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Multi-Parameter Optimization of Pharmaceuticals: the Big-Data Way - Poster 36 Cambridge Med Chem

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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

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Multi-Parameter Optimization of Pharmaceuticals: the Big-Data Way - Poster 36 Cambridge Med Chem

  1. 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.

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