Predicting Pharmacokinetic Profiles Using in Silico Derived Parameters
Abstract
Human pharmacokinetic (PK) predictions play a critical role in assessing the quality of potential clinical candidates where the accurate estimation of clearance, volume of distribution, bioavailability, and the plasma-concentration-time profiles are the desired end points. While many methods for conducting predictions utilize in vivo data, predictions can be conducted successfully from in vitro or in silico data, applying modeling and simulation techniques. This approach can be facilitated using commercially available prediction software such as GastroPlus which has been reported to accurately predict the oral PK profile of small drug-like molecules. Herein, case studies are described where GastroPlus modeling and simulation was employed using in silico or in vitro data to predict PK profiles in early discovery. The results obtained demonstrate the feasibility of adequately predicting plasma-concentration-time profiles with in silico derived as well as in vitro measured parameters and hence predicting PK profiles with minimal data. The applicability of this approach can provide key information enabling decisions on either dose selection, chemistry strategy to improve compounds, or clinical protocol design, thus demonstrating the value of modeling and simulation in both early discovery and exploratory development for predicting absorption and disposition profiles.
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Success in predicting clearance during early discovery was recently demonstrated by applying QSPR models within a chemical series.
“Predicting clearance during early discovery remains challenging [18, 26]; however, success was recently demonstrated by applying QSPR models within a chemical series [24, 26].”
Accurate prediction of pharmacokinetics using many properties predicted from a structure may be possible but is not guaranteed, and verification with compounds from each chemical class has been recommended.
“Although accurate prediction of pharmacokinetics using many properties predicted from a structure may be possible [23], it is not guaranteed [24] and verification with compounds from each chemical class has been recommended [25].”
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- Authors
- Hosea, N. A., & Jones, H. M.
- Affiliations
- Pfizer (United States) (US)
- Corresponding author
- Hosea, Natalie A.
- ORCID
- Jones, Hannah M.
- Venue
- Molecular Pharmaceutics
- Publisher
- American Chemical Society (ACS)
- Published
- 2013-03-07
- Volume
- 10
- Issue
- 4
- Pages
- 1207-1215
- ISSN
- 1543-8384 (ISSN-L)
- 1543-8392
- OpenAlex
- W2004576565
- Cited by
- 65
- References
- 22
- Citations by year
- 2026: 5 · 2025: 3 · 2024: 4 · 2023: 6 · 2022: 6 · 2021: 4 · 2020: 3 · 2019: 7 · 2018: 6 · 2017: 8 · 2016: 3 · 2015: 3 · 2014: 3 · 2013: 4
- FWCI
- 4.74
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Molecular Pharmaceutics (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Pharmaceutical Science › Drug Solubulity and Delivery Systems
- Health Sciences › Medicine › Pediatrics, Perinatology and Child Health › Pharmaceutical studies and practices
- Keywords
- pharmacokinetic prediction, in silico modeling, GastroPlus, oral drug absorption, plasma concentration-time curve, in vitro-in vivo extrapolation, drug clearance, volume of distribution, bioavailability, early drug discovery, dose selection, drug-likeness
- MeSH terms
- Administration, Oral, Animals, Biological Availability, Computer Simulation, Humans, Models, Chemical, Permeability, Pharmacokinetics, Software, Solubility, Tetrahydronaphthalenes, Valine, Drug Design, Area Under Curve, Mice
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