Harnessing Meta‐analysis to Refine an Oncology Patient Population for Physiology‐Based Pharmacokinetic Modeling of Drugs. Issue 2 (20th November 2017)

• Record Type: Journal Article
• Authors: Schwenger, Emily; Reddy, Venkatesh Pilla; Moorthy, Ganesh; Sharma, Pradeep; Tomkinson, Helen; Masson, Eric; Vishwanathan, Karthick
• Is Part Of: Clinical pharmacology & therapeutics. Volume 103:Issue 2(2018)
• Page Start: 271
• Page End: 280
• Publication Date: 2017-11-20
• Subjects: Pharmacology -- Periodicals; Therapeutics -- Periodicals; 615.5
• DOI: 10.1002/cpt.917
• Languages: English
• ISSNs: 0009-9236

Abstract

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.