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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