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

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

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

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

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

7. Department Author Distribution of Ns and Os in PDR and GPCR Libraries 0 5 10 15 20 25 30 35 40 048 121620 Ns and Os %Count % Ns Os PDR % Ns and Os GPCR Distribution of donors in PDR and GPCR Libraries 0 5 10 15 20 25 30 35 0123456 More donors % Count %dons PDR %dons GPCR Distribution of ACDlogPs in PDR and GPCR Libraries 0 5 10 15 20 25 -5-2 147 101316 ACDlogP % occur PDR ACDlogP GPCR ACDlogP Distribution of Mwt in PDR and GPCR Libraries 0 5 10 15 20 25 30 100200300400500600700800900 1000 Mwt % Occur PDR MWt GPCR Mwt Early GPCR Library

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

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

12. Department Author 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  G (kcal/mol) = -14 -0.7n DOF + 0.7 n Csp2 + 0.8 n Csp3 +11.5n N+ +1.2n N +8.2n CO2- + 10n PO4- + 2.5n OH + 3.4 n C=O +1.1 n O,S +1.3n hal D Williams  G HB = 0.5-1.5 kcal/mol  G lipo = 0.7 kcal/mol -CH 3  G rot = 0.4 - 1.4 kcal/mol Williams etal Chemtracts, 1994, 7, 133

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

14. Department Author Andrews - 2

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

16. Department Author Final Model - 0,1 charges

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

18. Department Author 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  M leads easier to optimise than large  M “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. Department Author HtL Examples - GPCR Project IC 50 = 4.6  M Mwt 268 ClogP 3.4 IC 50 = 0.55  M Mwt 350 clogP 3.7 IC 50 = 0.18  M Mwt 380 ClogP = 4.5

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

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

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

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

24. Department Author 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  G (kcal/mol) = -14 - 0.7n DOF (n = 1-8) + 0.75 n Csp2+sp3 (n=4-18) + 11.5n N+ (n=0,1) + 1.2n N (n=0-4) + 2.5n OH (n=0,1) + 3.4 n C=O (n=0-2) + 1.1 n O,S (n=0-2) + 1.3n hal (n=0,1)

25. Department Author Leadlike Bang for your Bucks HTS screening environment Small leads probably need 1 charge @10  M

26. Department Author 100 lead - drug pairs

27. Department Author 1998: less than 600 solid compounds with mwt <250 and clogP <2 1999: 3000 added by purchase. Synthesis added >30000 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

28. Department Author Distribution of Ns and Os in PDR and GPCR Libraries 0 5 10 15 20 25 30 35 40 048 121620 Ns and Os %Count % Ns Os PDR % Ns and Os GPCR Distribution of donors in PDR and GPCR Libraries 0 5 10 15 20 25 30 35 0123456 More donors % Count %dons PDR %dons GPCR Distribution of ACDlogPs in PDR and GPCR Libraries 0 5 10 15 20 25 -5-2 147 101316 ACDlogP % occur PDR ACDlogP GPCR ACDlogP Distribution of Mwt in PDR and GPCR Libraries 0 5 10 15 20 25 30 100200300400500600700800900 1000 Mwt % Occur PDR MWt GPCR Mwt Early GPCR Library

29. Department Author Mitsunobu Library

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

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

32. Department Author 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

33. Department Author