Referencehttps://link.springer.com/article/10.1007/s00204-024-03764-9
Geci R, Gadaleta D, Lomana MG de, Ortega-Vallbona R, Colombo E, Serrano-Candelas E, Paini A, Kuepfer L, Schaller S (2024) Systematic evaluation of high-throughput PBK modelling strategies for the prediction of intravenous and oral pharmacokinetics in humans. Arch Toxicol:1–18. doi: 10.1007/s00204-024-03764-9
Abstract
Physiologically based kinetic (PBK) modelling offers a mechanistic basis for predicting the pharmaco-/toxicokinetics of
compounds and thereby provides critical information for integrating toxicity and exposure data to replace animal testing
with in vitro or in silico methods. However, traditional PBK modelling depends on animal and human data, which limits
its usefulness for non-animal methods. To address this limitation, high-throughput PBK modelling aims to rely exclusively
on in vitro and in silico data for model generation. Here, we evaluate a variety of in silico tools and different strategies to
parameterise PBK models with input values from various sources in a high-throughput manner. We gather 2000 + publicly
available human in vivo concentration–time profiles of 200 + compounds (IV and oral administration), as well as in silico,
in vitro and in vivo determined compound-specific parameters required for the PBK modelling of these compounds. Then, we
systematically evaluate all possible PBK model parametrisation strategies in PK-Sim and quantify their prediction accuracy
against the collected in vivo concentration–time profiles. Our results show that even simple, generic high-throughput PBK
modelling can provide accurate predictions of the pharmacokinetics of most compounds (87% of Cmax and 84% of AUC
within tenfold). Nevertheless, we also observe major differences in prediction accuracies between the different parameterisa-
tion strategies, as well as between different compounds. Finally, we outline a strategy for high-throughput PBK modelling
that relies exclusively on freely available tools. Our findings contribute to a more robust understanding of the reliability of
high-throughput PBK modelling, which is essential to establish the confidence necessary for its utilisation in Next-Generation
Risk Assessment.
Copyright
Open Access. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Reference https://link.springer.com/article/10.1007/s00204-024-03764-9 Geci R, Gadaleta D, Lomana MG de, Ortega-Vallbona R, Colombo E, Serrano-Candelas E, Paini A, Kuepfer L, Schaller S (2024) Systematic evaluation of high-throughput PBK modelling strategies for the prediction of intravenous and oral pharmacokinetics in humans. Arch Toxicol:1–18. doi: 10.1007/s00204-024-03764-9
Abstract
Physiologically based kinetic (PBK) modelling offers a mechanistic basis for predicting the pharmaco-/toxicokinetics of compounds and thereby provides critical information for integrating toxicity and exposure data to replace animal testing with in vitro or in silico methods. However, traditional PBK modelling depends on animal and human data, which limits its usefulness for non-animal methods. To address this limitation, high-throughput PBK modelling aims to rely exclusively on in vitro and in silico data for model generation. Here, we evaluate a variety of in silico tools and different strategies to parameterise PBK models with input values from various sources in a high-throughput manner. We gather 2000 + publicly available human in vivo concentration–time profiles of 200 + compounds (IV and oral administration), as well as in silico, in vitro and in vivo determined compound-specific parameters required for the PBK modelling of these compounds. Then, we systematically evaluate all possible PBK model parametrisation strategies in PK-Sim and quantify their prediction accuracy against the collected in vivo concentration–time profiles. Our results show that even simple, generic high-throughput PBK modelling can provide accurate predictions of the pharmacokinetics of most compounds (87% of Cmax and 84% of AUC within tenfold). Nevertheless, we also observe major differences in prediction accuracies between the different parameterisa- tion strategies, as well as between different compounds. Finally, we outline a strategy for high-throughput PBK modelling that relies exclusively on freely available tools. Our findings contribute to a more robust understanding of the reliability of high-throughput PBK modelling, which is essential to establish the confidence necessary for its utilisation in Next-Generation Risk Assessment.
Keywords Physiologically based kinetic (PBK) modelling · New approach methodologies (NAMs) · Next-generation risk assessment (NGRA) · High-throughput PBK modelling · Pharmacokinetics
Copyright Open Access. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.