A physiologically based pharmacokinetic (PBPK) approach to evaluate pharmacokinetics in patients with cancer

Abstract

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. Demographic data such as age, sex and body weight, and clinical laboratory measurements such as albumin, alpha-1 acid glycoprotein (AAG) and hematocrit were collected in ~2500 patients with cancer. A custom oncology population profile was built using the observed relationships among demographic variables and laboratory measurements in Simcyp® software, a population based ADME simulator. Patients with cancer were older compared with the age distribution in a built-in healthy volunteer profile in Simcyp. Hematocrit and albumin levels were lower and AAG levels were higher in patients with cancer. The custom population profile was used to investigate the disease effect on the pharmacokinetics of two probe substrates, saquinavir and midazolam. Higher saquinavir exposure was predicted in patients relative to healthy subjects, which was explained by the altered drug binding due to elevated AAG levels in patients with cancer. Consistent with historical clinical data, similar midazolam exposure was predicted in patients and healthy subjects, supporting the hypothesis that the CYP3A activity is not altered in patients with cancer. These results suggest that the custom oncology population profile is a promising tool for the prediction of PK in patients with cancer. Further evaluation and extension of this population profile with more compounds and more data will be needed.

Population Pharmacokinetic Analysis of Abiraterone in Chemotherapy-Naïve and Docetaxel-Treated Patients with Metastatic Castration-Resistant Prostate Cancer

“…However, the influence of prednisone or prednisolone on the pharmacokinetics of abiraterone could not be formally tested since neither prednisone nor prednisolone was administered to any of the healthy subjects. Furthermore, though severe hepatic and renal impairment has been reported to alter drug pharmacokinetics [32,33], including abiraterone exposure [34], this was unlikely to affect abiraterone pharmacokinetics in this analysis owing to the population assessed. The molecular markers related to liver function (e.g., aspartate aminotransferase, alanine aminotransferase, total bilirubin, serum albumin, total protein) as well as renal function (e.g., creatinine clearance) were explored using stepwise regression analysis.…”

Physiologically Based Absorption Modelling to Predict the Impact of Drug Properties on Pharmacokinetics of Bitopertin

“…Physiologically based absorption models are increasingly replacing trial and error approaches as a part of a rational strategy for formulation development. This is evidenced by several recent publications from scientists working in the industry (12,11,18,15,3).…”

Use of Physiologically Based Pharmacokinetic Modeling to Identify Physiological and Molecular Characteristics Driving Variability in Axitinib Exposure: A Fresh Approach to Precision Dosing in Oncology

“…The axitinib compound model was developed and verified using the Sim Cancer model. Exploratory analyses were performed to compare simulated axitinib exposure using the Sim‐Cancer and Genentech Cancer population models . Simulations performed to characterize physiological and molecular markers of axitinib steady‐state exposure in oncology patients used the Genentech Cancer Population model to define characteristics driving variability in exposure in a cohort that was not used to develop or verify the axitinib compound model.…”