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. 2021 Oct 27;49(2):257–270. doi: 10.1007/s10928-021-09793-6

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© The Author(s) 2021

Open AccessThis 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/.

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Fig. 3 — Example of training output parameters for the two NN tasks. CL clearance, Fr bioavailability, Ka 1 order absorption, Km and Vm Michaelis–Menten elimination, Ktr transitory compartment, Mtt transitory compartment, NN neural network, Q2 and Q3 inter-compartmental clearance, TK0 0 order absorption, T_lag lag time, V volume for central compartment, V2 volume for second compartment, V3 volume for third compartment. Note For the regression task, individual pharmacokinetic parameters constitute the output to be predicted (top table). Data for the classification task can be derived from this by combining parameters into model components binarily labeled according to their presence or absence (bottom table)