leadlike properties, highthroughput screening and combinatorial

,Click to edit Master title style,Click to edit Master text styles,Second level,Third level,Fourth level,Fifth level,Department,Author,*,Department,Author,Click to edit Master title style,Click to edit Master text styles,Second level,Third level,Fourth level,Fifth level,*,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, 3743,1,Fastest - first and best,Target,HTS,Hit,Evaluation,Hit to,Lead,Lead,Optimisation,DESIGN AND SYNTHESIS,Potency Efficacy Selectivity,compounds,information,Kinetics Metabolism Enzymology,compounds,Lead,HTS + Combichem,2,Fisons History,Early lit work - largely peptidic,Approaches available to us,solid phase ?,Solution phase ?,Singles or mixtures ?,3,Design Criteria,Library Design Buzzwords and Concepts,“Diverse“,“Universal !”,Pharmacophore mapping libraries,focussed libraries,4,“Universal” Library,Approach 1,Approach 2,Walters and Teague Tet Lett. 2000, 41, 2023,5,Charnwood “Universal” Library,55,000 member library,6,Distribution of Ns and Os in PDR and,GPCR Libraries,0,5,10,15,20,25,30,35,40,0,4,8,12,16,20,Ns and Os,%Count,% Ns Os PDR,% Ns and Os GPCR,Distribution of donors in PDR and,GPCR Libraries,0,5,10,15,20,25,30,35,0,1,2,3,4,5,6,More,donors,% Count,%dons PDR,%dons GPCR,Distribution of ACDlogPs in PDR and,GPCR Libraries,0,5,10,15,20,25,-5,-2,1,4,7,10,13,16,ACDlogP,% occur,PDR ACDlogP,GPCR ACDlogP,Distribution of Mwt in PDR and GPCR,Libraries,0,5,10,15,20,25,30,100,200,300,400,500,600,700,800,900,1000,Mwt,% Occur,PDR MWt,GPCR Mwt,Early GPCR Library,7,The Age of Lipinski,HTS lead generation biases chemistry,alerts,HTS,8,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.,9,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,10,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,11,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,D,G (kcal/mol),=,n,DOF,+ 0.7,n,Csp2,+ 0.8,n,Csp3,n,N+,n,N,n,CO2-,+ 10,n,PO4-,n,OH,+ 3.4,n,C=O,+1.1,n,O,S,n,hal,D Williams,D,G,HB,= 0.5-1.5 kcal/mol,D,G,lipo,= 0.7 kcal/mol -CH,3,D,G,rot,= 0.4 - 1.4 kcal/mol,Williams etal Chemtracts, 1994, 7, 133,12,Andrews Analysis Training set,Significant ,model incl by 2 outliers,Biotin,13,Andrews - 2,14,Andrews - Coloured by Charge,Multiply charged compounds overpredicted,oral targets 0,1 charge,15,Final Model - 0,1 charges,16,Andrews predictions,HTS Obsd activities,HTS screening Hits,probabilities,predicted,p,(10nM) = 22%,obsd,p,(10nM) e-8%,Many hits underperform,17,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,M leads easier to optimise than large,m,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,18,HtL Examples - GPCR Project,IC,50,= 4.6,m,M,Mwt 268,IC,50,= 0.55,m,M,Mwt 350,clogP 3.7,IC,50,= 0.18,m,M,Mwt 380,19,GPCR Hit-to-Lead,Many analogues,same or loss potency,Many analogues,same potency,Both series dropped -,20,GPCR Hit-to-Lead,Rapid Hit-to-Lead optimisation,clear SAR,drug-like series with good DMPK,patentable,IC50 = 4.6,m,M,Mwt 268,IC50 = 0.02,m,M,Mwt 336,ClogP 5.3 (:-),21,22,“Difficult” Project - 2 Renin Inhibitors,No renin inhibitor went passed PII,all failed due to poor bioavailability, high cost,Process Lead Optimisation,PDR,Outside drug space,old Combi Library,Lead-like,Optimisation Hypothesis,23,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,D,n,DOF,(,n,= 1-8),+ 0.75,n,Csp2+sp3,(,n,=4-18),n,N+,(,n,=0,1),n,N,(,n,=0-4),n,OH,(,n,=0,1),+ 3.4,n,C=O,(,n,=0-2),+ 1.1,n,O,S,(,n,=0-2),n,hal,(,n,=0,1),24,Leadlike Bang for your Bucks,HTS screening environment,Small leads probably need 1 charge 10,m,M,25,100 lead - drug pairs,26,1998: less than 600 solid compounds with mwt 250 and clogP 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,27,Distribution of Ns and Os in PDR and,GPCR Libraries,0,5,10,15,20,25,30,35,40,0,4,8,12,16,20,Ns and Os,%Count,% Ns Os PDR,% Ns and Os GPCR,Distribution of donors in PDR and,GPCR Libraries,0,5,10,15,20,25,30,35,0,1,2,3,4,5,6,More,donors,% Count,%dons PDR,%dons GPCR,Distribution of ACDlogPs in PDR and,GPCR Libraries,0,5,10,15,20,25,-5,-2,1,4,7,10,13,16,ACDlogP,% occur,PDR ACDlogP,GPCR ACDlogP,Distribution of Mwt in PDR and GPCR,Libraries,0,5,10,15,20,25,30,100,200,300,400,500,600,700,800,900,1000,Mwt,% Occur,PDR MWt,GPCR Mwt,Early GPCR Library,28,Mitsunobu Library,29,Lead Continiuum -,350,Mwt 500,Mwt 200,Drug-like,Leadlike,HtL problems ?,Topical target ?,HTS screening,Non-HTS,Shapes (Vertex ),Needles(Roche),MULBITS(GSK),Crystallead(Abbott),30,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,31,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, 3743,32,33,