Molecular dynamics (MD) simulations were used to characterize the equilibrium size, shape, hydration, and self-assembly of dodecylphosphocholine (DPC) and dodecyl-β-D-maltoside (DDM) micelles. We show that DPC molecules self-assemble to form micelles with sizes within the range reported in the experimental literature. The equilibrium shape of DPC and DDM micelles as well as associated micellar radii are in agreement with small-angle X-ray scattering (SAXS) experiments and theoretical packing parameters. In addition, we show that hydration of the micelle interior is limited; however, flexibility of the acyl chains leads to dynamic encounters with the solvated outer shell of the micelle, providing an explanation for long-standing differences in models of micelle hydration. Altogether, our results provide fundamental understanding of physical characteristics of micelles that can be utilized to study other types of detergents and proteomicelle complexes.

Objective: The objective of this study was to measure the effect of micromagnetic stimulation (μMS) on hippocampal neurons, by using single microcoil (μcoil) prototype, Magnetic Pen (MagPen). MagPen will be used to stimulate the CA3 magnetically and excitatory post synaptic potential (EPSP) measurements will be made from the CA1. The threshold for μMS as a function of stimulation frequency of the current driving the µcoil will be demonstrated. Finally, the optimal stimulation frequency of the current driving the μcoil to minimize power will be estimated. Approach: A biocompatible prototype, MagPen was built, and customized such that it is easy to adjust the orientation of the μcoil over the hippocampal tissue in an in vitro setting. Finite element modeling (FEM) of the μcoil was performed to estimate the spatial profiles of the magnetic flux density (in T) and the induced electric fields (in V/m). The induced electric field profiles generated at different values of current applied to the µcoil whether can elicit a neuron response was validated by numerical modeling. The modeling settings were replicated in experiments on rat hippocampal neurons. Main results: The preferred orientation of MagPen over the Schaffer Collateral fibers was demonstrated such that they elicit a neuron response. The recorded EPSPs from CA1 due to μMS at CA3 were validated by applying tetrodotoxin (TTX). Finally, it was interpreted through numerical analysis that increasing frequency of the current driving the μcoil, led to a decrease in the current amplitude threshold for μMS. Significance: This work reports that μMS can be used to evoke population EPSPs in the CA1 of hippocampus. It demonstrates the strength-frequency curve for µMS and its unique features related to orientation dependence of the µcoils, spatial selectivity and distance dependence. Finally, the challenges related to µMS experiments were studied including ways to overcome them.

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Experimental and theoretical studies are presented on the design of perturbations that enhance desynchronization in populations of oscillators that are synchronized by periodic entrainment. A phase reduction approach is used to determine optimal perturbation timing based upon experimentally measured phase response curves. The effectiveness of the perturbation waveforms is tested experimentally in populations of periodically and stochastically synchronized chemical oscillators. The relevance of the approach to therapeutic methods for disrupting phase coherence in groups of stochastically synchronized neuronal oscillators is discussed.

Assessing drug permeability across the blood-brain barrier (BBB) is important when evaluating the abuse potential of new pharmaceuticals as well as developing novel therapeutics that target central nervous system disorders. One of the gold-standard in vivo methods for determining BBB permeability is rodent log BB; however, like most in vivo methods, it is time-consuming and expensive. In the present study, two statistical-based quantitative structure-activity relationship (QSAR) models were developed to predict BBB permeability of drugs based on their chemical structure. The in vivo BBB permeability data were harvested for 921 compounds from publicly available literature, non-proprietary drug approval packages, and University of Washington’s Drug Interaction Database. The cross-validation performance statistics for the BBB models ranged from 82 to 85% in sensitivity and 80–83% in negative predictivity. Additionally, the performance of newly developed models was assessed using an external validation set comprised of 83 chemicals. Overall, performance of individual models ranged from 70 to 75% in sensitivity, 70–72% in negative predictivity, and 78–86% in coverage. The predictive performance was further improved to 93% in coverage by combining predictions across the two software programs. These new models can be rapidly deployed to predict blood brain barrier permeability of pharmaceutical candidates and reduce the use of experimental animals.