Clinical trials in oncology recruit heterogeneous participants not often representative of the target cohort, leading to large variability in pharmacokinetics (PK). This increases the risk of toxicity and ineffective treatment. Physiologically-based pharmacokinetic (PBPK) modelling can be used as an alternative to clinical trials and inform drug labelling. These models require systems data, which are not fully characterized in cancer. In this study, scaling factors (e.g. microsomal protein per gram of liver (MPPGL)) for the in vitro-in vivo extrapolation of drug clearance were assessed in 16 colorectal cancer liver metastasis (CRLM) patients (paired histologically normal and cancerous livers). MPPGL was significantly lower in cancerous compared with histologically normal livers and was used to simulate plasma exposure of drugs, revealing a substantial decrease in drug exposure, when using typical scaling factors (healthy population) instead of cancer-related parameters in cancer population. Subsequently, LC-MS/MS based proteomic analysis of pooled healthy control, and histologically normal paired with cancerous liver samples from CRLM patients was carried out. Most cytochrome P450 (CYP), UDP-glucuronosyltransferase (UGT) and other drug metabolising enzymes (DMEs) were downregulated in cancer, indicating impaired drug metabolism. Similarly, most drug transporters were downregulated in CRLM, implying perturbed drug transport. A novel QconCAT standard (KinCAT) was designed to quantify receptor tyrosine kinases (RTKs). Several RTKs in addition to other pharmacodynamics protein markers were altered in CRLM. Application of perturbed CYP abundances on PBPK models demonstrated substantially higher drug exposure in cancer compared with healthy populations. These data were confirmed in individual liver samples (15 healthy, 18 cancer and paired normal). To our knowledge, this project provides the first comprehensive scaling factors for IVIVE and quantification of DMEs, transporters, RTKs and other important markers in cancer, with a focus on CRLM. The application of the experimentally-derived data on PBPK models showed the importance of using population-specific data in oncology and is a promising step towards the development of virtual cancer populations for optimal drug dosing.
|Date of Award||1 Aug 2021|
- The University of Manchester
|Supervisor||Jill Barber (Supervisor), Amin Rostami-Hochaghan (Supervisor) & Adam Darwich (Supervisor)|