Background: Ectodysplasin-A appears to be a critical component of branching morphogenesis. Mutations in mouse Eda or human EDA are associated with absent or hypoplastic sweat glands, sebaceous glands, lacrimal glands, salivary glands (SMGs), mammary glands and/or nipples, and mucous glands of the bronchial, esophageal and colonic mucosa. In this study, we utilized Eda Ta (Tabby) mutant mice to investigate how a marked reduction in functional Eda propagates with time through a defined genetic subcircuit and to test the proposition that canonical NFκB signaling is sufficient to account for the differential expression of developmentally regulated genes in the context of Eda polymorphism.

Objectives Insulin control of fatty acid metabolism has long been deemed dominated by suppression of adipose lipolysis. This study’s goal was to test the hypothesis that this single role of insulin is insufficient to explain observed fatty acid dynamics. Methods and Results Fatty acid kinetics were measured during a meal-tolerance test and insulin sensitivity assessed by IVGTT in overweight human subjects (n=15, BMI 35.8 ± 7.1 kg/m2). Non-steady state tracer kinetic models were formulated and tested using ProcessDB© software. Suppression of adipose release alone could not account for NEFA concentration changes postprandially, but when combined with insulin activation of fatty acid uptake was consistent with the NEFA data. The observed insulin Km for NEFA uptake was inversely correlated with both insulin sensitivity of glucose uptake (IVGTT Si) (r=−0.626, P=0.01), and whole body fat oxidation after the meal (r=−0.538, P=0.05). Conclusions These results support insulin regulation of fatty acid turnover by both release and uptake mechanisms. Activation of fatty acid uptake is consistent with the human data, has mechanistic precedent in cell culture, and highlights a new potential target for therapies aimed at improving the control of fatty acid metabolism in insulin-resistant disease states.

This paper discusses two approaches for the preparation of enzymatically controlled drug delivery systems: (i) a calcium‐responsive biodegradable drug delivery system based on a mixture of starch with HPMC (hydroxypropyl methyl cellulose ether) (biodegradable) and the starch hydrolytic enzyme, α‐amylase, in its non‐active form; (ii) a glucose‐responsive insulin delivery system based on the hydrogel poly(2‐hydroxyethyl methacrylate‐co‐N,N‐dimethylaminoethyl methacrylate), with entrapped glucose oxidase, catalase and insulin. In both systems, the sensitivity to a trigger molecule (calcium or glucose) was achieved by the incorporation of a specific enzyme that reacts with the trigger molecule. Based on these interactions we propose two different enzyme‐controlled drug release mechanisms for responsive drug delivery systems. Copyright © 2003 John Wiley & Sons, Ltd.

Ionic hydrogels can be used as controlled release systems that respond to an external substrate or trigger by swelling or de-swelling. One example is a glucose-sensitive system for insulin-controlled release based on pH-sensitive hydrogel. To enhance understanding of non-steady state swelling, and to facilitate design of specifications (e.g., glucose-sensitivity) of the pH-sensitive ionic hydrogel based on the copolymer poly (2-hydroxyethyl methacrylate-co-N, N-dimethylaminoethyl methacrylate) (poly (HEMA-co-DMAEMA)), we developed a mathematical compartmental model using the software SAAM II. Current analytical and computational methods focus on equilibrium swelling of hydrogels; although for many stimuli-responsive hydrogel applications, the dynamic process is significant. We now report, using a combination of experimental data and kinetic analysis that in the poly (HEMA-co-DMAEMA) the rate of proton entry is governed by a different rate coefficient than water entry rate. The transport coefficient governing water uptake is dependent upon three variables: pH of external media, amine groups incorporated into the polymer, and crosslinking density of the polymer. An additional result is that swelling equilibrium is reached when all the amine groups are protonated. In this study we also demonstrate the predictive capability of the model for both interpolated and extrapolated data, and its use in design of future bench experiments. Uncovering these fundamental properties of pH-sensitive hydrogels with the aid of a kinetic model suggests that the complexities of hydrogel research and development can be overcome by combining experimental and computational approaches.

The basic approach that drug concentration–effect relationships are significantly invariant as a function of time in humans has led to the development of constant‐rate drug delivery systems. Nevertheless, there are a number of clinical situations where such an approach may not be sufficient. These include the delivery of insulin for patients who have diabetes mellitus, antiarrhythmics for patients who have heart rhythm disorders, gastric acid inhibitors for ulcer control, nitrates for patients who have angina pectoris, as well as selective β‐blockade, birth control, general hormone replacement, immunization, and cancer chemotherapy.In recent years, several research groups have been developing responsive systems that could more closely resemble the normal physiological process where the amount of drug released can be effected according to physiological needs. Responsive polymeric delivery systems can be classified as open or closed‐loop systems. Open‐loop control systems are those where information about the controlled variable is not automatically used to adjust the system inputs to compensate for the change in the process variables. In closed‐loop control systems, the controlled variable is detected, and as a result the system output is adjusted accordingly. In the controlled drug delivery field, open‐loop systems are known as pulsatile or externally regulated, and closed‐loop systems as self‐regulated. The externally controlled devices apply external triggers such as magnetic, ultrasonic, thermal, or electric irradiation for pulsatile delivery. In self‐regulated devices, the release rate is controlled by feedback information without any external intervention. Self‐regulated systems use several approaches as rate control mechanisms: pH‐sensitive polymers, enzyme–substrate reactions, pH‐sensitive drug solubility, competitive binding, antibody interactions, and metal concentration‐dependent hydrolysis.