Antiprotozoan lead discovery by aligning dry and wet screening: prediction, synthesis, and biological assay of novel quinoxalinones.

Protozoan parasites have been one of the most significant public health problems for centuries and several human infections caused by them have massive global impact. Most of the current drugs used to treat these illnesses have been used for decades and have many limitations such as the emergence of drug resistance, severe side-effects, low-to-medium drug efficacy, administration routes, cost, etc. These drugs have been largely neglected as models for drug development because they are majorly used in countries with limited resources and as a consequence with scarce marketing possibilities. Nowadays, there is a pressing need to identify and develop new drug-based antiprotozoan therapies. In an effort to overcome this problem, the main purpose of this study is to develop a QSARs-based ensemble classifier for antiprotozoan drug-like entities from a heterogeneous compounds collection. Here, we use some of the TOMOCOMD-CARDD molecular descriptors and linear discriminant analysis (LDA) to derive individual linear classification functions in order to discriminate between antiprotozoan and non-antiprotozoan compounds as a way to enable the computational screening of virtual combinatorial datasets and/or drugs already approved. Firstly, we construct a wide-spectrum benchmark database comprising of 680 organic chemicals with great structural variability (254 of them antiprotozoan agents and 426 to drugs having other clinical uses). This series of compounds was processed by a k-means cluster analysis in order to design training and predicting sets. In total, seven discriminant functions were obtained, by using the whole set of atom-based linear indices. All the LDA-based QSAR models show accuracies above 85% in the training set and values of Matthews correlation coefficients (C) vary from 0.70 to 0.86. The external validation set shows rather-good global classifications of around 80% (92.05% for best equation). Later, we developed a multi-agent QSAR classification system, in which the individual QSAR outputs are the inputs of the aforementioned fusion approach. Finally, the fusion model was used for the identification of a novel generation of lead-like antiprotozoan compounds by using ligand-based virtual screening of 'available' small molecules (with synthetic feasibility) in our 'in-house' library. A new molecular subsystem (quinoxalinones) was then theoretically selected as a promising lead series, and its derivatives subsequently synthesized, structurally characterized, and experimentally assayed by using in vitro screening that took into consideration a battery of five parasite-based assays. The chemicals 11(12) and 16 are the most active (hits) against apicomplexa (sporozoa) and mastigophora (flagellata) subphylum parasites, respectively. Both compounds depicted good activity in every protozoan in vitro panel and they did not show unspecific cytotoxicity on the host cells. The described technical framework seems to be a promising QSAR-classifier tool for the molecular discovery and development of novel classes of broad-antiprotozoan-spectrum drugs, which may meet the dual challenges posed by drug-resistant parasites and the rapid progression of protozoan illnesses.

Synthesis, biological evaluation and chemometric analysis of indazole derivatives. 1,2-Disubstituted 5-nitroindazolinones, new prototypes of antichagasic drug.

Chagas disease chemotherapy, currently based on only two drugs, nifurtimox and benznidazole, is far from satisfactory and therefore the development of new antichagasic compounds remains an important goal. On the basis of antichagasic properties previously described for some 1,2-disubstituted 5-nitroindazolin-3-ones (21, 33) and in order to initiate the optimization of activity of this kind of compounds, we have prepared a series of related analogs (22-32, 34-38, 58 and 59) and tested in vitro these products against epimastigote forms of Trypanosoma cruzi. 2-Benzyl-1-propyl (22), 2-benzyl-1-isopropyl (23) and 2-benzyl-1-butyl (24) derivatives have shown high trypanocidal activity and low unspecific toxicity. Other indazole derivatives with different substitution patterns (1-substituted 3-alkoxy-1H-indazoles and 2-substituted 3-alkoxy-2H-indazoles), arising from the synthetic procedures used to prepare the mentioned indazolinones, have moderate to low effectiveness. The exploration of SAR information using the concept of an activity landscape has been carried out with SARANEA software. We have also searched for structural similarities between 225 known antiprotozoan drugs and compound 22. The results confirm that compounds 22-24 constitute promising leads and that 5-nitroindazolin-3-one system is a novel anti-T. cruzi scaffold which may represent an important therapeutic alternative for the treatment of Chagas disease.

Activity of a hydroxybibenzyl bryophyte constituent against Leishmania spp. and Trypanosoma cruzi: in silico, in vitro and in vivo activity studies.

The synthesis and potent antiprotozoal activity of 14-hydroxylunularin, a natural hydroxybibenzyl bryophyte constituent is reported. 14-hydroxylunularin was highly active in vitro assays against culture and intracellular forms of Leishmania spp. and Trypanosoma. cruzi, in absence of cytotoxicity against mammalian cells. Preliminary structure-activity relationship studies showed that the reported bioactivity depends on hybridization at the carbon-carbon bridge, position and number of free hydroxy group on the aromatic rings. The reported results were also in agreement with the in silico prediction using Non-Stochastic Quadratic Fingerprints-based algorithms. The same compound also showed antiprotozoal activity in Leishmania spp. infected mice by oral and subcutaneous administration routes, with an optimal treatment of a daily subcutaneous administration of 10 mg/kg of body weight for 15 days. This study suggested that 14-hydroxylunularin may be chosen as a new candidate in the development of leishmanicidal therapy.

Non-stochastic quadratic fingerprints and LDA-based QSAR models in hit and lead generation through virtual screening: theoretical and experimental assessment of a promising method for the discovery of new antimalarial compounds.

In order to explore the ability of non-stochastic quadratic indices to encode chemical information in antimalarials, four quantitative models for the discrimination of compounds having this property were generated and statistically compared. Accuracies of 90.2% and 83.3% for the training and test sets, respectively, were observed for the best of all the models, which included non-stochastic quadratic fingerprints weighted with Pauling electronegativities. With a comparative purpose and as a second validation experiment, an exercise of virtual screening of 65 already-reported antimalarials was carried out. Finally, 17 new compounds were classified as either active/inactive ones and experimentally evaluated for their potential antimalarial properties on the ferriprotoporphyrin (FP) IX biocrystallization inhibition test (FBIT). The theoretical predictions were in agreement with the experimental results. In the assayed test compound C5 resulted more active than chloroquine. The current result illustrates the usefulness of the TOMOCOMD-CARDD strategy in rational antimalarial-drug design, at the time that it introduces a new family of organic compounds as starting point for the development of promising antimalarials.

New ligand-based approach for the discovery of antitrypanosomal compounds.

The antitrypanosomal activity of 10 already synthesized compounds was in silico predicted as well as in vitro and in vivo explored against Trypanosoma cruzi. For the computational study, an approach based on non-stochastic linear fingerprints to the identification of potential antichagasic compounds is introduced. Molecular structures of 66 organic compounds, 28 with antitrypanosomal activity and 38 having other clinical uses, were parameterized by means of the TOMOCOMD-CARDD software. A linear classification function was derived allowing the discrimination between active and inactive compounds with a confidence of 95%. As predicted, seven compounds showed antitrypanosomal activity (%AE>70) against epimastigotic forms of T. cruzi at a concentration of 100mug/mL. After an unspecific cytotoxic assay, three compounds were evaluated against amastigote forms of the parasite. An in vivo test was carried out for one of the studied compounds.

A novel non-stochastic quadratic fingerprints-based approach for the 'in silico' discovery of new antitrypanosomal compounds.

A non-stochastic quadratic fingerprints-based approach is introduced to classify and design, in a rational way, new antitrypanosomal compounds. A data set of 153 organic chemicals, 62 with antitrypanosomal activity and 91 having other clinical uses, was processed by a k-means cluster analysis to design training and predicting data sets. Afterwards, a linear classification function was derived allowing the discrimination between active and inactive compounds. The model classifies correctly more than 93% of chemicals in both training and external prediction groups. The predictability of this discriminant function was also assessed by a leave-group-out experiment, in which 10% of the compounds were removed at random at each time and their activity predicted a posteriori. In addition, a comparison with models generated using four well-known families of 2D molecular descriptors was carried out. As an experiment of virtual lead generation, the present TOMOCOMD approach was finally satisfactorily applied on the virtual evaluation of 10 already synthesized compounds. The in vitro antitrypanosomal activity of this series against epimastigotes forms of Trypanosomal cruzi was assayed. The model was able to predict correctly the behaviour of these compounds in 90% of the cases.

Ligand-based virtual screening and in silico design of new antimalarial compounds using nonstochastic and stochastic total and atom-type quadratic maps.

Malaria has been one of the most significant public health problems for centuries. It affects many tropical and subtropical regions of the world. The increasing resistance of Plasmodium spp. to existing therapies has heightened alarms about malaria in the international health community. Nowadays, there is a pressing need for identifying and developing new drug-based antimalarial therapies. In an effort to overcome this problem, the main purpose of this study is to develop simple linear discriminant-based quantitative structure-activity relationship (QSAR) models for the classification and prediction of antimalarial activity using some of the TOMOCOMD-CARDD (TOpological MOlecular COMputer Design-Computer Aided "Rational" Drug Design) fingerprints, so as to enable computational screening from virtual combinatorial datasets. In this sense, a database of 1562 organic chemicals having great structural variability, 597 of them antimalarial agents and 965 compounds having other clinical uses, was analyzed and presented as a helpful tool, not only for theoretical chemists but also for other researchers in this area. This series of compounds was processed by a k-means cluster analysis in order to design training and predicting sets. Afterward, two linear classification functions were derived in order to discriminate between antimalarial and nonantimalarial compounds. The models (including nonstochastic and stochastic indices) correctly classify more than 93% of the compound set, in both training and external prediction datasets. They showed high Matthews' correlation coefficients, 0.889 and 0.866 for the training set and 0.855 and 0.857 for the test one. The models' predictivity was also assessed and validated by the random removal of 10% of the compounds to form a new test set, for which predictions were made using the models. The overall means of the correct classification for this process (leave group 10% full-out cross validation) using the equations with nonstochastic and stochastic atom-based quadratic fingerprints were 93.93% and 92.77%, respectively. The quadratic maps-based TOMOCOMD-CARDD approach implemented in this work was successfully compared with four of the most useful models for antimalarials selection reported to date. The developed models were then used in a simulation of a virtual search for Ras FTase (FTase = farnesyltransferase) inhibitors with antimalarial activity; 70% and 100% of the 10 inhibitors used in this virtual search were correctly classified, showing the ability of the models to identify new lead antimalarials. Finally, these two QSAR models were used in the identification of previously unknown antimalarials. In this sense, three synthetic intermediaries of quinolinic compounds were evaluated as active/inactive ones using the developed models. The synthesis and biological evaluation of these chemicals against two malaria strains, using chloroquine as a reference, was performed. An accuracy of 100% with the theoretical predictions was observed. Compound 3 showed antimalarial activity, being the first report of an arylaminomethylenemalonate having such behavior. This result opens a door to a virtual study considering a higher variability of the structural core already evaluated, as well as of other chemicals not included in this study. We conclude that the approach described here seems to be a promising QSAR tool for the molecular discovery of novel classes of antimalarial drugs, which may meet the dual challenges posed by drug-resistant parasites and the rapid progression of malaria illnesses.

A linear discrimination analysis based virtual screening of trichomonacidal lead-like compounds: outcomes of in silico studies supported by experimental results.

A computational (virtual) screening test to identify potential trichomonacidals has been developed. Molecular structures of trichomonacidal and non-trichomonacidal drugs were represented using stochastic and non-stochastic atom-based quadratic indices and a linear discrimination analysis (LDA) was trained to classify molecules regarding their antiprotozoan activity. Validation tests revealed that our LDA-QSAR models recognize at least 88.24% of trichomonacidal lead-like compounds and suggest using this methodology in virtual screening protocols. These classification functions were then applied to find new lead antitrichomonal compounds. In this connection, the biological assays of eight compounds, selected by computational screening using the present models, give good results (87.50% of good classification). In general, most of the compounds showed high activity against Trichomonas vaginalis at the concentration of 100 microg/ml and low cytotoxicity to this concentration. In particular, two heterocyclic derivatives (VA7-67 and VA7-69) maintained their efficacy at 10 microg/ml with an important trichomonacidal activity (100.00% of reduction), but it is remarkable that the compound VA7-67 did not show cytotoxic effects in macrophage cultivations. This result opens a door to a virtual study considering a higher variability of the structural core already evaluated, as well as of other chemicals not included in this study.

Ligand-Based Virtual Screening and in Silico Design of New Antimalarial Compounds Using Nonstochastic and Stochastic Total and Atom-Type Quadratic Maps

Malaria has been one of the most significant public health problems for centuries. It affects many tropical and subtropical regions of the world. The increasing resistance of Plasmodium spp. to existing therapies has heightened alarms about malaria in the international health community. Nowadays, there is a pressing need for identifying and developing new drug-based antimalarial therapies. In an effort to overcome this problem, the main purpose of this study is to develop simple linear discriminant-based quantitative structure-activity relationship (QSAR) models for the classification and prediction of antimalarial activity using some of the TOMOCOMD-CARDD (TOpological MOlecular COMputer Design-Computer Aided "Rational" Drug Design) fingerprints, so as to enable computational screening from virtual combinatorial datasets. In this sense, a database of 1562 organic chemicals having great structural variability, 597 of them antimalarial agents and 965 compounds having other clinical uses, was analyzed and presented as a helpful tool, not only for theoretical chemists but also for other researchers in this area. This series of compounds was processed by a k-means cluster analysis in order to design training and predicting sets. Afterward, two linear classification functions were derived in order to discriminate between antimalarial and nonantimalarial compounds. The models (including nonstochastic and stochastic indices) correctly classify more than 93% of the compound set, in both training and external prediction datasets. They showed high Matthews' correlation coefficients, 0.889 and 0.866 for the training set and 0.855 and 0.857 for the test one. The models' predictivity was also assessed and validated by the random removal of 10% of the compounds to form a new test set, for which predictions were made using the models. The overall means of the correct classification for this process (leave group 10% full-out cross validation) using the equations with nonstochastic and stochastic atom-based quadratic fingerprints were 93.93% and 92.77%, respectively. The quadratic maps-based TOMOCOMD-CARDD approach implemented in this work was successfully compared with four of the most useful models for antimalarials selection reported to date. The developed models were then used in a simulation of a virtual search for Ras FTase (FTase = farnesyltransferase) inhibitors with antimalarial activity; 70% and 100% of the 10 inhibitors used in...

Non-stochastic and stochastic linear indices of the 'molecular pseudograph's atom adjacency matrix': application to 'in silico' studies for the rational discovery of new antimalarial compounds.

Malaria is one of the most deadly diseases, affecting million of people especially in developing countries. Because of the rapidly increasing threat worldwide of malaria epidemics multidrugs resistant to therapies, there is an urgent global need to discover new classes of antimalarial compounds. In an effort to overcome this problem, we have investigated the use of structure-based classification models for the 'rational' selection/identification or design/optimization of new lead antimalarials from virtual combinatorial data sets. In this sense, TOpological MOlecular COMputer Design strategy (TOMOCOMD approach) has been introduced in order to obtain two quantitative models for the discrimination of antimalarials. A collected data set containing 597 antimalarial compounds is presented as a helpful tool not only for theoretical chemist but for other researchers in this area. The validated models (including non-stochastic and stochastic indices) classify correctly more than 90% of compounds in both training and external prediction data sets. They showed high Matthews' correlation coefficients; 0.87 and 0.82 for training and 0.86 and 0.79 for test set. The TOMOCOMD-CARDD approach implemented in this work was successfully compared with two of the most useful models for antimalarials selection reported so far. Thus we expect that these two QSAR models can be used in the identification of previously un-known antimalarials compounds.

A computer-based approach to the rational discovery of new trichomonacidal drugs by atom-type linear indices.

Computational approaches are developed to design or rationally select, from structural databases, new lead trichomonacidal compounds. First, a data set of 111 compounds was split (design) into training and predicting series using hierarchical and partitional cluster analyses. Later, two discriminant functions were derived with the use of non-stochastic and stochastic atom-type linear indices. The obtained LDA (linear discrimination analysis)-based QSAR (quantitative structure-activity relationship) models, using non-stochastic and stochastic descriptors were able to classify correctly 95.56% (90.48%) and 91.11% (85.71%) of the compounds in training (test) sets, respectively. The result of predictions on the 10% full-out cross-validation test also evidenced the quality (robustness, stability and predictive power) of the obtained models. These models were orthogonalized using the Randic orthogonalization procedure. Afterwards, a simulation experiment of virtual screening was conducted to test the possibilities of the classification models developed here in detecting antitrichomonal chemicals of diverse chemical structures. In this sense, the 100.00% and 77.77% of the screened compounds were detected by the LDA-based QSAR models (Eq. 13 and Eq. 14, correspondingly) as trichomonacidal. Finally, new lead trichomonacidals were discovered by prediction of their antirichomonal activity with obtained models. The most of tested chemicals exhibit the predicted antitrichomonal effect in the performed ligand-based virtual screening, yielding an accuracy of the 90.48% (19/21). These results support a role for TOMOCOMD-CARDD descriptors in the biosilico discovery of new compounds.