Current Topics in Medicinal Chemistry - Volume 15, Issue 18, 2015

SMILES notation based optimal descriptors as a universal tool for the QSAR analysis with further application in drug discovery and design is presented. The basis of this QSAR modeling is Monte Carlo method which has important advantages over other methods, like the possibility of analysis of a QSAR as a random event, is discussed. The advantages of SMILES notation based optimal descriptors in comparison to commonly used descriptors are defined. The published results of QSAR modeling with SMILES notation based optimal descriptors applied for various pharmacologically important endpoints are listed. The presented QSAR modeling approach obeys OECD principles and has mechanistic interpretation with possibility to identify molecular fragments that contribute in positive and negative way to studied biological activity, what is of big importance in computer aided drug design of new compounds with desired activity.

Natural products have been an integral part of sustaining civilizations because of their medicinal properties. Past discoveries of bioactive natural products have relied on serendipity, and these compounds serve as inspiration for the generation of analogs with desired physicochemical properties. Bioactive natural products with therapeutic potential are abundantly available in nature and some of them are beyond exploration by conventional methods. The effectiveness of computational approaches as versatile tools for facilitating drug discovery and development has been recognized for decades, without exception, in the case of natural products. In the post-genomic era, scientists are bombarded with data produced by advanced technologies. Thus, rendering these data into knowledge that is interpretable and meaningful becomes an essential issue. In this regard, computational approaches utilize the existing data to generate knowledge that provides valuable understanding for addressing current problems and guiding the further research and development of new natural-derived drugs. Furthermore, several medicinal plants have been continuously used in many traditional medicine systems since antiquity throughout the world, and their mechanisms have not yet been elucidated. Therefore, the utilization of computational approaches and advanced synthetic techniques would yield great benefit to improving the world’s health population and well-being.

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.

Inhibition of non-structural protein 5B (NS5B) represents an attractive strategy for the therapeutic treatment of hepatitis C virus (HCV). In this study, machine learning classifiers such as artificial neural network (ANN), support vector machine (SVM), random forest (RF) and decision tree (DT) analyses were used to classify 970 compounds based on their physicochemical properties, including quantum chemical descriptors, constitutional descriptors, functional groups and molecular properties. Good predictive performance was obtained from all classifiers, providing accuracies ranging from 82.47–89.61% for external validation set. SVM was noted as the best classifier, indicated by its highest accuracy of 89.61%. The analyses were performed on data sets stratified by structural scaffolds (nucleoside and non-nucleoside) and bioactivities (active and inactive properties). In addition, a molecular fragment analysis was performed to investigate molecular substructures corresponding to biological activities. Furthermore, common substructures and potential functional groups governing the activities of active and inactive inhibitors were noted for the benefit of rational design and high-throughput screening towards potential HCV NS5B inhibitors.

The increasing use of Nanoparticles (NPs) in a wide range of applications has led to a rising concern on the possible toxicological effects that this use may have on human health and the environment. Since experimental toxicity evaluation for the different types of NPs already available, is often expensive and time consuming, several computational approaches are proposed for the risk assessment of NPs. In this work, we have developed a predictive classification model for the toxicological assessment of iron oxide NPs with different core, coating and surface modification based on a number of different properties including size, relaxivities, zeta potential and type of coating. The model was fully validated based on several validation measurements and was released online via Enalos InSilicoNano Platform (http://enalos.insilicotox.com/QNAR_IronOxide_Toxicity/). The developed web service gives the interested user the opportunity to insert the indicated properties and get a toxicity prediction accompanied by an indication of its reliability based on the domain of applicability. This newly introduced web service complements our previously reported efforts to extract important information from available datasets and develop user friendly applications for the toxicity assessment of NPs.

The CORAL software (http://www.insilico.eu/coral) has been used to develop quantitative feature–property/activity relationships (QFPRs/QFARs) for the prediction of endpoints related to different categories of nanomaterials. In contrast to previous models built up by using CORAL from a representation of the molecular structure by using simplified molecular input-line entry system (SMILES), the current QFPR/QFARs are based on an integrated representation of acting conditions (i.e., a combination of physicochemical and/or biochemical factors) of nanomaterials via the so-called quasi-SMILES notation. In contrast to traditional quantitative structure – property / activity relationships (QSPRs/QSARs), the new models are able to provide new insight on the conditions of acting of substances (e.g., chemicals and nanomaterials) independently of their molecular structure. The development and validation of the QFPR/QFAR models was carried out following the OECD principles. The statistical quality of models developed from quasi-SMILES is acceptable, with values for the determination coefficient in the range of 0.70 to 0.85 for various endpoints of environmental and human health relevance. Perspectives of the QFPR/QFAR and their interaction and overlapping with traditional QSPR/QSAR are also discussed.

Fullerene and its derivatives are currently one of the most intensively investigated species in the area of nanomedicine and nanochemistry. Various unique properties of fullerenes are responsible for their wide range applications in industry, biology and medicine. A large pool of functionalized C60 and C70 fullerenes is investigated theoretically at different levels of quantum-mechanical theory. The semiempirial PM6 method, density functional theory with the B3LYP functional, and correlated ab initio MP2 method are employed to compute the optimized structures, and an array of properties for the considered species. In addition to the calculations for isolated molecules, the results of solution calculations are also reported at the DFT level, using the polarizable continuum model (PCM). Ionization potentials (IPs) and electron affinities (EAs) are computed by means of Koopmans’ theorem as well as with the more accurate but computationally expensive ΔSCF method. Both procedures yield comparable values, while comparison of IPs and EAs computed with different quantum-mechanical methods shows surprisingly large differences. Harmonic vibrational frequencies are computed at the PM6 and B3LYP levels of theory and compared with each other. A possible application of the frequencies as 3D descriptors in the EVA (EigenVAlues) method is shown. All the computed data are made available, and may be used to replace experimental data in routine applications where large amounts of data are required, e.g. in structure-activity relationship studies of the toxicity of fullerene derivatives.

Supported transition metals on TiO2 surfaces have shown exceptional catalytic properties in many important process such as CO oxidation, selective propane oxidation, hydrogenation, water adsorption and other catalytic and photocatalytic oxidation reaction at low-temperature. Among the three polymorphs of TiO2, the anatase crystal is the more photoactive. The anatase (101) surface attracts more attention since it has lower surface energy relative to (001) and (100) surfaces and it is observed to adsorb small molecules on its surface. Using density-functional theory (DFT) with on-site Coulomb interactions corrections, we have computed the structural and electronic properties of selected Au8 clusters interacting with clean and reduced anatase TiO2(101) surfaces. The computed adsorption energies are suggesting that the considered Au8 clusters are only physisorbed onto pristine TiO2(101) surface. Oxygen vacancies are found to enhance the absorption of Au8 on the Ti2(101) surface. Accurate simulations required spin polarized DFT since the ground state of Au8 interacting with defective TiO2(101) shows magnetic solutions. The results show that Au8 clusters are chemically bonded to the surface around the locality of the oxygen vacancy. The surface oxygen vacancy is found to be energetically more favourable than sub-surface oxygen vacancy configuration. These vacancy sites may act as nucleation sites for small Au clusters or Au atoms. Finally, the computed electronic structure of all the Au8/TiO2(101) configurations considered in this work are analysed in the light of available experimental data.

Evaluation of chemical and physical properties of nanomaterials is of critical importance in a broad variety of nanotechnology researches. There is an increasing interest in computational methods capable of predicting properties of new and modified nanomaterials in the absence of time-consuming and costly experimental studies. Quantitative Structure- Property Relationship (QSPR) approaches are progressive tools in modelling and prediction of many physicochemical properties of nanomaterials, which are also known as nano-QSPR. This review provides insight into the concepts, challenges and applications of QSPR modelling of carbon-based nanomaterials. First, we try to provide a general overview of QSPR implications, by focusing on the difficulties and limitations on each step of the QSPR modelling of nanomaterials. Then follows with the most significant achievements of QSPR methods in modelling of carbon-based nanomaterials properties and their recent applications to generate predictive models. This review specifically addresses the QSPR modelling of physicochemical properties of carbon-based nanomaterials including fullerenes, single-walled carbon nanotube (SWNT), multi-walled carbon nanotube (MWNT) and graphene.

Metal oxide nanomaterials have exhibited excellent performance as nanomedicines in photodynamic therapy (PDT) for cancer and infection treatment. Their unique and tunable physicochemical properties advance them as promising alternatives in drug delivery, early diagnosis, imaging, and treatment against various tumors and infectious diseases. Moreover, the implementation of nanophototherapy in deep tissue sites is enhanced by advancements in photosensitization technology. Notwithstanding the progress made in emerging metal oxide nanomaterials-derived PDT, the potential toxicity towards adjunct tissues associated with this approach remains challenging. Regulation and legislation have also been recommended and subsequently enacted in response to public concerns related to large-scale production, transportation, use, and disposal of those nanomaterials. Consequently, a quantitative structure-activity relationship (QSAR) paradigm has been adopted and is widely used in evaluating and predicting the side effects of nanomedicines, thus influencing their design and fabrication. This article briefly reviews the application of metal oxide nanomaterials in PDT and their associated adverse impacts as reported in recent publications. The future trends and implications of this platform in nanomedicine are also highlighted. However, more studies and efforts have to be carried out for developing novel nano-therapeutics with high selectivity, sensitivity, biocompatibility, and minimal side effects in PDT.

The problem in this work is the computational characterization of cyclodextrins, crown ethers and hyaluronan (HA) as hosts of inclusion complexes for nanosized drug delivery vehicles in pharmaceutical formulations. The difficulty is addressed through a computational study of some thermodynamic, geometric and topological properties of the hosts. The calculated properties of oligosaccharides of D-glucopyranoses allow these to act as co-solvents of polyanions in water. In crown ethers, the central channel is computed. Mucoadhesive polymer HA in formulations releases drugs in mucosas. Geometric, topological and fractal analyses are carried out with code TOPO. Reference calculations are performed with code GEPOL. From HA to HA·3Ca and hydrate, the hydrophilic solvent-accessible surface varies with the count of H-bonds. The fractal dimension rises. The dimension of external atoms rises resulting 1.725 for HA. It rises going to HA·3Ca and hydrate. Nonburied minus molecular dimension rises and decays. Hydrate globularity is lower than O(water), Ca2+ and O(HA). Ca2+ rugosity is smaller than for hydrate, O(HA) and O(water). Ca2+ and O(water) accessibilities are greater than hydrate. Conclusions are drawn on: (1) the relative stability of linear/cyclic and shorter/larger polymers; (2) the atomic analysis of properties allows determining the atoms with maximum reactivity.

The knowledge on potential harmful effects of metallic nanomaterials lags behind their increased use in consumer products and therefore, the safety data on various nanomaterials applicable for risk assessment are urgently needed. In this study, 11 metal oxide nanoparticles (MeOx NPs) prepared using flame pyrolysis method were analyzed for their toxicity against human alveolar epithelial cells A549, human epithelial colorectal cells Caco2 and murine fibroblast cell line Balb/c 3T3. The cell lines were exposed for 24 h to suspensions of 3-100 μg/mL MeOx NPs and cellular viability was evaluated using. Neutral Red Uptake (NRU) assay. In parallel to NPs, toxicity of soluble salts of respective metals was analyzed, to reveal the possible cellular effects of metal ions shedding from the NPs. The potency of MeOx to produce reactive oxygen species was evaluated in the cell-free assay. The used three cell lines showed comparable toxicity responses to NPs and their metal ion counterparts in the current test setting. Six MeOx NPs (Al2O3, Fe3O4, MgO, SiO2, TiO2, WO3) did not show toxic effects below 100 µg/mL. For five MeOx NPs, the averaged 24 h IC50 values for the three mammalian cell lines were 16.4 µg/mL for CuO, 22.4 µg/mL for ZnO, 57.3 µg/mL for Sb2O3, 132.3 µg/mL for Mn3O4 and 129 µg/mL for Co3O4. Comparison of the dissolution level of MeOx and the toxicity of soluble salts allowed to conclude that the toxicity of CuO, ZnO and Sb2O3 NPs was driven by release of metal ions. The toxic effects of Mn3O4 and Co3O4 could be attributed to the ROS-inducing ability of these NPs. All the NPs were internalized by the cells according to light microscopy studies but also proven by TEM, and internalization of Co3O4 NPs seemed to be most prominent in this aspect. In conclusion, this work provides valuable toxicological data for a library of 11 MeOx NPs. Combining the knowledge on toxic or non-toxic nature of nanomaterials may be used for safe-by-design approach.

Nanoparticles are likely to interact in real-case application scenarios with mixtures of proteins and biomolecules that will absorb onto their surface forming the so-called protein corona. Information related to the composition of the protein corona and net cell association was collected from literature for a library of surface-modified gold and silver nanoparticles. For each protein in the corona, sequence information was extracted and used to calculate physicochemical properties and statistical descriptors. Data cleaning and preprocessing techniques including statistical analysis and feature selection methods were applied to remove highly correlated, redundant and non-significant features. A weighting technique was applied to construct specific signatures that represent the corona composition for each nanoparticle. Using this basic set of protein descriptors, a new Protein Corona Structure-Activity Relationship (PCSAR) that relates net cell association with the physicochemical descriptors of the proteins that form the corona was developed and validated. The features that resulted from the feature selection were in line with already published literature, and the computational model constructed on these features had a good accuracy (R2LOO=0.76 and R2LMO(25%)=0.72) and stability, with the advantage that the fingerprints based on physicochemical descriptors were independent of the specific proteins that form the corona.