Negative Modeling Results: A Dime a Dozen or a Stepping Stone to Scientific Discovery?
Open accessAbstract
CPT: Pharmacometrics & Systems Pharmacology (2013) 2, e48; doi:10.1038/psp.2013.28; advance online publication 12 June 2013
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A disagreement between simulations and experimental observations, assuming the observations are directly pertinent, highlights that one or some of the underlying assumptions in the model are incorrect.
“A disagreement between simulations and experimental observations, assuming experimental observations can be considered to be directly pertinent, highlights the fact that one or some of the underlying assumptions in the model are incorrect.77”
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 [77]
In “Model Validation: Credibility vs. Evaluation” (page not provided), the paper says that observations can discredit proposed mechanisms that are inconsistent with data. It does not say that every disagreement establishes an incorrect model assumption; it asks whether a mismatch is a model shortcoming or a scientific finding. “The Value of Negative (Modeling) Results” also stresses the need to rule out technical implementation failure. (LLM, conf=0.84)
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Cites
Darwich, A. S., Pade, D., Rowland‐Yeo, K., Jamei, M., Åsberg, A., Christensen, H., Ashcroft, D. M., & Rostami‐Hodjegan, A. (2013) · CPT Pharmacometrics & Systems Pharmacology · doi:10.1038/psp.2013.23 · Cited by 29 · see citing works
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Recent developments in using mechanistic cardiac modelling for drug safety evaluation
Davies, M. R., Wang, K., Mirams, G. R., Caruso, A., Noble, D., Walz, A., Lavé, T., Schuler, F., Singer, T., & Polonchuk, L. (2016) · Drug Discovery Today · doi:10.1016/j.drudis.2016.02.003 · Cited by 66 · see citing works
Cucurull‐Sanchez, L., Chappell, M. J., Chelliah, V., Amy Cheung, S. Y., Derks, G., Penney, M., Phipps, A., Malik‐Sheriff, R. S., Timmis, J., Tindall, M. J., et al. (2019) · CPT: Pharmacometrics & Systems Pharmacology · doi:10.1002/psp4.12381 · Cited by 61 · see citing works
“, “our current understanding of the drug target biology does not explain the observed in vitro phenomena quantitatively”), which generates a revision of the initial hypothesis; the QSP model can then help identify the key questions needed to expand our knowledge sufficiently and design the studies required to inform t…”
BackgroundMethodologies for Quantitative Systems Pharmacology (QSP) Models: Design and Estimation
Ribba, B., Grimm, H., Agoram, B., Davies, M., Gadkar, K., Niederer, S., van Riel, N., Timmis, J., & van der Graaf, P. (2017) · CPT: Pharmacometrics & Systems Pharmacology · doi:10.1002/psp4.12206 · Cited by 43 · see citing works
Agoram, B. (2014) · CPT: Pharmacometrics & Systems Pharmacology · doi:10.1038/psp.2013.77 · Cited by 30 · see citing works
Benson, N., Metelkin, E., Demin, O. O., Li, G., Nichols, D., & van der Graaf, P. (2014) · CPT Pharmacometrics & Systems Pharmacology · doi:10.1038/psp.2013.72 · Cited by 26 · see citing works
Assessing the performance of QSP models: biology as the driver for validation
Singh, F. A., Afzal, N., Smithline, S. J., & Thalhauser, C. J. (2024) · Journal of Pharmacokinetics and Pharmacodynamics · doi:10.1007/s10928-023-09871-x · Cited by 15 · see citing works
- Authors
- Hendriks, B.
- Affiliations
- Merrimack Pharmaceuticals (United States) (US)
- Corresponding author
- Hendriks, B
- ORCID
- Hendriks, B
- Venue
- CPT: Pharmacometrics & Systems Pharmacology
- Publisher
- Wiley
- Published
- 2013-06
- Volume
- 2
- Issue
- 6
- Pages
- 1-2
- ISSN
- 2163-8306 (ISSN-L)
- PMC
- PMC3697039
- OpenAlex
- W2026029476
- Cited by
- 6
- References
- 1
- Citations by year
- 2023: 1 · 2019: 1 · 2017: 1 · 2016: 1 · 2014: 2
- FWCI
- 0.88
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- CPT Pharmacometrics & Systems Pharmacology (publishedVersion, cc-by-nc-nd)
- Europe PMC (PubMed Central) (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Health Sciences › Medicine › Pediatrics, Perinatology and Child Health › Pharmaceutical studies and practices
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Keywords
- scientific discovery
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