Prioritizing Scaffolds for Hit Selection in High Throughput Screening Programs
1 like1,010 views
The document discusses high throughput screening (HTS) programs at the NIH Chemical Genomics Center. It outlines the process of HTS including assay development, screening thousands of compounds, and the challenge of hit selection from large amounts of data. Specifically, it focuses on prioritizing scaffolds for hit selection in HTS campaigns to identify lead compounds for further optimization.
Prioritizing Scaffolds for Hit Selection in High Throughput Screening Programs
- 1. Priori%zing Scaffolds for Hit Selec%on in High Throughput Screening Programs Rajarshi Guha, Dac‐Trung Nguyen NIH Chemical Genomics Center 6th Indo‐US Workshop on Mathema%cal Chemistry 8th – 10th January, 2010
- 2. Outline • HTS and qHTS at the NCGC • Hit selec%on & cherry picks • The fragment based approach
- 3. NIH Chemical Genomics Center Assay development Compound and op2miza2on Op2miza2on Small Molecules Biology Chemistry NCGC Informa%cs ACOM Genome wide RNAi SAR analysis, method & Automa2on, Compound tool development management
- 4. High Throughput Screening • Assay thousands to hundreds of thousands of compounds – Biochemical, gene%c, pharmacological assays • Rapidly iden%fy novel modulators of biological systems • Lots of raw data – automated analy%cs are essen%al
- 5. Hun%ng for Leads Target Lead Lead Clinical Iden%fica%on Discovery Op%miza%on Development HTS Primary Confirma%on • Sensi%vity Screening • Select subset • Scaling • Fluorescence to follow up • Counter • High Content • Diversity screen • Explore SAR Assay Cherry Picking Op%miza%on
- 6. High Throughput Screening • Given a screen, we must iden%fy ac%ve compounds and confirm their ac%vity • Most HTS campaigns are run at a single concentra%on Negative 150 Positive ! ! ! ! ! ! ! Sample ! ! ! ! ! ! ! !!! ! ! ! ! ! ! ! !! ! ! ! ! ! !! ! ! ! ! ! !! ! ! ! ! !! !! ! ! ! ! ! !! ! ! !! ! !! ! !! ! ! ! ! ! ! ! ! ! !! !! ! ! ! ! !! ! ! ! ! ! ! ! !! ! !! ! ! !! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !! ! ! ! !! ! ! ! !! ! ! ! ! !! ! !! ! ! !! !! ! ! ! ! ! ! ! ! ! !! ! ! ! ! !! ! ! ! ! ! !! ! !! ! !! ! ! ! !! !! ! !! ! !! ! !! ! !! ! ! ! ! ! ! ! !! ! ! ! ! !! ! ! ! ! !! ! !! ! !! ! ! ! !! !! !! ! !! !! ! ! ! !! !! ! ! ! ! ! ! ! !! ! ! ! ! ! !! !! ! ! ! ! ! ! ! ! ! !! ! ! !!!! !! ! !! ! ! ! ! ! !! ! ! ! ! ! !! ! ! !! ! ! !! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !! ! ! ! !! ! ! ! ! ! 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- 7. The qHTS Paradigm Negative Positive Sample ! ! 150 ! ! ! ! ! ! ! ! ! ! ! ! !!! ! ! ! ! ! ! ! !! ! ! ! ! ! !! ! ! ! ! ! !! ! ! ! ! !! !! ! ! ! ! ! !! ! ! !! ! ! !! ! ! ! ! ! ! ! ! ! ! ! ! !! !! ! !! ! ! ! ! ! !! ! ! !! ! ! ! ! ! ! ! ! ! !! !! !! ! ! !! !! ! ! ! ! !! !! !! ! ! !! !! ! !!! !! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !! ! !! ! !! ! !! ! ! ! !! !! ! !! ! !! ! ! ! !! ! !! ! ! ! ! ! ! ! !! ! ! ! ! !! ! ! ! ! !! ! !! !! ! ! !! ! !! ! ! ! ! ! !! ! !!! ! !! ! ! ! ! !! ! ! ! ! ! !! ! ! !! !! ! ! ! !! !! ! ! ! ! ! ! ! !! ! ! ! !! ! ! ! ! ! !! ! ! !!! ! !! ! !! 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! ! can miss useful hits ! ! ! ! ! ! ! 0 1 2 3 4 5 6 7 8 9 10 Plate Number • qHTS involves concentra%on response assays on a high‐throughput scale • The CRC allows us to categorize hits in a more fine‐grained manner Inglese, J et al, Proc. Natl. Acad. Sci., 2006, 103, 11473‐11478
- 8. Concentra%on Response Curves SInf • Each compound is characterized by 4 Hill parameters 50% • Also evaluate Activity p‐values and R2 • Finally we classify each curve S0 AC50 Concentration
- 9. Curve Class Usage Inac2ve • Heuris%c assessment of the significance of a concentra%on response curve • We aggregate certain curve classes into “ac%ve”, “inconclusive” and “inac%ve” categories • Inconclusive is a “catch all” Inconclusive category (i.e., if it not clearly ‘ac%ve’ or ‘inac%ve’) Ac2ve 9
- 10. Hit Selec%on • Easy to iden%fy ini%al set of ac%ves 300K • Ranges from 50 to 5000 molecules HTS • How do we select a subset that will Number of Molecules likely retest as ac%ve in secondary assays? 1000 – Avoid false nega%ves as far as Cherry Picks possible • We’d also like to be able to explore SAR’s 300
- 11. Hit Selec%on – Prior Work • The simplest method is purely 1000 1200 sta%s%cal 800 Frequency – Select compounds above/below an 600 ac%vity threshold 400 • Many methods are based on 200 similarity to known ac%ves or 0 neighborhoods Activity – Hit‐directed nearest neighbor – Predic%ve models – Local hit rate analysis Shanmugasundaram, V. et al, J. Med. Chem., 2005, 48, 240‐248 Selzer P. et al, J. Chem. Inf. Model., 2006, 46, 2319‐2323 Posner, B. et al, J. Chem. Inf. Model., 2009, 49, 2202‐2210
- 12. Why Use Fragments? • Conceptually equivalent to clustering • Very fast with pre‐computed fragments compared to clustering – No similarity matrix calcula%ons • Simple to implement – Iden%fy fragments associated with ac%ve compounds – For a given fragment iden%fy the ac%ve, inac%ve, inconclusive members – Rank these fragment “series” for follow up
- 13. The Fragment Analysis Workflow Identify Scaffolds Promiscuity & Potency Flourescence Flagging Singletons Filter Scaffold Reduction Calculate Scaffold Statistics 1. Min/Max potency and efficacy 2. Ratio of actives to size of member set Flourescence Flagging 3. Complexity of the scaffold 4. Promiscuity (based on active members) Priority Non-Priority Series Series Promiscuity & Potency Promiscuity & Potency Filter Filter
- 14. Scaffold Genera%on • We employ Murcko fragments • Pre‐generate fragments on compound registra%on and store them in the database • For a given screen, we iden%fy fragments based on the ac%ve compounds and then proceed with the analysis – Ignore “common” fragments (benzene, furan, …)
- 16. Scaffold Reduc%on • The reduc%on process tries to iden%fy a subset of core‐like fragments • What is core‐like? – Somewhat subjec%ve • Ini%ally, we iden%fy fragments that are substructures of another fragment – Keep the smaller fragment – Not always the best choice
- 17. Scoring Scaffolds • What makes a good scaffold? – Size, complexity, … – Do the members represent an SAR or not? – Intui%on and experience also play a role
- 18. Scaffolds, R‐groups & SAR Fit PLS or ridge regression model 0 ! ! !! ! !2 ! ! ! ! Predicted ! !4 ! ! !! ! Evaluate topological ! ! and physicochemical ! !6 descriptors for the ! ! R‐groups !8 Characterize the !8 !6 !4 !2 0 Observed SAR landscape Guha, R et al. J. Chem. Inf. Model., 2008,48, 646‐658
- 19. Scaffold QSAR ‐ Drawbacks • Many scaffolds have few (5 to 10) members • Invariably, more features than observa%ons • If the number of R‐groups is large, the feature matrix can be very sparse – Less of a problem for combinatorial libraries • A linear fit may not be the best approach to correla%ng R‐groups to the ac%vi%es – Difficult to choose a model type a priori • SBll working on it …
- 20. Measuring Promiscuity • Promiscuity is measured on a compound‐wise basis • Defined in terms of how many %mes it is ac%ve in a pre‐defined set of assays versus how many %mes it’s not ac%ve • Promiscuity ranges from 1 to 10 – 1 indicates it was not ac%ve in any assay – 10 indicates it was ac%ve in all assays – 0 indicates it wasn’t tested • Scaffold promiscuity is the mean promiscuity of its ac%ve members
- 21. 2000 Measuring Promiscuity 0.7 0.6 1500 Width of Confidence Interval 0.5 Frequency 1000 0.4 0.3 500 0.2 0.1 0 1 2 3 4 5 6 7 1 2 3 4 5 6 7 Promiscuity Score Promiscuity Score 10N active + N inactive Promiscuity Score = N active + N inactive
- 22. Fluorescence Flagging • Depends on the nature of the screen • For a single compound, flag if ac%ve in spectral profiling assay or LDR fluorescence counterscreen • For a series – Flag if a single ac%ve compound in the series is ac%ve in spectral profiling assay or LDR fluorescence counterscreen – Or if > 50% of the ac%ve series members are ac%ve in LDR/Lamin‐A/Betaglobin
- 23. qHTS for Tau Inhibitors • Fibrils of the microtubule associated protein tau are observed in Alzheimers pa%ents • Goal was to iden%fy small molecule inhibitors of tau oligomeriza%on • Screened 292K compounds, iden%fied 4788 ac%ves • Selected a novel aminothienopyridazine series Crowe, A. et al, Biochemistry, 2009, 48, 7732‐7745
- 24. Fragment Analysis on Tau‐2 Identify Scaffolds Flourescence Flagging Potency & Promiscuity Filter 1. Consider the 4788 actives 2. Ignore non-specific scaffolds Identify singleton compounds 1. If compound is active in cho-gfp/spec-profile/ 1. Keep cmpds whose minimum potency < X uM LDR retest, the cluster is flagged fluorescent (i.e., compounds with no precomputed scaffold) 866 2. Keep cmpds with promiscuity < Y Identify singleton compounds Scaffold Reduction (i.e., compounds with no precomputed scaffold) ? Use a substructure method to collapse scaffolds into more "core-like" scaffolds. Calculate Scaffold Statistics Flourescence Flagging Selec2on Strategy 1. Min/Max potency and efficacy 1. If a single active member is active in cho-gfp, 2. Ratio of actives to size of member set the cluster is flagged fluorescent 3. Complexity of the scaffold 2. If a single active member is active in LDR 4. Promiscuity (based on active members) retest, the cluster is flagged fluorescent • All singletons 2. If 50% of the actives are active in LDR/ Lamin-A/Betaglobin, the cluster is flagged • 2 ac%ves, 1 inac%ve Non flourescent other scaffolds 514 from priority series • 1 ac%ve from non‐ Priority series (scaffolds with > 10% actives) 46 Non-priority series (scaffolds with < 10% actives) 459 priority series Potency & Promiscuity Filter Potency & Promiscuity Filter 1. Keep series whose minimum potency < X uM 1. Keep series whose minimum potency < X uM 2. Keep series with promiscuity < Y 2. Keep series with promiscuity < Y Priority series (scaffolds Non-priority series with > 10% actives) ? (scaffolds with < 10% actives) ?
- 26. The Top Priority Series Scaffold MCS ! ! ! ! ! ! ! ! 0 ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !20 ! ! ! ! ! ! ! Activity ! ! ! ! !40 ! ! ! ! ! ! ! !60 !80 ! !7.5 !7.0 !6.5 !6.0 !5.5 !5.0 !4.5 log Concentration (M)
- 27. The Top Priority Series Scaffold ! ! 0 ! ! ! MCS ! !10 ! Activity ! !20 ! ! ! ! !30 ! ! ! ! ! !7.5 !7.0 !6.5 !6.0 !5.5 !5.0 !4.5 log Concentration (M)
- 28. The Scaffold Explorer • Currently %ed to NCGC database infrastructure
- 29. Summary • Fragment based hit selec%on is a cheaper alterna%ve to clustering & neighborhood methods • Easily integrated with other metrics such as promiscuity • What makes a “good” fragment is s%ll tricky to encode algorithmically – Encoding the “SARability” of a fragment series is key – But, this is one component of a comprehensive fragment score • Allows us to rapidly drill into series of interest in qHTS projects
- 30. Acknowledgements • Ruili Huang • Wenwei Huang • Ajit Jadhav