Frontiers in pharmacology
Exploratory machine-learning analysis of bevacizumab exposure and circulating VEGF dynamics in patients with cancer
By Theofili I, Liolis E, Koutras A and 2 others.
Abstract
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.