Establishing a Successful Virtual Screening Process

Stephen Pickett

Roche Discovery Welwyn

Introduction

• Challenges facing lead generation and lead optimisation

• Overview of computational methods in lead generation

• “Needle” screening

• Model Validation

• Conclusions

Challenges Facing Lead Generation and Lead Optimisation

• Reduce fall-out rate in development

• Nature of compounds, not just number of compounds is important

• Require leads not hits

• Fail fast

Challenges Facing Lead Generation and Lead Optimisation

• Increase robustness of candidates in humans

• Simultaneous optimisation of – Biological activity– Physicochemical properties– Pharmaceutic properties– Pharmacokinetic properties

• In vitro screens - synthesised compounds

• Computational screens - virtual compounds

Role for Computational Techniques

Property Prediction

Genern & Applicn of Predictive Models

Compound Prioritisation

Purchase Synthesis Screening

Compound set comparisons

Compound filtering

Compound selection (virtual screening)

Library Design

Tasks

Overview

Virtual screening

• Application of computational models to prioritise a set of compounds for screening

• Similarity to lead(s)– 2D

› Substructural keys› BCUTS, topological pharmacophores (CATS)

– 3D› Pharmacophores› Pharmacophore fingerprints› Shape, surface properties, MFA

• Q/SAR models

• Fit to protein binding site

ProcessTargeted screening

Enumeration

Docking / Pharmacophore Scoring

Property Filtering

Compounds

ReactionIdeas

Reagents

Prioritised Syntheses

Prioritised Screening

Library design

Property Filtering

Reagent Scoring

Process Requirements

• Robust and iterative– Flexibility– Reliability– Usability

• Substructural filters– acid anhydrides, reactive alkyl halides ...– functional groups incompatible with chemistry

• Price, supplier, availability

• Reagent Scoring

• Rapid calculation of product properties

• Apply consistently across projects

Computational Methods in Lead Generation at RDW

• Biological Screening– Pharmacophore and/or docking for compound prioritisation.– Target families– Data analysis

• Needle Screening– Selection of diverse compound set for NMR screening library.– Designing a focussed needle set.

• Lead Generation libraries– Design of targeted libraries– Ligand-based design

Needle Screening: An application• IMPDH

– Inosine Monophosphate DeHydrogenase– Key enzyme in purine biosynthesis– Potential host target for halting viral replication.

• Known inhibitors

O

N

OO NH

NH

O

NH

O

O

O

OOH

O

O

OH

O

N

O NH

O

O

NH

F

VX-497 7nMMPA 20nM

BMS 17nM

O

O

N

N

“War-Head” 19M

MPA “warhead” bound to IMPDH

• Aim– Find novel replacements for phenyl oxazole “warhead”.

› Low molecular weight, chemically tractable “needles”.

• Methods– NMR screening– Structure-based virtual screening to select set of compounds for

biological evaluation.

Needle Screening: An application

Process

• Optimise virtual screening protocol (FlexX)

• Virtual screening of suitable small molecules– reagents available in-house

• Biological evaluation

• Develop chemistry around actives

Overview of FlexX

• Fragment based docking methodology– Break molecule into small fragments at rotatable single bonds– Dock multiple conformations of each fragment– Regenerate molecule from docked fragments

• Scoring Function– Trade-off between speed and accuracy– Focussed on identifying good intermolecular interactions– Takes no account of absent or poor interactions

• Post-processing of solutions required– Additional calculations– Visual inspection

Optimisation of Virtual Screening Protocol

• Dataset– 47 t-butyl oxamides (40nm to >>40M).

21 with IC50.

• Examine influence of

• Protein model– 2 X-ray structures

› oxamide› MPA analogue

• Crystal waters

• Scoring functions– Flex-X, ScreenScore and PLP

N

O

N

O

R

Y

Binding site with four waters

Binding site with oxamide

Summary of Results

• Prediction of pKi values of actives– ScreenScore best in this case– Less dependence on X-ray structure – Best results when incorporating crystal waters

• Docked orientations good

• Identified most appropriate model set up– Good correlation with actives but ...– Inactives cover range of scores

• 2 sub-classes of inactives poorly predicted– Intramolecular terms.

PCA analysis of docking scores

Correlation of Docking Score with pKi (N=21)

pKi vs FlexX score

D7WX

-20-30-40-50-60

pK

i-1.5

-2.0

-2.5

-3.0

-3.5

-4.0

-4.5

-5.0

-5.5

Virtual Screening

• Screening Sets– In-house available reagents: 3425 compounds after filtering

• Dock into best model from each X-ray structure

• Data analysis– Initial visual inspection of predicted binding mode– Clustering of structures– Further visual inspection and selection of 100 compounds

• 74 compounds available for biological evaluation

Frequency of Scores

0

5

10

15

20

25

30

-55 -50 -45 -40 -35 -30 -25 -20 -15 -10 -5 0

Score

% d

atab

ase

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

% c

um

ula

tiv

e

D5WX

D7WX

cum D5

cum D7

Screening results

• 8 compounds with % inhibition > 65% @250M.

10% hit-rate with 50-fold reduction in compounds screened.

Novel, patentable warheads

Uncompetitive inhibition with respect to IMP

Cmpd IC50 M Cmpd IC50 MCmpd1 31 Cmpd5 88Cmpd2 32 Cmpd6 99Cmpd3 32 Cmpd7 168Cmpd4 54 Cmpd8 620

Thoughts on Model Validation

• Validate against known actives

• Efficiency (enrichment)– Ratio No. Actives found/No. Hits : No. Actives/DB size

• Effectiveness (coverage)– Ratio No. Actives found : No. Actives in DB

• Beware of over-fitting– Coverage across structural classes

Pharmacophore Hypothesis ValidationEnrichment of hits and effectiveness of finding all possible hits.

0

10

20

30

40

50

60

70

80

90

100

hypo1 hypo2 hypo3 hypo4 hypo5 hypo6 hypo7 hypo8 hypo9 hypo10 by-hand all

effectiveness

enrichment factor

Docking Model Selection

Effectiveness

0

10

20

30

40

50

60

70

80

90

100

0 20 40 60 80 100

Screen (%)

Act

ives

(%

) M1

M2

M3

M4

Efficiency

0

10

20

30

40

50

60

70

80

90

100

0 20 40 60 80 100

Screen (%)

Hit

rat

e

M1

M2

M3

M4

Conclusions

• Effective virtual screening strategy established.

• Successfully applied to lead generation.

• Virtual needle screening powerful method for lead generation.

Acknowledgements

• Brad Sherborne, Ian Wall, John King-Underwood, Sami Raza

• Phil Jones, Mike Broadhurst, Ian Kilford, Murray McKinnell

• Neera Borkakoti