Mathematical models in percutaneous absorption

A number of mathematical models have been used to describe percutaneous absorption kinetics. In general, most of these models have used either diffusion-based or compartmental equations. The object of any mathematical model is to a) be able to represent the processes associated with absorption accurately, b) be able to describe/summarize experimental data with parametric equations or moments, and c) predict kinetics under varying conditions. However, in describing the processes involved, some developed models often suffer from being of too complex a form to be practically useful. In this chapter, we attempt to approach the issue of mathematical modeling in percutaneous absorption from four perspectives. These are to a) describe simple practical models, b) provide an overview of the more complex models, c) summarize some of the more important/useful models used to date, and d) examine sonic practical applications of the models. The range of processes involved in percutaneous absorption and considered in developing the mathematical models in this chapter is shown in Fig. 1. We initially address in vitro skin diffusion models and consider a) constant donor concentration and receptor conditions, b) the corresponding flux, donor, skin, and receptor amount-time profiles for solutions, and c) amount- and flux-time profiles when the donor phase is removed. More complex issues, such as finite-volume donor phase, finite-volume receptor phase, the presence of an efflux. rate constant at the membrane-receptor interphase, and two-layer diffusion, are then considered. We then look at specific models and issues concerned with a) release from topical products, b) use of compartmental models as alternatives to diffusion models, c) concentration-dependent absorption, d) modeling of skin metabolism, e) role of solute-skin-vehicle interactions, f) effects of vehicle loss, a) shunt transport, and h) in vivo diffusion, compartmental, physiological, and deconvolution models. We conclude by examining topics such as a) deep tissue penetration, b) pharmacodynamics, c) iontophoresis, d) sonophoresis, and e) pitfalls in modeling.

Is affective learning really resistant to extinction? Stimulus ratings and affective priming give different answers

Affective learning, the learning of likes and dislikes, is proposed to differ from signal learning, the learning of relationships between events. However, affective learning research varies in the methodology used, and in addition, researchers concerned primarily with affective learning tend to use different paradigms from those concerned with signal learning. The current research used an affective priming task in addition to verbal ratings to assess changes in the valence of neutral geometric shapes in an aversive differential conditioning procedure. After acquisition, affective learning was present as indexed by ratings and affective priming, whereas after extinction, affective learning remained significant only in the ratings. This study suggests that different measures of affective learning may be differentially sensitive to valence, which has implications for studies that employ verbal ratings as the sole measure of affective learning. Moreover, there is no evidence from the current study that affective learning differs from signal learning.

Subtype-selective noncompetitive or competitive inhibition of human alpha(1)-adrenergic receptors by rho-TIA

The 19-amino acid conopeptide (rho-TIA) was shown previously to antagonize noncompetitively alpha(1B)-adrenergic receptors (ARs). Because this is the first peptide ligand for these receptors, we compared its interactions with the three recombinant human alpha(1)-AR subtypes (alpha(1A), alpha(1B), and alpha(1D)). Radioligand binding assays showed that rho-TIA was 10-fold selective for human alpha(1B)- over alpha(1A)- and alpha(1D)-ARs. As observed with hamster alpha(1B)-ARs, rho-TIA decreased the number of binding sites (B-max) for human alpha(1B)-ARs without changing affinity (K-D), and this inhibition was unaffected by the length of incubation but was reversed by washing. However, rho-TIA had opposite effects at human alpha(1A)-ARs and alpha(1D)-ARs, decreasing KD without changing Bmax, suggesting it acts competitively at these subtypes. rho-TIA reduced maximal NE-stimulated [H-3] inositol phosphate formation in HEK293 cells expressing human alpha(1B)-ARs but competitively inhibited responses in cells expressing alpha(1A)- or alpha(1D)-ARs. Truncation mutants showed that the amino-terminal domains of alpha(1B)- or alpha(1D)-ARs are not involved in interaction with rho-TIA. Alanine-scanning mutagenesis of rho-TIA showed F18A had an increased selectivity for alpha(1B)-ARs, and F18N also increased subtype selectivity. I8A had a slightly reduced potency at alpha(1B)-ARs and was found to be a competitive, rather than noncompetitive, inhibitor in both radioligand and functional assays. Thus rho-TIA noncompetitively inhibits alpha(1B)-ARs but competitively inhibits the other two subtypes, and this selectivity can be increased by mutation. These differential interactions do not involve the receptor amino termini and are not because of the charged nature of the peptide, and isoleucine 8 is critical for its noncompetitive inhibition at alpha(1B)-ARs.