التفاصيل البيبلوغرافية
| العنوان: |
Computational Predictions of Nonclinical Pharmacokinetics at the Drug Design Stage |
| المؤلفون: |
Raya Stoyanova (14322613), Paul Maximilian Katzberger (14322616), Leonid Komissarov (10798712), Aous Khadhraoui (14322619), Lisa Sach-Peltason (1747699), Katrin Groebke Zbinden (14322622), Torsten Schindler (3013920), Nenad Manevski (276406) |
| سنة النشر: |
2023 |
| مصطلحات موضوعية: |
Biochemistry, Genetics, Biological Sciences not elsewhere classified, Mathematical Sciences not elsewhere classified, Information Systems not elsewhere classified, gaussian process regressor, drug design stage, chemical structure alone, average fold error, ten machine learning, including plasma clearance, chemprop neural networks, discovery data set, data set sizes, 98 – 1, 96 – 2, 45 – 1, 35 – 2, computed prediction variance, shapley additive explanations, emphasized human interpretability, e ., pretraining, combined results show, rat pk studies, transfer learning approaches, higher overprediction bias, best aafe values, clp ), volume |
| الوصف: |
Although computational predictions of pharmacokinetics (PK) are desirable at the drug design stage, existing approaches are often limited by prediction accuracy and human interpretability. Using a discovery data set of mouse and rat PK studies at Roche (9,685 unique compounds), we performed a proof-of-concept study to predict key PK properties from chemical structure alone, including plasma clearance (CLp), volume of distribution at steady-state (Vss), and oral bioavailability (F). Ten machine learning (ML) models were evaluated, including Single-Task, Multitask, and transfer learning approaches (i.e., pretraining with in vitro data). In addition to prediction accuracy, we emphasized human interpretability of outcomes, especially the quantification of uncertainty, applicability domains, and explanations of predictions in terms of molecular features. Results show that intravenous (IV) PK properties (CLp and Vss) can be predicted with good precision (average absolute fold error, AAFE of 1.96–2.84 depending on data split) and low bias (average fold error, AFE of 0.98–1.36), with AutoGluon, Gaussian Process Regressor (GP), and ChemProp displaying the best performance. Driven by higher complexity of oral PK studies, predictions of F were more challenging, with the best AAFE values of 2.35–2.60 and higher overprediction bias (AFE of 1.45–1.62). Multi-Task approaches and pretraining of ChemProp neural networks with in vitro data showed similar precision to Single-Task models but helped reduce the bias and increase correlations between observations and predictions. A combination of GP-computed prediction variance, molecular clustering, and dimensionality-reduction provided valuable quantitative insights into prediction uncertainty and applicability domains. SHAPley Additive exPlanations (SHAPs) highlighted molecular features contributing to prediction outcomes of Vss, providing explanations that could aid drug design. Combined results show that computational predictions of PK are feasible at the drug design stage, with ... |
| نوع الوثيقة: |
article in journal/newspaper |
| اللغة: |
unknown |
| العلاقة: |
https://figshare.com/articles/journal_contribution/Computational_Predictions_of_Nonclinical_Pharmacokinetics_at_the_Drug_Design_Stage/21809572Test |
| DOI: |
10.1021/acs.jcim.2c01134.s001 |
| الإتاحة: |
https://doi.org/10.1021/acs.jcim.2c01134.s001Test |
| حقوق: |
CC BY-NC 4.0 |
| رقم الانضمام: |
edsbas.31ACD156 |
| قاعدة البيانات: |
BASE |
