Clinical trial simulations of the interaction between cannabidiol and clobazam and effect on drop-seizure frequency.

With this study, we aim to test the hypothesis that the effect of cannabidiol on drop-seizure frequency in patients with Lennox-Gastaut syndrome and Dravet syndrome could be attributed to a drug-drug interaction with clobazam. We performed clinical trial simulations for the effect of 20 mg/kg/day cannabidiol on drop-seizure frequency in patients with Lennox-Gastaut syndrome. We assumed that patients taking 10 or 20 mg clobazam would have a 2- to 7-fold increase in N-desmethylclobazam exposure, whereas patients not taking clobazam would have a median reduction in drop-seizure frequency and a variability in the percent reduction similar to the placebo group. The results show that the effect of cannabidiol on the median reduction in drop-seizure frequency in patients with Lennox-Gastaut syndrome may be explained by a drug-drug interaction with clobazam. This may have important implications for the use of cannabidiol and its Food and Drug Administration registration.

The application of mathematical modelling to the design of bispecific monoclonal antibodies.

Targeting multiple receptors with bispecific antibodies is a novel approach that may prevent the development of resistance to cancer treatments. Despite the initial promise, full clinical benefit of this technology has yet to be realized. We hypothesized that in order to optimally exploit bispecific antibody technology, thorough fundamental knowledge of their pharmacological properties compared to that of single agent combinations was needed. Therefore, we developed a mathematical model for the binding of bispecific antibodies to their targets that accounts for the spatial distribution of the binding receptors and the kinetics of binding, and is scalable for increasing valency. The model provided an adequate description of internal and literature-reported in vitro data on bispecific binding. Simulations of in vitro binding with the model indicated that bispecific antibodies are not always superior in their binding potency to combination of antibodies, and the affinity of bispecific arms must be optimized for maximum binding potency. Our results suggest that this tool can be used for the design and development of the next generation of anti-cancer bispecific compounds.

Development of a restricted state space stochastic differential equation model for bacterial growth in rich media.

In the present study, bacterial growth in a rich media is analysed in a Stochastic Differential Equation (SDE) framework. It is demonstrated that the SDE formulation and smoothened state estimates provide a systematic framework for data driven model improvements, using random walk hidden states. Bacterial growth is limited by the available substrate and the inclusion of diffusion must obey this natural restriction. By inclusion of a modified logistic diffusion term it is possible to introduce a diffusion term flexible enough to capture both the growth phase and the stationary phase, while concentration is restricted to the natural state space (substrate and bacteria non-negative). The case considered is the growth of Salmonella and Enterococcus in a rich media. It is found that a hidden state is necessary to capture the lag phase of growth, and that a flexible logistic diffusion term is needed to capture the random behaviour of the growth model. Further, it is concluded that the Monod effect is not needed to capture the dynamics of bacterial growth in the data presented.