Super fast identification and optimization of high quality drug candidates

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

Preclinical Drug Discovery

We reduce lead identification and optimization to 1-3 months, and identify highest quality drug candidates

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

Competing state-of-the-art computational drug discovery technologies in Pharma

Experimental

Datasets

(drugs, Non-drugs,

agonists,

antagonists, inhibitors)

DLIand/or

MBI

ISE (Iterative Stochastic Elimination) engine

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)

MBI and DLI

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

High Throughput Screening

Combinatorial Synthesis

Hit to lead development

Lead optimization

Construction of Focused libraries

Molecular scaffold optimization

Selectivity optimization

MBI and DLI can make a difference in:

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

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

ISE for identification of high quality leads

ISE Engine

Huge CommercialDatabase of chemicals

TRAINING SET TEST SET

MBI MODEL

ValidationINPUT

Database orderedBy Bioactivity

Index

1-2

day

s

Huge CommercialDatabase ofchemicals

Database orderedBy Bioactivity

Index

Assumed high affinity leads

Validations: Docking, Scifinder, “fishing” tests

DLI Optimized leads for in vitro and animal tests

MBI MODEL

2 - 4 days

Few hours

Double focusing with MBI and DLI

MODELS

Matrix metalloproteinase-2 (MMP-2)

Endothelin receptor

D2- dopaminergic receptor

DHFR

Histaminergic receptors

HIV-1 protease

Cannabinoid receptor

And others..

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

Current technological status:

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

Drug Likeness Index (DLI):Randomly mixing 10 Drugs + 100 Non-drugs

Enrichment of ~7

DLI vs. the Medicinal Chemist-1

DLI vs. the Medicinal Chemist-2

5 top Medicinal chemists examined

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

Typical MMP-2 actives - nanomolar

Typically - hydroxamates and sulphonamides

ISE for identification of high quality leads

MBI MODELFor MMP-2

ZINC database with2 million molecules

Zinc ordered byMBI values

Picking 104 molecules with top MBI values above 30

Similarity between 104 prospective MMP-2 leads and the 650 MMP-2 leads used for model construction

0

10

20

30

40

50

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Tanimoto Index

Nu

mb

er o

f m

ole

cule

s

SimilarLess

Similar

NewChemicalEntities(> 90 !)

Non-typical MMP-2 suspected nanomolar candidates

1.00 0.04 0.02 0.09 0.04 0.08 0.11 0.020.04 1.00 0.16 0.04 0.26 0.15 0.17 0.090.02 0.16 1.00 0.07 0.14 0.08 0.09 0.060.09 0.04 0.07 1.00 0.12 0.06 0.21 0.110.04 0.26 0.14 0.12 1.00 0.15 0.15 0.140.08 0.15 0.08 0.06 0.15 1.00 0.20 0.060.11 0.17 0.09 0.21 0.15 0.20 1.00 0.070.02 0.09 0.06 0.11 0.14 0.06 0.07 1.00

8 of highest diversity were pickedScifinder – 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).

Independent validation by docking

7 out of the 8 dock well to the active site of MMP- 2

The Big Pharma technology testEnrichment Curves

Our ISE

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

We vs. chemical companies selling focused libraries

Company

name

Combinat.

algorithm

Novel detect

False positiv

False negat.

Enrichment Model speed

Virtual screening speed 106

3D structure required ?

Biofocus - Yes - - 10-100 - - Yes

Pharmacopeia No Yes High High 5-50 - 16,000 hours/CPU

Yes

Enamine No No S Yes D

Low S High D

High

High

10-1000 S 5-50 D

- 300-16,000 hours/CPU

No

Yes

TimeTec No No Low High 10-1000 - 300 hours/CPU

No

IBS-interbioscreen

No No Low High 10-1000 - 300 hours/CPU

No

Comgenex - No Low High 10-1000 - 300 hours/CPU

No

OSI pharmaceutical

- Yes High High 5-50 - 16,000 hours/CPU

Yes

Our algorithm Yes Yes Low Low 200 – 5,000 1-2 days

2 hours No

We vs. DockingDocking approaches

Results reported by GSK

Average enrichment factor found on top 10%

Ideal 10.0

Dock4 2.3

DockIt 1.9

FlexX 3.7

Flo+ 3.0

Fred 2.4

Glide 3.2

Glod 2.2

LigandFit 2.6

MOEDOCK 1.2

MVP 6.3

Ours – validation test of Big Pharma 9.5