A stochastic differential equation model for drug dissolution and its parameters

Mathematical Models as Tools to Predict the Release Kinetic of Fluorescein from Lyotropic Colloidal Liquid Crystals

In this study, we investigated the release kinetic of fluorescein from colloidal liquid crystals made from monoglyceride and different non-ionic surfactants. The crystals were physicochemically characterized and the release experiments were carried out under the sink conditions, while mathematical models were described as extrapolations from solutions of the diffusion equation, in different initial and boundary conditions imposed by pharmaceutical formulations. The diffusion equation was solved using Laplace and Fourier transformed functions for release kinetics from infinite reservoirs in a semi-infinite medium. Solutions represents a general square root law and can be applied for the release kinetic of fluorescein from lyotropic colloidal liquid crystals. Akaike, Schwartz, and Imbimbo criteria were used to establish the appropriate mathematical model and the hierarchy of the performances of different models applied to the release experiments. The Fisher statistic test was applied to obtain the significance of differences among mathematical models. Differences of mathematical criteria demonstrated that small or no significant statistic differences were carried out between the various applied models and colloidal formulations. Phenomenological models were preferred over the empirical and semi-empirical ones. The general square root model shows that the diffusion-controlled release of fluorescein is the mathematical models extrapolated for lyotropic colloidal liquid crystals.

On the estimate of the rate constant in the homogeneous dissolution model

The aim is to determine how well the rate parameter of the homogeneous model of dissolution can be estimated in dependency on the chosen times to measure the empirical data. The approach is based on the theory of Fisher information. We show that if the probability distribution of the measurement errors is known, the data should be collected at a single time instant or its close proximity in order to obtain the best estimate. This is in sharp contrast with commonly used experimental protocols. Further, from the properties of the Fisher information we deduce how suitable is the model of measurement error and we show that asymmetric distribution of data close to the time origin is unavoidable.

Modeling subject-specific phase-dependent effects and variations in longitudinal responses via a geometric Brownian motion process

We address statistical issues regarding modeling a collection of longitudinal response trajectories characterized by the presence of subject-specific phase-dependent effects and variation. To accommodate these two time-varying individual characteristics, we employ a geometric stochastic differential equation for modeling based on a Brownian motion process and develop a two-step paradigm for statistical analysis. This paradigm reverses the order of statistical inference in random effects model. We first extract individual information about phase-dependent treatment effects and volatility parameters for all subjects. Then, we derive the association relationship between the parameters characterizing the individual longitudinal trajectories and the corresponding covariates by means of multiple regression analysis. The stochastic differential equation model and the two-step paradigm together provide significant advantages both in modeling flexibility and in computational efficiency. The modeling flexibility is due to the easy adaptation of temporal change points for subject-specific phase transition in treatment effects, whereas the computational efficiency benefits in part from the independent increment property of Brownian motion that avoids high-dimensional integration. We demonstrate our modeling approach and statistical analysis on a real data set of longitudinal measurements of disease activity scores from a rheumatoid arthritis study.

Resistance to antitumor chemotherapy due to bounded-noise-induced transitions

Tumor angiogenesis is a landmark of solid tumor development, but it is also directly relevant to chemotherapy. Indeed, the density and quality of neovessels may influence the effectiveness of therapies based on blood-born agents. In this paper, first we define a deterministic model of antiproliferative chemotherapy in which the drug efficacy is a unimodal function of vessel density, and then we show that under constant continuous infusion therapy the tumor-vessel system may be multistable. However, the actual drug concentration profiles are affected by bounded even if possibly large fluctuations. Through numerical simulations, we show that the tumor volume may undergo transitions to the higher equilibrium value induced by the bounded noise. In case of periodically delivered boli-based chemotherapy, we model the fluctuations due to time variability of both the drug clearance rate and the distribution volume, as well as those due to irregularities in drug delivery. We observed noise-induced transitions also in case of periodic delivering. By applying a time dense scheduling with constant average delivered drug (metronomic scheduling), we observed an easier suppression of the transitions. Finally, we propose to interpret the above phenomena as an unexpected non-genetic kind of resistance to chemotherapy.