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A Promising New Approach for In Silico Prediction of Drug Concentration Profiles for Drug Candidates Lack of Experimental Pharmacokinetic Data
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  • Jingchen Zhai,
  • Beihong Ji,
  • Shuhan Liu,
  • Yuzhao Zhang,
  • Junmei Wang
Jingchen Zhai
University of Pittsburgh School of Pharmacy
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Beihong Ji
University of Pittsburgh School of Pharmacy
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Shuhan Liu
University of Pittsburgh School of Pharmacy
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Yuzhao Zhang
University of Pittsburgh School of Pharmacy
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Junmei Wang
University of Pittsburgh
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Abstract

Aim: The purpose of this study is to develop a novel protocol to predict the concentration profiles of a target drug based on the PBPK model of a structurally similar template drug by combining two software for PBPK modeling, the SimCYP simulator and ADMET Predictor. Methods: The method was evaluated by utilizing 13 drug pairs which come from 18 drugs in the built-in database of the SimCYP software. All drug pairs have their Tanimoto scores no less than 0.5. Three versions (V1, V2 and V3) of models for the target drug were constructed by gradually replacing the corresponding parameters of the template drug with those predicted by ADME Predictor for the target drug. Normalized RMSE and Wilcoxon rank-sum test were introduced for the evaluation of the model performance. Results: Overall, V3 models demonstrated better performance than the V1 and V2 models did. The relationship between the model performance and structural similarity of drug pairs was also explored. Three protocols have come out as guidance on how to build PBPK models for target drugs: (1) V1 models are recommended when the structural similarity is very high; (2) V2 models are recommended when the similarity is below 0.65 or high than 0.85; (3) V3 models are recommended when the similarity is below 0.85. Conclusion: By leveraging the prediction accuracy and application practicality, this novel approach has a great promise in predicting the preliminary PK profiles for novel drugs, propelling the drug discovery process by suggesting drug candidates with promising PK profiles.