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FDA-Industry Scientific Exchange on assessing quantitative systems pharmacology models in clinical drug development: a meeting report, summary of challenges/gaps, and future perspective

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Abstract

The pharmaceutical industry is actively applying quantitative systems pharmacology (QSP) to make internal decisions and guide drug development. To facilitate the eventual development of a common framework for assessing the credibility of QSP models for clinical drug development, scientists from US Food and Drug Administration and the pharmaceutical industry organized a full-day virtual Scientific Exchange on July 1, 2020. An assessment form was used to ensure consistency in the evaluation process. Among the cases presented, QSP was applied to various therapeutic areas. Applications mostly focused on phase 2 dose selection. Model transparency, including details on expert knowledge and data used for model development, was identified as a major factor for robust model assessment. The case studies demonstrated some commonalities in the workflow of QSP model development, calibration, and validation but differ in the size, scope, and complexity of QSP models, in the acceptance criteria for model calibration and validation, and in the algorithms/approaches used for creating virtual patient populations. Though efforts are being made to build the credibility of QSP models and the confidence is increasing in applying QSP for internal decisions at the clinical stages of drug development, there are still many challenges facing QSP application to late stage drug development. The QSP community needs a strategic plan that includes the ability and flexibility to Adapt, to establish Common expectations for model Credibility needed to inform drug Labeling and patient care, and to AIM to achieve the goal (ACCLAIM).

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References

  1. Zineh I. Quantitative systems pharmacology: a regulatory perspective on translation. CPT Pharmacometrics Syst Pharmacol. 2019;8(6):336–9.

    Article  CAS  Google Scholar 

  2. Gadkar K, Kirouac DC, Mager DE, van der Graaf PH, Ramanujan S. A six-stage workflow for robust application of systems pharmacology. CPT Pharmacometrics Syst Pharmacol. 2016;5(5):235–49.

    Article  CAS  Google Scholar 

  3. Jafarnejad M, Gong C, Gabrielson E, Bartelink IH, Vicini P, Wang B, et al. A Computational model of neoadjuvant PD-1 Inhibition in non-small cell lung cancer. AAPS J. 2019;21(5):79.

    Article  Google Scholar 

  4. Wang H, Milberg O, Bartelink IH, Vicini P, Wang B, Narwal R, et al. In silico simulation of a clinical trial with anti-CTLA-4 and anti-PD-L1 immunotherapies in metastatic breast cancer using a systems pharmacology model. R Soc Open Sci. 2019;6(5):190366.

    Article  CAS  Google Scholar 

  5. Musante CJ, Ramanujan S, Schmidt BJ, Ghobrial OG, Lu J, Heatherington AC. Quantitative systems pharmacology: a case for disease models. Clin Pharmacol Ther. 2017;101(1):24–7.

    Article  CAS  Google Scholar 

  6. Battista C, Howell BA, Siler, SQ & Watkins PB An introduction to DILIsym® software, a mechanistic mathematical representation of drug-induced liver injury. in Drug-Induced Liver Toxicity 2018:101-21.

  7. Kirouac DC, Cicali B, Schmidt S. Reproducibility of quantitative systems pharmacology models: current challenges and future opportunities. CPT Pharmacometrics Syst Pharmacol. 2019;8(4):205–10.

    Article  CAS  Google Scholar 

  8. Cucurull-Sanchez L, Chappell MJ, Chelliah V, Amy Cheung SY, Derks G, Penney M, et al. Best practices to maximize the use and reuse of quantitative and systems pharmacology models: recommendations from the United Kingdom quantitative and systems pharmacology network. CPT Pharmacometrics Syst Pharmacol. 2019;8(5):259–72.

    Article  CAS  Google Scholar 

  9. Friedrich CM. A model qualification method for mechanistic physiological QSP models to support model-informed drug development. CPT Pharmacometrics Syst Pharmacol. 2016;5(2):43–53.

    Article  CAS  Google Scholar 

  10. Bai JPF, Earp JC, Pillai VC. Translational Quantitative systems pharmacology in drug development: from current landscape to good practices. AAPS J. 2019;21(4):72.

    Article  Google Scholar 

  11. Andreas Viardot MG, Hess G, Neumann S, Pfreundschuh M, Adrian N, Zettl F, et al. Treatment of relapsed/refractory diffuse large b-cell lymphoma with the bispecific T-cell engager (BiTE®) antibody construct blinatumomab: primary Analysis results from an open-label, phase 2 study. Blood. 2014;124(21):4460.

    Article  Google Scholar 

  12. Youssef Hijazi MK, Schub A, Wu B, Zhu M, Wolf PKA, Nagorsen D. Blinatumomab exposure and pharmacodynamic response in patients with non-Hodgkin lymphoma (NHL). J Clin Oncol. 2013;31:3051.

    Article  Google Scholar 

  13. Lebwohl M, Strober B, Menter A, Gordon K, Weglowska J, Puig L, et al. Phase 3 studies comparing brodalumab with ustekinumab in psoriasis. N Engl J Med. 2015;373(14):1318–28.

    Article  CAS  Google Scholar 

  14. Leonardi CL, Kimball AB, Papp KA, Yeilding N, Guzzo C, Wang Y, et al. Efficacy and safety of ustekinumab, a human interleukin-12/23 monoclonal antibody, in patients with psoriasis: 76-week results from a randomised, double-blind, placebo-controlled trial (PHOENIX 1). Lancet. 2008;371(9625):1665–74.

    Article  CAS  Google Scholar 

  15. Papp KA, Langley RG, Lebwohl M, Krueger GG, Szapary P, Yeilding N, et al. Efficacy and safety of ustekinumab, a human interleukin-12/23 monoclonal antibody, in patients with psoriasis: 52-week results from a randomised, double-blind, placebo-controlled trial (PHOENIX 2). Lancet. 2008;371(9625):1675–84.

    Article  CAS  Google Scholar 

  16. Griffiths CE, Strober BE, van de Kerkhof P, Ho V, Fidelus-Gort R, Yeilding N, et al. Comparison of ustekinumab and etanercept for moderate-to-severe psoriasis. N Engl J Med. 2010;362(2):118–28.

    Article  CAS  Google Scholar 

  17. Langley RG, Elewski BE, Lebwohl M, Reich K, Griffiths CE, Papp K, et al. Secukinumab in plaque psoriasis--results of two phase 3 trials. N Engl J Med. 2014;371(4):326–38.

    Article  Google Scholar 

  18. Rich P, Sigurgeirsson B, Thaci D, Ortonne JP, Paul C, Schopf RE, et al. Secukinumab induction and maintenance therapy in moderate-to-severe plaque psoriasis: a randomized, double-blind, placebo-controlled, phase II regimen-finding study. Br J Dermatol. 2013;168(2):402–11.

    Article  CAS  Google Scholar 

  19. Schmidt BJ, Casey FP, Paterson T, Chan JR. Alternate virtual populations elucidate the type I interferon signature predictive of the response to rituximab in rheumatoid arthritis. BMC Bioinformatics. 2013;14:221.

    Article  CAS  Google Scholar 

  20. Allen RJ, Rieger TR, Musante CJ. Efficient generation and selection of virtual populations in quantitative systems pharmacology models. CPT Pharmacometrics Syst Pharmacol. 2016;5(3):140–6.

    Article  CAS  Google Scholar 

  21. Cheng Y, Thalhauser CJ, Smithline S, Pagidala J, Miladinov M, Vezina HE, et al. QSP Toolbox: Computational Implementation of Integrated workflow components for deploying multi-scale mechanistic models. AAPS J. 2017;19(4):1002–16.

    Article  CAS  Google Scholar 

  22. Samson A, Lavielle M, Mentré F. Extension of the SAEM algorithm to left-censored data in nonlinear mixed-effects model: application to HIV dynamics model. Comput Stat Data Anal. 2006;51:1562–74.

    Article  Google Scholar 

  23. Catlett IAU, Liu Y, Bei D, Girgis I, Murthy B, Honczarenko M, et al. A first-in-human, study of BMS-986165, a selective, potent, allosteric small molecule inhibitor of tyrosine kinase 2. Ann Rheum Dis. 2017;76:859.

    Google Scholar 

  24. Papp K, Gordon K, Thaci D, Morita A, Gooderham M, Foley P, et al. Phase 2 Trial of selective tyrosine kinase 2 inhibition in psoriasis. N Engl J Med. 2018;379(14):1313–21.

    Article  CAS  Google Scholar 

  25. Waters SB, Topp BG, Siler SQ, Alexander CM. Treatment with sitagliptin or metformin does not increase body weight despite predicted reductions in urinary glucose excretion. J Diabetes Sci Technol. 2009;3(1):68–82.

    Article  Google Scholar 

  26. Musante JRBSMJ. Systems pharmacology modeling in type 2 diabetes mellitus. Systems Pharmacology and Pharmacodynamics 2016: 465-507.

  27. Meeuwisse CM, van der Linden MP, Rullmann TA, Allaart CF, Nelissen R, Huizinga TW, et al. Identification of CXCL13 as a marker for rheumatoid arthritis outcome using an in silico model of the rheumatic joint. Arthritis Rheum. 2011;63(5):1265–73.

    Article  CAS  Google Scholar 

  28. Demin OO, Smirnov SV, Sokolov VV, Cucurull-Sanchez L, Pichardo-Almarza C, Flores MV, et al. Modeling of celiac disease immune response and the therapeutic effect of potential drugs. BMC Syst Biol. 2013;7:56.

    Article  CAS  Google Scholar 

  29. Hosseini I, Gadkar K, Stefanich E, Li CC, Sun LL, Chu YW, et al. Mitigating the risk of cytokine release syndrome in a phase I trial of CD20/CD3 bispecific antibody mosunetuzumab in NHL: impact of translational system modeling. NPJ Syst Biol Appl. 2020;6(1):28.

    Article  CAS  Google Scholar 

  30. Zhang XY, Trame MN, Lesko LJ, Schmidt S. Sobol Sensitivity analysis: a tool to guide the development and evaluation of systems pharmacology models. CPT Pharmacometrics Syst Pharmacol. 2015;4(2):69–79.

    Article  CAS  Google Scholar 

  31. Marino S, Hogue IB, Ray CJ, Kirschner DE. A methodology for performing global uncertainty and sensitivity analysis in systems biology. J Theor Biol. 2008;254(1):178–96.

    Article  Google Scholar 

  32. Campolongo F, Cariboni J, Saltelli A. An effective screening design for sensitivity analysis of large models. Environ Model Softw. 2007;22:1509–18.

    Article  Google Scholar 

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Acknowledgements

1) Case study presenters: Ted Rieger (Pfizer); Jason Chan (Eli Lilly); Iraj Hosseini (Genentech); Brian Topp (Merck); Annabelle Lemenuel (Roche); Tarek Leil (BMS); Sergio Ladevia (Takeda); Loveleena Bansal (Glaxo SmithKline (GSK); and lightning talk speakers (Mary Spilker (Pfizer); Richard Allen (Pfizer); Antoine Soubret (Roche); Lourdes Cucurull-Sanchez (GSK)

2) Case study assessment teams: Case Study 1A captain Weirong Wang (JNJ) and team members: Majid Vakilynejad (Takeda); Jared Weddell (Astellas); Tarek Leil (BMS); Case Study 1B captain Karim Azer (GMRI) and team members: Antoine Soubret (Roche); Lourdes Cucurull-Sanchez (GSK); Bradley Niesner (Sanofi); Case Study 1C captain Chris Penland (AZ) and team members: Andy Zhu (Takeda); Oleg Milberg (JNJ); Brian Schmidt (BMS); Spyros Stamatelos (Sanofi); Case Study 1D captain Abhishek Gulati (Astellas) and team members: Benjamin Ribba (Roche); Loveleena Bansal (GSK); Chris Penland (AZ); Case Study 2A captain Diana Clausznitzer (Abbvie) and team members: Lars Kuepfer (Bayer); Vikram Sinha (Merck); Eric Fernandez (Boehringer Ingelheim); Case Study 2B captain Jenny Chien (Lilly) and team members: Mary Spilker (Pfizer); Khamir Mehta (Amgen); Kapil Gadkar (Genentech); Case Study 2C captain Mary Spilker (Pfizer) and team members: Dinesh DeAlwis (Merck); Evan Wang (Lilly); Andy Stein (Novartis); Michael Block (Bayer); Case Study 2D captain Jeff Kearns (Novartis) and team members: Richard Allen (Pfizer); Iraj Hosseini (Genentech); Ramona Schmid (BI)

3) Meeting moderators: Cynthia Musante (Pfizer), Valeriu Damian (GSK)

4) 2019 Preliminary Group Discussion presenters: Chris Penland (Astra Zeneca); Tarek Leil (BMS); Kapil Gadkar (Genentech); Loveleena Bansal (Glaxo SmithKline (GSK); Nitin Mehrotra (Merck); Andrew Stein (Novartis); Susana Zaph (Sanofi); Timothy Nicholas (Pfizer); Antoine Soubret (Roche); Majid Vakilynejad (Takeda)

5) Selected participants who critically commented during the meeting or provided suggestions in the Discussion session engaging the audience: Holly Kimko (AstraZeneca); Dwaipayan Mukherjee (AbbVie); Sergio Iadevaia (Takeda); Jeff Kearns (Novartis); Andrew Stein (Novartis); Satyaprakash Nayak (Pfizer); Timothy Nicholas (Pfizer); Ruth Abram (Sanofi); Steven Chang (Immunetrics)

6) 2019 Preliminary Group Discussion moderators: Jane PF Bai (US FDA) Brian Schmidt (BMS), Kapil Gadkar (Genentech), Cynthia Musante (Pfizer),, Valeriu Damian (GSK)

7) The International Society of Pharmacometrics (ISoP) for providing a meeting room at ACoP10 for the purposes of the preliminary group discussion and also its virtual conferencing account to make the virtual meeting on July 1 successful

8) Enrico Smith of ISoP for assisting in setting the virtual meeting for several rounds of testing and dry-run and for live stream of the virtual meeting. Tarek Leil at Bristol Myers Squib (BMS) for his scientific discussion at the 2019 AAPS meeting

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Authors

Contributions

Jane PF Bai, Brian Schmidt, and Kapil Gadkar contributed equally to drafting the manuscript. Valeriu Damian, Justin C Earp, Christina Friedrich, Piet van der Graaf, Kunal Naik, Rajanikanth Madabushi, Cynthia J Musante, Mark Rogge, and Hao Zhu commented on the manuscript.

Corresponding authors

Correspondence to Jane P. F. Bai, Brian J. Schmidt or Kapil G. Gadkar.

Additional information

Organizing Committee: The FDA-Industry Scientific Exchange took place virtually on July 1, 2020. The Organizing Committee members include Jane PF Bai, Justin Earp, Kunal Naik, Rajanikanth Madabushi and Hao Zhu from the US Food and Drug Administration and Brian Schmidt, Kapil Gadkar, Christina Friedrich, Piet van der Graaf, Mark Rogge from the industry.

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Bai, J.P.F., Schmidt, B.J., Gadkar, K.G. et al. FDA-Industry Scientific Exchange on assessing quantitative systems pharmacology models in clinical drug development: a meeting report, summary of challenges/gaps, and future perspective. AAPS J 23, 60 (2021). https://doi.org/10.1208/s12248-021-00585-x

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