Publications

Cardiometabolic medications are prescribed to prevent and treat cardiometabolic diseases such as diabetes, high blood pressure, and high cholesterol. This study investigated whether genes could affect the way in which patients use cardiometabolic medications. The authors combined and analyzed cardiometabolic medication use history and genetic information of 567,671 patients from three large biobank studies: FinnGen, the Estonian Biobank and the UK Biobank. In total, 333 locations across several genes were found to be associated with total medication use, medication switching or treatment discontinuation. This study demonstrates how medication use history can help better understand the underlying biology of cardiometabolic diseases.

Dasatinib is a medication used to treat certain types of leukemia. Its effectiveness can be influenced by other drugs that patients might be taking. Some medications can affect the enzyme (CYP3A4) that breaks down dasatinib in the liver, while others, like antacids, can change stomach acidity and affect how dasatinib is absorbed.
Researchers created a so called physiologically based pharmacokinetic (PBPK) model to predict how dasatinib behaves in the body and interacts with other drugs. This model simulates the human body and helps understand how dasatinib is distributed, broken down, and eliminated over time.
The PBPK model was developed using dasatinib’s properties and clinical study data. It accurately described how dasatinib levels change in the blood. The model was then used to figure out how to adjust dasatinib doses when taken with other medications, for example:
• How the dasatinib dose should be reduced with enzyme blockers (CYP3A4 inhibitors), or
• How the dasatinib dose should be increased with enzyme boosters (CYP3A4 inducers).
This model helps physicians to personalize dasatinib treatment for patients, ensuring they receive the correct dose when taking other medications, thereby improving effectiveness and reducing side effects.

Imatinib is a medication used to treat chronic myeloid leukemia, a type of blood cancer. It is broken down in the body by enzymes called CYP2C8 and CYP3A4. Additionally, a protein called P-glycoprotein pumps imatinib out of cells, affecting its activity. Imatinib can interact with other medications, influencing the enzymes and the protein, which can impact the effectiveness and safety of both imatinib and the other drugs.
Researchers created a computer model, known as a physiologically-based pharmacokinetic (PBPK) model, to predict how imatinib and its byproduct behave in the body and interact with other drugs. This model simulates the human body and helps understand how imatinib is distributed, broken down, and eliminated over time.
The PBPK model was developed using imatinib’s properties, its breakdown by CYP3A4 and CYP2C8, and its interaction with P-glycoprotein. Clinical study data were used to validate the model, which accurately described imatinib levels in the blood over time. The model was then used to explore drug interactions:
• With drugs affecting enzyme activity, like rifampicin, ketoconazole, and gemfibrozil: These simulations showed how these drugs could change imatinib and its byproduct levels.
• With drugs affected by imatinib, like simvastatin and metoprolol: The simulations showed how imatinib could alter the levels of these drugs.
The model successfully predicted these interactions, matching observed data closely. It can help in developing new drugs and optimizing dosages for patients, ensuring safe and effective treatments when multiple medications are used together.

The analysis of substrates of polymorphic cytochrome P450 (CYP) enzymes is important information to enable drug–drug interactions (DDIs) analysis and the relevance of pharmacogenetics in this context in large datasets. Our aim was to compare different approaches to assess the substrate properties of drugs for certain polymorphic CYP2 enzymes. Methods: A standardized manual method and an automatic method were developed and compared to assess the substrate properties for the metabolism of drugs by CYP2D6, 2C9, and 2C19. The automatic method used a matching approach to three freely available resources. We applied the manual and automatic methods to a large real-world dataset deriving from a prospective multicenter study collecting adverse drug reactions in emergency departments in Germany (ADRED). Results: In total, 23,878 medication entries relating to 895 different drugs were analyzed in the real-world dataset. The manual method was able to assess 12.2% (n = 109) of drugs, and the automatic method between 12.1% (n = 109) and 88.9% (n = 796), depending on the resource used. The CYP substrate classifications demonstrated moderate to almost perfect agreements for CYP2D6 and CYP2C19 (Cohen’s Kappa (κ) 0.48–0.90) and fair to moderate agreements for CYP2C9 (κ 0.20–0.48). Conclusion: A closer look at different classifications between methods revealed that both methods are prone to error in different ways. While the automated method excels in time efficiency, completeness, and actuality, the manual method might be better able to identify CYP2 substrates with clinical relevance.

Pharmacogenetics (PGx) aims to optimize drug treatment outcomes by using a patient's genetic profile for individualized drug and dose selection. Currently, reactive and pretherapeutic single-gene PGx tests are increasingly applied in clinical practice in several countries and institutions. With over 95% of the population carrying at least one actionable PGx variant, and with drugs impacted by these genetic variants being in common use, pretherapeutic or preemptive PGx panel testing appears to be an attractive option for better-informed drug prescribing. Here, we discuss the current state of PGx panel testing and explore the potential for clinical implementation. We conclude that available evidence supports the implementation of pretherapeutic PGx panel testing for drugs covered in the PGx guidelines, yet identification of specific patient populations that benefit most and cost-effectiveness data are necessary to support large-scale implementation.