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Cited by 37Pharmaceutical studies and practicesStatistical Methods in Clinical TrialsComputational Drug Discovery Methods

An industry perspective on current QSP trends in drug development

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Cucurull-Sanchez, Lourdes·2024·Journal of Pharmacokinetics and Pharmacodynamics
DOI 10.1007/s10928-024-09905-yPMC PMC11576823
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Abstract

2023 marks the 10th anniversary of Natpara’s submission to the US FDA, which led to the first recorded regulatory interaction where a decision was supported by Quantitative and Systems Pharmacology (QSP) simulations. It had taken about 5 years for the timid QSP discipline to emerge as an effective Model-Informed Drug Development (MIDD) tool with visible impact in the pharmaceutical industry. Since then, the presence of QSP in the regulatory environment has continued to increase, to the point that the Agency reported 60 QSP submissions in 2020 alone, representing ~ 4% of their annual IND submissions [1]. What sort of industry mindset has enabled QSP to reach this level of success? How does QSP fit within the MIDD paradigm? Does QSP mean the same to Discovery and to Clinical Development projects? How do ‘platforms’ compare to ‘fit-for-purpose’ QSP models in an industrial setting? Can QSP and empirical Pharmacokinetic-Pharmacodynamic (PKPD) modelling be complementary? What level of validation is required to inform drug development decisions? This article reflects on all these questions, in particular addressing those audiences with limited line-of-sight into the drug industry decision-making machinery.

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  • Examples of QSP contexts of use in early clinical development include first-in-human dose projections, recommended phase 2 or 3 dose optimization/justification, mechanistic biomarker-based patient stratification, and mechanistic insight into combination treatment or dose optimization across indications.

    “In this setting, examples of QSP CoU could be first in human (FIH) dose projections, recommended phase 2 or 3 dose (RP2D/RP3D) optimization/justification; mechanistic biomarker-based patient stratification or providing mechanistic insight into combination treatment or dose optimization across indications (8) (9).”

    From Best Practices on QSP Model Reporting for Regulatory Use: perspectives from ISoP QSP SIG Working Group (2026) · cites this paper as [9]

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Research works printed but not linked: 37

  1. [1]Chan JR et al (2022) Current practices for QSP model assessment: an IQ consortium survey. J Pharmacokinet Pharmacodyn,: p. 1–13 PMC9371373 35953664
  2. [2]Peterson MC, Riggs MM (2015) FDA Advisory Meeting Clinical Pharmacology Review utilizes a quantitative systems Pharmacology (QSP) Model: a Watershed Moment? CPT Pharmacometrics Syst Pharmacol 4(3):e00020 26225239 PMC4394612 · DOI · Paper page
  3. [5]Vicini P, van der Graaf PH (2013) Systems pharmacology for drug discovery and development: paradigm shift or flash in the pan? Clin Pharmacol Ther 93(5):379–381 23598453 · DOI · Paper page
  4. [6]Danhof M (2022) Quantitative Systems Pharmacology Conference (QSPC). From PKPD to QSP [Online] 2022 [cited June 30th 2023]; Available from: https://www.qspc.eu/qspc
  5. [9]International Consortium for Innovation & Quality in Pharmaceutical Development. [Online] [cited June 30th 2023]; Available from: https://iqconsortium.org/ 36477720
  6. [11]Zineh I (2019) Quantitative systems Pharmacology: A Regulatory Perspective on translation. CPT Pharmacometrics Syst Pharmacol 8(6):336–339 30924594 PMC6618141 · DOI
  7. [12]Bai JPF et al (2021) Quantitative systems pharmacology: Landscape analysis of regulatory submissions to the US Food and Drug Administration. CPT Pharmacometrics Syst Pharmacol 10(12):1479–1484 34734497 PMC8673997 · DOI
  8. [14]Madabushi R et al (2022) Review: role of model-informed Drug Development approaches in the lifecycle of Drug Development and Regulatory decision-making. Pharm Res 39(8):1669–1680 35552984 PMC9097888 · DOI
  9. [15]Krishnaswami S et al (2020) MID3: Mission impossible or model-informed drug Discovery and Development? Point-counterpoint discussions on Key challenges. Clin Pharmacol Ther 107(4):762–772 31955417 PMC7158219 · DOI
  10. [16]Marshall S et al (2019) Model-informed drug Discovery and Development: current industry Good Practice and Regulatory expectations and Future perspectives. CPT Pharmacometrics Syst Pharmacol 8(2):87–96 30411538 PMC6389350 · DOI · Paper page
  11. [17]Manolis E et al (2017) Commentary on the MID3 good practices Paper. CPT Pharmacometrics Syst Pharmacol 6(7):416–417 28653481 PMC5529732 · DOI
  12. [18]Galluppi GR et al (2021) Industrial Perspective on the benefits realized from the FDA’s model-informed Drug Development Paired Meeting Pilot Program. Clin Pharmacol Ther 110(5):1172–1175 33991429 PMC8596613 · DOI
  13. [19]Madabushi R et al (2019) The US Food and Drug Administration’s model-informed Drug Development Paired Meeting Pilot Program: early experience and impact. Clin Pharmacol Ther 106(1):74–78 31081932 · DOI · Paper page
  14. [22]Bradshaw EL et al (2019) Applications of quantitative systems Pharmacology in Model-Informed Drug Discovery: perspective on Impact and opportunities. CPT Pharmacometrics Syst Pharmacol 8(11):777–791 31535440 PMC6875708 · DOI
  15. [24]Polasek TM, Rostami-Hodjegan A (2020) Virtual twins: understanding the data required for Model-Informed Precision Dosing. Clin Pharmacol Ther 107(4):742–745 32056199 · DOI
  16. [25]Venkatakrishnan K, van der Graaf PH (2022) Toward Project Optimus for Oncology Precision Medicine: multi-dimensional dose optimization enabled by quantitative clinical pharmacology. Clin Pharmacol Ther 112(5):927–932 36264968 · DOI
  17. [27]Crisp A et al (2018) Practical experiences of adopting assurance as a quantitative framework to support decision making in drug development. Pharm Stat 17(4):317–328 29635777 · DOI
  18. [29]Agoram B (2014) Evaluating systems Pharmacology Models is different from evaluating standard pharmacokinetic–pharmacodynamic models, vol 3. Pharmacometrics & Systems Pharmacology, CPT, p 101. 2 PMC3944115 24552986
  19. [30]Androulakis IP (2022) Towards a comprehensive assessment of QSP models: what would it take? J Pharmacokinet Pharmacodyn PMC9922790 35962928
  20. [31]Sové RJ et al (2020) QSP-IO: a quantitative systems Pharmacology Toolbox for mechanistic Multiscale modeling for Immuno-Oncology Applications. CPT Pharmacometrics Syst Pharmacol 9(9):484–497 32618119 PMC7499194 · DOI
  21. [32]Scheuher B et al (2023) Towards a platform quantitative systems pharmacology (QSP) model for preclinical to clinical translation of antibody drug conjugates (ADCs). J Pharmacokinet Pharmacodyn PMC11576657 37787918
  22. [33]Lu J et al (2014) An in-silico model of lipoprotein metabolism and kinetics for the evaluation of targets and biomarkers in the reverse cholesterol transport pathway. PLoS Comput Biol 10(3):e1003509 24625468 PMC3952822 · DOI
  23. [34]Karelina T et al (2017) Studying the Progression of Amyloid Pathology and its Therapy using Translational Longitudinal Model of Accumulation and distribution of amyloid Beta. CPT Pharmacometrics Syst Pharmacol 6(10):676–685 28913897 PMC5658285 · DOI
  24. [35]Klinke DJ (2008) 2nd, Integrating epidemiological data into a mechanistic model of type 2 diabetes: validating the prevalence of virtual patients Ann Biomed Eng, 36(2): p. 321 – 34 18046647
  25. [36]Schmidt BJ et al (2013) Alternate virtual populations elucidate the type I interferon signature predictive of the response to Rituximab in rheumatoid arthritis. BMC Bioinformatics 14:221 23841912 PMC3717130 · DOI
  26. [37]Gadkar K et al (2014) A mechanistic systems Pharmacology Model for prediction of LDL cholesterol lowering by PCSK9 antagonism in human dyslipidemic populations. CPT Pharmacometrics Syst Pharmacol 3:e149 25426564 PMC4260002 · DOI · Paper page
  27. [38]Allen RJ, Rieger TR, Musante CJ (2016) Efficient generation and selection of virtual populations in quantitative systems Pharmacology models. CPT Pharmacometrics Syst Pharmacol 5(3):140–146 27069777 PMC4809626 · DOI
  28. [39]Rieger TR et al (2018) Improving the generation and selection of virtual populations in quantitative systems pharmacology models. Prog Biophys Mol Biol 139:15–22 29902482 · DOI · Paper page
  29. [40]Milberg O et al (2019) A QSP Model for Predicting clinical responses to Monotherapy, Combination and Sequential Therapy following CTLA-4, PD-1, and PD-L1 checkpoint blockade. Sci Rep 9(1):11286 31375756 PMC6677731 · DOI
  30. [41]Wang H et al (2019) 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 6(5):190366 31218069 PMC6549962 · DOI
  31. [42]Wang H 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 32158754 PMC7051945 · DOI
  32. [43]Susilo ME et al (2023) Systems-based digital twins to help characterize clinical dose-response and propose predictive biomarkers in a phase I study of bispecific antibody, mosunetuzumab, in NHL. Clin Transl Sci PMC10339700 36908269
  33. [44]Cheng Y et al (2022) Virtual Populations for Quantitative Systems Pharmacology Models, in Systems Medicine, J.P.F. Bai and J. Hur, Editors. Springer US: New York, NY. p. 129–179 35437722
  34. [45]Zhang T et al (2022) Two heads are better than one: current landscape of integrating QSP and machine learning: an ISoP QSP SIG white paper by the working group on the integration of quantitative systems pharmacology and machine learning. J Pharmacokinet Pharmacodyn 49(1):5–18 35103884 PMC8837505 · DOI
  35. [46]Braakman S, Pathmanathan P, Moore H (2022) Evaluation framework for systems models. CPT Pharmacometrics Syst Pharmacol 11(3):264–289 34921743 PMC8923730 · DOI
  36. [47]Ramanujan S et al (2019) A Flexible Approach for Context-Dependent Assessment of quantitative systems Pharmacology models. CPT Pharmacometrics Syst Pharmacol 8(6):340–343 30983158 PMC6617835 · DOI
  37. [49]Musuamba FT et al (2021) Scientific and regulatory evaluation of mechanistic in silico drug and disease models in drug development: building model credibility. CPT Pharmacometrics Syst Pharmacol 10(8):804–825 34102034 PMC8376137 · DOI

Cited by

Sorted by citations

Authors
Cucurull-Sanchez, L.
Affiliations
GlaxoSmithKline (United Kingdom) (GB)
Corresponding author
Cucurull-Sanchez, Lourdes
ORCID
Cucurull-Sanchez, Lourdes
Venue
Journal of Pharmacokinetics and Pharmacodynamics
Publisher
Springer Science and Business Media LLC
Published
2024-03-05
Volume
51
Issue
5
Pages
511-520
ISSN
  • 1567-567X (ISSN-L)
  • 1573-8744
DOI
10.1007/s10928-024-09905-y
PMC
PMC11576823
OpenAlex
W4392450309
Cited by
37
References
6
Citations by year
2026: 12 · 2025: 15 · 2024: 10
FWCI
25.05
Type
journal article
Language
en
Open access
Licence
Locations
  • Journal of Pharmacokinetics and Pharmacodynamics (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
Topics
  • Health Sciences › Medicine › Pediatrics, Perinatology and Child Health › Pharmaceutical studies and practices
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
  • Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
Keywords
quantitative systems pharmacology, model-informed drug development, regulatory submissions, FDA, industry perspectives, pharmacokinetic-pharmacodynamic modeling, model validation, drug development decision-making
MeSH terms
Drug Development, Network Pharmacology, Drug Industry, Humans, Models, Biological, United States, United States Food and Drug Administration

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