Dataset Information

Applying interpretable machine learning workflow to evaluate exposure-response relationships for large-molecule oncology drugs.

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.

SUBMITTER: Liu G

PROVIDER: S-EPMC9755920 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature

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Applying interpretable machine learning workflow to evaluate exposure-response relationships for large-molecule oncology drugs.

Liu Gengbo G Lu James J Lim Hong Seo HS Jin Jin Yan JY Lu Dan D

CPT: pharmacometrics & systems pharmacology 20221020 12

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 exposu ...[more]

PMID: 36193885

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Project description:ObjectiveNasopharyngeal carcinoma (NPC) patients frequently present with distant metastasis (DM), which is typically associated with poor prognosis. This study aims to develop and apply machine learning models to predict DM, overall survival (OS), and cancer-specific survival (CSS) in NPC patients to provide optimal tools for improved predictive accuracy and performance.MethodsWe retrieved over 8,000 NPC patient samples with associated clinical information from the Surveillance, Epidemiology, and End Results (SEER) database. Utilizing two methods for handling missing values-imputation or deletion-we created various cohorts: DM-all, DM-slim, OS-all, OS-slim, CSS-all, and CSS-slim. Five machine learning models were deployed for the binary classification task of DM, and their performance was evaluated using the area under the curve (AUC). For the survival prediction tasks of OS and CSS, we constructed 45 combinations using nine survival machine learning algorithms. The Concordance Index (C-index), 5-year AUC, and Brier score assessed model accuracy. Patients were stratified into two risk groups for survival analysis, and the survival curves were presented.ResultsThis study examines the relationships between clinical factors and survival in NPC patients. The analysis, visualized through forest plots, indicates that demographic and clinical variables like gender, marital status, tumor grade, and stage significantly affect metastatic risks and survival. Specifically, factors such as advanced stages increase metastasis and survival risks, while enhanced treatments improve survival rates. In the cohort for DM prediction, results revealed that the random forest model was the most effective, with an AUC of 0.687. In contrast, when predicting overall survival (OS), the random survival forest (RSF) model consistently showed superior performance with the highest mean C-index of 0.802, a 5-year AUC of 0.857, and a Brier score of 0.167. Similarly, for cancer-specific survival (CSS) prediction, the RSF model demonstrated a mean C-index of 0.822, a 5-year AUC of 0.884, and a Brier score of 0.165. An online Shiny server was developed to allow the models to be used freely and efficiently via http://npcml.shinyapps.io/NPCpre.ConclusionThis study successfully established an online tool by machine learning models for NPC metastasis and survival prediction, providing valuable references for clinicians.

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Dataset Information

Applying interpretable machine learning workflow to evaluate exposure-response relationships for large-molecule oncology drugs.

Publications

Applying interpretable machine learning workflow to evaluate exposure-response relationships for large-molecule oncology drugs.

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