Assessment of predictivity of volatile organic compounds carcinogenicity and mutagenicity by freeware in silico models

A Comprehensive Review of Biomarker Sensors for a Breathalyzer Platform

Detecting volatile organic compounds (VOCs) is increasingly recognized as a pivotal tool in non-invasive disease diagnostics. VOCs are metabolic byproducts, mostly found in human breath, urine, feces, and sweat, whose profiles may shift significantly due to pathological conditions. This paper presents a thorough review of the latest advancements in sensor technologies for VOC detection, with a focus on their healthcare applications. It begins by introducing VOC detection principles, followed by a review of the rapidly evolving technologies in this area. Special emphasis is given to functionalized molecularly imprinted polymer-based biochemical sensors for detecting breath biomarkers, owing to their exceptional selectivity. The discussion examines SWaP-C considerations alongside the respective advantages and disadvantages of VOC sensing technologies. The paper also tackles the principal challenges facing the field and concludes by outlining the current status and proposing directions for future research.

Prenatal Exposure to Herbicide 2,4-Dichlorophenoxyacetic Acid (2,4D) Exacerbates Zika Virus Neurotoxicity In Vitro and In Vivo

Zika virus (ZIKV) infection during pregnancy can lead to a set of congenital malformations known as Congenital ZIKV syndrome (CZS), whose main feature is microcephaly. The geographic distribution of CZS in Brazil during the 2015-2017 outbreak was asymmetrical, with a higher prevalence in the Northeast and Central-West regions of the country, despite the ubiquitous distribution of the vector Aedes aegypti, indicating that environmental factors could influence ZIKV vertical transmission and/or severity. Here we investigate the involvement of the most used agrochemicals in Brazil with CZS. First, we exposed human neuroblastoma SK-N-AS cells to the 15 frequently used agrochemical molecules or derivative metabolites able to cross the blood-brain barrier. We found that a derived metabolite from a widely used herbicide in the Central-West region, 2,4-dichlorophenoxyacetic acid (2,4D), exacerbates ZIKV neurotoxic effects in vitro. We validate this observation by demonstrating vertical transmission leading to microcephaly in the offspring of immunocompetent C57BL/6J mice exposed to water contaminated with 0.025 mg/L of 2,4D. Newborn mice whose dams were exposed to 2,4D and infected with ZIKV presented a smaller brain area and cortical plate size compared to the control. Also, embryos from animals facing the co-insult of ZIKV and 2,4D exposition presented higher Caspase 3 positive cells in the cortex, fewer CTIP2+ neurons and proliferative cells at the ventricular zone, and a higher viral load. This phenotype is followed by placental alterations, such as vessel congestion, and apoptosis in the labyrinth and decidua. We also observed a mild spatial correlation between CZS prevalence and 2,4D use in Brazil's North and Central-West regions, with R2 = 0.4 and 0.46, respectively. Our results suggest that 2,4D exposition facilitates maternal vertical transmission of ZIKV, exacerbating CZS, possibly contributing to the high prevalence of this syndrome in Brazil's Central-West region compared to other regions.

Advances in Activity/Property Prediction from Chemical Structures

Recent technological advancement in AI modeling of molecular property databases has significantly expanded the opportunities for drug design and development. Quantitative structure-activity relationships (QSARs) are shown to provide more accurate predictions with regards to biological activity as well as toxicological assessment. By using a combination of in-silico models or by combining disparate structure-activity databases, researchers have been able to improve accuracy for a variety of drug discovery and analysis methods, generating viable compounds, which in certain cases, can be synthesized and further studied in vitro to find candidates for potential development. Additionally, the development of compounds of determined toxicology can be discontinued earlier, allowing alternative routes to be evaluated, preventing wasted time and resources. Although the progress that has been made is tremendous, expert review is still necessary for most in-silico generated predictions. Regardless, the scientific community continues to move ever closer to completely automated drug discovery and evaluation.

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.

Design and Analysis of Pharmacokinetics, Pharmacodynamics and Toxicological analysis of Cannabidiol analogs using in silico Tools

Background:

Background: Cannabidiol (CBD), a non‐psychoactive phytocannabinoid from Cannabis Sativa, has become an interesting option for medicinal chemists in the development of new drug candidates

Objective:

Objective: This study aims to propose analogs with therapeutic potential from the CBD scaffold. Methods: The 16 analogs proposed were designed using the PubChem Sketcher V. 2.4® software. Already, CBD analogs were subjected to different in silico tools, such as Molinspiration®; SwissADME®; SwissTargetPrediction® and OSIRIS Property Explorer

Results and Discussio:

Results and Discussion: The screening of CBD analogs carried out in this study showed compounds 9 and 16 with good affinity for interactions with CB1 and CB2 receptors. Pharmacokinetic data showed that these two compounds have good oral absorption. Finally, in silico toxicity data showed that these compounds pose no risk of a toxic event in humans

Conclusion:

Conclusion: CBD analogs 9 and 16 would have a better profile of drug candidates to be further tested in vitro and in vivo models.: CBD analogs 9 and 16 would have a better profile of drug candidates to be further tested in vitro and in vivo models.

Adsorption of Toluene and Water over Cationic-Exchanged Y Zeolites: A DFT Exploration

In this study, density functional theory (DFT) calculations have been performed to investigate the adsorption mechanisms of toluene and water onto various cationic forms of Y zeolite (LiY, NaY, KY, CsY, CuY and AgY). Our computational investigation revealed that toluene is mainly adsorbed via π-interactions on alkalis exchanged Y zeolites, where the adsorbed toluene moiety interacts with a single cation for all cases with the exception of CsY, where two cations can simultaneously contribute to the adsorption of the toluene, hence leading to the highest interaction observed among the series. Furthermore, we find that the interaction energies of toluene increase while moving down in the alkaline series where interaction energies are 87.8, 105.5, 97.8, and 114.4 kJ/mol for LiY, NaY, KY and CsY, respectively. For zeolites based on transition metals (CuY and AgY), our calculations reveal a different adsorption mode where only one cation interacts with toluene through two carbon atoms of the aromatic ring with interaction energies of 147.0 and 131.5 kJ/mol for CuY and AgY, respectively. More importantly, we show that water presents no inhibitory effect on the adsorption of toluene, where interaction energies of this latter were 10 kJ/mol (LiY) to 47 kJ/mol (CsY) higher than those of water. Our results point out that LiY would be less efficient for the toluene/water separation while CuY, AgY and CsY would be the ideal candidates for this application.

In silico studies: Physicochemical properties, drug score, toxicity predictions and molecular docking of organosulphur compounds against Diabetes mellitus

Diabetes mellitus (DM) is a significant common metabolic disorder seen all over the world. In 2020, according to the International Diabetes Federation (IDF), out of 463 million people who have diabetes all over the world, 77 million belong to India. As per the statistical prediction, the affected numbers are probably expected to rise to 642 million by 2040. The commercially available anti-diabetic drugs in the market include metformin, sulphonyl urea, meglitinides, miglitol, acarbose, biguanides, and thiazolidinediones cause side effects like hypoglycaemia, dizziness, liver cell injury, digestive discomfort, neurological defects, etc. Hence, bioactive organosulphur based functional ligands are chosen in this study to arrive at a newer drug for DM. In this work, in silico analysis of organosulphur molecular descriptors like physicochemical properties, solubility, drug score, and toxicity predictions are evaluated using OSIRIS and Toxtree freeware. The essential parameters for discovering drugs for biopharmaceutical formulations viz the solubility of drugs and toxicity have been calculated. The protein target Dipeptidyl peptidase DPP4 (PID: 2RIP) was docked against energy minimised sulphur compounds using Hex 6.3. The results indicate that the drug likeliness of the molecule 4, that is, N-[(3,3-dimethyl piperidin-2-yl) methyl]-4-ethyl sulphonyl aniline is active with decreasing binding energy score (-212.24 Kcal mol^-1 ) with no toxicity and also few sulphur compounds are active against diabetes compared to standard drug metformin (-158.33 Kcal mol^-1 ). The best drug-like ligand N-[(3,3-dimethyl piperidin-2-yl) methyl]-4-ethyl sulphonyl aniline, was docked using commercial Maestro Schrodinger software to predict the results.

Oil sludge washing with surfactants and co-solvents: oil recovery from different types of oil sludges

Different physicochemical and biological treatments have been used to treat oil sludges, and oil recovery techniques are preferred such as oil sludge washing (OSW) with surfactants and co-solvents. Toluene is commonly used as co-solvent, but it is non-benign to the environment. This study tested alternative co-solvents (n-pentane, n-hexane, cyclohexane, and isooctane) at 1:1 and 2:1 C/OS (co-solvent to oil sludge ratio). Also, this study evaluated the effect on the oil recovery rate (ORR) of three main parameters in the washing: type, concentration, and application ratio (S/OS) of surfactants to oil sludges. To date, no study has assessed these parameters in the washing of oil sludges from different sources. Four types of oil sludges and five surfactants (Triton X-100 and X-114, Tween 80, sodium dodecyl sulphate (SDS), and rhamnolipid) were used. The results showed that cyclohexane had high ORR and could be used instead of toluene because it is more benign to the environment. The S/OS ratio had a high effect on the ORR and depended on the type of oil sludge. Rhamnolipid, Triton X-100, and Triton X-114 had the highest oil recovery rates (40 - 70%). In addition, it was found that the surfactant concentration had no effect on the ORR. Consequently, the addition of surfactant was not significantly different compared to the washing with no surfactants, except for one sludge. The use of the surfactant in the washing solution can help in the selective extraction of specific oil hydrocarbon fractions in the recovered oil to assess its potential reuse as fuel. Further recommendations were given to improve the OSW process.

Computational Approaches in Preclinical Studies on Drug Discovery and Development

Because undesirable pharmacokinetics and toxicity are significant reasons for the failure of drug development in the costly late stage, it has been widely recognized that drug ADMET properties should be considered as early as possible to reduce failure rates in the clinical phase of drug discovery. Concurrently, drug recalls have become increasingly common in recent years, prompting pharmaceutical companies to increase attention toward the safety evaluation of preclinical drugs. In vitro and in vivo drug evaluation techniques are currently more mature in preclinical applications, but these technologies are costly. In recent years, with the rapid development of computer science, in silico technology has been widely used to evaluate the relevant properties of drugs in the preclinical stage and has produced many software programs and in silico models, further promoting the study of ADMET in vitro. In this review, we first introduce the two ADMET prediction categories (molecular modeling and data modeling). Then, we perform a systematic classification and description of the databases and software commonly used for ADMET prediction. We focus on some widely studied ADMT properties as well as PBPK simulation, and we list some applications that are related to the prediction categories and web tools. Finally, we discuss challenges and limitations in the preclinical area and propose some suggestions and prospects for the future.

Predicting PBT and CMR properties of substances of very high concern (SVHCs) using QSAR models, and application for K-REACH

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BIOWIN is effective for predicting persistence and bioaccumulation.

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Toxtree is effective for predicting carcinogenicity and mutagenicity.

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WoE approach enhances the sensitivity.

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It is recommended to set a conservative criteria of log Kow more than 4.5 in K-REACH.

Quantitative structure-activity relationship (QSAR) models have been applied to predict a variety of toxicity endpoints. Their performance needs to be validated, in a variety of cases, to increase their applicability to chemical regulation. Using the data set of substances of very high concern (SVHCs), the performance of QSAR models were evaluated to predict the persistence and bioaccumulation of PBT, and the carcinogenicity and mutagenicity of CMR. BIOWIN and Toxtree showed higher sensitivity than other QSAR models – the former for persistence and bioaccumulation, the latter for carcinogenicity. In terms of mutagenicity, the sensitivities of QSAR models were underestimated, Toxtree was more accurate and specific than lazy structure–activity relationships (LAZARs) and Computer Assisted Evaluation of industrial chemical Substances According to Regulations (CAESAR). Using the weight of evidence (WoE) approach, which integrates results of individual QSAR models, enhanced the sensitivity of each toxicity endpoint. On the basis of obtained results, in particular the prediction of persistence and bioaccumulation by KOWWIN, a conservative criterion is recommended of log Kow greater than 4.5 in K-REACH, without an upper limit. This study suggests that reliable production of toxicity data by QSAR models is facilitated by a better understanding of the performance of these models.

In Silico Approaches in Predictive Genetic Toxicology

Genetic toxicology testing is a weight-of-evidence approach to identify and characterize chemical substances that can cause genetic modifications in somatic and/or germ cells. Prediction of genetic toxicology using computational tools is gaining more attention and preferred by regulatory authorities as an alternate safety assessment for in vivo or in vitro approaches. Due to the cost and time associated with experimental genetic toxicity tests, it is essential to develop more robust in silico methods to predict chemical genetic toxicity. A number of in silico genotoxicity predictive tools/models are developed based on the experimental data gathered over the years. These in silico tools are divided into statistical quantitative structure-activity relationships (QSAR)-based approaches and expert-based systems. This chapter covers the state of the art in silico toxicology approaches and standardized protocols, essential for conducting genetic toxicity predictions of chemicals. This chapter also highlights various parameters for the validation of the prediction results obtained from QSAR models.

A first principle evaluation of the adsorption mechanism and stability of volatile organic compounds into NaY zeolite

Removal of volatile organic compounds (VOCs) from indoor or outdoor environments is an urgent challenge for the protection of human populations. Inorganic sorbents such as zeolites are a promising solution to tackle this issue. Using dispersion corrected periodic DFT calculations, we have studied the interaction between sodium-exchanged faujasite zeolite and a large set of VOCs including aromatics, oxygenates and chlorinated compounds. The computed interaction energies range from about −25 (methane) to −130 kJ/mol (styrene). Methane is by far the less interacting specie with the NaY zeolite. All other VOCs present interaction energies higher in absolute value than 69 kJ/mol. Most of them show a similar adsorption strength, between −70 and −100 kJ/mol. While the electrostatic interactions are important in the case of oxygenates and acrylonitrile, van der Waals interactions predominate in hydrocarbons and chlorides. By monitoring the variation of molecular bond lengths of the different VOCs before and after adsorption, we have then evaluated the tendency of adsorbate to react and form by-products, since a significant stretching would evidently lead to the activation of the bond. While hydrocarbons, tetrachloroethylene and acrylonitrile seem to be not activated upon adsorption, all oxygenates and 1,1,2-trichloroethane could possibly react once adsorbed.