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Julia Stingl

Director, Institute of Clinical Pharmacology University Hospital of RWTH Aachen, Germany

Julia is professor and director of the institute of clinical pharmacology at the university hospital of RWTH Aachen since 2019. From 2012-2019, she was head of research and vice president of the German drug regulatory authority, BfArM. Her research mostly focuses on Personalized Medicine and regulatory science. She pioneered the systematic development of personalized dose adjustments promoting the way of Pharmacogenetic information in drug labels. She is involved in many European projects, and is coordinator of a EraPerMed project on Artificial intelligence for personalised medicine in depression. J Stingl has authored more than 278 publications in peer-reviewed scientific journals, has been cited more than 12,000 times with an average citation of 32 per article and an H-index of 57 (ISI web of science, Aug 2023). 

Julia was recipient of the Utrecht Award for Pharmaceutical Research in 2009 and awarded with the Leon I. Goldberg Young Investigator Award of the American Society for Clinical Pharmacology and Therapeutics (ASCPT) in 2010. Julia was member of the former Pharmacogenomics Working Party (PGWP) and now serves as expert for the European Medicines Agency (EMA). Since 2019, she is member of the gene diagnostic committee in Germany. Since 2004 she is extraordinary member of the Drug Commission of the German Medical Association (AKDAE).

Precision dosing: phenotypic model based prediction of pharmacogenetic dose adjustments in situations of polypharmacy


Genetic polymorphisms in drug metabolizing enzymes and drug-drug interactions are major sources of inadequate drug exposure and ensuing adverse effects or insufficient responses. The current challenge of assessing drug-drug gene interactions for the development of precise dose adjustments for therapy recommendation systems is to take into account both, simultaneously, in situations of polypharmacy. Taking more than five medications which is termed polypharmacy, is more the rule than exemption in patients above the age of 65. Even if the indications are similar in multimorbidity conditions, the medication profiles are mostly unique in individual patients. The Pharmacogenetic profile fairly well predicts individual drug enzyme activity and can be translated into dose recommendations for drugs where the enzyme mediates a major metabolic pathway. To predict individual phenoconversion, the amount of deviation from the pharmacogenetic predicted phenotype, that is caused by drug interactions –either by inhibitors, inducers or competitive substrates in comedication, is a challenge and hurdle for precision dosing, today.


The aim is to develop static mechanistic methods for the prediction of the amount of phenoconversion by comedication in different Pharmacogenetic genotype groups


A metaanalytic approach compiling all available clinical study or real world TDM data on pharmacogenetic changes in drug clearance for drugs that are affected by pharmacogenetics to a major extent (the so-called actionable drugs) was used to develop a static mechanistic model for prediction of dose adjustments according to CYP enzyme activity predicted by pharmacogenotype. The model predicts the difference in CYP enzyme clearance between poor and extensive metabolizers of a CYP enzyme which can be used to estimate the shift in enzyme activity caused by drug interactions from comedication.


In the presence of strong inhibitors, the shift in enzyme clearance may be very similar, as the clearance at the Pharmacogenetic poor metabolizer level. Pharmacogenetic models generally assume linearity in the activity scores. When inhibitors affect CYP enzymes that play a major role in metabolism of the victim drug, the maximal inhibition would cause an effect similar to the poor metabolizer effect. However, competitive inhibitors or weak inhibitors may only cause a slight shift towards lower metabolism. When several substrates are taken simultaneously, it may be challenging to model the overall effects.


While Pharmacogenetic guided drug therapy has been shown to enhance medication safety and can be implemented for precision dosing, the real-world situation of polypharmacy may be better approached by developing a comprehensive phenoconversion model that integrates drug-drug interactions to the Pharmacogenetic profile, and estimates dose adjustments in situation of both, Pharmacogenetic and drug interaction effects on drug clearance.