Prospective Predictions of Human Pharmacokinetics for Eighteen Compounds

Zhang, Tao; Heimbach, Tycho; Lin, Wen; Zhang, Jin; He, Handan

doi:10.1002/jps.24373

Cited by 56Drug Solubulity and Delivery SystemsPharmaceutical studies and practicesComputational Drug Discovery Methods

Prospective Predictions of Human Pharmacokinetics for Eighteen Compounds

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Zhang, Tao, Heimbach, Tycho, Lin, Wen, Zhang, Jin, He, Handan·2015·Journal of Pharmaceutical Sciences

DOI 10.1002/jps.24373

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Abstract

Quantitative predictions of pharmacokinetics (PKs) and concentration-time profiles using in vitro and in vivo preclinical data are critical to estimate systemic exposures for first-in-human studies. Prospective prediction accuracies of human PKs for 18 compounds across all Biopharmaceutics Classification System/Biopharmaceutics Drug Disposition Classification System classes were evaluated. The a priori predicted profiles were then compared with clinical profiles. Predictions were conducted using advanced compartmental absorption and transit (ACAT) physiology based PK models. Human intravenous profiles were predicted with in vivo preclinical intravenous data using Wajima formulas. Human oral profiles were generated by combining intravenous PKs together with either physiologically based oral ACAT models utilizing solubility and permeability data or by using the average bioavailability (F) and absorption rate constant (ka ) from preclinical species. Key PK parameters evaluated were the maximum plasma concentration (Cmax ), the area under the plasma concentration-time curve (AUC), CL/F, and Vdss /F. A decision tree was provided to guide human PK and ACAT predictions. Our prospective human PK prediction methods yielded good prediction results. The predictions were within a twofold error for 80% (Cmax ), 65% (AUC), 65% (CL/F), and 80% (Vz /F) of the compounds. The methods described can be readily implemented with available in vitro and in vivo data during early drug development.

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Prediction accuracy is optimised by considering all available preclinical data, filling identified data gaps and verifying the preclinical PBPK model.
“Prediction accuracy is optimised by considering all available preclinical data [19], filling identified data gaps and verifying the preclinical PBPK model [18].”
From Physiologically Based Pharmacokinetic Modelling for First-In-Human Predictions: An Updated Model Building Strategy Illustrated with Challenging Industry Case Studies (2019) · cites this paper as [19]
Several publications from industry groups have confirmed the superiority of PBPK modelling for predicting pharmacokinetics.
“Since then, several publications from industry groups have confirmed the superiority of PBPK modelling for this application [17-19]”
From Physiologically Based Pharmacokinetic Modelling for First-In-Human Predictions: An Updated Model Building Strategy Illustrated with Challenging Industry Case Studies (2019) · cites this paper as [19]
If BCS class is predicted as I and II, mechanistic oral absorption modelling predictions may be straightforward, while class III and IV compounds may be more challenging.
“Thus, if the Biopharmaceutics Classification System (BCS) [27] class is predicted as I and II, mechanistic oral absorption modelling predictions may be straightforward, while class III and IV compounds may be more challenging [19, 28, 29].”
From Physiologically Based Pharmacokinetic Modelling for First-In-Human Predictions: An Updated Model Building Strategy Illustrated with Challenging Industry Case Studies (2019) · cites this paper as [19]
Use of deconvoluted biorelevant gut solubility based on modelling in preclinical species is supported by good predictions for a compound set, including all BCS/BDDCS classes.
“Use of deconvoluted biorelevant gut solubility based on modelling in preclinical species is supported by good predictions for a compound set, including all BCS/Biopharmaceutics Drug Disposition Classification classes [19].”
From Physiologically Based Pharmacokinetic Modelling for First-In-Human Predictions: An Updated Model Building Strategy Illustrated with Challenging Industry Case Studies (2019) · cites this paper as [19]

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Authors: Zhang, T., Heimbach, T., Lin, W., Zhang, J., & He, H.
Corresponding author: He, Handan
ORCID: Zhang, Tao
Heimbach, Tycho
Lin, Wen
Zhang, Jin
Venue: Journal of Pharmaceutical Sciences
Publisher: Elsevier BV
Published: 2015-09
Volume: 104
Issue: 9
Pages: 2795-2806
ISSN: 0022-3549 (ISSN-L)
DOI: 10.1002/jps.24373
OpenAlex: W2144951695
Cited by: 56
References: 49
Citations by year: 2026: 3 · 2025: 6 · 2024: 4 · 2023: 8 · 2022: 5 · 2021: 7 · 2020: 5 · 2019: 3 · 2018: 4 · 2017: 1 · 2016: 6 · 2015: 4
FWCI: 2.79
Type: journal article
Language: en
Open access: green
www.sciencedirect.com
Licence: cc-by-nc-nd
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
Locations: Journal of Pharmaceutical Sciences (publishedVersion)
PubMed (publishedVersion)
The Novartis Repository (Novartis) (submittedVersion, cc-by-nc-nd)
The Novartis Repository (Novartis) (submittedVersion)
Topics: 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
Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
Keywords: human pharmacokinetic prediction, concentration-time profiles, oral bioavailability, Biopharmaceutics Classification System
MeSH terms: Algorithms, Biological Availability, Biopharmaceutics, Chemistry, Pharmaceutical, Computer Simulation, Pharmaceutical Preparations, Humans, Intestinal Absorption, Models, Biological, Permeability, Pharmacokinetics, Predictive Value of Tests, Solubility, Reproducibility of Results, Area Under Curve

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Prospective Predictions of Human Pharmacokinetics for Eighteen Compounds

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