Cheminformatics and Machine Learning Approaches to Assess Aquatic Toxicity Profiles of Fullerene Derivatives.

Fullerene derivatives (FDs) are widely used in nanomaterials production, the pharmaceutical industry and biomedicine. In the present study, we focused on the potential toxic effects of FDs on the aquatic environment. First, we analyzed the binding affinity of 169 FDs to 10 human proteins (1D6U, 1E3K, 1GOS, 1GS4, 1H82, 1OG5, 1UOM, 2F9Q, 2J0D, 3ERT) obtained from the Protein Data Bank (PDB) and showing high similarity to proteins from aquatic species. Then, the binding activity of 169 FDs to the enzyme acetylcholinesterase (AChE)-as a known target of toxins in fathead minnows and Daphnia magna, causing the inhibition of AChE-was analyzed. Finally, the structural aquatic toxicity alerts obtained from ToxAlert were used to confirm the possible mechanism of action. Machine learning and cheminformatics tools were used to analyze the data. Counter-propagation artificial neural network (CPANN) models were used to determine key binding properties of FDs to proteins associated with aquatic toxicity. Predicting the binding affinity of unknown FDs using quantitative structure-activity relationship (QSAR) models eliminates the need for complex and time-consuming calculations. The results of the study show which structural features of FDs have the greatest impact on aquatic organisms and help prioritize FDs and make manufacturing decisions.

Toxicological evaluation of ergocalciferol, cholecalciferol, and their metabolites by a category approach.

Predictive toxicology plays an integral role in determining the toxicological profiles of chemicals for safety assessment. Vitamin D is an essential vitamin for the regulation of calcium absorption and homeostasis, as well as the treatment and prevention of several diseases such as rickets and osteomalacia. According to European Medicines Agency (EMA) Guideline on setting health-based exposure limits for use in risk identification in the manufacturing of different medicinal products in shared facilities, permitted daily exposure (PDE) calculation for active pharmaceutical ingredients (APIs) should be done by the medicinal product producers. PDE calculation is mainly based on critical toxicological endpoints such as repeated dose toxicity, genotoxicity, carcinogenicity, developmental and reproductive toxicity, and hypersensitivity potential. During this procedure, critical toxicological endpoints data of an API can be used to predict the PDE of another API that has a similar chemical structure. In the present paper, human toxicological endpoints of vitamin D2, D3, and their metabolites were evaluated and afterwards the data gaps in the toxicological endpoints were filled by forming a category. The read-across was justified by the structural and metabolic similarities. Molecular similarity and mechanistic relevance were found to be substantial, resulting in low uncertainty. The untested vitamin D analogs within the category can be read across with confidence to complete the data gaps related to the human health endpoints.

QSPR modelling of in vitro degradation half-life of acyl glucuronides.

Acyl glucuronidation is an important Phase II biotransformation, which is an efficient detoxification mechanism for the metabolism of carboxylic acid group-containing drugs. However, the reactivity of acyl glucuronide (AG) metabolites associated with short half-lives may be an indication of idiosyncratic drug toxicity. The degradation half-lives of AGs elucidate several important reactions such as hydrolysis, acyl migration and covalent binding to proteins. Prediction of degradation half-life using computational methods is a promising alternative approach to costly and time-consuming experiments, enabling a priori evaluation of the properties of drug candidates during the drug design process. The main objective of the present study was to develop a linear model for the quantitative prediction of half-lives of acyl glucuronidated drug-like compounds. The proposed model revealed that the number of total quaternary carbons, the complexity of the ring in the compound, Sanderson electronegativities, and dipole moment of the compound are important molecular features in predicting the half-life of an AG. The rigorously validated model can contribute to a better understanding of molecular features of these drugs to predict degradation half-lives.

How fullerene derivatives (FDs) act on therapeutically important targets associated with diabetic diseases.

Fullerene derivatives (FDs) belong to a relatively new family of nano-sized organic compounds. They are widely applied in materials science, pharmaceutical industry, and (bio) medicine. This research focused on the study of FDs in terms of their potential inhibitory effect on therapeutic targets associated with diabetic disease, as well as analysis of protein-ligand binding in order to identify the key binding characteristics of FDs. Therapeutic drug compounds when entering the biological system usually inevitably encounter and interact with a vast variety of biomolecules that are responsible for many different functions in organisms. Protein biomolecules are the most important functional components and used in this study as target structures. The structures of proteins [(PDB ID: 1BMQ, 1FM6, 1GPB, 1H5U, 1US0)] belonging to the class of anti-diabetes targets were obtained from the Protein Data Bank (PDB). Protein binding activity data (binding scores) were calculated for the dataset of 169 FDs related to these five proteins. Subsequently, the resulting data were analyzed using various machine learning and cheminformatics methods, including artificial neural network algorithms for variable selection and property prediction. The Quantitative Structure-Activity Relationship (QSAR) models for prediction of binding scores activity were built up according to five Organization for Economic Co-operation and Development (OECD) principles. All the data obtained can provide important information for further potential use of FDs with different functional groups as promising medical antidiabetic agents. Binding scores activity can be used for ranking of FDs in terms of their inhibitory activity (pharmacological properties) and potential toxicity.

Filling data gap for nicotinic acid, nicotinate esters and nicotinamide for the determination of permitted daily exposure by a category approach.

This study aimed to obtain necessary toxicological data using experimental and computational methods for the calculation of a common permitted daily exposure (PDE) which can be relevant for nicotinic acid and its esters and nicotinamide according to European Medicines Agency Guideline on setting health-based exposure limits. PDE calculation is mainly based on critical toxicological endpoints. During this procedure, critical toxicological endpoints data of an active pharmaceutical ingredient (API) may not be able to find satisfactorily. Hence, using toxicological data for another API that has a similar chemical structure can be a useful way. In this study, toxicological endpoints of nicotinic acid and its esters and nicotinamide were evaluated. Then, the data gaps in the toxicological endpoints were filledusing the read-across approach. Based on the current existing data, nicotinic acid and its esters and also nicotinamide are not genotoxic and do not have skin sensitization potential. These compounds do not present a concern for carcinogenicity and developmental/reproductive toxicity. Based on these critical endpoints and available experimental data, the final PDE of 10 mg/day was calculated for all category members. Our study showed the utility of the read-across for PDE calculation of APIs with experimental toxicological data gap.

In silico Modeling and Toxicity Profiling of a Set of Quinoline Derivatives as c-MET Inhibitors in the treatment of Human Tumors.

OBJECTIVES: 4-(2-fluorophenoxy) quinoline derivatives constitute one of the chemical classes of hepatocyte growth factor receptor (c-MET) inhibitors, a promising treatment against various human tumors. There are three aims of the present study: (1) To develop a robust and validated quantitative structure-activity relationship model to predict the c-Met kinase inhibition; (2) to examine the toxicity profiles of these compounds; (3) to design new quinoline derivatives and apply the developed model on these compounds to observe its pertinence. MATERIALS AND METHODS: A multiple linear regression method was used to develop the model with calculated descriptors. State-of-the-art internal and external validation parameters were calculated. The in silico toxicity profiles including structural alerts and the lowest observed adverse effect level (LOAEL) values were evaluated using online tools. New derivatives were designed and tested on the developed model. RESULTS: A series of 4-(2-fluorophenoxy) quinoline derivatives was linearly modeled and vigorously validated to predict the molecules' c-MET kinase inhibition potential. Statistical metrics of the developed model showed that it was robust and able to make successful predictions for this chemical class. The mass, electronegativity, partial charges, and the structure of the molecules had an effect on the activity. Moreover, the toxicity profiles of the studied compounds were found to be adequate. CONCLUSION: Five of the synthesized compounds were observed to be auspicious for the toxicity/activity ratio. The developed model is useful in the virtual screening and in the design of new anti-tumor compounds.

The integrated use of in silico methods for the hepatotoxicity potential of Piper methysticum.

Herbal products as supplements and therapeutic intervention have been used for centuries. However, their toxicities are not completely evaluated and the mechanisms are not clearly understood. Dried rhizome of the plant kava (Piper methysticum) is used for its anxiolytic, and sedative effects. The drug is also known for its hepatotoxicity potential. Major constituents of the plant were identified as kavalactones, alkaloids and chalcones in previous studies. Kava hepatotoxicity mechanism and the constituent that causes the toxicity have been debated for decades. In this paper, we illustrated the use of computational tools for the hepatotoxicity of kava constituents. The proposed mechanisms and major constituents that are most probably responsible for the toxicity have been scrutinized. According to the experimental and prediction results, the kava constituents play a substantial role in hepatotoxicity by some means or other via glutathione depletion, CYP inhibition, reactive metabolite formation, mitochondrial toxicity and cyclooxygenase activity. Some of the constituents, which have not been tested yet, were predicted to involve mitochondrial membrane potential, caspase-3 stimulation, and AhR activity. Since Nrf2 activation could be favorable for prevention of hepatotoxicity, we also suggest that these compounds should undergo testing given that they were predicted not to be activating Nrf2. Among the major constituents, alkaloids appear to be the least studied and the least toxic group in general. The outcomes of the study could help to appreciate the mechanisms and to prioritize the kava constituents for further testing.

Do We Build Similar Molecules for Comorbid Diseases? Tevarud in Drug Design, an Analysis for Depression and Inflammation.

Tevarud designates two poets coincidently writing a same verse in the Ottoman Divan literature. This study aims to analyze the structural similarity of molecules independently designed for inflammation and depression to determine if coincidentally we are building similar molecules for comorbid diseases. For this purpose, a molecule library was first constituted with structures that were developed as anti-inflammatory (AI) and antidepressant (AD) agents these last decades. Then, the similarity of the structures was determined by calculating the Tanimoto and Cosine similarity coefficients for each AD/AI pair. The highest scores were obtained for two theophylline derivatives: AD17 (for which some AI activity was found to be mentioned) and AI42. The study also pointed out the similarity of some AD coumarins with some AI flavonoids interestingly found to be highly similar to some AI coumarins and AD flavonoids, respectively. Thus, our investigation demonstrated that structures independently developed as AD and AI derivatives can present extremely high structural similarity, a finding that can suggest mechanistic interconnection for these comorbid diseases and also guide for the design of novel bioactive compounds.

A QSAR Study for Analgesic and Anti-inflammatory Activities of 5-/6-Acyl-3-alkyl-2-Benzoxazolinone Derivatives.

In this publication, QSAR models were developed to predict analgesic and anti-inflammatory activities of some 2-benzoxazolinone derivatives using multiple linear regression method. The models were validated internally and externally according to the OECD principles. With the help of these models, pronounced molecular properties of these compounds related to activities were also explored. The developed models demonstrated that hydrophobicity, the number of halogens, and the shape of the molecular structure of these candidate drugs are prominent to represent analgesic and anti-inflammatory activities. Based on the previously tested compounds and the developed models, 77 new compounds were designed as potential analgesic and anti-inflammatory drugs. Majority of the newly designed compounds demonstrated promising analgesic and anti-inflammatory activity.

Application of a Validated QSTR Model for Repurposing COX-2 Inhibitor Coumarin Derivatives as Potential Antitumor Agents.

BACKGROUND: The discovery of novel potent molecules for both cancer prevention and treatment has been continuing over the past decade. In recent years, identification of new, potent, and safe anticancer agents through drug repurposing has been regarded as an expeditious alternative to traditional drug development. The cyclooxygenase-2 is known to be over-expressed in several types of human cancer. For this reason cyclooxygenase-2 inhibition may be useful tool for cancer chemotherapy. OBJECTIVE: The first aim of the study was to develop a validated linear model to predict antitumor activity. Subsequently, applicability of the model for repurposing these cyclooxygenase-2 inhibitors as antitumor compounds to abridge drug development process. METHODS: We performed a quantitative structure-toxicity relationship (QSTR) study on a set of coumarin derivatives using a large set of molecular descriptors. A linear model predicting growth inhibition on leukemia CCRF cell lines was developed and consequently validated internally and externally. Accordingly, the model was applied on a set of 143 cyclooxygenase-2 inhibitor coumarin derivatives to explore their antitumor activity. RESULTS: The results indicated that the developed QSAR model would be useful for estimating inhibitory activity of coumarin derivatives on leukemia cell lines. Electronegativity was found to be a prominent property of the molecules in describing antitumor activity. The applicability domain of the developed model highlighted the potential antitumor compounds. CONCLUSION: The promising results revealed that applied integrated in silico approach for repurposing by combining both the biological activity similarity and the molecular similarity via the computational method could be efficiently used to screen potential antitumor compounds among cyclooxygenase-2 inhibitors.

Toxicological assessment of epinephrine and norepinephrine by analog approach.

Epinephrine and norepinephrine have been used in the management of anaphylactic reactions and cardiac resuscitation, along with treatment of asthma and glaucoma extensively, but their toxicological profiles are not yet completed. Based on this circumstance, various toxicological endpoints of epinephrine and norepinephrine were explored. Since there is a paucity of some endpoints' data, readacross was applied to fill the data gaps using analog approach. Along with structural similarity, biological and mechanistic plausibility were also considered in analog selection. The similarity justification and supporting experimental data were provided for uncertainty evaluation. Short term repeated dose toxicity values as NOAEL and LOAEL belonging to epinephrine were used to estimate the repeated dose toxicity of norepinephrine. The in vivo and in vitro mutagenicity tests were considered representative of genotoxicity. Both chemicals are showed to be non-genotoxic. They are experimentally reported to cause developmental and reproductive toxicity. For the carcinogenicity endpoint, a conclusion could not be reached because similar compounds were seen to show conflicting results.

A multipronged QSAR approach to predict algal low-toxic-effect concentrations of substituted phenols and anilines.

Environmental risk assessment procedures require acute and chronic toxicity values of hazardous chemicals. In this respect, the 96-h toxicity bioassays of nitro-, methyl-, methoxy-, chloro-, and nitrile- substituted phenols and anilines to Chlorella vulgaris were performed. Median inhibitory and low-toxic-effect concentrations were reported. Significant correlations between acute and chronic toxicities were found for the chemicals in the data set regardless of mode of action. Consequently, linear models employing theoretical and empirical descriptors were developed for the prediction of NOEC and IC20. The outcome of the study will be beneficial in the risk assessments of organic chemicals and setting water quality standards by the regulators.

On the aquatic toxicity of substituted phenols to Chlorella vulgaris: QSTR with an extended novel data set and interspecies models.

This study provides for the first time the 96-h toxicity of 16 nitro- and methyl- substituted phenols to Chlorella vulgaris. Enabling the circulation of new ecotoxicity data has expanded the previously reported toxicity data set of 30 phenols to C. vulgaris by our laboratory. In this respect, high quality, single source algal toxicity data, generated in the same laboratory according to a REACH (Registration, Evaluation, Authorization and Restriction of CHemicals) compatible endpoint, provided a sound basis to explore quantitative structure-toxicity relationship (QSTR), which can be used for regulatory purposes. Of the developed linear models on a new data set, the selected one was applied to a data set lack of toxicity values, and prediction ability of the model was discussed. Interspecies relations were sought related to Pseudokirchneriella subcapitata and Tetrahymena pyriformis. The developed models displayed decent predictivity, which can be used to predict the toxicity of untested phenols on C. vulgaris.

Comparative performance of descriptors in a multiple linear and Kriging models: a case study on the acute toxicity of organic chemicals to algae.

This study presents quantitative structure-toxicity relationship (QSTR) models on the toxicity of 91 organic compounds to Chlorella vulgaris using multiple linear regression (MLR) and Kriging techniques. The molecular descriptors were calculated using SPARTAN and DRAGON programs, and descriptor selection was made by "all subset" method available in the QSARINS software. MLR and Kriging models developed with the same descriptors were compared. In addition to these models, Kriging method was used for descriptor selection, and model development. The selected descriptors showed the importance of hydrophobicity, molecular weight and atomic ionization state in describing the toxicity of a diverse set of chemicals to C. vulgaris. A QSTR model should be associated with appropriate measures of goodness-of-fit, robustness, and predictivity in order to be used for regulatory purpose. Therefore, while the internal performances (goodness-of-fit and robustness) of the models were determined by using a training set, the predictive abilities of the models were determined by using a test set. The results of the study showed that while MLR method is easier to apply, the Kriging method was more successful in predicting toxicity.