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Pharmacophore extraction from Matched Molecular Pair Analysis

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Poster on extracting pharmacophores and toxophores from MMPA, first presented at the UKQSAR spring meeting 14th March 2016

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Pharmacophore extraction from Matched Molecular Pair Analysis

  1. 1. Toxophore Results Recommended safety targets with ChEMBL binding measurements: MedChemica Pharmacophore Extraction from Matched Molecular Pair Analysis (MMPA) Al G. Dossetter, Ed J. Griffen, Andrew G. Leach, Lauren Reid, Jess Stacey (MedChemica) References 1Griffen, E. et al. Matched Molecular Pairs as a Medicinal Chemistry Tool. J. Med. Chem. 2011, 54(22), pp.7739-7750. 2Leach, A.G. et. al. Matched Molecular Pairs as a Guide in the Optimization of Pharmaceutical Properties; a Study of Aqueous Solubility, Plasma Protein Binding and Oral Exposure. J. Med. Chem. 2006, 49(23), pp.6672-6682. 3Papadatos, G. et al. Lead Optimization Using Matched Molecular Pairs: Inclusion of Contextual Information for Enhanced of hERG Inhibition, Solubility, and Lipophilicity. J. Chem. Inf. Model. 2010, 50(10), pp.1872-1886. 4J.-F. Truchon and C. I. Bayly, “Evaluating Virtual Screening Methods: Good and Bad Metrics for the ‘Early Recognition’ Problem,” J. Chem. Inf. Model., vol. 47, no. 2, pp. 488–508, Mar. 2007. 5Minato Nakazawa (2015). fmsb: Functions for Medical Statistics Book with some Demographic Data. R package version 0.5.2. http://CRAN.R-project.org/package=fmsb 6Jacob Cohen (1988). Statistical Power Analysis for the Behavioral Sciences (second ed.). Lawrence Erlbaum Associates. 7Bold et al. A Novel Potent Oral Series of VEGFR2 Inhibitors Abrogate Tumor Growth by Inhibiting Angiogensis. J. Med. Chem. 2016, 59, pp 132-146. 8Mainolfi et al. Evolution of a New Class of VEGFR-2 Inhibitors from Morphing and Redesign. ACS Med. Chem. Lett. 2016 Problem Is it possible to extract pharmacophores from matched molecular pair analysis (MMPA)? Solution Post MMPA, pharmacophore dyads can be generated and predictions can be made from these through a PLS model   MMPA - a method of determining structure activity relationships (SAR’s) within sets of compounds. Matched molecular pairs (MMP’s) are identified and differences in their measured data are used to link properties to structure.1 Pharmacophore process explained in 4 steps: Pharmacophore Extraction • For a pharmacophore to be considered it must be sensitive and selective • The pair of biophores and the shortest path between them constitutes the pharmacophore dyad • Sensitivity is then found by finding the Cohen’s d coefficient of the pharmacophore dyad • A cross validated partial least squares (PLS) model is then run – potency predictions can be made from this model • Selectivity is found by finding the odds ratio of the pharmacophore dyad by comparison of ChEMBL18 database, a ROC and BEDROC curve is produced Case Study - Several different toxicity assays were then analysed to find toxophores, below are some examples Assay No. of compounds ROC score (against ChEMBL 18) BEDROC score (against ChEMBL 18) Geometric mean odds ratio R2 y-scrambled R2 Acetylcholinesterase human 383 0.80 0.35 3.77 0.43 -0.03 Beta-1 adrenergic receptor 505 0.96 0.85 832.89 0.64 0.00 Dopamine D2 receptor human 3873 0.70 0.02 110.34 0.42 0.00 Dopamine D2 receptor rat 1807 0.78 0.41 125.08 0.29 0.00 Dopamine transporter rat 1470 0.88 0.34 141.25 0.58 0.00 GABA-A receptor; anion channel rat 848 0.97 0.72 560.31 0.70 -0.01 hERG human 4189 0.92 0.49 55.46 0.61 -0.01 Monoamine oxidase A human 264 0.48 0.04 180.53 0.12 -0.02 Vascular Endothelial Growth Factor receptor 2 human 4466 0.95 0.76 79.44 0.64   0.00 contact@medchemica.com Critical Fragment Extraction Statistical analysis of data sets of SMIRKS to extract chemical fragments that are predominantly found in more or less potent compounds: 3)   Identify and group Frag A SMARTS Calculate parametric paramaters If n ≥ 8, perform a one-tailed binomial test to determine the significance of the ‘decrease’ or ‘increase’ occurrences Perform the Holm-Bonferroni adjustment on the p value If FragA >> FragB passes the 95% cut off, after the Holm-Bonferroni adjustment has been applied, Frag A is classified as a ‘biophore’ Search each significant biophore back in the original assay data set Compare the mean of the compounds containing the biophore with the mean of the remaining compounds for significance (Welch’s t test and effect size Cohen’s d) This yields a set of significant fragments 4)   These  data  sets  were  chosen  as  they  had  more  than  2000  compounds  in  and  that  there  were  from  a  wide  range  of  targets  to  show  that  this  does  not  just   work  for  one  target  type     Actual < 7 Actual >= 7 Predicted < 7 384 98 Predicted >= 7 129 682 Actual: 8.4 7 Predicted: 7.5 Actual: 7.6 7 Predicted: 7.5 Actual: 7.7 8 Predicted: 7.1 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● 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● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 4 6 8 10 5 7 9 pIC50_pred pIC50 Left: graph of the predicted pIC50 against the actual pIC50 for the VEGF set Right: ROC curve and BEDROC score4 to indicate how selective the pharmacophore dyads are 0 20 40 60 80 100 0 20 40 60 80 100 top % of ranked database %foundActivities(yield) area under the curve: 0.9537 BEDROC score: 0.7581 From the VEGFR model predictions of Novartis VEGF compounds could be made: Potency Predictions- Example recent Novartis compounds Confusion matrix for VEGF set Assay: Dopamine D2 receptor human Actual: 9.5 Predicted: 9.1 Mean with: 8.0 Mean without: 6.6 Odds Ratio: 339 Assay: Dopamine D2 receptor rat Actual: 9.4 Predicted: 9.2 Mean with: 7.6 Mean without: 6.5 Odds Ratio: 13 Assay: GABA-A Actual: 9.0 Predicted: 8.7 Mean with: 8.0 Mean without: 6.8 Odds Ratio: 1506 Assay: β1 Adrenergic receptor Actual: 7.8 Predicted: 7.7 Mean with: 6.5 Mean without: 5.7 Odds Ratio: 1500 Assay: Dopamine Transporter Actual: 9.1 Predicted: 9.1 Mean with: 8.1 Mean without: 6.7 Odds Ratio: 26.5 Cohen’s d pKi/ pIC50 Compounds containing pharmacophore dyad Remaining Compounds Effect size = Cohen’s d test • Measurement of distance between two means • Cohen’s d equals 6 • Where • This pharmacophore dyad has a Cohen’s d coefficient of 2.50 1 σ = A 2 σ + B 2 σ 2 d = A µ − B µ 1 σ Effect sizes: Large >= 0.8 Medium 0.5 – 0.8 Small 0.2 – 0.5 Trivial 0.1 – 0.2 No effect < 0.1 • What are the odds of the pharmacophore dyad hitting a molecule in the potency set against ChEMBL? Odds ratio and it's confidence limits calculated using the R fmsb package 5 • Odds of finding in potency set: • Odds of finding in ChEMBL: • Odds ratio = selectivity: •  Odds ratio = 257 (95% confidence limits 135 - 492) therefore odds of hitting a potent compound are 135 to 492 times greater than a random compound in ChEMBL Odds Ratio 17 4466 20 1348205 17 / 4466 20 /1348205 n(pharmacophoredyad hitsin potencyset) n(in potencyset) Oddsof findingin potencyset Oddsof findinginChEMBL(not potencyset) n(pharmacophoredyad hitsinChEMBLnotin potencyset) n(inChEMBL) Pharmacophore dyad example Fragment1 – yellow Fragment2 – purple Path – mixture or orange Advanced MMP’s • Two pair finding techniques are available • Not all pairs are found by a single method, both methods are needed to maximize the MMP output Molecules that differ only by a particular, well- defined, structural transformation2   A MMP found by both methods: 1)   CHEMBL318733 (VEGF inhibitor)CHEMBL101461 (VEGF inhibitor) FI method (MMP defining cut shown by red line) MCSS method (MCSS shown in red) Environment Capture • Chemical transformations are encoded as SMIRKS and recorded along with their delta property value(s) • The SMIRKS contain the structural change along with the chemical environment spanning up to 4 atoms out Essential for understanding the context of the transformation3 [c:6]1[c:4]([H])[c:2]([H])[c:1]([c:3]([H])[c:5]1[c: 7])([H])>>[c:6]1[c:4]([H])[c:2]([H])[c:1]([c:3] ([H])[c:5]1[c:7])[F] 2)   [c:4][c:2]([H])[c:1]([c:3]([H])[c:5])([H]) >>[c:4][c:2]([H])[c:1]([c:3]([H])[c:5])[F] [c:2][c:1]([c:3])([H])>>[c:2][c:1]([c:3])[F] [c:1]([H])>>[c:1][F] The MMP as a transformation: 4 atom environment: 3 atom environment: 2 atom environment: 1 atom environment: Δ data A to B Δ data A to BΔ data A to B Δ data A to B Fragment1 – yellow Fragment 2 – purple Path –mixture or orange FragA >> FragB O O O N N N N N O N N

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