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Lead-like Properties, High-throughput Screening and Combinatorial Library Design

Lead-like Properties, High-throughput Screening and Combinatorial Library Design. Andy Davis, Simon Teague, Tudor Oprea, John Steele, Paul Leeson. Teague, Leeson, Oprea, Davis, Angew Chem 1999, 38, 3743. Hit Evaluation. Hit to Lead. Lead Optimisation. Target. HTS.

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Lead-like Properties, High-throughput Screening and Combinatorial Library Design

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  1. Lead-like Properties, High-throughput Screening and Combinatorial Library Design Andy Davis, Simon Teague, Tudor Oprea, John Steele, Paul Leeson Teague, Leeson, Oprea, Davis, Angew Chem 1999, 38, 3743

  2. Hit Evaluation Hit to Lead Lead Optimisation Target HTS Fastest - first and best information Kinetics Metabolism Enzymology Potency Efficacy Selectivity DESIGN AND SYNTHESIS compounds compounds Lead HTS + Combichem

  3. Fisons History • Early lit work - largely peptidic • Approaches available to us • solid phase ? • Solution phase ? • Singles or mixtures ?

  4. Design Criteria • Library Design Buzzwords and Concepts • “Diverse“ • “Universal !” • Pharmacophore mapping libraries • focussed libraries

  5. “Universal” Library Approach 1 Approach 2 Walters and Teague Tet Lett. 2000, 41, 2023

  6. Charnwood “Universal” Library 55,000 member library

  7. Distribution of donors in PDR and Distribution of Mwt in PDR and GPCR GPCR Libraries Libraries 35 30 30 25 25 20 20 % Count 15 % Occur %dons PDR PDR MWt 15 %dons GPCR GPCR Mwt 10 10 5 5 0 0 100 200 300 400 500 600 700 800 900 1000 0 1 2 3 4 5 6 More Mwt donors Early GPCR Library Distribution of ACDlogPs in PDR and GPCR Libraries 25 20 PDR ACDlogP 15 GPCR ACDlogP % occur 10 5 0 4 7 1 -5 -2 10 13 16 ACDlogP Distribution of Ns and Os in PDR and GPCR Libraries 40 35 30 25 20 %Count % Ns Os PDR 15 % Ns and Os GPCR 10 5 0 0 4 8 20 12 16 Ns and Os

  8. The Age of Lipinski HTS alerts • HTS lead generation biases chemistry

  9. Design Criteria • Library Design Buzzwords and Concepts • “Diverse“ • “Universal” • Pharmacophore mapping libraries • Drug-like properties • Lipinski etal Adv Drug Del. Rev. 1997, 23, 3-25 • Sadowski, J. Med. Chem, 1998. 41, 3325. • Ajay etal, J.Med.Chem, 1998, 41, 3314 • focussed libraries etc etc.

  10. Our experiences ?? • by 1998 • 75%+ screening bank Combi derived • applied current design criteria • focussed upon “drug-like libraries” • we are looking for drug-like potency - • do we find it ?? 3000 hits 1e6 screen points

  11. Charnwood Confirmed HTS Hits 3000 hits 1e6 screen points • In > 1e6 screen tests - not 1 nM hit • probability of a nM hit < 1e-6 • But hits are already drug-like size

  12. Bang for your Buck • Andrews analysis (J Med Chem 1984, 27, 1648.) • scoring without a protein • analysed 200 good ligands for their receptor • assume all interactions are optimally made • apply fn group counts = regression vs potency DG (kcal/mol) = -14 -0.7nDOF + 0.7 nCsp2 + 0.8 nCsp3 +11.5nN++1.2nN +8.2n CO2- + 10n PO4- + 2.5n OH + 3.4 n C=O +1.1 nO,S +1.3nhal D Williams DGHB = 0.5-1.5 kcal/mol DGlipo = 0.7 kcal/mol -CH3 DGrot= 0.4 - 1.4 kcal/mol Williams etal Chemtracts, 1994, 7, 133

  13. Andrews Analysis Training set • Significant ,model incl by 2 outliers Biotin

  14. Andrews - 2

  15. Andrews - Coloured by Charge • Multiply charged compounds overpredicted • oral targets 0,1 charge

  16. Final Model - 0,1 charges

  17. HTS screening Hits Andrews predictions • probabilities • predicted • p(<10nM) = 22% • obsd • p(<10nM) <e-8% HTS Obsd activities Many hits underperform

  18. HTS Screening Hits • Drug-like hits • potency of many underperform • binding via weak non-specific interactions • not all interactions made or very suboptimal • would explain “flat SAR” in Hit-to-Lead activities • small mM leads easier to optimise than large mM • “easy” and “difficult” hit-to-lead projects • easy to increase Mwt/logP - increase potency • easy to demonstrate SAR, increase potency 10x • difficult because of flat SAR • difficult to reduce Mwt and logP maintaining potency

  19. HtL Examples - GPCR Project IC50 = 0.55 mM Mwt 350 clogP 3.7 IC50 = 4.6 mM Mwt 268 ClogP 3.4 IC50 = 0.18 mM Mwt 380 ClogP = 4.5

  20. GPCR Hit-to-Lead • Both series dropped - Many analogues same or loss potency Many analogues same potency

  21. GPCR Hit-to-Lead • Rapid Hit-to-Lead optimisation • clear SAR • drug-like series with good DMPK • patentable IC50 = 4.6 mM Mwt 268 ClogP 3.4 IC50 = 0.02 mM Mwt 336 ClogP 5.3 (:-<)

  22. “Difficult” Project - 2 Renin Inhibitors No renin inhibitor went passed PII all failed due to poor bioavailability, high cost

  23. Process Lead Optimisation • Optimisation Hypothesis Lead-like PDR Outside drug space old Combi Library

  24. Bang for your Buck - 2 Would a lead-like compound “hit” in HTS ? • Andrews analysis of leads • estimated pKi for “leadlike” ligand • 15,000 “random” drugs designed • random numbers of “features bounded by oral drugs filtered by est Mwt - and 0,1 charge DG (kcal/mol) = -14 - 0.7n DOF(n = 1-8) + 0.75 nCsp2+sp3(n=4-18) + 11.5nN+(n=0,1) + 1.2nN(n=0-4) + 2.5n OH(n=0,1) + 3.4 n C=O(n=0-2) + 1.1 nO,S(n=0-2) + 1.3nhal(n=0,1)

  25. Leadlike Bang for your Bucks • HTS screening environment • Small leads probably need 1 charge @10mM

  26. 100 lead - drug pairs

  27. Lead-like Profile • Mwt 200-350 • optimisation adds ca. 100 • logP 1-3 • optimisation may increase by 1-2 logunits • single charge • positive charge preferred • secondary or tertiary amine • 1998: less than 600 solid compounds with mwt <250 and clogP <2 • 1999: 3000 added by purchase. Synthesis added >30000

  28. Distribution of donors in PDR and Distribution of Mwt in PDR and GPCR GPCR Libraries Libraries 35 30 30 25 25 20 20 % Count 15 % Occur %dons PDR PDR MWt 15 %dons GPCR GPCR Mwt 10 10 5 5 0 0 100 200 300 400 500 600 700 800 900 1000 0 1 2 3 4 5 6 More Mwt donors Early GPCR Library Distribution of ACDlogPs in PDR and GPCR Libraries 25 20 PDR ACDlogP 15 GPCR ACDlogP % occur 10 5 0 4 7 1 -5 -2 10 13 16 ACDlogP Distribution of Ns and Os in PDR and GPCR Libraries 40 35 30 25 20 %Count % Ns Os PDR 15 % Ns and Os GPCR 10 5 0 0 4 8 20 12 16 Ns and Os

  29. Mitsunobu Library

  30. Lead Continiuum - Leadlike Drug-like HtL problems ? Topical target ? 350 Mwt >500 Mwt <200 HTS screening Non-HTS Shapes (Vertex ) Needles(Roche) MULBITS(GSK) Crystallead(Abbott)

  31. Screening File Split • Step taken by some companies - drivers • logical conclusion of leadlike paradigm • cost/feasibility some HTS technologies Screening file Bad - topical/desperate file Good oral file

  32. Summary • HTS • starting points are crucial to speed throughout process • screening file should reflect what chemists can easily work upon • ideally we all want to find drugs in our screening file • but generally a HTS finds only leads not drugs • file-size isnt everything = quality is equally important • Libraries • Many approaches - targeted libraries v successful • kinase libraries - 4x hit rate - screening file • libraries should reflect what you wish to find • leads not drugs Teague, Leeson, Oprea, Davis, Angew Chem 1999, 38, 3743

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