Academic Journal
Προβολή σε EDS

The Visual Predictive Check and Real-World Data.

Λεπτομέρειες βιβλιογραφικής εγγραφής
Τίτλος: The Visual Predictive Check and Real-World Data.
Συγγραφείς: Hughes JH; InsightRX, San Francisco, California, USA., Bergstrand M; Pharmetheus AB, Uppsala, Sweden., Keizer RJ; InsightRX, San Francisco, California, USA.
Πηγή: Clinical pharmacology and therapeutics [Clin Pharmacol Ther] 2026 Aug; Vol. 120 (2), pp. 394-398. Date of Electronic Publication: 2026 May 01.
Τύπος έκδοσης: Journal Article; Research Support, Non-U.S. Gov't
Γλώσσα: English
Στοιχεία περιοδικού: Publisher: Wiley Country of Publication: United States NLM ID: 0372741 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1532-6535 (Electronic) Linking ISSN: 00099236 NLM ISO Abbreviation: Clin Pharmacol Ther Subsets: MEDLINE
Imprint Name(s): Publication: 2015- : Hoboken, NJ : Wiley
Original Publication: St. Louis : C.V. Mosby
Ιατρικοί όροι (MeSH): Computer Simulation* , Models, Biological* , Pharmacokinetics*, Humans ; Dose-Response Relationship, Drug ; Prediction Algorithms
Περίληψη: The visual predictive check (VPC) is a standard tool for assessing pharmacometric model suitability, producing visualizations that compare observed data with simulated data such that both model structure and model variability terms can be assessed. However, real-world data commonly reflect clinical decision-making that adapts therapy in response to patient data. As a result, VPCs constructed from such data may display apparent model misspecification, even when models are well-specified. To illustrate possible confounders, four common real-world therapy adaptation scenarios were simulated: (1) varying dose amount with constant interval, (2) varying dosing interval with constant dose amount, (3) patient dropout after identification of a suitable maintenance dose, and (4) variability in sample timing based on individual pharmacokinetic parameter estimates. For all scenarios, simulated observations were generated using the same model used to produce the VPC simulations, ensuring that the model was unbiased. When only the dose amount varied, the prediction-corrected VPC (pcVPC) appropriately indicated a well-specified model. In all other cases, both VPC and pcVPC erroneously suggested a misspecified model. These simulated results, together with our broader experience with complex real-world data, demonstrate that VPCs can be misleading when applied to real-world data if dosing interval, sample timing, or sample frequency varies in response to measured drug concentrations. We propose these four scenarios as benchmarks for evaluating the robustness of current and future model diagnostics to real-world data characteristics.
(© 2026 InsightRx, Inc and Pharmetheus. Clinical Pharmacology & Therapeutics published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.)
References: Ho, Y., Hu, F. & Lee, P. The advantages and challenges of using real‐world data for patient care. Clin. Transl. Sci. 13, 4–7 (2020).
Keizer, R.J. et al. Get real: integration of real‐world data to improve patient care. Clin. Pharmacol. Ther. 107, 722–725 (2020).
Hughes, J.H., Tong, D.M.H., Lucas, S.S., Faldasz, J.D., Goswami, S. & Keizer, R.J. Continuous learning in model‐informed precision dosing: a case study in pediatric dosing of vancomycin. Clin. Pharmacol. Ther. 109, 233–242 (2021).
Choi, L. et al. Development of a system for Postmarketing population pharmacokinetic and pharmacodynamic studies using real‐world data from electronic health records. Clin. Pharmacol. Ther. 107, 934–943 (2020).
Darwich, A.S. et al. Model‐informed precision dosing: background, requirements, validation, implementation, and forward trajectory of individualizing drug therapy. Annu. Rev. Pharmacol. Toxicol. 61, 225–245 (2021).
Nguyen, T.H.T. et al. Model evaluation of continuous data pharmacometric models: metrics and graphics. CPT Pharmacometrics Syst. Pharmacol. 6, 87–109 (2017).
Bergstrand, M., Hooker, A.C., Wallin, J.E. & Karlsson, M.O. Prediction‐corrected visual predictive checks for diagnosing nonlinear mixed‐effects models. AAPS J. 13, 143–151 (2011).
El Hassani, M. & Marsot, A. External evaluation of population pharmacokinetic models for precision dosing: current state and knowledge gaps. Clin. Pharmacokinet. 62, 533–540 (2023).
Keizer, R.J., Hughes, J.H., Tong, D.M.H. & Woo, K. PKPDsim <https://github.com/InsightRX/PKPDsim&gt; (2025).
Keizer, R., Hughes, J.H. & Woo, K. PKPDmap: MAP Bayesian estimates <https://github.com/InsightRX/PKPDmap&gt; (2025).
Hughes, J.H. & Keizer, R.J. mipdtrial: Simulate MIPD trials <https://github.com/insightRX/mipdtrial&gt; (2025).
Keizer, R.J. vpc: Create Visual Predictive Checks <https://CRAN.R‐project.org/package=vpc> (2021).
Hughes, J.H., Woo, K.H., Keizer, R.J. & Goswami, S. Clinical decision support for precision dosing: opportunities for enhanced equity and inclusion in health care. Clin. Pharmacol. Ther. 113, 565–574 (2023).
Ruwaard, J. et al. Interval prolongation of etanercept in rheumatoid arthritis, ankylosing spondylitis, and psoriatic arthritis: a randomized controlled trial. Scand. J. Rheumatol. 52, 129–136 (2023).
Kopylov, U. et al. The efficacy of shortening the dosing interval to once every six weeks in Crohn's patients losing response to maintenance dose of infliximab: efficacy of infliximab dosing once in 6 weeks. Aliment. Pharmacol. Ther. 33, 349–357 (2011).
Johansson, Å.M., Hill, N., Perisoglou, M., Whelan, J., Karlsson, M.O. & Standing, J.F. A population pharmacokinetic/pharmacodynamic model of methotrexate and mucositis scores in osteosarcoma. Ther. Drug Monit. 33, 711–718 (2011).
Karlsson, M.O. & Savic, R.M. Diagnosing model diagnostics. Clin. Pharmacol. Ther. 82, 17–20 (2007).
Lindblom, L. et al. Perl‐speaks‐NONMEM <https://uupharmacometrics.github.io/PsN/&gt; (2025).
Keizer, R. & McCarthy, M. mipdeval: Run evaluation of iterative predictive performance based on historical datasets <https://github.com/InsightRX/mipdeval&gt; (2025).
Lommerse, J., Largajolli, A., Craig, J., Plock, N., Cheung, S.Y.A. & Sachs, J.R. Effective visualizations using “vachette” to assess and communicate Pharmacometric model results. AAPS J. 27, 163 (2025).
Ibrahim, M.M.A., Jonsson, E.N. & Bergstrand, M. The reference‐corrected visual predictive check: a more intuitive diagnostic for non‐linear mixed effects models. AAPS J. 27, 86 (2025).
Entry Date(s): Date Created: 20260501 Date Completed: 20260712 Latest Revision: 20260726
Update Code: 20260726
PubMed Central ID: PMC13339482
DOI: 10.1002/cpt.70306
PMID: 42067900
Βάση Δεδομένων: MEDLINE
Περιγραφή
ISSN:1532-6535
DOI:10.1002/cpt.70306