© 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

© 2007 IBM Corporation The problem

© 2007 IBM Corporation 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

© 2007 IBM Corporation 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

© 2007 IBM Corporation 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

© 2007 IBM Corporation 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

© 2007 IBM Corporation 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

© 2007 IBM Corporation 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

© 2007 IBM Corporation 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

© 2007 IBM Corporation EuResist at a glance EuResist database Training DB2 V9 ARCAArevirKarolinska Integrating Prediction System

© 2007 IBM Corporation 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

© 2007 IBM Corporation 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!

© 2007 IBM Corporation Alignment with standards 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

© 2007 IBM Corporation Alignment with standards 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.

© 2007 IBM Corporation Alignment with standards 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?

© 2007 IBM Corporation Alignment with standards HL7 3.0 Msg HL7 RIM Repository EuResist Integrated database HL7 v3 Export/Import HL7 3.0 Msg CG v3 Mapping HL7 V3.0 Certified

© 2007 IBM Corporation Collaborations Retrovirology Laboratory Luxembourg – Stanford University – The Rega Institute, Katholieke Universiteit Leuven – Carlos III HIV resistance center –Reference national coordinating center for HIV resistance in Spain –

© 2007 IBM Corporation Links Official EuResist site – IBM Haifa Research Lab – –

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