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© 2015 Optibrium Ltd.Optibrium™, StarDrop™, Auto-Modeller™ and Glowing Molecule™ are trademarks of Optibrium Ltd.
Guiding Optimal Compound Design and Development, March 19th 2015Matthew Segall, CEO, Optibrium Ltd.
Practical Application of Multi-Parameter Optimization to Guide Successful Drug Discovery
© 2015 Optibrium Ltd.
Overview
• Multi-parameter optimisation (MPO) in drug discovery
• Practical application of MPO
• Case Study: balancing properties in lead optimisation
• Tailoring property profiles to a project objective
• Example: Properties for CNS drugs
• Conclusions
2
© 2015 Optibrium Ltd.
Multi-Parameter OptimizationThe objectives
• Identify chemistries with an optimal balance of properties
• Quickly identify situations when such a balance is not possible
−Fail fast, fail cheap
−Only when confident
3
X
No good drug
Absorption
Metabolic
stability
PotencySafety
Property 1
Pro
pe
rty 2
X
Solubility
Absorption
Solubility
Metabolic
stability
Potency
Safety
Pro
pe
rty 2
Property 1
DrugHit
Segall, Curr. Pharm. Des. (2012) 18 p. 1292
© 2015 Optibrium Ltd.
Rules of Thumb‘Drug-like’ Properties
• The most famous – Lipinski’s Rule-of-Five for oral absorption
• Many other have been proposed, e.g. Hughes et al.* explored risk of adverse outcomes in in vivo toleration studies
• Simple, easy to apply and interpret
• But need to be aware of limitations:
− Rules tailored to specific objectives
− Simple parameters do not have strong correlations with outcome
4
logP<5 MW<500
HBD<5 HBA<10
logP<3 TPSA>75 Å2
* Hughes et al., Bioorg Med. Chem. Lett. (2008) 18 p. 4875
© 2015 Optibrium Ltd.
Likelihood of Finding a Drug vs. logP
5Yusof and Segall, Drug Discov. Today (2013) 18 p. 659
© 2015 Optibrium Ltd.
Ligand Efficiency Indices
• Combine multiple parameters into single metric for optimisation
− E.g. Ligand Efficiency,
• Poor Ligand Lipophilicity Efficiency (LLE) has been shown to increase risk of safety issues*
− LLE = pIC50 – logP
• (Usually) Easy to interpret – only have to monitor one parameter
• Similar limitations to rules of thumb
− Single non-potency property often has low correlation with outcome
− Often tailored to specific objective
6
𝐿𝐸 = 𝑅𝑇 × pIC50
𝑁𝐻=
1.4 × pIC50
𝑁𝐻
* Edwards et al. (2010) Ann. Rep. Med. Chem 45 p. 381
© 2015 Optibrium Ltd.
Relevance of Experimental DataE.g. Caco-2 vs Human Intestinal Absorption*
7* Irvine et al. J. Pharm. Sci. (1999) 88 p. 28
© 2015 Optibrium Ltd.
Uncertainty in Data
• Experimental variability
− Single measurements: assay variability
− Multiple replicates: mean and standard error
• Statistical uncertainty in predictions
− E.g. Solubility (logS)
R2=0.82RMSE = 0.8 log units
• Uncertainties combine in efficiency metrics, e.g. LLE
− 𝜎𝐿𝐿𝐸 = 𝜎𝑝𝐾𝑖2 + 𝜎𝑙𝑜𝑔𝑃
2
8
© 2015 Optibrium Ltd.
Filtering?
LogP
MW
Potency
Practical Application of MPO
© 2015 Optibrium Ltd.
Accounting for RelevanceDesirability Functions*
• Avoid ‘hard’ cut-offs
• Relate property values to how ‘desirable’ the outcome
• Combine multiple properties into ‘desirability index’
− Additive:
− Multiplicative:
11* Harrington EC. (1965) Ind. Qual. Control. 21 p. 494
Simple filter: >5
0
0.2
0.4
0.6
0.8
1
0 2 4 6 8 10
De
sira
bili
ty
Property
0
0.2
0.4
0.6
0.8
1
0 2 4 6 8 10
De
sira
bili
ty
Property
Desired value: >5Importance
0
0.2
0.4
0.6
0.8
1
0 2 4 6 8 10
De
sira
bili
ty
Property
Range: 4-6
0
0.2
0.4
0.6
0.8
1
0 2 4 6 8 10
De
sira
bili
ty
Property
Ideal value: 5
0
0.2
0.4
0.6
0.8
1
0 2 4 6 8 10
De
sira
bili
ty
Property
Trend: >8
0
0.2
0.4
0.6
0.8
1
0 2 4 6 8 10
De
sira
bili
ty
Property
Non-linear, ideal value: 5
(Derringer Function)
© 2015 Optibrium Ltd.
Desirability FunctionsE.g. CNS MPO*
• 74% of marketed CNS drugs achieved CNS MPO > 4 vs. 60% of Pfizer candidates
• Correlations observed between high CNS MPO score and good in vitro ADME properties, e.g. MDCK Papp, HLM stability, P-gptransport
12
0
0.2
0.4
0.6
0.8
1
-2 0 2 4 6 8
De
sira
bil
ity
clogP
0
0.2
0.4
0.6
0.8
1
-10 40 90 140
De
sira
bili
ty
TPSA
0
0.2
0.4
0.6
0.8
1
-6 -4 -2 0 2 4 6 8
De
sira
bili
ty
clogD
0
0.2
0.4
0.6
0.8
1
0 1 2 3 4 5
De
sira
bili
ty
HBD
0
0.2
0.4
0.6
0.8
1
100 300 500 700
De
sira
bili
ty
MW
0
0.2
0.4
0.6
0.8
1
0 2 4 6 8 10 12
De
sira
bili
ty
pKa
*Wager et al. (2010) ACS Chem. Neurosci. 1 p. 435
clogP TPSA clogD HBD MW pKa
CNS MPO = sum of desirabilities for each parameter
© 2015 Optibrium Ltd.
Desirability FunctionsE.g. Caco-2
• Objective: Human Intestinal Absorption > 50%
13
0
0.2
0.4
0.6
0.8
1
1 2 3 4 5
Pro
po
rtio
n m
eet
ing
crit
erio
n
Axis Title
0
0.2
0.4
0.6
0.8
1
-1 0 1 2 3 4
Like
liho
o o
f su
cces
s
Caco-2 log(Papp) (nm/s)
Segall and Champness J. Comp.-Aided Mol. Des. (in press)
© 2015 Optibrium Ltd.
X
Property Y
100 10 1 0.1
Desired value > Threshold
A B C
UNDESIRABLE DESIRABLE
Taking Uncertainty into Account
X X X X
© 2015 Optibrium Ltd.
Probabilistic ScoringScoring Profile
15
Desirability function
Segall et al. (2009) Chem. & Biodiv. 6 p. 2144
© 2015 Optibrium Ltd.
Probabilistic Scoring
• Property data
− Experimental or predicted
• Criteria for success
− Relative importance
• Uncertainties in data
− Experimental or statistical
• Score (Likelihood of Success)• Confidence in score
Sco
re
Best Worst
Error bars show confidence in overall score
Data do not separate these as error bars overlap
Bottom 50% may be rejected with confidence
Segall et al. (2009) Chem. & Biodiv. 6 p. 2144
© 2015 Optibrium Ltd.
Probabilistic ScoringGuide redesign to improve chance of success
17Segall et al. (2009) Chem. & Biodiv. 6 p. 2144
Case StudyBalancing Properties in Lead Optimization
© 2015 Optibrium Ltd. 19
Case Study*Goal: Orally dosed compound against CV target
• In vitro data for potency, selectivity, solubility, microsomalstability (human and rat) generated on ~150 compounds
• Original process focused on potency and selectivity, filteringcompounds that did not meet requirements. Results:
− Low but prolonged activity after IP dosing
− No correlation between in vitro and in vivo potency
− Problems with solubility and metabolic stability
• Profile for probabilistic scoring:
*Segall et al.,Expert Opin. Drug. Metab. Toxicol., 2 pp. 325-37 (2006)
© 2015 Optibrium Ltd.
Comparison of Strategies
20
Potency and selectivity
No uncertainty - filter
Potency and selectivity
Consider uncertainty
All properties
Consider uncertainty
… …• New series identified with oral
bioavailability and efficacy
•New direction for project
*Segall et al.,Expert Opin. Drug. Metab. Toxicol., 2 pp. 325-37 (2006)
Tailoring Profiles to a Project Objective
Patent pending
© 2015 Optibrium Ltd.
Finding Tailored ProfilesObjectives
• Use existing data to find scoring profiles that identify compounds with improved chance of success
− Any drug discovery objective, e.g. clinical, PK, toxicity...
− Once developed, a profile can be applied prospectively to find new compounds
• Identify most important data with which to distinguish between successful and unsuccessful compounds
− Any data can be used as input, calculated or experimental
• Explore multi-parametric data
− Consider properties simultaneously, not individually
− Avoid ‘over counting’ of correlated factors
• Criteria should interpretable and modifiable
− Avoid black boxes
− Synergy between computer and experts
22I. Yusof et al., (2014) Drug Discov. Today 19(5) pp. 680-687
© 2015 Optibrium Ltd.
What is a Rule?
• A Rule is a box in multi-dimensional property space containing significantly more ‘good’ than ‘bad’ compounds
23
Property X
Pro
pe
rty Y
Key
‘good’
‘bad’
I. Yusof et al., (2014) Drug Discov. Today 19(5) pp. 680-687
© 2015 Optibrium Ltd.
Rule Induction
• ‘Rule induction’ method identifies multi-parameter regions of property space with higher chance of success
− Also known as ‘bump hunting’ because it can find property regions corresponding to small increases in probability distribution
24
Property X
Pro
pe
rty Y
I. Yusof et al., (2014) Drug Discov. Today 19(5) pp. 680-687
Example Application‘CNS’ Properties
© 2015 Optibrium Ltd.
Desirability FunctionsE.g. CNS MPO*
• 74% of marketed CNS drugs achieved CNS MPO > 4 vs. 60% of Pfizer candidates
• Correlations observed between high CNS MPO score and good in vitro ADME properties, e.g. MDCK Papp, HLM stability, P-gptransport
26
0
0.2
0.4
0.6
0.8
1
-2 0 2 4 6 8
De
sira
bil
ity
clogP
0
0.2
0.4
0.6
0.8
1
-10 40 90 140
De
sira
bili
ty
TPSA
0
0.2
0.4
0.6
0.8
1
-6 -4 -2 0 2 4 6 8
De
sira
bili
ty
clogD
0
0.2
0.4
0.6
0.8
1
0 1 2 3 4 5
De
sira
bili
ty
HBD
0
0.2
0.4
0.6
0.8
1
100 300 500 700
De
sira
bili
ty
MW
0
0.2
0.4
0.6
0.8
1
0 2 4 6 8 10 12
De
sira
bili
ty
pKa
*Wager et al. (2010) ACS Chem. Neurosci. 1 p. 435
clogP TPSA clogD HBD MW pKa
CNS MPO = sum of desirabilities for each parameter
© 2015 Optibrium Ltd.
Finding CNS DrugsApplying Rule Induction
• Data set of 119 CNS drugs and 108 failed candidates published by Wager et al.*
• Divided into training and validation sets (70:30)
• Rule derived with 20% minimum coverage:
27*Wager et al. (2010) ACS Chem. Neurosci. 1 p. 435
PKA
© 2015 Optibrium Ltd.
Finding CNS DrugsValidation Results – ROC plot
28*Wager et al. (2010) ACS Chem. Neurosci. 1 p. 435
• 38 CNS drugs and 34 failed candidates from Wager dataset*
0
0.2
0.4
0.6
0.8
1
0 0.2 0.4 0.6 0.8 1
Tru
e P
osi
tive
Rat
e (
Sen
siti
vity
)
False Positive Rate (1 - Specificity)
Random
CNS MPO (AUC=0.60)
Rule Induction 20% coverage (AUC=0.74)
© 2015 Optibrium Ltd.
Finding CNS DrugsA more realistic external test
• 118 (different) CNS drugs and 1000 CNS ‘leads’ (measured Ki
< 1 µM against CNS target) from ChEMBL database
29
0
0.2
0.4
0.6
0.8
1
0 0.2 0.4 0.6 0.8 1
Tru
e P
osi
tive
Rat
e (
Sen
siti
vity
)
False Positive Rate (1 - Specificity)
Random
CNS MPO (AUC=0.63)
Rule Induction 20% coverage (AUC=0.78)
© 2015 Optibrium Ltd.
Conclusion
• MPO is a powerful approach to select and design high quality compounds
− Quickly target compounds with high chance of success
− Avoid missed opportunities
• Be aware of the limitations of drug discovery data− Relevance
− Uncertainty
• Tailor property criteria/profile to the objectives of your project− ‘One size fits all’ profile not realistic
− Rule Induction provides a powerful way to develop tailored profile based on existing data
• Download papers from:
− www.optibrium.com/community/publications
• For more information: www.optibrium.com
© 2015 Optibrium Ltd.
Acknowledgements
• Tatsu Hashimoto – MIT
• Optibrium team, including:
− Ed Champness
− Chris Leeding
− James Chisholm
− Peter Hunt
− Alex Elliott
− Sam Dowling
− Iskander Yusof