Multiquery Similarity Searching Models: An Alternative Approach for Predicting Hemolytic Activity from Peptide Sequence.

The desirable pharmacological properties and a broad number of therapeutic activities have made peptides promising drugs over small organic molecules and antibody drugs. Nevertheless, toxic effects, such as hemolysis, have hampered the development of such promising drugs. Hence, a reliable computational tool to predict peptide hemolytic toxicity is enormously useful before synthesis and experimental evaluation. Currently, four web servers that predict hemolytic activity using machine learning (ML) algorithms are available; however, they exhibit some limitations, such as the need for a reliable negative set and limited application domain. Hence, we developed a robust model based on a novel theoretical approach that combines network science and a multiquery similarity searching (MQSS) method. A total of 1152 initial models were constructed from 144 scaffolds generated in a previous report. These were evaluated on external data sets, and the best models were fused and improved. Our best MQSS model I1 outperformed all state-of-the-art ML-based models and was used to characterize the prevalence of hemolytic toxicity on therapeutic peptides. Based on our model's estimation, the number of hemolytic peptides might be 3.9-fold higher than the reported.

Rethinking the applicability domain analysis in QSAR models.

Notwithstanding the wide adoption of the OECD principles (or best practices) for QSAR modeling, disparities between in silico predictions and experimental results are frequent, suggesting that model predictions are often too optimistic. Of these OECD principles, the applicability domain (AD) estimation has been recognized in several reports in the literature to be one of the most challenging, implying that the actual reliability measures of model predictions are often unreliable. Applying tree-based error analysis workflows on 5 QSAR models reported in the literature and available in the QsarDB repository, i.e., androgen receptor bioactivity (agonists, antagonists, and binders, respectively) and membrane permeability (highest membrane permeability and the intrinsic permeability), we demonstrate that predictions erroneously tagged as reliable (AD prediction errors) overwhelmingly correspond to instances in subspaces (cohorts) with the highest prediction error rates, highlighting the inhomogeneity of the AD space. In this sense, we call for more stringent AD analysis guidelines which require the incorporation of model error analysis schemes, to provide critical insight on the reliability of underlying AD algorithms. Additionally, any selected AD method should be rigorously validated to demonstrate its suitability for the model space over which it is applied. These steps will ultimately contribute to more accurate estimations of the reliability of model predictions. Finally, error analysis may also be useful in "rational" model refinement in that data expansion efforts and model retraining are focused on cohorts with the highest error rates.

Validation of venous thromboembolism predictive model in hematologic malignancies.

Although several scores stratify venous thromboembolism (VTE) risk in solid tumors, hematologic malignancies (HM) are underrepresented. To develop an internal and external validation of a logistic regression model to predict VTE risk in hospitalized HM patients. Validation of the existing VTE predictive model was performed through a prospective case-control study in 496 hospitalized HM patients between December 2010 and 2020 at the Arnaldo Milián University Hospital, Cuba. The predictive model designed with data from 285 patients includes 5 predictive factors: hypercholesterolemia, tumoral activity, use of thrombogenic drugs, diabetes mellitus, and immobilization. The model was internally validated using bootstrap analysis. External validation was realized in a prospective cohort of 211 HM patients. The predictive model had a 76.4% negative predictive value (NPV) and an 81.7% positive predictive value (PPV) in the bootstrapping validation. The area under curve (AUC) in the bootstrapping set was 0.838. Accuracy was 80.1% and 82.9% in the internal and external validation, respectively. In the external validation, the model produced 89.7% of NPV, 67.7% of PPV, 74.6% of sensitivity, and 86.2% of specificity. The AUC in the external validation was 0.900. VTE predictive model is a reproducible and simple tool with good accuracy and discrimination.

Elucidating the Racemization Mechanism of Aliphatic and Aromatic Amino Acids by In Silico Tools.

The racemization of biomolecules in the active site can reduce the biological activity of drugs, and the mechanism involved in this process is still not fully comprehended. The present study investigates the impact of aromaticity on racemization using advanced theoretical techniques based on density functional theory. Calculations were performed at the ωb97xd/6-311++g(d,p) level of theory. A compelling explanation for the observed aromatic stabilization via resonance is put forward, involving a carbanion intermediate. The analysis, employing Hammett's parameters, convincingly supports the presence of a negative charge within the transition state of aromatic compounds. Moreover, the combined utilization of natural bond orbital (NBO) analysis and intrinsic reaction coordinate (IRC) calculations confirms the pronounced stabilization of electron distribution within the carbanion intermediate. To enhance our understanding of the racemization process, a thorough examination of the evolution of NBO charges and Wiberg bond indices (WBIs) at all points along the IRC profile is performed. This approach offers valuable insights into the synchronicity parameters governing the racemization reactions.

Synthetic Cinnamides and Cinnamates: Antimicrobial Activity, Mechanism of Action, and In Silico Study.

The severity of infectious diseases associated with the resistance of microorganisms to drugs highlights the importance of investigating bioactive compounds with antimicrobial potential. Therefore, nineteen synthetic cinnamides and cinnamates having a cinnamoyl nucleus were prepared and submitted for the evaluation of antimicrobial activity against pathogenic fungi and bacteria in this study. To determine the minimum inhibitory concentration (MIC) of the compounds, possible mechanisms of antifungal action, and synergistic effects, microdilution testing in broth was used. The structures of the synthesized products were characterized with FTIR spectroscopy, 1 H-NMR, 13 C-NMR, and HRMS. Derivative 6 presented the best antifungal profile, suggesting that the presence of the butyl substituent potentiates its biological response (MIC = 626.62 µM), followed by compound 4 (672.83 µM) and compound 3 (726.36 µM). All three compounds were fungicidal, with MFC/MIC ≤ 4. For mechanism of action, compounds 4 and 6 directly interacted with the ergosterol present in the fungal plasmatic membrane and with the cell wall. Compound 18 presented the best antibacterial profile (MIC = 458.15 µM), followed by compound 9 (550.96 µM) and compound 6 (626.62 µM), which suggested that the presence of an isopropyl group is important for antibacterial activity. The compounds were bactericidal, with MBC/MIC ≤ 4. Association tests were performed using the Checkerboard method to evaluate potential synergistic effects with nystatin (fungi) and amoxicillin (bacteria). Derivatives 6 and 18 presented additive effects. Molecular docking simulations suggested that the most likely targets of compound 6 in C. albicans were caHOS2 and caRPD3, while the most likely target of compound 18 in S. aureus was saFABH. Our results suggest that these compounds could be used as prototypes to obtain new antimicrobial drugs.

Molecular Modeling and In Vitro Evaluation of Piplartine Analogs against Oral Squamous Cell Carcinoma.

Cancer is a principal cause of death in the world, and providing a better quality of life and reducing mortality through effective pharmacological treatment remains a challenge. Among malignant tumor types, squamous cell carcinoma-esophageal cancer (EC) is usually located in the mouth, with approximately 90% located mainly on the tongue and floor of the mouth. Piplartine is an alkamide found in certain species of the genus Piper and presents many pharmacological properties including antitumor activity. In the present study, the cytotoxic potential of a collection of piplartine analogs against human oral SCC9 carcinoma cells was evaluated. The analogs were prepared via Fischer esterification reactions, alkyl and aryl halide esterification, and a coupling reaction with PyBOP using the natural compound 3,4,5-trimethoxybenzoic acid as a starting material. The products were structurally characterized using 1H and 13C nuclear magnetic resonance, infrared spectroscopy, and high-resolution mass spectrometry for the unpublished compounds. The compound 4-methoxy-benzyl 3,4,5-trimethoxybenzoate (9) presented an IC50 of 46.21 µM, high selectively (SI > 16), and caused apoptosis in SCC9 cancer cells. The molecular modeling study suggested a multi-target mechanism of action for the antitumor activity of compound 9 with CRM1 as the main target receptor.

Antileishmanial Activity of Cinnamic Acid Derivatives against Leishmania infantum.

Leishmania infantum is the etiological agent of visceral leishmaniasis (VL) in South America, the Mediterranean basin, and West and Central Asia. The most affected country, Brazil, reported 4297 VL cases in 2017. L. infantum is transmitted by female phlebotomine sand flies during successive blood meals. There are no validated vaccines to prevent the infection and the treatment relies on drugs that often present severe side effects, which justify the efforts to find new antileishmanial drugs. Cinnamic acid derivatives have shown several pharmacological activities, including antiparasitic action. Therefore, in the present study, the biological evaluation of cinnamic acid and thirty-four derivatives against L. infantum is reported. The compounds were prepared by several synthesis methods and characterized by spectroscopic techniques and high-resolution mass spectrometry. The results revealed that compound 32 (N-(4-isopropylbenzyl)cinnamamide) was the most potent antileishmanial agent (IC50 = 33.71 µM) with the highest selectivity index (SI > 42.46), followed by compound 15 (piperonyl cinnamate) with an IC50 = 42.80 µM and SI > 32.86. Compound 32 was slightly less potent and nineteen times more selective for the parasite than amphotericin B (MIC = 3.14 uM; SI = 2.24). In the molecular docking study, the most likely target for the compound in L. infantum was aspartyl aminopeptidase, followed by aldehyde dehydrogenase, mitochondrial. The data obtained show the antileishmanial potential of this class of compounds and may be used in the search for new drug candidates against Leishmania species.

Gallic Acid Alkyl Esters: Trypanocidal and Leishmanicidal Activity, and Target Identification via Modeling Studies.

Eight gallic acid alkyl esters (1−8) were synthesized via Fischer esterification and evaluated for their trypanocidal and leishmanicidal activity using bloodstream forms of Trypanosoma brucei and promastigotes of Leishmania major. The general cytotoxicity of the esters was evaluated with human HL-60 cells. The compounds displayed moderate to good trypanocidal but zero to low leishmanicidal activity. Gallic acid esters with alkyl chains of three or four carbon atoms in linear arrangement (propyl (4), butyl (5), and isopentyl (6)) were found to be the most trypanocidal compounds with 50% growth inhibition values of ~3 µM. On the other hand, HL-60 cells were less susceptible to the compounds, thus, resulting in moderate selectivity indices (ratio of cytotoxic to trypanocidal activity) of >20 for the esters 4−6. Modeling studies combining molecular docking and molecular dynamics simulations suggest that the trypanocidal mechanism of action of gallic acid alkyl esters could be related to the inhibition of the T. brucei alternative oxidase. This suggestion is supported by the observation that trypanosomes became immobile within minutes when incubated with the esters in the presence of glycerol as the sole substrate. These results indicate that gallic acid alkyl esters are interesting compounds to be considered for further antitrypanosomal drug development.

A population-based study of preeclampsia and eclampsia in Ecuador: ethnic, geographical and altitudes differences.

BACKGROUND: In Ecuador eclampsia and preeclampsia were identified as the third cause of maternal death. Like other Latin-American countries, Ecuador has human settlements living from 0 to more than 4000 m of altitude and comprising a wide ethnic-diversity across all these altitude changes. These characteristics offer the possibility to study a wide variety of possible risk factors for preeclampsia and eclampsia. METHODS: We conducted a population-based retrospective study of all deliveries in Ecuador from 2015 through 2017. The main variables analyzed were: altitude, ethnic self-identification, geographic location, and maternal age. The data comes from the Ecuadorian National Institute of Statistics and Census (INEC) and the Ecuadorian Ministry of Health. Data information regarding maternal parity and socioeconomic status was not available from official records. Logistic regression analysis was used to study the relationship between preeclampsia and eclampsia with the variable of interest. Geospatial statistical analysis was done to identify statistically significant spatial clusters of preeclampsia and eclampsia cases. RESULTS: The incidence of preeclampsia was estimated between 5.11 (5.05-5.18) and 6.23 (6.16-6.30), and 0.25 (0.23-0.26) for eclampsia. Native American have a lower incidence regarding preeclampsia compared to other ethnic groups. High altitude has a significant odds ratio (OR = 2.31, 1.93-2.78) of preeclampsia. Montubio residing in middle altitude (1500-3500 m) have the highest risk of preeclampsia (OR = 18.13, 9.53-34.50). Afro-Ecuadorians also have an increased risk of preeclampsia associated with altitude (OR = 2.36, 1.78-3.14). Ethnicity was not identified as a risk factor for eclampsia. Early and older maternal age was associated with an increased risk of preeclampsia and eclampsia. Women living more than 20 km from the obstetric unit have an OR = 2.61 (2.32-2.95, p-value< 0.01) and OR = 1.87 (1.82-1.92, p-value< 0.01) of developing eclampsia and preeclampsia respectively. CONCLUSIONS: Preeclampsia is widespread across low and high-altitude areas, while eclampsia is mostly located at lower altitudes. Montubios living at middle or high altitudes represents the ethnic group with a higher risk of preeclampsia. No ethnic effect was identified as a potential risk factor for eclampsia. Moreover, in eclampsia the associated risk of young women seems to be higher than in preeclampsia.

Structure-Function Relationships in the Human P-Glycoprotein (ABCB1): Insights from Molecular Dynamics Simulations.

P-Glycoprotein (P-gp) is a transmembrane protein belonging to the ATP binding cassette superfamily of transporters, and it is a xenobiotic efflux pump that limits intracellular drug accumulation by pumping compounds out of cells. P-gp contributes to a reduction in toxicity, and has broad substrate specificity. It is involved in the failure of many cancer and antiviral chemotherapies due to the phenomenon of multidrug resistance (MDR), in which the membrane transporter removes chemotherapeutic drugs from target cells. Understanding the details of the ligand-P-gp interaction is therefore critical for the development of drugs that can overcome the MDR phenomenon, for the early identification of P-gp substrates that will help us to obtain a more effective prediction of toxicity, and for the subsequent outdesign of substrate properties if needed. In this work, a series of molecular dynamics (MD) simulations of human P-gp (hP-gp) in an explicit membrane-and-water environment were performed to investigate the effects of binding different compounds on the conformational dynamics of P-gp. The results revealed significant differences in the behaviour of P-gp in the presence of active and non-active compounds within the binding pocket, as different patterns of movement were identified that could be correlated with conformational changes leading to the activation of the translocation mechanism. The predicted ligand-P-gp interactions are in good agreement with the available experimental data, as well as the estimation of the binding-free energies of the studied complexes, demonstrating the validity of the results derived from the MD simulations.

Antifungal Activity of N-(4-Halobenzyl)amides against Candida spp. and Molecular Modeling Studies.

Fungal infections remain a high-incidence worldwide health problem that is aggravated by limited therapeutic options and the emergence of drug-resistant strains. Cinnamic and benzoic acid amides have previously shown bioactivity against different species belonging to the Candida genus. Here, 20 cinnamic and benzoic acid amides were synthesized and tested for inhibition of C. krusei ATCC 14243 and C. parapsilosis ATCC 22019. Five compounds inhibited the Candida strains tested, with compound 16 (MIC = 7.8 µg/mL) producing stronger antifungal activity than fluconazole (MIC = 16 µg/mL) against C. krusei ATCC 14243. It was also tested against eight Candida strains, including five clinical strains resistant to fluconazole, and showed an inhibitory effect against all strains tested (MIC = 85.3-341.3 µg/mL). The MIC value against C. krusei ATCC 6258 was 85.3 mcg/mL, while against C. krusei ATCC 14243, it was 10.9 times smaller. This strain had greater sensitivity to the antifungal action of compound 16. The inhibition of C. krusei ATCC 14243 and C. parapsilosis ATCC 22019 was also achieved by compounds 2, 9, 12, 14 and 15. Computational experiments combining target fishing, molecular docking and molecular dynamics simulations were performed to study the potential mechanism of action of compound 16 against C. krusei. From these, a multi-target mechanism of action is proposed for this compound that involves proteins related to critical cellular processes such as the redox balance, kinases-mediated signaling, protein folding and cell wall synthesis. The modeling results might guide future experiments focusing on the wet-lab investigation of the mechanism of action of this series of compounds, as well as on the optimization of their inhibitory potency.

A Multi-Objective Approach for Drug Repurposing in Preeclampsia.

Preeclampsia is a hypertensive disorder that occurs during pregnancy. It is a complex disease with unknown pathogenesis and the leading cause of fetal and maternal mortality during pregnancy. Using all drugs currently under clinical trial for preeclampsia, we extracted all their possible targets from the DrugBank and ChEMBL databases and labeled them as "targets". The proteins labeled as "off-targets" were extracted in the same way but while taking all antihypertensive drugs which are inhibitors of ACE and/or angiotensin receptor antagonist as query molecules. Classification models were obtained for each of the 55 total proteins (45 targets and 10 off-targets) using the TPOT pipeline optimization tool. The average accuracy of the models in predicting the external dataset for targets and off-targets was 0.830 and 0.850, respectively. The combinations of models maximizing their virtual screening performance were explored by combining the desirability function and genetic algorithms. The virtual screening performance metrics for the best model were: the Boltzmann-Enhanced Discrimination of ROC (BEDROC)α=160.9 = 0.258, the Enrichment Factor (EF)1% = 31.55 and the Area Under the Accumulation Curve (AUAC) = 0.831. The most relevant targets for preeclampsia were: AR, VDR, SLC6A2, NOS3 and CHRM4, while ABCG2, ERBB2, CES1 and REN led to the most relevant off-targets. A virtual screening of the DrugBank database identified estradiol, estriol, vitamins E and D, lynestrenol, mifrepristone, simvastatin, ambroxol, and some antibiotics and antiparasitics as drugs with potential application in the treatment of preeclampsia.

LEGO-based generalized set of two linear algebraic 3D bio-macro-molecular descriptors: Theory and validation by QSARs.

Novel 3D protein descriptors based on bilinear, quadratic and linear algebraic maps in Rn are proposed. The latter employs the kth 2-tuple (dis) similarity matrix to codify information related to covalent and non-covalent interactions in these biopolymers. The calculation of the inter-amino acid distances is generalized by using several dis-similarity coefficients, where normalization procedures based on the simple stochastic and mutual probability schemes are applied. A new local-fragment approach based on amino acid-types and amino acid-groups is proposed to characterize regions of interest in proteins. Topological and geometric macromolecular cutoffs are defined using local and total indices to highlight non-covalent interactions existing between the side-chains of each amino acid. Moreover, local and total indices calculations are generalized considering a LEGO approach, by using several aggregation operators. Collinearity and variability analyses are performed to evaluate every generalizing component applied to the definition of these novel indices. These experiments are oriented to reduce the number of MDs obtained for performing prediction models. The predictive power of the proposed indices was evaluated using two benchmark datasets, folding rate and secondary structural classification of proteins. The proposed MDs are modeled using the following strategies: Multiple Linear Regression (MLR) and Support Vector Machine (SVM), respectively. The best regression model developed for the folding rate of proteins yields a cross-validation coefficient of 0.875 (Test Set) and the best model developed for secondary structural classification obtained 98% of instances correctly classified (Test Set). These statistical parameters are superior to the ones obtained with existing MDs reported in the literature. Overall, the new theoretical generalization enhanced the information extraction into the MDs, allowing a better correlation between these two evaluated benchmark datasets and the proposed indices. The optimal theoretical configurations defined for the calculation of these MDs consider low collinearity and less information redundancy among them. These theoretical configurations and the software are available at http://tomocomd.com/mulims-mcompas.

Gene Prioritization through Consensus Strategy, Enrichment Methodologies Analysis, and Networking for Osteosarcoma Pathogenesis.

Osteosarcoma is the most common subtype of primary bone cancer, affecting mostly adolescents. In recent years, several studies have focused on elucidating the molecular mechanisms of this sarcoma; however, its molecular etiology has still not been determined with precision. Therefore, we applied a consensus strategy with the use of several bioinformatics tools to prioritize genes involved in its pathogenesis. Subsequently, we assessed the physical interactions of the previously selected genes and applied a communality analysis to this protein-protein interaction network. The consensus strategy prioritized a total list of 553 genes. Our enrichment analysis validates several studies that describe the signaling pathways PI3K/AKT and MAPK/ERK as pathogenic. The gene ontology described TP53 as a principal signal transducer that chiefly mediates processes associated with cell cycle and DNA damage response It is interesting to note that the communality analysis clusters several members involved in metastasis events, such as MMP2 and MMP9, and genes associated with DNA repair complexes, like ATM, ATR, CHEK1, and RAD51. In this study, we have identified well-known pathogenic genes for osteosarcoma and prioritized genes that need to be further explored.

Drugs Repurposing Using QSAR, Docking and Molecular Dynamics for Possible Inhibitors of the SARS-CoV-2 Mpro Protease.

Wuhan, China was the epicenter of the first zoonotic transmission of the severe acute respiratory syndrome coronavirus clade 2 (SARS-CoV-2) in December 2019 and it is the causative agent of the novel human coronavirus disease 2019 (COVID-19). Almost from the beginning of the COVID-19 outbreak several attempts were made to predict possible drugs capable of inhibiting the virus replication. In the present work a drug repurposing study is performed to identify potential SARS-CoV-2 protease inhibitors. We created a Quantitative Structure-Activity Relationship (QSAR) model based on a machine learning strategy using hundreds of inhibitor molecules of the main protease (Mpro) of the SARS-CoV coronavirus. The QSAR model was used for virtual screening of a large list of drugs from the DrugBank database. The best 20 candidates were then evaluated in-silico against the Mpro of SARS-CoV-2 by using docking and molecular dynamics analyses. Docking was done by using the Gold software, and the free energies of binding were predicted with the MM-PBSA method as implemented in AMBER. Our results indicate that levothyroxine, amobarbital and ABP-700 are the best potential inhibitors of the SARS-CoV-2 virus through their binding to the Mpro enzyme. Five other compounds showed also a negative but small free energy of binding: nikethamide, nifurtimox, rebimastat, apomine and rebastinib.

Alkyl and Aryl Derivatives Based on p-Coumaric Acid Modification and Inhibitory Action against Leishmania braziliensis and Plasmodium falciparum.

In low-income populations, neglected diseases are the principal cause of mortality. Of these, leishmaniasis and malaria, being parasitic, protozoan infections, affect millions of people worldwide and are creating a public health problem. The present work evaluates the leishmanicidal and antiplasmodial action of a series of twelve p-coumaric acid derivatives. Of the tested derivatives, eight presented antiparasitic activities 1-3, 8-12. The hexyl p-coumarate derivative (9) (4.14 ± 0.55 µg/mL; selectivity index (SI) = 2.72) showed the highest leishmanicidal potency against the Leishmania braziliensis amastigote form. The results of the molecular docking study suggest that this compound inhibits aldehyde dehydrogenase (ALDH), mitogen-activated kinase protein (MPK4), and DNA topoisomerase 2 (TOP2), all of which are key enzymes in the development of Leishmania braziliensis. The data indicate that these enzymes interact via Van der Waals bonds, hydrophobic interactions, and hydrogen bonds with phenolic and aliphatic parts of this same compound. Of the other compounds analyzed, methyl p-coumarate (64.59 ± 2.89 µg/mL; IS = 0.1) demonstrated bioactivity against Plasmodium falciparum. The study reveals that esters presenting a p-coumarate substructure are promising for use in synthesis of derivatives with good antiparasitic profiles.

Larvicidal Activity of Cinnamic Acid Derivatives: Investigating Alternative Products for Aedes aegypti L. Control.

The mosquito Aedes aegypti transmits the virus that causes dengue, yellow fever, Zika and Chikungunya viruses, and in several regions of the planet represents a vector of great clinical importance. In terms of mortality and morbidity, infections caused by Ae. aegypti are among the most serious arthropod transmitted viral diseases. The present study investigated the larvicidal potential of seventeen cinnamic acid derivatives against fourth stage Ae. aegypti larvae. The larvicide assays were performed using larval mortality rates to determine lethal concentration (LC50). Compounds containing the medium alkyl chains butyl cinnamate (7) and pentyl cinnamate (8) presented excellent larvicidal activity with LC50 values of around 0.21-0.17 mM, respectively. While among the derivatives with aryl substituents, the best LC50 result was 0.55 mM for benzyl cinnamate (13). The tested derivatives were natural compounds and in pharmacology and antiparasitic studies, many have been evaluated using biological models for environmental and toxicological safety. Molecular modeling analyses suggest that the larvicidal activity of these compounds might be due to a multi-target mechanism of action involving inhibition of a carbonic anhydrase (CA), a histone deacetylase (HDAC2), and two sodium-dependent cation-chloride co-transporters (CCC2 e CCC3).

CompScore: Boosting Structure-Based Virtual Screening Performance by Incorporating Docking Scoring Function Components into Consensus Scoring.

Consensus scoring has become a commonly used strategy within structure-based virtual screening (VS) workflows with improved performance compared to those based in a single scoring function. However, no research has been devoted to analyze the worth of docking scoring functions components in consensus scoring. We implemented and tested a method that incorporates docking scoring functions components into the setting of high performance VS workflows. This method uses genetic algorithms for finding the combination of scoring components that maximizes the VS enrichment for any target. Our methodology was validated using a data set including ligands and decoys for 102 targets that have been widely used in VS validation studies. Results show that our approach outperforms other methods for all targets. It also boosts the initial enrichment performance of the traditional use of whole scoring functions in consensus scoring by an average of 45%. Our methodology showed to be outstandingly predictive when challenged to rescore external (previously unseen) data. Remarkably, CompScore was able not only to retain its performance after redocking with a different software, but also proved that the enrichment obtained was not artificial. CompScore is freely available at: http://bioquimio.udla.edu.ec/compscore/ .

NFBTA: A Potent Cytotoxic Agent against Glioblastoma.

Piplartine (PPL), also known as piperlongumine, is a biologically active alkaloid extracted from the Piper genus which has been found to have highly effective anticancer activity against several tumor cell lines. This study investigates in detail the antitumoral potential of a PPL analogue; (E)-N-(4-fluorobenzyl)-3-(3,4,5-trimethoxyphenyl) acrylamide (NFBTA). The anticancer potential of NFBTA on the glioblastoma multiforme (GBM) cell line (U87MG) was determined by 3-(4,5-dimethyl-2-thia-zolyl)-2, 5-diphenyl-2H-tetrazolium bromide (MTT), and lactate dehydrogenase (LDH) release analysis, and the selectivity index (SI) was calculated. To detect cell apoptosis, fluorescent staining via flow cytometry and Hoechst 33258 staining were performed. Oxidative alterations were assessed via colorimetric measurement methods. Alterations in expressions of key genes related to carcinogenesis were determined. Additionally, in terms of NFBTA cytotoxic, oxidative, and genotoxic damage potential, the biosafety of this novel agent was evaluated in cultured human whole blood cells. Cell viability analyses revealed that NFBTA exhibited strong cytotoxic activity in cultured U87MG cells, with high selectivity and inhibitory activity in apoptotic processes, as well as potential for altering the principal molecular genetic responses in U87MG cell growth. Molecular docking studies strongly suggested a plausible anti-proliferative mechanism for NBFTA. The results of the experimental in vitro human glioblastoma model and computational approach revealed promising cytotoxic activity for NFBTA, helping to orient further studies evaluating its antitumor profile for safe and effective therapeutic applications.

Toward the computer-aided discovery of FabH inhibitors. Do predictive QSAR models ensure high quality virtual screening performance?

Antibiotic resistance has increased over the past two decades. New approaches for the discovery of novel antibacterials are required and innovative strategies will be necessary to identify novel and effective candidates. Related to this problem, the exploration of bacterial targets that remain unexploited by the current antibiotics in clinical use is required. One of such targets is the ß-ketoacyl-acyl carrier protein synthase III (FabH). Here, we report a ligand-based modeling methodology for the virtual-screening of large collections of chemical compounds in the search of potential FabH inhibitors. QSAR models are developed for a diverse dataset of 296 FabH inhibitors using an in-house modeling framework. All models showed high fitting, robustness, and generalization capabilities. We further investigated the performance of the developed models in a virtual screening scenario. To carry out this investigation, we implemented a desirability-based algorithm for decoys selection that was shown effective in the selection of high quality decoys sets. Once the QSAR models were validated in the context of a virtual screening experiment their limitations arise. For this reason, we explored the potential of ensemble modeling to overcome the limitations associated to the use of single classifiers. Through a detailed evaluation of the virtual screening performance of ensemble models it was evidenced, for the first time to our knowledge, the benefits of this approach in a virtual screening scenario. From all the obtained results, we could arrive to a significant main conclusion: at least for FabH inhibitors, virtual screening performance is not guaranteed by predictive QSAR models.