Distributed Drug Discovery

William L. Scott

Department of Chemistry and Chemical BiologyIndiana University Purdue University Indianapolis,

Indianapolis IN 46202

1

- The Goal:

• Interdisciplinary education of undergraduates. • Accelerated discovery of drug leads for developing world or orphan diseases.

- The Resources: Distributed and Integrated

• Simple, inexpensive, widely applicable research tools.• Multiple (e.g.“distributed”) undergraduate labs.• Spare/idle critical resources in industry and academia.

IUPUI’s Distributed Drug Discovery Project

2

Distributedproblem solving

Many small, inexpensive, "distributed"problem-solver resources

LargeProblem

Centralizedproblem solving

LargeProblemSolver

Site

LargeSolution

LargeProblem

LargeSolution

Massive and expensive resource

Centralized vs. Distributed Problem Solving

3

Large numberof proposed

potential drugs

Large numberof molecules

synthesized forscreening

Large number ofmolecules

screened providinga small number

of promising leads

Very large libraryof molecules accessible by

known chemistryand equipment

Stage 1

Distributedcomputational

analysis

Stage 2

Distributed chemicalsynthesis

Stage 3

Distributedbiological screening

Many individualPCs

Many individuallaboratories

Many individuallaboratories

Overall information technology integration

Diagram Of A Distributed Problem SolvingProcess Applied To Drug Lead Discovery

4

Big computationallibrary of proposed

potential drugs.Sub-divide into

smaller sets A, B,C, D.....

Manageablenumber of

molecules showingpromise based on

computationalmodel

Uses untapped resources: Screen saver computationalprogram runs on many idle individual personal computers

Doug'sPC

Set A

Set B

Set C

Set A

Set B

Set C

Computational redundancy: Each set sent to multiple PCs toprovide a check for internal consistency and error identification.

Ryan'sPC

Jeff'sPC

Linda'sPC

Guyla'sPC

Tulay'sPC

Molecular modeling filters

Stage 1: Distributed Problem Solving AppliedTo Drug Discovery Computational AnalysisMillions of candidates Hundreds of candidates

5

Some Current Examples of DistributedComputational Analysis in Drug Discovery

• Novartis: Currently has linked 2,700 company PCs in drug discovery. Goal is to link 70,000 company computers.

• http://folding.Stanford.edu/ (Folding@Home)• www.Grid.org (Powered by United Devices software) Software: “LigandFit” (from Accelrys) Library sources: Asinex, Oxford, Maybridge, etc. Disease targets:

• Smallpox (examining 35 million drug candidates)• Anthrax (examining 3.5 billion drug candidates)• Cancer(See W.G. Richards, “Virtual Screening Using GRID Computing: TheScreensaver Project” Nature Reviews Drug Discovery, 1, 551-555 (2002);also his web site at http://bellatrix.pcl.ox.ac.uk/index.html) 6

IUPUI

Basel

Prague

Spain

Lublin

Spain

IUPUI

Lublin

Basel

Prague

Large numberof proposed

potential drugs.Sub-divide intosets A, B, C...

Large number ofnew molecules

screened inquadruplicate

Distributed chemicalsynthesis

Distributedbiological screening

Many individuallaboratories

Many individuallaboratories

Univ ofIndpls

Set A

Set B

Set C

Set A

Set B

Set C

Up to four-fold redundancy: Each set sent to at least two institutions. All molecules in each set synthesized and screened at least twice within that institution.

Univ ofIndpls

Stages 2 & 3: Distributed Synthesis and Screening

7

Large numberof proposed

potential drugs

Large numberof molecules

synthesized forscreening

Large number ofmolecules

screened providinga small number

of promising leads

Very large numberof molecules accessible by

known chemistryand equipment

Distributedcomputational

analysis

Distributedchemicalsynthesis

Distributedbiologicalscreening

Many individual PCs Many individuallaboratories

Many individuallaboratories

Key Question: Can Distributed ChemicalSynthesis and Biological Screening be

Carried Out in Undergraduate Laboratories?

8

• Simple, well documented chemistry to relevant drug targets.

- Use solid-phase combinatorial chemistry to substituted unnatural amino acids and peptidomimetics.

• Simple, inexpensive equipment to make the molecules.

- Use a Bill-Board to conduct up 6 solid phase reactions at the same time. Academic laboratories will train

students in solid-phase and combinatorial synthesis while simultaneously making new molecules as potential drugs.

Requirements at the Chemistry Level

9

N

NH

O

O

R3

R2

R2 NHOH

O

R1O

NH

OHN

O

R1

H2NO

O

R1

N CO2H

R1

R2

NH

NOO

R2

H2NO

O

R2

R2

R3

N CO2H

O

NH

R2

R1

R3

N

HN

O

R1

1 2 5

8

7

6

9

3

4

80,000 (200X200X2)Capped unnatural amino acids

1,200,000 (200X50X30X4)Diketopiperazines

160,000 (200X20X20X2)Benzodiazepines

32,000,000 (200X200X200X4)Lactam peptidomimetics

4,000 (200X10X2)1,4-Benzodiazepine-2,5-diones

20,000 (200X50X2)Hydantoins

120,000 (3X200X100X2)-substituted proline homologs

n

(Library numbers from conservativechoice of R groups and n stereoisomers)

n = 1-3

R1

R1

X

R1

Some Peptidomimetic “Virtual” LibrariesAvailable from Our Key Intermediate:Resin-Bound Unnatural Amino Acid 2

10

H2N

O

OW H2N

O

OmR1

W

W

mR1-X

= Polystyrene Wang resin bead

Keyintermediate

21

Library III Library IV Library V

Library I

Library II

DiversityStep 1

Other Diversity

Steps

11

Schematic of Potential Libraries Available

H2N

O

OW

HN

O

OH

O

nR2

mR1

H2N

O

OmR1

W

mR1-XnR2-CO2H

Keyintermediate

2 31Library I

DiversityStep 2

DiversityStep 1

Assume m = 400 for mR1 (e.g. there are 400 Alkylating agents R1 available commercially)Assume n = 1000 for nR2 (e.g. there are 1000 Carboxylic acids R2 available commercially)

A. "Virtual" Library: All 400,000 potential enumerated combinations of mR1 and nR2 (400 x 1000).

B. "Rehearsed Virtual" Library: If we identify a subset of xR1 (say 200 alkylating agents) and yR2 (say 600 carboxylic acids) that have been proven to individually work (i.e. "rehearsed") at the appropriate step in the synthesis we can create a "Rehearsed Virtual Library" based on these subsets.This library would consist of 120,000 members (200 x 600) and is much more realistically made.

C. "Real" Library: Library of molecules actually made and registered (already 800 from reagent rehearsal).

All Library levels are valuable to computational chemists for modeling selection of drug candidates for any diseases they can model. Structures picked for synthesis from Rehearsed Virtual Libraries have a good chance of being made. Real libraries are the repositories of real synthetic and biological data. There should be open access to all levels.

12

Example of Library Levels

IUPUI

Basel

Prague

Madrid

Lublin

Distributed chemicalsynthesis

Many individuallaboratories

NotreDame

Set A

Set B

Set C

Team 1

Team 3

Team 4

Team 5

Team 6

Large numberof alkylating agentsfor synthesis of 2,sub-divided into

Subsets A, B, C...

Distributed rehearsalof alkylating agents to afford

key combichem intermediate 2

IUPUI undergraduatelaboratories

Team 2

Set A

Set B

Set C

Large numberof targeted

peptidomimeticssub-divided into

Subsets A, B, C...

First Goal: Create a database of >200 "undergraduate rehearsed"resin-bound unnatural amino acids 2 for future library generation.

Ultimate Goal First Demonstration

13

Converting Theory to Practice With Undergrads

Inexpensive Components of Bill-Board Kit(For Up to 6 simultaneous Solid Phase Syntheses)

6, 3.5ml fritted glass reaction vesselsBill-Board, washing/drain tray, collection rack

Commercial benzophenoneimine of gly-Wang resin

Bill-Board rotisserie for up to 48 simultaneous reactions 14

• Fall 2004: Final Pilot Lab (Done)- Undergraduate chemistry lab (1 Section, 17 Students) incorporated past year’s refinements and evaluated, in duplicate, 8 new alkylating agents.

• Spring 2005: Full IUPUI Implementation (Done)- up to 20 new alkylating agents will be evaluated, in quadruplicate, by 80 students in 4 lab sections.

• Spring/Fall 2005: Other Institutions in Distributed Discovery- Replicate pilot lab in two or more institutions outside US: University of Barcelona, Moscow State University, Lublin School of Pharmacy (Poland) (Done)

• Fall/Spring 2005-2006: Involve Other Schools in Indianapolis- Train student from University of Indianapolis (Done), relay experience to undergrad lab at the University of Indianapolis in Spring or Summer of 2006 (in Process).

Distributed Chemistry Discovery Time Table

15

Pressing IT Needs

• Enumeration of libraries based on chemistry andreagent availability.

• Recording and management of “rehearsal” results.

• Tracking of reagents, compounds made, and allassociated data (e.g. analytical and biological).

• Making all these resources easily available toglobal “distributed drug discovery” community.

16

Distributed Drug Discovery

• Depends on simple technology, inexpensive equipment and underutilized resources (brains included!) to facilitate drug lead discovery for developing world diseases.

• Builds on interdisciplinary expertise.

• Provides students with opportunity to see how their work fits into a “bigger picture” and indicates how they can make a difference.

• Is rooted in the core scientific value of universal reproducibility.

• Requires commitment to free and widespread exchange of scientific ideas and information across geography and culture.

17