A Web-Based Computational Tool for Combinatorial Library Design that Simultaneously Optimises Multiple Properties

A Web-Based Computational Tool for Combinatorial Library Design that Simultaneously Optimises Multiple Properties

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## A Web-Based Computational Tool for Combinatorial Library Design that Simultaneously Optimises Multiple Properties

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**A Web-Based Computational Tool for Combinatorial Library**Design that Simultaneously Optimises Multiple Properties Weifan Zheng, Sunny T. Hung, Joel T. Saunders, Stephen R. Johnson, George L. Seibel A short paper: http://www-smi.stanford.edu/projects/helix/psb-online/**Outline**• Library Design - Problem Definition • Criteria in Early Computational Techniques • Important Developability Parameters • Multifactorial Nature of Library Design • PICCOLO • Optimisation Protocol • Individual Penalty Terms and Their Definition • Snapshots of the Intranet-Based System • Conclusions**R2**R1 5x5 full combination ? Library Design - Problem Definition 10 x 10 => 5 x 5**Criteria Used in Early Computational Design Techniques**• Diverse Design: • diversity analysis and void-filling • Targeted Design: • similarity to leads • docking to a binding site • predicted activity using QSAR/QualSAR models • Pphore models**Failure of Compounds in Development**• Poor biopharmaceutical properties, 39% • Lack of efficacy, 29% • Toxicity, 21% • Market reasons, 6% - Venkatesh & Lipper, J. Pharm. Sci. 89, 145-154 (2000) “an efficacious but non-absorbed agent is no better than a well absorbed but in-efficacious one” -Curatolo W. Pharm Sci Tech Today 1, 387 (1998)**Developability Should Be Considered in Library Design**To avoid serious ADME liabilities as early as possible in the drug discovery process • Empirical rules • Lipinski rules of 5 (MW, clogP, #HD, #HA) • Drug-likeness • Ajay & Murcko (JMC, 1998, 41, 3314-3324) • Sadowski & Kubinyi (JMC, 1998 , 41, 3325-3329)**Some Fundamental Properties Contributing to Pharmacokinetics**(PK) • Aqueous solubility • Membrane passive permeability • Cytochrome P450 activities • Plasma protein binding • Efflux pumping and active transport • ...**Factors That Are Optimised**• Similarity to leads • Reagent diversity/coverage • Product novelty with respect to the corporate compound inventory • Lipinski parameters • Liabilities against P450 enzymes • Aqueous solubility; [Permeability] • Molecular flexibility; MS redundancy; reagent price**R1**R2 PICCOLO: reagent PICking by COmbinatorial Library Optimisation R1 R2 Better Library Initial Library R1 R2 Optimal Library R1 Penalty Scores R2 P450 Activity Lipinski Properties Diversity Iteration**The Size of the Solution Space is Huge**50 Amines + 50 carboxylic acids • Total number of compounds 50 x 50 = 2500 • Total number of solutions for an 8 x 12 library 50!/(8!42!) * 50!/(12!38!) = 6.52 x 1019**Stochastic Optimisation to Sample the Solution Space**Randomly Pick 5x5 Calc penalty scores for the trial solution & save scores Reagent Pool Enumerate Swap a Fraction of Reagents Y Save the trial solution Metropolis criteria? N Reject trial solution**Perturbation Scheme**• Which R-group to perturb • bias toward the R-groups that need more sampling • Which new reagent to pick • uniform sampling by cycling through the selected R-group list • Which old reagent to kick out • randomly chosen**Total Penalty Score is the Weighted Sum of Individual**Penalty Terms**Similarity to Leads**• Esim(S) = Daylight Tanimoto “distances” between all the compounds in a given library and the lead, averaged over the size of the library • In case of multiple leads, the Tanimoto distance between a compound and the leads is defined as the nearest neighbour distance**N**D S = opt 1 å ( ) d y, D - y Î y D Reagent Diversity: S-Optimal Criterion • Esdiv (S) = Reverse S optimal scores for all R-groups averaged over the number of R-groups D: a set of design points (i.e., the selected reagents) d(x, A): minimum TD between point x and set of points A**Product Novelty with Respect to Corporate Collection**• All S.B. compounds were mapped onto a 6D cell space (PCA, or formed by selected features to distinguish biological activities) • Epn (S) = the smoothed average number of S.B. compounds in the neighbouring cells**Developability Penalty Scores**• Lipinski Parameters • MW < = 500 • ClogP: -1 to 5 • NHD <= 5 • NHA <= 10 • P450s - non-inhibitory predicted by the P450 classifiers • Solubility - should be higher than a limit Each penalty term is the percentage of library compounds that violate the limits for each term**P450 Classifiers and Solubility Predictor**• P450s: 2d6, 3a4, 1a2, 2c9 • dataset(2d6): Active: ~3500; Inactive: ~4000 • method: 3 layer ANN • FP: 20%; FN: 10%; Ambiguous - 12 - 18% • Solubility • N = ~550 • 3 layer ANN • rms error ~1.0 log unit**Conclusions**• PICCOLO is an in-house library design system that can simultaneously optimise all the factors we care about • Important developability parameters are taken into account • Expandable to include other criteria • A Web based system being used by SB chemists worldwide**Acknowledgements**Colleagues in Cheminformatics Department Ken Kopple Jie Liang (now at Univ. Illinois at Chicago) Medicinal Chemists Todd Graybill, Jian Jin , Ronggang Liu, Tom Ku, Dennis Yamashita, Scott Thompson, Jia-Ning Xiang