Tumor Drug Penetration Measurements Could Be the Neglected Piece of the Personalized Cancer Treatment Puzzle

Abstract

Precision medicine aims to use patient genomic, epigenomic, specific drug dose, and other data to define disease patterns that may potentially lead to an improved treatment outcome. Personalized dosing regimens based on tumor drug penetration can play a critical role in this approach. State-of-the-art techniques to measure tumor drug penetration focus on systemic exposure, tissue penetration, cellular or molecular engagement, and expression of pharmacological activity. Using in silico methods, this information can be integrated to bridge the gap between the therapeutic regimen and the pharmacological link with clinical outcome. These methodologies are described, and challenges ahead are discussed. Supported by many examples, this review shows how the combination of these techniques provides enhanced patient-specific information on drug accessibility at the tumor tissue level, target binding, and downstream pharmacology. Our vision of how to apply tumor drug penetration measurements offers a roadmap for the clinical implementation of precision dosing.

Dose Issues in Cancer Chemotherapy

“…5. Scarcity of data on drug concentrations (also varying with the route of administration) near the tumor and tumor drug penetration [50]…”

Opportunities for Quantitative Translational Modeling in Oncology

“…Further exploration is required of new sources of clinical and nonclinical in vivo data that could help parameterize models: greater use of computed tomography scan data, such as radiomic approaches, to inform on the physical structure of tumor lesions and development and validation of one-dimensional quantitative measures of spatial characteristics, which can be integrated in clinical modeling approaches; circulating biomarkers, such as tumor antigens (including PSA and CA125), circulating tumor cells and circulating free tumor DNA; imaging mass-spectrometry data to give spatial resolution on drug and biomarker distribution in tumor tissue. 105 Data sharing through public databases (e.g., Project Data Sphere and Vol-PACT: https://fnih.org/what-we-do/biomarkers-consortium/programs/vol-pact) and precompetitive industrial collaborations could facilitate this.…”

Modeling the dynamics of antibody–target binding in living tumors

“…Our understanding of antibody-target interactions, and particularly in the native physiological context, is still limited, mainly due to the lack of approaches to detect their binding dynamics with temporal and spatial resolution or specificity 18 . For example, immunohistochemistry (IHC) staining can quantify the spatial distribution but it often fails to incorporate the dynamic factors present in physiological situations 19 .…”