Mitochondrial Permeability Transition Pore (MPTP) is involved in neurodegeneration, hepatotoxicity, cardiac necrosis, nervous and muscular dystrophies. We used different experimental protocols to determine the mitoprotective activity (%P) of different carbon nanotubes (CNT) against mitochondrial swelling in multiple boundary conditions (bj). The experimental boundary conditions explored included different sub-sets of combinations of the following factors b0 = three different mitochondrial swelling assays using the MPT-inductor (Ca2+, Fe3+, H2O2) combined or not with a second MPT-inductor and swelling control assays using MPT-inhibitor (CsA, RR, EGTA), b1 = exposure time (0-600 s), and b2 = CNT concentrations (0-5 μg ml-1). Other boundary conditions (bk) changed were the CNT structural parameters b3 = CNT type (SW, SW + DW, MW), b4 = CNT functionalization type (H, OH, COOH). We also changed different of CNT like b5 = molecular weight/functionalization ratio (minW/maxW) or b6 = maximal and minimal diameter (Dmin/Dmax) as physic-chemical properties (Vk). Next, we employed chemoinformatics ideas to develop a new Perturbation Theory (PT) model able to predict the %P of CNT in multiple experimental conditions. We investigated different output functions of the absorbance ′f(εij) used in PL4/PL5 methods like (εij, 1/εij, 1/εij2, or -log εij) as alternative outputs of the model. The inputs are in the form an additive functions with linear/non-linear terms. The first term is a function 0f(〈εij〉) of the average absorbance 〈εij〉 (expected value) in different assays (bj). The concentration dependent terms are linear functions of concentration, or hill-shaped curves similar to PL4/PL5 functions (used in dose-response analysis). The CNT structure perturbation terms are linear/non-linear functions of Box-Jenkins operators (ΔVkj). The ΔVkj are moving averages (deviations) of the Vk of the CNT with respect to their expected values 〈Vkj〉. The best model found predicted the values of absorbance (measure of mitoprotective activity vs. mitochondrial swelling) with regression coefficient R2 = 0.997 for >6000 experimental data points (q2 = 0.994). Last, we used the model to carry out a simulation of the changes on mitoprotective activity for CNT family after one increase of 1-10% of the minWi and maxDi of CNT. © The Royal Society of Chemistry.

Tuning of surface properties plays an important role in applications ranging from material engineering to biomedicine/chemistry. The interactions of chains grafted to a solid support and exposed to a matrix of chemically identical chains represent an intriguing issue. In this work, the behavior of poly(ethylene oxide) (PEO) chains grafted irreversibly onto an amorphous silica and immersed in the matrix of free PEO chains of different polymerization degree is studied using molecular dynamics simulations. The density distributions of grafted and free PEO chains, the height of the grafted layer, overlap parameters, and orientation order parameters depend not only on the grafting density but also on the length of free chains which confirm the entropic nature of the interactions between the grafted and free chains. In order to achieve a complete expulsion of the free chains from the grafted layer, a grafting density as high as 3.5 nm(-2) is necessary. Free PEO chains of 9 monomers leave the grafted layer at lower grafting densities than the longer PEO chains of 18 monomers in contrast with the theoretical predictions. The height of the grafted layer evolves with the grafting density in the presence of free chains in qualitative agreement with the theoretical phase diagram.

The results of a. systematic molecular dynamics study of the interfacial structure between the gold (100) surface and two room-temperature ionic liquids, namely, 1-butyl-3-methylimidazolium hexafluorophosphate ([BMIm][PF6]) and 1-butyl-3-methylimadazolium bis(trifluoromethylsulfonyl)imide ([BMIm][NTf2]), are herein reported. It is found that near an uncharged surface the IL structure differs from its bulk, having an enhanced density extended until the two first layers. Interfacial layering is clearly observed at the gold surface, with a higher effect for the [BMIm][NTf2] IL but a higher packing for [BMIm][PF6]. In both ILs the alkyl side chains are oriented parallel to the interface while the imidazolium rings tend to be parallel to the interface in about 60% of the cases. The presence of the interface has a higher impact on the orientation of the cations than on the chemical properties of the counterion. The surface potential drop across the interface is more pronounced toward a negative value for ([BMIm][PF6]) than for ([BMIm][NTf2]), due to relatively larger local density of the anions for ([BMIm][PF6]) near the gold surface.

The present review deals with the sol-gel imprinting of both drug and non-drug templates of medical relevance, namely neurotransmitters, biomarkers, hormones, proteins and cells. Nearly a hundred recent works, either developmental or applied in a medical-related context, were critically analyzed. It may be concluded that, although research is still at an early stage, the potential of these sol-gel materials was well demonstrated in a few applications of critical interest for medicinal/biomedical science. The vast room left for expansion and improvement envisages a continuously growing interest by researchers in the future, eventually resulting in important medical applications able to enter the professional and consumer medical markets.

Drug discovery is aimed at finding therapeutic agents for the treatment of many diverse diseases and infections. However, this is a very slow an expensive process, and for this reason, in silico approaches are needed to rationalize the search for new molecular entities with desired biological profiles. Models focused on quantitative structure-activity relationships (QSAR) have constituted useful complementary tools in medicinal chemistry, allowing the virtual predictions of dissimilar pharmacological activities of compounds. In the last 10 years, multi-target (mt) QSAR models have been reported, representing great advances with respect to those models generated from classical approaches. Thus, mt-QSAR models can simultaneously predict activities against different biological targets (proteins, microorganisms, cell lines, etc.) by using large and heterogeneous datasets of chemicals. The present review is devoted to discuss the most promising mt-QSAR models, particularly those developed for the prediction of protein inhibitors. We also report the first multi-tasking QSAR (mtk-QSAR) model for simultaneous prediction of inhibitors against biomacromolecules (specifically proteins) present in Gram-negative bacteria. This model allowed us to consider both different proteins and multiple experimental conditions under which the inhibitory activities of the chemicals were determined. The mtk-QSAR model exhibited accuracies higher than 98% in both training and prediction sets, also displaying a very good performance in the classification of active and inactive cases that depended on the specific elements of the experimental conditions. The physicochemical interpretations of the molecular descriptors were also analyzed, providing important insights regarding the molecular patterns associated with the appearance/enhancement of the inhibitory potency.

Introduction: Drug discovery is the process of designing new candidate medications for the treatment of diseases. Over many years, drugs have been identified serendipitously. Nowadays, chemoinformatics has emerged as a great ally, helping to rationalize drug discovery. In this sense, quantitative structure-activity relationships (QSAR) models have become complementary tools, permitting the efficient virtual screening for a diverse number of pharmacological profiles. Despite the applications of current QSAR models in the search for new drug candidates, many aspects remain unresolved. To date, classical QSAR models are able to predict only one type of biological effect (activity, toxicity, etc.) against only one type of generic target. Areas covered: The present review discusses innovative and evolved QSAR models, which are focused on multitasking quantitative structure-biological effect relationships (mtk-QSBER). Such models can integrate multiple kinds of chemical and biological data, allowing the simultaneous prediction of pharmacological activities, toxicities and/or other safety profiles. Expert opinion: The authors strongly believe, given the potential of mtk-QSBER models to simultaneously predict the dissimilar biological effects of chemicals, that they have much value as in silico tools for drug discovery. Indeed, these models can speed up the search for efficacious drugs in a number of areas, including fragment-based drug discovery and drug repurposing.