Good Practices in Model‐Informed Drug Discovery and Development: Practice, Application, and Documentation
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This document was developed to enable greater consistency in the practice, application, and documentation of Model‐Informed Drug Discovery and Development (MID3) across the pharmaceutical industry. A collection of “good practice” recommendations are assembled here in order to minimize the heterogeneity in both the quality and content of MID3 implementation and documentation. The three major objectives of this white paper are to: i) inform company decision makers how the strategic integration of MID3 can benefit R&D efficiency; ii) provide MID3 analysts with sufficient material to enhance the planning, rigor, and consistency of the application of MID3; and iii) provide regulatory authorities with substrate to develop MID3 related and/or MID3 enabled guidelines.
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Careful documentation of the collation of sources and usage of input data, knowledge, and assumptions made in formulating and informing a model increases confidence in its results.
“Careful documentation of the collation of sources and usage of input data, knowledge, and assumptions made in formulating and informing a model increases the confidence in its results.”
Regulatory versus industry criteria for impact have been developed.
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Self-citationThe promises of quantitative systems pharmacology modelling for drug development
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How, When, and for Whom Should We Perform Therapeutic Drug Monitoring?
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Progress and Opportunities to Advance Clinical Cancer Therapeutics Using Tumor Dynamic Models
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Application of Machine Learning in Translational Medicine: Current Status and Future Opportunities
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Multi-scale Modeling in Clinical Oncology: Opportunities and Barriers to Success
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Model‐Informed Drug Development for Anti‐Infectives: State of the Art and Future
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Harvesting the promise of AOPs: An assessment and recommendations
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Quantitative Systems Pharmacology: A Case for Disease Models
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Quantitative Modeling and Simulation in PMDA: A Japanese Regulatory Perspective
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Clinical Drug–Drug Interaction Evaluations to Inform Drug Use and Enable Drug Access
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“It acts to ensure that quality assurance and quality control of the developed model have been undertaken.(41,42) A discussion of the potential limitations of the model in the context of the available data, knowledge, and assumptions also constitutes good practice because it allows modeling practitioners to reuse or ex…”
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Fast screening of covariates in population models empowered by machine learning
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Population pharmacokinetic model selection assisted by machine learning
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Semaglutide s.c. Once-Weekly in Type 2 Diabetes: A Population Pharmacokinetic Analysis
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Model-based drug development: application of modeling and simulation in drug development
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A Holistic and Integrative Approach for Advancing Model‐Informed Drug Development
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Open innovation: Towards sharing of data, models and workflows
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Role of pharmacokinetic modeling and simulation in precision dosing of anticancer drugs
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Impact of Model‐Informed Drug Development on Drug Development Cycle Times and Clinical Trial Cost
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Model‐Informed Drug Development: Steps Toward Harmonized Guidance
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Self-citationA Systematic Review of the Efforts and Hindrances of Modeling and Simulation of CAR T-cell Therapy
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Optimizing drug development in oncology by clinical trial simulation: Why and how?
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Commentary on the MID3 Good Practices Paper
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Model reduction in mathematical pharmacology
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Self-citationLinked Pharmacometric‐Pharmacoeconomic Modeling and Simulation in Clinical Drug Development
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Deep Learning Methods Applied to Drug Concentration Prediction of Olanzapine
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In Silico Clinical Trials: Is It Possible?
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Pharmacokinetics and Tissue Distribution of Enavogliflozin in Mice and Rats
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Gut Microbiome Integration in Drug Discovery and Development of Small Molecules
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Transforming Drug Design: Innovations in Computer-Aided Discovery for Biosimilar Agents
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PDUFA VI: It Is Time to Unleash the Full Potential of Model‐Informed Drug Development
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Model Informed Drug Development and Regulation in China: Challenges and Opportunities
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The implications of model-informed drug discovery and development for tuberculosis
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Self-citation
- Authors
- Workgroup, E. M., Marshall, S., Burghaus, R., Cosson, V, Cheung, S., Chenel, M., DellaPasqua, O., Frey, N., Hamrén, B., Harnisch, L., Ivanow, F., Kerbusch, T., Lippert, J., Milligan, P., Rohou, S., Staab, A., Steimer, J., Tornøe, C., & Visser, S.
- Affiliations
- Pfizer (United Kingdom) (GB)
- F. Hoffmann-La Roche AG (CH)
- AstraZeneca (United Kingdom) (GB)
- GlaxoSmithKline (United Kingdom) (GB)
- Clinical Pharmacology Modelling & Simulation GlaxoSmithKline R&D Ltd Uxbridge UK
- Janssen (United Kingdom) (GB)
- MSD (Netherlands) (NL)
- Pharmacometrics Pfizer Ltd Sandwich UK
- Global Regulatory Affairs & Policy AstraZeneca Paris France
- Novartis (Switzerland) (CH)
- Merck & Co., Inc., Rahway, NJ, USA (United States) (US)
- Quantitative Pharmacology & Pharmacometrics Merck & Co Kenilworth USA
- Corresponding author
- Marshall, SF
- Venue
- CPT: Pharmacometrics & Systems Pharmacology
- Publisher
- Wiley
- Published
- 2016-03
- Volume
- 5
- Issue
- 3
- Pages
- 93-122
- ISSN
- 2163-8306 (ISSN-L)
- PMC
- PMC4809625
- OpenAlex
- W2181278642
- Cited by
- 373
- References
- 82
- Citations by year
- 2026: 26 · 2025: 44 · 2024: 37 · 2023: 36 · 2022: 32 · 2021: 51 · 2020: 38 · 2019: 40 · 2018: 33 · 2017: 22 · 2016: 12 · 2015: 1
- FWCI
- 22.58
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- CPT Pharmacometrics & Systems Pharmacology (publishedVersion, cc-by-nc)
- Europe PMC (PubMed Central) (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- 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
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Pharmacology › Pharmacogenetics and Drug Metabolism
- Keywords
- good practice recommendations, pharmacokinetic-pharmacodynamic modeling, R&D efficiency, regulatory guidelines, pharmaceutical industry
- MeSH terms
- Documentation, Technology, Pharmaceutical, Drug Design, Guidelines as Topic
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