Pilot Study to Predict Pharmacokinetics of a Therapeutic Gemcitabine Dose From a Microdose

When to consider intra-target microdosing: physiologically based pharmacokinetic modeling approach to quantitatively identify key factors for observing target engagement

Intra-Target Microdosing (ITM), integral to Phase 0 clinical studies, offers a novel approach in drug development, effectively bridging the gap between preclinical and clinical phases. This methodology is especially relevant in streamlining early drug development stages. Our research utilized a Physiologically Based Pharmacokinetic (PBPK) model and Monte Carlo simulations to examine factors influencing the effectiveness of ITM in achieving target engagement. The study revealed that ITM is capable of engaging targets at levels akin to systemically administered therapeutic doses for specific compounds. However, we also observed a notable decrease in the probability of success when the predicted therapeutic dose exceeds 10 mg. Additionally, our findings identified several critical factors affecting the success of ITM. These encompass both lower dissociation constants, higher systemic clearance and an optimum abundance of receptors in the target organ. Target tissues characterized by relatively low blood flow rates and high drug clearance capacities were deemed more conducive to successful ITM. These insights emphasize the necessity of taking into account each drug's unique pharmacokinetic and pharmacodynamic properties, along with the physiological characteristics of the target tissue, in determining the suitability of ITM.

A naïve pooled data approach for extrapolation of Phase 0 microdose trials to therapeutic dosing regimens

Microdosing is a strategy to obtain knowledge of human pharmacokinetics prior to Phase I clinical trials. The most frequently used method to extrapolate microdose (≤100 μg) pharmacokinetics to therapeutic doses is based on linear extrapolation from a noncompartmental analysis (NCA) with a two-fold acceptance criterion between pharmacokinetic metrics of the extrapolated microdose and the therapeutic dose. The major disadvantage of NCA is the assumption of linear extrapolation of NCA metrics. In this study, we used a naïve pooled data (NPD) modeling approach to extrapolate microdose pharmacokinetics to therapeutic pharmacokinetics. Gemcitabine and anastrozole were used as examples of intravenous and oral drugs, respectively. Data from microdose studies were used to build a parent-metabolite model for gemcitabine and its metabolite 2',2'-difluorodeoxyuridine (dFdU) and a model for anastrozole. The pharmacokinetic microdose models were extrapolated to therapeutic doses. Extrapolation of the microdose showed differences in pharmacokinetic shape for gemcitabine and dFdU between the simulated and observed therapeutic concentrations, whereas the observed therapeutic concentrations for anastrozole were captured by the extrapolation. This study demonstrated the possible use and feasibility of an NPD modeling approach for the evaluation and application of microdose studies in early drug development. Last, physiologically-based pharmacokinetic modeling might be an alternative for microdose extrapolation of drugs with complex pharmacokinetics such as gemcitabine.

Phase 0/microdosing approaches: time for mainstream application in drug development?

Phase 0 approaches - which include microdosing - evaluate subtherapeutic exposures of new drugs in first-in-human studies known as exploratory clinical trials. Recent progress extends phase 0 benefits beyond assessment of pharmacokinetics to include understanding of mechanism of action and pharmacodynamics. Phase 0 approaches have the potential to improve preclinical candidate selection and enable safer, cheaper, quicker and more informed developmental decisions. Here, we discuss phase 0 methods and applications, highlight their advantages over traditional strategies and address concerns related to extrapolation and developmental timelines. Although challenges remain, we propose that phase 0 approaches be at least considered for application in most drug development scenarios.