Robert T. Dunn, II, Ph.D., DABT, SLAS ADMET Special Interest Group Meeting presentation, Jan. 27 at SLAS2016, San Diego.
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Deploying Automated Workstreams and Computational Approaches for Generation of Toxicity Data Used for Hazard Identification, by Robert T. Dunn, II, Ph.D., DABT
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Robert T. Dunn, II, Ph.D., DABT, SLAS ADMET Special Interest Group Meeting presentation, Jan. 27 at SLAS2016, San Diego.
- 1. Deploying Automated Workstreams and Computational Approaches for Generation of Toxicity Data Used for Hazard Identification Robert T. Dunn, II, Ph.D., DABT SLAS Annual Meeting: ADMET Special Interest Group January 27, 2016
- 2. Presentation Overview • Toxicity testing: Brief Historical Perspective • Predictive Safety • Deploying automated workstreams for key assay platforms: • Mitochondrial toxicity • Hepatobiliary transport • Computational approaches • Future Directions 2
- 3. Classical Approaches to Toxicity Testing • Labor intensive, costly, and time consuming • Animal-centric • 2 test species required for most regulatory submissions • Rodent and non-rodent • Uses fully integrated mammalian systems • Not fully predictive of similar toxicities in human • Species differences may lead to unexpected toxicities in early human trials • Species may be more or less sensitive than humans • Immune-mediated toxicities are poorly predicted using non- human systems 3
- 4. The Advent of Predictive Safety Goal: Reduce Late-Stage Safety Related Attrition • In Vitro Safety assays • Recent notable successes in earlier hazard identification • BSEP profiling • hERG (cardiac ion channel) testing • Binding very high throughput/early triage • Functional lower throughput but good correlation to in-vivo drug effects • Following the Success noted with In vitro ADME assays • In use for decades • High throughput • Simple in vitro systems • Reduced the number of drug failures due to poor pharmacokinetic properties 4
- 5. Predictive Safety • 3 Examples of assay platforms in use: • Mitochondrial toxicity: “Mitomics” • Hepatobiliary transport: BSEP • Computational Screening: using structural information from the drug and target to predict toxicity 5
- 6. Automation Example: Isolated Mitochondrial Function Platform (IMF) Automated assay initiation and data capture • Platform is comprised of 21 assays • 16 test articles/run • 5 Automation protocols per run 1. Test article dilution (5X in buffer and 10X in water) 2. Automated assay preparation: rat heart mitochondria isolated and placed on robotics deck 3. Stamping diluted 5X TA into 384 well plates 4. Stamping diluted 10X TA into 384 well plates 5. Full 21 assay run • Total automation run time = ~15hrs (overnight) • FTE Costs: 2 FTE, 2 business days (fully automated) • FTE Costs: 4 FTE, 5 business days (partially automated) 6
- 7. Isolated Mitochondrial Function Profiling Assays Overview Pathway Assay TCA Glutamate oxidation Pyruvate oxidation Succinate oxidation Citrate Synthase Fatty Acid Oxidation Acetyl-CoA oxidation Acetyl Carnitine oxidation Butyryl-CoA oxidation Octanoyl-CoA oxidation Palmitoyl Carnitine oxidation Palmitoyl-CoA oxidation Electron Transport Chain NADH oxidation Complex I Complex II Complex III Complex IV Complex V Uncoupling Oxidative Phosphorylation Uncoupling via respiratory burst Mitochondrial Membrane Potential (MMP) Mitochondrial Swelling Mitochondrial Permeability Transition (MPT) Calcium Homeostasis Calcium Loading Potential Oxidative Stress Aconitase Dykens and Will, 2007 Qu, Y., et al., 2013
- 8. Hamilton-Based Automation Systems Shakers Readers Reader eSwap Washer • Liquid Handler: Hamilton, MicroLab Star • Robotic Arm: Hamilton, MicroLab eSwap • Plate Reader Spectrophotometer: Molecular Devices, SpectraMax plus384 • Plate Reader Luminometer: Molecular Devices, LMax • Plate Reader (2): Multi-Mode: Molecular Devices, M5 • Plate Reader Electrochemiluminescence: MesoScale Discovery (MSD), Sector Imager 6000™ • Robotic Incubator: Thermo Scientific, Cytomat • Plate washer: Biotek, ELX405 • Plate Shakers (5): Thermo Scientiic, VARIOMAG® Teleshake 8 Front view Rear view
- 9. Automation System Control 9 GATE CPU Intra-network MOTHER CPU Robotic Arm Electrochemiluminescence Shakers Liquid Handler
- 10. Isolated Mitochondrial Function Profiling Dataset Examples • By comparing potencies, a primary mitochondrial target can be identified • Compounds can be compared via potencies at the same target or by their collection of targets • Results are reported to teams in a standard template that enables prioritization decisions Heat maps are generated for each compound (or series of compounds) Glutamate Pyruvate Succinate Acetyl CoA Acetyl Carnitine Butyryl CoA Octanoyl CoA Palmitoyl CoA Palmitoyl Carnitine Citrate Synthase NADH oxidation Complex II Complex III Complex IV Complex V MMP Uncoupling MPT Calcium Loading Aconitase concentration Series A Series B Concentration-response curves are created for each assay and compared for each compound Pyruvate Succinate AcetylCarnitine Palmitoyl Carnitine NADH Complex I Complex II Complex III Complex IV Complex V Acetyl-CoA Butyryl-CoA Octanoyl-CoA Palmitoyl-CoA Citrate Synthase MMP Uncoupling MPT Calcium Loading Aconitase Glutamate
- 11. Bile Salt Export Pump (BSEP) • Other names include: • ATP-binding cassette transporter ABCB11 • sister of p-glycoprotein (SPGP) • Originally discovered in 1995 as SPGP in pig • Discovered to play a major role in hepatobiliary excretion of conjugated bile salts • Mutations of ABCB11 gene (in humans) are associated with familial intrahepatic cholestasis 11 Morgan, R. et al (2013) Tox Sci
- 12. Bile Acid Trafficking: High Level OSTβ OST Conjugated BA IBAT Conjugated BA NTCP Conjugated BA Conjugated BA Portalvein Ileocyte Hepatocyte BSEPConjugated BA Cholesterol Bile acids Un-Conjugated + Un-Conjugated + Un-Conjugated + MRP3 MRP4 MRP2 12
- 13. Membrane Vesicle Assay for Transporter Function - Sf9 insect cells transfected with BSEP/Bsep - BSEP Assay is a highly stable platform - History: - Manual assay that was run as needed - Transferred to Amgen HTS group to assess 1000’s of compounds/year in-house - Now outsourced to a 3rd party Inverted membrane vesicle 3H-T ATP Test article BSEP BSEP 3H-T 3H-T 13 Morgan et al, 2010, ToxSci
- 14. Comparison of the Css (total)/BSEP IC50 ratio with the BSEP IC50 value alone for 109 marketed or withdrawn drugs 25 µM IC50 10x safety margin Morgan et al, 2013, ToxSci 14Human BSEP IC50 (µM)
- 15. Off-Target Screening Moving toward computational screens • Small molecule drugs are developed against specific targets to elicit a desired pharmacological effect • Receptor proteins • Enzymes • Nuclear targets • However small molecules often interact with other entities • So called “off-target” effects* • Off-target effects are identified using HT binding assays 15 *Fan, F., et al., Tox. Sci., 2015
- 16. Off-Target Screening • A retrospective analysis of ~6,000 small molecules tested against 140 off-target entities • For certain targets, high hit rates were observed • >30% of compounds had binding activity • Follow up functional assays were rarely positive • Low sensitivity and specificity • Creates additional work and resource drain to follow false hits • Can we derive a computational approach to prioritize early off-target screening? 16
- 17. Off-Target Screening • Computational platform: “Maestro” by Schrödinger • The present analysis may be used to: • Generate SAR for chemistry to “design out” off-target activities • Build target structure-based predictions • Build QSAR ligand-based predictions 17
- 18. Using assay data to identify “features” GPCR receptor example 18 EC50 data from 235 Amgen compounds Scaffold summary based on 235 Amgen compounds Importance analysis of key functional groups R5: most important for activities
- 19. Off-Target Screening • To avoid possible “hits” on the GPCR of interest we can virtually screen prior to ordering expensive assays • Enables Medicinal Chemists to build safer molecules • Prioritizes investment in cleaner molecular scaffolds • Avoids unnecessary spend on riskier scaffolds or molecules 19
- 20. The Future… • Using computational tools, build in early screening assays based solely on “in-silico” tools • Can rule out “risky” chemical structures in virtual space • Prevents unnecessary investment in assays for molecules that have a low likelihood of advancement • Share the structural information on safety endpoints with medicinal chemists to feedback into the design of safer molecules • Construct reference database of chemical structures that may pose a risk for various off-target entities (receptors, enzymes, etc) 20
- 21. Acknowledgements • Patrick Cosgrove • Rocio Hernandez • Ryan Morgan • Yuan Chen • Fan Fan • Cindy Afshari • Hisham Hamadeh • Craig Spruiell • Paul Santana • Jeff Lawrence • Paul Acton • Nianyu Li • Padma Narayanan • Jesse Campbell (Telos Scientific) 21