Evaluation of the drug–drug interaction potential of brigatinib using a physiologically‐based pharmacokinetic modeling approach

Author:

Hanley Michael J.1ORCID,Yeo Karen Rowland2ORCID,Tugnait Meera3,Iwasaki Shinji4,Narasimhan Narayana5,Zhang Pingkuan6,Venkatakrishnan Karthik7ORCID,Gupta Neeraj1ORCID

Affiliation:

1. Clinical Pharmacology, Takeda Development Center Americas, Inc. Lexington Massachusetts USA

2. Simcyp Division, Certara UK Limited Sheffield, South Yorkshire UK

3. Clinical Pharmacology, Cerevel Therapeutics Cambridge Massachusetts USA

4. Global DMPK, Takeda Development Center Americas, Inc. Lexington Massachusetts USA

5. DMPK & Preclinical Safety, Theseus Pharmaceuticals Cambridge Massachusetts USA

6. Clinical Science, Takeda Development Center Americas, Inc. Lexington Massachusetts USA

7. Quantitative Pharmacology, EMD Serono Research & Development Institute, Inc. Billerica Massachusetts USA

Abstract

AbstractBrigatinib is an oral anaplastic lymphoma kinase (ALK) inhibitor approved for the treatment of ALK‐positive metastatic non‐small cell lung cancer. In vitro studies indicated that brigatinib is primarily metabolized by CYP2C8 and CYP3A4 and inhibits P‐gp, BCRP, OCT1, MATE1, and MATE2K. Clinical drug–drug interaction (DDI) studies with the strong CYP3A inhibitor itraconazole or the strong CYP3A inducer rifampin demonstrated that CYP3A‐mediated metabolism was the primary contributor to overall brigatinib clearance in humans. A physiologically‐based pharmacokinetic (PBPK) model for brigatinib was developed to predict potential DDIs, including the effect of moderate CYP3A inhibitors or inducers on brigatinib pharmacokinetics (PK) and the effect of brigatinib on the PK of transporter substrates. The developed model was able to predict clinical DDIs with itraconazole (area under the plasma concentration–time curve from time 0 to infinity [AUC] ratio [with/without itraconazole]: predicted 1.86; observed 2.01) and rifampin (AUC ratio [with/without rifampin]: predicted 0.16; observed 0.20). Simulations using the developed model predicted that moderate CYP3A inhibitors (e.g., verapamil and diltiazem) may increase brigatinib AUC by ~40%, whereas moderate CYP3A inducers (e.g., efavirenz) may decrease brigatinib AUC by ~50%. Simulations of potential transporter‐mediated DDIs predicted that brigatinib may increase systemic exposures (AUC) of P‐gp substrates (e.g., digoxin and dabigatran) by 15%–43% and MATE1 substrates (e.g., metformin) by up to 29%; however, negligible effects were predicted on BCRP‐mediated efflux and OCT1‐mediated uptake. The PBPK analysis results informed dosing recommendations for patients receiving moderate CYP3A inhibitors (40% brigatinib dose reduction) or inducers (up to 100% increase in brigatinib dose) during treatment, as reflected in the brigatinib prescribing information.

Publisher

Wiley

Subject

Pharmacology (medical),Modeling and Simulation

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