The evolution of age-specific smoking cessation rates in the United States from 2009 to 2017: a Kalman filter based approach.

BACKGROUND: Tracking the US smoking cessation rate over time is of great interest to tobacco control researchers and policymakers since smoking cessation behaviors have a major effect on the public's health. Recent studies have employed dynamic models to estimate the US cessation rate through observed smoking prevalence. However, none of those studies has provided annual estimates of the cessation rate by age group. Hence, the primary objective of this study is to estimate annual smoking cessation rates specific to different age groups in the US from 2009 to 2017. METHODS: We employed a Kalman filter approach to investigate the annual evolution of age-group-specific cessation rates, unknown parameters of a mathematical model of smoking prevalence, during the 2009-2017 period using data from the 2009-2018 National Health Interview Surveys. We focused on cessation rates in the 25-44, 45-64 and 65 + age groups. RESULTS: The findings show that cessation rates followed a consistent u-shaped curve over time with respect to age (i.e., higher among the 25-44 and 65 + age groups, and lower among 45-64-year-olds). Over the course of the study, the cessation rates in the 25-44 and 65 + age groups remained nearly unchanged around 4.5% and 5.6%, respectively. However, the rate in the 45-64 age group exhibited a substantial increase of 70%, from 2.5% to 2009 to 4.2% in 2017. The estimated cessation rates in all three age groups tended to converge to the weighted average cessation rate over time. CONCLUSIONS: The Kalman filter approach offers a real-time estimation of cessation rates that can be helpful for monitoring smoking cessation behavior.

Biopharmaceutical studies of a novel sedative sublingual lozenge based on glycine and tryptophan: A rationale for mucoadhesive agent selection.

Effective sedative drugs are in great demand due to increasing incidence of nervous disorders. The present work was aimed to develop a novel sublingual sedative drug based on glycine and L-tryptophan amino acids. Carbopol and different hydroxypropyl methylcellulose species were alternatively tested as mucoadhesive agents intended to prolong tryptophan sublingual release time. A model lipid medium of fully hydrated L-α-dimyristoylphosphatidylcholine was used for optimal mucoadhesive agents selection. Simultaneous processes of drug release and diffusion in lipid medium were first investigated involving both experimental and theoretical approaches. Individual substances, their selected combinations as well as different drug formulations were consecutively examined. Application of kinetic differential scanning calorimetry method allowed us to reveal a number of specific drug-excipient effects. Lactose was found to essentially facilitate tryptophan release and provide its ability to get into the bloodstream simultaneously with glycine, which is necessary to achieve glycine-tryptophan synergism. Introduction of a mucoadhesive agent into the formulation was shown to change kinetics of drug-membrane interactions variously depending on viscosity grade. Among the mucoadhesive agents, hydroxypropyl methylcellulose species K4M and E4M were shown to further accelerate drug release, therefore they were selected as optimal. Thus, effectiveness of the novel sedative drug was provided by including some excipients, such as lactose and the selected mucoadhesive agent species. A dynamic mathematical model was developed properly describing release and diffusion in lipid medium of various drug substances. Our study clearly showed applicability of a lipid medium to meet challenges such as drug-excipient interactions and optimization of drug formulations.

DMPy: a Python package for automated mathematical model construction of large-scale metabolic systems.

BACKGROUND: Models of metabolism are often used in biotechnology and pharmaceutical research to identify drug targets or increase the direct production of valuable compounds. Due to the complexity of large metabolic systems, a number of conclusions have been drawn using mathematical methods with simplifying assumptions. For example, constraint-based models describe changes of internal concentrations that occur much quicker than alterations in cell physiology. Thus, metabolite concentrations and reaction fluxes are fixed to constant values. This greatly reduces the mathematical complexity, while providing a reasonably good description of the system in steady state. However, without a large number of constraints, many different flux sets can describe the optimal model and we obtain no information on how metabolite levels dynamically change. Thus, to accurately determine what is taking place within the cell, finer quality data and more detailed models need to be constructed. RESULTS: In this paper we present a computational framework, DMPy, that uses a network scheme as input to automatically search for kinetic rates and produce a mathematical model that describes temporal changes of metabolite fluxes. The parameter search utilises several online databases to find measured reaction parameters. From this, we take advantage of previous modelling efforts, such as Parameter Balancing, to produce an initial mathematical model of a metabolic pathway. We analyse the effect of parameter uncertainty on model dynamics and test how recent flux-based model reduction techniques alter system properties. To our knowledge this is the first time such analysis has been performed on large models of metabolism. Our results highlight that good estimates of at least 80% of the reaction rates are required to accurately model metabolic systems. Furthermore, reducing the size of the model by grouping reactions together based on fluxes alters the resulting system dynamics. CONCLUSION: The presented pipeline automates the modelling process for large metabolic networks. From this, users can simulate their pathway of interest and obtain a better understanding of how altering conditions influences cellular dynamics. By testing the effects of different parameterisations we are also able to provide suggestions to help construct more accurate models of complete metabolic systems in the future.