COMPUTATIONAL BIOLOGY IN DRUG DISCOVERY RAM SAMUDRALA ASSOCIATE PROFESSOR UNIVERSITY OF WASHINGTON

1. COMPUTATIONAL BIOLOGY IN DRUG DISCOVERY RAM SAMUDRALA ASSOCIATE PROFESSOR UNIVERSITY OF WASHINGTON How can we computationally screen compounds against protein structure targets to discover inhibitors with high affinity?

2. MOTIVATION Drug discovery as undertaken by the pharmaceutical company is time consuming and expensive, with very low hit rates for the amount of resources expended. Computational screening of compounds against structures of protein targets offers a way to speed up discovery time and reduce costs, but such techniques have typically had low accuracy and need high resolution structures. We will capitalise on advances in computational protein structure prediction and protein docking to improve accuracy of target-based in silico compound screening.

3. Methods for obtaining structure Experimental Computational X-ray crystallography NMR spectroscopy De novo prediction Homology modelling

4. Critical Assessment of Structure Prediction (CASP) Pre-CASP CASP Bias towards known structures Blind prediction

5. CASP6 prediction (model1) for T0281 4.3 Å Cα RMSD for all 70 residues http://protinfo.compbio.washington.edu/protinfo_abcmfr Ling-Hong Hung/Shing-Chung Ngan

6. CASP6 prediction (model1) for T0271 2.4 Å Cα RMSD for all 142 residues (46% identity) Tianyun Liu http://protinfo.compbio.washington.edu/protinfo_abcmfr

7. Prediction of HIV protease-inhibitor binding energies with dynamics Can predict resistance/susceptibility to six FDA approved inhibitors with 95% accuracy in conjunction with knowledge-based methods http://protinfo.compbio.washington.edu/pirspred/ Ekachai Jenwitheesuk

8. Drug discovery – current approach Pink et al, September 2005

9. Drug discovery – our approach Pink et al, September 2005 Computational protein docking with molecular dynamics protocol enables in silico discovery of compounds that inhibit multiple targets and diseases.

10. Multi-target multi-disease therapeutic discovery • Disease A • Protein A1 • Protein A2 • Protein A3 • … • … • Disease B • Protein B1 • Protein B2 • Protein B3 • … • … • Disease C • Protein C1 • Protein C2 • Protein C3 • … • … • Disease X • Protein X1 • Protein X2 • Protein X3 • … • … Screen library of FDA approved or experimental compounds using docking with dynamics protocol Binding affinity calculation using docking with dynamics protocol Disease A Disease B Disease C Disease X Protein A… Protein A2 Protein A1 1 … 2 … 3 … 4 Inhibitor A 5 … 6 … Protein B… Protein B2 Protein B1 1 … 2 Inhibitor B 3 … 4 … 5 … 6 … Protein C… Protein C2 Protein C1 1 … 2 … 3 … 4 … 5 Inhibitor C 6 … Protein X… Protein X2 Protein X1 1 … 2 … 3 Inhibitor X 4 … 5 … 6 … Rank of inhibitory concentration . . . . . . . . . . . Ekachai Jenwitheesuk

11. Multi-target inhibition of herpesvirus proteases Michael Lagunoff

12. Multi-target inhibition of herpesvirus proteases HSV CMV KHSV Ekachai Jenwitheesuk

13. Multi-target inhibition of herpesvirus proteases Our best prediction showed inhibitory activity against all three classes of herpesviruses (alpha, beta, and gamma) in cell culture, and is the only inhibitor known to do so. We have repeated experiments several times. Inhibition of viral growth is comparable to or better than known anti-herpes drugs in the market (acyclovir, gancylovir, foscarnet). Growing HSV in the presence of acylovir for a few days and measuring virus titer results in almost no reduction with and without drug, indicating growth of drug resistant virus. Our inhibitor continues to work well under the same conditions. Using low (sub-optimal) doses of both acyclvir and our inhibitor together results in much better inhibition than either alone. Higher doses result in the best inhibition we have observed. Circumstantial evidence that our inhibitor does work against protease. Inhibitory constant measurements and mouse studies are underway.

14. Multi-target inhibition of herpesvirus proteases • All these three viruses cause life-threatening diseases in immunocompromised patients. • HSV drugs alone represent a > $2 billion dollar yearly market and growing at a 10% rate. Nearly 90 million people worldwide are infected with the genital herpes virus, and about 25 million of them suffer frequent outbreaks of painful blisters and sores. • CMV is a major cause of mortality in transplant patients, and drugs against it represent a $300 million dollar yearly market. • Acylovir and related drugs are all nucleoside analogues/inhibitors whose patents will soon expire. Our protease inhibitor is a novel type of anti-herpes agent that may be used in combination therapy. • The inhibitor has been evaluated in mouse models of cancer and found to very nontoxic. • Topical applications are therefore possible with a high likelihood of success.

15. Multi-target inhibition of Plasmodium falciparum proteins Ekachai Jenwitheesuk/Wesley Van Voorhis

16. Multi-target inhibition of Plasmodium falciparum proteins We experimentally evaluated 16 of our top predictions against P. falciparum in cell culture. 6/16 had an ED50 of  1 M, with the best inhibitor having an ED50 of 127nM. A negative control of 5 randomly selected compounds predicted to not inhibit our fourteen targets did not inhibit P. falciparum growth. Chong et al.1 experimentally screened 2687 compounds and found 87 inhibitors against P. falciparum. Weisman et al.2 screened 2162 compounds found 72 inhibitors. Their hit rates are 3.2% (87/2687) and 3.3% (72/2162). We are thus able to obtain a much higher hit rate of 38% (6/16) for a fraction of the cost: Only 16 compounds costing ~$1000 needed to be tested. Computation is fully automated and takes only a few days. Examining overlap between our computational library and their experimental libraries resulted in 75 compounds of which we would have tested 15. 8/15 inhibitors had an ED50 of  1M, resulting in a hit rate of 53%. Ekachai Jenwitheesuk/Wesley Van Voorhis 1Nat Chem Biol 2: 415-6, 2006. 2Chem Biol Drug Des 409-16, 2006.

17. Other work and future directions Our predicted inhibitors against the dengue virus are more efficacious in cell culture than previously identified inhibitors We have predicted inhibitors against more than 100 protein targets for over 20 diseases, including HIV, SARS, Leishmania, Tuberculosis, and Influenza. Experimental testing is underway against some of the pathogens responsible. Computationally screen structurally-related compounds to experimentally verified inhibitors from a much larger library of 1 million compounds. Use data from experimental studies to figure out when our predicted inhibitors are likely to be cell-active and drug-like in their behaviour; use machine learning approaches to learn from compound characteristics (PK, ADME, toxicity), importance of protein targets, predicted binding energies and experimental inhibition. Works due to the use of a combination of knowledge- and biophysics-based methods for computational simulation.

18. Acknowledgements Current group members: Collaborators: • Baishali Chanda • Brady Bernard • Chuck Mader • David Nickle • Ersin Emre Oren • Ekachai Jenwitheesuk • Gong Cheng • Imran Rashid • Jason McDermott • Jeremy Horst • Ling-Hong Hung • Michal Guerquin • Rob Brasier • Rosalia Tungaraza • Shing-Chung Ngan • Siriphan Manocheewa • Somsak Phattarasukol • Stewart Moughon • Tianyun Liu • Weerayuth Kittichotirat • Zach Frazier • Kristina Montgomery, Program Manager • Michael Lagunoff • Wesley Van Voorhis • Roger Bumgarner Funding agencies: • National Institutes of Health • National Science Foundation • Searle Scholars Program • Puget Sound Partners in Global Health • UW Advanced Technology Initiative • Washington Research Foundation • UW TGIF

19. Advantages of our approach Probabily of success is higher: Multi-target inhibition Mechanism of action is understood Use of preapproved drugs Side effects may be predicted Costs are reduced: Computational discovery Use of preapproved drugs Lower number of failed drugs