Abstract 3027: Application of machine learning and grid search approaches to minimize lucitanib pharmacokinetic variability following different dosing regimens
Authors: Liao Michelle , Zhou Jessie , Sale Mark , Xiao Jim J. 
Published: September 2020

Background: Lucitanib is a potent tyrosine kinase inhibitor that selectively inhibits vascular endothelial growth factor receptors 1, 2, and 3 (VEGFR1-3), platelet-derived growth factor receptors alpha and beta (PDGFRα/β), and fibroblast growth factor receptors types 1, 2, and 3 (FGFR1-3). Lucitanib is being evaluated in patients with solid tumors in combination with other anticancer agents. The pharmacokinetics (PK) of lucitanib were described by a 2-compartment population PK model with first-order absorption and elimination. Body weight was a covariate on clearance and volume of distribution.

Objective: We sought to compare the variability of lucitanib PK between body weight-based and fixed-dose regimens to inform dosing regimen selection.

Methods: Lucitanib PK were simulated for 5000 virtual patients with a uniform distribution of body weight (40-120 kg). Patients either received a fixed dose (10 mg once daily) or a body weight-based dose. Genetic and grid search algorithms were used to identify the body weight-based dosing regimens that resulted in the smallest fraction of outliers, defined as subjects outside of the 5th and 95th prediction interval of the area under the curve during the dosing interval (tau) at steady state (AUCtau,ss) for 70-kg patients receiving the fixed dose. The genetic algorithm used a continuous body weight and dose search space, whereas the grid search algorithm limited the search space to discrete values of body weight and dose (ie, 5 kg increments for body weight and 0.25 mg increments for dose). The penalty function in both algorithms was the fraction of outliers. The optimized body weight-based regimens were compared to the fixed-dose regimen to evaluate lucitanib PK variability.

Results: The genetic and grid search algorithms identified similar and reasonable dosing regimens. Two or 3 body weight-based dosing regimens decreased the fraction of outliers by 1%-4% in the overall population (40-120 kg) versus the fixed dose. However, within the low body weight group (40-50 kg), body weight-based dosing regimens substantially decreased the fraction of outliers, with exposure greater than the 95th percentile reduced by 10%-15% compared with the fixed dose. On the other hand, the fixed-dose regimen predicted fewer outliers with exposure below the 5th percentile versus the body weight-based dosing regimens.

Conclusions: The genetic algorithm provided similar results to the more computationally demanding method of grid searching. Compared with a fixed-dose regimen, body weight-based regimens showed a small reduction in PK variability for lucitanib in the overall population, suggesting that there is limited benefit with body weight-based dosing. Dose optimization will be further evaluated with a growing understanding of the target therapeutic range and exposure-response relationships.

Citation Format: Michelle Liao, Jessie Zhou, Mark Sale, Jim J. Xiao. Application of machine learning and grid search approaches to minimize lucitanib pharmacokinetic variability following different dosing regimens [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 3027.