Drug Discovery & GPCR Models

Drug Discovery & GPCR Models Sheila DeWitt, PhDVP Discovery & ManufacturingOctober 25, 2007

Outline • Overview of EPIX’ Product Portfolio • Drug Discovery Strategy • Case Study – 5HT1A

Clinical Portfolio – Internally Discovered Three Drug Candidates in Phase 2 Development Product Target Lead Discovery Lead Optimization IND/GLP Tox Phase I Phase 2 Phase 3 NDA Approved PRX-08066 (5-HT2B) Pulmonary Hypertension w/ COPD Depression PRX-00023 (5-HT1A) Alzheimer's Disease (GSK has exclusive option) PRX-03140 (5-HT4) Obesity, Cognitive Impairment PRX-07034 (5-HT6) COPD = Chronic Obstructive Pulmonary Disease

Proprietary Drug Discovery Technology • GPCRs – Strategic drug development targets • Embedded proteins in surface membrane of all cells • Mediate biological signaling in health/disease • Commercially validated - 40% of top 100 drugs • Never crystallized – 3D Structures Unknown • SAR a “hit-or-miss” exercise requiring years • Side effect / selectivity issues remain problematic • Opportunity for EPIX • Proprietary modeling / screening technologies • Commitment to discovery triad: • Computational and medicinal chemistry integrated with biology

EPIX Discovery Strategy Drug Discovery • Modeling Novel GPCR modeling methodology (PREDICT™) • Screening in silico screening > 4 Mil commercially available cmpds • Hit Charact 3D Models & Purchased SAR (pSAR) to prioritize scaffolds • Lead Opt 3D Models, Biology, and Med Chem to optimize Model Development Screening Hit Characterization Lead Optimization Preclinical Development

Modeling GPCRs with PREDICT™ • Unique de novo GPCR structure prediction algorithm • Based on scientific understanding of GPCR folds • from experiments, simulations and theory • Folds the protein within its membrane environment • Does not rely on rhodopsin x-ray structure • Does not use homology modeling • Applicable (in principle) to any GPCR

Ser199 TM5 Asp116 TM3 PREDICT Model Two-Tier Modeling: ~5,000 Decoys Virtual Complex PREDICT Modeling Process GPCR sequence

6 5 4 7 3 2 1 PREDICTTM: Step I - Build 7 TMs • Represent each helix by a 2D dial • Generate all closed 2D configurations of 7 dials • under geometrical constraints • Optimize each 2D configuration • to maximize hydrophobic moment in the direction of the membrane (introduce experimental constraints) Binding pocket

6 5 4 7 3 2 1 PREDICTTM: Step II – Translate 2D to 3D • Extend each optimized 2D configuration into a 3D representation and optimize in 3D

Docking PREDICTTM Scoring Biological assays EPIX’ in silico Screening Process Data collection Target modeling Library generation Selection of virtual hits

EPIX Screening Libraries • Size: ~4 million drug-like compounds • Source: Catalogues of ~30 reputable vendors • Updates: Continuously (+before new projects) • Criteria: Availability for immediate purchase • Advantages: • Diverse • Rapid access to newest compounds (30% change per year) • Cheap to obtain and to maintain • Quick registration (buy only what is actually needed) • Limitations: • Non-standard targets may not be represented well • Need to improve IP properties since hits will be in public domain

in silico Screening & Hit Characterization • Datamine collection of >4 Mil commercially available cmpds • Select focused cmpd library for target (100,000–400,000) • In silico screening of focused library against target protein • Scoring & selection of prioritized cmpds (200-300 ‘virtual hits’) • Purchase and test ‘virtual hits’ in biological assay • Hit criteria Ki/IC50 < 10mM (validated dose response) • Datamine around hits to generate pSAR • Prioritize scaffold for Lead Optimization • Further optimize model for specific scaffold using pSAR

EPIX Paradigm for Lead Optimization • Integrated MedChem–CompChem teams (2:1 ratio) • Extensive use of computational tools (3D structures, predictive ADMET) to navigate the multiple possible optimization pathways: • Suggest/prioritize what to synthesize • Suggest/prioritize what NOT to synthesize • Efficient process, robust, agnostic to the receptor class

Efficient and Effective Discovery Engine Industry Standards EPIX Hits “wet” assay screen <1M compounds ~12 months Hits in silico screen 4M compounds ~ 6 months Lead Optimization ~1,000 compounds 2-5 years to clinical candidate Lead Optimization 100 compounds or less 6-12 months to clinical candidate

EPIX’ Lead Optimization Track Record (1) Estimated

PRX-00023 ~ Depression • 5-HT1A partial agonist, proven mechanism of action • Estimated world market for treatments $20 billion* • 35M in US (more than 16% of the population) suffer from depression severe enough to warrant treatment at some time in their lives • Substantial commercial opportunity for a selective, better tolerated alternative: • No withdrawal symptoms, sexual dysfunction, weight changes or sleep disturbances as observed with SSRIs • Lacks the addictive and sedative effects of the benzodiazepines • Does not have side effects of azapirones • Initiated Phase 2b trial March 2007 ~ results expected 1H08 • Achieved significant results on depression in Phase 3 anxiety clinical trial • Source: National Institute of Mental Health, 2003 National Comorbidity Study, Sponsored by the National Institutes of Health

Mechanisms of Other Drug Classes SSRI / SNRI • Mechanism results in increased levels of serotonin (5-HT), norepinephrine (NE) • Affects 5-HT (14), NE (>6) receptors • Affects sleep, sexual function, appetite • Withdrawal symptoms • Black box warning Azapirones • 5-HT1A agonists • Affinity for “off-target” GPCRs • Dopamine D2, alpha-1, alpha-2 • Nausea, lightheadedness, headache, restlessness • Slow dose escalation requirements

Mechanism of Action – PRX-00023 Potential advantages • Highly selective for 5-HT1A • No sexual dysfunction • No effects on sleep or appetite • No withdrawal symptoms • Do not expect black box warning • Well tolerated compared to azapirones, with minimal dose escalation required PRX-00023

PRX-00023 Superior to other 5-HT1A Agonists • Azapirones & other 5-HT1A agonists have selectivity issues and metabolic liabilities • PRX-00023 • Very high affinity for 5-HT1A (Ki = 5nM) • Better selectivity • minimal binding to alpha-1 (Ki = 1600nM), alpha-2 (> 3000nM) and dopamine D2 (Ki > 2000nM) receptors compared to Azapirones • Not metabolized to 1-(2-pyrimidimyl)-piperazine, a potent alpha2-adrenergic modulator • Better selectivity results in superior tolerability and no need to a few weeks of multi-step dose titration • Once daily dosing • No significant inhibition of CYP450 or hERG • Well tolerated in three Phase I and two Phase II clinical trials • No significant nausea / lightheadedness vs. azapirones

PRX-00023 Phase 2b in Depression in Progress ~ Data in 1H08 • Double-blind, randomized, placebo-controlled dose clinical trial of PRX-00023 in major depressive disorder (MDD) • 8-week study with 120mg twice daily flexible dosing • Approximately 330 MDD patients • Randomized 1:1 drug vs. placebo • Primary endpoint • Change from baseline in MADRS compared to placebo • Secondary endpoints • Changes in the Hamilton Anxiety Score (HAM-A) • Clinical Global Impressions Improvement Scale (CGI-I) • Clinical Global Severity of Illness Scale (CGI-S)

Outline • Overview of EPIX’ Product Portfolio • in silico Modeling Strategy • Discovery Case Study – 5HT1A

Drug Discovery & GPCR Models