A long-term mechanistic computational model of physiological factors driving the onset of type 2 diabetes in an individual.

A computational model of the physiological mechanisms driving an individual's health towards onset of type 2 diabetes (T2D) is described, calibrated and validated using data from the Diabetes Prevention Program (DPP). The objective of this model is to quantify the factors that can be used for prevention of T2D. The model is energy and mass balanced and continuously simulates trajectories of variables including body weight components, fasting plasma glucose, insulin, and glycosylated hemoglobin among others on the time-scale of years. Modeled mechanisms include dynamic representations of intracellular insulin resistance, pancreatic beta-cell insulin production, oxidation of macronutrients, ketogenesis, effects of inflammation and reactive oxygen species, and conversion between stored and activated metabolic species, with body-weight connected to mass and energy balance. The model was calibrated to 331 placebo and 315 lifestyle-intervention DPP subjects, and one year forecasts of all individuals were generated. Predicted population mean errors were less than or of the same magnitude as clinical measurement error; mean forecast errors for weight and HbA1c were ~5%, supporting predictive capabilities of the model. Validation of lifestyle-intervention prediction is demonstrated by synthetically imposing diet and physical activity changes on DPP placebo subjects. Using subject level parameters, comparisons were made between exogenous and endogenous characteristics of subjects who progressed toward T2D (HbA1c > 6.5) over the course of the DPP study to those who did not. The comparison revealed significant differences in diets and pancreatic sensitivity to hyperglycemia but not in propensity to develop insulin resistance. A computational experiment was performed to explore relative contributions of exogenous versus endogenous factors between these groups. Translational uses to applications in public health and personalized healthcare are discussed.

Templated nucleation of acetaminophen on spherical excipient agglomerates.

We investigated the effect of spherical agglomeration of heterogeneous crystalline substrates on the nucleation of acetaminophen (AAP). Optical and electron microscopy showed that the surface morphologies of single crystal triclinic lactose and D-mannitol differed significantly from their counterparts formed via spherical agglomeration. Spherical agglomerates of lactose were shown to enhance the nucleation rate of acetaminophen (AAP) by a factor of 11 compared to single crystal lactose; however, no such enhancement was observed for D-mannitol. X-ray powder diffraction identified the presence of new crystal faces of lactose present only in the spherical agglomerates However, D-mannitol did not show any significant change in crystal morphology. The new crystal faces of triclinic lactose were analyzed using geometric lattice matching software and molecular dynamics simulations to establish any new and significant epitaxial matches between lactose and AAP. A coincident lattice match and a large favorable energy interaction from hydrogen bonding were observed between the (141¯) and (001) crystal faces of lactose and AAP, respectively. The enhanced nucleation kinetics, X-ray data, and computational studies indicated that the spherical crystallization of lactose exposed the (141¯) face on the surface of the agglomerates, which subsequently enhanced the nucleation rate of AAP through geometric lattice matching and molecular functionality. This study highlights the importance of exploring different heterogeneous substrate morphologies for enhancing nucleation kinetics.

Material strategies for creating artificial cell-instructive niches.

There has been a tremendous growth in the use of biomaterials serving as cellular scaffolds for tissue engineering applications. Recently, advanced material strategies have been developed to incorporate structural, mechanical, and biochemical signals that can interact with the cell and the in vivo environment in a biologically specific manner. In this article, strategies such as the use of composite materials and material processing methods to better mimic the extracellular matrix, integration of mechanical and topographical properties of materials in scaffold design, and incorporation of biochemical cues such as cytokines in tethered, soluble, or time-released forms are presented. Finally, replication of the dynamic forces and biochemical gradients of the in vivo cellular environment through the use of microfluidics is highlighted.