Overcoming differential tumor penetration of BRAF inhibitors using computationally guided combination therapy

Area of Science:

• Oncology
• Pharmacology
• Biophysics

Background:

• BRAF-targeted kinase inhibitors (KIs) treat various cancers, but selection criteria and efficacy limitations are unclear.
• Understanding pharmacokinetic/pharmacodynamic (PK/PD) mechanisms is crucial for optimizing KI therapy.

Purpose of the Study:

• To investigate spatial PK/PD mechanisms limiting BRAF-targeted KI efficacy in BRAF-mutant cancers.
• To develop computational models for predicting and improving KI performance.
• To identify optimal strategies for combining BRAF inhibitors.

Main Methods:

• Utilized mouse models of BRAF-mutant cancers with fluorescent KI tracers and unlabeled drugs.
• Employed in silico spatial PK/PD modeling calibrated with imaging data.
• Performed retrospective clinical data analysis and preclinical combination studies.

Main Results:

• Drug lipophilicity, plasma clearance, target dissociation, and albumin binding influence dabrafenib efficacy, particularly in visceral metastases.
• High albumin binding was identified as a key factor limiting dabrafenib action compared to other KIs.
• A timed combination strategy of dabrafenib and encorafenib demonstrated enhanced BRAF inhibition and efficacy in preclinical models.

Conclusions:

• Spatial drug properties significantly impact BRAF-targeted KI efficacy in different cancer microenvironments.
• Computational modeling provides a framework for understanding and overcoming drug limitations.
• Optimized combination therapies hold promise for improving treatment outcomes in BRAF-mutant cancers.