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Accelerating robust plausible virtual patient cohort generation by substituting ODE simulations with parameter space mapping

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Abstract

The generation of plausible virtual patients (VPs) is an important step in most quantitative systems pharmacology (QSP) workflows, which requires time-intensive solving of ordinary differential equations (ODEs). However, non-physiological profiles of outputs of interest (OoI) are frequently produced, and additional acceptance/rejection steps are needed for comparing and removing VPs with predicted values outside a pre-defined range. Here, a new approach is developed to accelerate the acceptance/rejection steps by leveraging patterns of parameter associations with OoI. In most models, some parameters are monotonic with respect to OoI, such that an increase in a parameter value always induces an increase or decrease in the OoI. This monotonic property can be used to replace some ODE-solving steps with appropriate monotonic parameter value comparisons to extrapolate the rejection or interpolate the acceptance of some VPs (after simulation) to others. Two algorithms were built that directly extract plausible VPs from a pre-defined initial cohort. These algorithms were first tested using a simple tumor growth inhibition model. Analyzing 200,000 VPs took 50 s with a reference method and 3 to 41 s (depending on the initial set-up) with the first algorithm. The method was then applied to an apoptosis QSP model, in which the clinical phenotypes (i.e., treatment sensitive or resistant) of 200,000 VPs were fully characterized for four different drug regimens in 12 min as compared to over 80 min with the reference approach. Extraction of each phenotype can also be performed individually in 34 s to 8 min, demonstrating the time benefit and flexibility of this approach.

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References

  1. Sorger P (2011) Quantitative and systems pharmacology in the post-genomic era: new approaches to discovering drugs and understanding therapeutic. 48

  2. van der Graaf PH, Benson N (2011) Systems pharmacology: bridging systems biology and pharmacokinetics-pharmacodynamics (PKPD) in drug discovery and development. Pharm Res 28:1460–1464. https://doi.org/10.1007/s11095-011-0467-9

    Article  CAS  PubMed  Google Scholar 

  3. Ermakov S, Schmidt BJ, Musante CJ, Thalhauser CJ (2019) A survey of software tool utilization and capabilities for quantitative systems pharmacology: what we have and what we need. CPT Pharmacometrics Syst Pharmacol 8:62–76. https://doi.org/10.1002/psp4.12373

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Bradshaw EL, Spilker ME, Zang R et al (2019) Applications of quantitative systems pharmacology in model-informed drug discovery: perspective on impact and opportunities. CPT Pharmacometrics Syst Pharmacol 8:777–791. https://doi.org/10.1002/psp4.12463

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Gadkar K, Kirouac D, Mager D et al (2016) A six-stage workflow for robust application of systems pharmacology. CPT 5:235–249. https://doi.org/10.1002/psp4.12071

    Article  CAS  Google Scholar 

  6. Lazarou G, Chelliah V, Small BG et al (2020) Integration of omics data sources to inform mechanistic modeling of immune-oncology therapies: a tutorial for clinical pharmacologists. Clin Pharmacol Ther 107:858–870. https://doi.org/10.1002/cpt.1786

    Article  PubMed  PubMed Central  Google Scholar 

  7. Iyengar R, Zhao S, Chung S-W et al (2012) Merging systems biology with pharmacodynamics. Sci Transl Med 4:126ps7. https://doi.org/10.1126/scitranslmed.3003563

    Article  PubMed  PubMed Central  Google Scholar 

  8. Azizi T, Mugabi R (2020) Global sensitivity analysis in physiological systems. Appl Math 11:119–136. https://doi.org/10.4236/am.2020.113011

    Article  Google Scholar 

  9. Hsieh N-H, Reisfeld B, Bois FY, Chiu WA (2018) Applying a global sensitivity analysis workflow to improve the computational efficiencies in physiologically-based pharmacokinetic modeling. Front Pharmacol 9:588

    Article  PubMed  PubMed Central  Google Scholar 

  10. Zhang X-Y, Trame M, Lesko L, Schmidt S (2015) Sobol sensitivity analysis: a tool to guide the development and evaluation of systems pharmacology models. CPT 4:69–79. https://doi.org/10.1002/psp4.6

    Article  CAS  Google Scholar 

  11. Cheng Y, Straube R, Alnaif A et al (2021) Virtual populations for quantitative systems pharmacology models. Open Science Framework

  12. Duffull S, Gulati A (2020) Potential issues with virtual populations when applied to nonlinear quantitative systems pharmacology models. CPT 9:613–616. https://doi.org/10.1002/psp4.12559

    Article  CAS  Google Scholar 

  13. Wang H, Sové RJ, Jafarnejad M et al (2020) Conducting a virtual clinical trial in HER2-negative breast cancer using a quantitative systems pharmacology model with an epigenetic modulator and immune checkpoint inhibitors. Front Bioeng Biotechnol 8:141. https://doi.org/10.3389/fbioe.2020.00141

    Article  PubMed  PubMed Central  Google Scholar 

  14. Holford N, Ma SC, Ploeger BA (2010) Clinical trial simulation: a review. Clin Pharmacol Ther 88:166–182. https://doi.org/10.1038/clpt.2010.114

    Article  CAS  PubMed  Google Scholar 

  15. Sayama H, Marcantonio D, Nagashima T et al (2021) Virtual clinical trial simulations for a novel KRASG12C inhibitor (ASP2453) in non-small cell lung cancer. CPT 10:864–877. https://doi.org/10.1002/psp4.12661

    Article  CAS  Google Scholar 

  16. Allen R, Rieger T, Musante C (2016) Efficient generation and selection of virtual populations in quantitative systems pharmacology models. CPT Pharmacometrics Syst Pharmacol 5:140–146. https://doi.org/10.1002/psp4.12063

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Schmidt BJ, Casey FP, Paterson T, Chan JR (2013) Alternate virtual populations elucidate the type I interferon signature predictive of the response to rituximab in rheumatoid arthritis. BMC Bioinform 14:221. https://doi.org/10.1186/1471-2105-14-221

    Article  CAS  Google Scholar 

  18. Cheng Y, Thalhauser CJ, Smithline S et al (2017) QSP toolbox: computational implementation of integrated workflow components for deploying multi-scale mechanistic models. AAPS J 19:1002–1016. https://doi.org/10.1208/s12248-017-0100-x

    Article  CAS  PubMed  Google Scholar 

  19. Teutonico D, Musuamba F, Maas HJ et al (2015) Generating virtual patients by multivariate and discrete re-sampling techniques. Pharm Res 32:3228–3237. https://doi.org/10.1007/s11095-015-1699-x

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Zhang T, Androulakis IP, Bonate P et al (2022) Two heads are better than one: current landscape of integrating QSP and machine learning. J Pharmacokinet Pharmacodyn 49:5–18. https://doi.org/10.1007/s10928-022-09805-z

    Article  PubMed  PubMed Central  Google Scholar 

  21. Asher MJ, Croke BFW, Jakeman AJ, Peeters LJM (2015) A review of surrogate models and their application to groundwater modeling. Water Resour Res 51:5957–5973. https://doi.org/10.1002/2015WR016967

    Article  Google Scholar 

  22. Angione C, Silverman E, Yaneske E (2022) Using machine learning as a surrogate model for agent-based simulations. PLoS ONE 17:e0263150. https://doi.org/10.1371/journal.pone.0263150

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Chris R, Elliot S, Utkarsh R et al (2021) Composing modeling and simulation with machine learning in Julia, pp 97–107

  24. Estelle C, Andrew H, Mats O (2018) Karlsson Generation and Application of Avatars (Digital Twins) in Pharmacometric Modelling PAGE 27, Abstr 8666. www.page-meeting.org/?abstract=8666. Accessed 12 Sept 2022

  25. Kolesova G, Stepanov A, Lebedeva G, Demin O (2022) Application of different approaches to generate virtual patient populations for the quantitative systems pharmacology model of erythropoiesis. J Pharmacokinet Pharmacodyn. https://doi.org/10.1007/s10928-022-09814-y

    Article  PubMed  Google Scholar 

  26. Wang W, Hallow K, James D (2016) A tutorial on RxODE: simulating differential equation pharmacometric models in R. CPT Pharmacometrics Syst Pharmacol 5:3–10. https://doi.org/10.1002/psp4.12052

    Article  CAS  PubMed  Google Scholar 

  27. Victor S, Dolgun A, Voronova V et al (2018) Evaluation of the utility and efficiency of MATLAB and R-based packages for the development of quantitative systems pharmacology models—PAGE 2018

  28. Simeoni M, Magni P, Cammia C et al (2004) Predictive pharmacokinetic-pharmacodynamic modeling of tumor growth kinetics in xenograft models after administration of anticancer agents. Cancer Res 64:1094–1101. https://doi.org/10.1158/0008-5472.CAN-03-2524

    Article  CAS  PubMed  Google Scholar 

  29. Lindner AU, Concannon CG, Boukes GJ et al (2013) Systems analysis of BCL2 protein family interactions establishes a model to predict responses to chemotherapy. Cancer Res 73:519–528. https://doi.org/10.1158/0008-5472.CAN-12-2269

    Article  CAS  PubMed  Google Scholar 

  30. Carneiro BA, El-Deiry WS (2020) Targeting apoptosis in cancer therapy. Nat Rev Clin Oncol 17:395–417. https://doi.org/10.1038/s41571-020-0341-y

    Article  PubMed  PubMed Central  Google Scholar 

  31. Christina F, Rosa & Co., LLC, Renee M, Tongli Z, Michael Weis Surrogate Modeling with machine learning for faster VP cohort generation (QSP-235 Quantitative Systems Pharmacology)

  32. Rieger TR, Allen RJ, Bystricky L et al (2018) Improving the generation and selection of virtual populations in quantitative systems pharmacology models. Prog Biophys Mol Biol 139:15–22. https://doi.org/10.1016/j.pbiomolbio.2018.06.002

    Article  CAS  PubMed  Google Scholar 

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Funding

This study was funded by Servier as part of T. Derippe's Ph.D. program.

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Authors and Affiliations

  1. Institut de Recherches Internationales Servier, Suresnes, France

    Thibaud Derippe & Sylvain Fouliard

  2. Université Paris Cité, Inserm, UMRS-1144, Optimisation Thérapeutique en Neuropsychopharmacologie, 75006, Paris, France

    Thibaud Derippe & Xavier Declèves

  3. Department of Pharmaceutical Sciences, University at Buffalo, State University of New York, Buffalo, NY, 14214, USA

    Thibaud Derippe & Donald E. Mager

  4. Enhanced Pharmacodynamics, LLC, Buffalo, NY, USA

    Donald E. Mager

Authors
  1. Thibaud Derippe
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  2. Sylvain Fouliard
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  3. Xavier Declèves
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  4. Donald E. Mager
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Contributions

T. Derippe, S. Fouliard and D.E. Mager designed the study and developed the methodology. T. Derippe implemented the algorithms in R and performed their evaluations. The study was supervised by S. Fouliard, X. Declèves, D.E. Mager. All authors contributed to the manuscript's writing, review and/or revision.

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Correspondence to Donald E. Mager.

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Competing interest

D.E. Mager reports receiving commercial research grants from Servier. No potential conflicts of interest were disclosed by the other authors.

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Derippe, T., Fouliard, S., Declèves, X. et al. Accelerating robust plausible virtual patient cohort generation by substituting ODE simulations with parameter space mapping. J Pharmacokinet Pharmacodyn 49, 625–644 (2022). https://doi.org/10.1007/s10928-022-09826-8

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  • DOI: https://doi.org/10.1007/s10928-022-09826-8

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