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Cited by 22Pharmaceutical studies and practicesPharmacogenetics and Drug MetabolismStatistical Methods in Clinical Trials

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Development and Qualification of Physiologically Based Pharmacokinetic Models for Drugs With Atypical Distribution Behavior: A Desipramine Case Study

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Samant, TS, Lukacova, V, Schmidt, S·2017·CPT: Pharmacometrics & Systems Pharmacology
DOI 10.1002/psp4.12180PMC PMC5697013
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Abstract

Desipramine is a secondary tricyclic amine, which is primarily metabolized by cytochrome 2D6. It shows a high volume of distribution (Vss) (10–50 L/kg) due to its high lipophilicity, unspecific phospholipid binding, and lysosomal trapping. The objective of this study was to develop and qualify a physiologically based pharmacokinetic (PBPK) model for desipramine, which accounts for the high Vss of the drug following intravenous and oral administration of doses up to 100 mg. The model also accounts for the extended time to reach maximum concentration after oral dosing due to enterocyte trapping. Once developed and qualified in adults, we characterized the dynamic changes in metabolism and pharmacokinetics of desipramine after birth by scaling the system‐specific parameters of the model from adults to pediatrics. The developed modeling strategy provides a prototypical workflow that can also be applied to other drugs with similar properties and a high volume of distribution.

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Figure 1 Model building and qualification workflow for the establishment of an age‐ranging desipramine PBPK model.
Figure 1 Model building and qualification workflow for the establishment of an age‐ranging desipramine PBPK model.CC BY-NC · PubMed Central
Figure 2 Comparison of simulated (lines) and observed (points) plasma concentration–time profiles of desipramine following i.v. administration: (a) mean simulated Cp‐time profile after 12.5 mg i.v. bolus administration used for model building; (b) population simulation (mean, 2.5–97.5th percentiles) for 12.5 mg i.v. bolus administration used for model building; (c) mean simulated Cp‐time profile after 12.5 mg i.v. bolus administration without increased volume of distribution; (d) mean predicted Cp‐time profile for 50 mg i.v. bolus administration used for external model qualification; (e) population simulation (mean, 2.5–97.5th percentiles) for 50 mg i.v. bolus administration used for external model qualification. Observed data are obtained from refs. 22, 23.
Figure 2 Comparison of simulated (lines) and observed (points) plasma concentration–time profiles of desipramine following i.v. administration: (a) mean simulated Cp‐time profile after 12.5 mg i.v. bolus administration used for model building; (b) population simulation (mean, 2.5–97.5th percentiles) for 12.5 mg i.v. bolus administration used for model building; (c) mean simulated Cp‐time profile after 12.5 mg i.v. bolus administration without increased volume of distribution; (d) mean predicted Cp‐time profile for 50 mg i.v. bolus administration used for external model qualification; (e) population simulation (mean, 2.5–97.5th percentiles) for 50 mg i.v. bolus administration used for external model qualification. Observed data are obtained from refs. 22, 23.CC BY-NC · PubMed Central
Figure 3 Comparison of simulated (lines) and observed (points) plasma concentration–time profiles of desipramine following oral administration: (a) mean simulated Cp‐time profile after 50 mg oral administration used for model building; (b) population simulation (mean, 2.5–97.5th percentiles) for 50 mg oral administration used for model building; (c) mean simulated Cp‐time profile after 50 mg oral administration with no phospholipid binding or lysosomal trapping in enterocytes; (d) mean predicted Cp‐time profile for 100 mg oral administration used for external model qualification; (e) population simulation (mean, 2.5–97.5th percentiles) for 100 mg oral administration used for external model qualification. Observed data are obtained from refs. 24, 25.
Figure 3 Comparison of simulated (lines) and observed (points) plasma concentration–time profiles of desipramine following oral administration: (a) mean simulated Cp‐time profile after 50 mg oral administration used for model building; (b) population simulation (mean, 2.5–97.5th percentiles) for 50 mg oral administration used for model building; (c) mean simulated Cp‐time profile after 50 mg oral administration with no phospholipid binding or lysosomal trapping in enterocytes; (d) mean predicted Cp‐time profile for 100 mg oral administration used for external model qualification; (e) population simulation (mean, 2.5–97.5th percentiles) for 100 mg oral administration used for external model qualification. Observed data are obtained from refs. 24, 25.CC BY-NC · PubMed Central
Figure 4 Changes in body weight normalized clearance as a function of age. Model‐based predictions for males (blue solid line) and females (red solid line) were overlaid with respective mean (±SD) literature data (CL 1,22 CL 2,23 CL 328).
Figure 4 Changes in body weight normalized clearance as a function of age. Model‐based predictions for males (blue solid line) and females (red solid line) were overlaid with respective mean (±SD) literature data (CL 1,22 CL 2,23 CL 328).CC BY-NC · PubMed Central
Figure 5 Simulated concentration–time curves of desipramine in males of different ages following the i.v. administration of 12.5 mg desipramine.
Figure 5 Simulated concentration–time curves of desipramine in males of different ages following the i.v. administration of 12.5 mg desipramine.CC BY-NC · PubMed Central

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Metadata

Authors
Samant, T., Lukacova, V, & Schmidt, S.
Affiliations
  • University of Florida (US)
  • Simulations Plus (United States) (US)
Corresponding author
Schmidt, S
ORCID
Schmidt, S
Venue
CPT: Pharmacometrics & Systems Pharmacology
Publisher
Wiley
Published
2017-04-11
Volume
6
Issue
5
Pages
315-321
ISSN
2163-8306 (ISSN-L)
DOI
10.1002/psp4.12180
PMC
PMC5697013
OpenAlex
W2606308747
Cited by
22
References
1
Citations by year
2026: 2 · 2025: 3 · 2024: 1 · 2023: 2 · 2021: 1 · 2020: 6 · 2019: 3 · 2018: 2 · 2017: 2
FWCI
3.88
Type
journal article
Language
en
Open access
Licence
Locations
  • CPT Pharmacometrics & Systems Pharmacology (publishedVersion, cc-by-nc)
  • PubMed Central (publishedVersion, cc-by-nc)
  • PubMed (publishedVersion)
Topics
  • Health Sciences › Medicine › Pediatrics, Perinatology and Child Health › Pharmaceutical studies and practices
  • Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Pharmacology › Pharmacogenetics and Drug Metabolism
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
Keywords
desipramine, physiologically based pharmacokinetic modeling, lysosomal trapping, phospholipid binding, lipophilicity, pediatric pharmacokinetics, allometric scaling, model qualification
MeSH terms
Adult, Antidepressive Agents, Tricyclic, Child, Desipramine, Humans, Models, Biological, Tissue Distribution

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CoChat. (2026). Citation check: Development and Qualification of Physiologically Based Pharmacokinetic Models for Drugs With Atypical Distribution Behavior: A Desipramine Case Study. https://app.cochat.ai/papers/10.1002/psp4.12180
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Samant, T., Lukacova, V, & Schmidt, S. (2017). Development and Qualification of Physiologically Based Pharmacokinetic Models for Drugs With Atypical Distribution Behavior: A Desipramine Case Study. CPT: Pharmacometrics & Systems Pharmacology. https://doi.org/10.1002/psp4.12180