Disease impact on enzyme and transporter functions as well as other factors contributing to exposure variability in different types of cancer patients particularly in gastrointestinal (GI) and hepatic cancer

The exposures of advanced cancer therapeutics in cancer patients are associated with high variability (Sparreboom, 2009). Although many of these are kinase inhibitors with high protein binding and extensive CYP-mediated metabolism, the variations in plasma protein levels or CYP expression in the healthy population are unable to explain the extent of observed exposure variability in cancer patients. Potential differences in pharmacokinetics (PK) between healthy subjects and patients with cancer were investigated using a physiologically based pharmacokinetic approach integrating demographic and physiological data from patients with cancer (Cheeti, 2013). Demographic data included age, sex and body weight, and clinical laboratory measurements such as albumin, alpha-1 acid glycoprotein (AAG) and haematocrit were collected in ~2500 patients with cancer. In general, haematocrit and albumin levels were lower and AAG levels were higher in patients with cancer. However, the impact of differences in the activity and expression of drug metabolizing enzymes as well as uptake and efflux transporters between cancer patients and the healthy were not considered, although their potential to impact exposure is huge. Oncogenic signalling enhances protein synthesis by hyper-activating cellular protein translation machinery. At the same time, dysregulation of mitochondrial metabolism and overproduction of reactive oxygen species (ROS) in cancer patients can damage cellular proteins through oxidation, leading to misfolding and aggregation of proteins, and therefore to loss of enzyme abundance and activity. Considering the recent reports on high frequency of physiologically-based pharmacokinetic (PBPK) predictions in oncology area (Yoshida et al 2017), creation of reliable virtual populations for various cancer patients is essential. Proteomics technique have proved to be very usful in linking the in vitro data to clinical observations in this context (Al-Feteisi et al 2015).