In silico prediction of toxicity and its applications for chemicals at work

Chloropicrin induced ocular injury: Biomarkers, potential mechanisms, and treatments

Ocular tissue, especially the cornea, is overly sensitive to chemical exposures. The availability and adoption of chemical threat agent chloropicrin (CP) is growing in the United States as a pesticide and fumigant; thereby increasing the risk of its use in warfare, terrorist attacks and non-intentional exposure. Exposure to CP results in immediate ocular, respiratory, and dermal injury; however, we lack knowledge on its mechanism of toxicity as well as of its breakdown products like chlorine and phosgene, and effective therapies are elusive. Herein, we have reviewed the recent findings on exposure route, toxicity and likely mechanisms of CP induced ocular toxicity based on other vesicating chemical warfare agents that cause ocular injury. We have focused on the implication of their toxicity and mechanistic outcomes in the ocular tissue, especially the cornea, which could be useful in the development of broad-spectrum effective therapeutic options. We have discussed on the potential countermeasures, overall hallmarks and challenges involved in studying ocular injuries from chemical threat agent exposures. Finally, we reviewed useful available technologies and methods that can assist in the identification of effective medical countermeasures for chemical threat agents related ocular injuries.

In vitro and in silico studies of the potential cytotoxic, antioxidant, and HMG CoA reductase inhibitory effects of chitin from Indonesia mangrove crab (Scylla serrata) shells

This study aimed to characterize chitin extracted from Indonesia mangrove crab (Scylla serrata) shells, as well as to assess its in vitro cytotoxic, antioxidant, and HMG CoA reductase inhibitory potentials. In silico molecular docking, molecular dynamic, and ADMET prediction analyses were also carried out. Chitin was extracted from mangrove crab shells using deproteination and demineralization processes, Scanning Electron Microscopy (SEM) and Fourier Transform Infrared (FTIR) characterization are then performed. The MTT method was further tested in a study of cell viability, while in vitro method was used to assess HMG CoA reductase inhibitory and antioxidant activities. The extracted chitin was found to have a moderate level of cytotoxic and antioxidant activities. In vitro studies showed that it has an IC50 of 36,65 ± 0,082 μg/mL as an HMG CoA reductase inhibitor, and decreased enzyme activity by 68.733 % at 100 μg/mL as a concentration. Furthermore, in the in silico study, chitin showed a strong affinity to several targets, including HMG CoA reductase, HMG synthase, LDL receptor, PPAR-alfa, and HCAR-2 with binding energies of -5.7; -5.8; -3.6; -5.6; -4.6 kcal/mol, respectively. Based on the ADMET properties, it had non-toxic molecules, which were absorbed and distributed across the blood-brain barrier. The molecular dynamics (MD) simulation also showed that it remained stable in the active sites of HMG CoA reductase receptor for 100 ns. These results indicated that chitin from Indonesian mangrove crab shells can be used to develop more potent HMG CoA reductase inhibitor with antioxidant and cytotoxic activities for effective dyslipidemia therapy.

Piquing artificial intelligence towards drug discovery: Tools, techniques, and applications

The purpose of this study was to discuss how artificial intelligence (AI) methods have affected the field of drug development. It looks at how AI models and data resources are reshaping the drug development process by offering more affordable and expedient options to conventional approaches. The paper opens with an overview of well-known information sources for drug development. The discussion then moves on to molecular representation techniques that make it possible to convert data into representations that computers can understand. The paper also gives a general overview of the algorithms used in the creation of drug discovery models based on AI. In particular, the paper looks at how AI algorithms might be used to forecast drug toxicity, drug bioactivity, and drug physicochemical properties. De novo drug design, binding affinity prediction, and other AI-based models for drug-target interaction were covered in deeper detail. Modern applications of AI in nanomedicine design and pharmacological synergism/antagonism prediction were also covered. The potential advantages of AI in drug development are highlighted as the evaluation comes to a close. It underlines how AI may greatly speed up and improve the efficiency of drug discovery, resulting in the creation of new and better medicines. To fully realize the promise of AI in drug discovery, the review acknowledges the difficulties that come with its uses in this field and advocates for more study and development.

Endocrine disruptors in e-waste dismantling dust: In silico prediction of mixture-induced reproductive toxicity mechanisms

The constant exposure of humans to a mixture of low doses of toxic substances, emerging from the daily emission of toxic dust containing various metals and organic compounds in electrical and electronic waste (e-waste) recycling areas, poses potential harmful effects on health and the environment. While individually recognized as endocrine disruptors affecting hormonal balance, the combined impact of these toxic substances in a mixture remains insufficiently explored, particularly in relation to reproductive health. Thus, the aim of this in silico analysis was to: (i) assess the relationship between the exposure to a mixture of DBDE, DBDPE, TBBPA, Pb, Cd and Ni and development of male and female reproductive system disorders; and (ii) demonstrate the ability of in silico toxicogenomic tools in revealing the potential molecular mechanisms involved in the mixture toxicity. As the main data-mining tool, Comparative Toxicogenomics Database (CTD) was used, along with the ToppGene Suite portal and GeneMANIA online server. Our analysis identified 5 genes common to all the investigated substances and linked to reproductive system disorders. Notably, the most prominent interactions among these genes were physical interactions (77.64 %). Pathway enrichment analysis identified oxidative stress response as the central disrupted molecular pathway linked to reproductive pathology in the investigated mixture, while our chemical-phenotype CTD analysis uncovered additional affected pathways - apoptosis, hormonal regulation, and developmental functions. These findings highlight an increased risk of reproductive system disorders associated with the exposure to the investigated mixture of toxic substances in electronic waste recycling areas, emphasizing the urgent need for attention to address this environmental health concern. Hence, future laboratory studies should prioritize investigating the specific genes and common mechanisms identified in this study.

Network Toxicology Prediction and Molecular Docking-based Strategy to Explore the Potential Toxicity Mechanism of Metformin Chlorination Byproducts in Drinking Water

BACKGROUND

Metformin (MET), a worldwide used drug for treating type 2 diabetes but not metabolized by humans, has been found with the largest amount in the aquatic environment. Two MET chlorination byproducts, including Y and C, were transformed into drinking water during chlorination. However, the potential toxicity of the byproducts in hepatotoxicity and reproduction toxicity remains unclear.

METHODS

The TOPKAT database predicted the toxicological properties of metformin disinfection by-products. The targets of metformin disinfection by-products were mainly obtained from the PharmMapper database, and then the targets of hepatotoxicity and reproductive toxicity were screened from GeneCards. The overlapping targets of toxic component targets and the hepatotoxicity or reproduction toxicity targets were regarded as the key targets. Then, the STRING database analyzed the key target to construct a protein-protein interaction network (PPI) and GO, and KEGG analysis was performed by the DAVID platform. Meanwhile, the PPI network and compound- target network were constructed by Cytoscape 3.9.1. Finally, Discovery Studio 2019 software was used for molecular docking verification of the two toxic compounds and the core genes.

RESULTS

Y and C exhibited hepatotoxicity, carcinogenicity, and mutagenicity evaluated by TOPKAT. There were 22 potential targets relating to compound Y and hepatotoxicity and reproduction toxicity and 14 potential targets relating to compound C and hepatotoxicity and reproduction toxicity. PPI network analysis showed that SRC, MAPK14, F2, PTPN1, IL2, MMP3, HRAS, and RARA might be the key targets; the KEGG analysis indicated that compounds Y and C caused hepatotoxicity through Hepatitis B, Pathways in cancer, Chemical carcinogenesis-reactive oxygen species, Epstein-Barr virus infection; compound Y and C caused reproduction toxicity through GnRH signaling pathway, Endocrine resistance, Prostate cancer, Progesterone-mediated oocyte maturation. Molecular docking results showed that 2 compounds could fit in the binding pocket of the 7 hub genes.

CONCLUSION

This study preliminarily revealed the potential toxicity and possible toxicity mechanism of metformin disinfection by-products and provided a new idea for follow-up research.

Advances in Drug Discovery and Design using Computer-aided Molecular Modeling

Computer-aided molecular modeling is a rapidly emerging technology that is being used to accelerate the discovery and design of new drug therapies. It involves the use of computer algorithms and 3D structures of molecules to predict interactions between molecules and their behavior in the body. This has drastically improved the speed and accuracy of drug discovery and design. Additionally, computer-aided molecular modeling has the potential to reduce costs, increase the quality of data, and identify promising targets for drug development. Through the use of sophisticated methods, such as virtual screening, molecular docking, pharmacophore modeling, and quantitative structure-activity relationships, scientists can achieve higher levels of efficacy and safety for new drugs. Moreover, it can be used to understand the activity of known drugs and simplify the process of formulating, optimizing, and predicting the pharmacokinetics of new and existing drugs. In conclusion, computer-aided molecular modeling is an effective tool to rapidly progress drug discovery and design by predicting the interactions between molecules and anticipating the behavior of new drugs in the body.

Toxicological Profiling of Potential Shikimate Kinase Inhibitors Against Mycobacterium tuberculosis

Over the last decade, Mycobacterium tuberculosis has mutated into a putative 'superbug', as treatments against it have failed due to increasing antimicrobial resistance. As a result, the rising incidence of multidrug-resistant tuberculosis (MDR-TB) is posing a significant public health threat, thus, the need to develop effective drugs for MDR-TB has become an urgent priority. To identify new drug candidates for the treatment of MDR-TB, the present study was based on mycobacterial shikimate kinase (MtSK) as the pharmacological target. One hundred potential MtSK inhibitors were identified from literature and database searches to identify compounds that were designed to specifically function as MtSK antagonists. The ADME properties of these compounds were evaluated by using the SwissADME web tool. ProTox-II software was also used to investigate any potential endocrine disrupting effects, mediated through their interaction with oestrogenic and/or androgenic receptors. This study also aimed to predict LD50 values of potential drug candidates that would be active against the standard H37Rv strain of M. tuberculosis, by using the ProTox-II in silico tool. The molecules for which no structural hazard alerts were identified with these software tools were further subjected to molecular docking analyses and molecular dynamic simulations to estimate their ability to interact with the MtSK enzyme. Preliminary results from SwissADME indicated that 30 molecules were drug-like, due to their physicochemical and pharmacokinetic properties. However, subsequent analysis with ToxTree and ProTox-II indicated that only three of these 30 drug-like molecules were suitable for taking forward into further in vitro experiments. This study, which is based on the use of commonly used open-source in silico tools, identified new MtSK ligands for potential use in the development of new drugs for the therapeutic management of tuberculosis. An initial prediction of their safety profile was also generated.

Bioactive Molecules from the Innate Immunity of Ascidians and Innovative Methods of Drug Discovery: A Computational Approach Based on Artificial Intelligence

The study of bioactive molecules of marine origin has created an important bridge between biological knowledge and its applications in biotechnology and biomedicine. Current studies in different research fields, such as biomedicine, aim to discover marine molecules characterized by biological activities that can be used to produce potential drugs for human use. In recent decades, increasing attention has been paid to a particular group of marine invertebrates, the Ascidians, as they are a source of bioactive products. We describe omics data and computational methods relevant to identifying the mechanisms and processes of innate immunity underlying the biosynthesis of bioactive molecules, focusing on innovative computational approaches based on Artificial Intelligence. Since there is increasing attention on finding new solutions for a sustainable supply of bioactive compounds, we propose that a possible improvement in the biodiscovery pipeline might also come from the study and utilization of marine invertebrates' innate immunity.

Molecular modelling, synthesis and in vitro evaluation of quinazolinone hybrid analogues as potential pancreatic lipase inhibitors

Obesity is a multifactorial metabolic disorder, growing in an alarming rate across the world. Amongst the numerous targets explored for obesity management, inhibition of pancreatic lipase (PL) is considered as one of the promising approaches. Orlistat is the only PL inhibitory drug approved for long term treatment of obesity. However, it is reported to possess hepatotoxicity and nephrotoxicity. Thus, novel drug candidates that act through PL inhibition are considered the hour's need. Based on this aim, a series of quinazolinone hybrid analogues have been synthesized, characterized and evaluated for their PL inhibitory potential. The physicochemical properties and toxicity parameters suggested that these parameters are in an acceptable range for the screened analogues. Amongst the synthesised analogues, QH-25 exerted potential PL inhibition (IC50 = 16.99 ± 0.54 µM). Further, enzyme inhibition studies suggested a reversible competitive inhibition. Molecular docking of these analogues was in line with in vitro results, wherein the obtained MolDock scores exhibited a significant correlation with their inhibitory activity (Pearson's r = 0.6629). To further confirm the stability of the QH-25-PL complex in a dynamic environment, a molecular dynamics study (100 ns) was carried out and the results suggested that this complex is stable at dynamic conditions. Overall, these results shed light on the quinazolinone hybrids as potential PL inhibitors. Further structural modification may result in the development of potent anti-obesity agents which acts through PL inhibition.Communicated by Ramaswamy H. Sarma.

Progress and promise of alternative animal and non-animal methods in biomedical research

Cell culture and invertebrate animal models reflect a significant evolution in scientific research by providing reliable evidence on the physiopathology of diseases, screening for new drugs, and toxicological tests while reducing the need for mammals. In this review, we discuss the progress and promise of alternative animal and non-animal methods in biomedical research, with a special focus on drug toxicity.

Systematic computational toxicity analysis of the ozonolytic degraded compounds of azo dyes: Quantitative structure-activity relationship (QSAR) and adverse outcome pathway (AOP) based approach

The present study identifies and analyses the degraded products of three azo dyes (Reactive Orange 16, Reactive Red 120, and Direct Red 80) and proffers their in silico toxicity predictions. In our previously published work, the synthetic dye effluents were degraded using an ozonolysis-based Advanced Oxidation Process. In the present study, the degraded products of the three dyes were analysed using GC-MS at endpoint strategy and further subjected to in silico toxicity analysis using Toxicity Estimation Software Tool (TEST), Prediction Of TOXicity of chemicals (ProTox-II), and Estimation Programs Interface Suite (EPI Suite). Several physiological toxicity endpoints, such as hepatotoxicity, carcinogenicity, mutagenicity, cellular and molecular interactions, were considered to assess the Quantitative Structure-Activity Relationships (QSAR) and adverse outcome pathways. The environmental fate of the by-products in terms of their biodegradability and possible bioaccumulation was also assessed. Results of ProTox-II suggested that the azo dye degradation products are carcinogenic, immunotoxic, and cytotoxic and displayed toxicity towards Androgen Receptor and Mitochondrial Membrane Potential. TEST results predicted LC50 and IGC50 values for three organisms Tetrahymena pyriformis, Daphnia magna, and Pimephales promelas. EPISUITE software via the BCFBAF module surmises that the degradation products' bioaccumulation (BAF) and bioconcentration factors (BCF) are high. The cumulative inference of the results suggests that most degradation by-products are toxic and need further remediation strategies. The study aims to complement existing tests to predict toxicity and prioritise the elimination/reduction of harmful degradation products of primary treatment procedures. The novelty of this study is that it streamlines in silico approaches to predict the nature of toxicity of degradation by-products of toxic industrial affluents like azo dyes. These approaches can assist the first phase of toxicology assessments for any pollutant for regulatory decision-making bodies to chalk out appropriate action plans for their remediation.

Investigation of the metabolomic crosstalk between liver sinusoidal endothelial cells and hepatocytes exposed to paracetamol using organ-on-chip technology

Organ-on-chip technology is a promising in vitro approach recapitulating human physiology for the study of responses to drug exposure. Organ-on-chip cell cultures have paved new grounds for testing and understanding metabolic dose-responses when evaluating pharmaceutical and environmental toxicity. Here, we present a metabolomic investigation of a coculture of liver sinusoidal endothelial cells (LSECs, SK-HEP-1) with hepatocytes (HepG2/C3a) using advanced organ-on-chip technology. To reproduce the physiology of the sinusoidal barrier, LSECs were separated from hepatocytes by a membrane (culture insert integrated organ-on-chip platform). The tissues were exposed to acetaminophen (APAP), an analgesic drug widely used as a xenobiotic model in liver and HepG2/C3a studies. The differences between the SK-HEP-1, HepG2/C3a monocultures and SK-HEP-1/HepG2/C3a cocultures, treated or not with APAP, were identified from metabolomic profiles using supervised multivariate analysis. The pathway enrichment coupled with metabolite analysis of the corresponding metabolic fingerprints contributed to extracting the specificity of each type of culture and condition. In addition, we analysed the responses to APAP treatment by mapping the signatures with significant modulation of the biological processes of the SK-HEP-1 APAP, HepG2/C3a APAP and SK-HEP-1/HepG2/C3a APAP conditions. Furthermore, our model shows how the presence of the LSECs barrier and APAP first pass can modify the metabolism of HepG2/C3a. Altogether, this study demonstrates the potential of a "metabolomic-on-chip" strategy for pharmaco-metabolomic applications predicting individual response to drugs.

Integrative toxicogenomics: Advancing precision medicine and toxicology through artificial intelligence and OMICs technology

More information about a person's genetic makeup, drug response, multi-omics response, and genomic response is now available leading to a gradual shift towards personalized treatment. Additionally, the promotion of non-animal testing has fueled the computational toxicogenomics as a pivotal part of the next-gen risk assessment paradigm. Artificial Intelligence (AI) has the potential to provid new ways analyzing the patient data and making predictions about treatment outcomes or toxicity. As personalized medicine and toxicogenomics involve huge data processing, AI can expedite this process by providing powerful data processing, analysis, and interpretation algorithms. AI can process and integrate a multitude of data including genome data, patient records, clinical data and identify patterns to derive predictive models anticipating clinical outcomes and assessing the risk of any personalized medicine approaches. In this article, we have studied the current trends and future perspectives in personalized medicine & toxicology, the role of toxicogenomics in connecting the two fields, and the impact of AI on personalized medicine & toxicology. In this work, we also study the key challenges and limitations in personalized medicine, toxicogenomics, and AI in order to fully realize their potential.

Toxic External Exposure Leading to Ocular Surface Injury

The surface of the eye is directly exposed to the external environment, protected only by a thin tear film, and may therefore be damaged by contact with ambient particulate matter, liquids, aerosols, or vapors. In the workplace or home, the eye is subject to accidental or incidental exposure to cleaning products and pesticides. Organic matter may enter the eye and cause infection. Ocular surface damage can trigger a range of symptoms such as itch, discharge, hyperemia, photophobia, blurred vision, and foreign body sensation. Toxin exposure can be assessed clinically in multiple ways, including via measurement of tear production, slit-lamp examination, corneal staining, and conjunctival staining. At the cellular level, environmental toxins can cause oxidative damage, apoptosis of corneal and conjunctival cells, cell senescence, and impaired motility. Outcomes range from transient and reversible with complete healing to severe and sight-compromising structural changes. Classically, evaluation of tolerance and safety was carried out using live animal testing; however, new in vitro and computer-based, in silico modes are superseding the gold standard Draize test. This review examines how environmental features such as pollutants, temperature, and seasonality affect the ocular surface. Chemical burns to the eye are considered, and approaches to protect the ocular surface are detailed.

Docking and Molecular Dynamics Identify Leads against 5 Alpha Reductase 2 for Benign Prostate Hyperplasia Treatment

Steroid 5 alpha-reductase 2 (5αR-2) is a membrane-embedded protein that together with other isoforms plays a key role in the metabolism of steroids. This enzyme catalyzes the reduction of testosterone to the more potent ligand, dihydrotestosterone (DHT) in the prostate. Androgens, testosterone, and DHT play important roles in prostate growth, development, and function. At the same time, both testosterone and DHT have been implicated in the pathogenesis of benign prostate hyperplasia (BPH). Inhibition of the DHT formation, therefore, provides a therapeutic strategy that offers the possibility of preventing, delaying, or treating BPH. Currently, two steroidal drugs that inhibit 5αR-2, dutasteride and finasteride, have been approved for clinical use. These two come at a high cost and also portray undesirable sexual side effects which necessitate the need to find new chemotherapeutic alternatives for the disease. Based on the aforementioned, finasteride and dutasteride were subjected to scaffold hopping, fragment-based de novo design, molecular docking, and molecular dynamics simulations employing databases like ChEMBL, DrugBank, PubChem, ChemSpider, and Zinc15 in the identification of potential hits targeting 5αR-2. Altogether, ten novel compounds targeting 5αR-2 were identified with binding energies lower or comparable to finasteride and dutasteride, the main inhibitors for this target. Molecular docking and molecular dynamics simulations studies identify amino acid residues Glu57, Phe219, Phe223, and Leu224 to be critical for ligand binding and complex stability. The physicochemical and pharmacological profiling suggests the potential of the hit compounds to be drug-like and orally active. Similarly, the quality parameter assessments revealed the hits possess LELP greater than 3 implying their promise as lead-like molecules. The compounds A5, A9, and A10 were, respectively, predicted to treat prostate disorders with Pa (0.188, 0.361, and 0.270) and Pi (0.176, 0.050, and 0.093), while A8 and A9 were found to be associated with BPH treatment with Pa (0.09 and 0.127) and Pi (0.077 and 0.033), respectively. Structural similarity searches via DrugBank identified the drugs faropenem, acemetacin, estradiol valerate, and yohimbine to be useful for BPH treatment suggesting the de novo designed ligands as potential chemotherapeutic agents for treating this disease.

Integrated Approach for Synthetic Cathinone Drug Prioritization and Risk Assessment: In Silico Approach and Sub-Chronic Studies in Daphnia magna and Tetrahymena thermophila

Synthetic cathinones (SC) are drugs of abuse that have been reported in wastewaters and rivers raising concern about potential hazards to non-target organisms. In this work, 44 SC were selected for in silico studies, and a group of five emerging SC was prioritized for further in vivo ecotoxicity studies: buphedrone (BPD), 3,4-dimethylmethcathinone (3,4-DMMC), butylone (BTL), 3-methylmethcathinone (3-MMC), and 3,4-methylenedioxypyrovalerone (MDPV). In vivo short-term exposures were performed with the protozoan Tetrahymena thermophila (28 h growth inhibition assay) and the microcrustacean Daphnia magna by checking different indicators of toxicity across life stage (8 days sublethal assay at 10.00 µg L^-1). The in silico approaches predicted a higher toxic potential of MDPV and lower toxicity of BTL to the model organisms (green algae, protozoan, daphnia, and fish), regarding the selected SC for the in vivo experiments. The in vivo assays showed protozoan growth inhibition with MDPV > BPD > 3,4-DMMC, whereas no effects were observed for BTL and stimulation of growth was observed for 3-MMC. For daphnia, the responses were dependent on the substance and life stage. Briefly, all five SC interfered with the morphophysiological parameters of juveniles and/or adults. Changes in swimming behavior were observed for BPD and 3,4-DMMC, and reproductive parameters were affected by MDPV. Oxidative stress and changes in enzymatic activities were noted except for 3-MMC. Overall, the in silico data agreed with the in vivo protozoan experiments except for 3-MMC, whereas daphnia in vivo experiments showed that at sublethal concentrations, all selected SC interfered with different endpoints. This study shows the importance to assess SC ecotoxicity as it can distress aquatic species and interfere with food web ecology and ecosystem balance.

Artificial intelligence for drug discovery: Resources, methods, and applications

Conventional wet laboratory testing, validations, and synthetic procedures are costly and time-consuming for drug discovery. Advancements in artificial intelligence (AI) techniques have revolutionized their applications to drug discovery. Combined with accessible data resources, AI techniques are changing the landscape of drug discovery. In the past decades, a series of AI-based models have been developed for various steps of drug discovery. These models have been used as complements of conventional experiments and have accelerated the drug discovery process. In this review, we first introduced the widely used data resources in drug discovery, such as ChEMBL and DrugBank, followed by the molecular representation schemes that convert data into computer-readable formats. Meanwhile, we summarized the algorithms used to develop AI-based models for drug discovery. Subsequently, we discussed the applications of AI techniques in pharmaceutical analysis including predicting drug toxicity, drug bioactivity, and drug physicochemical property. Furthermore, we introduced the AI-based models for de novo drug design, drug-target structure prediction, drug-target interaction, and binding affinity prediction. Moreover, we also highlighted the advanced applications of AI in drug synergism/antagonism prediction and nanomedicine design. Finally, we discussed the challenges and future perspectives on the applications of AI to drug discovery.

Characteristics of cytochrome P450-dependent metabolism against acetamiprid in the musk shrew (Suncus murinus)

Soricidae spp. (shrews) play an essential role in soil ecosystems and, due to their habitat and behavior, are exposed to soil pollutants, such as pesticides. Still, toxicity risk in Soricidae spp. has not been appropriately assessed. In this study, the musk shrew (Suncus murinus) was used as a model organism for toxicity assessment in Soricidae. Considering their carnivorous diet, it is reasonable to assume that the musk shrew has unique metabolic traits that are different from those of other common experimental models. This study describes the cytochrome P450 (CYP)-dependent metabolism affected by acetamiprid (ACP), a neonicotinoid insecticide. Pharmacokinetics analysis, an in vitro metabolic assay, and genetic analysis of CYP were performed and compared with data from mice and rats. Through phylogenetic and syntenic analyses, three families of CYP were identified in the musk shrew. Pharmacokinetic analysis showed that the blood concentration of ACP decreased more quickly in musk shrews than in mice. Moreover, the in vitro metabolic assay suggested more efficient metabolic responses toward ACP in musk shrews than in mice or rats. One of the CYP2A isoforms in musk shrews might be linked to a better ACP metabolism. From the results above, we describe novel metabolic traits of the musk shrew. Future research on recombinant CYP enzymes is necessary to fully understand CYP-dependent metabolism of xenobiotics in musk shrews.

In Vivo Pharmacodynamics of Calophyllum soulattri as Antiobesity with In Silico Molecular Docking and ADME/Pharmacokinetic Prediction Studies

This study aims to determine the antiobesity activity of Calophyllum soulattri leaves extract (CSLE) on high fat diet-fed rats (HFD) and to predict the molecular docking and pharmacokinetics of selected compounds of Calophyllum soulattri to fat mass and obesity-associated protein (FTO). Daily body weight, organ, carcass fat (renal and anal), body mass index, total cholesterol, and total triglyceride levels were observed after CSLE was given orally for 50 days. Furthermore, body mass index of a CSLE dose of 50 mg/kgbw, 100 mg/kgbw and orlistat (120 mg/kgbw) group are 0.68, 0.57 and 0.52, respectively. The total body weight of the CLSE dose of 100 mg/kgbw group showed the lowest percentage change, followed by a CLSE dose of 50 mg/kgbw compared to the normal and positive control group. The carcass fat index of CSLE dose of 100 mg/kgbw was not significantly different from orlistat, which was in line with its total cholesterol level and triglyceride (p < 0.05). The binding affinity of selected compounds from Calophyllum soulattri (friedelin, caloxanthone B, macluraxanthone, stigmasterol, trapezifolixanthone, dombakinaxanthone, and brasixanthone B) to FTO are –8.27, –9.74, –8.48, –9.34, –8.85, –8.68 and –9.39 kcal/mol, which are better than that of orlistat at –4.80 kcal/mol. The molecular dynamics simulation showed that the interaction between Caloxanthone B compounds and obesity receptors was relatively stable. Lipinski’s rule determined the absorption percentage of all compounds above 90% with good drug-likeness. The results showed the potential of CSLE as an antiobesity drug candidate.

Cytotoxic evaluation of YSL-109 in a triple negative breast cancer cell line and toxicological evaluations

Breast cancer (BC) is the leading cause of cancer-related death in women worldwide. Triple negative breast cancer (TNBC) is the most aggressive form of BC being with the worst prognosis and the worst survival rates. There is no specific pharmacological target for the treatment of TNBC; conventional therapy includes the use of non-specific chemotherapy that generally has a poor prognosis. Therefore, the search of effective therapies against to TNBC continues at both preclinical and clinical level. In this sense, the exploration of different pharmacological targets is a continue task that pave the way to epigenetic modulation using novel small molecules. Lately, the inhibition of histone deacetylases (HDACs) has been explored to treat different BC, including TNBC. HDACs remove the acetyl groups from the ɛ-amino lysine resides on histone and non-histone proteins. In particular, the inhibition of HDAC6 has been suggested to be useful for the treatment of TNBC due to it is overexpressed in TNBC. Therefore, in this work, an HDAC6 selective inhibitor, the (S)-4-butyl-N-(1-(hydroxyamino)-3-(naphthalen-1-yl)-1-oxopropan-2-yl) benzamide (YSL-109), was assayed on TNBC cell line (MDA-MB231) showing an antiproliferative activity (IC₅₀ = 50.34 ± 1.11 µM), whereas on fibroblast, it was lesser toxic. After corroborating the in vitro antiproliferative activity of YSL-109 in TNBC, the toxicological profile was explored using combined approach with in silico tools and experimental assays. YSL-109 shows moderate mutagenic activity on TA-98 strain at 30 and 100 µM in the Ames test, whereas YSL-109 did not show in vivo genotoxicity and its oral acute toxicity (LD₅₀) in CD-1 female mice was higher than 2000 mg/kg, which is in agreement with our in silico predictions. According to these results, YSL-109 represents an interesting compound to be explored for the treatment of TNBC under preclinical in vivo models.

Juvenile hormone mimics with phenyl ether and amide functionality to be insect growth regulators (IGRs): synthesis, characterization, computational and biological study

A series of substituted phenyl ethers derivatives as juvenile hormone (JH) mimics (V₁-V₈) have been synthesized. Substituted phenoxyacetic acid and amino acid ethyl ester hydrochloride were prepared using NaOH, SOCl₂. DCC method has been used for amide linkage. The structure of prepared compounds has been confirmed by Fourier Transform Infra-Red (FT-IR), Electrospray ionization-Mass spectrometry (ESI-MS), Proton and Carbon-13 nuclear magnetic resonance (¹H-NMR, ¹³C-NMR) spectroscopic techniques. Biological efficacy of synthesized analogs has been carried out under laboratory conditions. Galleria mellonella (honey bee pest) has been chosen as testing insect. Juvenile hormone (JH) activity of synthesized compounds has been tested at different concentrations and compared with the standard juvenile hormone analogs (JHAs) pyriproxyfen (M₁) and fenoxycarb (M₂) against the fifth larval instar of G. mellonella. Compound ethyl 2-[2-(4-methylphenoxy)aminoacetyl]-3-phenyl-propanoate (V₆) exhibited better activity among all the synthesized compounds (V₁-V₈) with LC₅₀ and LC₉₀ values of 0.11 mg/mL and 0.56 mg/mL respectively. Compounds showed insect growth regulating (IGR) activity at lower concentrations. In silico screening of all synthesized compounds with the W-cavity of juvenile hormone-binding protein (JHBP) of insect G. mellonella has been carried out. Chemical reactivity of synthesized series has been studied using DFT/B3LYP/6-311 + G(d,2p) method. Non-toxic behavior of molecules has also been observed from ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) study using discovery studio client 3.0.Communicated by Ramaswamy H. Sarma.

Synthesis and greener pastures biological study of bis-thiadiazoles as potential Covid-19 drug candidates

A novel series of bis- (Abdelhamid et al., 2017, Banerjee et al., 2018, Bharanidharan et al., 2022)thiadiazoles was synthesized from the reaction of precursor dimethyl 2,2'-(1,2-diphenylethane-1,2-diylidene)-bis(hydrazine-1-carbodithioate) and hydrazonyl chlorides in ethanol under ultrasonic irradiation. Spectral tools (IR. NMR, MS, elemental analyses, molecular dynamic simulation, DFT and LUMO and HOMO) were used to elucidate the structure of the isolated products. Molecular docking for the precursor, 3 and ligands 6a-i to two COVID-19 important proteins M^pro and RdRp was compared with two approved drugs, Remdesivir and Ivermectin. The binding affinity varied between the ligands and the drugs. The highest recorded binding affinity of 6c with M^pro was (-9.2 kcal/mol), followed by 6b and 6a, (-8.9 and -8.5 kcal/mol), respectively. The lowest recorded binding affinity was (-7.0 kcal/mol) for 6 g. In comparison, the approved drugs showed binding affinity (-7.4 and -7.7 kcal/mol), for Remdesivir and Ivermectin, respectively, which are within the range of the binding affinity of our ligands. The binding affinity of the approved drug Ivermectin against RdRp recoded the highest (-8.6 kcal/mol), followed by 6a, 6 h, and 6i are the same have (-8.2 kcal/mol). The lowest reading was found for compound 3 ligand (-6.3 kcal/mol). On the other side, the amino acids also differed between the compounds studied in this project for both the viral proteins. The ligand 6a forms three H-bonds with Thr 319(A), Sr 255(A) and Arg 457(A), whereas Ivermectin forms three H-bonds with His 41(A), Gly143(A) and Gln 18(A) for viral M^pro. The RdRp amino acids residues could be divided into four groups based on the amino acids that interact with hydrogen or hydrophobic interactions. The first group contained 6d, 6b, 6 g, and Remdesivir with 1-4 hydrogen bonds and hydrophobic interactions 1 to 10. Group 2 is 6a and 6f exhibited 1 and 3 hydrogen bonds and 15 and 14 hydrophobic interactions. Group 3 has 6e and Ivermectin shows 4 and 3 hydrogen bonds, respectively and 11 hydrophobic interactions for both compounds. The last group contains ligands 3, 6c, 6 h, and 6i gave 1-3 hydrogen bonds and 6c and 3 recorded the highest number of hydrophobic interactions, 14 for both 6c and 6 h. Pro Tox-II estimated compounds' activities as Hepatoxic, Carcinogenic and Mutagenic, revealing that 6f-h were inactive in all five similar to that found with Remdesivir and Ivermectin. The drug-likeness prediction was carried out by studying physicochemical properties, lipophilicity, size, polarity, insolubility, unsaturation, and flexibility. Generally, some properties of the ligands were comparable to that of the standards used in this study, Remdesivir and Ivermectin.

Prior Knowledge for Predictive Modeling: The Case of Acute Aquatic Toxicity

Early assessment of the potential impact of chemicals on health and the environment requires toxicological properties of the molecules. Predictive modeling is often used to estimate the property values in silico from pre-existing experimental data, which is often scarce and uncertain. One of the ways to advance the predictive modeling procedure might be the use of knowledge existing in the field. Scientific publications contain a vast amount of knowledge. However, the amount of manual work required to process the enormous volumes of information gathered in scientific articles might hinder its utilization. This work explores the opportunity of semiautomated knowledge extraction from scientific papers and investigates a few potential ways of its use for predictive modeling. The knowledge extraction and predictive modeling are applied to the field of acute aquatic toxicity. Acute aquatic toxicity is an important parameter of the safety assessment of chemicals. The extensive amount of diverse information existing in the field makes acute aquatic toxicity an attractive area for investigation of knowledge use for predictive modeling. The work demonstrates that the knowledge collection and classification procedure could be useful in hybrid modeling studies concerning the model and predictor selection, addressing data gaps, and evaluation of models’ performance.

Identification of Potential Cytochrome P450 3A5 Inhibitors: An Extensive Virtual Screening through Molecular Docking, Negative Image-Based Screening, Machine Learning and Molecular Dynamics Simulation Studies

Cytochrome P450 3A5 (CYP3A5) is one of the crucial CYP family members and has already proven to be an important drug target for cardiovascular diseases. In the current study, the PubChem database was screened through molecular docking and high-affinity molecules were adopted for further assessment. A negative image-based (NIB) model was used for a similarity search by considering the complementary shape and electrostatics of the target and small molecules. Further, the molecules were segregated into active and inactive groups through six machine learning (ML) matrices. The active molecules found in each ML model were used for in silico pharmacokinetics and toxicity assessments. A total of five molecules followed the acceptable pharmacokinetics and toxicity profiles. Several potential binding interactions between the proposed molecules and CYP3A5 were observed. The dynamic behavior of the selected molecules in the CYP3A5 was explored through a molecular dynamics (MD) simulation study. Several parameters obtained from the MD simulation trajectory explained the stability of the protein–ligand complexes in dynamic states. The high binding affinity of each molecule was revealed by the binding free energy calculation through the MM-GBSA methods. Therefore, it can be concluded that the proposed molecules might be potential CYP3A5 molecules for therapeutic application in cardiovascular diseases subjected to in vitro/in vivo validations.

Critical features identification for chemical chronic toxicity based on mechanistic forecast models

Facing billions of tons of pollutants entering the ocean each year, aquatic toxicity is becoming a crucial endpoint for evaluating chemical adverse effects on ecosystems. Notably, huge amount of toxic chemicals at environmental relevant doses can cause potential adverse effects. However, chronic aquatic toxicity effects of chemicals are much scarcer, especially at population level. Rotifers are highly sensitive to toxicants even at chronic low-doses and their communities are usually considered as effective indicators for assessing the status of aquatic ecosystems. Therefore, the no observed effect concentration (NOEC) for population abundance of rotifers were selected as endpoints to develop machine learning models for the prediction of chemical aquatic chronic toxicity. In this study, forty-eight binary models were built by eight types of chemical descriptors combined with six machine learning algorithms. The best binary model was 1D & 2D molecular descriptors - random trees model (RT) with high balanced accuracy (BA) (0.83 for training and 0.83 for validation set), and Matthews correlation coefficient (MCC) (0.72 for training set and 0.67 for validation set). Moreover, the optimal model identified the primary factors (SpMAD_Dzp, AMW, MATS2v) and filtered out three high alerting substructures [c1cc(Cl)cc1, CNCO, CCOP(=S)(OCC)O] influencing the chronic aquatic toxicity. These results showed that the compounds with low molecular volume, high polarity and molecular weight could contribute to adverse effects on rotifers, facilitating the deeper understanding of chronic toxicity mechanisms. In addition, forecast models had better performances than the common models embedded into ECOSAR software. This study provided insights into structural features responsible for the toxicity of different groups of chemicals and thereby allowed for the rational design of green and safer alternatives.

Anti-Inflammatory and Anti-Rheumatic Potential of Selective Plant Compounds by Targeting TLR-4/AP-1 Signaling: A Comprehensive Molecular Docking and Simulation Approaches

Plants are an important source of drug development and numerous plant derived molecules have been used in clinical practice for the ailment of various diseases. The Toll-like receptor-4 (TLR-4) signaling pathway plays a crucial role in inflammation including rheumatoid arthritis. The TLR-4 binds with pro-inflammatory ligands such as lipopolysaccharide (LPS) to induce the downstream signaling mechanism such as nuclear factor κappa B (NF-κB) and mitogen activated protein kinases (MAPKs). This signaling activation leads to the onset of various diseases including inflammation. In the present study, 22 natural compounds were studied against TLR-4/AP-1 signaling, which is implicated in the inflammatory process using a computational approach. These compounds belong to various classes such as methylxanthine, sesquiterpene lactone, alkaloid, flavone glycosides, lignan, phenolic acid, etc. The compounds exhibited different binding affinities with the TLR-4, JNK, NF-κB, and AP-1 protein due to the formation of multiple hydrophilic and hydrophobic interactions. With TLR-4, rutin had the highest binding energy (−10.4 kcal/mol), poncirin had the highest binding energy (−9.4 kcal/mol) with NF-κB and JNK (−9.5 kcal/mol), respectively, and icariin had the highest binding affinity (−9.1 kcal/mol) with the AP-1 protein. The root means square deviation (RMSD), root mean square fraction (RMSF), and radius of gyration (RoG) for 150 ns were calculated using molecular dynamic simulation (MD simulation) based on rutin’s greatest binding energy with TLR-4. The RMSD, RMSF, and RoG were all within acceptable limits in the MD simulation, and the complex remained stable for 150 ns. Furthermore, these compounds were assessed for the potential toxic effect on various organs such as the liver, heart, genotoxicity, and oral maximum toxic dose. Moreover, the blood–brain barrier permeability and intestinal absorption were also predicted using SwissADME software (Lausanne, Switzerland). These compounds exhibited promising physico-chemical as well as drug-likeness properties. Consequently, these selected compounds portray promising anti-inflammatory and drug-likeness properties.

A predictive toolset for the identification of degradation pattern and toxic hazard estimation of multimeric hazardous compounds persists in water bodies

An array of industrial processing units generates many multimeric hazardous compounds, such as complex technical lignin and its toxic derivatives, thereby persist in expelled water bodies. The inclusion of some group of motifs in the complex technical lignin structure helps it resist degrade biologically, most often even recalcitrant. Relatively small concentrations of lignin are harmful to aquatic organisms and can trigger environmental hazards. Sadly, the entire biotransformation pathway and insightful information about these toxic derivatives are incomplete and missing in the literature. This is mainly because the current conventional treatments often fail to identify all transformed compounds and their environmental fate. Thus, a robust toolset is much needed to cover this literature gap. Inadequate performance of conventional remediation processes and biological degradation patterns can be maximally optimized with the aid of predictive toolset methods that could offer better degradability and complete transformed compound information. A predictive toolset-assisted biodegradation pattern determination is a multifaceted and reliable analytical technique that can help to overcome existing shortcomings by providing an entire transformation pathway. Considering the above critiques, this work reports on the degradation pattern, and toxicological endpoints of five hazardous compounds, i.e., 2-chlorosyringaldehyde, 5-chlorovanillin, catechol, guaiacyl 4-O-5 guaiacyl, and syringyl β-O-4 syringyl β-O-4 sinapyl alcohol, that persists in water matrices. The predictive transformation pattern was revealed notably less complex end-products of catechol as; succinate, and 2-Oxo-4-pentenoate. The gastrointestinal (GI) absorption rate was found high for all tested compounds, excluding trimer compound, i.e., syringyl β-O-4 syringyl β-O-4 sinapyl alcohol. The toxicity and persistence profile tested via Toxtree showed that the Cramer Rules, Verhaar Scheme, and Structural Alerts for Reactivity, (START) biodegradation ability as positive, and all five target compounds were found as class-II persistent compounds. Furthermore, the Ecological Structure-Activity Relationships (ECOSAR)assisted testing specifies that all tested derivatives have multiple aquatic toxic levels. In summary, the current findings endorse the hazardous compounds and undertake prescreening of the deprivation policy to protect the environment.

In silico analytical toolset for predictive degradation and toxicity of hazardous pollutants in water sources

Different phenolic compounds, including multimeric lignin derivatives in the β-O-4 form, are among the most prevalent compounds in wastewater, often generated from paper industries. Relatively small concentrations of lignin are hazardous to aquatic organisms and can trigger severe environmental hazards. Herein, we present a predictive toolset to insight the induced toxic hazards prediction, and their Lignin peroxidase (LiP)-assisted degradation mechanism of selected multimeric lignin model compounds. T.E.ST and Toxtree toolset were deployed for toxic hazards estimation in different endpoints. To minimize the concerning hazards, we screened multimeric compounds for binding affinity with LiP. The binding affinity was found to be significantly lower than the reference compound. An Extra precision (XP) Glide score of -6.796 kcal/mol was found for dimer (guaiacyl 4-O-5 guaiacyl) complex as lowest compared to reference compound (-4.007 kcal/mol). The active site residues ASP-153, HIP-226, VAL-227, ARG-244, GLU-215, 239, PHE-261 were identified as site-specific key binding AA residues actively involved with corresponding ligands, forming Hydrophobic, H-Bond, π-Stacking, π-π type interactions. The DESMOND-assisted molecular dynamics simulation's (MDS) trajectories of protein-ligand revealed the considerable binding behavior and attained stability and system equilibrium state. Such theoretical and predictive conclusions indicted the feasibility of LiP assisted sustainable mitigation of lignin-based compounds, and such could be used to protect the environment from the potential hazards posed by recognized similar pollutants.

Investigation of potential descriptors of chemical compounds on prevention of nephrotoxicity via QSAR approach

Drug-induced nephrotoxicity remains a common problem after exposure to medications and diagnostic agents, which may be heightened in the kidney microenvironment and deteriorate kidney function. In this study, the toxic effects of fourteen marked drugs with the individual chemical structure were evaluated in kidney cells. The quantitative structure–activity relationship (QSAR) approach was employed to investigate the potential structural descriptors of each drug-related to their toxic effects. The most reasonable equation of the QSAR model displayed that the estimated regression coefficients such as the number of ring assemblies, three-membered rings, and six-membered rings were strongly related to toxic effects on renal cells. Meanwhile, the chemical properties of the tested compounds including carbon atoms, bridge bonds, H-bond donors, negative atoms, and rotatable bonds were favored properties and promote the toxic effects on renal cells. Particularly, more numbers of rotatable bonds were positively correlated with strong toxic effects that displayed on the most toxic compound. The useful information discovered from our regression QSAR models may help to identify potential hazardous moiety to avoid nephrotoxicity in renal preventive medicine.

Concentrations and Toxic Implications of Dioxin-Like Polychlorinated Biphenyls in Soil Samples from Electrical Power Stations in Lagos, Nigeria

Dioxin-like polychlorinated biphenyls (dl-PCBs) are ubiquitous chemicals which mediate toxicity in a way similar to polychlorinated dibenzo-p-dioxins and polychlorinated dibenzofurans. In silico modeling was used to predict the absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties of eight dioxin-like PCBs in soil samples of 12 power stations in Lagos, Nigeria. Concentrations of Σdl-PCB₈ in soil samples ranged from 490 to 61,000 pg g^-1 , with mean concentrations of 17,000 pg g^-1 . The corresponding toxic equivalent (TEQ) concentrations of Ʃdl-PCB₈ ranged from 0.01 to 450 pg TEQ g^-1 , with a mean value of 42 pg TEQ g^-1 . Mean TEQ concentrations for Ʃdl-PCB₈ in soil samples from all but one of the sites exceeded the Canadian guideline value of 4 pg TEQ g^-1 and the US and German guideline values of 5-10 pg TEQ g^-1 . However, the TEQ concentrations obtained were all below the US action level of 1000 pg TEQ g^-1 . The ADMET predictions revealed that all studied dl-PCBs are inhibitors of three major isoforms (1A2, 2C9, and 2C19) of cytochrome P450 enzyme. Acute oral toxicity (median lethal dose) predictions revealed that all target dl-PCBs were class III compounds. Hepatotoxicity and carcinogenicity were positive, signifying that the studied compounds all have a tendency to elicit these effects. Occupational daily TEQ exposure via soil ingestion was estimated for an average adult worker weighing 70 kg. The maximum exposure obtained was 0.14 pg TEQ kg^-1  body weight day^-1 , which is half of the European Food Safety Authority (EFSA) tolerable daily intake (TDI) for dioxin-like compounds. This raises concern over the possible exceedance of the EFSA TDI for these workers if other dietary and nondietary exposure pathways and dioxin-like compounds are considered. Environ Toxicol Chem 2022;41:800-809. © 2021 SETAC.

Ethyl Acetate Fraction of Punica granatum and Its Galloyl-HHDP-Glucose Compound, Alone or in Combination with Fluconazole, Have Antifungal and Antivirulence Properties against Candida spp

Candidiasis is the most common fungal infection among immunocompromised patients. Its treatment includes the use of antifungals, which poses limitations such as toxicity and fungal resistance. Plant-derived extracts, such as Punica granatum, have been reported to have antimicrobial activity, but their antifungal effects are still unknown. We aimed to evaluate the antifungal and antiviral potential of the ethyl acetate fraction of P. granatum (PgEA) and its isolated compound galloyl-hexahydroxydiphenoyl-glucose (G-HHDP-G) against Candida spp. In silico analyses predicted the biological activity of G-HHDP-G. The minimum inhibitory concentrations (MIC) of PgEA and G-HHDP-G, and their effects on biofilm formation, preformed biofilms, and phospholipase production were determined. In silico analysis showed that G-HHDP-G has antifungal and hepatoprotective effects. An in vitro assay confirmed the antifungal effects of PgEA and G-HHDP-G, with MIC in the ranges of 31.25–250 μg/mL and 31.25 ≥ 500 μg/mL, respectively. G-HHDP-G and PgEA synergistically worked with fluconazole against planktonic cells. The substances showed antibiofilm action, alone or in combination with fluconazole, and interfered with phospholipase production. The antifungal and antibiofilm actions of PgEA and G-HHDP-G, alone or in combination with fluconazole, in addition to their effects on reducing Candida phospholipase production, identify them as promising candidates for therapeutics.

Strategies to apply 3Rs in preclinical testing

Animal experimentation has been fundamental in biological and biomedical research. To guarantee the maximum quality, efficacy and/or safety of products intended for the use in humans in vivo testing is necessary; however, for over 60 years, alternative methods have been developed in response to the necessity to reduce the number of animals used in experimentation, to guarantee their welfare; resorting to animal models only when strictly necessary. The three Rs (Replacement, Reduction, and Refinement), seek to ensure the rational and respectful use of laboratory animals and maintain an adequate projection in terms of bioethical considerations. This article describes different approaches to apply 3Rs in preclinical experimentation for either research or regulatory purposes.

Bioremediation of lignin derivatives and phenolics in wastewater with lignin modifying enzymes: Status, opportunities and challenges

Lignin modifying enzymes from fungi and bacteria are potential biocatalysts for sustainable mitigation of different potentially toxic pollutants in wastewater. Notably, the paper and pulp industry generates enormous amounts of wastewater containing high amounts of complex lignin-derived chlorinated phenolics and sulfonated pollutants. The presence of these compounds in wastewater is a critical issue from environmental and toxicological perspectives. Some chloro-phenols are harmful to the environment and human health, as they exert carcinogenic, mutagenic, cytotoxic, and endocrine-disrupting effects. In order to address these most urgent concerns, the use of oxidative lignin modifying enzymes for bioremediation has come into focus. These enzymes catalyze modification of phenolic and non-phenolic lignin-derived substances, and include laccase and a range of peroxidases, specifically lignin peroxidase (LiP), manganese peroxidase (MnP), versatile peroxidase (VP), and dye-decolorizing peroxidase (DyP). In this review, we explore the key pollutant-generating steps in paper and pulp processing, summarize the most recently reported toxicological effects of industrial lignin-derived phenolic compounds, especially chlorinated phenolic pollutants, and outline bioremediation approaches for pollutant mitigation in wastewater from this industry, emphasizing the oxidative catalytic potential of oxidative lignin modifying enzymes in this regard. We highlight other emerging biotechnical approaches, including phytobioremediation, bioaugmentation, Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)-based technology, protein engineering, and degradation pathways prediction, that are currently gathering momentum for the mitigation of wastewater pollutants. Finally, we address current research needs and options for maximizing sustainable biobased and biocatalytic degradation of toxic industrial wastewater pollutants.

Machine Learning for Predicting Risk of Drug-Induced Autoimmune Diseases by Structural Alerts and Daily Dose

An effective approach for assessing a drug’s potential to induce autoimmune diseases (ADs) is needed in drug development. Here, we aim to develop a workflow to examine the association between structural alerts and drugs-induced ADs to improve toxicological prescreening tools. Considering reactive metabolite (RM) formation as a well-documented mechanism for drug-induced ADs, we investigated whether the presence of certain RM-related structural alerts was predictive for the risk of drug-induced AD. We constructed a database containing 171 RM-related structural alerts, generated a dataset of 407 AD- and non-AD-associated drugs, and performed statistical analysis. The nitrogen-containing benzene substituent alerts were found to be significantly associated with the risk of drug-induced ADs (odds ratio = 2.95, p = 0.0036). Furthermore, we developed a machine-learning-based predictive model by using daily dose and nitrogen-containing benzene substituent alerts as the top inputs and achieved the predictive performance of area under curve (AUC) of 70%. Additionally, we confirmed the reactivity of the nitrogen-containing benzene substituent aniline and related metabolites using quantum chemistry analysis and explored the underlying mechanisms. These identified structural alerts could be helpful in identifying drug candidates that carry a potential risk of drug-induced ADs to improve their safety profiles.

Molecular docking studies of Nigella sativa L and Curcuma xanthorrhiza Roxb secondary metabolites against histamine N-methyltransferase with their ADMET prediction

OBJECTIVES

Histamine N-methyltransferase (HNMT) is an enzyme that plays a crucial role in the inactivation of histamine in central nervous system, kidneys and bronchi. Inhibition of HNMT is known to have a potential role in treating attention-deficit hyperactivity disorder, memory impairment, mental illness and neurodegenerative illnesses. Therefore, to find potential compounds that could be developed as novel HNMT inhibitors, this study conducted an in silico study of the secondary metabolites of Nigella sativa L and Curcuma xanthorrhiza Roxb.

METHODS

In this study, we conducted a molecular docking study of 36 secondary metabolites of N. sativa L and 26 secondary metabolites of C. xanthorrhiza Roxb using an in silico approach targeting HNMT protein (PDB ID: 2AOT) using AutoDockVina software. The prediction of ADMET characteristics was done using the pkCSM Online Tool.

RESULTS

This study obtained one metabolite from N. sativa L (longifolene) and seven metabolites from C. xanthorrhiza Roxb {(+)-beta-atlantone, humulene epoxide, (-)-beta-curcumene, (E)-caryophyllene, germacrone, (R)-(-)-xanthorrhizol, and (-)-beta-caryophyllene epoxide} which were predicted to have potential to be developed as HNMT inhibitors.

CONCLUSIONS

This study found several secondary metabolites of N. sativa L and C. xanthorrhiza Roxb which had activity as HNMT inhibitors. This research can likewise be utilized as a basis for further research, both in vitro, in vivo, and clinical trials related to the development of secondary metabolites from N. sativa L and C. xanthorrhiza Roxb as novel HNMT inhibitor compounds.

Trends in predictive biodegradation for sustainable mitigation of environmental pollutants: Recent progress and future outlook

The feasibility of in-silico techniques, together with the computational framework, has been applied to predictive bioremediation aiming to clean-up contaminants, toxicity evaluation, and possibilities for the degradation of complex recalcitrant compounds. Emerging contaminants from different industries have posed a significant hazard to the environment and public health. Given current bioremediation strategies, it is often a failure or inadequate for sustainable mitigation of hazardous pollutants. However, clear-cut vital information about biodegradation is quite incomplete from a conventional remediation techniques perspective. Lacking complete information on bio-transformed compounds leads to seeking alternative methods. Only scarce information about the transformed products and toxicity profile is available in the published literature. To fulfill this literature gap, various computational or in-silico technologies have emerged as alternating techniques, which are being recognized as in-silico approaches for bioremediation. Molecular docking, molecular dynamics simulation, and biodegradation pathways predictions are the vital part of predictive biodegradation, including the Quantitative Structure-Activity Relationship (QSAR), Quantitative structure-biodegradation relationship (QSBR) model system. Furthermore, machine learning (ML), artificial neural network (ANN), genetic algorithm (GA) based programs offer simultaneous biodegradation prediction along with toxicity and environmental fate prediction. Herein, we spotlight the feasibility of in-silico remediation approaches for various persistent, recalcitrant contaminants while traditional bioremediation fails to mitigate such pollutants. Such could be addressed by exploiting described model systems and algorithm-based programs. Furthermore, recent advances in QSAR modeling, algorithm, and dedicated biodegradation prediction system have been summarized with unique attributes.

Formaldehyde exposure and leukemia risk: a comprehensive review and network-based toxicogenomic approach

Formaldehyde is a widely used but highly reactive and toxic chemical. The International Agency for Research on Cancer classifies formaldehyde as a Group 1 carcinogen, based on nasopharyngeal cancer and leukemia studies. However, the correlation between formaldehyde exposure and leukemia incidence is a controversial issue. To understand the association between formaldehyde exposure and leukemia, we explored biological networks based on formaldehyde-related genes retrieved from public and commercial databases. Through the literature-based network approach, we summarized qualitative associations between formaldehyde exposure and leukemia. Our results indicate that oxidative stress-mediated genetic changes induced by formaldehyde could disturb the hematopoietic system, possibly leading to leukemia. Furthermore, we suggested major genes that are thought to be affected by formaldehyde exposure and associated with leukemia development. Our suggestions can be used to complement experimental data for understanding and identifying the leukemogenic mechanism of formaldehyde.

Phosgene: toxicology, animal models, and medical countermeasures

Phosgene is a gas crucial to industrial chemical processes with widespread production (∼1 million tons/year in the USA, 8.5 million tons/year worldwide). Phosgene's high toxicity and physical properties resulted in its use as a chemical warfare agent during the First World War with a designation of CG ('Choky Gas'). The industrial availability of phosgene makes it a compound of concern as a weapon of mass destruction by terrorist organizations. The hydrophobicity of phosgene exacerbates its toxicity often resulting in a delayed toxidrome as the upper airways are moderately irritated; by the time symptoms appear, significant damage has occurred. As the standard of care for phosgene intoxication is supportive therapy, a pressing need for effective therapeutics and treatment regimens exists. Proposed toxicity mechanisms for phosgene based on human and animal exposures are discussed. Whereas intermediary components in the phosgene intoxication pathways are under continued discussion, generation of reactive oxygen species and oxidative stress is a common factor. As animal models are required for the study of phosgene and for FDA approval via the Animal Rule; the status of existing models and their adherence to Haber's Rule is discussed. Finally, we review the continued search for efficacious therapeutics for phosgene intoxication; and present a rapid post-exposure response that places exogenous human heat shock protein 72, in the form of a cell-penetrating fusion protein (Fv-HSP72), into lung tissues to combat apoptosis resulting from oxidative stress. Despite significant progress, additional work is required to advance effective therapeutics for acute phosgene exposure.

'In silico' toxicology methods in drug safety assessment

While experimental animal investigation has historically been the most conventional approach conducted to assess drug safety and is currently considered the main method for determining drug toxicity, these studies are constricted by cost, time, and ethical approvals. Over the last 20 years, there have been significant advances in computational sciences and computer data processing, while knowledge of alternative techniques and their application has developed into a valuable skill in toxicology. Thus, the application of in silico methods in drug safety assessment is constantly increasing. They are very complex and are grounded on accumulated knowledge from toxicology, bioinformatics, biochemistry, statistics, mathematics, as well as molecular biology. This review will summarize current state-of-the-art scientific data on the use of in silico methods in toxicity testing, taking into account their shortcomings, and highlighting the strategies that should deliver consistent results, while covering the applications of in silico methods in preclinical trials and drug impurities toxicity testing.