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Introduction to DILIsym Services, Inc.
Dilisym Services Inc. develops and applies Quantitative Systems Pharmacology (QSP) and Quantitative Systems Toxicology (QST) modeling to support drug development, focusing on predicting drug-induced liver injury (DILI) and integrating various biological data. The document details case studies on lixivaptan, a vasopressin receptor antagonist, and pf-04895162, an epilepsy drug, demonstrating the utility of their modeling in assessing liver safety signals in different species. Dilisym's modeling capabilities aim to enhance drug safety and efficacy, ultimately benefiting patient outcomes.
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Introduction to DILIsym Services, Inc.
- 1. CONFIDENTIAL *DILIsym®, NAFLDsym®, MITOsym®,ADMET Predictor®, GastroPlus® and SimPops® are registered trademarks, and SimCohorts™, IPFsym™, RADAsym™, and RENAsym™ are trademarks, of DILIsym Services Inc. and/or SLP for computer modeling software and for consulting services.
- 2. DILIsym Services isa Member of the Simulations Plus Family 2 Cheminformatics, PBPK modeling Quantitative Systems Pharmacology and Toxicology (QSP/QST) Pharmacometrics We apply modeling and simulation to support all phases of drug development
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- 4. What is QSP/ QST? “QSP is defined as an approach to translational medicine that combines computational and experimental methods to elucidate, validate, and apply new pharmacological concepts to the development an use of small molecule and biologic drugs. QSP will provide an integrated “systems level” approach to determining mechanisms of action of new and existing drugs in preclinical and animal models and in patients….” QST combines QSP methodologies with the discipline of toxicology to address potential or existing issues of drug safety in animal models and in patients. 4 NIH White Paper 2011 Introduction to DILIsym Services QSP/QST Modeling Case Study: Lixivaptan Case Study: Kv7 Summary
- 5. QSP/QST Modeling Applications inMultiple Areas 5 Introduction to DILIsym Services QSP/QST Modeling Case Study: Lixivaptan Case Study: Kv7 Summary
- 6. DILIsym Predicts DILIvia the Intersection of Exposure, Mechanisms, and Inter-Patient Variability 6 Relevant Liver Biochemistry DILI Mechanisms Exposure DILI Introduction to DILIsym Services QSP/QST Modeling Case Study: Lixivaptan Case Study: Kv7 Summary
- 7. DILIsym Software Overview •Multiple species: human, rat, mouse, and dog - Population variability • The three primary acinar zones of liver represented • Essential cellular processes represented to multiple scales in interacting sub-models • Over 70 detailed representations of optimization or validation compounds with 80% success • Single and combination drug therapies 7 Introduction to DILIsym Services QSP/QST Modeling Case Study: Lixivaptan Case Study: Kv7 Summary
- 8. DILIsym Utilizes VariousData Types to Inform Modeling and Simulation 8 Simulated Protocol and Patients • Dosing Protocols - Fasting/fed state, meal times • Anthropometric data - Body weight, age, ethnicity • Comorbidities / comedication data - Type 2 diabetes, NASH, acetaminophen PBPK Prediction of In Vivo Exposure • Compound Properties - Tissue partition coefficients • Tissue penetration studies - Liver to blood ratio • Pharmacokinetic data - Absorption, extra-hepatic clearance, metabolites • in vitro data - Metabolite synthesis, active uptake Modeling & Simulation In vitro Mechanistic DILI Data Clinical Data Toxicity Parameter Values • Oxidative stress - Direct and reactive metabolite-mediated • Mitochondrial toxicity - ETC inhibition - Uncoupling • Bile acid / phospholipid transporter inhibition - BSEP, MRP3 and 4, NTCP, (MDR3) • Bilirubin transport/metabolism - OATP1B1, OATP1B3, UGT1A1, MRP2, MRP3 Exposure Data Simulations and Assays inform: • Prediction of DILI risk • Participating DILI mechanisms • Characteristics of patients at risk for DILI • Drug dosing paradigms • DILI monitoring strategies Introduction to DILIsym Services QSP/QST Modeling Case Study: Lixivaptan Case Study: Kv7 Summary
- 9. DILIsym Development isFunded and Directed by the DILI-sim Initiative 9 Multiple Industry Partners World-class Scientific Advisory Board Select Sample of Current Companies Licensing DILIsym Dr. Kevin Park University of Liverpool Dr. Robert Roth Michigan State University Dr. Frank Sistare Merck (retired) Dr. Neil Kaplowitz University of Southern California Dr. Paul B. Watkins University of North Carolina Dr. Jack Uetrecht University of Toronto Dr. David Pisetsky Duke University Introduction to DILIsym Services QSP/QST Modeling Case Study: Lixivaptan Case Study: Kv7 Summary
- 10. Recent DILIsym Publications 10 Introduction toDILIsym Services QSP/QST Modeling Case Study: Lixivaptan Case Study: Kv7 Summary
- 11. CASE STUDY: LIXIVAPTAN Investigationof a Next-in-Class Drug For Potential Differentiation on Liver Safety 11
- 12. Lixivaptan Background • Lixivaptanis Palladio Bio’s selective, competitive vasopressin V2 receptor antagonist • Palladio Biosciences acquired lixivaptan and intends to reposition lixivaptan for the treatment of Autosomal-Dominant Polycystic Kidney Disease (ADPKD) 12 Introduction to DILIsym Services QSP/QST Modeling Case Study: Lixivaptan Case Study: Kv7 Summary
- 13. Lixivaptan DILIsym Project DILIBackground • An approved compound in the same class had no DILI signals in hyponatremia, but signals were observed in ADPKD patients • Lixivaptan has had no DILI signals in hyponatremia Question • Will lixivaptan experience similar DILI liability as the competitor in ADPKD patients? Approach • Develop a mechanistic representation of lixivaptan in DILIsym, a QST model of drug-induced liver injury (DILI), to assess the potential for liver toxicity with the intended dosing for lixivaptan 13 Introduction to DILIsym Services QSP/QST Modeling Case Study: Lixivaptan Case Study: Kv7 Summary
- 14. Toxicity Parameter Valuesfor Lixivaptan, Three Metabolites, and Tolvaptan (competitor) 14 Mechanism DILIsym Parameter Unit Value**** Lixivaptan WAY-138451 WAY-141624 WAY-138758 Tolvaptan** Mitochondrial Dysfunction Coefficient for ETC inhibition µM 535 250 N/A N/A 729 Oxidative Stress RNS/ROS production rate constant mL/nmol/hr 5.45 x 10-4 2.12 x 10-3 N/A N/A N/A Bile Acid Transporter Inhibition BSEP inhibition constant µM 15* 8.6* 39.5* 5.6* 10*** NTCP inhibition constant µM 19* N/A 85.8* 8.9* N/A Basolateral inhibition constant** µM 70* 54* 16.3* 4* N/A * Values are IC50 values; mode of inhibition was not measured in vitro ** Tolvaptan parameters are taken from in vitro experiments undertaken for this research. Previously published DILIsym parameters are available in Woodhead et al., Tox. Sci. 2017 *** IC50 value for tolvaptan was measured for this research. A Ki value was measured for the previously published tolvaptan work; the published value is somewhat higher than the value reported here. However, personal communication with the experimentalists suggested that the initial IC50 value calculated in that experiment was not substantially different from that measured here. **** Comparisons of parameter values should be undertaken with caution, as they must be placed in context with exposure for their full usefulness. Introduction to DILIsym Services QSP/QST Modeling Case Study: Lixivaptan Case Study: Kv7 Summary
- 15. CONFIDENTIAL Lixivaptan Simulations Predict MinimalLiver Safety Signals at Clinical Dose • Lixivaptan simulated in SimPops of N = 285 • No ALT elevations simulated in 100 mg BID 60-day simulation – Consistent with observed clinical similarity to placebo (validation) • No ALT elevations simulated in 200/100 split daily dosing scenario for 12 weeks – Maximum intended clinical dosing for ADPKD 15 Simulation Results Introduction to DILIsym Services QSP/QST Modeling Case Study: Lixivaptan Case Study: Kv7 Summary
- 16. CONFIDENTIAL Post-hoc Analysis ofExposure vs. Predicted Toxicity Identified Safety Threshold • ALT elevations are correlated with total lixivaptan exposure • Project established exposure threshold below which lixivaptan is safe (AUC0-7 days< 350 µg*h/ml) 16 Lixivaptan 400mg BID, 7 days (n = 285) Lixivaptan plasma AUC (0-inf) (µg*h/ml) 2x ULN 3x ULN 0 5 10 15 20 25 30 0-15 15-30 30-45 45-60 60-75 75-90 90-105 105-120 120-135 135-150 150-165 165-180 180-195 195-210 210-225 225-240 240-255 255-270 270-285 285-300 300-315 315-330 330-345 345-360 360-375 375-390 390-405 405-420 420-435 435-450 450-465 465-480 480-495 495-510 510-525 400mg BID 100mg BID N 38 Lixivaptan plasma AUC(0-7 Day) (µg*h/ml) Lixivaptan 100 mg BID N = 72 Lixivaptan 400 mg BID N = 67 • Existing data indicate lixivaptan exposure rarely exceeds the exposure threshold • Intended clinical dose not expected to exceed threshold Introduction to DILIsym Services QSP/QST Modeling Case Study: Lixivaptan Case Study: Kv7 Summary Clinical Data and Simulation Results
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- 18. CASE STUDY: KV7 MechanisticInvestigation of a Compound that was Clean in Rats but Showed Evidence of Hepatotoxicity in Humans 18
- 19. Kv7 DILIsym Project DILIBackground • PF-04895162 is a small molecule that was being developed for epilepsy based on its ability to open Kv7.2/7.3 potassium channels • PF-04895162 was clean in rat toxicity studies but liver safety signals were seen in healthy human subjects Question • Can mechanisms of toxicity represented in DILIsym account for the observed species difference? Approach • Develop a mechanistic representation of PF-04895162 in DILIsym for simulated rats and simulated humans to determine if the experimental findings are reproduced 19 Introduction to DILIsym Services QSP/QST Modeling Case Study: Lixivaptan Case Study: Kv7 Summary
- 20. Species-Specific In VitroData Informed Parameter Values for Toxicity 20 Mechanisms of Toxicity Bile acid transporter inhibition Mitochondrial dysfunction Preclinical and Clinical Data RAT DATA HUMAN DATA Introduction to DILIsym Services QSP/QST Modeling Case Study: Lixivaptan Case Study: Kv7 Summary Generaux 2019
- 21. SimPops Results Reproduce SpeciesDifference in Liver Injury 21 Simulation Results RATS HUMANS No DILI in simulated rats (n=294) treated with PF-04895162 DILI in simulated humans (n=285) treated with PF-04895162 Generaux 2019 Introduction to DILIsym Services QSP/QST Modeling Case Study: Lixivaptan Case Study: Kv7 Summary
- 22. Analysis of SimulationResults Provided Mechanistic Insights into Toxicity 22 Simulation Results Variables used to create alternate simulated patients were investigated to identify predictors of DILI susceptibility Re-running simulations with each mechanism off reveals DILI is dependent on both mechanisms of toxicity Introduction to DILIsym Services QSP/QST Modeling Case Study: Lixivaptan Case Study: Kv7 Summary Generaux 2019
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- 24. DILIsym Services Developsand Applies QSP / QST Models To Support Drug Development • Modeling applies ordinary differential equations (ODEs) to mathematically represent biological processes and their interactions with compounds • DILIsym is the flagship platform and has been used to evaluate dozens of compounds, supporting internal decision-making as well as regulatory discussions • NAFLDsym has been applied to evaluate multiple compounds for therapeutic efficacy • Ongoing efforts underway to expand offerings in drug safety and efficacy • DILIsym Services offers the opportunity to impact and improve drug development, ultimately getting better, safer medicines to patients who need them 24 Introduction to DILIsym Services QSP/QST Modeling Case Study: Lixivaptan Case Study: Kv7 Summary
- 25. Introduction to DILIsym Services QSP/QST Modeling Case Study: Lixivaptan CaseStudy: Kv7 Summary . The DILIsym Services Team 25 Jeff Woodhead Senior Scientist RTP, NC Brett Howell President RTP, NC Bud Nelson Director of Operations RTP, NC Grant Generaux Senior Scientist Philadelphia, PA Diane Longo Senior Scientist Arlington, VA Shawn O’Connor CEO, Simulations Plus Inc. Lancaster, CA Zack Kenz Scientist II Dubuque, Iowa Lisl Shoda Principal Scientist Director of Immunology Bay Area, CA Christina Battista Scientist II Buffalo, NY Paul B. Watkins DILI-sim Initiative Founder and Scientific Advisory Board Chair RTP, NC Vinal Lakhani Scientist I RTP, NC Corey Berry Senior Software Engineer RTP, NC Yeshi Gebremichael Scientist II RTP, NC Scott Q Siler Chief Scientific Officer Bay Area, CA Patti Steele Executive Assistant RTP, NC Shailendra Tallapaka Scientist I RTP, NC Nader Hamzavi Postdoctoral Fellow RTP, NC Kyunghee Yang Senior Scientist Lawrence, KS Pallavi Bhargava Postdoctoral Fellow RTP, NC Michael Liu Senior Scientist RTP, NC Sergey Ermakov Principal Scientist Bay Area, CA Lara Clemens Postdoctoral Fellow RTP, NC James Beaudoin Scientist I RTP, NC
- 26. Come Meet Usin Person! DILIsym, Modeling Drug-Induced Liver Injury and Beyond Zack Kenz, PhD, Scientist II Wednesday, August 19th, from 2:30-3:00pm EDT QST Applications, Use of Data and Species Differences Christina Battista, PhD, Scientist II Wednesday, August 19th, from 3:00-3:30pm EDT 26 Lisl Shoda, PhD Principal Scientist Director of Immunology Christina Battista, PhD Scientist II Zackary Kenz, PhD Scientist II Introduction to DILIsym Services QSP/QST Modeling Case Study: Lixivaptan Case Study: Kv7 Summary