Exploratory machine-learning analysis of bevacizumab exposure and circulating VEGF dynamics in patients with cancer.

INTRODUCTION

This study explored pharmacokinetic and pharmacodynamic factors associated with circulating vascular endothelial growth factor (VEGF) dynamics during bevacizumab treatment.

METHODS

A secondary analysis was conducted using data from 74 patients with breast, ovarian, cervical, non-small cell lung, or colorectal cancer. Paired pre- and post-infusion serum concentrations of bevacizumab and VEGF were measured by ELISA. Exploratory dimensionality-reduction methods and supervised tree-based models were applied. Artificial neural networks were used only as a complementary exploratory analysis on the same dataset.

RESULTS

The main outcome of the analysis was VEGF_D, the absolute difference between the paired VEGF concentrations. The supervised models did not perform above the naive three-class benchmark; therefore, their variable-importance outputs were treated as secondary, model-specific, and hypothesis-generating observations. The results show exploratory model patterns of bevacizumab exposure and circulating VEGF dynamics. We did not assess clinical efficacy, safety results, and other dosing strategies in this study.

DISCUSSION: The results do not support clinical implementation, personalized dosing, or treatment optimization and need to be confirmed in large independent cohorts.