Suein Choi(1, 2), Sungpil Han(1) So Jin Lee(2, 3) Byunghee Lim(2) Soo Hyeon Bae(3) Seunghoon Han(1,2) Dong-Seok Yim(1,2)
(1) Pharmacometrics Institute for Practical Education and Training (PIPET), The Catholic University of Korea (2) Department of Pharmacology, The Catholic University of Korea (3)Q-fitter Inc., Seoul 06578, Republic of Korea
Objectives: In silico experiments and simulations using physiologically based pharmacokinetic (PBPK) and allometric approaches have played an important role in pharmaceutical research and drug development. These methods integrate diverse data from preclinical and clinical development, and have been widely applied to in vitro–in vivo extrapolation (IVIVE) of absorption, distribution, metabolism, and excretion (ADME). Commercial PBPK platforms or relevant services are costly and generally demand a large amount of input data, but their prediction methods or key references are not fully known to users. This software is designed to support the prompt switching from allometric scaling to PBPK methods, and vice versa. Researchers can conduct extrapolations across species and generate simulations of PK profiles under multiple physiological conditions. Most of all, we have focused on enabling the software to provide affordability and accessibility to researchers in academia or industry with the latest knowledge on human PK prediction. Currently, DallphinAtoM is freely downloadable (https://github.com/pipetcpt/dallphin/releases).
Methods: The current version of the DallphinAtoM (version 0.9.4, www.pipet.or.kr/board/apps_list.asp) works on Microsoft Windows 7 (64-bit) or later versions. It implements openJDK (version 1.8.0) and Java Runtime Environment (JRE, version 1.8.0). For data security, the results are completely calculated on a local computer running JRI (Java/R Interface), in conjunction with R statistical software and rJava.The source code for DallphinAtoM is written using R software (version 3.4.4, R Core Team, R Foundation for Statistical Computing), a suite of libraries of statistical and mathematical computations. Among various references reporting PBPK models and allometric methods for predicting human PK parameters, models and equations which are frequently used and cited by researchers were selected to build up DallphinAtoM. All the references are written in its manual and corresponding R scripts are accessible in the sub-directory (/DallphinAtoM/R/code) after installation. To develop a user-friendly open tool predicting human PK, we assessed various references on PBPK and allometric methods published so far. They were integrated into a software system named “DallphinAtoM” (Drugs with ALLometry and PHysiology Inside-Animal to huMan), which has a user-friendly platform that can handle complex PBPK models and allometric models with a relatively small amount of essential information of the drug. DallphinAtoM can predict human PK parameters and simulate plasma drug concentrations based on physicochemical properties, in vitro experiment data and in vivo animal PK data. The models of DallphinAtoM support the integration of data gained during the nonclinical development phase such as physicochemical properties, caco-2 permeability data, microsomal/hepatocyte intrinsic clearance and in vivo animal PK parameters, and enable translation from animal to human. It also allows the prediction of concentration-time profiles with predicted PK parameters.
Results: We presented two illustrative applications using DallphinAtoM: (1) human PK simulation of an orally administered drug using PBPK method; and (2) simulation of intravenous infusion following a two-compartment model using the allometric scaling method.
Conclusions: We conclude that this is a straightforward and transparent tool allowing fast and reliable human PK simulation based on the latest knowledge on biochemical processes and physiology and provides valuable information for decision making during the early-phase drug development.
References:
[1] A. Rostami-Hodjegan Physiologically based pharmacokinetics joined with in vitro–in vivo extrapolation of ADME: a marriage under the arch of systems pharmacology Clin. Pharmacol. Ther., 92 (2012), pp. 50-61
[2] H. Jones, K. Rowland-Yeo Basic concepts in physiologically based pharmacokinetic modeling in drug discovery and development CPT Pharm. Syst. Pharmacol., 2 (2013), p. e63
[3] G.W. Choi, Y.B. Lee, H.Y. Cho Interpretation of non-clinical data for prediction of human pharmacokinetic parameters: in vitro–in vivo extrapolation and allometric scaling Pharmaceutics, 11 (2019)
[4] S. Choi, S. Han, S.J. Lee,B. Lim, S.H. Bae, S. Han, DS Yim, DallphinAtoM: Physiologically based pharmacokinetics software predicting human PK parameters based on physicochemical properties, in vitro and animal in vivo data, Computer Methods and Programs in Biomedicine,
216 (2022)
Reference: PAGE 30 (2022) Abstr 10124 [www.page-meeting.org/?abstract=10124]
Poster: Methodology - New Modelling Approaches