Computational Toxicology and Virtual Development in Drug Design Dale E. Johnson, Pharm.D., Ph.D. Chief Scientific Officer ddplatform LLC

The “Problem” in pharmaceutical R&D ~ $700 MM and over 10 years to develop novel drug Approximately 75% of overall R&D cost attributed to failures The “Solution” for R&D Identify/eliminate problematic drugs early Design desirable properties into drugs

Drug Discovery: the hunting process where is toxicology today? Target Selection Lead Identification Lead Optimization Identification of potential targets Screen development Lead explosion/ optimization Target verification High-throughput screening Potency in disease Target selection Secondary assays/ mechanism of action Pharmacokinetics Hits to leads Early toxicology From: Rosamond and Allsop, Science 287, 1973 (2000)

Early toxicology at the Lead Optimization Step: still a high failure rate – high cost to R&D ADME, PK, TOX Lead optimization Primary & secondary efficacy screening Secondary in vitro screening In vivo and mechanistic screens Lead selection Chemical Libraries Chemical Libraries Development Candidate 65% Drop Out IND enabling studies Phase I, II

The toxicology solution Incorporate predictive toxicology concept throughout discovery & development Design reduced toxicity into chemical libraries Create expert systems to accelerate and increase success rate Expert systems must be multi-disciplinary for real impact

Major needs in Predictive Toxicology: Recent industry surveys Predictive software with updated databases Improved data mining capabilities Enhanced in vitro mechanistic screens Ready access to human hepatocytes and other cells Relevant application of new technologies ie. toxicogenomics

Major needs in Predictive Toxicology: Recent industry surveys Predictive software with updated databases Improved data mining capabilities Enhanced in vitro mechanistic screens Ready access to human hepatocytes and other cells Relevant application of new technologies ie. toxicogenomics

Missing elements in the toolbox Quality data from controlled sources Newly created database(s) using “pharmaceutical” chemical space Multi-disciplinary chem-tox Information / decision tools Data mining via “med chem building blocks” Flexibility to incorporate all data from internal and external sources Web-based, platform independent

LeadScopeTM Technology Structural analysis based on familiar structural features Powerful graphical representations and dynamic querying Refine structure alerts to reflect new assay results Statistically test structural hypotheses

RTECS database & liver toxicity ~7000 compounds with liver toxicity codes Expert conversion to grades (risk) Ordinal ranks using severity of findings, dose, regimen, species Create 1o liver tox – chemical space Data mining with ToxScopeTM: correlations between chemical structure and liver toxicity

Feature Hierarchy Graphic Panel Filter Panel Information Windows

Portion of the Heterocycles hierarchy showing 3 levels of the pyridine subhierarchy Selected subset of compounds containing a pyridine substructure with an acyclic alkenyl group in the 2-position Subset contains 2 compounds

Each structure feature in the hierarchy is defined as a substructure search query Structural definition atom and bond restrictions

Compounds containing a pyridine, 2-(alkenyl, acyc) substructure

Uncovering bias in chemical space within data sets Detect + and – coverage within a desired chemical space Understand decision errors that can be introduced with biased space

Structural alerts Can rapidly find structural alerts Can view new libraries in relation to structural alerts Can evaluate impact of alert on optimization scheme

RTECS grade 5 only

ToxScopeTM Components LeadScopeTM Enterprise Technology Several public or commercial databases New databases using “pharmaceutical" chemical space New specific organ toxicity database Structural alerts Continual updates on target organs

Conclusion “… an in silico revolution is emerging that will alter the conduct of early drug development in the future.” “Preclinical safety must transition from an experimental-based process into a knowledge-based, predictive process, where experimentation is used primarily to confirm existing knowledge”

Acknowledgements Grushenka Wolfgang, Co-author Julie Roberts Kevin Cross Bill Snyder Michael Crump Chris Freeman Jeff Miller Don Swartz Michael Murray Ilya Utkin Mark Balbes Wayne Johnson Zhicheng Li Allen Richon Yan Wang Paul Blower Limin Yu Glenn Myatt Sighle Brackman Emily Johnson Lisa Balbes