Markovian chemicals "in silico" design (MARCH-INSIDE), a promising approach for computer aided molecular design II: experimental and theoretical assessment of a novel method for virtual screening of fasciolicides

A novel method for in silico selection of fluckicidal drugs is introduced. Two QSARs that permit us to discriminate between fasciolicide and non-fasciolicide drugs (the first) and to outline some conclusions about the possible mechanism of action of a chemical (the second) are performed. The first model correctly classified 93.85% of compounds in the training series and 89.5% of the compounds in the predicting one. This model correctly classified 87.7, 93.8, 92.2 and 93.9% of compounds in leave- n-out cross validation procedures when n takes values from 2 to until 6. The model seems to be stable in around 92% of good classification in leave- n-out cross validation analysis when n>6. The second model correctly classified 70% of non-fasciolicide compounds, 85.71% of beta-tubulin inhibitors and 100% of proton ionophores in the training set. This model recognizes as proton ionophores 100% of any nitrosalicylanilides in the predicting series. Both models have a low p-level <0.05. Finally, the experimental assay of six organic chemicals by an in vivo test permit us to carry out an assessment of the model with a fairly good 100% agreement between experiment and theoretical prediction.     

Multi-target spectral moment QSAR versus ANN for antiparasitic drugs against different parasite species

There are many of pathogen parasite species with different susceptibility profile to antiparasitic drugs. Unfortunately, almost QSAR models predict the biological activity of drugs against only one parasite species. Consequently, predicting the probability with which a drug is active against different species with a single unify model is a goal of the major importance. In so doing, we use Markov Chains theory to calculate new multi-target spectral moments to fit a QSAR model that predict by the first time a mt-QSAR model for 500 drugs tested in the literature against 16 parasite species and other 207 drugs no tested in the literature using spectral moments. The data was processed by linear discriminant analysis (LDA) classifying drugs as active or non-active against the different tested parasite species. The model correctly classifies 311 out of 358 active compounds (86.9%) and 2328 out of 2577 non-active compounds (90.3%) in training series. Overall training performance was 89.9%. Validation of the model was carried out by means of external predicting series. In these series the model classified correctly 157 out 190, 82.6% of antiparasitic compounds and 1151 out of 1277 non-active compounds (90.1%). Overall predictability performance was 89.2%. In addition we developed four types of non Linear Artificial neural networks (ANN) and we compared with the mt-QSAR model. The improved ANN model had an overall training performance was 87%. The present work report the first attempts to calculate within a unify framework probabilities of antiparasitic action of drugs against different parasite species based on spectral moment analysis.     

3D QSAR Markov model for drug-induced eosinophilia--theoretical prediction and preliminary experimental assay of the antimicrobial drug G1

The application of 3D-MEDNEs as a novel alternative technique to reduce the use of animal experimentation in toxicology in the early stages of medicinal chemistry research has been extended from agranulocytosis to chemically induced eosinophilia. Firstly, a heterogeneous series of organic compounds, which are classified either as eosinophilia inductors or noninductors, was collected. A linear discriminant analysis was subsequently used to obtain a QSTR that gave rise to a very good classification of 91.82% (110 chemicals within training series). Eosinophilia inductors (88.89%) composed the first group while the other one contained only harmless compounds (97.37%). The total predictability (88.1%) was tested by means of an external validation series (42 compounds). The model correctly classifies 88.89% of harmless compounds and 87.5% of toxic ones. Finally, comparison of predicted versus experimental results for G1 [2-bromo-5-(2-bromo-2-nitroethenyl)furan, which is a promising antibacterial-antifungal compound] illustrates the practical application of the method. A dose-dependent study of G1 (9.8-185.6 mg/Kg) at 48, 72 and 96 h after oral administration in rats is reported here for the first time. The study has shown that G1 does not affect the murine eosinophils count under these conditions--a situation in total agreement with the model prediction.     

TOPS-MODE based QSARs derived from heterogeneous series of compounds. Applications to the design of new anti-inflammatory compounds

A new application of TOPological Sub-structural MOlecular DEsign (TOPS-MODE) was carried out in anti-inflammatory compounds using computer-aided molecular design. Two series of compounds, one containing anti-inflammatory and the other containing nonanti-inflammatory compounds were processed by a k-means cluster analysis in order to design the training and prediction sets. A linear classification function to discriminate the anti-inflammatory from the inactive compounds was developed. The model correctly and clearly classified 88% of active and 91% of inactive compounds in the training set. More specifically, the model showed a good global classification of 90%, that is, (399 cases out of 441). While in the prediction set, they showed an overall predictability of 88% and 84% for active and inactive compounds, being the global percentage of good classification of 85%. Furthermore this paper describes a fragment analysis in order to determine the contribution of several fragments towards anti-inflammatory property, also the present of halogens in the selected fragments were analyzed. It seems that the present TOPS-MODE based QSAR is the first alternate general 'in silico' technique to experimentation in anti-inflammatory discovery.     

Unified QSAR approach to antimicrobials. Part 2: predicting activity against more than 90 different species in order to halt antibacterial resistance

There are many different kinds of pathogenic bacteria species with very different susceptibility profiles to different antibacterial drugs. One limitation of QSAR models is that they consider the biological activity of drugs against only one species of bacteria. In a previous paper, we developed a unified Markov model to describe the biological activity of different drugs tested in the literature against some antimicrobial species. Consequently, predicting the probability with which a drug is active against different species of bacteria with a single unified model is a goal of major importance. The work described here develops the unified Markov model to describe the biological activity of more than 70 drugs from the literature tested against 96 species of bacteria. We applied linear discriminant analysis (LDA) to classify drugs as active or inactive against the different tested bacterial species. The model correctly classified 199 out of 237 active compounds (83.9%) and 168 out of 200 inactive compounds (84%). Overall training predictability was 84% (367 out of 437 cases). Validation of the model was carried out using an external predicting series, with the model classifying 202 out of 243 (i.e., 83.13%) of the compounds. In order to show how the model functions in practice, a virtual screening was carried out and the model recognized as active 84.5% (480 out of 568) antibacterial compounds not used in the training or predicting series. The current study is an attempt to calculate within a unified framework the probabilities of antibacterial action of drugs against many different species.     

Synthesis,cytotoxic evaluation,docking and in silico pharmacokinetic prediction of 4-arylideneamino/cycloalkylidineamino 1, 2-naphthoquinone thiosemicarbazones

In an attempt to develop potent anticancer agents, a series of 4-arylideneamino/cycloalkylidineamino-1, 2-naphthoquinone thiosemicarbazones were synthesized and characterized using FT-IR, ¹H NMR, ¹³C NMR spectroscopy and elemental analysis. The compounds were screened for antiproliferative activity against three human cancer cell lines (Hep-G2, MG-63 and MCF-7) using the MTT assay. Significant anticancer activity was observed for several members of the series. The compounds 4-(3, 4, 5-trimethoxybenzylidene amino) 1, 2-naphthoquinone-2-thiosemicarbazone (TS10) and 4-(4-hydroxy-3-methoxy benzylideneamino) 1, 2-naphthoquinone-2-thiosemicarbazone (TS13) were active cytotoxic agents in all three cancer cell lines, with IC₅₀ values in the range of 3.5–6.4 µM. Further evaluation of some of these potent cytotoxic compounds demonstrated their good safety profile in a normal cell line (MCF-12A). Docking experiments showed a good correlation between the predicted glide scores and the IC₅₀ values of these compounds. In silico ADME studies revealed that these compounds can be used for second generation development.     

Discovery,synthesis and molecular substantiation of N-(benzo[d]thiazol-2-yl)-2-hydroxyquinoline-4-carboxamides as anticancer agents

The effort was taken to develop a series of benzothiazole and quinoline fused bioactive compounds obtained through a four-step synthetic route using a range of substituted acetoacetanilides. Achieved N-(benzo[d]thiazol-2-yl)-2-hydroxyquinoline-4-carboxamides (6a-l) were produced up to 96% of yield while the eco-friendly p-TSA used as a catalyst. Further, the anticancer activity of these compounds was determined using a range of cancer cell lines starting from MCF-7 (Breast cancer), HCT-116 (Colon cancer), PC-3 & LNCaP (Prostate) and SK-HEP-1 (Liver cancer). Present study compounds were also testified for antioxidant properties prior to anticancer studies since the Reactive Oxygen Species (ROS) being vital in cancer development. To determine the cell membrane stability effects of the compounds, human red blood cells (HRBC) based membrane protection assay was determined. In the results, compounds 6a-l were able to produce a dominated result values over PC3 cell lines (Prostate cancer) than the other cell lines used in this study. Since the connectivity of human germ cell alkaline phosphatase (hGC-ALP) in the development of prostate cancer is known, the most active compounds were evaluated for the hGC-ALP inhibition in order to ensure a mechanism of anticancer action of these compounds. The mode of interaction and binding affinity of these compounds was also investigated by a molecular docking study. In the results, 6d, 6i, 6k, and 6l were found with least IC₅₀ values <0.075 µM and highest relative activity of 92%, 90%, and 96% respectively. The need for further animal model evaluation and pre-clinical studies recognized.     

Synthesis, growth inhibition, and cell cycle evaluations of novel flavonoid derivatives

As a continuation of our search for potential new anticancer agents, a series of ten flavonoid derivatives has been synthesized by cyclization of substituted chalcones. Target compounds were evaluated for their biological activity. Among them, compounds 1–4 and 9 displayed a significant growth inhibitory action against a panel of tumor cell lines including Jurkat, PC-3, and Colon 205. On treatment with an equitoxic (IC₅₀) concentration, compounds 1–5 and 7–9 blocked cells in the G2/M phase of the Jurkat cell cycle, whereas compound 6 blocked the same in the G0/G1 phase.     

Unified QSAR approach to antimicrobials. Part 3: first multi-tasking QSAR model for input-coded prediction, structural back-projection, and complex networks clustering of antiprotozoal compounds

Several pathogen parasite species show different susceptibilities to different antiparasite drugs. Unfortunately, almost all structure-based methods are one-task or one-target Quantitative Structure-Activity Relationships (ot-QSAR) that predict the biological activity of drugs against only one parasite species. Consequently, multi-tasking learning to predict drugs activity against different species by a single model (mt-QSAR) is vitally important. In the two previous works of the present series we reported two single mt-QSAR models in order to predict the antimicrobial activity against different fungal (Bioorg. Med. Chem.2006, 14, 5973-5980) or bacterial species (Bioorg. Med. Chem.2007, 15, 897-902). These mt-QSARs offer a good opportunity (unpractical with ot-QSAR) to construct drug-drug similarity Complex Networks and to map the contribution of sub-structures to function for multiple species. These possibilities were unattended in our previous works. In the present work, we continue this series toward other important direction of chemotherapy (antiparasite drugs) with the development of an mt-QSAR for more than 500 drugs tested in the literature against different parasites. The data were processed by Linear Discriminant Analysis (LDA) classifying drugs as active or non-active against the different tested parasite species. The model correctly classifies 212 out of 244 (87.0%) cases in training series and 207 out of 243 compounds (85.4%) in external validation series. In order to illustrate the performance of the QSAR for the selection of active drugs we carried out an additional virtual screening of antiparasite compounds not used in training or predicting series; the model recognized 97 out of 114 (85.1%) of them. We also give the procedures to construct back-projection maps and to calculate sub-structures contribution to the biological activity. Finally, we used the outputs of the QSAR to construct, by the first time, a multi-species Complex Networks of antiparasite drugs. The network predicted has 380 nodes (compounds), 634 edges (pairs of compounds with similar activity). This network allows us to cluster different compounds and identify on average three known compounds similar to a new query compound according to their profile of biological activity. This is the first attempt to calculate probabilities of antiparasitic action of drugs against different parasites.     

Synthesis, dihydrofolate reductase inhibition, antitumor testing, and molecular modeling study of some new 4(3H)-quinazolinone analogs

In order to produce potent new leads for anticancer drugs, a new series of quinazoline analogs was designed to resemble methotrexate (MTX, 1) structure features and fitted with functional groups believed to enhance inhibition of mammalian DHFR activity. Molecular modeling studies were used to assess the fit of these compounds within the active site of human DHFR. The synthesized compounds were evaluated for their ability to inhibit mammalian DHFR in vitro and for their antitumor activity in a standard in vitro tissue culture assay panel. Compounds 28, 30, and 31 were the most active DHFR inhibitors with IC₅₀ values of 0.5, 0.4, and 0.4 μM, respectively. The most active antitumor agents in this study were compounds 19, 31, 41, and 47 with median growth inhibitory concentrations (GI₅₀) of 20.1, 23.5, 26.7, and 9.1 μM, respectively. Of this series of compounds, only compound 31 combined antitumor potency with potent DHFR inhibition; the other active antitumor compounds (19, 41, and 47) all had DHFR IC₅₀ values above 15 μM, suggesting that they might exert their antitumor potency through some other mode of action. Alternatively, the compounds could differ significantly in uptake or concentration within mammalian cells.     

TOPS-MODE model of multiplexing neuroprotective effects of drugs and experimental-theoretic study of new 1,3-rasagiline derivatives potentially useful in neurodegenerative diseases

The interest on computational techniques for the discovery of neuroprotective drugs has increased due to recent fail of important clinical trials. In fact, there is a huge amount of data accumulated in public databases like CHEMBL with respect to structurally heterogeneous series of drugs, multiple assays, drug targets, and model organisms. However, there are no reports of multi-target or multiplexing Quantitative Structure–Property Relationships (mt-QSAR/mx-QSAR) models of these multiplexing assay outcomes reported in CHEMBL for neurotoxicity/neuroprotective effects of drugs. Accordingly, in this paper we develop the first mx-QSAR model for multiplexing assays of neurotoxicity/neuroprotective effects of drugs. We used the method TOPS-MODE to calculate the structural parameters of drugs. The best model found correctly classified 4393 out of 4915 total cases in both training and validation. This is representative of overall train and validation Accuracy, Sensitivity, and Specificity values near to 90%, 98%, and 80%, respectively. This dataset includes multiplexing assay endpoints of 2217 compounds. Every one compound was assayed in at least one out of 338 assays, which involved 148 molecular or cellular targets and 35 standard type measures in 11 model organisms (including human). The second aim of this work is the exemplification of the use of the new mx-QSAR model with a practical case of study. To this end, we obtained again by organic synthesis and reported, by the first time, experimental assays of the new 1,3-rasagiline derivatives 3 different tests: assay (1) in absence of neurotoxic agents, (2) in the presence of glutamate, and (3) in the presence of H₂O₂. The higher neuroprotective effects found for each one of these assays were for the stereoisomers of compound 7: compound 7b with protection = 23.4% in assay (1) and protection = 15.2% in assay (2); and for compound 7a with protection = 46.2% in assay (3). Interestingly, almost all compounds show protection values >10% in assay (3) but not in the other 2 assays. After that, we used the mx-QSAR model to predict the more probable response of the new compounds in 559 unique pharmacological tests not carried out experimentally. The results obtained are very significant because they complement the pharmacological studies of these promising rasagiline derivatives. This work paves the way for further developments in the multi-target/multiplexing screening of large libraries of compounds potentially useful in the treatment of neurodegenerative diseases.     

Identification of components of fibroadenoma in cytology preparations using texture analysis: a morphometric study

S. Singh and R. Gupta Identification of components of fibroadenoma in cytology preparations using texture analysis: a morphometric study Objectives: To evaluate the utility of image analysis using textural parameters obtained from a co‐occurrence matrix in differentiating the three components of fibroadenoma of the breast, in fine needle aspirate smears. Methods: Sixty cases of histologically proven fibroadenoma were included in this study. Of these, 40 cases were used as a training set and 20 cases were taken as a test set for the discriminant analysis. Digital images were acquired from cytological preparations of all the cases and three components of fibroadenoma (namely, monolayered cell clusters, stromal fragments and background with bare nuclei) were selected for image analysis. A co‐occurrence matrix was generated and a texture parameter vector (sum mean, energy, entropy, contrast, cluster tendency and homogeneity) was calculated for each pixel. The percentage of pixels correctly classified to a component of fibroadenoma on discriminant analysis was noted. Results: The textural parameters, when considered in isolation, showed considerable overlap in their values of the three cytological components of fibroadenoma. However, the stepwise discriminant analysis revealed that all six textural parameters contributed significantly to the discriminant functions. Discriminant analysis using all the six parameters showed that the numbers of pixels correctly classified in training and tests sets were 96.7% and 93.0%, respectively. Conclusion: Textural analysis using a co‐occurrence matrix appears to be useful in differentiating the three cytological components of fibroadenoma. These results could further be utilized in developing algorithms for image segmentation and automated diagnosis, but need to be confirmed in further studies.