1. Development and validation of DMET quantitative proteomics methods and their application in characterizing interindividual variability
Our research is based on robust LC-MS/MS proteomics approach to quantify DMET proteins (particularly, non-CYP enzymes and transporters) in human and animal tissues and cell lines. We quantify proteins using targeted and untargeted methods using triple quadrupole and Orbitrap MS systems coupled with micro and nano-LC instruments. We have developed a repository of LC-MS/MS methods for quantification of >280 human DMET proteins. Further, we are developing novel non-invasive methods for predicting drug disposition, e.g., by quantifying drug transporters in exosomes. These methods are being utilized to quantify effect of age, genotype and disease condition on expression of DMET proteins.
2. PBPK prediction of ontogeny mediated hepatic and renal drug disposition
Our NICHD funded research utilizes in-house generated protein quantification data of hepatic DMET proteins to build PBPK models for prediction of age-dependent hepatic drug disposition. Such approaches are critical in determining first-in-children dose.
3. Integrated proteomics and metabolomics analysis of androgen homeostasis
We are investigating age-dependent changes in steroidogenic pathway with respect to the role of hepatic and intestinal DMET proteins during human development. The study involves effect of androgen glucuronidation, sulfation, transport and microbial deconjugation in regulating androgen homeostasis.
4. PBPK prediction of drug absorption including food effect
The overarching objective of this project is to establish a mechanism-based biopharmaceutical classification system (mBCS) for better prediction of drug bio-availability in fasted and fed conditions. To establish this, we are investigating effect of factors influencing oral drug absorption and integrating drug-dependent (e.g., solubility, logP, transport and metabolism kinetics) and system-dependent (e.g., gastric emptying time, regional transporter, DME, and water channels abundances) parameters into PBPK models.