PROJECT FINAL REPORT...Prof. Miriam CJM Sturkenboom, Erasmus Universitair Medisch Centrum Rotterdam...

ARITMO Final Report

© Copyright 2013 ARITMO Consortium 1

PROJECT FINAL REPORT

Grant Agreement number: 241679

Project acronym: ARITMO

Project title: Arrhythmogenic potential of drugs

Funding Scheme: Small or Medium-Scale Focused Research Project

Period covered: from 1st

January 2010 to 30th

June 2013

Name of the scientific representative of the project's co-ordinator, Title and Organisation:

Prof. Miriam CJM Sturkenboom, Erasmus Universitair Medisch Centrum Rotterdam

Tel: +31 10 704 4126

Fax: +31 10 704 4722

E-mail: [email protected]

Project website1 address:

www.aritmo-project.org

1 The home page of the website should contain the generic European flag and the FP7 logo which are available in

electronic format at the Europa website (logo of the European flag: http://europa.eu/abc/symbols/emblem/index_en.htm ; logo of the 7th FP: http://ec.europa.eu/research/fp7/index_en.cfm?pg=logos). The area of activity of the project should also be mentioned.

mailto:[email protected]

http://www.aritmo-project.org/

http://europa.eu/abc/symbols/emblem/index_en.htm

http://ec.europa.eu/research/fp7/index_en.cfm?pg=logos

ARITMO Final Report


Table of contents

Table of contents ................................................................................................................................................................. 2

1. Final publishable summary report ................................................................................................................................ 3

1.1 Executive summary ................................................................................................................................................. 3

1.2 Description of project context and objectives ....................................................................................................... 4

1.3 Description of the main S&T results/foregrounds ................................................................................................ 8

1.4 The potential impact and the main dissemination activities and exploitation of results ................................. 47

1.5 Address of the project public website and relevant contact details. ................................................................. 49

2. Use and dissemination of foreground .......................................................................................................................... 50

Section A (public) ........................................................................................................................................................ 50

Section B (Confidential or public: confidential information to be marked clearly) .............................................. 65 Part B1 ............................................................................................................................................................... 65

Part B2 ............................................................................................................................................................... 66

3. Report on societal implications .................................................................................................................................... 69

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1. Final publishable summary report

1.1 Executive summary

The ability of some compounds to prolong the QT interval of the electrocardiogram and to precipitate torsade de pointes (TdP, a potentially fatal arrhythmia) has caused several regulatory interventions: a number of drugs were withdrawn from the market, whereas others were restricted in use. Specific guidelines have been implemented to detect QT liability of new compounds as early as possible. However there is increasing evidence showing that an increase in the QT interval does not necessarily lead to TdP which further increased the regulatory and clinical difficulties, intrinsically due to the fact that TdP is a serious, albeit rare event. The ARITMO project assessed the arrhythmogenic potential of antipsychotics, antihistamines and anti-infectives (588 individual compounds). This was done for each drug by systematically investigating pharmacokinetics and dynamics (hERG affinity, the most important mechanism of drug-induced TdP) from literature, experimental and in silico data, and by estimating the effect of the drug on the QT interval (literature), risk of Torsade de Pointes (Pharmacovigilance, field studies), QT prolongation (pharmacovigilance and field studies), as well as ventricular arrhythmia (VA) or sudden cardiac death (SCD) (epidemiological studies & pharmacovigilance). Patient related factors that may influence arrhythmogenicity were investigated by an attempt to find new ECG parameters that would predict TdP, and genetic analyses. All new ARITMO drug related evidence was aggregated in an overall score that was presenting the level of evidence of arrhythmogenic risk based on a Dempster Shafer model. This was compared to the knowledge we have on hERG affinity, QT prolongation and VA/SCD risk from published studies. The project has yielded a wide variety of data based on the work that was done in each of the workpackages. Drug related information: a database with pharmacokinetic parameters of all drugs, a databases with the cardiac safety profile (in silico) of 413 ARITMO drugs listing hERG, Nav, Cav pIC 50 values, a sheet with the number of case reports and other data to evaluate causality assessment (traditional approach) and new pharmacovigilance score based on case reports from the French, German, UK, and Italian national spontaneous reporting databases as well as the AERS (the FDA archive) and EUDRAVIGILANCE (the EMA archive) international databases. From these pharmacovigilance analyses new signals were identified as well as a ranking of drugs based on their arrhythmogenic risk. Overall, 167 ARITMO drugs had pharmacovigilance data (i.e., they received a pharmacovigilance score), drugs with highest scores were oxatomide, rupatadine (antihistamines) ziprasidone, pimozide, thioridazine (antipsychotics, all included in AZCERT lists) plus halofantrine (antiprotozoal). The association between ARITMO and non-ARITMO drugs and severe symptomatic confirmed QT prolongation was studied using existing field studies in the UK, Netherlands and Germany, as well as medical record databases. The risk of ventricular arrhythmia could be studied for 38 antibiotics, 15 antihistamines and 19 antipsychotics. The studies on VA were conducted using data from 7 different health care databases in 5 countries, whereas the association with SCD was studied in two databases. Literature on hERG affinity, clinical studies on QT prolongation and observational studies on VA or SCD was systematically reviewed and abstracted for 220 selected ARITMO drug, this subset was created based on frequency of use and other relevant parameters. The literature review data were integrated into different levels of evidence and contrasted with the derived integrated data in the ARITMO project on hERG affinity, pharmacovigilance and epidemiological risk. For each drug, these three independent pieces of information were integrated into an overall ARITMO score using the Dempster Shafer (DS) model and compared with the literature data. Evidence integration was achieved for 450 drugs: among these, 79 had all the three different sources of information quoted above. In addition, a comparison between literature review and DS assessment could be provided for 205 drugs: 26 antihistamines, 36 antipsychotics and 143 anti-infective drugs. Among antihistamines, for 16 drugs new evidence emerged from ARITMO, for 3 drugs ARITMO confirmed literature evidence and for 7 drugs ARITMO refined the information. Fexofenadine, cetirizine and levocetirizine belong to low risk category (green traffic light). Among antipsychotics, for 20 drugs there was no data in literature and ARITMO provided new evidence, for 5 drugs ARITMO confirms literature data (notably, for thioridazine, mesoridazine, droperidol and risperidone high arrhythmogenic risk has been confirmed) and for 11 drugs ARITMO refines the evidence. Among the anti-infective drugs, for 122 drugs no information was retrieved from literature and ARITMO could provide additional data on risk. From the DS analysis emerged that anti-infective drugs mainly belong to low risk categories (i.e., green or green-orange). In addition, different aminoglycosides, azole antifungals, antivirals (protease inhibitors) and anti-malarials were classified as high arrhythmogenic risk drugs (i.e., red traffic light). Patient related information: For ECG parameters a pipeline was developed for paper-to-digital ECG conversion, to allow for automated measurement of short-term QT variability. The ECGs from TdP cases were analysed, changes in QT interval were observed towards the event, no new parameters could be identified. Candidate genes were identified based on in silico predictions done in ARITMO.

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We conclude that the ARITMO consortium has developed a workflow that has proven to yield comprehensive and multifaceted information on the arrhythmogenic potential of many drugs by exploiting and integrating existing data sources. This information can be used for clinical and regulatory decision making. The consortium advocates that the accumulated information should be made available to the different stakeholders and that the workflow is maintained to investigate the arrhythmogenic potential of other drugs much faster.

1.2 Description of project context and objectives

Cardiac ventricular arrhythmia as a side effect of anti-arrhythmic and non-antiarrhythmic drugs has become a major pharmacological safety concern for the pharmaceutical industry and the health regulatory authorities

1,2. Among drug-

induced arrhythmias, Torsade de Pointes (TdP) is by far the most important and worrisome. Electrophysiology of heart rhythm The rhythm of the heart is controlled by a balance of ions flowing into and out of individual cardiac cells. Most of this ionic traffic flows through membrane-spanning proteins known as voltage-dependent ion channels. The three most important types of channels governing electrical activity in the human heart are those that carry Na

+, Ca

2+, and K

+,

respectively. When they work in concert, the activity of these ion channels gives rise to the shape and duration of the action potential on the cellular level and to the electrocardiogram (ECG) measured clinically. The influx of Na

+ and Ca

2+

into heart cells, through their respective channels, produces excitation and contraction of the myocardium. Activation of K

+ channels allows for the efflux of this ion out of cardiac cells. Several types of voltage-dependent K

+ channels exist

in the human myocardium, and their activity promotes repolarization of the heart, termination of the action potential, and an end to the ECG waveform

24.

Figure 1: Action potential and ion in-outflow

Torsade de Pointes and risk factors Torsade de Pointes is a polymorphic ventricular tachycardia, if it occurs it may lead to syncope, seizure, ventricular fibrillation and sudden death

3. The most important risk factor for TdP is prolongation of the QT interval, which can be

congenital but is mostly drug-induced. Other risk factors are: hypokalaemia, heart failure, myocardial infarction, diabetes mellitus and bradycardia

3,13. Prolongation of the QT interval may result in early after depolarizations (EAD),

which in turn may induce re-entry and thereby provoke Torsade de Pointes and fatal ventricular fibrillation leading to sudden cardiac death

1,9,10. Although QTc (QT interval corrected for the heart rate)

14 prolongation is almost always

present in cases with TdP3 and in itself has been directly associated with sudden cardiac death in epidemiological

studies15,16

, QTc prolongation itself is not a perfect predictive marker of TdP risk (TdP liability). Many persons have QTc prolongation and will not develop TdP. Moreover, there is no consensus on the magnitude of prolongation raising concern, although the threshold on the extent of prolongation for regulatory purposes is a mean increase of 10 ms. Some drugs prolong the QTc interval, but appear devoid of torsadogenic effects (e.g. verapamil), whereas others seem not to be associated with a significant QTc prolongation, but are still considered to be associated with cardiac arrhythmias

11,17. It is to be expected that both drug as well as person based risk factors may interact to cause TdP.

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QTc prolongation and mechanisms The vast majority of drugs associated with QTc prolongation are thought to act by reducing the IKr current in the heart

3.

This IKr current is conducted by tetrameric pores with the individual subunits encoded by the human ether-a-go-go-related gene (hERG or now termed KCNH2)

3,25. Blockade of hERG K

+ channels has been studied mostly and is widely

regarded as the predominant cause of drug-induced QT prolongation. Due to the role of hERG in causing QTc prolongation and potentially TdP and the availability of in vitro methods to model hERG binding, a common first step of screening drugs for potential torsadogenic effects is to look at hERG affinity

25.

Figure 2: Potential mechanism for TdP (from Roden & Viswanathan. J Clin Invest 2005; 115: 2025-32) Unfortunately hERG binding does not predict TdP perfectly

26. Terfenadine is a potent IKr blocker, but usually does not

prolong the QTc interval because it is readily transformed into a metabolite with no QT liability. In any case, because of the possible occurrence of drug interactions diseases that inhibit metabolism and due to the existence of alternatives, its use has been restricted

9,11,26. It is therefore likely that other drug and patient related factors/variables (and not only

IKr blockade) are involved in provoking or preventing Torsade de Pointes. These actions may be either direct (by blunting early after depolarizations) or indirect (by blunting the prolongation of the action potential)

26. Moreover, TdP

may be mediated by other ion channels such as the Ca2+

or Na+ channels

25. In addition, the assessment of the net

arrhythmic effects should also comprise pharmacokinetic aspects including metabolism since a very low baseline pro-arrhythmic effects may become clinically important in case of drug interactions leading to higher than expected plasma levels. Regulatory actions In the 1980s, large case series of TdP were accumulated, which showed the overwhelming preponderance of non-antiarrhythmic drug therapy to cause TdP. Risk estimates range in the order of 1-8% for quinidine and sotalol, and similar rates have also been recorded for ibutilide and dofetilide

18. Non-antiarrhythmic drugs that have been associated

with TdP and sudden death include antipsychotics7,19,20

, antihistamines (especially diphenhydramine and astemizole)

18,21, anti-infectives (macrolides, quinolones, imidazole antifungals) and gastrointestinal drugs (cisapride and

domperidone)22

, but incidence with non-antiarrhythmic drugs (whenever available) is less common (1-5 per 100,000)

5,23. This long and growing list of non-antiarrhythmic drugs linked to TdP has contributed to the unjustified view

that QT prolongation is usually an effect of a whole therapeutic class, whereas, in many cases, it is displayed only by specific compounds within a given class. Rarely however, all drugs in the entire class are investigated. In ARITMO, we look at all drugs in 3 main classes: anti-histamines, anti-infectives and anti-psychotics. In recent years, a number of blockbuster antipsychotic, antihistaminic, gastrointestinal and anti-infective drugs (e.g. thioridazine, astemizole, cisapride, grepafloxacin)

3 have been withdrawn from the market because of reports of

Torsade de Pointes (TdP) and sudden death or cardiac death (SD or SCD), and several others were restricted in use (e.g. terfenadine, haloperidol, sertindole). This has resulted in health concerns for patients as well as billions of dollars of lost revenues for the pharmaceutical industry

4. The relative rarity of drug-induced TdP in non-antiarrhythmic drugs

5 and our

imperfect prediction of risk for a given individual, make this a particularly vexing problem for clinicians. Several attempts have been made to list the drugs that are associated with QTc prolongation and cardiac arrhythmias

1,10-12. Although these lists provide some ranking, the absolute and relative risks of TdP and sudden death for

the listed drugs are mostly unknown as most are based on case reports and incomplete evidence.

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In the absence of complete explanatory factors but the link with QTc prolongation, regulatory agencies have taken several measures to reduce the risk of licensing potentially torsadogenic drugs by implementing guidelines (ICH S7B and ICH E14) for the early identification of TdP. This involves a battery of preclinical and clinical tests (in vitro and in vivo models of a delayed cardiac ventricular repolarization, or QTc interval prolongation, as surrogate biomarkers of TdP risk

6). However the link between the primary abnormalities such as hERG channel inhibition and TdP or sudden death is

a complex one, as it is modulated by a number of factors. The low specificity (i.e. the chances to label as negative those drugs carrying no risk) of the current in vitro test battery leads to many false positive screens, which may cause many potentially valuable drugs to be terminated during non-clinical or clinical development

28,29. Some attempts were made

to associate in vitro measures with clinical measures or spontaneous reports of ventricular arrhythmia or sudden death but a systematic comprehensive assessment that includes epidemiological studies as well was still lacking

11,30. Since

apparently not all TdP can be predicted accurately by either hERG blockade or QTc prolongation, it is likely that other genetic and environmental risk factors may affect or modify the association between QTc-prolongation, TdP and sudden death. In ARITMO we tried to integrate all factors. Genetics and drug-induced long QT syndrome, TdP or cardiac arrest Until the start of ARITMO, the search for sequence variants contributing to sudden death (SD) risk was restricted to several candidate genes known for their role in arrhythmogenesis. Yet, the relatively small size of existing sudden death collections and etiologic heterogeneity limit the statistical power to detect causal variants. Therefore, initial attention has focused on quantitative sudden death risk factors available in large cohorts, such as QT prolongation. Approximately 35% of the variation in QT interval duration in unselected community-based samples is heritable

32,33.

Hundreds of mutations in 10 genes linked to the long-QT syndrome have been identified34

. Mutations in three genes, each encoding a cardiac ion channel that is important for ventricular repolarization, account for the vast majority of cases; the resulting subtypes are called LQT1 (loss of function mutations in KCNQ1), LQT2 (loss of function mutations in KCNH2 or HERG) and LQT3 (mutations that disrupt the cardiac sodium channel SCN5A). Other mutations associated with long-QT are LQT5-9

34,35 Most reported mutations are in coding regions, although non coding mutations and

‘private’ (family bases) mutations have been seen34

. The prevalence of mutations is at least 1 per 2000 persons, however most remain asymptomatic during life

34. More recently the NOS1AP gene was reproducibly associated with QT

interval variation in several large population samples36,37

. The interaction between LQT mutations or NOS1AP variants and drug-induced symptomatic QT prolongation is largely unknown due to the rarity of the event and the lack of large case series. The identification of common variants that modify risk of drug-induced arrhythmia would be exciting from a clinical perspective. Genetic variation is now well-recognized as one source of variable response to drug therapy including variable susceptibility to adverse drug reactions. An association study utilising a candidate SNP chip array was utilised in cases of drug induced TDP and controls that were either from the general population or patients challenged with a QT prolonging drug without evidence for drug-induced long QT syndrome (DiLQTS). It identified the uncommon missense variant D85N in the KCNE1 gene, the beta subunit of the IKs current and implicated in congenital LQTS, as being more prevalent (8.6% vs 2.9% vs 1.8%).(3) In addition, Sanger sequencing studies have identified rare likely disease causing genetic variants in ion channel genes associated with LQTS in approximately 10% of cases. (1;2) With the current next generation sequencing technology, it has been possible to sequence larger numbers of cardiac ion channel function genes in less time despite the computational difficulties involved in data analysis. One recent study evaluated the frequency of rare non-synonymous variants in genes contributing to the maintenance of heart rhythm in cases of diLQTS using targeted capture coupled to next-generation sequencing.(4) In their cohort, 11 of 31 DiLQTS subjects (36%) carried a novel missense mutation in genes with known congenital arrhythmia associations or with a known LQTS mutation. In the 26 Caucasian subjects, 23% carried a highly conserved rare variant predicted to be deleterious to protein function in these genes compared with only 2-4% in public databases. The authors concluded that the rare variation in genes responsible for congenital arrhythmia syndromes is frequent in diLQTS and their findings demonstrate that diLQTS is a pharmacogenomic syndrome predisposed by rare genetic variants.(4) The overall goal of the ARITMO project was to analyse the arrhythmic potential of drugs in the following classes of study drugs, all included the EC FP7 call: antipsychotics (ATC N05A), anti-infectives (J), and H1-antihistamines (ATC R06), globally and in specific subgroups (age, co-morbidity, genetically). The specific objectives were: 1) To predict QT liability and TdP propensity for study drugs via in silico modelling.

ARITMO Final Report


2) To annotate relevant pharmacokinetic parameters of the study drugs. 3) To review the published pre-clinical in vitro and in vivo evidence for the study drugs. 4) To review the occurrence and extent of QTc prolongation from published clinical trials and the risk of ventricular fibrillation, TdP and sudden death from published epidemiological studies or large simple trials on study drugs. 5) To assess the reporting rate and relative risk (disproportionality) of QTc prolongation, TdP, ventricular fibrillation and sudden death from regional and international pharmacovigilance databases. 6) To assess the rate and relative risk of symptomatic QTc prolongation, TdP, ventricular arrhythmia and sudden death during use of study drugs. 7) To assess effect modification by concomitant drug use, co-morbidities, and genetic factors for drug induced symptomatic QTc prolongation, ventricular fibrillation and sudden death. 9) To further explore the relationship between new electrophysiological parameters and arrhythmias. All objectives were addressed through the workplan that split the work over various areas of expertise, whereas it was integrated in the ‘integration’ work package.

ARITMO Final Report


1.3 Description of the main S&T results/foregrounds

1.3.1 Source documents

The multifaceted workplan for ARITMO was started by creating source documents that described:

1) The ARITMO study drugs 2) The ARITMO outcome definitions 3) The ARITMO covariates

These list were followed by all workpackages. The ARITMO drug list ARITMO aimed to analyse the proarrhythmic potential of the following classes of medications: antipsychotics, anti-infectives and H1-antihistamines. To this aim, a list of all drugs to be investigated was created according to ATC classification by considering: (1) all V level ATC codes in the classes J (anti-infectives for systemic use, except for vaccines), N05A (antipsycotics, except for lithium (N05AN)), P01 (antiprotozoals) and R06A (antihistamines for systemic use); (2) V level ATC codes of molecules belonging to the aforementioned pharmacological groups, but included in other ATC classes, provided that they can have systemic effects (e.g. A02BD which corresponds to combinations of antibacterials used in the treatment of peptic ulcer). This list has been named as “the broad ARITMO drug list”. The broad ARITMO drug list contains multiple ATC codes for the same active substances, as some active substances have different ATC codes, based on different indications for use; ‐ drugs that are currently no longer or seldom used in Europe (e.g. astemizole and thioridazine, which, however, can be interesting to study as well). Overall, the broad ARITMO drug list includes 588 different ATC codes (V level), corresponding to 485 active substances. This broad list was taken into account in the activities of all the Work Packages except for WP6 (Literature review) and docking of molecules (WP 7). The narrow ARITMO drug list comprising 220 drugs has been prepared using various criteria based on frequency of use and publications or thorough QT studies. The drug lists are available on the ARITMO website. The ARITMO outcomes The cardiologists and epidemiologists defined the ARITMO outcome list, which was used across all of the workpackages (figure 1). Since the different workpackages use different type of data, that may be coded with different terminologies. D5.1 describes the mapping of the terminologies for the outcomes using the Unified Medical Language System. This approach offered the opportunity to provide consortium with a shared semantic basis for the creation of queries, to be adapted to the heterogeneity of different databases. ARITMO covariates As covariates of interest, we identified all the potential risk factors of QT prolongation, Torsade de pointes, ventricular fibrillation and sudden cardiac death. As there is a common pathway leading from QT prolongation to sudden cardiac death we did not distinguish at this stage the risk factors for individual study outcomes. Apart from the risk factors of QT prolongation and related cardiac arrhythmias, we identified also the main indication for use of the different study drugs. A source document with disease and drugs that were considered as covariates in the observational studies in WP 4 & 5 is available on the ARITMO website (source documents).

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Figure 3: ARITMO outcomes

1.3.2 A platform for collaborative work

The analyses on healthcare data, epidemiological data and pharmacovigilance data was performed in a distributed fashion by using a common Remote Research Environment (RRE) which is located at EMC. The RRE allows for loading, retrieving, extracting, and transforming of the data. The security measures taken for the Remote Research Environment (RRE) ensure the high level of stored data protection as described in article 34 of the legislative decree 196/2003 and Directive 95/46/EC for processing of healthcare data. The RRE consist of two servers, a database server (RRE-DB) located at the Department of Medical Informatics of EMC and an application server (RRE-APP) located in the demilitarized zone (DMZ) of EMC hosted at the Rotterdam Internet Exchange (R-iX) (see fig 4). RRE-DB is placed in a locked server room and was not directly connected to the RRE-APP server in de DMZ. Furthermore, there is a firewall that isolates RRE-DB from the Local Area Network (LAN). The RRE-APP server is secured by the EMC firewall and will not have any direct connections to the LAN of the hosting institute. ARITMO data are accessible for partners on the RRE for the coming years and data will be archived after publications of the papers. The exact details are described in the RRE security document that was part of the WP 1 deliverables. All partners signed confidentiality agreements.

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Figure 4: Remote Research Environment

1.3.3 Newly generated data for ARITMO drugs: drug related factors

1.3.3.1 Pharmacodyamic data: hERG, NaV, CaV target interactions

As explained above, the earliest indicator used to assess torsadogenic risk is usually based on the hERG pIC50 value. However other ion channels are important as well and there is evidence of cases of ventricular arrhythmia arising from pharmacological blockade of ion channels different from hERG: particularly susceptible are I (fast sodium channel, Nav1.5) and INaCaL (the L-type calcium channel, Cav1.2). Blockade of these channels will lead to anomalies in the action potential raising the risk of arrhythmic episodes. Accordingly it has been suggested that multi-channel effects must be considered when evaluating arrhythmic risk (see figure 5). WP 7 provided a cardiac safety profile (including hERG, Nav and Cav channel information) for the ARITMO drugs

Figure 5: Schematic description of Cav1.2, Nav1.5 and hERG channels block and the related anomalies of cardiac

action potential, ECG and types of arrhythmias

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Using several strategies a database containing in vitro compound-target interactions and in silico predictions thereof have been compiled for the ARITMO drugs. Three targets with established impact on the cardiac profile were selected to set up the risk profile, hERG, Nav1.5 and Cav1.2. Data on target interactions were retrieved and described in D7.2 following Experimental data: AstraZeneca extraction of internal in vitro assay data resulted in 35 reported pIC50s for hERG and 78 reported classification values. For Nav1.5 26 pIC50s were reported and 69 binary responses and finally for Cav1.2 3 cases with pIC50 values and 35 binary. This was complemented with the public data extraction approach performed by FIMIM, 60 pIC50 hERG values were obtained. 3D QSAR modelling (figure 6) was used for the ARITMO drugs The hERG blocking activity of most of the drugs (172 out of 218) belonging to the narrow ARITMO list was predicted using the 3D

QSAR equation derived from the newly developed CoMFA model. In silico target profiling could be used to predict 36 drugs in the hERG model, 10 Cav1.2 and one for Nav1.5. Machine learning based QSAR models available from AstraZeneca on hERG, CaV, NaV An excel sheet with the different pIC50 values is available for the ARITMO drugs (CSPD_v5.xls) Figure 6: 3D QSAR modelling

1.3.3.2 Pharmacokinetic data for ARITMO drugs

The ability of compounds to inhibit hERG potassium currents in recombinant cell systems has been extensively used in the early assessment of compounds likely to prolong the QT interval. The close correlation between free plasma concentrations associated with QT prolongation in both dog and man and the concentration associated with inhibition of the hERG channel in vitro have been demonstrated for terfenadine, terodiline and cisapride. An analysis of available data relating to QT prolongation demonstrated the dependence of QT prolongation on free plasma concentrations and lent support to the application of a 30-fold safety multiple between therapeutic activity and concentration causing QT prolongation. This can be further refined by the incorporation of a pharmacokinetic component to provide greater assurance that clinical exposure at proposed therapeutic doses will not approach free plasma concentrations expected to cause this adverse pharmacology. For ARITMO AstraZeneca has searched several databases containing published PK data. The results show that we currently have at least one PK parameter for 76% of the drugs and a fairly complete set of PK parameters for 60% of the ARITMO drugs. The results show that for the drugs for which we have data we generally have a lot of replicate measures. However a complete set of the core PK parameters for a drug (i.e. Cmax, Clearance, Tmax, Plasma Protein Binding and Bioavailability) is not that common. For 150 compounds we could provide the full profile of the above parameters, and for another 150 one parameter were lacking. Data were made available in an excel sheet (D7.1)

1.3.3.3 Pharmacovigilance data for ARITMO drugs

The WP3 approach (Pharmacovigilance) posed the basis to create a report on the methods, opportunities and limitations for monitoring of QT and TdP liability of drugs marketed in the European Union. This method can also be adopted by regulators to periodically monitor and prioritize potential signals arising from spontaneous reporting systems, especially for newly marketed drugs.

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Spontaneous reports of drug-event combinations are collected by drug companies and at a regional, national and international level through different databases. Each European country collects its own reports in a national spontaneous reporting database, although also international archives gather reports originating from different countries. Among international archives, the Eudravigilance database (European Union Drug Regulating Authorities Pharmacovigilance) is held by the European Medicines Agency and collects/exchanges electronically ADRs coming from national regulatory authorities, marketing authorization holders and sponsors of interventional clinical trials and non-interventional studies in Europe. The Uppsala Monitoring Centre in Sweden is responsible for the worldwide gathering of all serious ADRs received by regulatory authorities and companies. The FDA Adverse Event Reporting System (FAERS) collects ADRs from the US as well as rare/severe events from Europe and offers public access to raw data from 2004. Two different approaches were used for providing the required data for each ARITMO drug while using the multiple resources: a traditional approach and a novel approach. A traditional approach of disproportionality analyses was taken for the EUDRAVIGILANCE (EV) and FAERS databases using stringent case definitions (TdP or QT prolongation), an easily replicable score was created giving one point to each of the following criteria 1) number of cases for TdP/QT (> 3 cases); 2) number of cases for TdP/QT abnormalities without drugs listed by AZCERT (> 3 cases); 3) significance of the Reporting Odds Ratio (ROR, 95%CI>1) for TdP/QT when using all other drugs as reference; 4) significance of the Reporting Odds Ratio (ROR) for VA/SCD, in both EUDRAVIGILANCE as well as FAERS. Among the 588 ARITMO drugs with an ATC code (i.e., the original full ARITMO drug list used in the search strategy), 148 drugs fulfilled at least one criterium. Notably, only olanzapine fulfilled all the criteria in FAERS and EV, thus it was the only agent reaching the maximum score of 8/8. Seven agents resulted in a score of 7/8: clozapine, haloperidol, quetiapine and ziprasidone are included in AZCERT lists and, therefore are positive controls, whereas fluphenazine, levomepromazine, and zuclopenthixol are not labelled by AZCERT and, therefore can be considered as potential emerging signals deserving case-by-case evaluation. Twelve compounds reached a score of 6/8: amantadine, amisulpride, aripirpazole, azithromycin, ceftazidime, chlorpromazine, clarithromycin, moxifloxacin, posaconazole, prothypendyl, risperidone and rupatadine: aripiprazole, ceftazidime, posaconazole, prothipendyl and rupatadine are not included in AZCERT lists and, therefore, could be considered as potential emerging signals deserving case-by-case evaluation. Fifteen agents reached a score of 5/8, of which the following could be potential emerging signals: asenapine, bromperidol, ceftriaxone, chlorprothixene, dexchlorpheniramine, fexofenadine, linezolid, metronidazole, promazine, saquinavir. The list of scores is available in deliverable D 3.3. Novel approach. A new ARITMO Pharmacovigilance Score was developed that goes beyond the current standard and that allows for integration of evidence from multiple sources (see figures 7 and 8). Criterium Description Threshold Value Coefficient

1. cases of groups I and II 3 or more cases Number of cases per 1000 reports

0,7

2. cases of groups I and II without concomitant drugs listed in AZCERT I or II

3 or more cases Number of cases per 1000 reports

0,95

3. cases of groups I and II without concomitant cardio-vascular drugs

3 or more cases Number of cases per 1000 reports

0,95

4. ROR for groups I and II (complete database)

Significant ROR (i.e., Lower Limit 95%CI>1) and 3 or more cases

Lower Limit 95%CI value 0,5

5. ROR for groups III and IV (complete database)



6. ROR for groups I and II of the pharmacological class*



7. ROR for groups III and IV of the pharmacological class*



8. ROR for groups I and II within pharmacological class**



9. ROR for groups III and IV within pharmacological class**



10. ROR for groups I and II within pharmacological subclass***



11. ROR for groups III and IV within pharmacological subclass***



Figure 7: description of criteria used to calculate the pharmacovigilance score, with relevant thresholds, the value used in the calculation and coefficient used to weight each criterium (obtained through a consensus approach among

ARITMO Final Report


WP3 partners). The full description of criteria and relevant information was provided in a dedicated Deliverable (D3.3).

Figure 8: approach for novel pharmacovigilance score (left) and the number of drugs for which a novel PhV score could be calculated. The score was calculated by integrating evidence from 5 databases, and figure 8 shows that the combination of those databases allows for information on many more drugs than what would have been possible from a single source. A score for a drug could only be calculated if there were at least 3 case reports in each of the databases. Overall, 157 drugs received in integrated score and a value of uncertainty (i.e., a qualitative measure of the score that was calculated by using data on drug consumption, time on the market and consistency among the different scores considered in couple). Use of the novel PhV score allowed for graphical display of the score across the classes (figure 9). Notably, there are inter and intra-class differences in the distribution of the scores: for instance, nine antipsychotics received a score higher than moxifloxacin (the drug with the highest score among antibacterials). Among antipsychotics, the score spans from 0.915 (ziprasidone) to 0.018 (clotiapine). Oxatomide received the highest maximum score (=1), but with high degree of uncertainty (0.867), followed by rupatadine (score=0.935; uncertainty=0.636) and ziprasidone (0.915; 0.572).

Figure 9: Distribution of PhV score (y-axis) for the different drug classes

weighted by coefficient

Sum of criteria in each source

FAERS EV FRA GER ITA

Normalization (0-1 range)

Normalization (0-1 range)

INTEGRATED SCORE(mean value of the scores) Total drugs with an Integrated PhV Score: 157

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ARITMO Final Report


Key message from pharmacovigilance

Pharmacovigilance Score and uncertainty were calculated for 157 drugs. All 5 SRS databases have significantly contributed in the assignment of the score, as demonstrated by the fact

that there are some drugs that received a given score only from one source. There are inter and intra-class differences in the distribution of the score that may allow provisional risk

ranking. For WP8 integration, 240 drugs were categorized. Please remember that, for WP8 purpose, we also identified

low risk drugs, i.e., those without a pharmacovigilance score because less than 3 cases in all databases were reported, but with an uncertainty calculated (both drug utilization data and time on the market available).

High risk category (h3 in WP8): 38 drugs with a Pharmacovigilance score≥0.363 (0.363 represents the median value of the score for drugs in AZCERT lists 1, 2 and 3) were prioritized.

Of these, 20 drugs could be potential novel signals (i.e., because they were previously unknown for this risk based on the information obtained from the AZCERT lists): 10 antipsychotics, 4 antihistamines, 2 antiprotozoals, 2 antifungals, 2 antivirals.

For the remaining 18 drugs, the signal was confirmed (already listed by AZCERT) or strengthened (possible refinement of the AZCERT lists).

Added value of the Pharmacovigilance Score (calculation of the uncertainty and increased positive predictive value as compared to traditional disproportionality approach): detection of tiapride and oxatomide, drugs which already had provisional evidence of pro-arrhythmic risk.

1.3.3.4 Associations between (a) symptomatic QT prolongation and ARITMO drugs

In order to assess the risk of symptomatic and severe asymptomatic QTc prolongation for the ARITMO drugs a large case control study was conducted in a population from 4 European countries. QTc prolongation or TdP are not easy to measure in many of the health care databases since there are no ICD-9 or ICD-10 codes coding for these conditions. The study was therefore piggy backed on existing field studies: the Berlin Case-Control Surveillance Study (Germany), the Rotterdam Cohort Study (Netherlands), the Bologna Cohort Study (Italy), the Amsterdam Resuscitation Studies (Netherlands) and on three electronic medical record databases (HSD [Italy], IPCI [Netherlands], and THIN [UK]) providing information on QTc prolongation or TdP in free-text fields available in these databases. A matched case-control design was chosen to analyse the data. Two different case definitions were established to which the different data sources contributed to different extent depending on their data availability: Case definition 1 was based on ECG-confirmed cases with moderate or severe QTc prolongation with clinical symptoms including TdP and VF, or severe QTc prolongation without clinical symptoms. Case definition 2 included patients with evidence of QTc prolongation as found via free-text search in the electronic healthcare databases or QTc prolongation comparing two standardised subsequent ECG readings. Controls were ascertained from the same data source and matched by age and sex. Exposure to the ARITMO drugs (anti-infectives, antihistamines and antipsychotics) as well as to all other drugs was analysed. In an initial approach, three different ATC levels were evaluated for the ARITMO drugs: the therapeutic group (ATC level 2), the pharmacological subgroup (ATC level 3) and the single substance (ATC level 5). As these analyses suffered from limited study power for case definition 1, but also from instable statistical models for case definition 2, another approach was chosen presented here. In this second approach, drugs were grouped according to their indication and by their known or assumed potential to cause QTc prolongation or TdP. Drugs were identified from the Arizona Cert List (http://crediblemeds.org). We also consulted the drug database of the German "arznei-telegramm" (http://www.arznei-telegramm.de/db/01pin.php3) searching for drugs reportedly associated with the adverse events "QT prolongation" and "torsade de pointes". For all these analyses, as confounders, cardiologic and other comorbidities were taken into account. For case definitions 1 and 2, regression models were fitted regarding the different exposure definitions. First, univariate conditional logistic regressions were conducted for each drug group/substance to provide an unadjusted risk estimate for comparison. Second, the odds ratio (OR) for current use (i.e. on the index date) relative to non-use was estimated for each drug group and each single substance using multivariate conditional logistic regression while adjusting in one analysis for potential confounders except other drugs (“single drug assessment”) and in another analysis additionally for all other significant drugs (“joint drug assessment”). All comorbidities or comorbidity groups were forced into the model. To reduce the number of parameters and to exclude too rare medication exposures, only drugs that were observed in at least three cases at the index date for case definition 1 or 2 combined and that showed an elevated risk in the single drug assessment were considered to be of interest for the main analysis of the joint drug assessment. Table 1 shows the number of cases per case group and datasource.

http://crediblemeds.org/

http://www.arznei-telegramm.de/db/01pin.php3)%20searching

ARITMO Final Report


Table 1: Cases by datasource in the case control study on ARITMO drugs & QTc prolongation

Case definition

Event Total ARREST BOLOGNA FAKOS HSD IPCI ROTTERDAM THIN

N=899 N=6 N=16 N=59 N=95 N=411 N=288 N=24

1 N=356

Long QT with TdP 24 - - 24 - - - -

Long QT with VF 11 6 - 5 - - - -

Long QT with Syncope 10 - - 10 - - - -

Severe long QT w/o symptoms

311 - 3 20 - - 288 -

2 N=543

Delta QTc > 50ms 13 - 13 - - - - -

Found in free-text 530 - - - 95 411 - 24

Although case definition 1 included 356 cases and case definition 2 included 546 cases, very few cases and controls were exposed to any of the ARITMO drugs or to any of the other drugs potentially causing QTc prolongation and therefore did not contribute to the analyses. Table 2: Association between ARITMO drugs and symptomatic & non-symptomatic QTc prolongation with respect to

case definition 1 Case definition 1 ATC-level 5 + lumped drug groups ARITMO and other drugs Only significant drugs (pseudo-univariate) in the model

POOLED (all contributing data sources)

Variable

Frequency of Unadj.

OR Adj. OR

Cases Controls

Exp. Unexp. Exp. Unexp.

Amiodarone - C01BD01 26 330 1 961 26 24.991 [ 2.09,299.3]*

Domperidone - A03FA03 2 354 1 961 2 1.26E6 [ 0.00, . ]

Flecainide - C01BC04 5 351 3 959 1.667 3.658 [ 0.39,34.00]

Ondansetron - A04AA01 3 353 2 960 2.85 1.752 [ 0.11,27.37]

Sotalol - C07AA07 13 343 9 953 5.428 12.893 [ 2.05,80.91]*

*** Intestinal antiinfectives *** 2 354 1 961 14.65 7.759 [ 0.03, 2089]

*** Agents acting on the renin-angiotensin system ***

20 336 19 943 2.087 1.502 [ 0.44, 5.07]

*** Benzodiazepines *** 10 346 5 957 3.187 1.173 [ 0.18, 7.52]

*** Digitalis *** 30 326 16 946 2.133 1.150 [ 0.45, 2.96]

*** Diuretics *** 21 335 14 948 3.408 2.637 [ 0.70, 9.97]

*** Drugs for obstructive airway diseases *** 9 347 5 957 3.427 4.971 [ 0.50,49.46]

*** Expectorants *** 6 350 3 959 2.498 1.364 [ 0.10,18.02]

*** Iron preparations *** 3 353 1 961 3 0.917 [ 0.05,15.49]

*** Mineral supplements *** 14 342 5 957 3.463 2.169 [ 0.44,10.75]

*** Non-selective monoamine reuptake inhibitors ***

8 348 5 957 1.6 6.918 [ 0.95,50.39]

*** Other antihistamines for systemic use *** 4 352 14 948 3.124 2.528 [ 0.39,16.26]

(Chronic) heart diseases other than arrhythmias - - - - - -

Arrhythmias - - - - - -

COPD - - - - - -

Cancer - - - - - -

Electrolyte imbalances - - - - - -

Hypertension 28 317 111 840 1.176 0.815 [ 0.38, 1.76]

Metabolic and endocrine disorders 42 314 88 874 1.262 0.951 [ 0.47, 1.93]

Neurological and psychiatric diseases - - - - - -

Renal or liver disease 91 190 108 770 0.737 0.763 [ 0.43, 1.37]

ARITMO Final Report


We dispensed with pooling of case definitions 1 and 2, since the major number of cases for case definition 1 came from the Rotterdam cohort for which we had very limited confounder information. Table 2 shows the results for case definition 1 for the joint drug assessment. Significantly elevated risks of a-symptomatic and symptomatic QTc prolongation were not observed for any of the ARITMO drugs. Among the non-ARITMO drugs, the known QTc-prolonging and arrhythmogenic potential of amiodarone and sotalol was confirmed. The analysis regarding case definition 2 revealed increased risks for the antipsychotics quetiapine and thioridazine. Thioridazine is listed as drug with known TdP list on the Arizona Cert list. Among the newer antipsychotic drugs, quetiapine appears to prolong the QTc interval to the most marked extent. Severe QTc interval prolongation under quetiapine has been described to range between 3.1% and 7.8% (Wenzel-Seifert, K.; Wittmann, M.; Haen, E. QTc prolongation by psychotropic drugs and the risk of Torsade de Pointes. Dtsch. Arztebl. Int. 2011, 108, 687-693). Among anti-infective drugs, a significantly increased risk was observed for the fluoroquinolones moxifloxacin, norfloxacin and ciprofloxacin. Based on the HERG inhibition in in-vitro studies, the potency of the fluoroquinolones in terms of QT-interval prolongation has the following rank order: sparfloxacin > grepafloxacin > moxifloxacin > gatifloxacin > levofloxacin > ciprofloxacin > ofloxacin (Briasoulis, A.; Agarwal, V.; Pierce, W.J. QT prolongation and torsade de pointes induced by fluoroquinolones: infrequent side effects from commonly used medications. Cardiology 2011, 120, 103-110). Our results indicate that even fluoroquinolones with lower HERG affinity may be associated with an increased risk of QTc prolongation. Norfloxacin is a very old drug and there is not much information available on its QT prolonging potential. There was no increased risk observed for any drug of the group of antihistamines.

The results of the analysis for case definition 2 also confirmed the known arrythomogenic risks of the antiarrythmic drugs amiodarone, flecainide, disopyramide and sotalol. All these drugs are listed as drugs with known TdP risk on the Arizona Cert list. Among the other non-ARITMO drugs, other single drug substances with significantly increased risks included domperidone and ondansetron. Both drugs are also listed as drugs with known TdP risk on the Arizona Cert list. There was no increased risk observed for any other single drug substance. The increased risk observed for some lumped drug groups are not interpretable and therefore cannot provide relevant signals. Often drugs from many different drug classes were combined in these groups in order to reduce the number of covariables in the statistical model, given the limited number of cases. Drugs grouped for obstructive airway diseases thus e.g. include ß1-and ß2-sympathomimetic drugs, inhaled glucocorticoids, anticholinergic drugs and theophylline. Among the comorbidities, we observed quite expectedly increased risks for arrhythmias and electrolyte imbalances. We further observed an increased risk also for cancer diseases.

ARITMO Final Report


Table 3: Association between ARITMO drugs and symptomatic & non-symptomatic QTc prolongation with respect to case definition 2 Case definition 2 ATC-level 5 + lumped drug groups ARITMO and other drugs. Only significant drugs (pseudo-univariate) in the model

POOLED (all contributing data sources)

Variable

Frequency of Unadj.

OR Adj. OR

Cases Controls

Exp. Unexp. Exp. Unexp.

Clozapine - N05AH02 1 542 7 52058 14.74 6.935 [ 0.71,67.45]

Haloperidol - N05AD01 3 540 61 52004 4.815 2.191 [ 0.54, 8.86]

Quetiapine - N05AH04 3 540 31 52034 9.602 8.408 [ 2.47,28.58]*

Thioridazine - N05AC02 1 542 3 52062 30.23 32.261 [ 2.95,352.8]*

Ciprofloxacin - J01MA02 3 540 34 52031 8.817 5.062 [ 1.45,17.71]*

Moxifloxacin - J01MA14 1 542 2 52063 49.84 23.777 [ 1.58,356.8]*

Norfloxacin - J01MA06 3 540 28 52037 10.8 9.404 [ 2.72,32.50]*

Promethazine - R06AD02 1 542 35 52030 2.861 1.899 [ 0.25,14.42]

Amiodarone - C01BD01 15 528 164 51901 9.344 7.584 [ 4.13,13.93]*

Citalopram - N06AB04 8 535 297 51768 2.508 2.015 [ 0.93, 4.39]

Disopyramide - C01BA03 2 541 4 52061 50 50.116 [ 9.10,275.9]*

Domperidone - A03FA03 3 540 76 51989 3.938 3.466 [ 1.05,11.40]*

Escitalopram - N06AB10 2 541 32 52033 6.587 3.811 [ 0.78,18.56]

Flecainide - C01BC04 10 533 146 51919 7.036 4.700 [ 2.37, 9.31]*

Ondansetron - A04AA01 9 534 7 52058 19.79 22.335 [ 1.97,252.9]*

Sotalol - C07AA07 39 504 642 51423 6.883 6.148 [ 4.29, 8.81]*

Tamoxifen - L02BA01 1 542 20 52045 5.113 1.636 [ 0.13,19.87]

*** Anti-parkinson drugs *** 3 540 41 52024 6.924 5.662 [ 1.54,20.85]*

*** Beta-lactam antibacterials *** 8 535 183 51882 3.953 2.840 [ 1.31, 6.14]*

*** Intestinal antiinfectives *** 4 539 100 51965 3.388 3.089 [ 1.07, 8.91]*

*** Sulfonamides and trimethoprim *** 2 541 74 51991 2.717 2.557 [ 0.61,10.80]

*** Tetracyclines *** 6 537 127 51938 4.787 4.518 [ 1.94,10.53]*

*** Agents acting on the renin-angiotensin system *** 23 520 992 51073 2.365 1.256 [ 0.67, 2.35]

*** Antithrombotics *** 15 528 571 51494 2.699 0.948 [ 0.45, 2.00]

*** Benzodiazepines *** 7 536 83 51982 5.737 4.473 [ 1.70,11.77]*

*** Corticosteroids for systemic use *** 13 530 76 51989 5.349 1.230 [ 0.33, 4.55]

*** Digitalis *** 3 540 35 52030 7.079 4.818 [ 1.30,17.81]*

*** Diuretics *** 15 528 250 51815 5.651 1.870 [ 0.85, 4.13]

*** Expectorants *** 1 542 9 52056 9.994 4.201 [ 0.33,52.99]

*** Hormonal contraceptives *** 1 542 8 52057 14.13 11.342 [ 1.07,120.1]*

*** Iron preparations *** 2 541 45 52020 4.401 2.741 [ 0.60,12.52]

*** Laxatives *** 1 542 9 52056 11.03 4.079 [ 0.41,40.60]

*** Mineral supplements *** 2 541 21 52044 7.664 5.228 [ 0.98,28.01]

*** NSAIDs topical *** 2 541 35 52030 5.223 5.581 [ 1.21,25.70]*

*** Nitrates *** 4 539 124 51941 2.872 0.665 [ 0.19, 2.32]

*** Other antihistamines for systemic use *** 22 521 826 51239 1.987 1.572 [ 0.93, 2.67]

*** Other antiprotozoals *** 3 540 103 51962 2.901 2.175 [ 0.66, 7.14]

*** Other beta-blocking agents except sotalol *** 11 532 283 51782 3.283 2.513 [ 1.18, 5.33]*

*** Other selective calcium channel blockers *** 7 536 336 51729 1.899 1.197 [ 0.48, 3.01]

*** Proton pump inhibitors *** 17 526 325 51740 5.763 3.175 [ 1.55, 6.49]*

*** Statins ***

*** Vitamins *** 3 540 20 52045 15.32 7.102 [ 1.59,31.70]*

(Chronic) heart diseases other than arrhythmias 49 494 8096 43969 0.544 0.518 [ 0.37, 0.72]

Arrhythmias 36 507 1295 50770 2.898 2.110 [ 1.42, 3.13]

COPD 39 504 4011 48054 0.958 0.998 [ 0.70, 1.43]

Cancer 86 457 4812 47253 1.608 1.705 [ 1.31, 2.22]

Electrolyte imbalances 20 523 319 51746 6.636 5.806 [ 3.57, 9.44]

Hypertension 47 496 7726 44339 0.543 0.604 [ 0.44, 0.84]

Metabolic and endocrine disorders 45 498 4952 47113 0.82 0.871 [ 0.62, 1.23]

Neurological and psychiatric diseases 25 518 2750 49315 0.898 0.904 [ 0.59, 1.39]

Renal or liver disease 9 534 1141 50924 0.774 0.986 [ 0.49, 1.97]

ARITMO Final Report


1.3.3.5 Associations between VA, SCD and ARITMO drugs

Retrospective, population-based, multi-database, nested case-control studies in cohorts of users of the drug class were conducted to assess in a large population from 5 European Countries the risk of VA and SCD associated with current use of individual ARITMO drugs. These studies were registered as ENCePP studies. Different drug class and outcome-specific case-control sets were created at the site of the database custodian using dedicated standardized software, known as Jerboa©, which was originally developed in the EU-ADR project. All data were analysed and pooled on a remote research environment that is managed by Erasmus University Medical Center and analysed in a distributed fashion with all database custodians through secured remote access (see figure 10).

Figure 10: Distributed database approach utilizing common input files, standardized Jerboa scripts and the OCTOPUS

remote research environment Using these data drug utilization studies, incidence rate studies of VA and SCD and association studies were conducted. Drug utilization data were delivered in D5.4 and have resulted in several abstracts, papers and presentations. Figure 11 shows the incidence of VA after the harmonization of events. During a period ranging from 1997 to 2010 we identified overall 31,353 cases of VA (age standardized incidence rate: 0.2 per 1000 person years) from seven healthcare databases (AARHUS [Denmark], GEPARD [Germany], Health-Search/Thales (HSD) and Emilia-Romagna Regional Database (ERD) [Italy], PHARMO and IPCI [Netherlands], and THIN [UK]), covering a total population of around 27 million individuals and 150 million person years of follow-up time and 33,607 cases of SCD (age standardized incidence rate: 1.4 per 1000 person-years) from two healthcare databases (IPCI & Aarhus).

Figure 11: Age-specific incidence of VA (left) and SCD (right)

ARITMO Final Report


For each individual drug class and outcome, a cohort of incident users of that drug class during a period ranging from 1997 to 2010 was identified in each database. Within this cohort all cases of VA and SCD were identified using harmonized and validated DB-specific codes based on diagnostic codes and free-text search. Up to 100 controls were then drawn from the same source population and matched to each case by index date, sex, age and database. Exposure to study drugs was categorized into mutually exclusive groups of current (if exposure was at the index-date and/or in a carry-over period of either 7 or 30 days, depending on the drug class), recent (if exposure period ended between 7 (or 30) and 90 days before the index date), past (if the exposure period ended between 90 and 365 days before the index date), and non-use (if there was no exposure within 365 days prior to index date). Estimates were calculated only for drugs with at least 3 exposed cases to avoid instable estimates. For each drug class the odds ratio (OR) for current use for individual medications relative to non-use was estimated using multivariate conditional logistic regression while adjusting for recent and past use and for potential confounders. Since infections/fever is a risk factor for VA we also estimated the effect for antibiotics against current use of amoxicillin in a sensitivity analysis. In the final models we included as confounders all the known strong risk factors of VA (pre-defined) plus other weaker risk factors of VA based on modeling. Risk estimates were reported for each database separately. Data pooling was done by using a meta-analysis of single database estimates, as well as by pooling all data together (unweighted). Antibiotics Ventricular arrhythmia Overall, in the pooled analysis the risk of VA could be explored for 38 antibiotics (as individual ATC codes), separately. Current use of the most frequently used penicillins (in ranked order, phenoxymethylpenicillin, amoxicillin with enzyme inhibitor, amoxicillin, pivmecillinam and pivampicillin), cephalosporins (ceftriaxone, cefpodoxime and cefaclor) and macrolides (azithromycin, clarithromycin, roxithromycin and erythromycin) showed a statistically significant increase in the risk of VA as compared with non-use, while no increased risk was documented for any of the fluoroquinolones (ciprofloxacin, levofloxacin, norfloxacin and moxifloxacin) and tetracyclines. However, in comparison to non-use, higher risk of VA (OR=3.6; 95%CI: 1.4-9.3) was reported for moxifloxacin in one database (ERD). In general, these findings were confirmed in the meta-analyses of single database risk estimates. When using current use of amoxicillin as comparator, only ceftriaxone was associated with a statistically significant increased risk of VA (OR= 4.1; 95% CI: 1.7 - 9.9). Sensitivity analyses confirmed the robustness of the main findings. Current use of azithromycin versus non-use resulted in an OR of 1.9 (95% CI: 1.3 - 2.7) when using pooled data from the seven databases; however, no increase in the risk was observed when current use of amoxicillin was used as comparator (OR=1.1; 95% CI: 0.7 - 1.9). Sudden cardiac death Overall, the risk of SCD could be explored for 19 antibiotics in AARHUS and 1 in IPCI (as individual ATC codes). In AARHUS, the vast majority of antibiotics (including penicillins, cephalosporins, aminoglycosides, macrolides and fluoroquinolones), with some heterogeneity across different compounds, were also associated with increased risk of SCD as compared to non-use, confounding by indication may not be totally dealt with in these analyses. Interestingly, azithromycin was not associated with an increased risk of SCD (OR= 1.1; 95%CI: 0.8-1.5) as compared to non-use. In IPCI current use of amoxicillin plus clavulanic acid was not associated with statistically significant increased risk of SCD as compared to non-use. Anti-histamines Ventricular arrhythmia The risk of VA could be explored for 15 different anti-histamines. Only current use of cyclizine (OR= 5.2; 95% CI: 3.2 – 7.6) showed a statistically significant increase in the risk of VA as compared to non-use. Looking at database-specific estimates, the increased risk for cyclizine could be documented only in THIN. Removal of carry-over from the current use risk window changed the results slightly: a statistically significant increase in the risk of VA was documented now also for clemastine, whereas the risk estimatefor cyclizine increased. No dose effects were observed. Interestingly, loratadine was associated with a protective effect towards VA as compared to none use (OR=0.6; 95% CI: 0.4-0.9). This finding was confirmed in the meta-analysis of single database estimates. When using cetirizine as comparator to take away potential confounding by indication, the statistically significant increase in the risk for VA was retained for cyclizine (OR= 6.3; 95% CI: 3.6 - 11.0) .The risk of VA seems to be lower with longer duration of cyclizine treatment as compared to short-term use of that drug (<30 days).

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Sudden cardiac death Overall, the risk of SCD could be explored only in AARHUS for 9 antihistamines (as individual ATC codes). A statistically significant increased risk of SCD was observed for promethazine (OR= 5.1; 95% CI:1.8 - 14.8) and cetirizine (OR= 1.4; 95% CI:1.2 – 1.5). Antipsychotics Ventricular arrhythmia Overall, in the pooled analysis the risk of VA could be estimated for 17 antipsychotics, and, of these, 4 were atypical antipsychotics (olanzapine, quetiapine, risperidone, and amisulpride). Current use of haloperidol (OR: 2.0; 95%CI: 1.4 - 3.0) showed a statistically significantly increased risk of VA as compared to non-use. No increased risk of VA could be observed for any of the atypical antipsychotics, neither in the pooled analysis nor in the meta-analysis of database-specific estimates. The main findings were observed also in the sensitivity analyses. After removing the carry-over period,an increased risk of VA was seen also for thioridazine (OR: 2.6;95%CI: 1.3 - 5.3). For the drugs with an increased risk, a trend towards a lower risk for medium/long term use versus short term use was observed. Sudden cardiac death Overall, the risk of SCD could be explored for 20 antipsychotics in AARHUS and 1 in IPCI. In AARHUS, in addition to haloperidol, a statistically significant increased risk of SCD was observed for levomepromazine, chlorpromazine, chlorprothixene, zuclopenthixol, risperidone, olanzapine, melperone and penfluridol. Antimycotics Ventricular arrhythmia Overall, in the pooled analysis the risk of VA could be estimated for 7 antimycotics. Among drugs with enough exposure for measuring risk estimates, no antimycotic drug was found to be associated with VA. As regards fluconazole, an association with VA was reported only in Aarhus (OR=2.4; 95% CI: 1.1-5.1). Sudden cardiac death The risk of SCD could be explored only in AARHUS for 7 antimycotics (as individual ATC codes). In ranked order, nystatin, ketoconazole, fluconazole and itraconazole were associated with a statistically significant increased risk of SCD as compared to non-use of any antimycotic. A protective effect was reported for terbinafine. Antiprotozoals Ventricular arrhythmia Overall, in the pooled analysis the risk of VA could be estimated for 4 antiprotozoals. Among drugs with enough exposure, no antiprotozoal drug was found to be associated with VA. Sudden cardiac death The risk of SCD could be explored in AARHUS only for 3 anti-protozoals. Current use of metronidazole (OR= 2.7; 95%CI: 2.2 - 3.3) was associated with a significantly increased risk of SCD as compared to non-use. Antivirals Ventricular arrhythmia Overall, in the pooled analysis the risk of VA could be estimated only for 3 antivirals (acyclovir, amantadine and valaciclovir). Current use of these drugs did not show any statistically significant association with the outcome. Sudden cardiac death The risk of SCD could be explored only in AARHUS for 2 antivirals (acyclovir and valaciclovir). Current use of these drugs did not show any statistically significant association with SCD.

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1.3.4 ARITMO Literature review of drug related factors

The objective of the ARITMO literature review is to identify, assess and summarise the existing evidence from the published literature for the arrhythmogenic potential of selected, marketed drugs within three therapeutic classes: antihistamines, antipsychotics and anti-infective agents. Evidence from published preclinical, clinical and observational studies was collected using a systematic (Cochrane-style) approach involving a comprehensive search of the published literature and selection of relevant publications according to pre-defined eligibility criteria. This was done for all drugs on the ARITMO short list. These methods have been fully described in Deliverables 6.1 and 6.2. Deliverable 6.3 provides a description of the analysis of the evidence for each drug of interest and is accompanied by a Microsoft Access databases that provides evidence ‘profiles’ for each drug that summarises the quantitative evidence of arrhythmogenic potential. Inclusion criteria for the different types of evidence were:

• Preclinical – hERG/IKr assays providing IC50 only

• Clinical trials – Placebo-controlled TQT & ECG studies, RCTs reporting QTc data

• Observational studies – Comparative cohort/case control studies reporting estimates for VA/SCD

In total, 245 eligible publications were selected for review (figure 12). This included: 84 studies in which data on hERG or IKr channel block were provided, 145 randomised, placebo-controlled clinical trial studies providing one or more risk estimate relating to QTc prolongation and 16 observational studies providing one or more risk estimates relating to ventricular arrhythmia or sudden cardiac death.

Figure 12: Literature review process

Figure 13 shows the distribution of the types of evidence by drug class. Of the 67 studies identified for antihistamines, 29 (43%), 35 (52%) and 3 (4%) were found for hERG/IKr, clinical trials and observational studies respectively. The corresponding proportions for the 112 studies identified for anti-infectives were 37 (33%), 66 (59%) and 9 (8%); and for the 90 studies identified for antipsychotics were 35 (39%), 49 (54%) and 6 (7%). Clinical trial evidence represented the highest proportion of eligible studies for all three drug classes with a relatively low proportion of observational studies.

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Figure 13: Type of studies by drug class

In order to standardize the interpretation of results for the different types of outcome measures generated by the studies in the literature review, results were classified into 4 categories, coded according to a ‘traffic light’ system: ‘Significant’ is defined by the perceived practical or clinical importance of the results rather than statistical significance. The criteria for clinical importance are based on pragmatic selection of pre-defined arbitrary thresholds. Table 3 shows the criteria used to define each of the 4 categories across the different outcomes reported.

Table 3: Criteria for interpretation of quantitative data from WP6 (literature review)

Outcome No evidence

available ‘0’

No evidence of significant risk

‘1’

Evidence for significant risk inconclusive

‘2’

Evidence of significant risk ‘3’

Dichotomous outcomes* [Relative Risk, Odds Ratio or Incidence Rate Ratio]

(VA, SCD, QTc prolongation) No studies

Upper limit of 95% CI <2 or zero events

95% CI spans 2 Lower limit of 95% CI >2

Continuous outcomes* (milliseconds)

(MD in dQTc, ddQTc) No studies

Mean effect <5 msec or upper bound <10 msec

Mean effect 5-20 msec Mean effect >20 msec or upper bound >30 msec

hERG (margin) No studies Central margin >30 Central margin spans

30 Central margin <30

VA: Ventricular arrhythmia; SCD: Sudden Cardiac Death; CI: Confidence interval; MD: Mean difference in delta QTc; ddQTC: placebo subtracted delta QTc * The full description of dichotomous and continuous outcomes is provided in Deliverable 6.3.

As described in Deliverable 6.3, the hERG safety margin was calculated from the published IC50 divided by the peak plasma concentration (Cmax) that was retrieved for each drug from the pharmacokinetics database on the ARITMO drugs in the ARITMO project (WP7). This approach of categorisation of quantitative results does not, however, provide any indication of the quality of the evidence which, of course, may be highly variable across different studies, different outcomes and across different drugs. A strategy was therefore also developed to provide a means of summarising the quality of evidence from the literature. Interpretation of Quality of Evidence in the Literature Review For each outcome, five ‘uncertainty factors’ were identified that were considered to have a bearing on the interpretation of the quantitative results. The body of evidence for each drug was assessed according to each of these five factors and given a pre-defined score of 0.2 for each one that applied (equal weighting assumed). Table 4 shows the criteria that were used to allocate uncertainty factors. An overall ‘uncertainty score’ was computed from the sum of these individual factors (from 0-1) and presented alongside the quantitative results to assist interpretation.

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Table 4: Criteria for assessment of level of uncertainty in evidence Uncertainty Factor Criteria

hERG Data Clinical Data1

Volume If only one study available If only one study available

Precision If central range not precise enough to dichotomize between red and green categories

If 95% CI for the risk estimate spans the pre-defined clinically important threshold or if evidence underpowered or if missing data/imputations used

Consistency If full range of data highly variable, e.g. IC50 or Cmax varies by an order of magnitude or if there were clear outliers in the dataset.

If statistical heterogeneity observed (e.g. I2>40%) or study characteristics very different

in terms of population, dose/duration, design.

Validity If most studies did not involve use of positive or negative control compounds

If any study has high risk of bias or particular weakness

2; or if most have low level of detail in

reporting ECG data

Representativeness If studies use only one, less useful test condition e.g. only uses high potassium levels or only tests at room temperature.

If only selected populations studies e.g. only children, only healthy volunteers.

1 From either experimental or observational studies.

2 Note that for the clinical evidence, ‘validity’ is measured according to the overall risk of bias or methodological weakness from the quality assessments of individual studies that were performed using the Cochrane Collaboration Tool or Newcastle Ottawa Scale and are detailed in Deliverable 6.2.

Overall Summary of Evidence from Literature review In order to provide a simple summary that incorporates both quantitative and qualitative results an overall evidence categorisation was adopted and was based on consideration of all of the available evidence. An overall colour banding was then assigned according to the criteria in Table 5.

Table 5: Overall Classification of Evidence about Arrhythmogenic risk for Each Drug

A quality control check was carried out to ensure data accuracy. Two reviewers, independently, assessed the data. The overall conclusion statements and the uncertainty for hERG data have been completed, whereas the cross-check on clinical and observational studies was carried out on a sample of data.

ANTIHISTAMINES Among the 28 antihistamines that were reviewed (Table 6), most drugs had grey level of evidence (n=17), one was green (fexofenadine), one is red (terfenadine) and for 9 drugs there was uncertainty in the available evidence.

Classification of Evidence Criteria Final Colour Banding

No evidence available No data from any source Grey

Insufficient evidence available

If evidence available from only one source Grey

Uncertainty in existing evidence

If evidence is inconclusive across outcomes or high uncertainty within outcomes

Orange

No evidence of significant risk in existing data

If no evidence of risk and low uncertainty overall Green

Some evidence of significant risk

Evidence of significant risk in at least one CLINICAL outcome (i.e. regardless of consistency across outcomes or level of certainty within outcomes)

Red

Consistent evidence of significant risk

If evidence of significant risk across all outcomes Red

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Table 6. Output of the Literature Review for Antihistamines Year of marketing Drug hERG Clinical trials Observational Conclusion

1 2 3 4 5 VA SCD

1946 diphenhydramine 0,4 Insufficient evidence available

1951 promethazine 0,6 Insufficient evidence available

1963 chlorphenamine 0,4 Insufficient evidence available

1980 ketotifen No evidence available

1983 astemizole 0,2 0,6 0,2 Uncertainty in existing evidence

1984 mequitazine No evidence available

1992 clemastine 0,6 0,2 Uncertainty in existing evidence

1993 loratadine 0,4 0,4 0,6 0,4 0,4 0,6 Uncertainty in existing evidence

1993 terfenadine 0,2 0,8 0,4 0,6 0,6 0,4 Some evidence of significant risk

1994 acrivastine 0,6 Insufficient evidence available

1995 cetirizine 0,2 0,6 0,4 0,6 0,6 Uncertainty in existing evidence

1996 fexofenadine 0,8 0,2 0 No evidence of significant risk

1997 mizolastine 0,8 0,6 0,4 Uncertainty in existing evidence

2001 desloratadine 0,6 0,4 0,4 0,4 0,4 Uncertainty in existing evidence

2001 ebastine 0,6 0,2 0,6 0,4 Uncertainty in existing evidence

2001 levocetirizine 0,6 0,8 0,4 0,6 Uncertainty in existing evidence

2005 cyclizine No evidence available

2005 oxatomide 0,6 Insufficient evidence available

2008 rupatadine 0,8 0,8 0,6 Uncertainty in existing evidence

2009 alimemazine No evidence available

Unknown cyproheptadine No evidence available

Unknown deptropine No evidence available

Unknown dexchlorpheniramine No evidence available

Unknown dimetindene No evidence available

Unknown mebhydrolin No evidence available

Unknown meclozine No evidence available

Unknown oxomemazine No evidence available

Unknown thiazinam No evidence available

1= undefined QTc prolongation; 2= mild ormoderate QTc prolongation; 3= difference in mean delta QTc for drug and placebo; 4= placebo-adjusted delta QTc; 5= severe QTc prolongation; VA: ventricular Arrhythmia; SCD: Sudden Cardiac Death.

ANTIPSYCHOTICS Among the 37 antipsychotics that were reviewed (Table 7), most drugs had grey level of evidence (n=20), 4 were red (thioridazine, mesoridazine, droperidol and risperidone) and for 13 drugs there was uncertainty in the available evidence.

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Table 7. Output of the Literature Review for Antipsychotics Year of marketing Drug hERG Clinical Obs Conclusion

1 2 3 4 5 VA SCD

1956 prochlorperazine 0.4 Insufficient evidence available

1956 promazine No evidence available

1957 chlorpromazine 0.4 0,6 0,4 Uncertainty in existing evidence

1957 perphenazine 0.4 Insufficient evidence available

1958 perazine No evidence available

1959 chlorprothixene No evidence available

1959 fluphenazine 0.4 0,4 Uncertainty in existing evidence

1959 levomepromazine No evidence available

1962 thioridazine 0.2 0,6 0,4 0,6 0,4 Some evidence of significant risk

1966 mesoridazine 0.2 0,6 0,6 0,4 0,6 Some evidence of significant risk

1966 periciazine No evidence available

1967 haloperidol 0.2 0,6 0,4 0,2 0,2 0,6 Uncertainty in existing evidence

1970 pipamperone 0,6 Insufficient evidence available

1980 melperone No evidence available

1980 tiapride

No evidence available

1983 flupentixol 0,6 0,4

Uncertainty in existing evidence

1984 pimozide 0.4 0,6 Uncertainty in existing evidence

1984 sulpiride 0,4 Insufficient evidence available

1984 zuclopenthixol 0,4 Insufficient evidence available

1985 levosulpiride No evidence available

1986 droperidol 0.4 0,4 0,2 0,2 0,4 0,2 0,4 Some evidence of significant risk

1989 clozapine 0.2 0,4 Uncertainty in existing evidence

1993 amisulpride 0.4 Insufficient evidence available

1993 risperidone 0.4 0,8 0,4 0,4 0,4 0,4 Some evidence of significant risk

1996 olanzapine 0.4 0,4 0,4 0,6 0,4 0,4 Uncertainty in existing evidence

1996 sertindole 0.6 0,4 Uncertainty in existing evidence

1997 quetiapine 0.8 0,6 0,4 0,4 0,2 0,4 Uncertainty in existing evidence

1998 penfluridol 0,2 Insufficient evidence available

2001 ziprasidone 0.4 0,4 0,4 Uncertainty in existing evidence

2002 aripiprazole 0.4 0,2 0,2 0,4 Uncertainty in existing evidence

2002 clotiapine No evidence available

2005 bromperidol No evidence available

2006 paliperidone 0,2 0,2 0,2 0,4 0,2 Uncertainty in existing evidence

2009 asenapine 0,6 0,4 0,6 0,6 0,2 Uncertainty in existing evidence

Unknown fluspirilene No evidence available

Unknown mosapramine No evidence available

Unknown prothipendyl No evidence available


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ANTI-INFECTIVES Among the 154 anti-infectives that were reviewed (Table 8), most drugs had grey level of evidence (n=132) and for 22 drugs there was uncertainty in the available evidence. Table 8. Output of the Literature Review for Anti-infectives

Year of marketing Drug hERG

Clinical Obs

Conclusion 1 2 3 4 5 VA SCD

1941 sulfadiazine No evidence available

1949 chloroquine 0,8 Insufficient evidence available

1949 meglumine antimonate No evidence available

1952 pyrimethamine 0,4 Insufficient evidence available

1953 tetracycline No evidence available

1955 hydroxychloroquine No evidence available

1958 nitrofurantoin No evidence available

1963 metronidazole No evidence available

1963 metronidazole No evidence available

1964 erythromycin 0,2 0,8 0,6 0,4 Uncertainty in existing evidence

1964 vancomycin No evidence available

1965 ampicillin No evidence available

1966 amphotericin B No evidence available

1967 doxycycline No evidence available

1967 ethambutol No evidence available

1970 clindamycin No evidence available

1971 cefalexin No evidence available

1971 flucytosine No evidence available

1971 minocycline No evidence available

1971 pyrazinamide No evidence available

1973 cefazolin No evidence available

1974 amoxicillin 0,4 0,4 Uncertainty in existing evidence

1974 miconazole 0,6 Insufficient evidence available

1977 pivmecillinam No evidence available

1978 cefamandole No evidence available

1978 sulfamethoxazole No evidence available

1979 colistin No evidence available

1979 cotrimoxazole 0,4 Insufficient evidence available

1979 dapsone No evidence available

1980 gentamicin No evidence available

1981 amikacin No evidence available

1981 ketoconazole 0 0,6 0,6 Uncertainty in existing evidence

1981 piperacillin No evidence available

1982 aciclovir No evidence available

1982 dicloxacillin No evidence available

1982 nystatin No evidence available

1982 trimethoprim 0,4 Insufficient evidence available

1983 cefotaxime No evidence available

1983 cefuroxime No evidence available

1983 inosine pranobex No evidence available

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Clinical Obs


1983 isoniazid No evidence available

1983 pipemidic acid No evidence available

1984 ceftriaxone No evidence available

1984 co-amoxiclav No evidence available

1984 pentamidine 0,4 0,4 0,6 Uncertainty in existing evidence

1984 tobramycin No evidence available

1985 ceftazidime No evidence available

1985 phenoxymethylpenicillin No evidence available

1985 ribavirin No evidence available

1985 spiramycin No evidence available

1986 amantadine 0,4 Insufficient evidence available

1986 aztreonam No evidence available

1986 norfloxacin No evidence available

1986 sulbactam No evidence available

1987 ciprofloxacin 0,6 0,6 0,6 0,6 Uncertainty in existing evidence

1987 zidovudine No evidence available

1988 clotrimazole No evidence available

1988 halofantrine 0,4 Insufficient evidence available

1989 cefixime No evidence available

1989 ganciclovir No evidence available

1989 mefloquine 0,2 0,8 Uncertainty in existing evidence

1989 rifampicin 0,8 Insufficient evidence available

1989 roxithromycin 0,6 Insufficient evidence available

1989 teicoplanin No evidence available

1990 fluconazole 0,6 0,4 Uncertainty in existing evidence

1990 ofloxacin 0,8 Insufficient evidence available

1991 clarithromycin 0,4 0,6 0,6 0,2 Uncertainty in existing evidence

1991 didanosine No evidence available

1991 foscarnet No evidence available

1992 itraconazole No evidence available

1992 rifabutin No evidence available

1993 tazobactam No evidence available

1994 azithromycin 0,8 Insufficient evidence available

1994 famciclovir No evidence available

1994 quinine No evidence available

1994 stavudine No evidence available

1995 ceftibuten No evidence available

1995 lamivudine No evidence available

1995 saquinavir 0,8 Insufficient evidence available

1996 atovaquone No evidence available

1996 cefepime No evidence available

1996 fosfomycin No evidence available

1996 indinavir No evidence available

1996 levofloxacin 0,4 0,4 0,6 0,6 0,4 Uncertainty in existing evidence

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Clinical Obs


1996 meropenem No evidence available

1996 nevirapine 0,4 Insufficient evidence available

1996 ritonavir 0,8 0,4 0,2 0,4 Uncertainty in existing evidence

1996 terbinafine 0,6 Insufficient evidence available

1997 cefaclor No evidence available

1997 nelfinavir 0,8 0,4 0,4 0,6 Uncertainty in existing evidence

1997 paromomycin No evidence available

1998 abacavir No evidence available

1998 benzylpenicillin No evidence available

1998 efavirenz 0,4 Insufficient evidence available

1998 valaciclovir No evidence available

1999 artemether No evidence available

1999 flucloxacillin No evidence available

1999 lumefantrine (with artemether) No evidence available

1999 moxifloxacin 0,2 0,6 0,6 0,6 Uncertainty in existing evidence

1999 oseltamivir 0,6 Insufficient evidence available

1999 zanamivir No evidence available

2000 brivudine No evidence available

2000 griseofulvin No evidence available

2000 linezolid No evidence available

2001 caspofungin No evidence available

2001 lopinavir 0,8 0,4 Uncertainty in existing evidence

2001 netilmicin No evidence available

2001 valganciclovir No evidence available

2002 adefovir No evidence available

2002 cefpodoxime No evidence available

2002 tenofovir No evidence available

2002 voriconazole 0,6 Insufficient evidence available

2003 atazanavir 0,4 Insufficient evidence available

2003 enfuvirtide No evidence available

2003 fosamprenavir No evidence available

2003 gemifloxacin 0,6 Insufficient evidence available

2004 emtricitabine No evidence available

2004 prulifloxacin 0,6 0,4 Uncertainty in existing evidence

2004 rifaximin No evidence available

2004 telithromycin 0,6 0,4 0,4 Uncertainty in existing evidence

2004 tinidazole No evidence available

2005 micafungin No evidence available

2005 rifamycin No evidence available

2005 tigecycline No evidence available

2005 tipranavir No evidence available

2006 posaconazole 0,8 Insufficient evidence available

2007 raltegravir 0,6 0,4 Uncertainty in existing evidence

2008 doripenem 0,6 0,4 0,6 Uncertainty in existing evidence

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Clinical Obs


2008 etravirine 0,6 0,4 0,6 Uncertainty in existing evidence

2010 cilastatin (with imipenem) No evidence available

2010 imipenem plus cilastatin No evidence available

2012 telavancin 0,6 0,6 0,6 Uncertainty in existing evidence

Unknown azanidazole No evidence available

Unknown broxyquinoline No evidence available

Unknown butconazole No evidence available

Unknown clioquinol No evidence available

Unknown econazole No evidence available

Unknown ethionamide No evidence available

Unknown fenticonazole No evidence available

Unknown gatifloxacin 0,6 0,6 0,6 Uncertainty in existing evidence

Unknown grepafloxacin 0,8 Insufficient evidence available

Unknown isoconazole No evidence available

Unknown lysozyme No evidence available

Unknown mepartricin No evidence available

Unknown oritavancin 0,6 0,6 0,6 Uncertainty in existing evidence

Unknown oxicolazole No evidence available

Unknown pheneticillin No evidence available

Unknown pivampicillin No evidence available

Unknown primaquine 0,6 Insufficient evidence available

Unknown proguanil No evidence available

Unknown protionamide No evidence available

Unknown sparfloxacin 0,6 0,4 0,4 0,4 Uncertainty in existing evidence

Unknown sulfamethizole No evidence available

Unknown tioconazole No evidence available


1.3.5 Evidence integration on drug related factors

The ultimate objective of the ARITMO integration model was to actually translate results from the ARITMO project into useful recommendations for clinicians and regulators (see figure 14).

From a population perspective, drug characteristics were assessed through the following approaches:

1) Systematic reviews and syntheses of information from pre-clinical information, clinical trials and observational studies on a selected list of ARITMO drugs that are frequently used (WP6).

2) Assessment of pharmacovigilance data for all ARITMO drugs (WP3); 3) Design and conduct of multi-country studies in currently available health care databases in the UK,

Netherlands, Denmark, Germany and Italy for all ARITMO drugs (WP4 & 5) 4) In silico predictions of QT liability and proarrhythmic potential of drugs using various approaches

(pharmacophoric, target profiling and molecular modelling) (WP7)

From an individual perspective, patient characteristics were identified through:

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1) Novel markers for Torsade de Pointes on ECGs (WP7) 2) Genetic factors that can modify drug risk (WP7)

Figure 14: ARITMO model for data integration The main results from WP 3 (pharmacovigilance), WP 5 (database studies) and 7 (hERG affinity) were integrated in a Dempster Shafer (DS) model (WP8) the output of which was a traffic light. The literature review in WP also resulted in a traffic light, with different interpretation and value; it was not about the level or risk but the level of evidence of risk. The DS model allows combing evidence from heterogeneous and independent sources using expert judgment. Basically, it combines the “evidential weight” (also known as hypotheses) originated from WP3, 5 and 7 into a “combined mass”. The drug risk category is defined by considering the hypothesis with the highest value of the mass, provided that it is ≥0.300. In other circumstances, a grey category (representing uncertainty) is assigned. Combination of the two results from the two traffic light systems (WP6-literature review and WP8-DS evidence integration) may lead to different scenarios: THEORETICAL SCENARIO 1: No evidence from literature review (WP6), and some evidence in Dempster Shafer analysis (WP8) which is based on data from pharmacovigilance (WP3), pharmacoepidemiology (WP5) or hERG affinity (WP7). THEORETICAL SCENARIO 2: Evidence from literature review (WP6), and consistent traffic lights from Dempster Shafer analysis (WP8) which is based on data from pharmacovigilance (WP3), pharmacoepidemiology (WP5) or hERG affinity (WP7)

THEORETICAL SCENARIO 3: Uncertainty in evidence from literature review (WP6), and green or red traffic lights from Dempster Shafer analysis (WP8) which is based on data from pharmacovigilance (WP3), pharmacoepidemiology (WP5) or hERG affinity data (WP7).

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THEORETICAL SCENARIO 4: Evidence from literature review (WP6), and inconsistent traffic lights from Dempster Shafer analysis (WP8) which is based on data from pharmacovigilance (WP3), pharmacoepidemiology (WP5) or hERG affinity (WP7). This theoretical example never occurred in ARITMO FOR CLINICIANS: the overall traffic light system results may be very informative to guide the choice of therapy in patients susceptible of arrhythmia (e.g., because of the presence of multiple risk factors, which may be modifiable or not). We developed the following proposal (Figure 15)

FOR REGULATORS: the most important use of ARITMO results may be the identification of newly available evidence from the ARITMO model, i.e., ARITMO provides new data (evidence of high or low risk) that did not arise from the literature (i.e., grey area due to missing information). An algorithm could be developed (beyond the ARITMO project) with the aim of supporting EMA in revising information reported in the summary of product characteristics, in particular in sections 4.3 (contraindications), 4.4 (warnings and precautions) and 4.8 (side effects).

KEY RESULTS

Notably, the DS model was applied only if at least 2 WPs provided data. After merging data from the three WPs, 450 drugs had data from at least 2 WPs (among WP3, WP5 or WP7) and were processed by the DS approach for evidence integration. Please note that, in the following tables, an asterisk in the DS column indicates that all 3 WPs provided hypotheses. Comparison with the literature For 205 drugs, out of 450, a comparison between WP6 (literature review, which was performed on the ARITMO narrow drug list) and DS assessment is provided: 26 antihistamines (15 with asterisk), 36 antipsychotics (16 with asterisk) and 143 anti-infectives (44 with asterisk). Antihistamines Among antihistamines, 16 drugs fulfill scenario 1 (new evidence emerged from ARITMO DS), 3 drugs fulfill scenario 2 (ARITMO DS confirmed literature evidence: notably, for terfenadine, red light has been confirmed, and for fexofenadine, green light has been confirmed) and 7 fulfill scenario 3 (refinement of intermediate score to high or low risk category). Fexofenadine, cetirizine and levocetirizine belong to low risk category (green light).

N° ATC_V Drug literature DS Scenario

1 R06AA02 diphenhydramine 1

2 R06AA04 clemastine * 3

3 R06AB02 dexchlorpheniramine * 1

4 R06AB03 dimetindene * 1

Potentially usable in arrhythmia-susceptible patients*

* see also risk factors identified in WP7 (ECG and genetics). Please note that the level of plausibility/uncertainty of the DS approach should be considered.

Use with caution in arrhythmia-susceptible patients* * see also risk factors identified in WP7 (ECG and genetics).

Avoid in arrhythmia-susceptible patients* * see also risk factors identified in WP7 (ECG and genetics).

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Antipsychotics Among antipsychotics, 20 drugs fulfill scenario 1, 5 drugs fulfill scenario 2 (notably, for thioridazine, mesoridazine, droperidol and risperidone red light has been confirmed) and 11 fulfill scenario 3. Most benzamides resulted in low risk categories: sulpiride, tiapride and levosulpiride in the green group, whereas amisulpride in the green-orange group.


1 N05AA01 chlorpromazine * 3

2 N05AA02 levomepromazine * 1

3 N05AA03 promazine * 1

4 N05AB02 fluphenazine 3

5 N05AB03 perphenazine * 1

6 N05AB04 prochlorperazine 1

7 N05AB10 perazine 1

8 N05AC01 periciazine 1

9 N05AC02 thioridazine * 2

10 N05AC03 mesoridazine 2

11 N05AD01 haloperidol * 3

12 N05AD03 melperone * 1

13 N05AD05 pipamperone * 1

14 N05AD06 bromperidol 1

15 N05AD08 droperidol 2

5 R06AB04 chlorphenamine * 1

6 R06AD01 alimemazine 1

7 R06AD02 promethazine * 1

8 R06AD07 mequitazine 1

9 R06AD08 oxomemazine 1

10 R06AE03 cyclizine * 1

11 R06AE05 meclozine 1

12 R06AE06 oxatomide 1

13 R06AE07 cetirizine * 3

14 R06AE09 levocetirizine * 3

15 R06AX02 cyproheptadine 1

16 R06AX12 terfenadine * 2

17 R06AX13 loratadine * 3

18 R06AX15 mebhydrolin 1

19 R06AX16 deptropine 1

20 R06AX17 ketotifen 1

21 R06AX18 acrivastine * 1

22 R06AX22 ebastine * 3

23 R06AX25 mizolastine * 3

24 R06AX26 fexofenadine * 2

25 R06AX27 desloratadine * 2

26 R06AX28 rupatadine 3

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16 N05AE04 ziprasidone 3

17 N05AF01 flupentixol * 3

18 N05AF03 chlorprothixene * 1

19 N05AF05 zuclopenthixol * 1

20 N05AG01 fluspirilene 1

21 N05AG02 pimozide 3

22 N05AG03 penfluridol 1

23 N05AH02 clozapine 3

24 N05AH03 olanzapine * 2

25 N05AH04 quetiapine * 3

26 N05AH05 asenapine 3

27 N05AH06 clotiapine 1

28 N05AL01 sulpiride * 1

29 N05AL03 tiapride 1

30 N05AL05 amisulpride * 1

31 N05AL07 levosulpiride 1

32 N05AX07 prothipendyl 1

33 N05AX08 risperidone * 2

34 N05AX10 mosapramine 1

35 N05AX12 aripiprazole 3

36 N05AX13 paliperidone 3

Anti-infectives Among anti-infectives, 122 drugs fulfill scenario 1, 4 drugs fulfill scenario 2 and 16 fulfill scenario 3. In comparison with the other ARITMO drug classes, in most cases literature did not provide definite evidence (from WP6 emerged 142 drugs, of which only 19 belonged to orange class and all remaining drugs to grey class). Notably, from DS analysis emerged that anti-infectives mainly belong to low risk categories (i.e., green or green-orange). By contrast, different aminoglycosides, azole antifungals, antivirals (protease inhibitors) and antimalarials were classified as high risk drugs (red traffic light). Only for telithromycin, DS analysis did not provide a definite category as compared to the literature (orange traffic light).


1 J01AA02 doxycycline * 1

2 J01AA07 tetracycline 1

3 J01AA08 minocycline * 1

4 J01AA12 tigecycline 1

5 J01CA01 ampicillin 1

6 J01CA02 pivampicillin * 1

7 J01CA04 amoxicillin * 2

8 J01CA08 pivmecillinam * 1

9 J01CA12 piperacillin 1

10 J01CE01 benzylpenicillin 1

11 J01CE02 phenoxymethylpenicillin * 1

12 J01CE05 pheneticillin * 1

13 J01CF01 dicloxacillin * 1

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14 J01CF05 flucloxacillin * 1

15 J01CG01 sulbactam 1

16 J01CG02 tazobactam 1

17 J01DB01 cefalexin * 1

18 J01DB04 cefazolin 1

19 J01DC02 cefuroxime * 1

20 J01DC03 cefamandole 1

21 J01DC04 cefaclor * 1

22 J01DD01 cefotaxime 1

23 J01DD02 ceftazidime 1

24 J01DD04 ceftriaxone * 1

25 J01DD08 cefixime * 1

26 J01DD13 cefpodoxime * 1

27 J01DD14 ceftibuten 1

28 J01DE01 cefepime 1

29 J01DF01 aztreonam 1

30 J01DH02 meropenem 1

31 J01DH04 doripenem 3

32 J01EA01 trimethoprim * 1

33 J01EB02 sulfamethizole * 1

34 J01EC01 sulfamethoxazole 1

35 J01EC02 sulfadiazine 1

36 J01FA01 erythromycin * 3

37 J01FA02 spiramycin 1

38 J01FA06 roxithromycin * 1

39 J01FA09 clarithromycin * 2

40 J01FA10 azithromycin * 1

41 J01FA15 telithromycin -

42 J01FF01 clindamycin * 1

43 J01GB01 tobramycin 1

44 J01GB03 gentamicin 1

45 J01GB06 amikacin 1

46 J01GB07 netilmicin 1

47 J01MA01 ofloxacin * 1

48 J01MA02 ciprofloxacin * 3

49 J01MA06 norfloxacin * 1

50 J01MA11 grepafloxacin 1

51 J01MA12 levofloxacin * 3

52 J01MA14 moxifloxacin * 3

53 J01MA15 gemifloxacin 1

54 J01MA17 prulifloxacin 3

55 J01MB04 pipemidic acid 1

56 J01XA01 vancomycin 1

57 J01XA02 teicoplanin 1

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58 J01XA03 telavancin 3

59 J01XB01 colistin 1

60 J01XD01 metronidazole * 1

61 J01XD02 tinidazole 1

62 J01XE01 nitrofurantoin * 1

63 J01XX01 fosfomycin * 1

64 J01XX08 linezolid 1

65 A07AA11 rifaximin * 1

66 J02AA01 amphotericin B 1

67 J02AB01 miconazole * 1

68 J02AB02 ketoconazole 3

69 J02AC01 fluconazole * 3

70 J02AC02 itraconazole * 1

71 J02AC03 voriconazole 1

72 J02AC04 posaconazole 1

73 J02AX01 flucytosine 1

74 J02AX04 caspofungin 1

75 J02AX05 micafungin 1

76 A07AA02 nystatin *

1

77 N04BB01 amantadine * 1

78 D01AA08 griseofulvin 1

79 D01AE15 terbinafine * 1

80 G01AF02 clotrimazole *

1

81 G01AF05 econazole 1

82 G01AF08 tioconazole

1

83 G01AF07 isoconazole

1

84 G01AF12 fenticonazole

1

85 G01AF15 butoconazole

1

85 G01AF17 oxiconazole

1

86 J04AB02 rifampicin 1

87 J04AB03 rifamycin 1

88 J04AB04 rifabutin 1

89 J04AD01 protionamide 1

90 J04AD03 ethionamide 1

91 J04AK01 pyrazinamide 1

92 J04AK02 ethambutol 1

93 J04BA02 dapsone 1

94 J05AB01 aciclovir * 1

95 J05AB04 ribavirin 1

96 J05AB06 ganciclovir 1

97 J05AB09 famciclovir 1

98 J05AB11 valaciclovir * 1

99 J05AB14 valganciclovir 1

100 J05AB15 brivudine 1

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101 J05AD01 foscarnet 1

102 J05AE01 saquinavir 1

103 J05AE02 indinavir 1

104 J05AE03 ritonavir 3

105 J05AE04 nelfinavir 3

106 J05AE06 lopinavir 3

107 J05AE07 fosamprenavir 1

108 J05AE08 atazanavir 2

109 J05AE09 tipranavir 1

110 J05AF01 zidovudine 1

111 J05AF02 didanosine 1

112 J05AF04 stavudine 1

113 J05AF05 lamivudine 1

114 J05AF06 abacavir 1

115 J05AF08 adefovir 1

116 J05AF09 emtricitabine 1

117 J05AG01 nevirapine 1

118 J05AG03 efavirenz 1

119 J05AG04 etravirine 3

120 J05AH01 zanamivir 1

121 J05AH02 oseltamivir 1

122 J05AX02 lysozyme 1

123 J05AX05 inosine pranobex 1

124 J05AX07 enfuvirtide 1

125 J05AX08 raltegravir 3

126 P01AA01 broxyquinoline 2

127 P01AA02 clioquinol 1

128 P01AB01 metronidazole * 1

129 P01AB04 azanidazole 1

130 P01AX06 atovaquone 1

131 P01BA01 chloroquine * 1

132 P01BA02 hydroxychloroquine * 1

133 P01BA03 primaquine 1

134 P01BB01 proguanil 1

135 P01BC01 quinine * 1

136 P01BC02 mefloquine 3

137 P01BD01 pyrimethamine 1

138 P01BE02 artemether 1

139 P01BX01 halofantrine 1

140 P01CB01 meglumine antimonate 1

141 P01CX01 pentamidine 3

142 A07AA06 paromomycin 1

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Additional results from DS The following tables provided the DS output for drugs not investigated by the literature review (WP6). In fact, the literature search strategy was performed by considering a reduced number of compounds (i.e., the narrow ARITMO drug list) as compared to broad ARITMO drug list. Both lists are available as a source document in the project website (www.aritmo-project.org). Among antihistamines, 18 received red, 9 received red-orange classification, 2 classified as orange, only 1 is green classification (epinastine), whereas 2 drugs did not have sufficient data to provide a traffic light classification (grey area). Also in case of antipsychotics, most drugs (17) belong to red class, 3 received red-orange classification, only 1 is orange and 2 are green-orange (benzamides: sultopride and veralipride). Only 1 ARITMO drug remains in the grey area and only one carries an asterisk (see above for definition). By contrast, most anti-infectives listed below are categorized into lower risk classes: 54 out of 189 are green (29%), 99 green-orange (52%) and 5 orange. Sixteen received red-orange classification and only 6 resulted as red. The remaining 9 are grey and only 3 carry an asterisk. Antihistamines

N° ATC_V Drug Literature DS

1 R06AA01 bromazine -

2 R06AA06 chlorphenoxamine -

3 R06AA07 diphenylpyraline -

4 R06AA08 carbinoxamine -

5 R06AA09 Doxylamine -

6 R06AB01 brompheniramine -

7 R06AB05 pheniramine -

8 R06AB06 dexbrompheniramine -

9 R06AB07 talastine -

10 R06AC01 mepyramine -

11 R06AC02 histapyrrodine -

12 R06AC03 chloropyramine -

13 R06AC04 tripelennamine -

14 R06AC05 methapyrilene -

15 R06AC06 thonzylamine -

16 R06AD03 thiethylperazine -

17 R06AD04 methdilazine -

18 R06AD05 hydroxyethylpromethazine -

19 R06AD09 isothipendyl -

20 R06AE01 buclizine -

21 R06AE04 chlorcyclizine -

22 R06AX01 bamipine -

23 R06AX03 thenalidine -

24 R06AX03 thenalidine -

25 R06AX04 phenindamine -

26 R06AX05 antazoline -

27 R06AX07 triprolidine -

28 R06AX08 pyrrobutamine -

29 R06AX19 azelastine -

30 R06AX21 tritoqualine -

31 R06AX23 pimethixene -

32 R06AX24 epinastine -

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Antipsychotics

N° ATC_V Drug literature DS

1 N05AA04 acepromazine -

2 N05AA05 triflupromazine -

3 N05AA06 cyamemazine -

4 N05AA07 chlorproethazine -

5 N05AB01 dixyrazine -

6 N05AB05 thiopropazate -

7 N05AB06 trifluoperazine -

*

8 N05AB07 acetophenazine -

9 N05AB08 thioproperazine -

10 N05AB09 butaperazine -

11 N05AC04 pipotiazine -

12 N05AD02 trifluperidol -

13 N05AD04 moperone -

14 N05AD07 benperidol -

15 N05AD09 fluanisone -

16 N05AE01 oxypertine -

17 N05AE02 molindone -

18 N05AF02 clopenthixol -

19 N05AF04 tiotixene -

20 N05AH01 loxapine -

21 N05AL02 sultopride -

22 N05AL04 remoxipride -

23 N05AL06 veralipride -

24 N05AX11 zotepine -

Anti-infectives

N° ATC_V Drug literature DS

1 J01AA01 demeclocycline -

2 J01AA03 chlortetracycline -

3 J01AA04 lymecycline - *

4 J01AA05 metacycline -

5 J01AA06 oxytetracycline -

6 J01AA09 rolitetracycline - 7 J01AA10 penimepicycline - 8 J01AA11 clomocycline -

9 J01BA02 thiamphenicol - 10 J01CA03 carbenicillin - 11 J01CA05 carindacillin - 12 J01CA06 bacampicillin - 13 J01CA07 epicillin - 14 J01CA09 azlocillin -

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15 J01CA10 mezlocillin -

16 J01CA11 mecillinam - 17 J01CA13 ticarcillin and enzyme inhibitor - 18 J01CA14 metampicillin - 19 J01CA15 talampicillin -

20 J01CA16 sulbenicillin - 21 J01CA17 temocillin - 22 J01CA18 hetacillin -

23 J01CE03 propicillin - 24 J01CE04 azidocillin - 25 J01CE06 penamecillin - 26 J01CE07 clometocillin -

27 J01CF02 cloxacillin - 28 J01CF03 meticillin - 29 J01CF04 oxacillin -

30 J01CR04 sultamicillin - *

31 J01DB02 cefaloridine -

32 J01DB03 cefalotin -

33 J01DB05 cefadroxil - 34 J01DB06 cefazedone - 35 J01DB07 cefatrizine - 36 J01DB08 cefapirin -

37 J01DB09 cefradine - *

38 J01DB10 cefacetrile 39 J01DB11 cefroxadine - 40 J01DB12 ceftezole -

41 J01DC01 cefoxitin - 42 J01DC05 cefotetan - 43 J01DC06 cefonicide - 44 J01DC07 cefotiam -

45 J01DC08 loracarbef - 46 J01DC09 cefmetazole - 47 J01DC10 cefprozil - 48 J01DC11 ceforanide -

49 J01DD03 cefsulodin - 50 J01DD05 cefmenoxime - 51 J01DD06 latamoxef - 52 J01DD07 ceftizoxime -

53 J01DD09 cefodizime - 54 J01DD10 cefetamet - 55 J01DD11 cefpiramide - 56 J01DD12 cefoperazone - 57 J01DD15 Cefdinir - 58 J01DD16 Cefditoren - 59 J01DE02 Cefpirome - 60 J01DH03 Ertapenem -

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61 J01DI01 ceftobiprole medocaril -

62 J01EB01 sulfaisodimidine - 63 J01EB03 Sulfadimidine - 64 J01EB03 Sulfadimidine - 65 J01EB04 Sulfapyridine - 66 J01EB05 Sulfafurazole -

67 J01EB06 Sulfanilamide -

68 J01EB07 Sulfathiazole - 69 J01EB08 Sulfathiourea -

70 J01EC03 Sulfamoxole - 71 J01ED01 sulfadimethoxine -

72 J01ED02 Sulfalene -

73 J01ED03 sulfametomidine -

74 J01ED04 sulfametoxydiazine - 75 J01ED05 sulfamethoxypyridazine -

76 J01ED06 Sulfaperin - 77 J01ED07 sulfamerazine - 78 J01ED08 sulfaphenazole - 79 J01ED09 Sulfamazone - 80 J01FA03 Midecamycin - 81 J01FA05 oleandomycin -

82 J01FA07 Josamycin - 83 J01FA08 troleandomycin - 84 J01FA11 Miocamycin - 85 J01FA12 Rokitamycin - 86 J01FA13 Dirithromycin -

87 J01FA14 flurithromycin - 88 J01FF02 Lincomycin - 89 J01FG01 Pristinamycin - 90 J01GA01 Streptomycin - 91 J01GB04 Kanamycin - 92 J01GB05 Neomycin -

93 J01GB08 Sisomicin - 94 J01GB09 Dibekacin - 95 J01GB10 Ribostamycin - 96 J01GB11 Isepamicin -

97 J01GB12 arbekacin - 98 J01MA03 pefloxacin - 99 J01MA04 enoxacin -

100 J01MA05 temafloxacin - 101 J01MA07 lomefloxacin - 102 J01MA08 fleroxacin - 103 J01MA10 rufloxacin - 104 J01MA13 trovafloxacin - 105 J01MA18 pazufloxacin - 106 J01MB01 rosoxacin -

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107 J01MB02 nalidixic acid - 108 J01MB03 piromidic acid - 109 J01MB05 oxolinic acid -

110 J01MB06 cinoxacin -

111 J01MB07 flumequine - 112 J01XB02 polymyxin b -

113 J01XC01 fusidic acid -

114 J01XD03 ornidazole -

115 J01XE02 nifurtoinol - 116 J01XX02 xibornol -

117 J01XX03 clofoctol -

118 J01XX04 spectinomycin - 119 J01XX05 methenamine -

120 J01XX06 mandelic acid -

121 J01XX07 nitroxoline - 122 J01XX09 daptomycin -

123 J01XX10 bacitracin -

124 A07AB03 sulfaguanidine - 125 A07AB04 succinylsulfathiazole -

126 J02AX06 anidulafungin - 127 G01AF18 flutrimazole - 128 G01AG02 terconazole -

129 J04AA01 aminosalicylic acid -

130 J04AB01 cycloserine -

131 J04AB05 rifapentine - 132 J04AB30 capreomycin -

133 J04AD02 tiocarlide -

134 J04AK03 terizidone - 135 J04AK04 morinamide -

136 J04BA01 clofazimine - 137 J05AA01 metisazone - 138 J05AB02 idoxuridine - 139 J05AB03 vidarabine - 140 J05AB12 cidofovir -

141 J05AB12 penciclovir -

142 J05AC02 rimantadine -

143 J05AC03 tromantadine - 144 J05AD02 fosfonet -

145 J05AE05 amprenavir - 146 J05AE10 darunavir - 147 J05AF03 zalcitabine - 148 J05AF08 adefovir dipivoxil - 149 J05AF10 entecavir - 150 J05AF11 telbivudine - 151 J05AF12 clevudine - 152 J05AG02 delavirdine -

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153 J05AX01 moroxydine -

154 J05AX06 pleconaril -

155 J05AX09 maraviroc -

156 P01AA04 chlorquinaldol - 157 P01AA05 tilbroquinol - 158 P01AB05 propenidazole -

159 P01AB06 nimorazole - 160 P01AB07 secnidazole -

161 P01AC01 diloxanide -

162 P01AC03 etofamide -

163 P01AC04 teclozan - 164 P01AR01 arsthinol - 165 P01AR02 difetarsone - 166 P01AR03 glycobiarsol -

167 P01AX01 chiniofon -

168 P01AX02 emetine - 169 P01AX04 phanquinone - 170 P01AX05 mepacrine -

171 P01AX07 trimetrexate -

172 P01AX08 tenonitrozole - 173 P01AX09 dihydroemetine - 174 P01AX10 fumagillin - 175 P01AX11 nitazoxanide - 176 P01BA06 amodiaquine -

177 P01BB02 cycloguanil embonate -

178 P01BE01 artemisinin -

179 P01BE03 artesunate -

180 P01BE05 artenimol -

181 P01CA02 benznidazole - 182 P01CC01 nifurtimox - 183 P01CC02 nitrofural - 184 P01CD01 melarsoprol -

185 P01CD02 acetarsol -

186 P01CX02 suramin sodium - 187 P01CX03 eflornithine - 188 R02AB02 tyrothricin - 189 R02AB30 gramicidin -

KEY MESSAGE from evidence integration

Evidence integration represents the final step of the ARITMO project. We have adopted a multidisciplinary consensus process to aggregate heterogeneous data from various WPs, assigning a certain degree of confidence to each type of evidence to yield a final risk score, an innovative component of the ARITMO project.

The ARITMO results can be used in a flexible fashion, depending on the issue under scrutiny. For example, future applications include:

ARITMO Final Report


- WP8 results, especially when the final traffic light carries an asterisk (denoting evidence from three independent sources), can be used as an aid for decision making by regulators;

- WP3 results (pharmacovigilance) can be used to identify drugs to be prioritized in safety evaluation also in the light of drug utilization data; high sensitivity of this approach (possible false positives) should be taken into account;

- WP5 results can be used to quantify risk in a population perspective; high specificity of this approach (possible false negatives) should be taken into account

- WP3 and WP5 results, taken together, can help to validate preclinical predictive models by providing positive and negative control drugs.

1.3.6 ECG parameters

Recently, a number of other ECG parameters that reflect heterogeneity of repolarization have been proposed, based on T-wave loop morphology and beat-to-beat variability of repolarization. In activity 7.4 of WP7, we explored these ECG parameters in their potential to predict TdP. Deliverable 7.5 reports on the methods and results of the ECG analyses as part of that activity. Since most of the ECGs from the contributing studies were only available on paper, and the analyses required the ECGs to be in digital format, we had to develop a paper-to-digital ECG conversion pipeline. The data in our analyses were taken from four participating studies in the ARITMO project: DARE (Drug-Induced Arrhythmia Risk Evaluation Study), FAKOS (Fall-Kontroll Surveillance), EUDRAGENE, and the Rotterdam Study. A comprehensive description of these studies has been given in Deliverable 4.1 – Protocol for Multinational Analytic Field Studies. Briefly, the DARE study is a case-control study that recruited patients with TdP or QTc prolongation with syncope or severe asymptomatic QTc prolongation following exposure to drugs from 2003 onwards in England. FAKOS is a hospital-based prospective case-control surveillance study that collects adult patients with symptomatic long QT syndrome in Berlin. EUDRAGENE is a European case-control study of genetic susceptibility to adverse drug reactions (ADRs), including LQT/TdP ascertained from pharmacovigilance reports, hospitals, and primary care. The Rotterdam Study is a prospective population-based cohort study, which started in 1990 and includes 15,000 elderly inhabitants from Rotterdam. For the purpose of our analyses, we focused on cases of TdP, ventricular fibrillation (VF), and ventricular tachycardia (VT). For each case, at least one ECG that documented the event of interest had to be present. Databases that contributed cases were DARE, FAKOS, and EUDRAGENE, for a total of 113 cases. Table 1 shows the number of cases per database. Note that a case can have multiple documented events, e.g., TdP and VF. Controls were taken from the Rotterdam Study.

Table 1. Number of cases per database.

Database Cases TdP VT VF

DARE 69 48 16 17

FAKOS 36 30 0 15

EUDRAGENE 8 8 0 0

Digital ECGs were also collected and processed from drug-related cohort studies in Bologna and Newcastle, but since none of the patients in these studies experienced an event (TdP, VF, or VT), these ECGs were not used in the present analyses. All ECGs that were available in FAKOS and EUDRAGENE, were paper-based, i.e., the ECGs were only available as hard copies of the original paper ECG plots. In DARE, most ECGs were also paper-based, but some ECGs were stored in a digital format. The ECGs in the Rotterdam Study were all digitally stored. The ECG analysis program that computes the ECG parameters of interest requires the ECGs to be available in a digital format. Therefore, the paper-based ECGs had to be digitized. For this purpose, we developed a paper-to-digital ECG conversion pipeline.

ARITMO Final Report


Figure 15. Paper-to-digital ECG conversion pipeline. We measured a number of ECG parameters that reflect heterogeneity of repolarization, and were considered to be promising potential markers of TdP:

QTc interval

Maximum spatial T amplitude

Frontal T axis

QRS-T angle

Short-term QT variability

Other measures of repolarization disturbance that have been suggested as new markers of risk for arrhythmias, are QT dispersion and the peak-to-end T interval. In previous studies, we and others have shown that both these parameters are epiphenomena of the T-loop morphology and have no intrinsic value. We therefore did not further consider them in our present analyses. The ECG parameters described above were automatically measured with our Modular ECG Analysis System (MEANS). For the measurement of STV, we developed a special measurement technique (fiducial segment averaging) to accurately measure cardiac time intervals. With the use of this pipeline we processed 679 ECGs, of which 317 (47%) were converted successfully. Another important effort has been the development and validation of an algorithm for the automatic measurement of STV, one of the novel ECG risk markers. Manual measurement of STV is cumbersome and error-prone. The technique that we developed is fully automatic and was shown to provide more reliable STV estimates than a beat-by-beat measurement approach that resembles manual measurement. Our dataset contains 102 cases with documented events, with a total of 382 ECGs that were recorded before or after the event. To our knowledge, this is the largest TdP dataset with documented events to date. To investigate the predictive value of ECG parameters, ECGs that are recorded prior to the event are especially useful. Unfortunately, the number of ECGs in our dataset before the event was relatively low. Moreover, since most of these ECGs did not have a rhythm strip, STV could only be computed in a few ECGs prior to the event. The trend analyses show mixed results for the various ECG parameters. In the uncontrolled analysis, the QTc interval showed an increase towards the event and a decrease after the event. There are differences between QT corrected according to Bazett’s or Fridericia’s formula, but the trends for both parameters are very similar. The frontal T axis and QRS-T angle show large variability and basically no trend. The spatial T amplitude also shows large variability and little change in the median values, except for an increasing trend in the last time periods after the event. The STV finally shows a clear decrease after the event; the values before the event should be interpreted with caution because they

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are based on very few cases. Similar patterns were observed when the analyses were restricted to the cases with sotalol and amiodarone as the culpable drugs. For the case-control analysis, the QTc intervals again show an increase around the event. Interestingly, the median QTc values 3 months before or after the event remain increased as compared to the control values. For the spatial T amplitude the cases show consistently lower median values than controls, without any observable trend. T axis and QRS-T angle again show large variability and no clear differences between cases and controls. STV values of cases are larger than controls, without showing a trend. In the case-control analysis for sotalol and amiodarone cases, drug-matched controls were not always available, reducing the number of cases and complicating the interpretation. The self-controlled analysis (Figures 16) was only done on the 65 DARE cases that had a digital ECG. Due to the lower number of cases as compared to the previous analyses, variability in the estimates increases, obscuring trends (if any). Again, QTc intervals show an increased trend around the event. Remarkably, spatial T amplitude is reduced for all time periods. Summarizing, our analyses show clear trends in increasing QTc intervals before an event. This pattern was not observed for the other ECG parameters that we studied. An interesting finding was that the spatial T amplitude was reduced in cases as compared to controls, possibly suggesting that this parameter may be used as a general marker of risk. Although our uncontrolled analyses show that STV is increased around the event, too few ECGs prior to the event were available to establish a possible predictive value of this parameter. Overall, our analyses suffered from a lack of data. However, if larger datasets become available in the future, the tools and techniques that were developed as part of this workpackage provide a framework for large-scale ECG processing and exploration.

Figure 16. Number of ECGs per database in time periods relative to the event.

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Delta QTc interval Bazett

Delta QTc interval Fridericia

Delta spatial T amplitude

Delta frontal T axis

Delta QRS-T angle

Delta STV

Figure 17. Boxplots using all cases in the self-controlled analysis on the DARE cases.

1.3.7 Patient related factors: Genetics

The aim of the genetic study was to identify mutations in known and novel candidate genes associated with drug exposure (DiLQTS). 159 patients were enrolled for the genetic study at multiple centres in Europe and UK. Cases were included if they met criteria for case definition 1 (QT prolongation with documented Torsades de Pointes, ventricular fibrillation, cardiac syncope or QT prolongation alone) or case definition 2 (cardiac arrest and/or sudden death without further documentation). In addition exposure to one or more medications deemed culpable for the event was required. Blood samples were sent from Berlin, Germany and DNA was extracted in SGUL. DNA samples were received from Italy,

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Denmark and Netherlands. While the majority of the cases were thought to be Caucasian this was not reported for the Berlin and Denmark cases. 103/159 (65%) were female with a mean age of 59.0 years (range 14-88). The final list of targets for genetic studies was generated by taking the top 15 targets from an in silico list that was created in workpackage 7 and supplementing these with genes known to be involved in the congenital LQTS and additional targets generated from amiodarone and sotalol, two of the principal agents from the collection of cases. We identified putative mutations in cardiac ion channel associated genes, and a number of novel targets that require replication in other series. The details are described D 7.4. The ARITMO genetic study has identified putative mutations in cardiac ion channel associated genes, two neurological targets and a number of CYP enzymes. The details are described D 7.4. As part of ARITMO's work samples from Pavia and SGUL were pre-screened with Sanger sequencing technology for mutations in the genes involved in the congenital long QT syndrome: KCNQ1, KCNH2, SCN5A, KCNE1 and KCNE2. These data were then incorporated into the overall results of the sequencing study for samples accumulated as part of ARITMO.

1.4 The potential impact and the main dissemination activities and exploitation of results

Impact Regarding the project’s impact, the ARITMO project addressed the topic published by the EC Health DG Health-2009.4.2.2 – Study of the arrhythmogenic potential of different classes of medicines. ARITMO has specifically been devoted to analyse the cardiac safety profile of one of the commonly-used classes of medications: antipsychotics, anti-infectives and H1-anti-histamines. It should be stressed that this topic was promoted by the European Medicines Agency (EMA), thus acknowledging the importance and public health impact. A clear example of the impact reached by the ARITMO project is the intense communication and collaboration with the EMA, which is using ARITMO generated information into its decision-making process and consults ARITMO experts regularly. See also some examples of exploitation of ARITMO results in section 1.3.5. Dissemination Activities It is noteworthy that the most important scientific event in the field is the Annual Conference of the International Society of Pharmacoepidemiology, and the project has been increasingly present at this conference, to the point that in the 2013 edition to held in Montreal in August had a full workshop session devoted to ARITMO. The workshop was entitled The ARITMO results: prediction of the arrhythmogenic risk of antihistamines, antipsychotics and anti-infectives by integration of translational evidence and covered the main results derived from the project. As a general policy, only those activities that effectively reflect on the project dissemination and include the required EC funding acknowledgement have been taken into account. A Protocol for tracking dissemination activities in the Consortium was set up from the onset, consisting of a centralized repository of all Consortium dissemination activities, maintained by the Dissemination WP Leader and based on information reported by the partners. Outreach to additional audiences and other initiatives ARITMO has established links with related projects in the field of drug safety and beyond. In fact, ARITMO has used knowledge and tools generated by the EC-funded project EU-ADR and from previous projects (give examples) are being applied to ARITMO and will be further applied to the EC-funded project SAFEGUARD, conducting research in the field of drug safety on safety evaluation of adverse reactions in diabetes. The project was firstly presented at EMA in October 2010 and in June 2013 and at the Dutch pharmacovigilance network in June 2010. The European Network of Centres for Pharmacoepidemiology and Pharmacovigilance (ENCePP) was interested in the project and for this reason it was presented in some of its plenary sessions in London and in Washington (both of them took place in June 2010). The project was also presented at the Brookings Institute located in Washington in June 2010. Add more recent presentations?

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The EMA and the PRAC (Pharmacovigilance Risk Assessment Committee) have been one of the main target audiences of the project. Information about the relationship established and the communication guidelines developed can be found in deliverable D9.5 Final Report on Dissemination Activities. Communication tools From the initial stages of the project it was considered a priority to create a “corporate” identity of the project in order to maximise the impacts of homogeneous and consistent communication across partners. For this purpose a set of dissemination tools was designed and produced; namely the project logo, website, project launch press release, a flyer and poster and presentation templates. These have been widely used in all project communication activities and have contributed to visualize ARITMO as a relevant international research initiative. Some examples of tools are shown below:

Figure 18: ARITMO logo

Figure 19: ARITMO homepage screenshot

Exploitation of Results The primary results of the ARITMO project are the integrated evidence to allow for better regulatory and clinical decision making and the infrastructure established that may be used to look at other drugs. The plan is that the

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ARITMO generated infrastructure and decision models will be adopted by regulatory agencies and clinicians. A proof of success of the project results uptake is clearly on the regulatory side, where EMA is waiting for final ARITMO results to incorporate them in their decision making processes. This makes the project special, as it is not just a research project, with the target to set up a collaborative network for doing research, it actually needs to deliver state of the art results, that can be utilized directly, without peer review. Some of the outputs created by ARITMO are already being used in other drug safety related projects (i.e. SAFEGUARD), such as the terminology mapping results, methodology for conducting the literature review analyses (both on clinical trials and on observational studies), the tools for pooling data from different electronic healthcare records databases, the secure repository for data storage and analysis and the advanced statistical methods applied for the results analysis. Many of the lessons learned in ARITMO are in the form of experience at the various sites and managing arising difficulties that may arise when workflows have dependencies. This experience will be invaluable for future projects that have similar goals such as ARITMO, SAFEGUARD, and GRIP (Global Research in Pediatrics)

1.5 Address of the project public website and relevant contact details.

Project coordinator: Prof. Miriam CJM Sturkenboom, Erasmus Universitair Medisch Centrum Rotterdam Project manager: Eva Molero, Synapse Research Management Partners Contact details:

www.aritmo-project.org List of Partners

Erasmus Universitair Medisch Centrum Rotterdam (Netherlands) - Coordinator

Fundació Institut Mar d'Investigacions Mèdiques (Spain)

London School of Hygiene and Tropical Medicine (UK)

Alma Mater Studiorum, University of Bologna (Italy)

Universitaet Bremen (Germany)

University of Newcastle (UK)

Université Victor-Segalen Bordeaux II (France)

Fondazione Salvatore Maugeri Clinica del Lavoro e Della Riabilitazione (Italy)

Charite - Universitaetsmedizin Berlin (Germany)

Università Degli Studi di Verona (Italy)

St. George's Hospital Medical School (UK)

Astra Zeneca AB (Sweden)

PHARMO Coöperatie UA (Netherlands)

Fondazione Scientifica SIMG-ONLUS (Italy)

Aarhus Univesitetshospital, Aarhus Sygehus (Denmark)

Academisch Medisch Centrum bij de Universiteit van Amsterdam (Netherlands)

Drug Safety Research Trust (UK)

Institut für Epidemiologie und Präventionsforschung GmbH (Germany)

Synapse Research Management Partners SL (Spain)

http://www.aritmo-project.org/

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2. Use and dissemination of foreground

Section A (public)

TEMPLATE A1 - LIST OF SCIENTIFIC PUBLICATIONS, STARTING WITH THE MOST IMPORTANT ONES

No. Title Main author Title of the periodical or the series

Number, date or frequency

Publisher Place of publication

Date of publication

Relevant pages

Permanent identifiers (3)

(if applicable)

Is open access

(4)

provided to this publication?

Type

1 QT interval shortening in spontaneous reports submitted to the FDA: the need for consensus

Raschi E, Poluzzi E, Koci A, Boriani G, De Ponti F

Br J Clin Pharmacol Vol 72, Issue 5

11/2011 839-841

10.1111/j.1365-2125.2011.04065.x

No

2 ARITMO Project: Arrhythmia-related risks of drugs

Sturkenboom M, Molokhia M BJCN 5 (5) 2010 255 No

3 Differential changes in QTc duration during in-hospital haloperidol use

Blom MA, Bardai A, van Munster BC, Nieuwland MI, de Jong H, van Hoeijen DA, Spanjaart AM, de Boer A, de Rooij SE, Tan HL

PLoS One 6 (9) 2011 e23728

10.1371/journal.pone.0023728

Yes

4 Computational design and discovery of "minimally structured" hERG blockers

Cavalli A, Buonfliglio R, Ianni C, Masetti M, Ceccarini L, Caves R, Chang MW, Mitcheson JS, Roberti M, Recanatini M

J Med Chem 55(8) 4/2012 4010-4

10.1021/jm201194q

No

5 An Automated Docking Protocol for hERG Channel Blockers

Di Martino GP, Masetti M, Ceccarini L, Cavalli A, Recanatini M

J Chem Inf Model 53(1) 2013 159-75

10.1021/ci300326d

No

6 Ion Conduction through the hERG Potassium Channel

Ceccarini L, Masetti M, Cavalli A, Recanatini M

PLoS One 7(11) 2012 e49017

10.1371/journal.pone.0049017

Yes

7 Academic output from EU-funded health research projects

Molokhia M, De Ponti F, Behr E, Trifirò G, Sturkenboom M

The Lancet 380(9857)

2012 1903-04

10.1016/S0140-6736(12)62103-5

Yes

8 Macrolides and torsadogenic risk: Raschi E, Poluzzi E, Koci A, Moretti J Pharmacovigilance 1 2013 104 10.4172/23 No

http://www.thelancet.com/popup?fileName=cite-using-doi

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emerging issues from the FDA pharmacovigilance database

U, Sturkenboom M, de Ponti F 29-6887.1000104

9 Antipsychotics and torsadogenic risk: signals emerging from the US FDA Adverse Event Reporting System database

Poluzzi E, Raschi E, Koci A, Moretti U, Spina E, Behr ER, Sturkenboom M, de Ponti F

Drug Saf 36(6) 2013 467-79

10.1007/s40264-013-0032-z

No

10 Increased risk of sudden cardiac arrest in obstructive pulmonary disease: a case-control study

Warnier MJ, Blom MT, Bardai A, Berdowksi J, Souverein PC, Hoes AW, Rutten FH, de Boer A, Koster RW, de Bruin ML, Tan HL

PLoS One 8 2013 e65638

10.1371/journal.pone.0065638

Yes

11 Sudden cardiac arrest associated with use of a non-cardiac drug that reduces cardiac excitability: evidence from bench, bedside, and community

Bardai A, Amin AS, Blom MT, Bezzina CR, Berdowski J, Langendijk PNJ, Beekman L, Klemens CA, Souverein PC, Koster RW, de Boer A, Tan HL

Eur Heart J 34 2013 1506-16

10.1093/eurheartj/eht054

No

12 Reduced in-hospital survival rates of out-of-hospital cardiac arrest victims with obstructive pulmonary disease

Blom MT, Warnier MJ, Bardai A, Berdowski J, Koster RW, Souverein PC, Hoes AW, Rutten FH, de Boer A, de Bruin ML, Tan HL

Resuscitation 84 2013 569-574

10.1016/j.resuscitation.2012.10.009

No

13 Torsadogenic risk of antipsychotics: combining adverse event reports with drug utilization data across Europe

Raschi E, Poluzzi E, Godman B, Koci A, Moretti U, Kalaba M, Bennie M, Barbui C, Wettermark B, Sturkenboom M, De Ponti F

PLoS ONE 8(11) 2013 e81208

10.1371/journal.pone.0081208

Yes

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TEMPLATE A2 - LIST OF DISSEMINATION ACTIVITIES

ID Type of Activity Partners Involved Title Date Place Type of Audience

1 Press Release FIMIM, LSHTM ARITMO press release 2010-03-08 general public

2 Meeting EMC, SGUL SAE Phenotyping Standardisation Project Consensus Conference

2010-03-16 Cambridge scientific community

3 Press Release LSHTM ARITMO press release 2010-04-07 UK scientific community

4 Press Release FIMIM El projecte ARITMO investiga el risc d’arítmia que poden provocar alguns medicaments antipsicòtics, antihistamínics i antiinfecciosos

2010-04-26 Spain general public

5 Other FIMIM ARITMO website: www.aritmo-project.org 2010-04-28 general public

6 Publication LSHTM, EMC ARITMO Project: Arrhythmia-related risks of drugs

2010-05-05 UK scientific community

7 Press Release Uni-HB EU-Projekt zur Arzneimittelsicherheit 2010-05-31 Germany general public

8 Press Release Uni-HB Neues EU-Projekt soll Risiken von Arzneimitteln für Herzrhythmusstörungen und plötzlichen Herztod aufdecken

2010-05-31 Germany general public

9 Press Release Uni-HB EU-Studie mit Bremer Beteiligung 2010-06-02 Germany general public

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10 Conference EMC Sturkenboom M. Pharmacovigilance in motion. Dutch Pharmacovigilance network. 2010 Jun 6; Breda, The Netherlands.

2010-06-06 Breda, The Netherlands

scientific community

11 Conference EMC Sturkenboom M. The EU-drug. Brookings Institute. 2010 Jun 7;Washington (DC), United States

2010-06-07 Washington (DC), United States


12 Conference EMC Sturkenboom M. “Networking in drug safety in the EU: examples from EU-ADR, SOS and ARITMO”. 2010 Jun 10; London, United Kingdom.

2010-06-10 London, United Kingdom


13 Conference EMC Sturkenboom M. A changing world in pharmacovigilance. University 2, Napels. 2010 Jun 14; Naples, Italy.

2010-06-14 Naples, Italy scientific community

14 Conference EMC FDA, White Oak, Washington DC, June 18: EU-ADR, SOS, ARITMO and VAESCO EU projects on drug safety. ENCePP plenary session.

2010-06-18 Washington (DC), United States


15 Press Release UNIBO Aritmo fa tappa a Bologna 2010-06-21 Italy general public

16 Press Release Uni-HB Uni Bremen forscht über Herztod 2010-08-18 Germany general public

17 Poster UNIBO TdP liability of fluoroquinolones: integrated apporach based on FDA spontaneous reports and literature data

2010-08-16 Brighton scientific community

18 Conference EMC ICPE 2010 2010-08-16 Brighton scientific community

19 Conference EMC, F-SIMG ISPE 2011 Mid-year meeting 2011-04-09 Florence, Italy


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20 Poster UNIBO Comparing signals of Torsades de Pointes originated from pharmacovigilance with literature data: a pilot study on fluoroquinolones

2010-09-30 Dresden scientific community

21 Other LSHTM Discussion with MHRA representative regarding risk communication with professionals and patients



22 Other LSHTM Dissemination of study with cardiologists in Abetawe Bro Morgannwg University NHS Health Board, Wales



24 Other LSHTM Planned discussion with user groups 2010-09-29 London, United Kingdom

general public

25 Meeting ALL EMA meeting 2010-10-18 London, United Kingdom


26 Meeting ALL PWP meeting 2010-10-18 London, United Kingdom


27 Other LSHTM Submission of protocol for case ascertainment to MHRA



28 Meeting LSHTM Dissemination to GPs and research networks in UK

ongoing London, United Kingdom


29 Other LSHTM Discussion with Uppsala Univeristy colleagues working in pharmacoepidemiology and pharmacogenetics

15/9/10 & 20/10/2010 London, United Kingdom

general public

30 Conference LSHTM Patient feedback regarding risk communication ongoing London, United Kingdom


31 Presentation FAKOS Long QT Syndrome and Torsade de Pointes as Adverse Drug Reactions - Preliminary Results from the National Pharmacovigilance Center Berlin (PVZ-FAKOS)

2010-11-03 Würzburg, Germany


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32 Press Release Uni-HB Risiken und Nebenwirkungen. EU-Projekt erforscht Arxneimittel als Ursache für Herzrhythmusstörungen und plötzlichen Herztod

2010-07-03 Germany general public

33 Meeting UNIBO Fourth regional course in Pharmacovigilance 2010-11-19 Bologna scientific community

34 Meeting DSRU CVS Working Party meeting 2010-11-23 London, United Kingdom


35 News DSRU DSRU website 2010-02-01 UK general public

36 Other DSRU DSRU website 2010-11-01 UK general public

37 News DSRU DSRU GP newsletter 2011-04-01 UK scientific community

38 Presentation FAKOS Long QT Syndrome and Torsade de Pointes 2010-11-18 Berlin, Germany


39 Presentation FAKOS Long QT Syndrome and Torsade de Pointes. Pharmacovigilance BfArM Meeting

2010-11-29 Berlin, Germany


40 Presentation FAKOS ARITMO Project Presentation 2011-03-09 Berlin, Germany


41 Poster UNIBO Torsade de Pointes and QT prolongation with antipsychotics: a disproportionality analysis in the FDA Adverse Event Reporting System (FDA_AERS)

2011-09-14 Bologna scientific community

42 Other AUH-AS Description of and link to the project on partner’s website

2011-01-06 Aarhus, DK general public

43 Publication F-SIMG Health Search Report 2009-2010 2010-11-23 Florence, Italy

general public

44 Meeting LSHTM Meeting with psychiatrists to discuss LQT cases 2011-02-23 UK scientific community

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45 Meeting LSHTM Meeting with cardiologist KCL 2011-04-19 UK scientific community

46 Publication UNIBO QT-interval shortening in spontaneous reports submitted to the FDA: the need for consensus


47 Poster UNIBO QT shortening among suspected adverse drug reactions reported to the FDA: A case-by-case analysis

2011-06-27 Budapest scientific community

48 Poster UNIBO Reporting frequency of QT-interval abnormalitites in the FDA adverse event reporting system (FDA_AERS)

2011-06-27 Budapest scientific community

51 Presentation FAKOS FAKOS methodology and ARITMO Project Presentation









54 News PHARMO ARITMO in PHARMO newsletter 2011-06-01 scientific community

55 Poster UNIBO Pro-arrhythmic risk of antipsychotics: the importance of combining the FDA Adverse Event Reporting System with drug utilization data

2011-11-30 Antwerp, Belgium


56 Presentation UNIBO Federation of databases to evaluate the arrhythmogenic potential of drugs:the ARITMO project

2011-04-11 Florence, Italy


57 News UNIBO ARITMO in EuroDURG newsletter 2011-01-10 scientific community

58 Presentation EMC Monitoring a signal in postmarketing setting: new approaches

2011-09-29 scientific community

59 Presentation EMC Health Records databases, pharmacoepidemiology and drug development

2011-09-03 Rome, Italy scientific community

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60 Presentation EMC Communication about Early Safety Findings from Active Surveillance Studies

2011-08-15 Chicago scientific community

61 Poster UB2 ARITMO project. Ventricular arrhythmia associated to antimicrobials, antipsychotics and H1-antihistamines: an analysis of the French spontaneous reporting database

2011-11-26 Istanbul, Turkey


62 Poster UB2 ARITMO project. Competition bias for ventricular arrhythmia between AZERT drugs and antipshychotics: an analysis of the French spontaneous reporting database

2011-11-26 Istanbul, Turkey


63 Presentation AZ The Reality of Using Computational Tools to Reduce Safety Risk

2011-07-19 Mt. Snow, VT, USA


64 Presentation AZ Influencing drug design using safety data 2011-09-15 Vienna, Austria


65 Presentation AZ The Use of In Silico Technology to Predict Secondary Pharmacology Effects During High Throughput Screening of Compounds

2011-09-20 Innbruck, Austria


66 Presentation AZ Designing Safer Drugs: Past lessons and future challenges

2011-09-29 Bled, Slovenia


67 Presentation AZ Influencing drug design using safety data 2011-10-04 Lausanne, Switzerland


68 Other LSHTM/KCL What are the cardiac effects of antipsychotic medications? A systematic review and meta-analysis of the cardiac risk of antipsychotic medication



69 Publication LSHTM/KCL What are the cardiac effects of antipsychotic medications? A systematic review and meta-analysis of the cardiac risk of antipsychotic medication



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70 Presentation FAKOS Psychotropic drug induced Long QT Syndrome (LQTS) and Torsade de Pointes (TdP) Arrhythmia. Results from the National Pharmacovigilance Center Berlin (PVZ-FAKOS)

2011-11-10 Bonn, Germany


71 Publication AMC Differential Changes in QTc Duration during In-Hospital Haloperidol Use


72 Poster F-SIMG Antibiotic use across five European countries: a drug utilization study from the ARITMO project


73 Poster BIPS Antibiotic prescription rates in paediatric populations of 5 European countries: a drug utilization study from the ARITMO project


74 Presentation EMC Was the use of antipsychotic drugs with arrythmogenic potential changed in the last years? A population-based, database study in five European Countries


75 Poster UNIBO Antimycotic use across Europe: a drug utilization study from the ARITMO project


76 Poster LSHTM Utility of pharmacovigilance reports for LQT/TdP case ascertainment


77 Poster UNIBO Pro-arrhythmic risk of oral antihistamines (H1): combining adverse event reporting data with drug utilization data across Europe


78 Poster UNIBO Mapping the pro-arrhythmic risk of antipsychotics: combining adverse drug reactions with drug utilization data across Europe


79 Poster BIPS Antiviral prescription rates in paediatric populations of 5 European countries: a drug utilization study from the ARITMO project


80 Presentation UB2 ARITMO project. Torsade de pointes associated to antimicrobials, antipsychotics and antihistamines: an analysis of the French spontaneous reporting database.


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81 Poster UB2 ARITMO project. QT prolongation associated to antimicrobials, antipsychotics and antihistamines: an analysis of the French spontaneous reporting database.


82 Presentation BIPS Paediatric macrolide prescription rates in 5 European countries

2012-09-26 Regensburg, Germany


83 Abstract EMC, F-SIMG Use antibiotics in the ARITMO database network 2012-10-30 scientific community

84 Publication BIPS Holstiege J, Schink T, Molokhia M, Mazzaglia G, Trifirò G, Innocenti F, Oteri A, Herings R, Bezemer I, Poluzzi E, Puccini A, Pilgaard S, Pedersen L, Sturkenboom MC, Garbe E. Outpatient prescribing of systemic antibiotics in paediatric populations of 5 European countries. To be published soon.


85 Publication in preparation

EMC Epidemiology of non-fatal and fatal ventricular arrhythmia in five European Countries: a population-based, database study


86 Publication accepted UNIBO, UNIVR, EMC Antipsychotics and pro-arrhythmic risk: signals emerging from the FDA Adverse Event Reporting System


87 Presentation UNIBO, Univ Antwerp, Karolinska Institutet

Azithromycin and pro-arrhythmic risk: gaining insight through the FDA pharmacovigilance database and drug utilization data

2012-09-19 Rimini, Italy scientific community

88 Poster UNIBO, Univ Antwerp, Karolinska Institutet

Azithromycin and pro-arrhythmic risk: gaining insight through the FDA pharmacovigilance database and drug utilization data

2012-09-19 Rimini, Italy scientific community

89 Presentation UNIBO, Univ Antwerp, Karolinska Institutet

Il progetto Europeo ARITMO: il contributo della farmaco-utilizzazione nella valutazione del rischio di aritmie da farmaci anti-infettivi, antipsicotici ed anti-istaminici

2012-12-10 Rome, Italy scientific community

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90 Publication UNIBO, other WP3 partners, Univ Antewerp, Karolinska Institutet

Macrolides and pro-arrhythmic risk: combining pharmacovigilance reports with drug utilization data


91 Poster UNIBO Computational design and discovery of "minimally structured" hERG blockers.

2012-04-15 Riccione, Italy


92 Publication UNIBO Computational design and discovery of "minimally structured" hERG blockers


93 Publication UNIBO An automated docking protocol for hERG channel blockers


94 Publication UNIBO Ion conduction through the hERG potassium channel


95 Other LSHTM/KCL Correspondence to GPs regarding patients with suspected cardiac arrhythmias from MHRA data



96 Other LSHTM/KCL Interview of patients with suspected cardiac arrhythmias from MHRA data



97 Other LSHTM/KCL "Systematic review of Antihistamines in relation to risk of cardiac arrhythmias: QT prolongation, atrial fibrillation; ventricular fibrillation"



98 Publication LSHTM, UNIBO, SGUL, EMC

Molokhia M, De Ponti F, Behr E, Trifirò G, Sturkenboom M. Academic output from EU-funded health research projects. The Lancet 2012;380(9857):1903-04

2012-12-01 general public

99 Poster UNIBO, UNIVR, EMC, KI Mapping the pro-arrhythmic risk of antipsychotics: combining adverse drug reactions with drug utilization data across Europe

2013-01-24 UK scientific community

101 Publication UNIBO, UNIVR, EMC, KI Pro-arrhythmic risk of oral antihistamines (H1): combining adverse drug reactions with drug utilization data across Europe


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103 Abstract LSHTM Ataklte F, Molokhia M. Systematic review of antihistamines in relation to risk of cardiac arrhythmias: QT prolongation, atrial fibrillation; ventricular fibrillation

2013-01-31 United Kingdom


104 News UNIBO EuroDURG Bulletin 23 (Jan 2013) 2013-01-30 scientific community

105 Presentation UNIBO Invited lecture: QT lungo da farmaci antiaritmici: quale sorveglianza? Il parere del farmacologo. 10th Congress of the Italian Association of Arrhythmology and Cardiostimulation (AIAC)


106 Workshop EMC and all The ARITMO results: prediction of the arrhythmogenic risk of antihistamines, antipsychotics and anti-infectives by integration of translational evidence

2013-08-28 Montreal, Canada


107 Publication UNIBO Raschi E, Poluzzi E, Koci A, Moretti U, Sturkenboom M, de Ponti F. Macrolides and torsadogenic risk: emerging issues from the FDA pharmacovigilance database. J Pharmacovigilance 2013;1:104


108 Publication accepted in Plos One

UNIBO Raschi E, Poluzzi E, Godman B, Koci A, Moretti U, Kalaba M, Bennie M, Barbui C, Wettermark B, Sturkenboom M, De Ponti F.Torsadogenic risk of antipsychotics: combining adverse event reports with drug utilization data across Europe


109 Poster EMC, UNIBO, BIPS, AUH, LSHTM

Oteri A. Use of antibiotics and risk of ventricular arrhythmia: a nested case-control multi-database study in 5 European countries


110 Poster EMC, UNIBO, BIPS, AUH, LSHTM

Oteri A. Use of antipsychotics and risk of ventricular arrhythmia: a nested case-control multi-database study in 5 European countries


111 Presentation UNIBO, EMC, BIPS, AUH, LSHTM

Poluzzi E. Use of antihistamines and risk of ventricular arrhythmia: a nested case-control multi-database study in 5 European countries

2013-10-02 Pisa scientific community

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112 Publication UNIBO Poluzzi E, Raschi E, Koci A, Moretti U, Spina E, Behr ER, Sturkenboom M, de Ponti F. Antipsychotics and torsadogenic risk: signals emerging from the US FDA Adverse Event Reporting System database. Drug Saf 2013 Jun;36(6):467-79


113 Poster UNIBO, UNIVR Raschi E, Poluzzi E, Godman B, Koci A, Bishop I, Kalaba M, Laius O, Moretti U, Sermet C, Sturkenboom M, De Ponti F. Exposure to antipsychotics with pro-arrhythmic risk: combining adverse drug reactions with drug utilization data

2013-05-31 Geneva scientific community

114 Poster UNIBO, UNIVR, UB2, BIPS, DSRU, EMC

Raschi E, Salvo F, Poluzzi E, Moretti U, Koci A, Suling M, Hazell L, Sturkenboom M, Pariente A, De Ponti F. Comparison of torsadogenic events extracted from national pharmacovigilance databases: an overview from the ARITMO project



Raschi E, Salvo F, Poluzzi E, Moretti U, Chiarolanza A, Suling M, Hazell L, Sturkenboom M, Pariente A, De Ponti F. Development of an integrated torsadogenic score: the experience of the ARITMO project with a focus on antipsychotics


116 Poster UNIBO, UNIVR, UB2, EMC

Salvo F, Raschi E, Moretti U, Chiarolanza A, Moretti U, Fourrier-Réglat A, Moore N, Sturkemboom MC, De Ponti F, Poluzzi E, Pariente A. Criteria for signals of disproportionate reporting prioritisation: a proposal from the ARITMO project.


117 Abstract UNIBO, UNIVR, EMC U. Moretti, E. Raschi, E. Poluzzi, R. Lora, I. Dienberger, M. Sturkenboom, F. De Ponti. Case definition of torsades des pointes and related clinical events in spontaneous reporting: the ARITMO experience


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Raschi E, Salvo F, Poluzzi E, Moretti U, Chiarolanza A, Suling M, Hazell L, Sturkenboom M, Pariente A, De Ponti F. Novel integrated pharmacovigilance score to prioritize torsadogenic signals: the experience of the ARITMO project

2013-10-23 Turin scientific community


DSRU, UNIBO Hazell L, Raschi E, de Ponti F, Sturkenboom M, Shakir S. A systematic review of selected preclinical evidence for the torsadogenic potential of antihistamines, antipsychotics and anti-infective agents from the ARITMO project



UB2, UniBo, DSRU, EMC Criteria for signals of disproportionate reporting prioritisation: a proposal from the ARITMO project.



DSRU, UNIBO, UNEW, UB2

Summary paper of results of SR/MA on antihistamines




Summary paper of results of SR/MA on antipsychotics




Summary paper of results of SR/MA on anti-infectives


124 Publication planned UB2 Paper on MA of observational studies on antipsychotics


125 Publication AMC Warnier MJ, Blom MT, Bardai A, Berdowksi J, Souverein PC, Hoes AW, Rutten FH, de Boer A, Koster RW, de Bruin ML, Tan HL. Increased risk of sudden cardiac arrest in obstructive pulmonary disease: a case-control study. PLoS One 2013;8:e65638


126 Publication AMC Bardai A, Amin AS, Blom MT, Bezzina CR, Berdowski J, Langendijk PNJ, Beekman L, Klemens CA, Souverein PC, Koster RW, de Boer A, Tan HL. Sudden cardiac arrest associated with use of a non-cardiac drug that reduces cardiac excitability: evidence from bench, bedside, and community. Eur Heart J 2013;34:1506-16


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127 Publication AMC Blom MT, Warnier MJ, Bardai A, Berdowski J, Koster RW, Souverein PC, Hoes AW, Rutten FH, de Boer A, de Bruin ML, Tan HL. Reduced in-hospital survival rates of out-of-hospital cardiac arrest victims with obstructive pulmonary disease. Resuscitation 2013;84:569-574


128 Interview LSHTM Interview with Highlight project 20/02/2013 General public

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Section B (Confidential or public: confidential information to be marked clearly)

Part B1

The applications for patents, trademarks, registered designs, etc. shall be listed according to the template B1 provided hereafter. The list should, specify at least one unique identifier e.g. European Patent application reference. For patent applications, only if applicable, contributions to standards should be specified. This table is cumulative, which means that it should always show all applications from the beginning until after the end of the project. Template B1: List of applications for patents, trademarks, registered designs, etc.

Type of IP Rights

2:

Confidential Click on YES/NO

Foreseen embargo date dd/mm/yyyy

Application reference(s) (e.g. EP123456)

Subject or title of application

Applicant (s) (as on the application)

2 A drop down list allows choosing the type of IP rights: Patents, Trademarks, Registered designs, Utility models, Others.

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Part B2

Type of Exploitable Foreground

3

Description of exploitable foreground Confidential Click on YES/NO

Exploitable product(s) or measure(s)

Sector(s) of application4 Timetable,

commercial or any other use

Patents or other IPR exploitation (licences)

Owner & Other Beneficiary(s) involved

General advancement of knowledge

Evidence Profiles from the Literature Review Evidence profiles were built for each ARITMO drug where we found at least one eligible study in the literature. These profiles summarise the existing published evidence for each drug and have been collated in three Annexes

Yes DSRU, UB2, UNIBO


Summary Matrix of Published Evidence for ARITMO drugs Colour coded ‘traffic light’ results that present the overall results from the literature review incorporating the quantitative and qualitative interpretation of the published evidence

Yes DSRU, UB2, UNIBO


Antibiotics, antipsychotics antihistamines and the risk of Ventricular arrhythmia ANTIBIOTICS Overall, 25,952 cases and 2,594,738 matched controls were identified. Among cases, 2,298 (8.9%) were current users of antibiotics. Current use of beta-lactam antibiotics and macrolides showed higher risk of VA as compared with no-use. Current use of azithromycin (ORAdj: 2.12[1.62-2.77]), clarithromycin (ORAdj. 2.12 [95%CI:1.72-2.61]) and erythromycin (ORAdj.1.66 [1.28-2.13]) was

yes EMC: Gianluca Trifiró, Alessandro Oteri, Maria de Ridder, Miriam Sturkenboom BIPS: Edeltraut Garbe, Tania Schink, Marieke Niemeyer UNIBO: Elisabetta

19 A drop down list allows choosing the type of foreground: General advancement of knowledge, Commercial exploitation of R&D results, Exploitation of R&D results via

standards, exploitation of results through EU policies, exploitation of results through (social) innovation. 4 A drop down list allows choosing the type sector (NACE nomenclature) : http://ec.europa.eu/competition/mergers/cases/index/nace_all.html

http://ec.europa.eu/competition/mergers/cases/index/nace_all.html

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associated with significantly increased risk of VA (p<0.05). ORs from single database and meta-analyses of database-specific estimates were in line with results from the pooled analysis. Among fluoroquinolones, current use of ciprofloxacin (ORAdj1.43 [1.14-1.79]) was associated with a statistically significant increased risk of VA, while moxifloxacin showed an increased, although non-significant, risk of VA (ORAdj.1.54 [0.94-2.77]). ANTIPSYCHOTICS Overall,1,676 cases and 164,968 matched controls were identified. Of all cases, 629 (37.5%) were currently exposed to APDs. Current use of levosulpiride (ORAdj.12.90 [95%CI:5.41-30.68]), haloperidol (ORAdj. 2.70 [2.10-3.47]),chlorprothixene (ORAdj. 1.81 [1.11-2.93]), thioridazine (ORAdj.1.75 [1.06-2.89]), levomepromazine (ORAdj.1.61 [1.02-2.55]), fluopentixol (ORAdj. 1.61 [1.10-2.38]), quetiapine (ORAdj.1.53 [1.18-1.98]) and olanzapine (ORAdj. 1.33 [1.05-1.70]) was associated with a statistically significant increased risk of VA (p<0.05). ORs from single database and meta-analyses of database-specific estimates were in line with those observed in the pooled analysis. ANTIHISTAMINES Overall, 5,228 cases and 521,596 matched controls were identified. Out of all cases, 759 were currently exposed to antihistamines. Dimetindene (ORAdj.=3.08 [1.31-7.27]), promethazine (ORAdj.=1.46 [1.16-1.83]) and cyclizine, (ORAdj.=5.28 [4.12-6.77]) were associated with a statistically significantly increased risk of VA (p<0.05) in the pooled

Poluzzi, Fabrizio De Ponti PHARMO: Ron Herings, Irene Bezemer F-SIMG: Serena Pecchioli AUH-AS: Lars Pedersen, Trine Frøslev, Sinna Pilgaard Ulrichsen LSHTM: Mariam Molokhia

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analysis. No statistically significant associations in AARHUS, ERD, HSD and IPCI were observed, when DB-specific analyses were conducted, except for dimetindene and ebastine in GEPARD, dexchlorpheniramine and promethazine in PHARMO and cyclizine in THIN. *All the estimates reported in this overview should be considered as partial and potentially subject to variation


UMLS concepts and terminologies’ codes for outcomes and covariates Starting from the outcomes and covariates identified in the Aritmo project, the list of the corresponding UMLS concepts, together with the list of related codes and terms in free text (for the ICD10, ICD9-CM, ICPC, Read version 2 terminologies), were used by the database owners to build the event search queries in each own database

yes J63.1.1 - Data processing, hosting and related activities

None UB2-LESIM, All partners


Case definition of the torsadogenic potential of drugs to be used in spontaneous reporting system (and its comparison with current Standardized MedDRA Query)

YES UNIBO, UNIVR, UB2, BIPS, DSRU, LSHTM


Development of the pharmacovigilance score to prioritize signals of torsadogenicity, with relevant uncertainty: 38 drugs to be prioritized, of which 20 could be new signals.

YES UNIBO, UNIVR, UB2, BIPS, DSRU, LSHTM

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3. Report on societal implications

Replies to the following questions will assist the Commission to obtain statistics and indicators on societal and socio-economic issues addressed by projects. The questions are arranged in a number of key themes. As well as producing certain statistics, the replies will also help identify those projects that have shown a real engagement with wider societal issues, and thereby identify interesting approaches to these issues and best practices. The replies for individual projects will not be made public.

A General Information (completed automatically when Grant Agreement number is entered. Grant Agreement Number:

241679 Title of Project:

Arrhythmogenic potential of drugs Name and Title of Coordinator:

Prof. Miriam CJM Sturkenboom B Ethics

1. Did your project undergo an Ethics Review (and/or Screening)?

If Yes: have you described the progress of compliance with the relevant Ethics Review/Screening Requirements in the frame of the periodic/final project reports? Special Reminder: the progress of compliance with the Ethics Review/Screening Requirements should be described in the Period/Final Project Reports under the Section 3.2.2 'Work Progress and Achievements'

Yes 0No

2. Please indicate whether your project involved any of the following issues (tick box) : YES

Research on Humans

Did the project involve children?

Did the project involve patients?

Did the project involve persons not able to give consent?

Did the project involve adult healthy volunteers?

Did the project involve Human genetic material?

Did the project involve Human biological samples?

Did the project involve Human data collection?

Research on Human embryo/foetus

Did the project involve Human Embryos?

Did the project involve Human Foetal Tissue / Cells?

Did the project involve Human Embryonic Stem Cells (hESCs)?

Did the project on human Embryonic Stem Cells involve cells in culture?

Did the project on human Embryonic Stem Cells involve the derivation of cells from Embryos?

Privacy

Did the project involve processing of genetic information or personal data (eg. health, sexual lifestyle, ethnicity, political opinion, religious or philosophical conviction)?

Did the project involve tracking the location or observation of people?

Research on Animals

Did the project involve research on animals?

Were those animals transgenic small laboratory animals?

Were those animals transgenic farm animals?

Were those animals cloned farm animals?

Were those animals non-human primates?

Research Involving Developing Countries

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Did the project involve the use of local resources (genetic, animal, plant etc)?

Was the project of benefit to local community (capacity building, access to healthcare, education etc)?

Dual Use

Research having direct military use

Research having the potential for terrorist abuse

C Workforce Statistics

3. Workforce statistics for the project: Please indicate in the table below the number of people who worked on the project (on a headcount basis).

Type of Position Number of Women Number of Men

Scientific Coordinator 1

Work package leaders 5 3

Experienced researchers (i.e. PhD holders) 12 15

PhD Students 2 3

Other 29 14

4. How many additional researchers (in companies and universities) were recruited specifically for this project?

9

Of which, indicate the number of men:

5

D Gender Aspects

5. Did you carry out specific Gender Equality Actions under the project?

Yes No

6. Which of the following actions did you carry out and how effective were they? Not at all

effective Very

effective

Design and implement an equal opportunity policy

Set targets to achieve a gender balance in the workforce

Organise conferences and workshops on gender

Actions to improve work-life balance

Other:

7. Was there a gender dimension associated with the research content – i.e. wherever people were

the focus of the research as, for example, consumers, users, patients or in trials, was the issue of gender considered and addressed? Yes- please specify

No

E Synergies with Science Education

8. Did your project involve working with students and/or school pupils (e.g. open days, participation in science festivals and events, prizes/competitions or joint projects)? Yes- please specify

No

9. Did the project generate any science education material (e.g. kits, websites, explanatory booklets, DVDs)? Yes- please specify

No

F Interdisciplinarity

10. Which disciplines (see list below) are involved in your project? Main discipline

5:

5 Insert number from list below (Frascati Manual).

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Associated discipline5: Associated discipline

5:

G Engaging with Civil society and policy makers

11a Did your project engage with societal actors beyond the research community? (if 'No', go to Question 14)

Yes No

11b If yes, did you engage with citizens (citizens' panels / juries) or organised civil society (NGOs, patients' groups etc.)? No Yes- in determining what research should be performed Yes - in implementing the research Yes, in communicating /disseminating / using the results of the project

11c In doing so, did your project involve actors whose role is mainly to organise the dialogue with citizens and organised civil society (e.g. professional mediator; communication company, science museums)?

Yes No

12. Did you engage with government / public bodies or policy makers (including international organisations)

No Yes- in framing the research agenda Yes - in implementing the research agenda

Yes, in communicating /disseminating / using the results of the project

13a Will the project generate outputs (expertise or scientific advice) which could be used by policy makers? Yes – as a primary objective (please indicate areas below- multiple answers possible) Yes – as a secondary objective (please indicate areas below - multiple answer possible) No

13b If Yes, in which fields? Agriculture Audiovisual and Media

Budget

Competition Consumers

Culture

Customs Development Economic and

Monetary Affairs

Education, Training, Youth Employment and Social Affairs

Energy Enlargement

Enterprise

Environment External Relations

External Trade

Fisheries and Maritime Affairs Food Safety

Foreign and Security Policy

Fraud Humanitarian aid

Human rights Information Society

Institutional affairs

Internal Market Justice, freedom and security

Public Health

Regional Policy Research and Innovation

Space Taxation Transport

13c If Yes, at which level? Local / regional levels National level European level International level

H Use and dissemination

14. How many Articles were published/accepted for publication in peer-reviewed journals?

13

To how many of these is open access6 provided? 6

How many of these are published in open access journals?

How many of these are published in open repositories?

To how many of these is open access not provided? 7

Please check all applicable reasons for not providing open access:

6 Open Access is defined as free of charge access for anyone via Internet.

http://europa.eu/pol/agr/index_en.htm

http://europa.eu/pol/av/index_en.htm

http://europa.eu/pol/financ/index_en.htm

http://europa.eu/pol/comp/index_en.htm

http://europa.eu/pol/cons/index_en.htm

http://europa.eu/pol/cult/index_en.htm

http://europa.eu/pol/cust/index_en.htm

http://europa.eu/pol/dev/index_en.htm

http://europa.eu/pol/emu/index_en.htm

http://europa.eu/pol/emu/index_en.htm

http://europa.eu/pol/educ/index_en.htm

http://europa.eu/pol/socio/index_en.htm

http://europa.eu/pol/ener/index_en.htm

http://europa.eu/pol/enlarg/index_en.htm

http://europa.eu/pol/enter/index_en.htm

http://europa.eu/pol/env/index_en.htm

http://europa.eu/pol/ext/index_en.htm

http://europa.eu/pol/comm/index_en.htm

http://europa.eu/pol/fish/index_en.htm

http://europa.eu/pol/food/index_en.htm

http://europa.eu/pol/cfsp/index_en.htm

http://europa.eu/pol/fraud/index_en.htm

http://europa.eu/pol/hum/index_en.htm

http://europa.eu/pol/rights/index_en.htm

http://europa.eu/pol/infso/index_en.htm

http://europa.eu/pol/inst/index_en.htm

http://europa.eu/pol/singl/index_en.htm

http://europa.eu/pol/justice/index_en.htm

http://europa.eu/pol/health/index_en.htm

http://europa.eu/pol/reg/index_en.htm

http://europa.eu/pol/rd/index_en.htm

http://europa.eu/pol/tax/index_en.htm

http://europa.eu/pol/trans/index_en.htm

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publisher's licensing agreement would not permit publishing in a repository no suitable repository available no suitable open access journal available no funds available to publish in an open access journal lack of time and resources lack of information on open access other

7: ……………

15. How many new patent applications (‘priority filings’) have been made? ("Technologically unique": multiple applications for the same invention in different jurisdictions should be counted as just one application of grant).

N/A

16. Indicate how many of the following Intellectual Property Rights were applied for (give number in each box).

Trademark N/A

Registered design N/A

Other N/A

17. How many spin-off companies were created / are planned as a direct result of the project?

Indicate the approximate number of additional jobs in these companies: N/A

18. Please indicate whether your project has a potential impact on employment, in comparison with the situation before your project: Increase in employment, or In small & medium-sized enterprises Safeguard employment, or In large companies Decrease in employment, None of the above / not relevant to the project Difficult to estimate / not possible to quantify

19. For your project partnership please estimate the employment effect resulting directly from your participation in Full Time Equivalent (FTE = one person working fulltime for a year) jobs: Difficult to estimate / not possible to quantify

Indicate figure:

I Media and Communication to the general public

20. As part of the project, were any of the beneficiaries professionals in communication or media relations? Yes No

21. As part of the project, have any beneficiaries received professional media / communication training / advice to improve communication with the general public? Yes No

22 Which of the following have been used to communicate information about your project to the general public, or have resulted from your project? Press Release Coverage in specialist press Media briefing Coverage in general (non-specialist) press TV coverage / report Coverage in national press Radio coverage / report Coverage in international press Brochures /posters / flyers Website for the general public / internet DVD /Film /Multimedia Event targeting general public (festival, conference,

exhibition, science café)

23 In which languages are the information products for the general public produced?

Language of the coordinator English Other language(s)

7 For instance: classification for security project.

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Question F-10: Classification of Scientific Disciplines according to the Frascati Manual 2002 (Proposed

Standard Practice for Surveys on Research and Experimental Development, OECD 2002): Fields of science and technology 1. Natural Sciences 1.1 Mathematics and computer sciences [mathematics and other allied fields: computer sciences and other allied subjects (software development only; hardware development should be classified in the engineering fields)] 1.2 Physical sciences (astronomy and space sciences, physics and other allied subjects) 1.3 Chemical sciences (chemistry, other allied subjects) 1.4 Earth and related environmental sciences (geology, geophysics, mineralogy, physical geography and other geosciences, meteorology and other atmospheric sciences including climatic research, oceanography, vulcanology, palaeoecology, other allied sciences) 1.5 Biological sciences (biology, botany, bacteriology, microbiology, zoology, entomology, genetics, biochemistry, biophysics, other allied sciences, excluding clinical and veterinary sciences) 2 Engineering and technology 2.1 Civil engineering (architecture engineering, building science and engineering, construction engineering, municipal and structural engineering and other allied subjects) 2.2 Electrical engineering, electronics [electrical engineering, electronics, communication engineering and systems, computer engineering (hardware only) and other allied subjects] 2.3. Other engineering sciences (such as chemical, aeronautical and space, mechanical, metallurgical and materials engineering, and their specialised subdivisions; forest products; applied sciences such as geodesy, industrial chemistry, etc.; the science and technology of food production; specialised technologies of interdisciplinary fields, e.g. systems analysis, metallurgy, mining, textile technology and other applied subjects) 3. Medical Sciences 3.1 Basic medicine (anatomy, cytology, physiology, genetics, pharmacy, pharmacology, toxicology, immunology and immunohaematology, clinical chemistry, clinical microbiology, pathology) 3.2 Clinical medicine (anaesthesiology, paediatrics, obstetrics and gynaecology, internal medicine, surgery, dentistry, neurology, psychiatry, radiology, therapeutics, otorhinolaryngology, ophthalmology) 3.3 Health sciences (public health services, social medicine, hygiene, nursing, epidemiology) 4. Agricultural sciences 4.1 Agriculture, forestry, fisheries and allied sciences (agronomy, animal husbandry, fisheries, forestry, horticulture, other allied subjects) 4.2 Veterinary medicine 5. Social sciences 5.1 Psychology 5.2 Economics 5.3 Educational sciences (education and training and other allied subjects) 5.4 Other social sciences [anthropology (social and cultural) and ethnology, demography, geography (human, economic and social), town and country planning, management, law, linguistics, political sciences, sociology, organisation and methods, miscellaneous social sciences and interdisciplinary , methodological and historical S1T activities relating to subjects in this group. Physical anthropology, physical geography and psychophysiology should normally be classified with the natural sciences]. 6. Humanities 6.1 History (history, prehistory and history, together with auxiliary historical disciplines such as archaeology, numismatics, palaeography, genealogy, etc.) 6.2 Languages and literature (ancient and modern)

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6.3 Other humanities [philosophy (including the history of science and technology) arts, history of art, art criticism, painting, sculpture, musicology, dramatic art excluding artistic "research" of any kind, religion, theology, other fields and subjects pertaining to the humanities, methodological, historical and other S1T activities relating to the subjects in this group]

PROJECT FINAL REPORT...Prof. Miriam CJM Sturkenboom, Erasmus Universitair Medisch Centrum Rotterdam...

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