Applying interpretable machine learning workflow to evaluate exposure-response relationships for large-molecule oncology drugs.
CPT Pharmacometrics Syst Pharmacol
; 11(12): 1614-1627, 2022 12.
Article
em En
| MEDLINE
| ID: mdl-36193885
ABSTRACT
The application of logistic regression (LR) and Cox Proportional Hazard (CoxPH) models are well-established for evaluating exposure-response (E-R) relationship in large molecule oncology drugs. However, applying machine learning (ML) models on evaluating E-R relationships has not been widely explored. We developed a workflow to train regularized LR/CoxPH and tree-based XGboost (XGB) models, and derive the odds ratios for best overall response and hazard ratios for overall survival, across exposure quantiles to evaluate the E-R relationship using clinical trial datasets. The E-R conclusions between LR/CoxPH and XGB models are overall consistent, and largely aligned with historical pharmacometric analyses findings. Overall, applying this interpretable ML workflow provides a promising alternative method to assess E-R relationships for impacting key dosing decisions in drug development.
Texto completo:
1
Coleções:
01-internacional
Temas:
Geral
Base de dados:
MEDLINE
Assunto principal:
Aprendizado de Máquina
Tipo de estudo:
Prognostic_studies
Limite:
Humans
Idioma:
En
Revista:
CPT Pharmacometrics Syst Pharmacol
Ano de publicação:
2022
Tipo de documento:
Article
País de afiliação:
Estados Unidos