NOVEL PARADIGMS FOR DRUG DISCOVERY SHOTGUN COMPUTATIONAL MULTITARGET SCREENING RAM SAMUDRALA

1. NOVEL PARADIGMS FOR DRUG DISCOVERY SHOTGUN COMPUTATIONAL MULTITARGET SCREENING RAM SAMUDRALA ASSOCIATE PROFESSOR UNIVERSITY OF WASHINGTON

2. SHOTGUN MULTITARGET DOCKING WITH DYNAMICS ALL KNOWN DRUGS (~5,000 FROM FDA) FRAGMENT BASED DOCKING WITH DYNAMICS (~50,000,000) PRIORITISED HITS DISSOCIATION CONSTANTS (KD) (~300-500) + ALL TARGETS WITH KNOWN STRUCTURE (~5,000-10,000) MACHINE LEARNING IN VITRO STUDIES INITIAL CLINICAL TRIALS IN VIVO STUDIES herpes, malaria, dengue hepatitis C, dental caries HIV, HBRV, XMRV CLINICAL STUDIES/APPLICATION M Lagunoff (UW), W Van Woorhis (UW), S Michael (FCGU), J Mittler/J Mullins (UW), G Wong/A Mason/L Tyrell (U Alberta), W Chantratita/P Palittapongarnpim (Thailand)

3. PROSPECTIVE PRELIMINARY VERIFICATION HERPES (HSV, CMV, KSHV) DENGUE Predicted protease (dimer) + inhibitor: Viral E protein Prediction #1 Prediction #2 Observed: Function is inactivated. KD protease ligand ≤μM KD protease dimer ≤ μM Experiment 1 Experiment 2 2/4 ≤µM ED50 against dengue virus Herpes viral load PLoS Neglected Tropical Diseases, 2010. MALARIA 14 targets Multitarget protocol: 2,344 → 16 → 6 ≤ 1 µM ED50 HTS protocol: 2,687 → 19 ≤ 1 µM ED50 HTS protocol: 2,160 → 36 ≤ 1 µM ED50 Docking protocol: 355,000 → 100 → 1 ≤ 10 µM ED50 Docking protocol: 241,000 → 84 → 4 ≤ 10 µM ED50 Trends in Pharmacological Sciences, 2010.

4. SHOTGUN MULTITARGET DOCKING WITH DYNAMICS FRAGMENT BASED DOCKING WITH DYNAMICS (~50,000,000) PRIORITISED HITS DISSOCIATION CONSTANTS (KD) (~300-500) ALL KNOWN DRUGS (~5,000 FROM FDA) + ALL TARGETS WITH KNOWN STRUCTURE (~5,000-10,000) MACHINE LEARNING Docking with dynamics Fragment based Multitargeting Use of existing drugs Drug/target maching learning matrix PK/ADME/bioavailability/toxicity/etc. Biophysics + knowledge iteration Fast track to clinic (paradigm shift) Cocktails/NCEs/optimisation Translative: atomic → clinic herpes, malaria, dengue HIV, HBRV, XMRV hepatitis C, dental caries CLINICAL STUDIES/APPLICATION M Lagunoff (UW), W Van Woorhis (UW), S Michael (FCGU), J Mittler/J Mullins (UW), G Wong/A Mason/L Tyrell (U Alberta), W Chantratita/P Palittapongarnpim (Thailand) DISCOVER NOVEL OFFLABEL USES OF MAJOR THERAPEUTIC VALUE

6. PROTEIN INHIBITOR DOCKING WITH DYNAMICS 1.0 0.5 with MD without MD Correlation coefficient ps 0 0.2 0.4 0.6 0.8 1.0 MD simulation time HIV protease Jenwitheesuk

7. ACCURACY COMPARISON Bernard & Samudrala. Proteins (2009).

8. BACKGROUND AND MOTIVATION My research on protein and proteome structure, function, and interaction is directed to understanding how genomes specify phenotype and behaviour; my goal is to use this information to improve human health and quality of life. Protein functions and interactions are mediated by atomic three dimensional structure. We are applying all our structure prediction technologies to the area of small molecule therapeutic discovery. The goal is to create a comprehensive in silico drug discovery pipeline to increase the odds of initial preclinical hits and leads leading to significantly better outcomes downstream in the clinic. The knowledge-based drug discovery pipeline will adopt a shotgun approach that screens all known FDA approved drug and drug-like compounds against all known target proteins of known structure, simultaneously examining how a small molecule therapeutic interacts with targets, antitargets, metabolic pathways, to obtain a holistic picture of drug efficacy and side effects. Find new uses for existing drugs that can be used in the clinic, with a focus on third world and neglected diseases with poor or nonexisting treatments.

9. MULTITARGET DOCKING WITH DYNAMICS COMPOUND SELECTION Compound database (~300,000) Multiple disease related proteins DRUG-LIKE (~5000 from FDA) Compound library Computational docking with dynamics High throughput screen Computational docking Experimental verificationSuccess rate +Time .Cost $$$$$ Initial candidates Initial candidates Experimental verificationSuccess rate ++Time .Cost $$$ Experimental verificationSuccess rate +++++Time .Cost $ Disease &target identification NOVEL FRAGMENT BASED TRADITIONAL SINGLE MULTITARGET SCREENING TARGET SCREENING Single disease related protein

10. INHIBITION OF ALL REPRESENTATIVE HERPES PROTEASES Predicted: Observed: Function is inactivated. protease ligand KD < μM protease dimer KD < μM Jenwitheesuk/Myszka

11. INHIBITION OF ALL HERPESVIRUSES Computationally predicted broad spectrum human herpesvirus protease inhibitors is effective in vitro against members from all three classes and is comparable or better than antiherpes drugs CMV HSV KSHV Our protease inhibitor acts synergistically with acyclovir (a nucleoside analogue that inhibits replication) and it is less likely to lead to resistant strains compared to acyclovir HSV HSV Viral load Experiment 1 Experiment 2 Experiment 3 Viral load Fold inhibition Lagunoff

12. MALARIA INHIBITOR DISCOVERY Predicted inhibitory constant 10-13 10-12 10-11 10-10 10-9 10-8 10-7 None Jenwitheesuk/ Van Voorhis/Rivas/Chong/Weismann Trends in Pharmacological Sciences, 2010.

13. MALARIA INHIBITOR DISCOVERY +++++$ ++ $$$$$ +++ $$$ COMPARISON OF APPROACHES High throughput protocol 2 2,160 compounds high throughput screen 36 ED50 ≤ 1 μM Multitarget computational protocol 2,344 compounds simulation 16top predictions experiment 6 ED50 ≤ 1 μM High throughput protocol 1 2,687 compounds high throughput screen 19 ED50 ≤ 1 μM Computational protocol 1 241,000 compounds simulation 84top predictions experiment 4 ED50 ≤ 10 μM In comparison to other approaches, including experimental high throughput screens, our multitarget docking with dynamics protocol combining theory and experiment is more efficient and accurate. Computational protocol 1 355,000 compounds simulation 100top predictions experiment 1 ED50 ≤ 10 μM Jenwitheesuk/Van Voorhis/Rivas Trends in Pharmacological Sciences, 2010.

14. DENGUE INHIBITOR DISCOVERY Prediction #1 Prediction #2 Jenwitheesuk/Michael PLoS Neglected Tropical Diseases, 2010.

15. WHY WILL IT WORK Fragment based docking with dynamics: dynamics improves accuracy; fragmentation exploits redundancy in existing drugs; most accurate docking protocol out there. Use of existing drugs: exploits all the knowledge from Pharma. Multitargeting: multiple low Kd can work synergistically; screening for targets and antitargets simultaneously. Knowledge based: potential from known structures, will have a big matrix relating drugs, targets, PK, ADME, solubility, bioavailability, toxicity, etc.; rich dataset for combining our biophysics based methods with machine learning tools in an iterative manner. Targets with known structure docking score, Kd, PK, ADME, absorption, bioavailability, toxicity Known drugs

16. BROADER IMPACT Multiple drugs can be combined to produce therapeutic effect and overcome disease resistance. Good for any condition where one or more viable targets exist. Harnesses the power of all the drug discovery done thusfar; new paradigm for fast track FDA approval Translational approach goes from providing atomic mechanistic detail to measuring clinical efficacy in one shot. Protocol can be used to design novel drugs also.

17. SUITABILITY FOR THE PIONEER AWARD Not good for Pharma because of reuse of existing drugs (most profit in novel compounds) Not good for Pharma because of focus on third world/neglected diseases. Not good for Pharma because of nonfocus on single target model they love. Marked departure from my protein structure prediction work, but now applied research from basic protein folding to producing therapeutics in a clinic. Funding will help focus work on drug discovery which until now has been done on a shoestring.

18. CONCLUSION High risk endeavour is successful if one or more diseases currently without an effective treatment can be treated completely.