Screening heuristics pope-final
Upcoming SlideShare
Loading in...5

Like this? Share it with your network


Screening heuristics pope-final



Podium Presentation at SLAS Annual Meeting, San Diego, Feb 4-8, 2012 entitled "Screening Heursitcs and Chemical Propery BIas; New Directions for Lead Identification and Optimization"

Podium Presentation at SLAS Annual Meeting, San Diego, Feb 4-8, 2012 entitled "Screening Heursitcs and Chemical Propery BIas; New Directions for Lead Identification and Optimization"



Total Views
Views on SlideShare
Embed Views



2 Embeds 26 22 4



Upload Details

Uploaded via as Adobe PDF

Usage Rights

© All Rights Reserved

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
Post Comment
Edit your comment

Screening heuristics pope-final Presentation Transcript

  • 1. Screening Heuristics & Chemical PropertyBias - New directions for Lead Identification andOptimization Andy Pope Platform Technology & Science, GlaxoSmithKline, Collegeville PA, USA SLAS 2012, San Diego February 4-8, 2012
  • 2. Screening Heuristics
  • 3. Why Screening Heuristics?1. Huge complex datasets screening wisdom? (customers)2. Refining approaches/deliverables success rates attrition
  • 4. Some available datasets inside GSK Descriptor Descriptor Descriptor Descriptor metadata metadata metadata metadata Hit ID Compound Structures Profiles Public + Properties Public Data HTS Program Data GSK >300 profiling Compounds >500 + Data Descriptor metadata Descriptor metadatae.g. PubChem e.g. Literature, >>106 Descriptor metadata FS Connectivity Target class Maps >200 profiling Phys-chem >300 DMPK ELT >105 >150 Safety Marketed FBDD profiling Drugs et. >50 >103 >20 Other GSK Data – e.g. genomic, bio-informatic, clinical
  • 5. 300+ HTS Campaigns – 2004-11 Target class (13 classes) Assay technology (15 classes) 2007-11 screens – sized by count of screens
  • 6. Twin approaches to screening heuristics1. Building Collective wisdom 2. New “big” data analysis/ insights- Capture, combine and share the - Look for data patterns in large experiences of screeners and data aggregated datasets from screens (and screeners) e.g. e.g. How well do different assay methods perform? Do chemical properties influence the results of screens? What is the impact of screen quality and what should be targeted in assay development? How are screen results related between targets What policies do I need in place to have a high and assay methods? quality screening process? Which is the best method to use to discover hits? Which assay technology works best? How are library properties reflected in the hits?
  • 7. Building Collective Wisdom – a simple example Some Questions; - What actually happens in practice as z’ varies? - What z’ should we be aiming for? - Is this affected by the type of assay? - What is the appropriate trade off between cost, robustness and sensitivity? - How are we doing?From SBS Virtual Seminar Series 2007 - HTS Module 1
  • 8. Z’ Heuristics Statistical cut-off (% effect)- Z’ >0.8 is ideal, >0.7 acceptable- Z’ <0.7 many aspects of performance degrade (e.g. failures, cycle times, false +ve/-ve, hit confirmation)- Z’ vs “sensitivity” trade-off arguments may be based on false hunches- Target & assay type does not make a major difference Average Z’ of assay in HTS production Avge. Z’ 0.4-0.5Production failure rate (% of plates) 0.5-0.55 Cycle time (weeks/campaign) 0.55-0.6 0.6-0.65 0.7-0.75 0.65-0.7 0.75-0.8 >0.8 Average Z’ of assay in HTS production Average Z’ of assay in HTS production
  • 9. Properties, properties, properties…..….But, do they affect screening data?….are we selecting hits with the best properties? ….Bottom line; High cLogP (greasiness) is BAD ...This needs to be fixed at the start ..i.e in hit ID ….and tends to creep up during Lead Op.
  • 10. Do compound molecular properties impact how they behave in screens? Aggregate results from all 330 campaigns 2005-2010 withe.g. Compound total polar surface area (tPSA) >500K tests makes no difference Compounds with tPSA 80-85 Å2 26M measured responses in this bin - 485k marked as “hit” Hit rate = 100*(485k/26M) = 1.86% “hit” = % effect => 3 RSD of sample population in Hit Rate (%) that specific screen The total polar surface area (tPSA) is defined as the surface sum over all - Hit rate for Compounds polar atoms in specific tPSA bin < 60 A2 predicts brain penetration > 140 A2 predicts poor cell penetration Polar Surface Area (tPSA, Å2)
  • 11. Size Matters…… Middle 80% of Cpds 270 470 Cumulative % Cpds % Cpds in MW Bin 4.0%Hit Rate (%) 2.62% 1.50% MW 1.2%  Overall Hit rate rises 1.7-fold across the middle 80% of the screening deck i.e. 70% rise in hit rate from MW = 270 to Molecular Weight (MW) MW = 470 - Only bins containing 1M or more records are shown  3.3-fold rise across full MW range
  • 12. Greasiness matters most…… Middle 80% of Cpds 1 5 Cumulative % Cpds % Cpds in ClogP Bin 4.5% 3.31%Hit Rate (%) ClogP 1.14% 1.1%  Overall hit rate rises 2.9-fold across the middle 80% of the screening deck i.e. from ClogP = 1  5 ClogP  4.1-fold rise across full ClogP range - Only bins containing 1M or more records are shown
  • 13. HTS Promiscuity - cLogP Compounds Compounds hitting hitting ~1 target >10% of targetscLogP Note; Compounds required to have been run in 50 HTS and yielded > 50% effect in a single screen to be included Frequency at bin > Frequency at bin > Frequency at bin > Frequency at bin > Inhibition frequency Index* (%) *Inhibition frequency index (IFI) = % of screens where cpd yielded >50% inhibition, where total screens run => 50
  • 14. “Dark” Matter is small and polar – Compounds which have not yielded >50% effect once in >50 screens Molecular Weight (Da)cLogP
  • 15. Biases translate to full-curve follow-up and beyond Property bias in primary HTS hit marking are propagated forward to dose-response follow-up SS testing FC testing FC – SS differential% Compounds Tested % Compounds Tested cLogP Molecular Weight Elevated testing of large, lipophilic Reduced testing of small, polar compounds compounds in the full-curve phase of HTS in the full-curve phase of HTS Note; Plots represent data from 402M single-concentration responses & 2.1M full-curve results
  • 16. Property bias detection at an individual screen level e.g. Screens with largest response to cLogPHit rate as % of HR at cLogP =3.5 cLogP
  • 17. Assay Technology vs. property bias e.g. By assay technology, normalized to HR for that screen at median collection cLogP value Colored by Hit rate (%)Hit rate as % of HR at cLogP =3.5 e.g. No clear origins in any meta-data - Assay Technology, Target class, Screen quality etc. …. But effects detectable even at single screen level cLogP
  • 18. Lipophilicity trends in PubChem HTS Data Primary data from around 100 Academic HTS campaigns obtained from PubChem BioAssay Lipophilicity – similar to GSK HTS Compound size – little effect 3.80% Hit Rate (%)Hit Rate (%) Pretty flat 2.27% 2.14% 1.28% ClogP (MW)  GSK screening deck (>50 HTSs, 2.01M cpds) ClogP = 0.00835*MW – 0.058, R2 = 0.18  PubChem Compounds (405k) ClogP = 0.00554*MW + 0.97, R2 = 0.09
  • 19. Not just HTS… Lipophilicity trends in kinase focused set screens Primary data from ~50 focused screen campaigns against protein kinases Lipophilicity and size – similar to GSK HTS Y% Y% Hit Rate (% of cpds >50% I) at 10 uMHit Rate (% of cpds >50% I) at 10 uM X% X% ClogP MW
  • 20. Bias from other simple chemical properties? Property R2, ± vs MW R2, ± vs ClogP +ve -ve MW 1, + 0.21, + cLogP fCsp3 ClogP 0.21, + 1.0, + MW (HAC) flexibility HAC 0.92, + 0.19, + fCsp3 0.15, + 0.00 RotBonds 0.36, + 0.04, +Hit Rate (%) tPSA 0.16, + 0.08, - Chiral 0.02, + 0.00 HetAtmRatio 0.02, - 0.34, - Complexity 0.31, + 0.02, + Flexibility 0.02, + 0.00 AromRings 0.22, + 0.16, + Fraction of carbons that are sp3 (fCsp3) HBA 0.11, + 0.10, - HBD 0.01, + 0.02, -
  • 21. Improving hit marking – Property Biasing Mean + 3 x RSD cut-off Hit Rate (%) Ordinary HTS Hit Marking Property-biased Hit Marking More attractive properties% Compounds - promote MW Less attractive Hit Rate (%) properties - demote Ordinary HTS Hit Marking Property-biased Hit Marking RESPONSE (% control) ClogP
  • 22. Evolving the screening collection…  GSK’s Compound Collection Enhancement (CCE) strategy - moving the HTS deck towards decreased size and lipophilicity with the aim of improving chemical starting points Compounds tested in HTS test datasets % Compounds Exceeding Property Limit - 2004(% of total compounds in HTS) - 2010 - D 2010 <> 2004 ClogP > 5 MW > 500 New 2011 ClogP Year CCE Acquisition, Property Bounds 2004-05: Lipinski criteria (MW<500, ClogP<5) Most recently: MW<360, ClogP<3 Inclusion of DPU lead-op cpds: MW<500, ClogP<5
  • 23. Can property biases translate into lead optimization? Cellular Med. Biochemical “mechanistic” Rodent DMPK, chem target assay efficacy model target assay More potent in cell Example from current Lead Optimization“patient in a Program pIC50 Cell - Biochem plate” -Cellular activity favorsOr……. cLogP >4 - Directional “pull” to More potent in biochem more lipophilic cpds?“biochemistry -Good DMPK at cLogP <3 in a (grease- - Value of cellular assay? selective) bag”! Binned cLogP
  • 24. Property bias in broad pharmacological profilingEarly safety cross screening panel (eXP) GSK Lead Op. compounds 2009-11 Marketed drugs n = ~1000 Average % of assays giving IC50 <=10 uMAverage % of assays giving IC50 <=10 uM GSK Terminated Leads & Candidates n = ~2500 n = ~2500 n = ~400 GPCR’s – 17 Binned ClogP Ion Channels – 8 Binned ClogP Enzymes – 3 Kinases – 4 Nuclear Receptors – 2 Transporter – 3 Phenotypic – 3 (Blue Screen, Cell Heath, Phospholipidoses)
  • 25. Property bias in broad pharmacological profiling Early safety cross screening panel (eXP) GSK Lead Op. compounds 2009-11Average % of assays giving IC50 <=10 uM n = ~2500 n = ~2500 GPCR’s – 17 Binned ClogP Ion Channels – 8 Enzymes – 3 Kinases – 4 Nuclear Receptors – 2 Transporter – 3 Phenotypic – 3 (Blue Screen, Cell Heath, Phospholipidoses)
  • 26. Kinome profiling – no impact of cLogP ~400 kinase Lead Op % inhibition values (>300 kinase assays) Compounds vs 300 protein kinases Binned ClogP(>300 kinase assays)% inhibition values Kinase structural classifier
  • 27. Conclusions Heuristic approaches allow both refinement of best practice and new insights Standard screening processes favor the selection of lipophilic compounds - A contributing factor in current issues with drug Lead/Candidate property space occupancy - Improvement in screening collections and analysis methods can overcome this, BUT - All this effort is wasted if Lead Optimization pathways pull compounds back towards unfavorable property space!! The very large datasets generated from screening have considerable value beyond the lifetime of individual campaigns - Particularly crucial now that quality and cycle time problems are largely solved - Many other examples exist beyond those shown here - Please go look for these effects in your data!
  • 28. Snehal Bhatt Acknowledgements Stuart Baddeley James Chan Sue CrimminPat Brady Tony Jurewicz Emilio DiezDarren Green Glenn Hofmann Maite De Los FrailesStephen Pickett Stan Martens Bob HertzbergSunny Hung Deb Jaworski Jeff Gross Ricardo MacarronSubhas Chakravorty Carl MachuttaNicola Richmond Julio Martin-PlazaJesus Herranz Barry MorganGonzalo Colmeranjo-Sanchez Juan Antonio Mostacero Dave Morris Dwight Morrow Mehul Patel …and numerous others who contributed Amy Quinn to programs run by GSK 2004-2011….. Geoff Quinique Mike Schaber Zining Wu Ana Roa And colleagues... Screening & Compound Profiling