Harnessing Meta‐analysis to Refine an Oncology Patient Population for Physiology‐Based Pharmacokinetic Modeling of Drugs

Abstract

Certain oncology compounds exhibit fundamental pharmacokinetic (PK) disparities between healthy and malignant conditions. Given the effects of tumor-associated inflammation on enzyme and transporter expression, we performed a meta-analysis of CYP- and transporter-sensitive substrate clinical PK to quantitatively compare enzyme and transporter abundances between healthy volunteers (HV) and cancer patients (CP). Hepatic and intestinal CYP1A2, CYP2C19, and CYP3A4 abundance were subsequently adjusted via Simcyp's sensitivity analysis tool. Of the 11 substrates we investigated, seven displayed marked exposure differences >1.25-fold between CP and HV. Although CP studies are limited, meta-analysis-based reduction in CYP1A2, CYP2C19, and CYP3A4 enzyme abundances in a virtual oncology population effectively captures CP-PK for caffeine, theophylline, midazolam, simvastatin, omeprazole, and a subset of oncology compounds. These changes allow extrapolation from HV to CP, enhancing predictive capability; therefore, conducting simulations in this CYP-modified oncology (MOD-CP) population provides a more relevant characterization of CP-PK.

Intrinsic and Extrinsic Pharmacokinetic Variability of Small Molecule Targeted Cancer Therapy

“…Our study also identified intrinsic and extrinsic factors associated with PK variability in patients with cancer, and these are consistent with previous publications . Although regulatory approval documents can provide useful information on intrinsic and extrinsic factors important for PK variability, the analyses and subsequent documentation may be incomplete as it may be unfeasible or impractical to look beyond a limited “typical” set of factors.…”

“…Our study also identified intrinsic and extrinsic factors associated with PK variability in patients with cancer, and these are consistent with previous publications. 2,3,7,12 Although regulatory approval documents can provide useful information on intrinsic and extrinsic factors important for PK variability, the analyses and subsequent documentation may be incomplete as it may be unfeasible or impractical to look beyond a limited "typical" set of factors. A case in point is the documentation of intrinsic factors associated with PK variability in the current study where 40% of the molecules surveyed report no intrinsic factors associated with PK ( Figure 4).…”

Ribociclib Drug‐Drug Interactions: Clinical Evaluations and Physiologically‐Based Pharmacokinetic Modeling to Guide Drug Labeling

“…It has been published that CYP3A4 has down regulation in patients with cancer, which could result in 20-70% reductions in the expression (Supplementary Document SA). [16][17][18] The cancer population file in Simcyp version 18 was modified by reducing the abundance of CYP3A4 enzymes in the intestine and in the liver by 40% to match with the observed data in patients with cancer (study CLEE011X2101 (NCT01237236), phase I study of ribociclib in patients with advanced solid tumors or lymphomas following oral administration of 50-1,200 mg; henceforth referred to as X2101; Figure S2). 19 Prediction accuracy for the PK parameters (peak plasma concentration (C max ) and area under the plasma concentration-time curve (AUC)) was calculated by residual values (i.e., (predicted-observed)/observed*100), with a values within −50% to +100% considered as an acceptable prediction.…”

Physiologically‐based pharmacokinetic modelling to predict oprozomib CYP3A drug–drug interaction potential in patients with advanced malignancies

“…The simulated oncology population size was 100 (10 trials with 10 virtual patients each), and was built using data from 2597 cancer patients . In addition, because a 28% reduction in CYP3A4 abundance in the liver and gut has been noted in the cancer population , we adjusted our simulated oncology population in Simcyp accordingly. Virtual patients were 18–50 years of age, with a 1:1 male:female ratio.…”