|since 08/2012||PhD student (Graduate Research Training Progam PharMetrX, Pharmacometrics & Computational Disease Modelling) at the Dept. of Clinical Pharmacy and Biochemistry supervised by Prof. Dr. Charlotte Kloft|
|08/2012||Registered as Pharmacist|
|02/2012-07/2012||Pre-registration Internship at DocMorris Apotek Bohus (Bohus, Sweden)|
|08/2011-01/2012||Master thesis "Population Pharmacokinetics of Efavirenz in HIV-Infected Patients" at the Unit for Pharmacokinetics and Drug Metabolism, Dept. of Pharmacology, Sahlgrenska Academy, University of Gothenburg (Gothenburg, Sweden)|
|08/2007-07/2012||M. Sc. in Pharmaceutical Science at the University of Gothenburg (Gothenburg, Sweden)|
[Forschungslinie 2]: Pharmacometric data analysis
TNFα antagonists have been shown to have clear benefits for inducing and maintaining clinical remission in both Crohn’s disease and Ulcerative colitis. Despite its proven efficacy, not all patients respond to induction therapy and up to 50% of the patients who initially responded lose response over time.
The available TNFα antagonists are monoclonal antibodies (mAb) or parts of mAbs. The exact mechanism by which mAbs are cleared from the body is not fully understood and the variability of the elimination rate is high in this population.
Additionally, administration of mAbs may lead to development of anti-drug antibodies (ADA). ADA development is a common cause of treatment failure associated with increased risk of infusion reactions and reduced duration of response. ADAs are more frequently seen in patients receiving episodic therapy compared to scheduled therapy. Hence, it is important that the concentration of TNFα antagonists is at a measurable level, at all times, in all patients.
Application of mixed-effects models to data from a population allows for analysis of sparse clinical data. This method also allows investigation of multiple demographic covariates that may explain variability among individuals. The objectives of my Ph.D. project include development of a population pharmacokinetic (PK) model for infliximab (a TNFα antagonist), accounting for sources of variability between individuals as well as the development of ADAs. We are seeking to quantify the influence of these factors on the PK properties and subsequently its effect on the clinical outcome. Development of a more mechanistic model may also contribute to a better understanding of the elimination mechanism of mAbs, the development of ADAs, and make it possible to differentiate between disease specific and drug specific characteristics. Hopefully, the result will provide guidance on how to more efficiently use infliximab and give a better understanding on factors influencing the PK, not only the PK of infliximab but also of other TNFα antagonists.