Modeling the Effect of Binding Kinetics in Spatial Drug Distribution in the Brain

A 3-dimensional mathematical model is developed to determine the effect of drug binding kinetics on the spatial distribution of a drug within the brain. The key components, namely, transport across the blood-brain barrier (BBB), drug distribution in the brain extracellular fluid (ECF), and drug binding kinetics are coupled with the bidirectional bulk flow of the brain ECF to enhance the visualization of drug concentration in the brain. The model is developed based on the cubical volume of a brain unit, which is a union of three subdomains: the brain ECF, the BBB, and the blood plasma. The model is a set of partial differential equations and the associated initial and boundary conditions through which the drug distribution process in the mentioned subdomains is described. Effects of drug binding kinetics are investigated by varying the binding parameter values for both nonspecific and specific binding sites. All variations of binding parameter values are discussed, and the results show the improved visualization of the effect of binding kinetics in the drug distribution within the brain. For more realistic visualization, we suggest incorporating more brain components that make up the large volume of the brain tissue.

Learning Time Patterns

This chapter is important in that it lays a foundation for claims throughout this book that entrainment serves a platform for learning. In this chapter, this idea is developed in the context of learning categories of meter (e.g., duple meter vs. triple meter). The key difference is that entrainment depends on coupling parameters supplied by external driving rhythm force, whereas learning depends on a binding parameter which is strengthened simply by repeated synchronous activity of two or more oscillations. Against a backdrop of evidence indicating that musicians especially possess skill in recognizing metric categories, this chapter develops the coupling–binding distinction with the aim of showing that what people learn when exposed to metrical time patterns are global attractors instilled by learning a variety of different instances in a given metric category.

In-vitro Equilibrium Phosphate Binding Study of Sevelamer Carbonate by UV-Vis Spectrophotometry

Sevelamer carbonate is a cross-linked polymeric amine; it is the active ingredient in Renvela® tablets. US FDA provides recommendation for demonstrating bioequivalence for the development of a generic product of sevelamer carbonte using in-vitro equilibrium binding study. A simple UV-vis spectrophotometry method was developed and validated for quantification of free phosphate to determine the binding parameter constant of sevelamer. The method validation demonstrated the specificity, limit of quantification, accuracy and precision of measurements. The validated method has been successfully used to analyze samples in in-vitro equilibrium binding study for demonstrating bioequivalence.

The structure of binding curves and practical identifiability of equilibrium ligand-binding parameters

A critical but often overlooked question in the study of ligands binding to proteins is whether the parameters obtained from analyzing binding data are practically identifiable (PI), i.e., whether the estimates obtained from fitting models to noisy data are accurate and unique. Here we report a general approach to assess and understand binding parameter identifiability, which provides a toolkit to assist experimentalists in the design of binding studies and in the analysis of binding data. The partial fraction (PF) expansion technique is used to decompose binding curves for proteins with n ligand-binding sites exactly and uniquely into n components, each of which has the form of a one-site binding curve. The association constants of the PF component curves, being the roots of an n-th order polynomial, may be real or complex. We demonstrate a fundamental connection between binding parameter identifiability and the nature of these one-site association constants: all binding parameters are identifiable if the constants are all real and distinct; otherwise, at least some of the parameters are not identifiable. The theory is used to construct identifiability maps from which the practical identifiability of binding parameters for any two-, three-, or four-site binding curve can be assessed. Instructions for extending the method to generate identifiability maps for proteins with more than four binding sites are also given. Further analysis of the identifiability maps leads to the simple rule that the maximum number of structurally identifiable binding parameters (shown in the previous paper to be equal to n) will also be PI only if the binding curve line shape contains n resolved components.