Examples from Industrial Practice in Lead Development

Wolfgang MusterF. Hoffmann-La Roche Ltd.

Areas

Computer-Aided Molecular Modeling (CAMM) *

Absorption, Distribution, Metabolism and Excretion (ADME) – Physicochemical Properties

Predictive ToxicologyGenotoxicity/CarcinogenicityPhospholipidosisGenotoxic impurities

* Alternative terms applied to this area:Computer-Aided Drug Design (CADD)Computational Drug Design (CDD)Computer-Aided Molecular Design (CAMD)Rational Drug DesignIn silico Drug DesignComputer-Aided Rational Drug DesignComputer-Aided Drug Discovery and Development (CADDD)Cheminformatics and Molecular ModelingSustainable Pharmacy, Osnabrück 2008

Areas

DrugDrugCandidateCandidate

ADMEProperties

SafetyProfileEfficacy

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Sustainable Pharmacy, Osnabrück 2008

Safety42%

Efficacy28%

Business22%

ADME8%

PredictiveToxicology

In silico ADME

Bettertarget validation

Failure reasons

Stahl et al. (2006) Drug Discover Today 11(7-8): 326-33.Kapetanovic (2008) Chem-Biol Interactions 171: 165-76.

Remark: 3D-receptor modeling for prediction of potential side effects are presently devised (Vedani et al.)

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Computer-Aided Molecular Modeling (CAMM)

* nature of known ligands,homology to related targets,the size, polarity and shape of binding sites in known target 3D structures,knowledge of the key amino acids modulating selective binding or functional activity

*

Stahl et al. (2006) Drug Discover Today 11(7-8): 326-33.Sustainable Pharmacy, Osnabrück 2008

Computer-Aided Molecular Modeling (CAMM)

Various computational methods, such as

Virtual screeningMany computational techniques are available to compile focused compound sets, with most of them falling under the umbrella term ‘virtual screening’

Fragment-based screening

Fragment screening is an additional ‘focused screening’ technique; small libraries of several hundred to several thousand low molecular weight substances that are screened by direct-binding methods in combination with X-ray crystallography

Chemogenomics search strategies(for target classes without structure information, especially for G-protein-coupled receptors)

Multidimensional similarity paradigm: ligand structure similarity, target sequence similarity and similarity of biological effects are combined. Biological similarity is determined in terms of affinity fingerprints of compounds against a set of targets.

Classic structure-based design (QSARs)

should be seen as multifaceted disciplines contributing to the early drug discovery process.

Fostel, J.Predictive ADME-Tox

2005

Stahl et al. (2006) Drug Discover Today 11(7-8): 326-33.Sustainable Pharmacy, Osnabrück 2008

Predictive ADME – Molecular properties

Optimization of chemical series (quality of leads)

All activities of promising compound classes should focus on multiple ADME–Tox-related parameters in parallel to activity and selectivity

Results of commercially available tools for calculating physicochemical properties and ADME-related parameters have to be interpreted with great care

The use of generic models can only be recommended if they have been validated for a particular project; results of new compounds outside of the training sets can be misleading (ionization constants, lipophilicity and solubility)

Shift in optimization strategy, use of measured values calls for high quality, fast and standardized assays (100–500 compounds per week)

Generally, the aim of a local model is to rank compounds and not to predict the absolute magnitude of an in vivo or in vitro effect

Allows project teams to abandon the classic paradigm of sequential filtering in more complex and expensive models (continuous model building; in vivo spot checks)

Use of in silico tools within toxicology:

In silico prediction of toxic effects at early development stages – before drug candidate selection

Hypothesis generation for structural mechanisms of action

In later stages: first assessment of impurities, degradation products, side products, metabolites,...e.g. structural evaluation of synthesis schemes

In silico prediction systems – Toxicology

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System name Short description Predicted endpointsClassical QSAR approaches Correlate structural or property descriptors of compounds with

biological activitiesQSARs for various endpoints published

DEREK for Windows Knowledge(rule)-based expert system M/C/SS/I and more (>40)

MCASE(CASE, CASETOX)

Machine-learning approach to identify molecular fragments with a high probability of being associated with an observed biological activity

Available modules: M/C/T/I/H/MTD/BD/AT and more

OncoLogic Knowledge-based expert system, mimicking the decision logic of human experts

C

MDL QSAR QSAR modeling system to establish structure-property relationships, create new calculators and generate new compound libraries

M/C/hERG inhib/AT/LD50

lazar Derives predictions from toxicity data by searching the database for compounds that are similar with respect to a given toxic activity

M/C/H/ET

TOPKAT TOPKAT employs cross-validated QSTR models for assessing various measures of toxicity; each module consists of a specificdatabase

Available modules:M/C/T/LD50/SS/I/ET and more

ToxScope ToxScope correlates toxicity information with structural features of chemical libraries, and creates a data mining system

M/C/I/H/T and more

HazardExpert Knowledge(rule)-based expert system M/C/I/SS/IT/NT

COMPACT COMPACT is a procedure for the rapid identification of potentialcarcinogenicity or toxicities mediated by CYP450s

C and P450-mediated toxicities

PASS Based on the comparison of new structures with structures of well-known biological activity profiles by using MNA structure descriptors

Multiple endpoints

Cerius2 Molecular modeling software with a ADME/Tox tool package provides computational models for the prediction of ADME properties

ADME/H

In silico prediction systems – Summary table

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System name Short description Predicted endpoints

Tox Boxes Modules generated by a machine-learning approach implemented in a fragment-based Advanced Algorithm Builder (AAB)

M/AT/C/LD50 and more

MetaDrug Assessment of toxicity by generating networks around proteins and genes (toxicogenomics platform)

>40 QSAR models for ADME/Toxproperties

DICAS Cascade model with the capability to mine for local correlations in datasets with large number of attributes

C

CADD Computer-aided drug design (CADD) by multi-dimensional QSARs applied to toxicity-relevant targets

Receptor- and CYP450-mediated toxicities, ED

CSGeno Tox QSTR-based package employing electrotopological state indexes, connectivity indexes and shape indices

M

Admensa Interactive QSAR-based system primarily for ADME optimization CT

PreADMET Calculation of important descriptors and neural network for the construction of prediction system

M/C

BfR Decision Support System Rule-based system using physicochemical properties and substructures I and corrosion

M=Mutagenicity, C=Carcinogenicity, SS=Skin Sensitisation, I=Irritancy, H=Hepatotoxicity, T=Teratogenicity, MTD=Maximum Tolerated Dose, LD50=, BD=Biodegradation, AT=Acute Toxicity, ET=Environmental Toxicities, IT=Immunotoxicity, NT=Neurotoxicity, CT=Cardiotoxicity, ED= Endocrine disruption, ADME=Absorption Distribution Metabolism Excretion, QSTR=Quantitative Structure Toxicity Relationship, MNA=Multilevel Neighborhoods of Atoms

Muster et al. (2008) Drug Discovery Today 13/7-8, 303-310.

In silico prediction systems – Summary table continued

Genotoxicity endpoint represented by 139 rules* (51 chromosomal damage*)Carcinogenicity endpoint represented by 54 rules*Irritation (skin, eye and respiratory tract) (33 rules*)Sensitisation (skin and respiratory tract) (76 rules*)Thyroid toxicity, hERG channel inhibition, oestrogenicity, photo-induced effects, neurotoxicity, teratogencity: less well coveredNegative in DfW means: really negative or not covered!

DEREK for Windows (DfW)Deductive Estimation of Risk from Existing Knowledge

DfW is a knowledge-based expert system for the qualitative prediction of toxicity. DfW is not a database system but a rulebase system. Each rule describes relationship between a structural feature (toxicophore) and its associated toxicity.

Sustainable Pharmacy, Osnabrück 2008* DfWV9.0.0

In silico prediction systems – Toxicology

MCASE tries to predict toxicity on the basis of discrete structural fragments found to be statistically relevant to specific biological activity (biophores).The differences between active and inactive molecules are investigated with the help of a so-called ‘learning dataset’, to deduce the attributes or substructures (so-called biophores) responsible for activity. From the frequency with which a particular biophore is identified in all active and all inactive molecules, one can calculate the probability with which this fragment is associated with biological activity.

MultiCASE (MCASE)Multiple Computer Automated Structure Evaluation

Ames modules for each strain +/- rat or hamster S9 availableFour carcinogenicity modules incl. proprietary data male/female rats and mice

Modules and the underlying database have been developed with FDAHigh prediction accuracy of the MCASE modules (mainly based on the unique dataset)

Teratogenicity/Developmental toxicity/Male fertility/Behavioral toxicity in diff species (49 modules)Hepatotoxicity in humans (14 modules)GSH adduct formation (in-house) rat and human microsomesFurther available modules: antibacterial (pharm), ADME, cytotoxicity, ecotoxicity, skin/eye irritations, allergies, enzyme inhibition, biodegradation, bioaccumulation

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In silico prediction systems – Toxicology

Sustainable Pharmacy, Osnabrück 2008

DEREK for Windows (DfW)DEREK is a knowledge-based expert system for the qualitative prediction of toxicity. DEREK is not a database system but a rulebase system. Each rule describes relationship between a structural feature (toxicophore) and its associated toxicity.

METEORMeteor is a computer program that helps scientists who need information about the metabolic fate of chemicals. The program uses expert knowledge rules in metabolism to predict the metabolic fate of chemicals and the predictions are presented in metabolic trees. The only information needed by the program to make its prediction is the molecular structure of the chemical.

VITIC Toxicology DatabaseVitic is a chemically intelligent toxicology database, which can recognise and search for similarities in chemical structures. Vitic is especially useful in (Quantitative) Structure-Activity Relationship (QSAR) modelling.

In silico prediction systems – Toxicology

Sustainable Pharmacy, Osnabrück 2008

MultiCASE (MCASE)MCASE tries to predict toxicity on the basis of discrete structural fragments found to be statistically relevant to specific biological activity (biophores).The differences between active and inactive molecules are investigated with the help of a so-called ‘learning dataset’, to deduce the attributes or substructures (so-called biophores) responsible for activity. From the frequency with which a particular biophore is identified in all active and all inactive molecules, one cancalculate the probability with which this fragment is associated with biological activity.

In silico phospholipidosis tool (CAFCA)In-house tool predicts amphiphilic properties of charged small molecules expresed in terms of free energy of amphiphilicity (DDGAM). Amphiphiliccompounds have the potential to accumulate in lipid bilayers, interfering with the phopholipid metabolism and turnover, therefore causing adverse effects.

In silico phototoxicity predictionPhototoxicity prediction based on chemical structure or chemical structure in combination with measured UV spectra

Further endpoints in developmentPromising results with local models with the potential to be generally applicable (e.g. prediction of hERG channel inhibition, GSH adduct formation)

In silico prediction systems – Toxicology

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Expert vs data-driven (QSAR) systems - Toxicology

Local SARs (project-specific SARs) based on 5 to maximally 30 data points; can be evaluated by eye

(Q)SAR systems will get increasing importance if HCS for more toxicological endpoints are validated and implemented

(Q)SAR systems are normally not used for genotoxicity and/or carcinogenicity at Roche

Commercial systems are predicting well and can be optimizedAcceptance of regulatoriesEstablished for other endpoints (e.g. phototoxicity, phospholipidosis, hERG assay)

(Q)SAR systems might be also helpful, if additional in vitro HCSparameters or cross-reactivities have been measured

DEREK / MCASE analysis

MNT in vitro

Ames micro

Ames GLP

MNT in vitro

MNT in vivo

tbd

ML/TK

Gene mutations

Chromosomalaberration

HCAone or both

required forphase II

Crosscheck VITIC,

METEOR,SciFinder,TOXNET

In silico

(HTS)

In vitro

optimize LI/LO

CCS

RDC1 In vivo

Rodent cancer bioassay

Use of in silico genotoxicity prediction

optimize

On-the-fly Prediction/ClassificationDEREK combined with MCASE

Structural assessments of synthesis

scheme, impurities, metabolites

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The success of early genotoxicity screeningYear Ames micro

number of positive (incl. weak pos. and inconclusive ones) compounds b

Full Ames (GLP)number of positive (incl. weak pos.

and inconclusive ones) compounds b

1996 - 33 (48 %)

1997 - 25 (37 %)

1998 9 (15 %) 11 (24 %)

1999 5 (11 %) 9 (18 %)

2000 11 (11 %) 5 (20 %)

2001 6 (7 %) 3 (21 %)

2002 7 (9 %) 1 (6 %)

2003 3 (3 %) 0

2004 0 0

2007 2 (1 %) 0

2005 3 (2 %) 0

2006 3 (2 %) 0

2008a 1 (2 %) 0

a until March 2008b expected mutagens, intermediates/reactants and positives results due to impurities excluded

Start of routine in silico

screening

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Phospholipidosis

Drug-induced phospholipidosis is a reversible storage disorder characterized by accumulation of phospholipids within cells, i.e., in the lysosomes

Caused by cationic amphiphilic drugs (CADs) and some cationic hydrophilic drugs (e.g. Aminoglycoside gentamicin)

Drug-induced phospholipidosis is a generalized condition in humans and animals; it may occur in virtually any tissue characterized by accumulation of one, or several classes of phospholipids within the cell

Phospholipidosis may or may not be accompanied by organ toxicity although their association has not been proven (except for gentamicin)

Cationic hydrophilic

Hydrophobicresidues

O

N

N

OO

O

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N

O

O

IO

N

I

pKapKa

Negative

ΔΔGAM >= -6 kJ/molpKa < 6.3

ΔΔGAM < -6 kJ/molpKa >= 6.3

Positive

Free Energyof

Amphiphilicity(ΔΔGAM )

In silico classification of phospholipidosis potential

CAFCA (CAlculated Free energy of Charged Amphiphiles) Fischer, H. et al. (2000) Chimia 54, 640-645.

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Techniques to detect phospholipidosisIn silico tool

From in vivo findings to predictive in vitro assay to HT in silico tool

Calculation for large data set possible

Accessible on the Intranet - optimization of pKa value as well as amphiphilic properties

Identification of “clear positive” chemical series rather than single molecules

Useful in Lead Identification and early Lead Optimization (depends on the indication, potency/dose and duration of treatment)

Overall predictability of the in silico tool is very high for the in vitro assay;in vitro test normally not conducted anymore

Amiodaroneas an example of a cationic amphiphilic drug (CAD)

In silico classification of genotoxic impuritiesIn principle, any impurity that is present below the threshold of qualification (0.15%) needs notto be toxicologically „qualified“ or „characterized“ (ICH)

For a drug of 1 g daily intake this implies that a chronic intake of less than 1.5 μg of an impurityin that drug is considered toxicologically insignificant, however, ICH guidelines do indicate that “lower thresholds (for reporting, identification & qualification) can be appropriate if the impurity is unusually toxic” - but do not give guidance on what this is or how to handle

Synthesis of APIs often involves reactive starting materials, intermediates or process steps; synthesis pathways frequently involve known or suspected genotoxic compounds

Unknown/undetermined low levels of genotoxic impurities may be present (such as e.g. sulfonic acid esters)

Issue not directly addressed in ICH guidelines -> new draft of the EMEA ’guideline on the limits of genotoxic impurities’ with new concept

Clinical developments put on hold, because the synthesis pathways contains intermediateswith alerting structures; Companies were requested to either show that the alertingintermediates are below 1 ppm in the drug or provide data on genotoxicity

Solution: use a generic TTC (Threshold of Toxicological Concern) based on historicalexperience with genotoxic carcinogens; staged TTC taking treatment duration into accout

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Step 1: Identify and classify structural alerts in parent compound and impurities

Step 2: Establish a qualification strategy

A: Limitation based on structural information, chemistry andanalytical capabilities

B: Testing of “neat“ impurity; limitation based on outcome

C: Testing of spiked material; limitation based on outcome

Step 3: Establish acceptable limits

Proposal of acceptable intake levels without appreciable risk based on dose, duration of use, indication and patient/volunteer population(staged TTC)

In silico classification of genotoxic impurities

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Class 1: Genotoxic

Carcinogens

Class 2: Genotoxic,

Carc unknown

Class 3: Alert –Unrelated to parent

Class 4: Alert – Related to

parent

Class 5: No Alerts

Eliminate Impurity?

Staged TTC

Threshold Mechanism?

No or unknown

PDE(e.g. ICH Q3 appendix

2 reference

Control as an ordinary impurity

Impurity Genotoxic?

1

API Genotoxic2

Yes

Yes

/N

ot te

sted

No

1 Either tested neat or spiked into API and tested up to 250 μg/plate2 If API is positive, risk benefit analysis required3 Quantitative risk assessment to determine ADI

Risk Assess-ment?3

No

No

In silico classification of genotoxic impurities

In silico classification of genotoxic impurities

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BasicResearch

PreclinicalDevelopment

LeadOptimization

LeadIdentification

Targetidentification,assessment

and validation

ClinicalDevelopment

Filing/Approval& Launch

Phase 1 Phase 2 Phase 3

ADME / MolecPropclogP / PSA / cPAMPA / cpKaMetabolic clearance

In silico systems during drug development process

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PredTox 1: DEREK / MCASE / VITIC / METEOR

CAMM

PredTox 2:PL / PhototoxhERG / GSH adducts

Adequately predict complex toxicological endpoints (e.g. hepatotoxicity, cardiotoxicity, nephrotoxicity) – need for standardized high-quality data (Innovative Medicine Initiative)

Design in silico tools to cope with the enormous amount of data generated by new techniques – HTS/HCS, omics, system biology, biomarkers, etc.

Establish closer link from preclinical to clinical development

Future challenges for drug design and early screening

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In silico systems are extensively used during the early phases of drug development until selection of the clinical candidate (e.g. 3D-modeling, expert systems, QSARtools)

Applying in silico and in vitro screening significantly reduced failures in early project phases, increased efficiency and improved thquality of clinical candidates

The number of ADME-Tox in silico and (HTS)-in vitro screens are rapidly increasing

DEREK/MCASE and other commercially available systems are predicting toxicity endpoints like mutagenicity, carcinogenicity, skin sensitisation and irritancy well; in-house optimization is essential for high performance

Further endpoints are less-well covered, mainly due to the lack of comprehensive, high quality and standardized databases

QSAR tools can be established, based on internal standardized datasets, e.g. phospholipidosis, phototoxicity, hERG channel inhibition, GSH adduct formation

Challenge how to predict adequately potential genotoxic impurities from structures in synthesis scheme; further regulations needed?

Conclusions

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Sustainable Pharmacy, Osnabrück 2008

Sustainable Pharmacy, Osnabrück 2008

Sustainable Pharmacy, Osnabrück 2008

“Are you sure, Stan, that a pointy head and a long beak is what makes them fly?”

Sustainable Pharmacy, Osnabrück 2008

Alessandro BrigoStephan Kirchner Edith Brandt

Raymond Schmitt Wolfgang HeringJoelle MullerSabine Marget-MullerNicole Helt

Lutz MüllerHolger FischerManfred Kansy

Flavio CrameriLaura Suter-DickThomas WeiserThomas Singer

Acknowledgements