Prediction of acute mammalian toxicity using QSAR methods: a case study of sulfur mustard and its breakdown products

Attenuation of cannabis withdrawal symptoms by Prosopis farcta extract, its luteolin and melatonin in mice: Involvement of brain-derived neurotrophic factor and dopamine

The aim of this study was the identification of luteolin in Prosopis farcta extract (PFE) and melatonin to evaluate its effect on THC withdrawal syndrome in mice. Luteolin was identified by high-performance liquid chromatography (HPCL). Signs of toxicity of mice in PFE and luteolin were monitored for LD50 calculation. The behavioral symptoms of THC withdrawal (stereotypies, ambulation, and inactivity time) induced by the rimonabant challenge were illustrated in THC-dependent mice receiving PFE, luteolin, and melatonin. The expression of mature BDNF (mBDNF) was evaluated by Western blot analysis. The dopamine concentrations were measured using HPLC. PFE and luteolin LD50 were 650 and 220 mg/kg, respectively. PFE (300 mg/kg), all doses of luteolin, and melatonin increased significantly the mBDNF expression and decreased the dopamine concentration. The findings suggest that PFE, luteolin, and melatonin are mighty in reducing the signs of THC withdrawal. It seems these effects were due to a decrease in dopamine concentration level and an increase in mBDNF protein expression in mice brains.

Development of terpenoid repellents against Aedes albopictus: a combined study of biological activity evaluation and computational modelling

To explore novel terpenoid repellents, 22 candidate terpenoid derivatives were synthesized and tested for their electroantennogram (EAG) responses and repellent activities against Aedes albopictus. The results from the EAG experiments revealed that 5-(2-hydroxypropan-2-yl)-2-methylcyclohex-2-en-1-yl formate (compound 1) induced distinct EAG responses in female Aedes albopictus. At concentrations of 0.1, 1, 10, 100, and 1000 mg/L, the EAG response values for compound 1 were 179.59, 183.99, 190.38, 193.80, and 196.66 mV, demonstrating comparable or superior effectiveness to DEET. Repellent activity analysis indicated significant repellent activity for compound 1, closest to the positive control DEET. The in silico assessment of the ADMET profile of compound 1 indicates that it successfully passed the ADMET evaluation. Molecular docking studies exhibited favourable binding of compound 1 to the active site of the odorant binding protein (OBP) of Aedes albopictus, involving hydrophobic forces and hydrogen bond interactions with residues in the OBP pocket. The QSAR model highlighted the influential role of hydrogen-bonding receptors, positively charged surface area of weighted atoms, polarity parameters of molecules, and maximum nuclear-nuclear repulsion force of carbon-carbon bonds on the relative EAG response values of the tested compounds. This study holds substantial significance for the advancement of new terpenoid repellents.

Novel Design of Citral-Thiourea Derivatives for Enhancing Antifungal Potential against Colletotrichum gloeosporioides

Although much progress has been made in developing botanical fungicides to combat fungal diseases in crops, there remains a great need to improve the efficiency and long-term safety of these fungicides. This study proposes a novel strategy for designing citral-thiourea derivatives that feature such desirable properties. The motivation of the work herein was to enhance the antifungal activity of citral against C. gloeosprioides by exploiting the synergistic effect that arises from combining citral and thiourea compounds, thereby producing citral-thiourea derivatives that exhibit good long-term safety. The results revealed that the generated compounds e1, e3, e6, e18, and g showed remarkable antifungal activities against C. gloeosprioides, with corresponding EC50 values reaching 0.16, 1.66, 1.37, 4.76, and 4.60 mg/L, respectively, showing that the compounds significantly outperformed both the positive control kresoxim-methyl and the commercially available fungicide carbendazim. Furthermore, compound g showed stronger protective efficacy against C. gloeosprioides than carbendazim on mango fruit at 25 mg/L. Investigating the preliminary structure-activity relationship (SAR) of the compounds also revealed that the citral-thiourea derivatives exhibited higher antifungal activities against C. gloeosprioides compared to citral and thiourea compounds. This reinforcement of antifungal activity observed in the derivatives was found to be attributable to the two characteristics of low molecular size and the presence of a fluorine atom in the meta-position of the benzene ring. Beyond this, it was determined from QSAR that two molecular descriptors (the Kier-Hall index (order 3) and Tot dipole of the molecules) were negatively related to the antifungal activity of the citral-thiourea derivatives, while one other (the maximum resonance energy of a C-H bond) was positively related to their antifungal activity. More importantly, the citral-thiourea derivatives with high antifungal activities (i.e., compounds e1, e3, e6, e14, e15, e18, and g) exhibited negligible cytotoxicity to LO2 and HEK293T cell lines. The antifungal mechanism of the generated citral-thiourea derivatives was investigated by scanning electron microscopy (SEM) and relative conductivity. The derivatives were found to affect mycelial morphology and increase fungal cell membrane permeability, thereby resulting in the destruction of fungal cell membranes. These promising results provide novel insights into the study and potential application value of citral-thiourea derivatives as high-efficiency antifungal agents against C. gloeosprioides.

Computer-aided drug design combined network pharmacology to explore anti-SARS-CoV-2 or anti-inflammatory targets and mechanisms of Qingfei Paidu Decoction for COVID-19

Introduction

Coronavirus Disease-2019 (COVID-19) is an infectious disease caused by SARS-CoV-2. Severe cases of COVID-19 are characterized by an intense inflammatory process that may ultimately lead to organ failure and patient death. Qingfei Paidu Decoction (QFPD), a traditional Chines e medicine (TCM) formula, is widely used in China as anti-SARS-CoV-2 and anti-inflammatory. However, the potential targets and mechanisms for QFPD to exert anti-SARS-CoV-2 or anti-inflammatory effects remain unclear.

Methods

In this study, Computer-Aided Drug Design was performed to identify the antiviral or anti-inflammatory components in QFPD and their targets using Discovery Studio 2020 software. We then investigated the mechanisms associated with QFPD for treating COVID-19 with the help of multiple network pharmacology approaches.

Results and discussion

By overlapping the targets of QFPD and COVID-19, we discovered 8 common targets (RBP4, IL1RN, TTR, FYN, SFTPD, TP53, SRPK1, and AKT1) of 62 active components in QFPD. These may represent potential targets for QFPD to exert anti-SARS-CoV-2 or anti-inflammatory effects. The result showed that QFPD might have therapeutic effects on COVID-19 by regulating viral infection, immune and inflammation-related pathways. Our work will promote the development of new drugs for COVID-19.

Experimental evaluation and structure-activity relationship analysis of bridged-ring terpenoid derivatives as novel Blattella germanica repellent

Cockroaches are urban pests that are very difficult to control. Using repellents is a green, safe and effective strategy for their control. In order to find novel cockroach repellents, the repellent activity of 45 bridged-ring terpenoid derivatives synthesized from β-pinene against Blattella germanica was tested. The relationship between the molecular structure of these bridged-ring terpenoid derivatives and their repellent activity against Blattella germanica was also analysed. The results show that some of the bridged-ring terpenoid derivatives exhibit good repellent activity against Blattella germanica, and six compounds (RR = 60.44-87.32%) show higher repellent activity against Blattella germanica than DEET (RR = 54.77%), making them promising for development as new cockroach repellents. Quantitative structure-activity relationship (QSAR) analysis revealed that the HOMO-1 energy, Kier and Hall index (order 2), Balaban index, and relative positive charged surface area of bridged-ring terpenoid derivatives have effects on repellent activity against Blattella germanica. The present study may provide a theoretical basis for the high-value use of β-pinene and can be helpful to the development of novel repellents against Blattella germanica.

Principles and Procedures for Assessment of Acute Toxicity Incorporating In Silico Methods

Acute toxicity in silico models are being used to support an increasing number of application areas including (1) product research and development, (2) product approval and registration as well as (3) the transport, storage and handling of chemicals. The adoption of such models is being hindered, in part, because of a lack of guidance describing how to perform and document an in silico analysis. To address this issue, a framework for an acute toxicity hazard assessment is proposed. This framework combines results from different sources including in silico methods and in vitro or in vivo experiments. In silico methods that can assist the prediction of in vivo outcomes (i.e., LD50) are analyzed concluding that predictions obtained using in silico approaches are now well-suited for reliably supporting assessment of LD50-based acute toxicity for the purpose of GHS classification. A general overview is provided of the endpoints from in vitro studies commonly evaluated for predicting acute toxicity (e.g., cytotoxicity/cytolethality as well as assays targeting specific mechanisms). The increased understanding of pathways and key triggering mechanisms underlying toxicity and the increased availability of in vitro data allow for a shift away from assessments solely based on endpoints such as LD50, to mechanism-based endpoints that can be accurately assessed in vitro or by using in silico prediction models. This paper also highlights the importance of an expert review of all available information using weight-of-evidence considerations and illustrates, using a series of diverse practical use cases, how in silico approaches support the assessment of acute toxicity.

Toxicological assessment of SGLT2 inhibitors metabolites using in silico approach

Sodium-glucose cotransporter 2 inhibitors (SGLT2i) are the latest class of drugs approved to treat type 2 DM (T2DM). Although adverse effects are often caused by a metabolite rather than the drug itself, only the safety assessment of disproportionate drug metabolites is usually performed, which is of particular concern for drugs of chronic use, such as SGLT2i. Bearing this in mind, in silico tools are efficient strategies to reveal the risk assessment of metabolites, being endorsed by many regulatory agencies. Thereby, the goal of this study was to apply in silico methods to provide the metabolites toxicity assessment of the SGLT2i. Toxicological assessment from SGLT2i metabolites retrieved from the literature was estimated using the structure and/or statistical-based alert implemented in DataWarrior and ADMET predictorTM softwares. The drugs and their metabolites displayed no mutagenic, tumorigenic or cardiotoxic risks. Still, M1-2 and M3-1 were recognized as potential hepatotoxic compounds and M1-2, M1-3, M3-1, M3-2, M3-3 and M4-3, were estimated to have very toxic LD50 values in rats. All SGLT2i and the metabolites M3-4, M4-1 and M4-2, were predicted to have reproductive toxicity. These results support the awareness that metabolites may be potential mediators of drug-induced toxicities of the therapeutic agents.

Thymol Nanopolymer Synthesis and Its Effects on Morphine Withdrawal Syndrome in Comparison With Clonidine in Rats

The drug delivery system is valuable in the treatment of the disease. A nanopolymer as a thymol and Thymbra spicata release system was synthesized and its effects on morphine withdrawal syndrome in comparison with clonidine in rats were studied. The nanopolymer was characterized by different methods, namely, IR, HNMR, CNMR, GPC, DLS, and AFM. Thymol in T. spicata extract was assessed. The loading and release rate of thymol and T. spicata extract on the nanopolymer were evaluated by HPLC. The median lethal dose (LD50) of the T. spicata extract, thymol, extract nanopolymer, and thymol nanopolymer was studied. The frequency of jumping, rearing, and teeth chattering in naloxone-induced morphine withdrawal syndrome was studied. Synthesized nanopolymer was desirable as a carrier for the drug. The loaded amount of extract and thymol on nanopolymer was estimated 55 ± 3.2% and 48 ± 2.6% and the drug released was 71 and 68%, respectively. LD50 of the T. spicata extract, thymol, extract nanopolymer, and thymol nanopolymer was 975, 580, 1,250, and 650 mg/kg, respectively. This study showed that thymol nanopolymer was more effective than clonidine to reduce the frequency of morphine withdrawal symptoms. Our results suggest that T. spicata extract, thymol, extract nanopolymer, and thymol nanopolymer are mighty in reducing the narcotic withdrawal signs. The mechanism of action and therapeutic potential is maybe similar to clonidine.

The role of eugenol and ferulic acid as the competitive inhibitors of transcriptional regulator RhlR in P. aeruginosa

Search inhibitors of Quorum Sensing (QS) in Pseudomonas aeruginosa are challenging to find therapies due to the broad antibiotic resistance. Therefore, this study aimed to probe ten aromatic compounds as inhibitors of three transcriptional regulators of QS in P. aeruginosa. The methodology consisted in determining the Binding Gibbs Energy (BGE) with software Chimera (tool vina) and Mcule, comparing the averages by the Tukey method (p≤0.05) to find inhibitors of QS. Subsequently, the LD50 in the mice model was evaluated by three QSAR models, and the in silico pharmacokinetic values were obtained from the ADME (the absorption distribution metabolism excretion) and PubChem databases. Found three potential inhibitors of RhlR with the lower BGE values in the range -6.70±0.21 to -7.43±0.35 kcal/mol. On the other side, all compounds were acceptable for Lipinski's rule of fives and the in silico oral mice LD50 and ADME values. Concluding, the ferulic acid and eugenol showed the best total BGE values (-75.07±0.892 and -70.36±1.022 kcal/mol), proposing them as a new therapy against the virulence of P. aeruginosa. Finally, the in silico studies have demonstrated are reproducible and valuable for putative QS inhibitors predicting and obtaining new studies derivatives from the results obtained in the present study. • The key benefits of this methodology are: Use free, licensed, flexible, and efficient software for in silico molecular docking. • Validation and comparison of BGE employing two molecular docking software in three different proteins. • Use classical molecular dynamics to define the stability and the total BGE of interaction protein-ligand and find the best inhibitor of a protein for proposing them as a possible therapy against the virulence of specific pathogens.

Reducing health & environmental impacts of chemical warfare agents: Computational chemistry contributions

This review article summarizes advances in computational chemistry and cheminformatics methods and techniques that are used or have potential for use in reducing health and environmental impacts of Chemical Warfare Agents (CWA). These methods, include, but are not limited to, predictive modeling, data mining and virtual screening, similarity searching, molecular docking and dynamics and are briefly presented here. Applications of these in silico approaches, specifically for the protection of personnel and civilians against CWA, but also beyond, are discussed. CWA include toxic chemicals that can cause death, injury, or temporary incapacitation through their chemical action. CWA impose a significant worldwide threat and as such, destruction, remediation as well as protection measurements need to be carefully designed. Towards this goal computational chemistry and cheminformatics can play a key role specifically as far as decontamination, risk assessment and risk management are concerned. Among the wide range of in silico techniques applied for CWA, specific previously published paradigms are presented, including toxicity and property prediction, CWA simulant identification and CWA detoxification. Beyond CWA research, other applications with military interest are briefly presented and emerging trends of potential relevance noted.

Appraisal of Bioactive Compounds of Betel Fruit as Antimalarial Agents by Targeting Plasmepsin 1 and 2: A Computational Approach

In many countries, the fruit of betel (Piper betle Linn) is traditionally used as medicine for treating malaria. It is a fatal disease, and existing medications are rapidly losing potency, necessitating the development of innovative pharmaceutics. The current study attempted to determine the compounds in the n-hexane fraction of betel fruit extract and investigate the potential inhibition of bioactive compounds against aspartic protease plasmepsin 1 (PDB ID: 3QS1) and plasmepsin 2 (PDB ID: 1LEE) of Plasmodium falciparum using a computational approach. The ethanol extract was fractionated into n-hexane and further analyzed using gas chromatography-mass spectrometry (GC-MS) to obtain information regarding the compounds contained in betel fruit. Each compound's potential antimalarial activity was evaluated using AutoDock Vina and compared to artemisinin, an antimalarial drug. Molecular dynamics simulations (MDSs) were performed to evaluate the stability of the interaction between the ligand and receptors. Results detected 20 probable compounds in the n-hexane extract of betel fruit based on GC-MS analysis. The docking study revealed that androstan-17-one,3-ethyl-3-hydroxy-, (5 alpha)- has the highest binding affinity for plasmepsin 1 and plasmepsin 2. The compound exhibits a similar interaction with artemisinin at the active site of the receptors. The compound does not violate Lipinski's rules of five. It belongs to class 5 toxicity with an LD₅₀ of 3000 mg/kg. MDS results showed stable interactions between the compound and the receptors. Our study concluded that androstan-17-one,3-ethyl-3-hydroxy-, (5 alpha)- from betel fruit has the potential to be further investigated as a potential inhibitor of the aspartic protease plasmepsin 1 and plasmepsin 2 of Plasmodium falciparum.

New insights into toxicity of microcystins produced by cyanobacteria using in silico ADMET prediction

In silico methodologies can be used in the discovery of new drugs for measuring toxicity, predicting effects of substances not yet analyzed by in vivo methodologies. The ADMET Predictor® software (absorption, distribution, metabolism, elimination, and toxicity [ADMET]) was used in this work to predict toxic effects of microcystin variants MC-LR, MC-YR, MC-RR, and MC-HarHar. In the case of rodents, predictive results for all analyzed variants indicated carcinogenic potential. The predictive model of respiratory sensitivity in this group differentiated microcystins into 2 categories: sensitizer (MC-LR and -YR) and non-sensitizer (MC-HarHar and -RR). Predictive results for humans indicated that MC-LR and -RR are phospholipidosis inducers; on the other hand, MC-LR showed the highest predictive value of permeability in rabbit cornea and probability of crossing lipoprotein barriers (MC-LR>-YR>-HarHar>-RR). Considering bioavailable fractions, microcystins are more likely to cause biological effects in rats than humans, showing significant differences between models. The results of ADMET predictions add valuable information on microcystin toxicity, especially in the case of variants not yet studied experimentally.

Virtual Screening for Potential Inhibitors of Human Hexokinase II for the Development of Anti-Dengue Therapeutics

Dengue fever, which is a disease caused by the dengue virus (DENV), is a major unsolved issue in many tropical and sub-tropical regions of the world. The absence of treatment that effectively prevent further viral propagation inside the human’s body resulted in a high number of deaths globally each year. Thus, novel anti-dengue therapies are required for effective treatment. Human hexokinase II (HKII), which is the first enzyme in the glycolytic pathway, is an important drug target due to its significant impact on viral replication and survival in host cells. In this study, 23.1 million compounds were computationally-screened against HKII using the Ultrafast Shape Recognition with a CREDO Atom Types (USRCAT) algorithm. In total, 300 compounds with the highest similarity scores relative to three reference molecules, known as Alpha-D-glucose (GLC), Beta-D-glucose-6-phosphate (BG6), and 2-deoxyglucose (2DG), were aligned. Of these 300 compounds, 165 were chosen for further structure-based screening, based on their similarity scores, ADME analysis, the Lipinski’s Rule of Five, and virtual toxicity test results. The selected analogues were subsequently docked against each domain of the HKII structure (PDB ID: 2NZT) using AutoDock Vina programme. The three top-ranked compounds for each query were then selected from the docking results based on their binding energy, the number of hydrogen bonds formed, and the specific catalytic residues. The best docking results for each analogue were observed for the C-terminus of Chain B. The top-ranked analogues of GLC, compound 10, compound 26, and compound 58, showed predicted binding energies of −7.2, −7.0, and −6.10 kcal/mol and 7, 5, and 2 hydrogen bonds, respectively. The analogues of BG6, compound 30, compound 36, and compound 38, showed predicted binding energies of −7.8, −7.4, and −7.0 kcal/mol and 11, 9, and 5 hydrogen bonds, while the top three analogues of 2DG, known as compound 1, compound 4, and compound 31, showed predicted binding energies of −6.8, −6.3, and −6.3 kcal/mol and 4, 3, and 1 hydrogen bonds, sequentially. The highest-ranked compounds in the docking analysis were then selected for molecular dynamics simulation, where compound 10, compound 30, and compound 1, which are the analogues of GLC, BG6, and 2DG, have shown strong protein-ligand stability with an RMSD value of ±5.0 A° with a 5 H bond, ±4.0 A° with an 8 H bond, and ±0.5 A° with a 2 H bond, respectively, compared to the reference molecules throughout the 20 ns simulation time. Therefore, by using the computational studies, we proposed novel compounds, which may act as potential drugs against DENV by inhibiting HKII’s activity.

Prediction of toxicity of secondary metabolites

The prediction of toxicological endpoints has gained broad acceptance; it is widely applied in early stages of drug discovery as well as for impurities obtained in the production of generic or equivalent products. In this work, we describe methodologies for the prediction of toxicological endpoints compounds, with a particular focus on secondary metabolites. Case studies include toxicity prediction of natural compound databases with anti-diabetic, anti-malaria and anti-HIV properties.

SAR and QSAR modeling of a large collection of LD50 rat acute oral toxicity data

The median lethal dose for rodent oral acute toxicity (LD50) is a standard piece of information required to categorize chemicals in terms of the potential hazard posed to human health after acute exposure. The exclusive use of in vivo testing is limited by the time and costs required for performing experiments and by the need to sacrifice a number of animals. (Quantitative) structure-activity relationships [(Q)SAR] proved a valid alternative to reduce and assist in vivo assays for assessing acute toxicological hazard. In the framework of a new international collaborative project, the NTP Interagency Center for the Evaluation of Alternative Toxicological Methods and the U.S. Environmental Protection Agency's National Center for Computational Toxicology compiled a large database of rat acute oral LD50 data, with the aim of supporting the development of new computational models for predicting five regulatory relevant acute toxicity endpoints. In this article, a series of regression and classification computational models were developed by employing different statistical and knowledge-based methodologies. External validation was performed to demonstrate the real-life predictability of models. Integrated modeling was then applied to improve performance of single models. Statistical results confirmed the relevance of developed models in regulatory frameworks, and confirmed the effectiveness of integrated modeling. The best integrated strategies reached RMSEs lower than 0.50 and the best classification models reached balanced accuracies over 0.70 for multi-class and over 0.80 for binary endpoints. Computed predictions will be hosted on the EPA's Chemistry Dashboard and made freely available to the scientific community.

Exploring current read-across applications and needs among selected U.S. Federal Agencies

Read-across is a well-established data gap-filling technique applied for regulatory purposes. In US Environmental Protection Agency's New Chemicals Program under TSCA, read-across has been used extensively for decades, however the extent of application and acceptance of read-across among U.S. federal agencies is less clear. In an effort to build read-across capacity, raise awareness of the state of the science, and work towards a harmonization of read-across approaches across U.S. agencies, a new read-across workgroup was established under the Interagency Coordinating Committee on the Validation of Alternative Methods (ICCVAM). This is one of several ad hoc groups ICCVAM has convened to implement the ICCVAM Strategic Roadmap. In this article, we outline the charge and scope of the workgroup and summarize the current applications, tools used, and needs of the agencies represented on the workgroup for read-across. Of the agencies surveyed, the Environmental Protection Agency had the greatest experience in using read-across whereas other agencies indicated that they would benefit from gaining a perspective of the landscape of the tools and available guidance. Two practical case studies are also described to illustrate how the read-across approaches applied by two agencies vary on account of decision context.

Distribution of toxicity values across different species and modes of action of pesticides from PESTIMEP and PPDB databases

The continuous use of compounds contained in commodities such as processed food, medicines, and pesticides, demands safety measures, in particular, for those in direct contact with humans and the environment. Safety measures have evolved and regulations are now in place around the globe. In the case of pesticides, attempts have been made to use toxicological data to inform of potentially harmful compounds either across species, on different routes of exposure, or entirely new chemicals. The generation of models, based on statistical and molecular modeling studies, allows for such predictions. However, the use of these models is framed by the available data, the experimental errors, the complexity of the measurement, and the available computational algorithms, among other factors. In this work, we present the methodologies used for extrapolation across different species and routes of administration and show the appropriateness of developing predictive models of pesticides based on their type and mode of action. The analyses include comparisons based on structural characteristics and physicochemical properties. Whenever possible, the scope and limitations of the methodologies are discussed. We expect that this work will serve as a useful introductory guide of the tools employed in the toxicity assessment of agrochemical compounds.

Attenuation of Morphine Withdrawal Syndrome by Prosopis Farcta Extract and Its Bioactive Component Luteolin in Comparison with Clonidine in Rats

Background

Today, the plant Prosopis farcta is frequently used for traditional medicinal purposes. The aim of this study was the identification of luteolin in P. farcta extract (PFE) and to evaluate its effect on morphine discontinuation syndrome in rats.

Material/Methods

Using high-performance liquid chromatography (HPCL), luteolin was evaluated in PFE. The frequency of behavioral symptoms of morphine withdrawal (jumping, rearing, and teeth chattering) induced by naloxone challenge were illustrated in morphine-dependent rats receiving PFE, luteolin, saline, or clonidine. LD50 of PFE and luteolin was 540 mg/kg and 150 mg/kg, respectively. Signs of behavioral morphine withdrawal in rats were significantly inhibited by chronic co-administration of PFE, luteolin, or clonidine with morphine.

Results

This study showed that PFE was less effective than clonidine at a dose of 100 mg/kg, and at doses of 200 mg/kg and 300 mg/kg it was comparable to clonidine, and did not show a significant difference in the reduction of morphine withdrawal symptoms. Luteolin was comparable in 30 mg/kg, 60 mg/kg, and 90 mg/kg with clonidine to reduce the frequency of morphine withdrawal symptoms. PFE can be used as a source of luteolin.

Conclusions

The study findings suggest that PFE and luteolin might reduce the signs of narcotic withdrawal. Due to a similar effect to clonidine, its mechanism of action might be through the protein kinase A pathway and might have human therapeutic potential.

Integration of in silico methods and computational systems biology to explore endocrine-disrupting chemical binding with nuclear hormone receptors

Thousands of potential endocrine-disrupting chemicals present difficult regulatory challenges. Endocrine-disrupting chemicals can interfere with several nuclear hormone receptors associated with a variety of adverse health effects. The U.S. Environmental Protection Agency (U.S. EPA) has released its reviews of Tier 1 screening assay results for a set of pesticides in the Endocrine Disruptor Screening Program (EDSP), and recently, the Collaborative Estrogen Receptor Activity Prediction Project (CERAPP) data. In this study, the predictive ability of QSAR and docking approaches is evaluated using these data sets. This study also presents a computational systems biology approach using carbaryl (1-naphthyl methylcarbamate) as a case study. For estrogen receptor and androgen receptor binding predictions, two commercial and two open source QSAR tools were used, as was the publicly available docking tool Endocrine Disruptome. For estrogen receptor binding predictions, the ADMET Predictor, VEGA, and OCHEM models (specificity: 0.88, 0.88, and 0.86, and accuracy: 0.81, 0.84, and 0.88, respectively) were each more reliable than the MetaDrug™ model (specificity 0.81 and accuracy 0.77). For androgen receptor binding predictions, the Endocrine Disruptome and ADMET Predictor models (specificity: 0.94 and 0.8, and accuracy: 0.78 and 0.71, respectively) were more reliable than the MetaDrug™ model (specificity 0.33 and accuracy 0.4). A consensus approach is proposed that reaches general agreement among the models (specificity 0.94 and accuracy 0.89). This study integrates QSAR, docking, and systems biology approaches as a virtual screening tool for use in risk assessment. As such, this systems biology pathways and network analysis approach provides a means to more critically assess the potential effects of endocrine-disrupting chemicals.

QSAR Modeling for Acute Toxicity Prediction in Rat by Common Painkiller Drugs

Painkiller drugs or analgesics are potent pain reliever chemical agents, which are commonly used in pain therapy. Mathematical modeling by QSAR (quantitative structure activity relationship) methods are well known practices to determine predictive toxicity in biota. Now-a-days, an easy screening of chemicals, QSAR can be done by using several recommended softwares. The present study was carried out by using software namely T.E.S.T. (Toxicity estimation software tool) for rat oral LD50 (median lethal dose) predictive toxicity for common painkiller drugs. These painkiller drugs were selected as 35 compounds and tabulated on the basis characteristics of one non-narcotic viz. acetaminophen, twenty non-steroidal anti-inflammatory such as bromofenac, diclofenac, diflunsial, etodolac, fenoprofen, flurbiprofen, ibuprofen, indomethacin, ketoprofen, ketorolac, maclofenamate sodium, mefenamic acid, meloxicam, nabumetone, naproxen, oxaprozin, phenylbutazone, piroxicam, sulindac and tolmetin as well as fourteen narcotic viz. buprenorphine, butorphanol, codeine, hydrocodone, hydromorphone, levorphanol, meperidine, methadone, morphine, nalbuphine, oxycodone, pentazocine, dextropropoxyphene and tapentadol. The data were tabulated on experimental (bioassay) from ChemIDPlus and predictive toxicity of 30 compounds out of 35 compounds by using T.E.S.T. The predictive data were found by T.E.S.T. that 20 and 10 compounds were very toxic and moderately toxic respectively but not extremely, super toxic and non-toxic in rat model after acute oral exposure. It is suggested to evaluate the predicted data further with other available recommended softwares with different test models like daphnia, fish etc. to know aquatic toxicity when these compounds may discharge into waterbodies.

Novel derivatives of nitro-substituted salicylic acids: Synthesis, antimicrobial activity and cytotoxicity

Inspired by the high antituberculous activity of novel nitro-substituted derivatives and based on promising predicted ADMET properties we have synthesized a series of 33 salicylanilides containing nitro-group in their salicylic part and evaluated them for their in vitro antimycobacterial, antimicrobial and antifungal activities. The presence of nitro-group in position 4 of the salicylic acid was found to be beneficial and the resulting molecules exhibited minimum inhibitory concentrations (MICs) ranging from 2 to 32 μM against Mycobacterium tuberculosis. The best activity was found for 2-hydroxy-4-nitro-N-[4-(trifluoromethyl)phenyl]benzamide (MIC=2 μM). 4-Nitrosalicylanilides were also found to be active against all Staphylococcus species tested while for MRSA strain 2-hydroxy-4-nitro-N-[4-(trifluoromethyl)phenyl]benzamide's MIC was 0.98 μM. None of the nitrosalicylanilides was active against Enterococcus sp. J 14365/08 and no considerable activity was found against Gram-negative bacteria or fungi. The hepatotoxicity of all nitrosalicylanilides was found to be in the range of their MICs for HepG2 cells.

Using quantitative structure-activity relationship modeling to quantitatively predict the developmental toxicity of halogenated azole compounds

Developmental toxicity is a relevant endpoint for the comprehensive assessment of human health risk from chemical exposure. However, animal developmental toxicity data remain unavailable for many environmental contaminants due to the complexity and cost of these types of analyses. Here we describe an approach that uses quantitative structure-activity relationship modeling as an alternative methodology to fill data gaps in the developmental toxicity profile of certain halogenated compounds. Chemical information was obtained and curated using the OECD Quantitative Structure-Activity Relationship Toolbox, version 3.0. Data from 35 curated compounds were analyzed via linear regression to build the predictive model, which has an R(2) of 0.79 and a Q(2) of 0.77. The applicability domain (AD) was defined by chemical category and structural similarity. Seven halogenated chemicals that fit the AD but are not part of the training set were employed for external validation purposes. Our model predicted lowest observed adverse effect level values with a maximal threefold deviation from the observed experimental values for all chemicals that fit the AD. The good predictability of our model suggests that this method may be applicable to the analysis of qualifying compounds whenever developmental toxicity information is lacking or incomplete for risk assessment considerations.

Improvement of carcinogenicity prediction performances based on sensitivity analysis in variable selection of SVM models

A new sensitivity analysis (SA) method for variable selection in support vector machine (SVM) was proposed to improve the performance level of the QSAR model to predict carcinogenicity based on the correlation coefficient (CC) method used in our preceding study. The performances of both methods were also compared with that of the F-score (FS) method proposed by Chang and Lin. The 911 non-congeneric chemicals were classified into 20 mutually overlapping groups according to contained substructures, and a specific SVM model created on chemicals belonging to each group was optimized by searching the best set of SVM parameters while successively omitting descriptors of lower absolute values of sensitivity, CC or FS until the maximum predictive performance was obtained. The SA method improves the overall accuracy from 80% of CC and FS to 84%, which is considerably higher than those of existing models for predicting the carcinogenicity of non-congeneric chemicals. It selects the optimum sets of effective descriptors fewer than the CC and FS methods, and is not time-consuming and can be applied to a large set of initial descriptors. It is concluded that SA is superior as a variable selection method in SVM models.