Academic Journal
A Review of Virtual Twins in Physiologically-Based Pharmacokinetic Modeling and Simulation.
| Τίτλος: | A Review of Virtual Twins in Physiologically-Based Pharmacokinetic Modeling and Simulation. |
|---|---|
| Συγγραφείς: | Mannix E; Faculty of Pharmacy and Pharmaceutical Sciences, Monash University, Parkville, Victoria, Australia., Mostafa S; Faculty of Pharmacy and Pharmaceutical Sciences, Monash University, Parkville, Victoria, Australia.; MyDNA Life, South Yarra, Victoria, Australia., Rostami-Hodjegan A; Centre for Applied Pharmacokinetic Research, The University of Manchester, Manchester, UK.; Certara Predictive Technologies, Sheffield, UK., Polasek TM; Faculty of Pharmacy and Pharmaceutical Sciences, Monash University, Parkville, Victoria, Australia.; CMAX Clinical Research, Adelaide, South Australia, Australia., Kirkpatrick CMJ; Faculty of Pharmacy and Pharmaceutical Sciences, Monash University, Parkville, Victoria, Australia. |
| Πηγή: | Clinical pharmacology and therapeutics [Clin Pharmacol Ther] 2026 Aug; Vol. 120 (2), pp. 324-333. Date of Electronic Publication: 2026 May 07. |
| Τύπος έκδοσης: | Journal Article; Review; 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): | Models, Biological* , Computer Simulation* , Pharmacokinetics*, Pharmaceutical Preparations/metabolism ; Pharmaceutical Preparations/administration & dosage ; Precision Medicine/methods ; Humans |
| Περίληψη: | The novel application of Virtual Twins (VT) in PBPK (VT-PBPK) presents the opportunity to advance precision dosing and accelerate the shift from one-size-fits-all to targeted, individualized treatments. This review aims to: (1) critically evaluate existing research on the use of VTs in PBPK, (2) develop a conceptual definition of VT-PBPK, (3) describe and evaluate VT methodological diversity, (4) examine existing regulatory frameworks and guidance governing the integration of VT-PBPK, and (5) identify opportunities and challenges for advancing next-generation VTs. A structured literature search was conducted to identify studies describing VT-PBPK of a whole human body for the purpose of predicting drug concentration and/or effect. Details of the VT-PBPK models and VT design were extracted from each study. A framework assessing and categorizing methods of simulation and virtualization was applied to the extracted data. Twenty-two (22) studies were included which demonstrated the application of VT-PBPK across a range of populations, disease states, and drug classes. All studies applied VT-PBPK to real-world patient-specific covariate data retrospectively for the purpose of PBPK model development and evaluation, or model-informed precision dosing (MIPD). In the VT approaches, three levels of virtualization were identified; low, medium, and high, as determined by the number of covariates integrated into the model. To date there is no specific regulatory guidance on the appropriate use of VT-PBPK. A shift in application of PBPK modeling from population-based to specific, individualized predictions is required to advance VTs toward clinical implementation. Achieving rigorous design and evaluation of VT models will require strong interdisciplinary collaboration. (© 2026 The Author(s). Clinical Pharmacology & Therapeutics published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.) |
| References: | Polasek, T.M. & Rostami‐Hodjegan, A. Virtual twins: understanding the data required for model‐informed precision dosing. Clin. Pharmacol. Ther. 107, 742–745 (2020). Wang, K., Parrott, N.J. & Lave, T. Embracing the future of medicine with virtual patients. Drug Discov. Today 30, 104322 (2025). Mathur, S. & Sutton, J. Personalized medicine could transform healthcare. Biomed Rep. 7, 3–5 (2017). Holford, N., Ma, G. & Metz, D. TDM is dead. Long live TCI! Br. J. Clin. Pharmacol. 88, 1406–1413 (2022). Holford, N.H. Target concentration intervention: beyond Y2K. Br. J. Clin. Pharmacol. 48, 9–13 (1999). Sun, Z., Zhao, N., Zhao, X., Wang, Z., Liu, Z. & Cui, Y. Application of physiologically based pharmacokinetic modeling of novel drugs approved by the U.S. food and drug administration. Eur. J. Pharm. Sci. 200, 106838 (2024). Paul, P., Colin, P.J., Musuamba Tshinanu, F., Versantvoort, C., Manolis, E. & Blake, K. Current use of physiologically based pharmacokinetic modeling in new medicinal product approvals at EMA. Clin. Pharmacol. Ther. 117, 808–817 (2025). Rostami‐Hodjegan, A. Physiologically based pharmacokinetics joined with in vitro‐in vivo extrapolation of ADME: a marriage under the arch of systems pharmacology. Clin. Pharmacol. Ther. 92, 50–61 (2012). 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). Darwich, A.S. et al. Why has model‐informed precision dosing not yet become common clinical reality? Lessons from the past and a roadmap for the future. Clin. Pharmacol. Ther. 101, 646–656 (2017). Dibbets, A.C., Koldeweij, C., Osinga, E.P., Scheepers, H.C.J. & de Wildt, S.N. Barriers and facilitators for bringing model‐informed precision dosing to the Patient's bedside: a systematic review. Clin. Pharmacol. Ther. 117, 633–645 (2025). Mostafa, S. et al. Delineating gene‐environment effects using virtual twins of patients treated with clozapine. CPT Pharmacometrics Syst. Pharmacol. 12, 168–179 (2023). Fendt, R. et al. Data‐driven personalization of a physiologically based pharmacokinetic model for caffeine: a systematic assessment. CPT Pharmacometrics Syst. Pharmacol. 10, 782–793 (2021). Gaspar, F., Terrier, J., Jacot‐Descombes, C. et al. Virtual twin approach using physiologically based pharmacokinetic modelling in hospitalized patients treated with apixaban or rivaroxaban. Br. J. Clin. Pharmacol. 91, 2057–2069 (2025). https://doi.org/10.1002/bcp.70032. Kangne, H., Izat, N., Chen, G. et al. Virtual twin‐PBPK modelling: a step toward precision dosing in patients with obesity. AAPS J. 28, 46 (2026). Emoto, C., Hahn, D., Euteneuer, J.C., Mizuno, T., Vinks, A.A. & Fukuda, T. Next challenge from the variance in individual physiologically‐based pharmacokinetic model‐predicted to observed morphine concentration in critically ill neonates. Clin. Pharmacol. Ther. 107, 319–320 (2020). Hassine, K. et al. A Physiologically Based Pharmacokinetic Modelling and “Virtual Twin” Simulation Approach for the Prediction of Busulfan Exposure in Young Children Undergoing Hematopoietic Stem Cell Transplantation (Population Approach Group Europe, Ljubljana, Slovenia, 2022). https://www.page‐meeting.org/default.asp?abstract=10207. Mseddi, M. et al. Physiologically based pharmacokinetic virtual twin approach for Fludarabine dosing in pediatric hematopoietic stem cell transplantation. CPT Pharmacometrics Syst. Pharmacol. 15, e70218 (2026). Radke, C. et al. Development of a physiologically based pharmacokinetic modelling approach to predict the pharmacokinetics of vancomycin in critically ill septic patients. Clin. Pharmacokinet. 56, 759–779 (2017). Polasek, T.M. et al. Prediction of olanzapine exposure in individual patients using physiologically based pharmacokinetic modelling and simulation. Br. J. Clin. Pharmacol. 84, 462–476 (2018). U.S. Food and Drug Administration. PDUFA VI: Fiscal Years 2018–2022 (2017). European Medicines Agency CfMPfHUC. Guideline on the reporting of physiologically based pharmacokinetic (PBPK) modelling and simulation (2018). U.S. Food and Drug Administration CfDEaRC. Physiologically Based Pharmacokinetic Analyses—Format and Content: Guidance for Industry. Report (2018). (ICH) ICfHoTRfPfHU. ICH M15 Guideline on General Principles for Model‐Informed Drug Development (2026) EMA/CHMP/ICH/496426/2024 <https://www.ema.europa.eu/en/documents/scientific‐guideline/ich‐m15‐guideline‐general‐principles‐model‐informed‐drug‐development‐step‐5_en.pdf>. Accessed 23 March 2026. Li, Y., Sun, H. & Zhang, Z. The evolution and future directions of PBPK modeling in FDA regulatory review. Pharmaceutics. 17, 1413 (2025). https://doi.org/10.3390/pharmaceutics17111413. Agency EM. Qualification Opinion for Simcyp Simulator (2025). Mostafa, S. et al. Virtual twins for model‐informed precision dosing of clozapine in patients with treatment‐resistant schizophrenia. CPT Pharmacometrics Syst. Pharmacol. 13, 424–436 (2024). Susilo, M.E. et al. 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. 16, 1134–1148 (2023). Rostami‐Hodjegan, A., Al‐Majdoub, Z.M., von Grabowiecki, Y. et al. Dealing with variable drug exposure due to variable hepatic metabolism: a proof‐of‐concept application of liquid biopsy in renal impairment. Clin. Pharmacol. Ther. 116, 814–823 (2024). Bahrami, F., Rossi, R.M., De Nys, K. & Defraeye, T. An individualized digital twin of a patient for transdermal fentanyl therapy for chronic pain management. Drug Deliv. Transl. Res. 13, 2272–2285 (2023). Bahrami, F., Rossi, R.M., De Nys, K., Joerger, M., Radenkovic, M.C. & Defraeye, T. Implementing physics‐based digital patient twins to tailor the switch of oral morphine to transdermal fentanyl patches based on patient physiology. Eur. J. Pharm. Sci. 195, 106727 (2024). Cukic, M., Annaheim, S., Bahrami, F., Defraeye, T., De Nys, K. & Jorger, M. Is personal physiology‐based rapid prediction digital twin for minimal effective fentanyl dose better than standard practice: a pilot study protocol. BMJ Open 14, e085296 (2024). Martischang, R., Nikolaou, A., Daali, Y., Samer, C.F. & Terrier, J. Guidance on selecting optimal steady‐state tacrolimus concentrations for continuous IV perfusion: insights from physiologically based pharmacokinetic modeling. Pharmaceuticals 17, 1047 (2024). https://doi.org/10.3390/ph17081047. van Hoogdalem, M.W. et al. Forecasting fetal buprenorphine exposure through maternal‐fetal physiologically based pharmacokinetic modeling. Pharmaceutics. 16, 375 (2024). https://doi.org/10.3390/pharmaceutics16030375. Mostafa, S. et al. Pharmacogenomics in psychiatry ‐ the challenge of cytochrome P450 enzyme phenoconversion and solutions to assist precision dosing. Pharmacogenomics 23, 857–867 (2022). Society RCoPaBP. Personalised Prescribing: Using Pharmacogenomics to Improve Patient Outcomes. Report of the PGx working party. <https://www.bps.ac.uk/getmedia/b43a3dca‐1bbf‐4bff‐9379‐20bef9349a8c/Personalised‐prescribing‐full‐report.pdf.aspx> (2022). Koldeweij, C.J.M. et al. Willingness‐to‐use and preferences for model‐informed antenatal doses: a cross‐sectional study among European healthcare practitioners and pregnant women. Front. Pharmacol. 15, 1403747 (2024). Zapke, S.E., Willmann, S., Grebe, S.O., Menke, K., Thurmann, P.A. & Schmiedl, S. Comparing predictions of a PBPK model for cyclosporine with drug levels from therapeutic drug monitoring. Front. Pharmacol. 12, 630904 (2021). Gerhart, J.G. et al. Development and evaluation of a virtual population of children with obesity for physiologically based pharmacokinetic modeling. Clin. Pharmacokinet. 61, 307–320 (2022). Ford, J.L., Gerhart, J.G., Edginton, A.N., Yanovski, J.A., Hon, Y.Y. & Gonzalez, D. Physiologically based pharmacokinetic modeling of metformin in children and adolescents with obesity. J. Clin. Pharmacol. 62, 960–969 (2022). van Hoogdalem, M.W., Johnson, T.N., McPhail, B.T. et al. Physiologically‐based pharmacokinetic modeling to investigate the effect of maturation on buprenorphine pharmacokinetics in newborns with neonatal opioid withdrawal syndrome. Clin. Pharmacol. Ther. 111, 496–508 (2022). Gerhart, J.G. et al. Use of real‐world data and physiologically‐based pharmacokinetic modeling to characterize enoxaparin disposition in children with obesity. Clin. Pharmacol. Ther. 112, 391–403 (2022). Mahdy, W.Y.B. et al. Physiologically‐based pharmacokinetic model to investigate the effect of pregnancy on risperidone and paliperidone pharmacokinetics: application to a pregnant woman and her neonate. Clin. Transl. Sci. 16, 618–630 (2023). Silva, J.Q.D. et al. Amikacin dosing adjustment in critically ill oncologic patients: a study with real‐world patients, PBPK analysis, and digital twins. Pharmaceutics. 17, 297 (2025). https://doi.org/10.3390/pharmaceutics17030297. Simcyp Simulator. Certara <https://www.certara.com/simcyp/> (2025). PK‐Sim. Open Systems Pharmacology <https://www.open‐systems‐pharmacology.org/> (2025). MoBi. Open Systems Pharmacology <https://www.open‐systems‐pharmacology.org/> (2025). MATLAB. MathWorks <https://au.mathworks.com/products/matlab.html> (2025). COMSOL Multiphysics. COMSOL <https://www.comsol.com/> (2025). |
| Substance Nomenclature: | 0 (Pharmaceutical Preparations) |
| Entry Date(s): | Date Created: 20260507 Date Completed: 20260712 Latest Revision: 20260726 |
| Update Code: | 20260726 |
| PubMed Central ID: | PMC13339568 |
| DOI: | 10.1002/cpt.70313 |
| PMID: | 42095647 |
| Βάση Δεδομένων: | MEDLINE |
καταχωρήστε σχόλιο πρώτοι!