The DOI matches its Crossref record; 81 of the 82 references it cites were found in a registry or an index.
Good Practices in Model‐Informed Drug Discovery and Development: Practice, Application, and Documentation
Open accessAbstract
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Figures



Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source says what the claim attributes to it.
In the citing paper
“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.”
The cited source says what the claim attributes to it.
In the citing paper
“It is for this reason that regulatory vs. industry criteria for impact have been developed.41, 79”
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Prediction-Corrected Visual Predictive Checks for Diagnosing Nonlinear Mixed-Effects Models
Bergstrand, M., Hooker, A. C., Wallin, J. E., & Karlsson, M. O. (2011) · The AAPS Journal · doi:10.1208/s12248-011-9255-z · Cited by 1,435
Ways to Fit a PK Model with Some Data Below the Quantification Limit
Beal, S. L. (2001) · Journal of Pharmacokinetics and Pharmacodynamics · doi:10.1023/a:1012299115260 · Cited by 1,156
Understanding the Dose-Effect Relationship
Holford, N. H. G., & Sheiner, L. B. (1981) · Clinical Pharmacokinetics · doi:10.2165/00003088-198106060-00002 · Cited by 1,147
Caro, J., Briggs, A., Siebert, U., & Kuntz, K. M. (2012) · Value in Health · doi:10.1016/j.jval.2012.06.012 · Cited by 1,006
Mould, D., & Upton, R. N. (2013) · CPT Pharmacometrics & Systems Pharmacology · doi:10.1038/psp.2013.14 · Cited by 906
Importance of Shrinkage in Empirical Bayes Estimates for Diagnostics: Problems and Solutions
Savić, R. M., & Karlsson, M. O. (2009) · The AAPS Journal · doi:10.1208/s12248-009-9133-0 · Cited by 641
Learning versus confirming in clinical drug development*
Sheiner, L. B. (1997) · Clinical Pharmacology & Therapeutics · doi:10.1016/s0009-9236(97)90160-0 · Cited by 633
Basic Concepts in Physiologically Based Pharmacokinetic Modeling in Drug Discovery and Development
Jones, H., & Rowland‐Yeo, K. (2013) · CPT: Pharmacometrics & Systems Pharmacology · doi:10.1038/psp.2013.41 · Cited by 626
Basic Concepts in Population Modeling, Simulation, and Model‐Based Drug Development
Mould, D., & Upton, R. N. (2012) · CPT Pharmacometrics & Systems Pharmacology · doi:10.1038/psp.2012.4 · Cited by 589
Sager, J. E., Yu, J., Ragueneau, I., & Isoherranen, N. (2015) · Drug Metabolism and Disposition · doi:10.1124/dmd.115.065920 · Cited by 541
Research works printed but not linked: 6
- EMA/EFPIA M&S workshop 2011.http://www.ema.europa.eu/ema/index.jsp?curl=pages/news_and_events/events/2011/07/event_detail_000440.jsp&mid=WC0b01ac058004d5c3(2011).
- EMA/EFPIAEuropean Medicines Agency/European Federation of Pharmaceutical Industries and Associations workshop on the importance of dose finding and dose selection for the successful development licensing and lifecycle management of medicinal products.http://www.ema.europa.eu/ema/index.jsp?curl=pages/news_and_events/events/2014/06/event_detail_000993.jsp&mid=WC0b01ac058004d5c3(2014).
- Holford N.H.G., 2006, Principles of Clinical Pharmacology 2nd edn, 369
- Meibohm B., 1997, Basic concept of PK/PD modeling, Int. J. Clin. Pharmacol. Ther., 35, 401
- 10.1038/clpt.2012.53 · DOI
- Goggin T., 2003, Simulation for designing clinical trials, 227
Cited by
Sorted by citations
A Review on Applications of Computational Methods in Drug Screening and Design
Lin, X., Li, X., & Lin, X. (2020) · Molecules · doi:10.3390/molecules25061375 · Cited by 718
Scientific white paper on concentration-QTc modeling
Garnett, C. E., Bonate, P. L., Dang, Q., Ferber, G., Huang, D. P., Liu, J., Mehrotra, D. V., Riley, S., Sager, P. T., Tornøe, C. W., et al. (2017) · Journal of Pharmacokinetics and Pharmacodynamics · doi:10.1007/s10928-017-9558-5 · Cited by 249
Self-citationModel‐Informed Drug Development: Current US Regulatory Practice and Future Considerations
Wang, Y., Zhu, H., Madabushi, R., Liu, Q., Huang, S., & Zineh, I. (2019) · Clinical Pharmacology & Therapeutics · doi:10.1002/cpt.1363 · Cited by 228
Musuamba, F. T., Rusten, I. S., Lesage, R., Russo, G. L., Bursi, R., Emili, L., Wangorsch, G., Manolis, E., Karlsson, K. E., Kulesza, A. J., et al. (2021) · CPT Pharmacometrics & Systems Pharmacology · doi:10.1002/psp4.12669 · Cited by 175
An in vitro toolbox to accelerate anti-malarial drug discovery and development
Charman, S. A., Andreu, A., Barker, H., Blundell, S. A., Campbell, A., Campbell, M., Chen, G., Chiu, F. C. K., Crighton, E., Katneni, K., et al. (2020) · Malaria Journal · doi:10.1186/s12936-019-3075-5 · Cited by 154
Metadata
- 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
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review