Describes the science behind Matched Molecular Pair Analysis (MMPA) which can help Multi-Parameter Opimisation (MPO) of new molecules to treat disease. The process is data hungry and the case for combining the data from pharmaceutical companies is made (and so a consortium). This Big Data analysis will provide more high context specific design rules to further aid drug design

1. MedChemica
MedChemica
For Example
Does adding a 4-F to a phenyl ring actually reduce
metabolism?
Mean change in human liver microsome log(Clint).
497 examples Mean (m) = 0.06, SE = 0.02
Probability change >0.5 log improvement : 0.086 = 8.6%
NO – the evidence is that 4-H>>4-F on average will give no difference
and only 8.6% of the time will it make a significant difference.
Medicinal Chemistry
transformations
Classic changes on med chem?
How often do they work?
How can we test the reliability of the
“rules” of medicinal chemistry?
Multiparameter Optimization of Pharmaceuticals: the Big-Data Way
Alexander G. Dossetter, Andrew G. Leach, Edward J. Griffen.
al.dossetter@medchemica.com
Ebenezer House, Ryecroft, Newcastle-under-Lyme, Staffordshire, ST5 2BE
Can we analyse multi-pharma datasets to produce the largest electronic textbook of Medicinal Chemistry?
If so can we reduce the number of compounds we make (costs) and increase quality?
Does it work? Multi-Objective Optimisation using Knowledge base - Project Examples
Identify Matched Pairs
in a supervised manner
on a dataset
Results Log10
and
subtracted
OR via an algorithm
that finds all pairs in
large datasets
Matched Molecular Pair Analysis
(MMPA)
Diversity testing
n = number of matched pairs
Statistical analysis (μ,σ,SEM)
frequency of increase
frequency of increase > 0.5
Main focus: In vitro ADMET assays
KNOWLEDGE BASE
Coded transforms
+ plus data
analysis
- Novel more efficient core required, improve hERG for CD
- CNS penetration and deliver tool for in vivo testing
McCoull, Dossetter et al, Med. Chem. Commun., (2013), 4, 456
Ghrelin Inverse agonists
MMPA
Cores
- Fix hERG problem whilst maintaining potency
Waring et al, Med. Chem. Commun., (2011), 2, 775
Glucokinase Activators
MMPA
∆pEC50: -0.1 ∆logD: -0.6 ∆hERG pIC50 :-0.5
n=33 n=32 n=22
MMPA
∆pEC50: +0.3 ∆logD: +0.3 ∆hERG pIC50 :-0.3
n=20 n=23 n=19
MMPA
∆pEC50: -0.1 ∆logD: -0.6 ∆hERG pIC50 :-0.5
n=27 n=27 n=7
Gleeson, 2009 and Papadatos, 2010
Human Liver Microsomal stabilityGroups that reduce cytochrome P450
inhibition or hERG ion channel binding
A1 = alkyl
A1 = aryl
A1 = all
What sort of Design Rules do you get? Transforms from the MMPA literature - Dossetter, A.G.; Griffen, E.J.; Leach A.G. Drug Discov. Today 2013, 18, 724
Dossetter 2012
Human Liver Microsomal stability
R1 = N, O, S, alkyl, arom. R2 = alkyl, arom.
Dossetter 2012
We can make it be better: More data will mean more specific transforms, with the best chance of delivering progress
• Distribution of clogP change for
18484 transformations where
hERG pIC50 decreases by at
least 0.3 log units and n >=3
• 25% of the hERG transforms
above show clogP >0
- Rules from analysis of 60K hERG datapoints
- Matched Pair Enantiomers produce different results
Surprises from MMPA – its more than lipophilicity
hERG If physical properties drove ADMET
then enantiomeric pairs should be
equivalent
Enantiomeric pairs reveal that key medicinal
chemistry parameters differ between R and S
Andrew G. Leach et al,
Med. Chem. Commun., 2012,3, 528-540.
6 Great Things to do next
1. Make better molecules faster – more data better rules
2. Generate new rules by suggesting extra compounds to test – fill in
the gaps
3. Critical toxicology SARs – more assays analyzed
4. Understand species differences in clearance
5. Combine rules to create “meta-rules”
6. Combine with shape and color to generate pharmacophores
7800k
Unique Transforms
1200k n>1
218k n>4
73k n>9
Pfizer authors extracted their HLM
dataset of 225k results – only a
fraction (<1%) had enough MPs to
reach statistical significance
Keefer, C.E. et al. Bioorg. Med. Chem. (2011)
19, 3739–3749
Bayer and AZ compound collections
overlap by just 5%!
Kogej, T. et al. Drug Discov. Today. 2012
There is so much we don’t know about
Combining data will find more rules
-1.7-1.4-1.1-0.8-0.5-0.20 .2 .4 .6 .8 1 1.2 1.5
pIC50 -0.4
logD -1.8
hERG pIC50 +0.4
pIC50 9.9
logD 5.0
hERG pIC50 5.0
LLE 4.9
very potent
very lipophilic
pIC50 +0.9
logD +0.2
hERG pIC50 -0.3
pIC50 8.2
logD 1.3
hERG pIC50 4.4
LLE 6.9
pIC50 -2.2
logD -2.2
hERG pIC50 -0.7
100
compounds
made
LLE 6.4
LLE 6.9
Better Worse Indistinguishable
Conclusions
• Projects can be accelerated by delivering statistically sound medicinal chemistry knowledge.
• The design rules found can be counter-intuitive to current ideas in medicinal chemistry.
• Pooling data can provide more rules including more structurally specific ones.
• By focusing on ADMET, rules are found that help reduce efficacy and potency failures in the clinic.