Immune Checkpoint Inhibitor (ICI)-Drug-Drug Interaction (DDI) Study

Findings suggest that adverse events during checkpoint inhibitor therapy may, in part, be caused by drug-drug interactions that increase the risk of adverse events with co-administered medications. Identifying these novel drug-drug interactions will likely inform clinical strategies to reduce adverse events during checkpoint inhibitor therapy and enhance their clinical benefits. This current research aims to systematically explore the impact of ICIs on CYP/transporter function and the associated risks for adverse events, thereby informing clinical strategies to mitigate these risks and optimize the therapeutic benefits of checkpoint inhibitors. By employing a rigorous crossover drug-drug interaction design, this study seeks to enhance understanding of drug interactions during ICI therapy, ultimately improving patient outcomes in oncology. The long-term goal of this research is to find ways to manage adverse events that occur during treatment with ICIs. The research has two main aims: 1. To understand how ICI therapy affects the metabolism of certain drugs named CYP/transporter probe drugs and to also understand the risk of side effects from commonly prescribed drugs that are affected by these enzymes and transporters in cancer patients. 2. To examine the relationship between levels of pro-inflammatory cytokines (signaling molecules involved in inflammation) and how well these probe drugs are metabolized before and during ICI therapy. A two-phase clinical study will be conducted to achieve these aims: patients will be given seven different probe drugs that interact with key enzymes and transporters (CYP1A2, CYP2B6, CYP2C9, CYP2C19, CYP2D6, CYP3A, BCRP, and SLCO1B1) both before they start ICI treatment and after they have begun therapy. Adverse events will be assessed by looking at how changes in the function of these enzymes and transporters affect the metabolism of drugs that cancer patients commonly use. Special computer models, known as physiologically-based pharmacokinetic (PBPK) models, will be used to simulate how these drugs behave in the body and predict potential serious adverse events.