Quantitative Structure-activity Relationship Tools Research Articles

Identification of apigenin-4'-glucoside as bacterial DNA gyrase inhibitor by QSAR modeling, molecular docking, DFT, molecular dynamics, and in vitro confirmation studies.

It is well known that antibiotic resistance is a major health hazard. To eradicate antibiotic-resistant bacterial infections, it is essential to find a novel antibacterial agent. Hence, in this study, a quantitative structure-activity relationship (QSAR) model was developed using 43 DNA gyrase inhibitors, and 700 natural compounds were screened for their antibacterial properties. Based on molecular docking and absorption, distribution, metabolism, excretion, and toxicity (ADMET) studies, the top three leads viz., apigenin-4'-glucoside, 8-deoxygartanin, and cryptodorine were selected and structurally optimized using density functional theory (DFT) studies. The optimized structures were redocked, and molecular dynamic (MD) simulations were performed. Binding energies were calculated by molecular mechanics/Poisson-Boltzmann surface area solvation (MM-PBSA). Based on the above studies, apigenin-4'-glucoside was identified as a potent antibacterial lead. Further in vitro confirmation studies were performed using the plant Lawsonia inermis containing apigenin-4'-glucoside to confirm the antibacterial activity. For QSAR modeling, 2D descriptors were calculated by PaDEL-Descriptors v2.21 software, and the model was developed using the DTClab QSAR tool. Docking was performed using PyRx v0.8 software. ORCA v5.0.1 computational package was used to optimize the structures. The job type used in optimization was equilibrium structure search using the DFT hybrid functional ORCA method B3LYP. The basis set was 6-311G (3df, 3pd) plus four polarization functions for all atoms. Accurate docking was performed for optimized leads using the iGEMDOCK v2.1 tool with a genetic algorithm by 10 solutions each of 80 generations. Molecular dynamic simulations were performed using GROMACS 2020.04 software with CHARMM36 all-atom force field.

Marbofloxacin mitigation by simultaneous process of adsorption and advanced oxidative process: An approach to the degradation mechanism and evaluation of the eco-toxicological impact using the QSAR tool

The present study investigated the adsorption and oxidation of the antibiotic Marbofloxacin (MBX) individually and simultaneously. The concentrations of MBX were monitored over time to assess the efficiency of the simultaneous process besides High-Performance Liquid Chromatography (HPLC) was employed to quantify the MBX degradation. An experimental design of three Box-Behnken Factors was performed to study the influence between the variables. To adsorption as an individual method, the best conditions were achieved at a pH of 7.8, 0.85 g of activated carbon, and a contact time of 42 min. To Fenton, the best conditions were achieved at a ratio of 6.25 (r = [H2O2]/[Fe2+]) and a time of 30 min. The final concentration reached by adsorption and Fenton as individual processes were 0.49 and 0.12 mg L−1, respectively, while the concentration of MBX in the simultaneous process reached a final concentration of 0.078 mg L−1. The results indicated that the simultaneous process exhibited greater efficiency in decreasing MBX concentration when compared to individual processes. The formation of degradation products also decreased after treatment, suggesting the potential environmental safety of the combined treatment. Finally, to further comprehend the environmental impact of MBX in water, a toxicity Quantitative Structure-Activity Relationship (QSAR) analysis was conducted to predict its potential toxicity, enabling a more thorough assessment of its ecological risks. It was concluded, that the method offers promising implications for environmental safety.

Physiologically-based pharmacokinetic modeling of the postbiotic supplement urolithin A predicts its bioavailability is orders of magnitudes lower than concentrations that induce toxicity, but also neuroprotective effects.

A range of health benefits are attributed to consuming urolithin A (UA), such as improved muscle health, anti-aging activity and neuroprotection, whereas few studies raise possible adverse effects at high doses, including genotoxicity and estrogenic effects. Therefore, understanding UA bioactivity and safety depends on its pharmacokinetics. However, there is no physiologically-based pharmacokinetic (PBPK) model available for UA, thus limiting reliable assessment of effects observed from in vitro experimentation. We characterized glucuronidation rates of UA by human S9 fractions. Partitioning and other physicochemical parameters were predicted using quantitative structure-activity relationship tools. Solubility and dissolution kinetics were determined experimentally. These parameters were used to construct a PBPK model, and results were compared with data from human intervention studies. We evaluated how different supplementation scenarios may influence UA plasma and tissue concentrations. Concentrations at which either toxic or beneficial effects were previously observed in vitro appear unlikely to be achieved in vivo. We established a first PBPK model for UA. It enables prediction of systemic UA concentrations and is critical for extrapolating in vitro results to in vivo uses. Results support the safety of UA, but also challenge the potential for readily achieving beneficial effects by postbiotic supplementation. This article is protected by copyright. All rights reserved.

Prioritization of mycotoxins based on mutagenicity and carcinogenicity evaluation using combined in silico QSAR methods

Mycotoxins and their metabolites are a family of compounds that contains a great diversity of both structure and biological properties. Information on their toxicity is spread within several databases and in scientific literature. Due to the number of molecules and their structure diversity, the cost and time required for hazard evaluation of each compound is unrealistic.In that purpose, new approach methodologies (NAMs) can be applied to evaluate such large set of molecules. Among them, quantitative structure-activity relationship (QSAR) in silico models could be useful to predict the mutagenic and carcinogenic properties of mycotoxins.First, a complete list of 904 mycotoxins and metabolites was built. Then, some known mycotoxins were used to determine the best QSAR tools for mutagenicity and carcinogenicity predictions. The best tool was further applied to the whole list of 904 mycotoxins. At the end, 95 mycotoxins were identified as both mutagen and carcinogen and should be prioritized for further evaluation.

Web-Based Quantitative Structure-Activity Relationship Resources Facilitate Effective Drug Discovery.

Traditional drug discovery effectively contributes to the treatment of many diseases but is limited by high costs and long cycles. Quantitative structure-activity relationship (QSAR) methods were introduced to evaluate the activity of compounds virtually, which saves the significant cost of determining the activities of the compounds experimentally. Over the past two decades, many web tools for QSAR modeling with various features have been developed to facilitate the usage of QSAR methods. These web tools significantly reduce the difficulty of using QSAR and indirectly promote drug discovery. However, there are few comprehensive summaries of these QSAR tools, and researchers may have difficulty determining which tool to use. Hence, we systematically surveyed the mainstream web tools for QSAR modeling. This work may guide researchers in choosing appropriate web tools for developing QSAR models, and may also help develop more bioinformatics tools based on these existing resources. For nonprofessionals, we also hope to make more people aware of QSAR methods and expand their use.

Improvement of quantitative structure-activity relationship (QSAR) tools for predicting Ames mutagenicity: outcomes of the Ames/QSAR International Challenge Project.

The International Conference on Harmonization (ICH) M7 guideline allows the use of in silico approaches for predicting Ames mutagenicity for the initial assessment of impurities in pharmaceuticals. This is the first international guideline that addresses the use of quantitative structure–activity relationship (QSAR) models in lieu of actual toxicological studies for human health assessment. Therefore, QSAR models for Ames mutagenicity now require higher predictive power for identifying mutagenic chemicals. To increase the predictive power of QSAR models, larger experimental datasets from reliable sources are required. The Division of Genetics and Mutagenesis, National Institute of Health Sciences (DGM/NIHS) of Japan recently established a unique proprietary Ames mutagenicity database containing 12140 new chemicals that have not been previously used for developing QSAR models. The DGM/NIHS provided this Ames database to QSAR vendors to validate and improve their QSAR tools. The Ames/QSAR International Challenge Project was initiated in 2014 with 12 QSAR vendors testing 17 QSAR tools against these compounds in three phases. We now present the final results. All tools were considerably improved by participation in this project. Most tools achieved >50% sensitivity (positive prediction among all Ames positives) and predictive power (accuracy) was as high as 80%, almost equivalent to the inter-laboratory reproducibility of Ames tests. To further increase the predictive power of QSAR tools, accumulation of additional Ames test data is required as well as re-evaluation of some previous Ames test results. Indeed, some Ames-positive or Ames-negative chemicals may have previously been incorrectly classified because of methodological weakness, resulting in false-positive or false-negative predictions by QSAR tools. These incorrect data hamper prediction and are a source of noise in the development of QSAR models. It is thus essential to establish a large benchmark database consisting only of well-validated Ames test results to build more accurate QSAR models.

Combination of experimental and in silico methods for the assessment of the phototransformation products of the antipsychotic drug/metabolite Mesoridazine

The lack of studies on the fate and effects of drug metabolites in the environment is of concern. As their parent compounds, metabolites enter the aquatic environment and are subject to biotic and abiotic process. In this regard, photolysis plays an important role. This study combined experimental and in silico quantitative structure-activity relationship (QSAR) methods to assess the fate and effects of Mesoridazine (MESO), a pharmacologically active human drug and metabolite of the antipsychotic agent Thioridazine, and its transformation products (TPs) formed through a Xenon lamp irradiation. After 256min, the photodegradation of MESO⋅besylate (50mgL−1) achieved 90.4% and 6.9% of primary elimination and mineralization, respectively. The photon flux emitted by the lamp (200–600nm) was 169.55Jcm−2. Sixteen TPs were detected by means of liquid chromatography-high resolution mass spectrometry (LC-HRMS), and the structures were proposed based on MSn fragmentation patterns. The main transformation reactions were sulfoxidation, hydroxylation, dehydrogenation, and sulfoxide elimination. A back-transformation of MESO to Thioridazine was evidenced. Aerobic biodegradation tests (OECD 301 D and 301F) were applied to MESO and the mixture of TPs present after 256min of photolysis. Most of TPs were not biodegraded, demonstrating their tendency to persist in aquatic environments. The ecotoxicity towards Vibrio fischeri showed a decrease in toxicity during the photolysis process. The in silico QSAR tools QSARINS and US-EPA PBT profiler were applied for the screening of TPs with character of persistence, bioaccumulation, and toxicity (PBT). They have revealed the carbazole derivatives TP 355 and TP 337 as PBT/vPvB (very persistent and very bioaccumulative) compounds. In silico QSAR predictions for mutagenicity and genotoxicity provided by CASE Ultra and Leadscope® indicated positive alerts for mutagenicity on TP 355 and TP 337. Further studies regarding the carbazole derivative TPs should be considered to confirm their hazardous character.

Improved algorithms for symmetry analysis: structure preserving permutations

We propose an improved algorithm for calculating Avnir’s continuous symmetry and chirality measures of molecules. These measures evaluate the deviation of a given structure from symmetry by calculating the distance between the structure and its nearest symmetric counterpart. Our new algorithm utilizes structural properties of the given molecule to increase the accuracy of the calculation and dramatically reduce the running time by up to tens orders of magnitude. Consequently, a wide variety of molecules of medium size with ca. 100 atoms and even more can be analyzed within seconds. Numerical evidence of the algorithm’s efficiency is presented for several families of molecules such as helicenes, porphyrins, dendrimers building blocks, fullerene and more. The ease and efficiency of the calculation make the continuous symmetry and chirality measures promising descriptors for integration in quantitative structure–activity relationship tools, as well as chemical databases and molecular visualization software.

Designing Anti-Influenza Aptamers: Novel Quantitative Structure Activity Relationship Approach Gives Insights into Aptamer – Virus Interaction

This study describes the development of aptamers as a therapy against influenza virus infection. Aptamers are oligonucleotides (like ssDNA or RNA) that are capable of binding to a variety of molecular targets with high affinity and specificity. We have studied the ssDNA aptamer BV02, which was designed to inhibit influenza infection by targeting the hemagglutinin viral protein, a protein that facilitates the first stage of the virus’ infection. While testing other aptamers and during lead optimization, we realized that the dominant characteristics that determine the aptamer’s binding to the influenza virus may not necessarily be sequence-specific, as with other known aptamers, but rather depend on general 2D structural motifs. We adopted QSAR (quantitative structure activity relationship) tool and developed computational algorithm that correlate six calculated structural and physicochemical properties to the aptamers’ binding affinity to the virus. The QSAR study provided us with a predictive tool of the binding potential of an aptamer to the influenza virus. The correlation between the calculated and actual binding was R2 = 0.702 for the training set, and R2 = 0.66 for the independent test set. Moreover, in the test set the model’s sensitivity was 89%, and the specificity was 87%, in selecting aptamers with enhanced viral binding. The most important properties that positively correlated with the aptamer’s binding were the aptamer length, 2D-loops and repeating sequences of C nucleotides. Based on the structure-activity study, we have managed to produce aptamers having viral affinity that was more than 20 times higher than that of the original BV02 aptamer. Further testing of influenza infection in cell culture and animal models yielded aptamers with 10 to 15 times greater anti-viral activity than the BV02 aptamer. Our insights concerning the mechanism of action and the structural and physicochemical properties that govern the interaction with the influenza virus are discussed.

Development of a chronic fish toxicity model for predicting sub-lethal NOEC values for non-polar narcotics

To comply with the REACH (Registration, Evaluation, Authorisation and restriction of Chemicals) regulations, the generation of chronic fish toxicity data is required for chemicals produced or imported within or into the EU in quantities greater than 100 tonnes per year. This comes at a great cost to industry and consumers alike and requires the sacrifice of many vertebrates. In acknowledgment of these issues the REACH regulations encourage the use of non-testing methods (NTM). These include read-across, weight-of-evidence and QSAR (quantitative structure–activity relationship) techniques. There are many QSAR tools available to generate predictive values for a number of physico-chemical properties, as well as human and environmental health end points; however, close analysis of the currently available chronic fish models identified room for improvement in both the selection of data used and in its application in model creation. In light of this a model was developed using only sub-lethal no-observed-effect concentration (NOEC) end-point data according to best practice QSAR development. Only the lowest value was taken for each compound, in line with the conservative approach taken by the European Chemicals Agency (ECHA). The model developed meets the Organisation for Economic Co-operation and Development (OECD) principles, has strong internal and external validation statistics, and can reliably predict sub-lethal chronic NOEC values for fish within its defined applicability domain.

Regulatory use of computational toxicology tools and databases at the United States Food and Drug Administration's Office of Food Additive Safety

Over 10 years ago, the Office of Food Additive Safety (OFAS) in the FDA's Center for Food Safety and Applied Nutrition implemented the formal use of structure–activity relationship analysis and quantitative structure–activity relationship (QSAR) analysis in the premarket review of food-contact substances. More recently, OFAS has implemented the use of multiple QSAR software packages and has begun investigating the use of metabolism data and metabolism predictive models in our QSAR evaluations of food-contact substances. In this article, we provide an overview of the programs used in OFAS as well as a perspective on how to apply multiple QSAR tools in the review process of a new food-contact substance.

Combinatorial QSAR Modeling of Chemical Toxicants Tested against Tetrahymena pyriformis

Selecting most rigorous quantitative structure-activity relationship (QSAR) approaches is of great importance in the development of robust and predictive models of chemical toxicity. To address this issue in a systematic way, we have formed an international virtual collaboratory consisting of six independent groups with shared interests in computational chemical toxicology. We have compiled an aqueous toxicity data set containing 983 unique compounds tested in the same laboratory over a decade against Tetrahymena pyriformis. A modeling set including 644 compounds was selected randomly from the original set and distributed to all groups that used their own QSAR tools for model development. The remaining 339 compounds in the original set (external set I) as well as 110 additional compounds (external set II) published recently by the same laboratory (after this computational study was already in progress) were used as two independent validation sets to assess the external predictive power of individual models. In total, our virtual collaboratory has developed 15 different types of QSAR models of aquatic toxicity for the training set. The internal prediction accuracy for the modeling set ranged from 0.76 to 0.93 as measured by the leave-one-out cross-validation correlation coefficient ( Q abs2). The prediction accuracy for the external validation sets I and II ranged from 0.71 to 0.85 (linear regression coefficient R absI2) and from 0.38 to 0.83 (linear regression coefficient R absII2), respectively. The use of an applicability domain threshold implemented in most models generally improved the external prediction accuracy but at the same time led to a decrease in chemical space coverage. Finally, several consensus models were developed by averaging the predicted aquatic toxicity for every compound using all 15 models, with or without taking into account their respective applicability domains. We find that consensus models afford higher prediction accuracy for the external validation data sets with the highest space coverage as compared to individual constituent models. Our studies prove the power of a collaborative and consensual approach to QSAR model development. The best validated models of aquatic toxicity developed by our collaboratory (both individual and consensus) can be used as reliable computational predictors of aquatic toxicity and are available from any of the participating laboratories.

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.

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.