Optimisation of antiinfective dosing regimens through complementary pharmacometric approaches

Invasive fungal infections (IFIs) remain a major clinical concern due to their increased incidence, and high morbidity and mortality rates especially in critically ill children and immunocompromised individuals. One of the most commonly used drugs for preventing and treating IFIs is voriconazole (VRC), a second-generation triazole. It was approved to be clinically used in 2002 by the FDA and is listed by the WHO as an essential medicine for adults and children from the age of 2 years and above for the first-line treatment of IFIs as aspergillosis or candidemia, and as primary or secondary prophylactic in immunocompromised patients. However, VRC exhibits nonlinear pharmacokinetics, along with large inter- and intraindividual variability. This might be partly due its complex metabolism, which has not been fully elucidated. Underexposure of VRC may decrease efficacy leading to therapeutic failure, whereas overexposure increases the risk mainly for neural and hepatic toxicity. These unresolved challenges result in difficulties for clinicians to choose appropriate VRC dosing regimens to target its narrow therapeutic range, especially in the case of high doses in severe infections, or for long-term treatments.

To tackle the previously mentioned challenges, two mathematical modelling approaches can be employed: Nonlinear mixed-effect (NLME) modelling and physiologically-based pharmacokinetic (PBPK) modelling. Conceptually, NLME (top-down approach) modelling is an empirical to semi-mechanistic approach that utilises clinically observed data, with subsequent covariate analysis, to characterise the typical population PK parameters, describe the underlying PK mechanisms over time accounting for different levels of variabilities, and identify influencing factors on VRC exposure and effects in the body. On the other hand, PBPK (bottom-up approach) modelling is a literature-driven approach, that combines in vitro information of the drug with prior knowledge of the physiological and anatomical characteristics of the organism to achieve a mechanistic representation of the drug in biological systems, allowing the a priori prediction of drug exposure. Both, top-down and bottom-up approaches can be combined using a middle-out approach to elucidate the relationship between dosing regimens, drug exposure and effect. The advantage of the middle-out approach is that it allows to integrate information from in vitro experiments or in silico analysis together with information deriving from observed clinical data and therefore can maximise the mechanistic insights regarding VRC PK and helps to generate additional hypotheses, as well as to confirm and refine them in an iterative process.

The aim of my doctoral project is to comprehensively investigate the metabolism of VRC to provide a basis for safe and effective therapeutic use. As a first step, the effect of VRC on its metabolising enzyme CYP3A4 will be quantified by developing a PBPK model of the CYP3A4 probe substrate midazolam. In a second step, a PBPK model for VRC will be developed to integrate in-house generated in vitro hepatic and gut metabolism data, VRC physicochemical properties, and other physiological processes. This will also allow further extrapolation to other patient populations (e.g. paediatrics) to aid in dosing decisions. Simulations based on the developed model can be used to i) inform and qualify the model using clinically observed data, and ii) recommend optimal dosing regimens for patients who have developed IFIs. This could translate research results into clinics, supporting future therapeutic decisions.