Transcript Document

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
Overview
Property
Prediction
Genern & Applicn of Predictive
Models
Compound Prioritisation
Purchase Synthesis Screening
Tasks
 Compound set comparisons
 Compound filtering
 Compound selection
(virtual screening)
Library Design
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
Process
Targeted screening
Reagents
Reaction
Ideas
Property Filtering
Library design
Reagent Scoring
Enumeration
Property Filtering
Compounds
Docking /
Pharmacophore Scoring
Prioritised
Syntheses
Prioritised
Screening
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
OH
O
O
H
N
O
O
O
H
N
N
H
O
O
OH
O
O
O
MPA 20nM
N
VX-497 7nM
O
O
N
O
N
F
O
H
N
O
O
N
H
BMS 17nM
N
“War-Head” 19mM
MPA “warhead” bound to IMPDH
Needle Screening: An application
• 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.
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 >>40mM).
21 with IC50.
• Examine influence of
O
N
N
Y
O
R
• Protein model
– 2 X-ray structures
› oxamide
› MPA analogue
• Crystal waters
• Scoring functions
– Flex-X, ScreenScore and PLP
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
-1.5
-2.0
-2.5
pKi
-3.0
-3.5
-4.0
-4.5
-5.0
-5.5
-60
-50
-40
D7WX
-30
-20
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
30
100%
90%
80%
70%
20
60%
15
50%
40%
10
30%
20%
5
10%
0
0%
-55 -50 -45 -40 -35 -30 -25 -20 -15 -10
Score
-5
0
% cumulative
% database
25
D5WX
D7WX
cum D5
cum D7
Screening results
• 8 compounds with % inhibition > 65% @250mM.
Cmpd
Cmpd1
Cmpd2
Cmpd3
Cmpd4
IC50 mM
31
32
32
54
Cmpd
Cmpd5
Cmpd6
Cmpd7
Cmpd8
IC50 mM
88
99
168
620
 10% hit-rate with 50-fold reduction in compounds screened.
 Novel, patentable warheads
 Uncompetitive inhibition with respect to IMP
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 Validation
Enrichment of hits and effectiveness of finding all possible hits.
100
effectiveness
90
enrichment factor
80
70
60
50
40
30
20
10
0
hypo1
hypo2
hypo3
hypo4
hypo5
hypo6
hypo7
hypo8
hypo9 hypo10 by-hand
all
Docking Model Selection
Efficiency
100
100
90
90
80
80
70
70
M1
60
Hit rate
Actives (%)
Effectiveness
M2
M3
50
M4
40
M1
M2
M3
M4
60
50
40
30
30
20
20
10
10
0
0
0
20
40
60
Screen (%)
80
100
0
20
40
60
Screen (%)
80
100
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