論文

査読有り 国際誌
2019年12月11日

Development of an in silico prediction system of human renal excretion and clearance from chemical structure information incorporating fraction unbound in plasma as a descriptor.

Scientific reports
  • Reiko Watanabe
  • ,
  • Rikiya Ohashi
  • ,
  • Tsuyoshi Esaki
  • ,
  • Hitoshi Kawashima
  • ,
  • Yayoi Natsume-Kitatani
  • ,
  • Chioko Nagao
  • ,
  • Kenji Mizuguchi

9
1
開始ページ
18782
終了ページ
18782
記述言語
英語
掲載種別
研究論文(学術雑誌)
DOI
10.1038/s41598-019-55325-1

Prediction of pharmacokinetic profiles of new chemical entities is essential in drug development to minimize the risks of potential withdrawals. The excretion of unchanged compounds by the kidney constitutes a major route in drug elimination and plays an important role in pharmacokinetics. Herein, we created in silico prediction models of the fraction of drug excreted unchanged in the urine (fe) and renal clearance (CLr), with datasets of 411 and 401 compounds using freely available software; notably, all models require chemical structure information alone. The binary classification model for fe demonstrated a balanced accuracy of 0.74. The two-step prediction system for CLr was generated using a combination of the classification model to predict excretion-type compounds and regression models to predict the CLr value for each excretion type. The accuracies of the regression models increased upon adding a descriptor, which was the observed and predicted fraction unbound in plasma (fu,p); 78.6% of the samples in the higher range of renal clearance fell within 2-fold error with predicted fu,p value. Our prediction system for renal excretion is freely available to the public and can be used as a practical tool for prioritization and optimization of compound synthesis in the early stage of drug discovery.

リンク情報
DOI
https://doi.org/10.1038/s41598-019-55325-1
PubMed
https://www.ncbi.nlm.nih.gov/pubmed/31827176
PubMed Central
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6906481
URL
http://orcid.org/0000-0003-3021-7078

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