Guidelines for the Quality Control of Population Pharmacokinetic–Pharmacodynamic Analyses: an Industry Perspective
Open accessAbstract
Quality population modeling and simulation analyses and reports are something every modeler desires. However, little attention in the literature has been paid to what constitutes quality regarding population analyses. Very rarely do published manuscripts contain any statement about quality assurance of the modeling results contained therein. The purpose of this manuscript is to present guidelines for the quality assurance of population analyses, particularly with regards to the use of NONMEM from an industrial perspective. Quality guidelines are developed for the NONMEM installation itself, NONMEM data sets, control streams, output listings, output data files and resultant post-processing, reporting of results, and the review processes. These guidelines were developed to be thorough yet practical, though are not meant to be completely comprehensive. It is our desire to ensure that what is reported accurately reflects the collected data, the modeling process, and model outputs for a modeling project.
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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.”
From Best Practices to Maximize the Use and Reuse of Quantitative and Systems Pharmacology Models: Recommendations From the United Kingdom Quantitative and Systems Pharmacology Network (2019) · cites this paper as [42]
The Conclusions (page unavailable) link report quality to the credibility of model results, and the NONMEM Input Data Files section emphasizes checking data merged from multiple sources. The paper does not specifically say that documenting the sources and use of input knowledge and modeling assumptions increases confidence in the results. (LLM, conf=0.82)
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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…”
Background
- Authors
- Bonate, P. L., Strougo, A., Desai, A., Roy, M., Yassen, A., van der Walt, J. S., Kaibara, A., & Tannenbaum, S.
- Affiliations
- Astellas Pharma (United States) (US)
- Astellas Pharma (Netherlands) (NL)
- Astellas Pharma (Japan) (JP)
- Corresponding author
- Bonate, P. L.
- Venue
- The AAPS Journal
- Published
- 2012-07-24
- Volume
- 14
- Issue
- 4
- Pages
- 749-758
- ISSN
- 1550-7416 (ISSN-L)
- PMC
- PMC3475847
- OpenAlex
- W2084152398
- Cited by
- 39
- References
- 12
- Citations by year
- 2026: 4 · 2025: 4 · 2024: 4 · 2023: 4 · 2022: 3 · 2021: 3 · 2020: 4 · 2019: 3 · 2016: 1 · 2015: 6 · 2014: 2 · 2013: 1
- FWCI
- 2.53
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- The AAPS Journal (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- 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
- quality control, population pharmacokinetics, pharmacodynamics, NONMEM, quality assurance, model validation, reporting guidelines, model review, industrial practice
- MeSH terms
- Drug Industry, Humans, Models, Biological, Quality Control, Drug Design, Guidelines as Topic, Nonlinear Dynamics
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