Gabapentin and morphine combined achieved better analgesia at lower doses of each drug than either as a single agent, with constipation, sedation, and dry mouth as the most frequent adverse effects.

A large number of methods are available for modeling quantitative structure-activity relationships (QSAR). We examine the predictive accuracy of several methods applied to data sets of inhibitors for angiotensin converting enzyme, acetylcholinesterase, benzodiazepine receptor, cyclooxygenase-2, dihydrofolate reductase, glycogen phosphorylase b, thermolysin, and thrombin. Descriptors calculated with CoMFA, CoMSIA, EVA, HQSAR, and traditional 2D and 2.5D descriptors were used for developing models with partial least squares (PLS). In addition, the genetic function approximation algorithm, genetic PLS, and back-propagation neural networks were used for deriving models from 2.5D descriptors (i.e., 2D descriptors and 3D descriptors calculated from CORINA structures and Gasteiger-Marsili charges). Predictive accuracy was assessed using designed test sets. It was found that HQSAR generally performs as well as CoMFA and CoMSIA; other descriptor sets performed less well. When 2.5D descriptors were used, only neural network ensembles were found to be similarly or more predictive than PLS models. In addition, we show that many cross-validation procedures yield similar estimates of the interpolative accuracy of methods. However, the lack of correspondence between cross-validated and test set predictive accuracy for four sets underscores the benefit of using designed test sets.

The blood–brain barrier (BBB) protects the brain from the toxic side effects of drugs and exogenous molecules. However, it is crucial that medications developed for neurological disorders cross into the brain in therapeutic concentrations. Understanding the BBB interaction with drug molecules based on physicochemical property space can guide effective and efficient drug design. An algorithm, designated “BBB Score”, composed of stepwise and polynomial piecewise functions, is herein proposed for predicting BBB penetration based on five physicochemical descriptors: number of aromatic rings, heavy atoms, MWHBN (a descriptor comprising molecular weight, hydrogen bond donor, and hydrogen bond acceptors), topological polar surface area, and pKa. On the basis of statistical analyses of our results, the BBB Score outperformed (AUC = 0.86) currently employed MPO approaches (MPO, AUC = 0.61; MPO_V2, AUC = 0.67). Initial evaluation of physicochemical property space using the BBB Score is a valuable addition to currently available drug design algorithms.

A role for Zn2+ in a variety of neurological conditions such as stroke, epilepsy and Alzheimer's disease has been postulated. In many instances, susceptible neurons are located in regions rich in Zn2+ where nerve growth factor (NGF) levels rise as a result of insult. Although the interaction of Zn2+ with this neurotrophin has previously been suggested, the direct actions of the ion on NGF function have not been explored. Molecular modeling studies predict that Zn2+ binding to NGF will induce structural changes within domains of this neurotrophin that participate in the recognition of TrkA and p75NTR. We demonstrate here that Zn2+ alters the conformation of NGF, rendering it unable to bind to p75NTR or TrkA receptors or to activate signal transduction pathways and biological outcomes normally induced by this protein. Similar actions of Zn2+ are also observed with other members of the NGF family, suggesting a modulatory role for this metal ion in neurotrophin function.

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A homology model for the A2 domain of von Willebrand factor (VWF) is presented. A large number of target-template alignments were combined into a consensus alignment and used for constructing the model from the structures of six template proteins. Molecular dynamics simulation was used to study the structural and dynamic effects of eight mutations introduced into the model, all associated with type 2A von Willebrand disease. It was found that the group I mutations G1505R, L1540P and S1506L cause significant deviations over multiple regions of the protein, coupled to significant thermal fluctuations for G1505R and L1540P. This suggests that protein instability may be responsible for their intracellular retention. The group II mutations R1597W, E1638K and G1505E caused single loop displacements near the physiologic VWF proteolysis site between Y1605-M1606. These modest structural changes may affect interactions between VWF and the ADAMTS13 protease. The group II mutations I1628T and L1503Q caused no significant structural change in the protein, suggesting that inclusion of the protease in this model is necessary for understanding their effect. [Figure: see text]. Homology model of the von Willebrand factor A2 domain

Existing treatments for Alzheimer's disease (AD) fail to address the underlying pathology of the disease; they merely provide short-lived symptomatic relief. Consequently, the progression of AD is unrelenting, leading to a continual decrease in cognitive abilities. Recent advances in understanding the genetic factors that predispose to AD, as well as in biomarker development, have brought with them the promise of earlier and more reliable diagnosis of this disease. As improvements continue to be made in these areas, the shortcomings of current AD treatments appear all the more acute because opportunities for early intervention are hindered by a lack of "curative" or even disease-modifying drugs. This State of the Art report reviews existing AD therapeutics and highlights recent progress made in the design and development of drugs that are aimed at disrupting AD disease progression by inhibition of the protein misfolding of β-amyloid (Aβ) into neurotoxic oligomeric aggregates.

In this study, noncovalent interactions between aromatic groups and side-chain amides in proteins were characterized. To elucidate the nature and structure−strength relationship of the interaction, the geometries and interaction potential energy surfaces for the benzene−formamide model complex were exhaustively and systematically studied at the MP2 level of theory. The effects of basis set size and basis set superposition error were investigated for 15 selected complex structures. The results indicate that the aromatic−amide (side-chain) interaction can achieve a significant binding energy of up to 4.0 kcal/mol over a wide conformational space. The interaction involves the entire side-chain amide group rather than only its amine portion. Both dispersion and electrostatic interactions are the major contributors for the binding energy, and the π electron charge distributions in both groups and the dipole moment of the side-chain amide group are crucial to the interaction. The importance of such an interaction in proteins was verified through data mining analyses of 1029 X-ray protein structures. The interaction naturally occurs in proteins with a frequency of more than one per two proteins on a statistical average and is of significance for some protein structure. The interaction was also found to play a role in determining the biological activity of some proteins. Our study not only emphasizes the significance of aromatic−amide(side-chain) interactions in proteins but also deepens our understanding of noncovalent interactions involving benzene or other aromatic groups.