Enhancing Prioritization & Discovery of Novel Combinations using an HTS Platform

1. Enhancing Prioritization & Discovery of Novel Combinations using an HTS Platform Rajarshi Guha NIH NCATS ACoP 7 Bellevue, WA

2. Screening for novel drug combinations • Increased efficacy • Delay resistance • Attenuate toxicity • Treat multiple aspects of a disease • Inform signaling pathway connectivity • Identify synthetic lethality • Polypharmacology Translational Interest Basic Interest

3. Mechanism Interrogation PlateE • 1911 small molecules, with a primary focus on oncology, but also addressing infectious disease and stem cell biology • Diverse and redundant MoA’s • Employed in 1-vs-all & all-vs-all modes AMG-47a Lck inhibitor Preclinical belinostat HDAC inhibitor Phase II GSK-1995010 FAS inhibitor Preclinical Approved Phase III Phase II Phase I Preclinical Other

4. High Throughput Combination Screening Run single agent dose responses 6x6 matrices for potential synergies 10x10 for confirmation + self-cross Acoustic dispense, 15 min for 1260 wells, 14 min for 1200 wells

5. Where are we now? • 81 projects, 773 screens • 140,730 combinations • 4.8M wells • 320 cell lines • Opportunities to look at global trends in combination behavior in the context of physicochemical properties, biological functionality, … 0 50 100 150 200 2011 2012 2013 2014 2015 2016 Year NumberCombinationScreens • Cancers • Hodgkins lymphoma • DLBCL • Neuroblastoma • Leukemia • Malaria • Transcriptional mechanics Baranello, L et al, Cell, 2016 Jun, W et al, PNAS, 2016 Lewis, R et al, J. Cheminf, 2015 Bogen, D et al, Oncotarget, 2015 Mott BT et al, Sci Rep, 2015 Zhang, M et al, PNAS, 2015 Ceribelli, M et al, PNAS, 2014 Mathews, L et al, PNAS 2014

6. Digging into the data • Lots of data across lots of cell lines for lots of (mostly annotated) compounds • How can we slice & dice? • How do we characterize quality of combination response? • Are there global trends in synergy based on target class, MoA, chemical structure/property? • What is the role of selectivity vs promiscuity? • What is the relation between single & combination responses? • Can we better prioritize large sets of combinations? • Can we find interesting subsets of combinations? • Are there alternatives to the table view? • How does (can) the data inform us on polypharmacology? • How do we prospectively predict combination responses

7. Quantifying combination quality • A key challenge is automated quality control • Control separation – control performance ≠ combination performance • Intra-plate or inter-plate pattern – no room for lots of replicates and – the assumption used in primary screen can’t be satisfied • Data consistency – IC50 not always available (we are searching for synergy!) – Consistent single agent IC50 ≠ consistent synergy Lu Chen (NCATS)

8. Deviation of block control mQC: Interpretable QC model Feature name Importance Explanation dmso.v 20.71 Normalized response of the negative control smoothness.p 18.88 p-value for smoothness moran.p 18.82 p-value for spatial autocorrelation (tested by Moran’s I) mono.v 12.62 Likelihood of monotonic dose responses sa.min 12.84 The smaller relative standard deviation of the single-agent dose response sa.matrix 8.78 The relative standard deviation of the dose combination sub-matrix sa.max 7.36 The larger relative standard deviation of the single-agent dose response Smoothness Randomness Monotonicity Activity variance Feature importance encoded by mQC is consistent with human intuition Chen, L. et al, Sci. Rep., submitted https://matrix.ncats.nih.gov/mQC/ Lu Chen (NCATS)

9. Visualization & Ranking 3D7 DD2 HB3 Azalomycin−B ABT−263 (Navitoclax) Cabozantinib AZD−2014 Selumetinib Volasertib Midostaurin SB−415286 IC−87114 GDC−0941 Neratinib NCGC00021305 LY2157299 GMX−1778 PCI−32765 Torin−2 BEZ−235 Ruxolitinib INK−128 Tipifarnib MK−2206 PD 0325901 Imatinib G−Strophanthin Ketotifen Clomipramine NCGC00014925 2−Fluoroadenosine MK−0752 Rolipram Alvespimycin hydrochloride Ganetespib NCGC00183656 Sulindac Carfilzomib Bardoxolone methyl LLL−12 JQ1 Suberoylanilide hydroxamic acid Panobinostat Azalo m ycin −B ABT−263 (N avitocla x) C abozantin ib AZD −2014 Selu m etin ib Vola sertib M id ostaurin SB−415286 IC −87114 G D C −0941 N eratin ib N C G C 00021305 LY2157299 G M X−1778 PC I−32765 Torin −2 BEZ−235 R uxolitin ib IN K−128 Tip ifarnib M K−2206 PD 0325901 Im atin ib G −Strophanthin Ketotifen C lo m ip ram in e N C G C 00014925 2−Flu oroadenosin e M K−0752 R olipram Alvespim ycin hydrochlo rid e G anetespib N C G C 00183656 Sulindac C arfilzom ib Bardoxolo ne m ethyl LLL−12JQ 1 Suberoyla nilid e hydroxam ic acid Panobin ostat DBSumNeg (−7,−4] (−4,−3] (−3,−2] (−2,−1] (−1,0] Azalomycin−B ABT−263 (Navitoclax) Cabozantinib AZD−2014 Selumetinib Volasertib Midostaurin SB−415286 IC−87114 GDC−0941 Neratinib NCGC00021305 LY2157299 GMX−1778 PCI−32765 Torin−2 BEZ−235 Ruxolitinib INK−128 Tipifarnib MK−2206 PD 0325901 Imatinib G−Strophanthin Ketotifen Clomipramine NCGC00014925 2−Fluoroadenosine MK−0752 Rolipram Alvespimycin hydrochloride Ganetespib NCGC00183656 Sulindac Carfilzomib Bardoxolone methyl LLL−12 JQ1 Suberoylanilide hydroxamic acid Panobinostat Azalo m ycin −B ABT−263 (N avitocla x) C abozantin ib AZD −2014 Selu m etin ib Vola sertib M id ostaurin SB−415286 IC −87114 G D C −0941 N eratin ib N C G C 00021305 LY2157299 G M X−1778 PC I−32765 Torin −2 BEZ−235 R uxolitin ib IN K−128 Tip ifarnib M K−2206 PD 0325901 Im atin ib G −Strophanthin Ketotifen C lo m ip ram in e N C G C 00014925 2−Flu oroadenosin e M K−0752 R olipram Alvespim ycin hydrochlo rid e G anetespib N C G C 00183656 Sulindac C arfilzom ib Bardoxolo ne m ethyl LLL−12JQ 1 Suberoyla nilid e hydroxam ic acid Panobin ostat DBSumNeg (−7,−4] (−4,−3] (−3,−2] (−2,−1] (−1,0] 0.00.20.40.60.8

10. LogP & Synergy? • Yilancioglu et al (JCIM 2014) suggested that you can predict synergicity using only logP • Synergicity of a compound is the frequency of synergistic pairs involving the compound Synergy doesn’t correlate with logP 10 20 30 -4 0 4 8 logP Numberofsynergisticcombinations Synergicity may correlate with logP http://blog.rguha.net/?p=1265

11. Predicting Synergies • Related to response surface methodologies • Little work on predicting drug response surfaces • Peng et al, PLoS One, 2011 • Boik & Newman, BMC Pharmacology, 2008 • Lehar et al, Mol Syst Bio, 2007 & Yin et al, PLoS One, 2014 • AZ-DREAM Challenge & Chen et al, PLoS Comp Bio, 2016 • But synergy is not always objective and doesn’t really correlate with structure

12. -3 -2 -1 0 0.0 0.1 0.2 0.3 0.4 Tanimoto Similarity DBSumNeg Structural similarity vs synergy? • Do structurally similar compounds lead to synergistic combinations? • No reason they should • Synergy driven by (off-)targets

13. Structural similarity vs synergy? beta gamma ssnum Win 3x3 0.1 0.2 0.3 0.4 0.1 0.2 0.3 0.4 0.1 0.2 0.3 0.4 0.1 0.2 0.3 0.4 0.85 0.90 0.95 1.00 1.05 1.10 1.15 0.75 0.85 0.95 1.05 0 5 10 15 20 25 -40 -30 -20 -10 0 Synergy measure Similarity

14. Predictive models (fail) • 10x10, all-vs-all screen • Random forest, ECFP6 • Predict value of a synergy metric https://tripod.nih.gov/matrix-client/rest/matrix/blocks/1763/table -10.0 -7.5 -5.0 -2.5 0.0 -10.0 -7.5 -5.0 -2.5 0.0 Observed DBSumNeg PredictedDBSumNeg Test Train 0.8 0.9 1.0 1.1 1.2 1.3 0.8 0.9 1.0 1.1 1.2 1.3 Observed Beta PredictedBeta Test Train

15. Descriptors matter Cell lines from data set 5 fold Multiple splitting 80%, training sets 20%, validation sets 1) Different descriptors 2) Selection of the decision threshold for each model Models creation Models validation 54 data sets, 127119 mixtures Alexey Zakharov (NCATS) 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00 PEO1 RH30 RH41 JHH136 BIRCH RH5 JHM1 MT1 SAOS2 Cal-1 PANC1 Cal27 UOK161 ipNF95.6 JHH520 TC71 FL3 KMS28BM_onyx TMD8 DD2 RDES L1236 SCC47 HFF EW8 Rec-1 HF4B Balanced Accuracy QNA descriptors_RF RDkit_RF (and classification is easier)

16. Explicitly consider targets Descriptors used for learning Three classes of descriptors generated per combination • StructuralFingerprint • Morgan, 2,048 bits, radius 2 (RDKit). • PredictedTargets • 1,080 human target probabilities of affinity (PIDGIN V1) • Combined • StructuralFingerprint and PredictedTargets. Input data required: • Compound structure for training and test data (names, SMILES) • Combination data (which compounds, synergy score) Output: • New combinations predicted to be synergistic • Probability of being synergistic (classifier model, worked best for this project) • Predicted synergy value (quantitative model, did not work so well for this project) Dan Mason, Andreas Bender (U. Cambridge)

17. Going in vivo? • Translating combinations to in vivo setting is complex • How does PK/PD affect combinations? • What dosing schedule works? Is it optimal? • Currently an open question from computational PoV • Lack of PK/PD parameters and ability to generate data are critical bottlenecks • We depend on clinician input & experience

18. Outlook • Accurate predictions will enable virtual screening of combinations • Many aspects of the process are yet to be explored • Differential analysis of combination response • Are some pathways or mechanisms more amenable to combination screening than others? • Viability is easy to measure. What about other readouts? • Is there a better way to characterize synergy? • Tang, J. et al, Frontiers. Pharmacol., 2015 https://tripod.nih.gov/matrix-client

19. Acknowledgements • Lu Chen • Alexey Zakharov • Kelli Wilson • Mindy Davis • Xiaohu Zhang • Richard Eastman • Bryan Mott • Craig Thomas • Marc Ferrer • Paul Shinn • Crystal McKnight • Carleen Klumpp- Thomas • Anton Simeonov • Dan Mason • Rich Lewis • Yasaman Kalantar Motamedi • Krishna Bulusu • Andreas Bender

Enhancing Prioritization & Discovery of Novel Combinations using an HTS Platform

Estás leyendo una previsualización.

Eche un vistazo a continuación

Enhancing Prioritization & Discovery of Novel Combinations using an HTS Platform

Más Contenido Relacionado

Presentaciones para usted (20)

Similares a Enhancing Prioritization & Discovery of Novel Combinations using an HTS Platform (20)

Más de Rajarshi Guha (20)

Más reciente (20)