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.

ABSTRACT: 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 (f_e) and renal clearance (CL_r), with datasets of 411 and 401 compounds using freely available software; notably, all models require chemical structure information alone. The binary classification model for f_e demonstrated a balanced accuracy of 0.74. The two-step prediction system for CL_r was generated using a combination of the classification model to predict

SUBMITTER: Watanabe R 

PROVIDER: S-EPMC6906481 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature