Article ; Online: Applying interpretable machine learning workflow to evaluate exposure-response relationships for large-molecule oncology drugs.
CPT: pharmacometrics & systems pharmacology
2022 Volume 11, Issue 12, Page(s) 1614–1627
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 ...
| 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. |
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| MeSH term(s) | Humans ; Workflow ; Machine Learning ; Logistic Models ; Proportional Hazards Models |
| Language | English |
| Publishing date | 2022-10-20 |
| Publishing country | United States |
| Document type | Journal Article ; Research Support, Non-U.S. Gov't |
| ZDB-ID | 2697010-7 |
| ISSN | 2163-8306 ; 2163-8306 |
| ISSN (online) | 2163-8306 |
| ISSN | 2163-8306 |
| DOI | 10.1002/psp4.12871 |
| Database | MEDical Literature Analysis and Retrieval System OnLINE |
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