The goal of this research study is to reduce adverse effects of anti-cancer medications known as kinase inhibitors, while preserving their therapeutic benefits. There have been major advances in the way cancers are treated. A class of drugs known as kinase inhibitors have proven to be highly effective for the treatment of cancers, including lung, kidney, gastrointestinal, and breast cancers. However, kinase inhibitor medications are well known for having unpredictable side effects. The body breaks down (metabolizes) medications such as kinase inhibitors using an enzyme known as cytochrome P450 3A4 (CYP3A4). CYP3A4 is the most important drug metabolizing enzyme in humans due to the fact that it is involved in the metabolism of nearly half of all prescribed medications and almost all of the currently prescribed kinase inhibitors. Among patients, there can be nearly 400 times difference in how efficiently CYP3A4 metabolizes drugs. Remarkably, very little is known about the role of genetic differences in CYP3A4 and a gene known as pregnane X receptor (PXR). PXR serves as a sensor for drugs in the body and helps to regulate how much CYP3A4 is made. Currently, there are no predictive biomarkers, whether in genes (genomic) or circulating in blood (endogenous) that could aid treating oncologists with regards predicting adverse effects or suboptimal response to this important class of anticancer drugs. The goal is to carry out DNA sequencing for genetic changes in CYP3A4 and PXR to assess differences in enzyme activity of not only common, but also rare genetic variants. CYP3A4 activity will be determined in participants blood samples based on break down products of the drug tamoxifen as well as cholesterol, the latter called 4β-hydroxycholesterol, both known to be influenced by the CYP3A4 enzyme. Since cholesterol is naturally made in the body, comparing blood cholesterol to 4β-hydroxycholesterol levels will be a biomarker of CYP3A4 metabolic activity that can be measured in anyone. Furthermore, circulating CYP3A4 messenger RNA from human blood will be measured as another independent marker of CYP3A4 gene expression. A model will be generated that includes these biomarkers of CYP3A4 expression and function, as well as genetic variation in CYP3A4 and PXR, that will aid in better identifying which patients may be at risk for loss of benefit or toxicity from kinase inhibitor therapy. This model will be evaluated in 100 breast cancer patients undergoing chemotherapy with kinase inhibitors, namely cyclin-dependent kinases CDK4 and CDK6 inhibitor (abemaciclib, ribociclib or palbociclib), as such kinase inhibitors are widely prescribed for breast cancer and are known to be broken down by CYP3A4.
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Predictive model for CDK4/6 inhibitor concentrations
Timeframe: From enrollment to 3 months