Super fast identification and optimization of high quality drug candidates

1. Super fast identification and optimization of high quality drug candidates

2. Our Goals • Constructing highly enriched and efficient molecular libraries for the development of new and selective drug-like leads • Minimizing false positives by early identification of drug failures, resulting in reduced cost/time of drug development

3. Preclinical Drug Discovery We reduce lead identification and optimization to 1-3 months, and identify highestquality drug candidates

4. Competing state-of-the-art computational drug discovery technologies in Pharma • Rules for drug-like properties(Lipinski, Veber): binary, many false positives • Data Mining from HTS: requires innovative algortihms • “Similarity” searches (mostly structural) : limit innovation • Drug-target “Docking” algorithms: at their infancy, false positives & negatives • ADME/Tox models: can not accurately predict a molecule’s chance to become a drug

5. Our Technology:what do we do best ? Grading drug likeness and molecular bioactivity Drug-Target: “Molecular Bioactivity Index”(MBI) Drug-Body: “Drug Like Index”(DLI) ISE (Iterative Stochastic Elimination) engine Experimental Datasets (drugs, Non-drugs, agonists, antagonists, inhibitors) DLI and/or MBI

6. MBI andDLI • MBI is a number that expresses the chance of a molecule being a high affinity ligand for a specific biological target • DLI is a number that expresses the chance of a molecule to become a drug • Double focusing using MBI and DLI provides: combined target specificity and drug-likeness

7. MBI and DLI can make a difference in: • High Throughput Screening • Combinatorial Synthesis • Hit to lead development • Lead optimization • Construction of Focused libraries • Molecular scaffold optimization • Selectivity optimization

8. Iterative Stochastic Elimination: A new tool for optimizing highly complex problems • First prize in emerging technologies symposium of ACS • Patent in National phase examination in several countries • PCT on the derived technology of DLI

9. IPA stochastic method to determine in silico the drug like character of molecules • By Rayan, Goldblum, Yissum (PCT stage) • A new provisional patent application covering the MBI algorithm will be submitted

10. 1-2 days ISE for identification of high quality leads TRAINING SET TEST SET Validation INPUT ISE Engine MBI MODEL Huge Commercial Database of chemicals Database ordered By Bioactivity Index

11. Double focusing with MBI and DLI Database ordered By Bioactivity Index MBI MODEL Huge Commercial Database of chemicals Few hours DLI Assumed high affinity leads Validations: Docking, Scifinder, “fishing” tests Optimized leads for in vitro and animal tests 2 - 4 days

12. MODELS • Matrix metalloproteinase-2 (MMP-2) • Endothelin receptor • D2- dopaminergic receptor • DHFR • Histaminergic receptors • HIV-1 protease • Cannabinoid receptor • And others..

13. Current technological status: • Excellent enrichment of “actives” from “non-actives” using MBI • Excellent separation of drugs from “non-drugs” using DLI • Discovering molecules for a known drug target, validated by a docking algorithm • Successful validation of MBI technology by big Pharma

14. Molecular Bioactivity Index (MBI): Fishing actives from a “bath” of “non-actives” Mix 10 in 100,000 - find 9 in best 100, 5 in best 10 Enrichment of 5000

15. Drug Likeness Index (DLI): Randomly mixing 10 Drugs + 100 Non-drugs Enrichment of ~7

16. DLI vs. the Medicinal Chemist-1

17. DLI vs. the Medicinal Chemist-2 5 top Medicinal chemists examined

18. MMP-2 as a target for POC • Identifying high affinity ligands for Matrix metalloproteinase-2 (MMP-2) was chosen as proof of concept for our technology • MMP-2 (or Gelatinase A) is involved in several types of cancer, such as Breast cancer, Hepatocellular carcinoma, Smooth muscle hyperplasia and possibly others • We have large datasets for training • Chemicals easy to purchase • In vitro assay available • Animal model available (murine leukemia) • Israel Science Foundation collaboration

19. Typical MMP-2 actives - nanomolar Typically - hydroxamates and sulphonamides

20. ISE for identification of high quality leads MBI MODEL For MMP-2 ZINC database with 2 million molecules Zinc ordered by MBI values Picking 104 molecules with top MBI values above 30

21. New Chemical Entities (> 90 !) Less Similar Similar

22. Non-typical MMP-2 suspected nanomolar candidates 1.00 0.04 0.02 0.09 0.04 0.08 0.11 0.02 0.04 1.00 0.16 0.04 0.26 0.15 0.17 0.09 0.02 0.16 1.00 0.07 0.14 0.08 0.09 0.06 0.09 0.04 0.07 1.00 0.12 0.06 0.21 0.11 0.04 0.26 0.14 0.12 1.00 0.15 0.15 0.14 0.08 0.15 0.08 0.06 0.15 1.00 0.20 0.06 0.11 0.17 0.09 0.21 0.15 0.20 1.00 0.07 0.02 0.09 0.06 0.11 0.14 0.06 0.07 1.00 8 of highest diversity were picked Scifinder – none ever examined on any MMP The first MMP-2 candidate inhibitors picked for purchasing and testing in the lab are devoid of the characteristics of MMP-2 or other MMP inhibitors. These molecules are not known to have any prior biological activity and have a very low similarity index (Tanimoto) to each other (the highest similarities are marked in yellow in the matrix above).

23. Independent validation by docking 7 out of the 8 dock well to the active site of MMP- 2

24. The Big Pharma technology test Enrichment Curves Our ISE

25. Our superiority claim • Highly innovative Prize winning optimization algorithm • The best enrichment algorithm currently available • MBI: “actives” from “non-actives” • DLI: drugs from “non-drugs” • Identification of highly diverse drug candidates • Reduction of time for lead identification and optimization

26. We vs. chemical companies selling focused libraries

27. We vs. Docking