Stephen Brent Duffull

The development of fit‐for‐purpose pharmacokinetic‐pharmacodynamic (PKPD) models based on clinical and pre‐clinical data is a critically important process in model informed drug development. This process is often hampered by modeling stability issues that are often multifactorial in nature and difficult to overcome, leading to protracted model building and arbitrary simplification of the model. This tutorial provides a heuristic workflow to help identify and resolve issues relating to model instability. The approach is centered on analyses undertaken using NONMEM, but the concepts can be generalized to other software used for population analysis of pharmacokinetics (PK) or PKPD data.

Model simplification is a process to simplify large‐scale mathematical models to enable easy applications such as simulation and parameter estimation. A novel heuristic machine analogy method of model simplification was developed and applied to a motivating example of a model for cAMP signaling switch induced by Gi/Gs pathway competition for the CB₁ receptor (consisting of 31 species and 76 parameters) to enable its use in estimation. The method first acquired an understanding of the mechanism by full model simulation, and then the mechanism was abstracted to a machine analogy. The machine analogy included signal start, signal mode selector, signal size regulator, and final effector, representing functions of different parts of the full model. The simplified minimal model (consisting of 11 species and 13 estimated parameters) was used for parameter estimation for Gi/Gs signaling of six CB₁ agonists. The results of the minimal model suggested that six CB₁ agonists have similar ratios of Gi/Gs activation, indicating Gi/Gs preference was more of a system effect rather than a ligand‐specific effect. In conclusion, the novel machine analogy method can be used to heuristically simplify a larger‐scale model while maintaining the important mechanisms. In the example here, the full Gi/Gs model of CB₁ was successfully simplified, and the results indicated Gi/Gs preference is a system‐dependent effect.

Medicine and pharmacy students benefit from interprofessional education that supports the learning of collaboration skills for medicines management decision-making. However, if and how students undertake collaborative decision-making processes in interprofessional student teams has not been investigated. The aim of this study was to determine the level to which pharmacy and medical students collaborate to make medicines management decisions in a learning activity. Eight pharmacy and 13 medical students completed a 9-item survey to evaluate a interprofessional learning activity. A secondary analysis of survey data using a deductive approach, according to the Model of Interprofessional Decision-Making was undertaken. Different levels of collaboration were identified in the students’ responses ranging from the first and lowest level in the model, Individual Decision Making, to the fourth and highest level, Shared Decision. Most student responses were categorised as level three-Deliberation. Findings from this study indicate that pharmacy and medical students collaborate to different levels with each other to make patient care decisions. Further examination is required to see if this is usual variation or related to the design of the learning activity.

Introduction: Docetaxel, a taxane used in the treatment of solid tumours, exerts pharmacological activity when in its unbound form. We report a sensitive assay to quantify unbound docetaxel after oral administration of docetaxel plus encequidar (oDox+E). Unbound drug quantification is important due to its direct correlation with drug-related toxicity and therapeutic efficacy. We improve on the sensitivity of current assay methods and demonstrate the utility of the assay on a novel formulation of oral docetaxel. Methods: Ultrafiltration followed by high-performance liquid chromatography and tandem mass spectrometry (HPLC-MS/MS) was utilized. Long-term stability, precision, accuracy, and recovery experiments were conducted to validate the assay. Additionally, patient samples from a Phase I dose-escalation pharmacokinetic study were analyzed using the developed assay. Results: The assay method exhibited long-term stability with an observed change between 0.8 and 6.9% after 131 days of storage at −60 °C. Precision and accuracy quality controls met the FDA acceptance criteria. An average recovery of 88% was obtained. Patient sample analysis demonstrated successful implementation of the assay. Conclusion: A validated sensitive assay was developed with an LLOQ of 0.084 ng/mL using 485 µL of human plasma. The sensitivity of the assay allowed quantification of unbound docetaxel concentrations in an early-phase oDox+E clinical study to compare it against IV docetaxel using pharmacokinetic modelling. Successful development of oDox+E represents an opportunity to replace the current IV docetaxel regimen with an oral regimen with lower cost, decreased side effects, and improve patient quality of life and experience.

The development of optimized dosing regimens plays a crucial role in oncology drug development. This study focused on the population pharmacokinetic modelling and simulation of docetaxel, comparing the pharmacokinetic exposure of oral docetaxel plus encequidar (oDox + E) with the standard of care intravenous (IV) docetaxel regimen. The aim was to evaluate the feasibility of oDox + E as a potential alternative to IV docetaxel. The article demonstrates an approach which aligns with the FDA’s Project Optimus which aims to improve oncology drug development through model informed drug development (MIDD). The key question answered by this study was whether a feasible regimen of oDox + E existed. The purpose of this question was to provide an early GO / NO-GO decision point to guide drug development and improve development efficiency. Methods: A stepwise approach was employed to develop a population pharmacokinetic model for total and unbound docetaxel plasma concentrations after IV docetaxel and oDox + E administration. Simulations were performed from the final model to assess the probability of target attainment (PTA) for different oDox + E dose regimens (including multiple dose regimens) in relation to IV docetaxel using AUC over effective concentration (AUCOEC) metric across a range of effective concentrations (EC). A Go / No-Go framework was defined—the first part of the framework assessed whether a feasible oDox + E regimen existed (i.e., a PTA ≥ 80%), and the second part defined the conditions to proceed with a Go decision. Results: The overall population pharmacokinetic model consisted of a 3-compartment model with linear elimination, constant bioavailability, constant binding mechanics, and a combined error model. Simulations revealed that single dose oDox + E regimens did not achieve a PTA greater than 80%. However, two- and three-dose regimens at 600 mg achieved PTAs exceeding 80% for certain EC levels. Conclusion: The study demonstrates the benefits of MIDD using oDox + E as a motivating example. A population pharmacokinetic model was developed for the total and unbound concentration in plasma of docetaxel after administration of IV docetaxel and oDox + E. The model was used to simulate oDox + E dose regimens which were compared to the current standard of care IV docetaxel regimen. A GO / NO-GO framework was applied to determine whether oDox + E should progress to the next phase of drug development and whether any conditions should apply. A two or three-dose regimen of oDox + E at 600 mg was able to achieve non-inferior pharmacokinetic exposure to current standard of care IV docetaxel in simulations. A Conditional GO decision was made based on this result and further quantification of the “effective concentration” would improve the ability to optimise the dose regimen.