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In Silico Prediction of Pharmacokinetic Profile for Human Oral Drug Candidates Which Lack Clinical Pharmacokinetic Experiment Data

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European Journal of Drug Metabolism and Pharmacokinetics Aims and scope Submit manuscript

Abstract

Backgrounds and Objectives

In silico methods which can generate high-quality physiologically based pharmacokinetic (PBPK) models for arbitrary drug candidates are greatly needed to select developable drug candidates that escape drug attrition because of the poor pharmacokinetic profile. The purpose of this study is to develop a novel protocol to preliminarily predict the concentration profile of a target drug based on the PBPK model of a structurally similar template drug by combining two software platforms for PBPK modeling, the SimCYP simulator and ADMET Predictor.

Methods

The method was evaluated by utilizing 13 drug pairs from 18 drugs in the built-in database of the SimCYP software. All drug pairs have Tanimoto scores (TS) no less than 0.5. As each drug in a drug pair can serve as both target and template, 26 sets were studied in this work. Three versions (V1, V2 and V3) of models for the target drug were constructed by replacing the corresponding parameters of the template drug step by step with those predicted by ADMET Predictor for the target drug. V1 represents the replacement of molecular weight (MW), V2 includes the replacement of parameter MW, fraction unbound in plasma (fu), blood-to-plasma partition ratio (B/P), logarithm of the octanol-buffer partition coefficient (log Po:w) and acid dissociation constant (pKa). In V3, all above-mentioned parameters as well as human jejunum effective permeability (Peff), Vd and cytochrome P450 (CYP) metabolism parameters (Km, Vmax or CLint) are modified. Normalized root mean square error (NRMSE) was used for the evaluation of the model performance.

Results

We found that the performance of the three versions of the models depends on structural similarity of the drug pairs. For Group I drug pairs (TS ≤ 0.7), V2 and V3 performed better than V1 in terms of NRMSE; for Group II drug pairs (0.7 < TS ≤ 0.9), 8 out of 10 V3 models had NRMSE < 0.2, the cutoff we applied to judge whether the simulated concentration-time (C–T) curve was satisfactory or not. V3 outperformed the V1 and V2 versions. For the two drug pairs belonging to Group III (TS > 0.9), V2 outperformed V1 and V3, suggesting more unnecessary replacement can lower the performance of PBPK models. We also investigated how the prediction accuracy of ADMET Predictor as well as its collaboration with SimCYP influences the quality of PBPK models constructed using SimCYP.

Conclusion

In conclusion, we generated practical guidance on applying two mainstream software packages, ADMET Predictor and SimCYP, to construct PBPK models for drugs or drug candidates that lack ADME parameters in model construction.

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Author notes

  1. Jingchen Zhai and Beihong Ji contribute equally to this paper.

Authors and Affiliations

  1. Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy, University of Pittsburgh, 06407 Salk Hall, Terrace Street, Pittsburgh, PA, 15261, USA

    Jingchen Zhai, Beihong Ji, Shuhan Liu, Yuzhao Zhang, Lianjin Cai & Junmei Wang

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  1. Jingchen Zhai
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  2. Beihong Ji
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  3. Shuhan Liu
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Corresponding author

Correspondence to Junmei Wang.

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Conflict of interest

The authors declare no competing interests.

Funding

The authors gratefully acknowledge the funding support from the National Science Foundation (1955260) and National Institutes of Health of the USA (R01GM079383, R21GM097617 and P30DA035778).

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All data have been provided in the manuscript and supplemental information.

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Author contributions

JZ, JW and SL designed the research. JZ and BJ performed the research and analyzed the data, all authors wrote the manuscript.

Supplementary Information

Below is the link to the electronic supplementary material.

13318_2022_758_MOESM1_ESM.docx

The input parameters of 18 drugs and predicted pharmacokinetic profiles of models in V1-V3 are listed in Table S1-S3. (DOCX 43 kb)

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Zhai, J., Ji, B., Liu, S. et al. In Silico Prediction of Pharmacokinetic Profile for Human Oral Drug Candidates Which Lack Clinical Pharmacokinetic Experiment Data. Eur J Drug Metab Pharmacokinet 47, 403–417 (2022). https://doi.org/10.1007/s13318-022-00758-9

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  • DOI: https://doi.org/10.1007/s13318-022-00758-9

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