M. Natália D. S. Cordeiro

Applications of 2D Descriptors in Drug Design: A DRAGON Tale

In order to minimize expensive drug failures, is essential to determine potential activity, toxicity and ADME problems as early as possible. In view of the large libraries of compounds now being handled by combinatorial chemistry and high-throughput screening, identification of potential drug is advisable even before synthesis using computational techniques such as QSAR modeling. A great number of in silico approaches to activity/toxicity prediction have been described in the literature, using molecular 0D, 1D, 2D and 3D descriptors. Also these descriptors have been implemented in available computational tools such as DRAGON, SYBYL and CODESSA for it easy use. However, many of them only have been used to explain a few prediction problems. This review attempts to summarize present knowledge related to the computational biological activity prediction based in 2D molecular descriptors implemented in the DRAGON software. These applications rely on new computational techniques such as virtual combinatorial synthesis, virtual computational screening or inverse. Several topological molecular descriptors applications are described, ranging from simple topological indices to topological indices derived from matrices weighted with atomic and bond properties. Their advantages, limitations and its possibilities in drug design are also discussed.

Quinoxaline, its derivatives and applications: A State of the Art review

Quinoxaline derivatives are an important class of heterocycle compounds, where N replaces some carbon atoms in the ring of naphthalene. Its molecular formula is C8H6N2, formed by the fusion of two aromatic rings, benzene and pyrazine. It is rare in natural state, but their synthesis is easy to perform. In this review the State of the Art will be presented, which includes a summary of the progress made over the past years in the knowledge of the structure and mechanism of the quinoxaline and quinoxaline derivatives, associated medical and biomedical value as well as industrial value. Modifying quinoxaline structure it is possible to obtain a wide variety of biomedical applications, namely antimicrobial activities and chronic and metabolic diseases treatment.

Computational tool for risk assessment of nanomaterials: novel QSTR-perturbation model for simultaneous prediction of ecotoxicity and cytotoxicity of uncoated and coated nanoparticles under multiple experimental conditions.

Nanomaterials have revolutionized modern science and technology due to their multiple applications in engineering, physics, chemistry, and biomedicine. Nevertheless, the use and manipulation of nanoparticles (NPs) can bring serious damages to living organisms and their ecosystems. For this reason, ecotoxicity and cytotoxicity assays are of special interest in order to determine the potential harmful effects of NPs. Processes based on ecotoxicity and cytotoxicity tests can significantly consume time and financial resources. In this sense, alternative approaches such as quantitative structure-activity/toxicity relationships (QSAR/QSTR) modeling have provided important insights for the better understanding of the biological behavior of NPs that may be responsible for causing toxicity. Until now, QSAR/QSTR models have predicted ecotoxicity or cytotoxicity separately against only one organism (bioindicator species or cell line) and have not reported information regarding the quantitative influence of characteristics other than composition or size. In this work, we developed a unified QSTR-perturbation model to simultaneously probe ecotoxicity and cytotoxicity of NPs under different experimental conditions, including diverse measures of toxicities, multiple biological targets, compositions, sizes and conditions to measure those sizes, shapes, times during which the biological targets were exposed to NPs, and coating agents. The model was created from 36488 cases (NP-NP pairs) and exhibited accuracies higher than 98% in both training and prediction sets. The model was used to predict toxicities of several NPs that were not included in the original data set. The results of the predictions suggest that the present QSTR-perturbation model can be employed as a highly promising tool for the fast and efficient assessment of ecotoxicity and cytotoxicity of NPs.

Rational drug design for anti-cancer chemotherapy: multi-target QSAR models for the in silico discovery of anti-colorectal cancer agents.

The discovery of new and more potent anti-cancer agents constitutes one of the most active fields of research in chemotherapy. Colorectal cancer (CRC) is one of the most studied cancers because of its high prevalence and number of deaths. In the current pharmaceutical design of more efficient anti-CRC drugs, the use of methodologies based on Chemoinformatics has played a decisive role, including Quantitative-Structure-Activity Relationship (QSAR) techniques. However, until now, there is no methodology able to predict anti-CRC activity of compounds against more than one CRC cell line, which should constitute the principal goal. In an attempt to overcome this problem we develop here the first multi-target (mt) approach for the virtual screening and rational in silico discovery of anti-CRC agents against ten cell lines. Here, two mt-QSAR classification models were constructed using a large and heterogeneous database of compounds. The first model was based on linear discriminant analysis (mt-QSAR-LDA) employing fragment-based descriptors while the second model was obtained using artificial neural networks (mt-QSAR-ANN) with global 2D descriptors. Both models correctly classified more than 90% of active and inactive compounds in training and prediction sets. Some fragments were extracted from the molecules and their contributions to anti-CRC activity were calculated using mt-QSAR-LDA model. Several fragments were identified as potential substructural features responsible for the anti-CRC activity and new molecules designed from those fragments with positive contributions were suggested and correctly predicted by the two models as possible potent and versatile anti-CRC agents.

Two New Parameters Based on Distances in a Receiver Operating Characteristic Chart for the Selection of Classification Models

There are several indices that provide an indication of different types on the performance of QSAR classification models, being the area under a Receiver Operating Characteristic (ROC) curve still the most powerful test to overall assess such performance. All ROC related parameters can be calculated for both the training and test sets, but, nevertheless, neither of them constitutes an absolute indicator of the classification performance by themselves. Moreover, one of the biggest drawbacks is the computing time needed to obtain the area under the ROC curve, which naturally slows down any calculation algorithm. The present study proposes two new parameters based on distances in a ROC curve for the selection of classification models with an appropriate balance in both training and test sets, namely the following: the ROC graph Euclidean distance (ROCED) and the ROC graph Euclidean distance corrected with Fitness Function (FIT(λ)) (ROCFIT). The behavior of these indices was observed through the study on the mutagenicity for four genotoxicity end points of a number of nonaromatic halogenated derivatives. It was found that the ROCED parameter gets a better balance between sensitivity and specificity for both the training and prediction sets than other indices such as the Matthews correlation coefficient, the Wilks lambda, or parameters like the area under the ROC curve. However, when the ROCED parameter was used, the follow-on linear discriminant models showed the lower statistical significance. But the other parameter, ROCFIT, maintains the ROCED capabilities while improving the significance of the models due to the inclusion of FIT(λ).

Density functional theory study of the water dissociation on platinum surfaces: general trends.

We report a comparative periodic density functional theory study of the reaction of water dissociation on five platinum surfaces, e.g., Pt(111) Pt(100), Pt(110), Pt(211), and Pt(321). These surfaces were chosen to study the surface structural effects in the reaction of water dissociation. It was found that water molecules adsorb stronger on surfaces presenting low coordinated atoms in the surface. In the cases of the stepped Pt(110) and kinked Pt(321) surfaces, the activation energy barriers are smaller than the adsorption energies for the water molecule on the corresponding surfaces. Therefore, the calculations suggest that the dissociation reaction will take place preferentially at corner or edge sites on platinum particles with the (110) orientation. The inclusion of the results obtained in this work in previous derived BEP relationships confirms that the adsorption energy of the reaction products arises as the most appropriate descriptor for water dissociation on transition metal surfaces.

What does an ionic liquid surface really look like? Unprecedented details from molecular simulations

We present the first intrinsic analysis of the surface of the [bmim][PF(6)] room-temperature ionic liquid. Our detailed analysis reveals unprecedented details about the structure of the interface by providing the relative prevalence of different molecular orientations. These results suggest that experimental data should be reinterpreted considering a distribution of molecular arrangements.

Desirability-Based Multiobjective Optimization for Global QSAR Studies: Application to the Design of Novel NSAIDs with Improved Analgesic, Antiinflammatory, and Ulcerogenic Profiles

Up to now, very few reports have been published concerning the application of multiobjective optimization (MOOP) techniques to quantitative structure–activity relationship (QSAR) studies. However, none reports the optimization of objectives related directly to the desired pharmaceutical profile of the drug. In this work, for the first time, it is proposed a MOOP method based on Derringers desirability function that allows conducting global QSAR studies considering simultaneously the pharmacological, pharmacokinetic and toxicological profile of a set of molecule candidates. The usefulness of the method is demonstrated by applying it to the simultaneous optimization of the analgesic, antiinflammatory, and ulcerogenic properties of a library of fifteen 3‐(3‐methylphenyl)‐2‐substituted amino‐3H‐quinazolin‐4‐one compounds. The levels of the predictor variables producing concurrently the best possible compromise between these properties is found and used to design a set of new optimized drug candidates. Our results also suggest the relevant role of the bulkiness of alkyl substituents on the C‐2 position of the quinazoline ring over the ulcerogenic properties for this family of compounds. Finally, and most importantly, the desirability‐based MOOP method proposed is a valuable tool and shall aid in the future rational design of novel successful drugs.

Chemoinformatics in anti-cancer chemotherapy: Multi-target QSAR model for the in silico discovery of anti-breast cancer agents

The discovery of new and more efficient anti-cancer chemotherapies is a field of research in expansion and growth. Breast cancer (BC) is one of the most studied cancers because it is the principal cause of cancer deaths in women. In the active area for the search of more potent anti-BC drugs, the use of approaches based on Chemoinformatics has played a very important role. However, until now there is no methodology able to predict anti-BC activity of compounds against more than one BC cell line, which should constitute a greater interest. In this study we introduce the first chemoinformatic multi-target (mt) approach for the in silico design and virtual screening of anti-BC agents against 13 cell lines. Here, an mt-QSAR discriminant model was developed using a large and heterogeneous database of compounds. The model correctly classified 88.47% and 92.75% of active and inactive compounds respectively, in training set. The validation of the model was carried out by using a prediction set which showed 89.79% of correct classification for active and 92.49% for inactive compounds. Some fragments were extracted from the molecules and their contributions to anti-BC activity were calculated. Several fragments were identified as potential substructural features responsible for anti-BC activity and new molecules designed from those fragments with positive contributions were suggested as possible potent and versatile anti-BC agents.

Long-term follow-up of breast capsule contracture rates in cosmetic and reconstructive cases.

Background: Silicone gel breast implants are associated with long-term adverse events, including capsular contracture, with reported incidence rates as high as 50 percent. However, it is not clear how long the follow-up period should be and whether there is any association with estrogen or menopausal status. In addition, the placement of Baker grade II subjects in the majority of reports has been in data sets of controls instead of capsular contracture. Methods: A retrospective medical study (1998 to 2004) was performed in women (n = 157) who received textured silicone breast implants for aesthetic or reconstructive procedures at the Hospital of S. João (Portugal). Medical data were collected that included the following: patient demographics, history, lifestyle factors, surgical procedures, and postoperative complications. Statistical analyses included Pearson chi-square testing, logistic regression modeling, and chi-squared automatic interaction detection (CHAID) methods. Results: The reconstructive cohort had a great incidence of capsular contracture compared with the cosmetic cohort. If one considered no capsular contracture versus capsular contracture, the follow-up period should be longer than 42 months. However, if considering no capsular contracture and grade II subjects versus grade III or IV subjects, a longer follow-up period of 64 months was determined. There was no association between capsular contracture and menopause/estrogen status. Conclusions: Increased frequencies of capsular contracture were recorded in breast reconstruction that were not attributable to estrogen or menopausal status. On the basis of these results, the authors propose a follow-up period longer than 42 months and the inclusion of Baker grade II subjects.