Absorption, distribution, metabolism and elimination (collectively called pharmacokinetics, PK), and time-dependent drug actions in target organs (pharmacodynamics, PD) play critical roles in efficacy and toxicity of all drugs. We will develop and implement new methods for modeling PK-PD at multiple scales from cells to patients, new methods for measuring PK at the cellular and subcellular levels, and new cell culture systems that better mimic the tumor environment, thus increasing our ability to predict patient responses from cell culture data. Our translational goal is to create new single-cell resolution methods to integrate a molecular understanding of drug-target interaction with measures of target engagement and induction of drug response in tissues and organisms.
Aim 3. 1 involves technology development for sub-cellular resolution PK measurement by fluorescence imaging of Companion Imaging Drugs (CIDs), small molecule or protein drugs tagged with a fluorophore for imaging in a manner that retains the bioactivity and pharmacokinetics of the parent compound. The properties of CIDs will be optimized (Aim 3.1.1) and the compounds used for intravital imaging of drug distribution and response in living mice (Aim 3.1.2). CIDs will be used to directly assay drug-target interaction in single cells by fluorescence correlation microscopy (Aim 3.1.3).
Aim 3. 2 will develop a novel quantitative, multiplexed mass spectrometry method for assaying structure-activity relationships at a cellular and sub-cellular level based on covalent modification of target proteins. This will involve creation of novel chemical drug-like probes (Aim 3.2.1) that will then be subjected to systematic chemical modification to explore the impact of physic0-chemical properties such as cLogP, pKa etc.
(Aim 3. 2.2).
Aim 3. 3 will involve development of methods for integrating pathway-level knowledge and biomarkers (both predictive and response) into the kind of PK-PD models that are routinely used for translational pharmacology and clinical trial design in industry.
Aim 3. 4 will attempt to recreate key features of the tumor microenvironment in culture to increase the predictivity of cell culture models. This will involve reproducing time-varying drug exposure as observed in animals and patients (Aim 3.4.1), systematic variation of the soluble environment (Aim 3.4.2), manipulation of the physical environment through changes in substrate elasticity (Aim 3.4.3) and direct assessment of the relationship between drug response in culture and patients across all the data collected Aims 1-3.
|AlQuraishi, Mohammed; Koytiger, Grigoriy; Jenney, Anne et al. (2014) A multiscale statistical mechanical framework integrates biophysical and genomic data to assemble cancer networks. Nat Genet 46:1363-71|