Pharmacokinetics and Quantitative Pharmacology
Our research focuses on investigations of pharmacokinetics and associated relations with efficacy and safety (pharmacodynamics) using state-of-art techniques for pharmacometric model-based analysis. We integrate work from pre-clinical to clinical setting in order to support decisions related to translational medicine, drug development and strategies for individualized dosing i.e. therapeutic drug monitoring and dose adjustments due to pharmacokinetic or pharmacodynamics interactions. We apply innovative techniques in order to support precise drug use and development using artificial intelligence and machine learning techniques.
Research group leader: prof Ulrika Simonsson (firstname.lastname@example.org)
Predicting human response based on pre-clinical information is key in order to secure the development of new anti-infective agents from risks related to resistance development. We use multi-scale mechanistic models in order to describe biomarker response to drugs and to account for translational factors (see figure). One example is the Multi-State Pharmacometrics Model (Clewe et al 2016 JAC) which successfully have been applied to in vitro and in vivo data, and used for prediction of short term response in TB patients based on pre-clinical information (Wicha et al 2018 CPT, Susanto et al 2020 CPT). The work was selected by the ASCPT Quantitative Pharmacology (QP) network to be included within the Impact and Influence Initiative in order to illustrate the role played by QP in influencing key decisions in the drug development process and in advancing translational medicine and therapeutics.
We are working closely with clinical trialists, physicians and drug companies in order to support decisions of clinical trial designs using clinical trial simulations of individual and population level pharmacokinetic and pharmacodynamics. We have developed novel mechanistic models for biomarker response for TB drugs (exemplified by Svensson et al 2017 JAC, Svensson et al 2016 CPT:PSP) and have been able to detect significant exposure-response relationships for drugs where traditional statistical methods have failed which proves the higher power of our innovative approaches. The biomarker models are further linked in order to simulate clinical outcome such as relapse or cure.
Strategies for individualized dosing
Drug-drug interactions may result from concurrent administration of drugs leading to diminished therapeutic efficacy of or increased toxicity from one or more of the administered drugs, which needs an individualized dosing. Our research focuses on characterizing pharmacokinetic drug-drug interactions using novel model-based techniques. Drug-drug interaction can also occur at the pharmacodynamic level. We have developed an innovative model-based tool to assess pharmacodynamics interactions – The General Pharmacodynamic Interaction (GPDI) model (Wicha et al 2017 Nature Comm). The GPDI model can handle time- and concentration- dependent interactions for synergy and antagonism, has been shown to be superior to standard approaches for evaluation, and can handle more than two interacting drugs (Chen C 2018 AAPS J). Our research focus also in suggesting dosing recommendations for children based on scaling from adult data or evaluation of pediatric data and how to predict optimal personalized treatment using individual concentration and/or microbiology information using Bayesian techniques (Keutzer et al 2020 Front Pharmacol).
COVID-19 vaccination research
The respiratory disease COVID-19, caused by coronavirus SARS-CoV-2, has resulted in a pandemic with worldwide health impact. Vaccination against this viral infection is essential in prevention of morbidity and mortality, and restoration of pre-pandemic healthcare, and economic, mobility, and other processes. Our group is part of the EDCTP funded Re-BCG-CoV-19 consortium which explores the effect of BCG revaccination in preventing COVID-19 morbidity and mortality in health care workers in South Africa. In this Phase III vaccination trial, high time resolution data is acquired in 1000 participants working in South African healthcare on COVID-19 specifically and respiratory tract infections in general. We develop advanced computational pharmacometric models to elucidate the impact of risk factors as well as the effect of BCG revaccination on prevention of disease, as well as elevation of symptoms over time.
Shiny application for parametric time to event analysis
We have developed an innovative Shiny application to guide the pharmacometric modeller through parametric time to event model development process. Parametric time to event analysis is an important tool in the pharmacometrician’s toolbox to analyse event type data. Examples of event type data are the occurrence of a respiratory tract infection like COVID-19, a myocardial infarction, or time to positivity in a tuberculosis liquid culture assay. The probability of an event happening at a certain timepoint can be quantified as a function of risk factors, including drug exposure, using this full parametric approach.
Link to the Shiny application