© 2007 IBM Corporation EuResist From Data to Knowledge Integration of viral genomics with clinical...

© 2007 IBM Corporation© 2007 IBM Corporation

EuResist From Data to Knowledge

Integration of viral genomics with clinical data

to predict response to anti-HIV treatment

IBM Haifa Research Lab

© 2007 IBM Corporation

Outline• The Problem

• Present solutions

• The project

– Vision, objectives, and value

– Partners

– Preliminary results

• Alignment with standards

• Collaborations

• Links


The problem


The problem

• HIV is a deadly virus

• Effective treatment available since 1996

– HAART = “Highly Active Antiretroviral Therapy”

• (combined use of 3 – 4 anti-HIV drugs)

• Anti-HIV drugs work, but the virus is not eradicated

• The virus replicates itself and generates variants with reduced suceptibility to anti-HIV drugs

– Drug resistance

• Testing for drug resistance has been recommended in clinical practice since 2001


Present solutions

• How do we measure antiretroviral resistance?

– Phenotypic test

• The virus “behavior” towards the drugs is observed “in vitro”

– Pick the virus from the patient blood and replicate it in the lab in the presence of any single antiretroviral drug

• Costly, lengthy, complex – some drugs work “in vitro” and develop resistance “in vivo”

• Important for the formal characterization of drug resistance mutations, but not suitable for routine application


Present solutions

• How do we measure antiretroviral resistance?

– Genotypic test

• The virus genetic make-up is investigated; its “behavior” is inferred

– Pick the virus from the patient blood and read the virus’ genetic code, then infer its drug susceptibility profile based on our knowledge of the relationship between mutations and drug resistance

• Easier than phenotypic tests, but

– We still have limited knowledge about the newest drugs

– Some important mutations and interactions among mutations have not yet been identified

• Imperfect tool, but definitely the reference test for routine application


EuResist vision

• To predict the in-vivo efficacy of anti-retroviral drug regimens against a given HIV, based on the use of viral genotype data integrated with treatment response data derived from clinical practice


EuResist objectives• Integrate biomedical information from three

large genotype-response correlation databases – Karolinska

– ARCA

– Arevir

• Develop an array of engines for effective prediction of the response to treatment

– Case-based reasoning

– Machine learning (Bayesian networks, support vector machines)

– Graph theory

– Evolutionary models

– Fuzzy logic

• Combine the engines into a predictive system publicly available on the web


EuResist value • Largest resistance database in the

world

• Innovative prediction system, based on state-of-the-art methods

– Some of the methods were not applicable before due to insufficient amounts of data

• More effective care for patients– Harness the power of information technology

• Significant decrease in global therapy management costs

• A pilot for hepatitis (HCV and HBV) where development of drug resistance can be foreseen


EuResist partners• Informa S.r.l., Italy, Rome

• Università degli Studi di Siena, Italy, Siena (ARCA database)

• Karolinska Institutet, Sweden, Stockholm (Karolinska database)

• University of Cologne – University hospital, Germany, Cologne (Arevir database)

• Max-Planck-Society for Advancement of Science, Germany Saarbrucken

• Research Institute for Particle and Nuclear Physics of the Hungarian Academy of Sciences, Hungary, Budapest

• European Federation of Pharmaceutical Industries and Associations (EFPIA)

• Kingston University, United Kingdom, Kingston upon Thames

• University of Roma TRE, Italy

• IBM Haifa Research Lab


EuResist at a glance EuResist at a glance

EuResist database

Training

DB2 V9

ARCA Arevir Karolinska

Integrating

Prediction System


Preliminary results• Very encouraging preliminary results • Integrating clinical, demographic and viral genomic data, and applying ML

techniques improves the quality of treatment while significantly reducing costs

– “The whole is more than its parts”• Success rate of individual prediction engines varies between 75%-

78%• Their combination gives a success rate of 80%

– Clinical and demographic data significantly improves prediction • Prediction based on clinical, demographic and viral genomic data

outperforms predictions based solely on viral genomic data


Alignment with standards

• EuResist defines an HIV specific data-mart

– Researchers run their analytical tools on this data-mart

• HL7 RIM based repository (e.g., IBM CG v3)

– Provides a standard way of storing healthcare information

– Supports HL7 v3 messages (e.g., CDA)

• Specialization vs. Generalization => Tension!



• EuResist infrastructure should be open and interoperable, supporting standard interfaces

– EuResist HIV specific data-mart can be derived from HL7 RIM

– EuResist defines a CDA template for HIV treatment



• CDA template for HIV treatment– Holds demographical and clinical data for a

patient, as well as viral genomic data through links to the HL7 Clinical Genomics models

– Provides a much-needed standard way for HIV scientists/caregivers to exchange information.



• What are the recommended tools for CDA template definition?

– Authoring tools

– Validation mechanism

• What is the recommended mechanism to disseminate a CDA template?

– Implementation guides?– Balloting?– NIST/HL7 Registry?



HL7 3.0 Msg

HL7 RIMRepository

EuResistIntegrateddatabase

HL7 v3 Export/Import

HL7 3.0 Msg

CG v3

Mapping

HL7 V3.0Certified


Collaborations

• Retrovirology Laboratory Luxembourg– http://www.retrovirology.lu/

• Stanford University – http://hidb.stanford.edu

• The Rega Institute, Katholieke Universiteit Leuven – http://www.kuleuven.be/rega/

• Carlos III HIV resistance center– Reference national coordinating center for HIV resistance in

Spain

– http://www.fundacionies.com/


Links

• Official EuResist site– http://www.euresist.org/

• IBM Haifa Research Lab– http://www.haifa.ibm.com/projects/software/euresist– http://www.haifa.ibm.com/projects/verification/ml_euresist


תודהHebrew (Toda)

Thank You!

MerciGrazie

Gracias

Obrigado

Danke

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Köszönöm

Hungarian

© 2007 IBM Corporation EuResist From Data to Knowledge Integration of viral genomics with clinical...

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