Thorsten Lehr is Professor of Clinical Pharmacy at Saarland University, Germany since 2017. His research focuses on the individualization of pharmacotherapy using biomathematical modelling and simulation techniques, with a particular emphasis on population approaches and PBPK/QSP modelling. With several years of experience in the pharmaceutical industry, Thorsten Lehr managed various national and international research projects. He was part of the consortium for the Horizon EU project UPGx which successfully demonstrated improvement of drug safety by tackling pharmacogenetics. He is currently Coordinator of the EU-funded SafePolyMed project, which aims to improve drug safety by managing drug-drug-gene interactions and empowering patients and healthcare providers through innovative technologies.
Decoding the Complexity of Drug-Drug-Gene Interactions: A Pathway to Safer, Personalised Treatment
Adverse drug reactions (ADRs) place a significant burden on healthcare systems and economies, with polypharmacy, multimorbidity, and genetic variability impacting drug efficacy and ADR risk. Over 65% of ADRs are linked to drug-drug interactions (DDIs), while more than 60% of ADRs involve drug-gene interactions (DGIs), caused by variations in absorption, distribution, metabolism, and excretion genes. In current clinical practice, DDIs and DGIs are typically treated as separate entities, though they are often highly interconnected. Here, studying their combined effects (DDGIs) in clinical trials is unfeasible due to complexity, costs, and ethical concerns, leading to knowledge gaps and challenges in creating guidelines for the management of DDGIs.
The SafePolyMed project aims to improve the management of these complex DDGIs. A “Medication Management Center” (MMC) will support patients in actively managing their medication therapy and assist physicians in evaluating individual ADR risks and making informed therapy decisions.
Machine learning and artificial intelligence are applied to analyse large real-world patient databases to identify the relationships between genetic factors, demographics, diseases, medications, and ADR risk. Dose-related ADRs can be reduced through model-based precision dosing, guiding personalized drug treatment. Here, physiologically based pharmacokinetic (PBPK) drug models are being developed to assess complex DDGIs and calculate patient-specific dosages. To further identify patients at risk for ADRs, not only an assessment from clinicians is important, also the patient’s own perception of their health status is valuable, which could be captured through questionnaires. Specific symptom questionnaires are being designed to help detect ADRs early.
All tools will be integrated into the MMC as a comprehensive software solution, enabling patients to manage their polypharmacy and check for drug interactions to optimize therapy. The MMC and its functionalities will be evaluated in a multicentre proof-of-concept study across Europe to assess the tool’s applicability, patient involvement, and its contribution to reducing ADRs.