Model-based optimization of controlled release formulation of levodopa for Parkinson's disease.

Levodopa is currently the standard of care treatment for Parkinson's disease, but chronic therapy has been linked to motor complications. Designing a controlled release formulation (CRF) that maintains sustained and constant blood concentrations may reduce these complications. Still, it is challenging due to levodopa's pharmacokinetic properties and the notion that it is absorbed only in the upper small intestine (i.e., exhibits an "absorption window"). We created and validated a physiologically based mathematical model to aid the development of such a formulation. Analysis of experimental results using the model revealed that levodopa is well absorbed throughout the entire small intestine (i.e., no "absorption window") and that levodopa in the stomach causes fluctuations during the first 3 h after administration. Based on these insights, we developed guidelines for an improved CRF for various stages of Parkinson's disease. Such a formulation is expected to produce steady concentrations and prolong therapeutic duration compared to a common CRF with a smaller dose per day and a lower overall dose of levodopa, thereby improving patient compliance with the dosage regime.

Theoretical investigation of pre-symptomatic SARS-CoV-2 person-to-person transmission in households.

Since its emergence, the phenomenon of SARS-CoV-2 transmission by seemingly healthy individuals has become a major challenge in the effort to achieve control of the pandemic. Identifying the modes of transmission that drive this phenomenon is a perquisite in devising effective control measures, but to date it is still under debate. To address this problem, we have formulated a detailed mathematical model of discrete human actions (such as coughs, sneezes, and touching) and the continuous decay of the virus in the environment. To take into account those discrete and continuous events we have extended the common modelling approach and employed a hybrid stochastic mathematical framework. This allowed us to calculate higher order statistics which are crucial for the reconstruction of the observed distributions. We focused on transmission within a household, the venue with the highest risk of infection and validated the model results against the observed secondary attack rate and the serial interval distribution. Detailed analysis of the model results identified the dominant driver of pre-symptomatic transmission as the contact route via hand-face transfer and showed that wearing masks and avoiding physical contact are an effective prevention strategy. These results provide a sound scientific basis to the present recommendations of the WHO and the CDC.

Selecting the particle size distribution for drugs with low water solubility - mathematical model.

PURPOSE: To introduce guidelines in selecting the particle size distribution (n(0), cm(-1)) that will guarantee optimal oral absorption for drugs with low solubility. METHODS: Unlike other multi-compartmental models the gastrointestinal tract is modeled as a continuous tube with spatially varying properties. The transport through the intestinal lumen is described using the dispersion model. The model accounts for the dissolution of poly-dispersed powders. RESULTS: The model was used to examine the sensitivity of the absorption on permeability (P) and water solubility (C(s)) following administration in different log-normal powders. The absorption exhibits inverse sigmoidal dependence on the mean particle size (r(m), µm) regardless of the administrated dose or drug properties. Thus, there is an optimal r(m) that maximizes the benefit-cost ratio of the formulation; finer particles do not improve the absorption while coarser particles decrease it. Using the model we find that the optimal r(m) depends mainly on the drug C(s) and on the geometrical standard deviation (gSTD). CONCLUSIONS: The results of this work provide the formulator with guidelines to select both r(m) and gSTD that guarantee optimal absorption.