Pharmacometric Research of Infectious Diseases

The Pharmacometric Research of Infectious Disesase is undertaking pharmacometric work mainly related to drugs used for treatment of tuberculosis (TB) but also drugs used for treatment of human immunodeficiency virus (HIV) and plasmodium malaria as patients often are co-infected with these diseases.

Our focus is to investigate 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 involve innovative techniques in our work in order to support precise drug use and development in an emerging era of digitalization and globalization.

Translational medicine

Predicting human response based on pre-clinical information is key in order to secure the development of new antibiotics due to the high emerging resistance development. We use multi-scale mechanistic models in order to describe biomarker response to drugs and to account for translational factors. One example is the Multi-State Pharmacometrics Model (Clewe et al., 2016 JAC) which successfully have been applied to in vitro, in vivo data and used for prediction of short term response in TB patients based on pre-clinical information (Wicha et al., 2018 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.  

Figure: An outline of the different components of the preclinical to clinical forecasting in tuberculosis drug development using the translational MTP model approach.

Drug development

We are working closely with clinical trialists, infectious disease 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 use an integrated pooling approach where all relevant data is pooled in order to support the models developed from which simulations for future trials are made.

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

Due to the geographical overlap of malaria, HIV and TB prevalence, the diseases are likely to co-exist in a great number of individuals. For these individuals, there is an obvious need for concomitant use of antimalarial, antiretroviral and antitubercular drugs. 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. We have importantly shown that model-based pharmacometric tools are superior compared to traditional statistical assessment for quantifying pharmacokinetic drug-drug interactions (Svensson et al. AAPSJ, 2016) which also has been highlighted in the FDA guideline “Clinical Drug Interaction Studies — Study Design, Data Analysis, and Clinical Implications Guidance for Industry”. Drug-drug interaction can also occur at the level of pharmacodynamics. 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 et al AAPS J 2018). 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.