Exploring Compound Combinations in High Throughput Settings: Going Beyond 1D Metrics
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The document describes efforts to screen drug combinations in high throughput settings beyond traditional one-dimensional metrics. It discusses the infrastructure and workflows used to screen compound combinations against a library of over 2000 small molecules with diverse mechanisms of action. Quality control of combination screening experiments poses challenges due to the multi-dimensional nature of the data. The researchers are exploring various metrics and analytical approaches to characterize synergistic, additive and antagonistic combination responses across different cell lines and combinations.
Exploring Compound Combinations in High Throughput Settings: Going Beyond 1D Metrics
- 1. Exploring Compound Combina1ons in High Throughput Se9ngs Going Beyond 1D Metrics Rajarshi Guha, Lesley Mathews, John Keller, Paul Shinn, Dongbo Liu, Craig Thomas, Anton Simeonov, Marc Ferrer NIH-‐NCATS January 2013, San Diego
- 2. Outline Why combine? Physical infrastructure & workflow Summarizing and exploring the data hKp://origin.arstechnica.com/news.media/pills-‐4.jpg
- 3. Screening for Novel Drug Combina1ons Transla5onal Interest • Increased efficacy • Delay resistance • AKenuate toxicity Basic Interest • Inform signaling pathway connec[vity • Iden[fy synthe[c lethality • Highlight polypharmacology
- 4. How to Test Combina1ons • Many procedures described in the literature – Fixed dose ra[o (aka ray) – Ray contour – Checkerboard – Gene[c algorithm C5 C5,D5 C4 C4,D4 C3 C3,D3 C2 C2,D2 C1,D5 C1,D4 C1,D3 C1,D2 C1,D1 D5 D4 D3 D2 D1 C1 0
- 5. Scaling Response Surface Screening 5e+07 Combination type • Response surfaces imply a DxD matrix for each combina[on • All pairs screening is imprac[cal for more than tens of compounds • Instead we consider N compounds versus a fixed size library All pairs Fixed library Number of combinations 4e+07 Dose matrix size 4 6 10 3e+07 2e+07 1e+07 0e+00 250 500 750 Number of compounds 1000
- 6. Mechanism Interroga1on PlateE • Collec[on of ~ 2000 small molecules of diverse mechanism of ac[on. • 745 approved drugs • 420 phase I-‐III inves[ga[onal drugs • 767 preclinical molecules • Diverse and redundant MOAs represented belinostat HDAC inhibitor Phase II AMG-47a Lck inhibitor Preclinical GSK-1995010 FAS inhibitor Preclinical JZL-184 MAGL inhibitor Preclinical JNJ-38877605 HGFR inhibitor Phase I Eliprodil NMDA antagonist Phase III
- 7. Mechanism Interroga1on PlateE Top 10 enriched GeneGo pathway maps Development EGFR signaling pathway Some pathways of EMT in cancer cells Development VEGF signaling via VEGFR2 - generic cascades Apoptosis and survival Anti-apoptotic action of Gastrin Cell adhesion Chemokines and adhesion Cytoskeleton remodeling TGF, WNT and cytoskeletal remodeling Regulation of lipid metabolism RXR-dependent regulation of lipid metabolism via PPAR, RAR and VDR Transcription PPAR Pathway Translation Non-genomic (rapid) action of Androgen Receptor Development VEGF signaling and activation 0 5 -log10(pValue) 10 15
- 8. Combina1on Screening Workflow Run single agent dose responses 6x6 matrices for poten1al synergies 10x10 for confirma1on + self-‐cross Acoustic dispense, 15 min for 1260 wells, 14 min for 1200 wells"
- 9. Where Are We Now? • 238 screens in total – 30 screens against full MIPE3 or MIPE4 • 200 cell lines – Various cancers – Mainly human Number of assays 150 100 50 0 0 500 1000 1500 Number of combinations • Combined with target annota[ons we can look at combina[on behavior as a func[on of various factors 2000
- 10. Screening Challenges • A key challenge is automated quality control • Plate level data employs standard metrics focusing on control performance • Combina[on level is more challenging – Single agent performance is one approach – MSR across all combina[on can provide a high level view – But how to iden[fy bad blocks?
- 11. QC Examples • Inves[ga[ng an[-‐malarial combina[ons • 300 10x10 combina[ons in duplicate • 15 compounds included more than ten [mes -1.5 log IC50 (uM) -2.0 -2.5 -3.0 -3.5 -4.0 Artemether Artesunate Dihydro artemisinin Halofantrine Lumefantrine
- 12. QC Examples Compound • Single agents with very high MSR’s could be used to flag combina[ons containing them • Doesn’t help for compounds with only one or two replicates • S[ll requires manual inspec[on Freq 40 30 20 10 0 5 10 MSR 15 20
- 15. Exploring Combina1on Metrics • We implement a variety of metrics to characterize synergy/addi[vity/antagonism • Lots of possible ques[ons – How is a metric distributed in a given assay? – How does a metric vary with cell line? – Do metrics correlate? – How does a certain combina[on behave across cell lines?
- 16. 8226 AMO-1 ANBL-6 ARP-1 EJM FR4 INA-6 JJN3 JK-6L KMS-11 KMS-11LB KMS-12BM KMS-12PE KMS-18 KMS-20 KMS-26 KMS-28BM KMS-28PE KMS-34 L363 LP-1 MM-MM1 MM.1.144 MOLP-8 OCI-MY-5 OCI-MY1 OPM-1 OPM-2 RPMI-8226 SACHI SKMM-1 U266 XG-1 XG-2 XG-6 XG-7 Delta Bliss Neg Sum Exploring Combina1on Metrics 0 -5 -10
- 17. Repor1ng Combina1on Results • These web pages and matrix layouts are a useful first step • Does not scale as we grow MIPE • S[ll need to do a beKer job of ranking and aggrega[ng combina[on responses taking into account – Response matrix – Compounds, targets and pathways
- 18. When are Combina1ons Similar? • Differences and their aggregates such as RMSD can lead to degeneracy • Instead we’re interested in the shape of the surface • How to characterize shape? – Parametrized fits – Distribu[on of responses 0.06 0.010 0.04 0.005 0.02 0.00 0.000 0 25 50 75 100 0 0.15 0.10 0.05 0.00 0 50 100 D, p value 25 50 75 100
- 19. Similarity via the KS Test • Quan[fy distance between response distribu[ons via KS test – If p-‐value > 0.05, we assume distance is 0 9 density • But ignores the spa1al distribu[on of the responses on the concentra[on grid 6 3 0 0.00 0.25 0.50 D 0.75 1.00
- 20. Similarity via the Syrjala Test • Syrjala test used to compare popula[on distribu[ons over a spa[al grid density – Invariant to grid orienta[on – Provides an empirical p-‐value • Less degenerate than just considering 1D distribu[ons 10.0 7.5 5.0 2.5 0.0 0.00 Syrjala, S.E., “A Sta[s[cal Test for a Difference between the Spa[al Distribu[ons of Two Popula[ons”, Ecology, 1996, 77(1), 75-‐80 0.25 D 0.50 0.75
- 21. Ibru1nib Combina1ons For DLBCL • Primary focus is on inves[ga[ng combina[ons with Ibru[nib for treatment of DLBCL – Btk inhibitor in Phase II trials – Experiments run in the TMD8 cell line, tes[ng for cell viability Viable Cells (% DMSO) Ibrutinib MK-2206 Ibrutinib* + MK-2206 Ibrutinib* (nM) MK-2206 (µM) Mathews-‐Griner, Guha, Shinn et al. PNAS, 2014, in press
- 22. 0.8 Clustering Response Surfaces 0.4 0.6 C1 (24) C3(35) 0.2 C2(47) 0.0 C4(24)
- 23. 302 281 128 174 285 153 177 210 144 35 60 457 180 39 111 272 288 166 231 104 106 417 319 44 218 279 219 121 119 34 102 286 230 178 179 0.00 0.05 0.10 0.15 0.20 0.25 0.30 Cluster C3 macromolecule catabolic process • Vargatef, vorinostat, flavopiridol, … • Not par[cularly specific given the range of primary targets regulation of interferon-gamma-mediated signaling pathway ubiquitin-dependent protein catabolic process cellular process involved in reproduction negative regulation of cell cycle peptidyl-amino acid modification interphase cell cycle checkpoint peptidyl-tyrosine phosphorylation response to stress 0 1 -log10(Pvalue) 2 3
- 24. 52 136 150 184 217 322 339 384 165 163 371 139 116 145 194 241 327 125 82 143 164 215 254 361 0.00 0.02 0.04 0.06 0.08 Cluster C4 cellular carbohydrate biosynthetic process • Focus on sugar metabolism • Ruboxistaurin, cycloheximide, 2-‐ methoxyestradiol, … • PI3K/Akt/mTOR signalling pathways regulation of polysaccharide biosynthetic process cellular macromolecule localization peptidyl-serine phosphorylation regulation of generation of precursor metabolites and energy cellular polysaccharide metabolic process glucan metabolic process glucan biosynthetic process regulation of glycogen biosynthetic process glycogen metabolic process 0 1 -log10(Pvalue) 2 3
- 25. Combina1ons across Cell Lines • Cellular background affects responses • Can we group cell lines based on combina[on response?
- 26. Working in Combina1on Space • Each cell line is represented as a vector of response matrices L L • “Distance” between two , cell lines is a func[on of the , distance between component response matrices , , D ( L1, L2 ) = F({d1, d2 ,…, dn }) , • F can be min, max, mean, … 1 2 = d1 = d2 = d3 = d4 = d5
- 28. Exploi1ng Polypharmacology • Vargatef exhibited anomalous matrix response compared to other VEGFR inhibitors Linifanib Sorafenib Vatalanib Motesanib Tivozanib Brivanib Telatinib Cabozantinib Cediranib BMS-794833 Lenvatinib OSI-632 Vargatef Axitinib Foretinib Regorafenib
- 29. Exploi1ng Polypharmacology DCC-2036 PD-166285 GDC-0941 PI-103 GDC-0980 Bardoxolone methyl AT-7519 AT7519 SNS-032 NCGC00188382-01 Lestaurtinib CNF-2024 ISOX • PD-‐166285 is a SRC & FGFR inhibitor • Lestaurnib has ac[vity against FLT3 Vargatef Belinostat PF-477736 AZD-7762 Src Lyn Lck Flt-3 PDGFRb PDGFRa FGFR-4 FGFR-3 Chk1 IC50 = 105 nM FGFR-2 FGFR-1 VEGFR-3 VEGFR-2 VEGFR-1 0 200 Potency (nM) Hilberg, F. et al, Cancer Res., 2008, 68, 4774-‐4782 400 600
- 30. Predic1ng Synergies • Related to response surface methodologies • LiKle work on predic[ng drug response surfaces – Peng et al, PLoS One, 2011 – Jin et al, Bioinforma1cs, 2011 – Boik & Newman, BMC Pharmacology, 2008 – Lehar et al, Mol Syst Bio, 2007 • But synergy is not always objec[ve and doesn’t really correlate with structure
- 31. Structural Similarity vs Synergy beta gamma 0.4 0.3 0.3 0.2 0.2 0.1 0.1 Similarity 0.4 0.85 0.90 0.95 1.00 ssnum 1.05 1.10 1.15 0.4 -30 -20 1.05 0.2 0.1 0.95 Win 3x3 0.3 0.2 0.85 0.4 0.3 0.75 0.1 0 5 10 15 20 25 -40 Synergy measure -10 0
- 32. Predic1on Strategy • Don’t directly predict synergy • Use single agent data to generate a model surface • Predict combina[on responses • Characterize synergy of predicted response with respect to model surface • Reduced to a mixture predic[on problem • Need to incorporate target connec[vity
- 33. Conclusions • Use response surfaces as first class descriptors of drug combina[ons – Surrogate for underlying target network connec[vity (?) • Response surface similarity based on distribu[ons is (fundamentally) non-‐parametric • Going from single -‐ chemical space to combina[on space opens up interes[ng possibili[es • Manual inspec[on is s[ll a vital step
- 34. Acknowledgements • Lou Staudt • Beverly Mock, John Simmons