Computer prediction of possible toxic action from chemical structure: an update on the DEREK system

Recent progress in artificial intelligence and machine learning for novel diabetes mellitus medications development

Diabetes mellitus, stemming from either insulin resistance or inadequate insulin secretion, represents a complex ailment that results in prolonged hyperglycemia and severe complications. Patients endure severe ramifications such as kidney disease, vision impairment, cardiovascular disorders, and susceptibility to infections, leading to significant physical suffering and imposing substantial socio-economic burdens. This condition has evolved into an increasingly severe health crisis. There is an urgent need to develop new treatments with improved efficacy and fewer adverse effects to meet clinical demands. However, novel drug development is costly, time-consuming, and often associated with side effects and suboptimal efficacy, making it a major challenge. Artificial Intelligence (AI) and Machine Learning (ML) have revolutionized drug development across its comprehensive lifecycle, spanning drug discovery, preclinical studies, clinical trials, and post-market surveillance. These technologies have significantly accelerated the identification of promising therapeutic candidates, optimized trial designs, and enhanced post-approval safety monitoring. Recent advances in AI, including data augmentation, interpretable AI, and integration of AI with traditional experimental methods, offer promising strategies for overcoming the challenges inherent in AI-based drug discovery. Despite these advancements, there exists a notable gap in comprehensive reviews detailing AI and ML applications throughout the entirety of developing medications for diabetes mellitus. This review aims to fill this gap by evaluating the impact and potential of AI and ML technologies at various stages of diabetes mellitus drug development. It does that by synthesizing current research findings and technological advances so as to effectively control diabetes mellitus and mitigate its far-reaching social and economic impacts. The integration of AI and ML promises to revolutionize diabetes mellitus treatment strategies, offering hope for improved patient outcomes and reduced healthcare burdens worldwide.

Applications of intelligent technology in the evaluation of mutagenicity

Bioassays are widely used in assessment of mutagenicity. Alternative methods have also been developed, including "intelligent evaluation", which depends on the quality of data, strategies, and techniques. CISOC-PSMT is an Ames test prediction system. The strategies and techniques for intelligent evaluation and four applications of CISOC-PSMT are presented; roles in pesticide management, environmental protection, drug discovery, and safety management of chemicals are introduced.

Synthesis and Trace-Level Quantification of Mutagenic and Cohort-of-Concern Ciprofloxacin Nitroso Drug Substance-Related Impurities (NDSRIs) and Other Nitroso Impurities Using UPLC-ESI-MS/MS-Method Optimization Using I-Optimal Mixture Design

Globally, the pharmaceutical industry has been facing challenges from nitroso drug substance-related impurities (NDSRIs). In the current study, we synthesized and developed a rapid new UPLC-MS/MS method for the trace-level quantification of ciprofloxacin NDSRIs and a couple of N-nitroso impurities simultaneously. (Q)-SAR methodology was employed to assess and categorize the genotoxicity of all ciprofloxacin N-nitroso impurities. The projected results were positive, and the cohort of concern (CoC) for all three N-nitroso impurities indicates potential genotoxicity. AQbD-driven I-optimal mixture design was used to optimize the mixture of solvents in the method. The chromatographic resolution was accomplished using an Agilent Poroshell 120 Aq-C18 column (150 mm × 4.6 mm, 2.7 μm) in isocratic elution mode with 0.1% formic acid in a mixture of water, acetonitrile, and methanol in the ratio of 475:500:25 v/v/v at a flow rate of 0.5 mL/min. Quantification was carried out using triple quadrupole mass detection with electrospray ionization (ESI) in a multiple reaction monitoring technique. The finalized method was validated successfully, affording ICH guidelines. All N-nitroso impurities revealed excellent linearity over the concentration range of 0.00125-0.0250 ppm. The Pearson correlation coefficient of each N-nitroso impurity was >0.999. The method accuracy recoveries ranged from 93.98 to 108.08% for the aforementioned N-nitrosamine impurities. Furthermore, the method was effectively applied to quantify N-nitrosamine impurities simultaneously in commercially available formulated samples, with its efficiency recurring at trace levels. Thus, the current method is capable of determining the trace levels of three N-nitroso ciprofloxacin impurities simultaneously from the marketed tablet dosage forms for commercial release and stability testing.

A sensitive UPLC-MS/MS method for the simultaneous assay and trace level genotoxic impurities quantification of SARS-CoV-2 inhibitor-Molnupiravir in its pure and formulation dosage forms using fractional factorial design

Two potential genotoxic impurities were identified (PGTIs)-viz. 4-amino-1-((2R,3R,4S,5R)-3,4-dihydroxy-5-(hydroxymethyl)tetrahydrofuran-2-yl)pyrimidin-2(1H)-one (PGTI-1), and 1-(2R,3R,4S,5R)-3,4-dihydroxy-5-(hydroxymethyl)tetrahydrofuran-2-yl)pyrimidin-2,4(1H,3H)-one (PGTI-II) in the Molnupiravir (MOPR) synthetic routes. COVID-19 disease was treated with MOPR when mild to moderate symptoms occurred. Two (Q)-SAR methods were used to assess the genotoxicity, and projected results were positive and categorized into Class-3 for both PGTIs. A simple, accurate and highly sensitive ultra-performance liquid chromatography-mass spectrometry (UPLC-MS/MS) method was optimized for the simultaneous quantification of the assay, and these impurities in MOPR drug substance and formulation dosage form. The multiple reaction monitoring (MRM) technique was utilized for the quantification. Prior to the validation study, the UPLC-MS method conditions were optimised using fractional factorial design (FrFD). The optimized Critical Method Parameters (CMPs) include the percentage of Acetonitrile in MP B, Concentration of Formic acid in MP A, Cone Voltage, Capillary Voltage, Collision gas flow and Desolvation temperature were determined from the numerical optimization to be 12.50 %, 0.13 %, 13.6 V, 2.6 kV, 850 L/hr and 375 °C, respectively. The optimized chromatographic separation achieved on Waters Acquity HSS T3 C18 column (100 mm × 2.1 mm, 1.8 µm) in a gradient elution mode with 0.13% formic acid in water and acetonitrile as mobile phases, column temperature kept at 35 °C and flow rate at 0.5 mL/min. The method was successfully validated as per ICH guidelines, and demonstrated excellent linearity over the concentration range of 0.5-10 ppm for both PGTIs. The Pearson correlation coefficient of each impurity and MOPR was found to be higher than 0.999, and the recoveries were in between the range of 94.62 to 104.05% for both PGTIs and 99.10 to 100.25% for MOPR. It is also feasible to utilise this rapid method to quantify MOPR accurately in biological samples.

Therapeutic Potential of the Genus Zanthoxylum Phytochemicals: A Theoretical ADME/Tox Analysis

Natural products (NPs) are essential in the search for new drugs to treat a wide range of diseases, including infectious and malignant disorders. However, despite the discovery of many bioactive NPs, they often do not make it to market as drugs due to toxicity and other challenges. The development of NPs into drugs is a long and expensive process, and many promising compounds are abandoned along the way. These molecules require in silico ADMET profiling in order to speed up their development into drugs lower costs, and the high attrition rate. The objective of this work was to produce thorough ADMET profiles of secondary metabolites from several classes that were isolated from Zanthoxylum species. The genus has a long history of therapeutic use, including treating tumours, hypertension, gonorrhoea, coughs, bilharzia, chest pains, and toothaches. The study used a dataset of 406 compounds from the genus for theoretical ADMET analysis. The findings revealed that 81% of the compounds met Lipinski's rule of five, indicating good oral bioavailability. The drug-likeness criteria were taken into account, with percentages ranging from 66.2 to 88.1 percent. Additionally, 9.2% of the compounds were predicted to be lead-like, demonstrating their potential as promising drug development candidates. Interestingly, none of the compounds inhibited hERG I, while 33% inhibited hERG II, potentially having cardiac implications. Additionally, 30% of the compounds exhibited AMES toxicity inhibition, while 23.6% were identified as hepatotoxic and 22.2% would cause skin sensitivity. Moreover, 81.8% of the compounds demonstrated high intestinal absorption, making them desirable for oral drugs. In conclusion, these findings highlight the diverse properties of the investigated compounds and their potential for drug development.

Phototoxicity─Medicinal Chemistry Strategies for Risk Mitigation in Drug Discovery

Phototoxicity is a common safety concern encountered by project teams in pharmaceutical research and has the potential to stop progression of an otherwise promising candidate molecule. This perspective aims to provide an overview of the approaches toward mitigation of phototoxicity that medicinal chemists have taken during the lead optimization phase in the context of regulatory standards for photosafety evaluation. Various strategies are laid out based on available literature examples in order to highlight how structural modification can be utilized toward successful mitigation of a phototoxicity liability. A proposed flowchart is presented as a guidance tool to be used by the practicing medicinal chemist when facing a phototoxicity risk. The description of available tools to consider in the drug design process will include an overview of the evolution of in silico methods and their application as well as structure alerts for consideration as potential phototoxicophores.

Scaffold Hopping and Optimization of Small Molecule Soluble Adenyl Cyclase Inhibitors Led by Free Energy Perturbation

Free energy perturbation is a computational technique that can be used to predict how small changes to an inhibitor structure will affect the binding free energy to its target. In this paper, we describe the utility of free energy perturbation with FEP+ in the hit-to-lead stage of a drug discovery project targeting soluble adenyl cyclase. The project was structurally enabled by X-ray crystallography throughout. We employed free energy perturbation to first scaffold hop to a preferable chemotype and then optimize the binding affinity to sub-nanomolar levels while retaining druglike properties. The results illustrate that effective use of free energy perturbation can enable a drug discovery campaign to progress rapidly from hit to lead, facilitating proof-of-concept studies that enable target validation.

Automated detection of toxicophores and prediction of mutagenicity using PMCSFG algorithm

Maximum common substructures (MCS) have received a lot of attention in the chemoinformatics community. They are typically used as a similarity measure between molecules, showing high predictive performance when used in classification tasks, while being easily explainable substructures. In the present work, we applied the Pairwise Maximum Common Subgraph Feature Generation (PMCSFG) algorithm to automatically detect toxicophores (structural alerts) and to compute fingerprints based on MCS. We present a comparison between our MCS-based fingerprints and 12 well-known chemical fingerprints when used as features in machine learning models. We provide an experimental evaluation and discuss the usefulness of the different methods on mutagenicity data. The features generated by the MCS method have a state-of-the-art performance when predicting mutagenicity, while they are more interpretable than the traditional chemical fingerprints.

Genotoxicity evaluation of a valsartan-related complex N-nitroso-impurity

Recently, the formation of genotoxic and carcinogenic N-nitrosamines impurities during drug manufacturing of tetrazole-containing angiotensin-II blockers has been described. However, drug-related (complex) nitrosamines may also be generated under certain conditions, i.e., through nitrosation of vulnerable amines in drug substances in the presence of nitrite. An investigation of valsartan drug substance showed that a complex API-related N-nitrosamine chemically designated as (S)-2-(((2'-(1H-tetrazol-5-yl)-[1,1'-biphenyl]-4-yl)methyl)(nitroso)amino)-3-methylbutanoic acid (named 181-14) may be generated. 181-14 was shown to be devoid of a mutagenic potential in the Non-GLP Ames test. According to ICH M7 (R1) (2018), impurities that are not mutagenic in the Ames test would be considered Class 5 impurities and limited according to ICH Q3A (R2) and B (R2) (2006) guidelines. However, certain regulatory authorities raised the concern that the Ames test may not be sufficiently sensitive to detect a mutagenic potential of nitrosamines and requested a confirmatory in vivo study using a transgenic animal genotoxicity model. Our data show that 181-14 was not mutagenic in the transgenic gene mutation assay in Muta^TMMice. The data support the conclusion that the Ames test is an adequate and sensitive test system to assess a mutagenic potential of nitrosamines.

In-Silico Drug Toxicity and Interaction Prediction for Plant Complexes Based on Virtual Screening and Text Mining

Potential drug toxicities and drug interactions of redundant compounds of plant complexes may cause unexpected clinical responses or even severe adverse events. On the other hand, super-additivity of drug interactions between natural products and synthetic drugs may be utilized to gain better performance in disease management. Although without enough datasets for prediction model training, based on the SwissSimilarity and PubChem platforms, for the first time, a feasible workflow of prediction of both toxicity and drug interaction of plant complexes was built in this study. The optimal similarity score threshold for toxicity prediction of this system is 0.6171, based on an analysis of 20 different herbal medicines. From the PubChem database, 31 different sections of toxicity information such as "Acute Effects", "NIOSH Toxicity Data", "Interactions", "Hepatotoxicity", "Carcinogenicity", "Symptoms", and "Human Toxicity Values" sections have been retrieved, with dozens of active compounds predicted to exert potential toxicities. In Spatholobus suberectus Dunn (SSD), there are 9 out of 24 active compounds predicted to play synergistic effects on cancer management with various drugs or factors. The synergism between SSD, luteolin and docetaxel in the management of triple-negative breast cancer was proved by the combination index assay, synergy score detection assay, and xenograft model.

Identification of Potential New Aedes aegypti Juvenile Hormone Inhibitors from N-Acyl Piperidine Derivatives: A Bioinformatics Approach

Aedes aegypti mosquitoes transmit several human pathogens that cause millions of deaths worldwide, mainly in Latin America. The indiscriminate use of insecticides has resulted in the development of species resistance to some such compounds. Piperidine, a natural alkaloid isolated from Piper nigrum, has been used as a hit compound due to its larvicidal activity against Aedes aegypti. In the present study, piperidine derivatives were studied through in silico methods: pharmacophoric evaluation (PharmaGist), pharmacophoric virtual screening (Pharmit), ADME/Tox prediction (Preadmet/Derek 10.0^®), docking calculations (AutoDock 4.2) and molecular dynamics (MD) simulation on GROMACS-5.1.4. MP-416 and MP-073 molecules exhibiting ΔG binding (MMPBSA −265.95 ± 1.32 kJ/mol and −124.412 ± 1.08 kJ/mol, respectively) and comparable to holo (ΔG binding = −216.21 ± 0.97) and pyriproxyfen (a well-known larvicidal, ΔG binding= −435.95 ± 2.06 kJ/mol). Considering future in vivo assays, we elaborated the theoretical synthetic route and made predictions of the synthetic accessibility (SA) (SwissADME), lipophilicity and water solubility (SwissADME) of the promising compounds identified in the present study. Our in silico results show that MP-416 and MP-073 molecules could be potent insecticides against the Aedes aegypti mosquitoes.

An Evaluation of the Occupational Health Hazards of Peptide Couplers

Peptide couplers (also known as amide bond-forming reagents or coupling reagents) are broadly used in organic chemical syntheses, especially in the pharmaceutical industry. Yet, occupational health hazards associated with this chemical class are largely unexplored, which is disconcerting given the intrinsic reactivity of these compounds. Several case studies involving occupational exposures reported adverse respiratory and dermal health effects, providing initial evidence of chemical sensitization. To address the paucity of toxicological data, a pharmaceutical cross-industry task force was formed to evaluate and assess the potential of these compounds to cause eye and dermal irritation as well as corrosivity and dermal sensitization. The goal of our work was to inform health and safety professionals as well as pharmaceutical and organic chemists of the occupational health hazards associated with this chemical class. To that end, 25 of the most commonly used peptide couplers and five hydrolysis products were selected for in vivo, in vitro, and in silico testing. Our findings confirmed that dermal sensitization is a concern for this chemical class with 21/25 peptide couplers testing positive for dermal sensitization and 15 of these being strong/extreme sensitizers. We also found that dermal corrosion and irritation (8/25) as well as eye irritation (9/25) were health hazards associated with peptide couplers and their hydrolysis products (4/5 were dermal irritants or corrosive and 4/5 were eye irritants). Resulting outcomes were synthesized to inform decision making in peptide coupler selection and enable data-driven hazard communication to workers. The latter includes harmonized hazard classifications, appropriate handling recommendations, and accurate safety data sheets, which support the industrial hygiene hierarchy of control strategies and risk assessment. Our study demonstrates the merits of an integrated, in vivo -in silico analysis, applied here to the skin sensitization endpoint using the Computer-Aided Discovery and REdesign (CADRE) and Derek Nexus programs. We show that experimental data can improve predictive models by filling existing data gaps while, concurrently, providing computational insights into key initiating events and elucidating the chemical structural features contributing to adverse health effects. This interactive, interdisciplinary approach is consistent with Green Chemistry principles that seek to improve the selection and design of less hazardous reagents in industrial processes and applications.

Natural product drug discovery in the artificial intelligence era

Natural products (NPs) are primarily recognized as privileged structures to interact with protein drug targets. Their unique characteristics and structural diversity continue to marvel scientists for developing NP-inspired medicines, even though the pharmaceutical industry has largely given up. High-performance computer hardware, extensive storage, accessible software and affordable online education have democratized the use of artificial intelligence (AI) in many sectors and research areas. The last decades have introduced natural language processing and machine learning algorithms, two subfields of AI, to tackle NP drug discovery challenges and open up opportunities. In this article, we review and discuss the rational applications of AI approaches developed to assist in discovering bioactive NPs and capturing the molecular "patterns" of these privileged structures for combinatorial design or target selectivity.

Potential implications of vouacapan compounds for insecticidal activity: an in silico study

BACKGROUND

From the fruits and seeds of the species of Pterodon, it is possible to obtain two main products: the essential oil and oleoresin. In oleoresin, numerous vouacapan compounds have been demonstrated to have biological potential, including insecticidal activity.

OBJECTIVE

In silico studies were performed to identify potential candidates for natural insecticides among the vouacapans present in the genus Pterodon.

MATERIALS AND METHODS

Molecular docking and molecular dynamics studies were performed to analyze the interaction of vouacapan compounds with acetylcholinesterase of Drosophila melanogaster. Pharmacokinetic parameters regarding physicochemical properties, plasma protein binding, and activity in the central nervous system were evaluated. The toxicological properties of the selected molecules were predicted using Malathion as the reference compound.

RESULTS

6α,7β-dimethoxivouacapan-17-ene (15) showed a high number of interactions and scores in molecular docking studies. This result suggests that this compound exhibits an inhibitory activity of the enzyme acetylcholinesterase. Regarding physicochemical properties, this compound showed the best results, besides presenting low cutaneous permeability values, suggesting null absorption. Molecular dynamics studies demonstrated few conformational changes in the structure of the complex formed by compound 4 and acetylcholinesterase enzyme throughout the simulation time.

CONCLUSION

It was determined that compound 4 (vouacapan 6α,7β,17β,19-tetraol) is an excellent candidate for usage as a natural insecticide.

In Silico Methods for Carcinogenicity Assessment

Screening compounds for potential carcinogenicity is of major importance for prevention of environmentally induced cancers. A large sequence of predictive models, ranging from short-term biological assays (e.g., mutagenicity tests) to theoretical models, has been attempted in this field. Theoretical approaches such as (Q)SAR are highly desirable for identifying carcinogens, since they actively promote the replacement, reduction, and refinement of animal tests. This chapter reports and describes some of the most noted (Q)SAR models based on human expert knowledge and statistical approaches, aiming at predicting the carcinogenicity of chemicals. Additionally, the performance of the selected models has been evaluated, and the results are interpreted in details by applying these predictive models to some pharmaceutical molecules.

Online Prioritization of Toxic Compounds in Water Samples through Intelligent HRMS Data Acquisition

LC-HRMS-based nontarget screening (NTS) has become the method of choice to monitor organic micropollutants (OMPs) in drinking water and its sources. OMPs are identified by matching experimental fragmentation (MS2) spectra with library or in silico-predicted spectra. This requires informative experimental spectra and prioritization to reduce feature numbers, currently performed post data acquisition. Here, we propose a different prioritization strategy to ensure high-quality MS2 spectra for OMPs that pose an environmental or human health risk. This online prioritization triggers MS2 events based on detection of suspect list entries or isotopic patterns in the full scan or an additional MS2 event based on fragment ion(s)/patterns detected in a first MS2 spectrum. Triggers were determined using cheminformatics; potentially toxic compounds were selected based on the presence of structural alerts, in silico-fragmented, and recurring fragments and mass shifts characteristic for a given structural alert identified. After MS acquisition parameter optimization, performance of the online prioritization was experimentally examined. Triggered methods led to increased percentages of MS2 spectra and additional MS2 spectra for compounds with a structural alert. Application to surface water samples resulted in additional MS2 spectra of potentially toxic compounds, facilitating more confident identification and emphasizing the method's potential to improve monitoring studies.

Synthesis, Docking, Computational Studies, and Antimicrobial Evaluations of New Dipeptide Derivatives Based on Nicotinoylglycylglycine Hydrazide

Within a series of dipeptide derivatives (5–11), compound 4 was refluxed with d-glucose, d-xylose, acetylacetone, diethylmalonate, carbon disulfide, ethyl cyanoacetate, and ethyl acetoacetate which yielded 5–11, respectively. The candidates 5–11 were characterized and their biological activities were evaluated where they showed different anti-microbial inhibitory activities based on the type of pathogenic microorganisms. Moreover, to understand modes of binding, molecular docking was used of Nicotinoylglycine derivatives with the active site of the penicillin-binding protein 3 (PBP3) and sterol 14-alpha demethylase’s (CYP51), and the results, which were achieved via covalent and non-covalent docking, were harmonized with the biological activity results. Therefore, it was extrapolated that compounds 4, 7, 8, 9, and 10 had good potential to inhibit sterol 14-alpha demethylase and penicillin-binding protein 3; consequently, these compounds are possibly suitable for the development of a novel antibacterial and antifungal therapeutic drug. In addition, in silico properties of absorption, distribution, metabolism, and excretion (ADME) indicated drug likeness with low to very low oral absorption in most compounds, and undefined blood–brain barrier permeability in all compounds. Furthermore, toxicity (TOPKAT) prediction showed probability values for all carcinogenicity models were medium to pretty low for all compounds.

Current development of integrated web servers for preclinical safety and pharmacokinetics assessments in drug development

In drug development, preclinical safety and pharmacokinetics assessments of candidate drugs to ensure the safety profile are a must. While in vivo and in vitro tests are traditionally used, experimental determinations have disadvantages, as they are usually time-consuming and costly. In silico predictions of these preclinical endpoints have each been developed in the past decades. However, only a few web-based tools have integrated different models to provide a simple one-step platform to help researchers thoroughly evaluate potential drug candidates. To efficiently achieve this approach, a platform for preclinical evaluation must not only predict key ADMET (absorption, distribution, metabolism, excretion and toxicity) properties but also provide some guidance on structural modifications to improve the undesired properties. In this review, we organized and compared several existing integrated web servers that can be adopted in preclinical drug development projects to evaluate the subject of interest. We also introduced our new web server, Virtual Rat, as an alternative choice to profile the properties of drug candidates. In Virtual Rat, we provide not only predictions of important ADMET properties but also possible reasons as to why the model made those structural predictions. Multiple models were implemented into Virtual Rat, including models for predicting human ether-a-go-go-related gene (hERG) inhibition, cytochrome P450 (CYP) inhibition, mutagenicity (Ames test), blood-brain barrier penetration, cytotoxicity and Caco-2 permeability. Virtual Rat is free and has been made publicly available at https://virtualrat.cmdm.tw/.

Virtual screening, ADME/Tox predictions and the drug repurposing concept for future use of old drugs against the COVID-19

The new Coronavirus (SARS-CoV-2) is the cause of a serious infection in the respiratory tract called COVID-19. Structures of the main protease of SARS-CoV-2 (M^pro), responsible for the replication of the virus, have been solved and quickly made available, thus allowing the design of compounds that could interact with this protease and thus to prevent the progression of the disease by avoiding the viral peptide to be cleaved, so that smaller viral proteins can be released into the host's plasma. These structural data are extremely important for in silico design and development of compounds as well, being possible to quick and effectively identify potential inhibitors addressed to such enzyme's structure. Therefore, in order to identify potential inhibitors for M^pro, we used virtual screening approaches based with the structure of the enzyme and two compounds libraries, targeted to SARS-CoV-2, containing compounds with predicted activity against M^pro. In this way, we selected, through docking studies, the 100 top-ranked compounds, which followed to subsequent studies of pharmacokinetic and toxicity predictions. After all the simulations and predictions here performed, we obtained 10 top-ranked compounds that were again in silico analyzed inside the M^pro catalytic site, together some drugs that are being currently investigated for treatment of COVID-19. After proposing and analyzing the interaction modes of these compounds, we submitted one molecule then selected as template to a 2D similarity study in a database containing drugs approved by FDA and we have found and indicated Apixaban as a potential drug for future treatment of COVID-19.

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The new coronavirus (SARS-CoV-2) is the cause of a serious infection in the respiratory tract called COVID-19.

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The main protease SARS-CoV-2 (M^pro) is essential in the process of maturation and infectivity of the virus.

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In silico methodologies are extremely important to identify potential inhibitors for the target structure quickly and effectively.

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The drug repurposing is an important concept for future use of old drugs.

Molecular docking and dynamics simulations studies of OmpATb identifies four potential novel natural product-derived anti-Mycobacterium tuberculosis compounds

The outer membrane protein A (OmpATb) of Mycobacterium tuberculosis is a virulence factor that neutralizes the host pH to impede the uptake of hydrophilic antitubercular drugs. Identifying natural compounds with the potential to inhibit OmpATb could allow circumvention of the porin-like activities of OmpATb. Four potential leads comprising ZINC000003958185, ZINC000000157405, ZINC000000001392 and ZINC000034268676 were obtained by virtual screening of 6394 diverse natural products. Characterization of the binding interactions of the potential leads with OmpATb revealed nine critical residues comprising ARG86, LEU110, LEU113, LEU114, ALA115, PHE142, SER145, VAL146, and PHE151. Molecular dynamics simulations also revealed very stable protein-lead complexes. Most residues contributed lower binding energies to the overall molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) binding free energies of the interactions between the molecules and OmpATb protein. Induced Fit Docking (IFD) of the compounds regenerated poses of the molecular docking using AutoDock Vina. These molecules could be starting templates for designing inhibitors to bypass the pore mediating activities of OmpATb. Based on structural similarity, ZINC000034268676 was suggested as a potential scaffold for designing efflux pump inhibitors of the gate mediating activities of OmpATb and may enhance the uptake of hydrophilic drugs to reduce the duration time of tuberculosis treatment. Furthermore, structurally similar compounds available in the DrugBank database with a similarity threshold of 0.7 have been reported to exhibit antitubercular and anti-mycobacterial activities. These biomolecules can be further characterized experimentally to corroborate their antitubercular activity. Also, the skeletons of the molecules can be adopted as sub-structures for the design of future anti-mycobacterial drugs.

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

OBJECTIVE AND METHODS

This study reviewed the concept of in silico prediction of chemical toxicity for prevention of occupational cancer and future prospects in workers' health. In this review, a new approach to determine the credibility of in silico predictions with raw data is explored, and the method of determining the confidence level of evaluation based on the credibility of data is discussed. I searched various papers and books related to the in silico prediction of chemical toxicity and carcinogenicity. The intention was to utilize the most recent reports after 2015 regarding in silico prediction.

RESULTS AND CONCLUSION

The application of in silico methods is increasing with the prediction of toxic risks to human and the environment. The various toxic effects of industrial chemicals have triggered the recognition of the importance of using a combination of in silico models in the risk assessments. In silico occupational exposure models, industrial accidents, and occupational cancers are effectively managed and chemicals evaluated. It is important to identify and manage hazardous substances proactively through the rigorous evaluation of chemicals.

Broad-Spectrum Profiling of Drug Safety via Learning Complex Network

Drug safety is a severe clinical pharmacology and toxicology problem that has caused immense medical and social burdens every year. Regretfully, a reproducible method to assess drug safety systematically and quantitatively is still missing. In this study, we developed an advanced machine learning model for de novo drug safety assessment by solving the multilayer drug‐gene‐adverse drug reaction (ADR) interaction network. For the first time, the drug safety was assessed in a broad landscape of 1,156 distinct ADRs. We also designed a parameter ToxicityScore to quantify the overall drug safety. Moreover, we determined association strength for every 3,807,631 gene‐ADR interactions, which clues mechanistic exploration of ADRs. For convenience, we deployed the model as a web service ADRAlert‐gene at http://www.bio-add.org/ADRAlert/. In summary, this study offers insights into prioritizing safe drug therapy. It helps reduce the attrition rate of new drug discovery by providing a reliable ADR profile in the early preclinical stage.

Proposition of Potential GSK-3β Inhibitors for the Treatment of Alzheimer's Disease: A Molecular Modeling Study

INTRODUCTION

The enzyme Glycogen Synthase Kinase 3-β (GSK-3β) is related to neuronal cell degeneration, representing a promising target to treat Alzheimer's Disease (AD).

METHODS

In this work, we performed a molecular modeling study of existing GSK-3β inhibitors by means of evaluation of their IC50 values, derivation of a pharmacophore model, molecular docking simulations, ADME/Tox properties predictions, molecular modifications and prediction of synthetic viability.

RESULTS

In this manner, inhibitor 15 (CID 57399952) was elected a template molecule, since it demonstrated to bear relevant structural groups able to interact with GSK-3β, and also presented favorable ADME/Tox predicted properties, except for mutagenicity. Based on this inhibitor chemical structure we proposed six analogues that presented the absence of alerts for mutagenic and carcinogenic activity, both for rats and mouse; likewise they all presented low risk alerts for inhibition of hERG and medium prediction of synthetic viability.

CONCLUSION

It is concluded that the analogues of GSK-3β inhibitors were optimized in relation to the toxicity endpoint of the template molecule, being, therefore, presented as novel and promising drug candidates for AD treatment.

Potential inhibitors of the enzyme acetylcholinesterase and juvenile hormone with insecticidal activity: study of the binding mode via docking and molecular dynamics simulations

Models validation in QSAR, pharmacophore, docking and others can ensure the accuracy and reliability of future predictions in design and selection of molecules with biological activity. In this study, pyriproxyfen was used as a pivot/template to search the database of the Maybridge Database for potential inhibitors of the enzymes acetylcholinesterase and juvenile hormone as well. The initial virtual screening based on the 3D shape resulted in 2000 molecules with Tanimoto index ranging from 0.58 to 0.88. A new reclassification was performed on the overlapping of positive and negative charges, which resulted in 100 molecules with Tanimoto's electrostatic score ranging from 0.627 to 0.87. Using parameters related to absorption, distribution, metabolism and excretion and the pivot molecule, the molecules selected in the previous stage were evaluated regarding these criteria, and 21 were then selected. The pharmacokinetic and toxicological properties were considered and for 12 molecules, the DEREK software not fired any alert of toxicity, which were thus considered satisfactory for prediction of biological activity using the Web server PASS. In the molecular docking with insect acetylcholinesterase, the Maybridge3_002654 molecule had binding affinity of -11.1 kcal/mol, whereas in human acetylcholinesterase, the Maybridge4_001571molecule show in silico affinity of -10.2 kcal/mol, and in the juvenile hormone, the molecule MCULE-8839595892 show in silico affinity value of -11.6 kcal/mol. Subsequent long-trajectory molecular dynamics studies indicated considerable stability of the novel molecules compared to the controls.AbbreviationsQSARquantitative structure-activity relationshipsPASSprediction of activity spectra for substancesCommunicated by Ramaswamy H. Sarma.

In Silico Evaluation of Ibuprofen and Two Benzoylpropionic Acid Derivatives with Potential Anti-Inflammatory Activity

Inflammation is a complex reaction involving cellular and molecular components and an unspecific response to a specific aggression. The use of scientific and technological innovations as a research tool combining multidisciplinary knowledge in informatics, biotechnology, chemistry and biology are essential for optimizing time and reducing costs in the drug design. Thus, the integration of these in silico techniques makes it possible to search for new anti-inflammatory drugs with better pharmacokinetic and toxicological profiles compared to commercially used drugs. This in silico study evaluated the anti-inflammatory potential of two benzoylpropionic acid derivatives (MBPA and DHBPA) using molecular docking and their thermodynamic profiles by molecular dynamics, in addition to predicting oral bioavailability, bioactivity and toxicity. In accordance to our predictions the derivatives proposed here had the potential capacity for COX-2 inhibition in the human and mice enzyme, due to containing similar interactions with the control compound (ibuprofen). Ibuprofen showed toxic predictions of hepatotoxicity (in human, mouse and rat; toxicophoric group 2-arylacetic or 3-arylpropionic acid) and irritation of the gastrointestinal tract (in human, mouse and rat; toxicophoric group alpha-substituted propionic acid or ester) confirming the literature data, as well as the efficiency of the DEREK 10.0.2 program. Moreover, the proposed compounds are predicted to have a good oral bioavailability profile and low toxicity (LD50 < 700 mg/kg) and safety when compared to the commercial compound. Therefore, future studies are necessary to confirm the anti-inflammatory potential of these compounds.

Structure–Activity Relationships and Prediction of the Phototoxicity and Phototoxic Potential of New Drugs

Relationships between the structure and properties of chemicals can be programmed into knowledge-based systems such as DEREK for Windows (DEREK is an acronym for "Deductive Estimation of Risk from Existing Knowledge"). The DEREK for Windows computer system contains a subset of over 60 rules describing chemical substructures (toxophores) responsible for skin sensitisation. As part of the European Phototox Project, the rule base was supplemented by a number of rules for the prospective identification of photoallergens, either by extension of the scope of existing rules or by the generation of new rules where a sound mechanistic rationale for the biological activity could be established. The scope of the rules for photoallergenicity was then further refined by assessment against a list of chemicals identified as photosensitisers by the Centro de Farmacovigilancia de la Comunidad Valenciana, Valencia, Spain. This paper contains an analysis of the mechanistic bases of activity for eight important groups of photoallergens and phototoxins, together with rules for the prospective identification of the photobiological activity of new or untested chemicals belonging to those classes. The mechanism of action of one additional chemical, nitrofurantoin, is well established; however, it was deemed inappropriate to write a rule on the basis of a single chemical structure.

Identification of Potential Inhibitors from Pyriproxyfen with Insecticidal Activity by Virtual Screening

Aedes aegypti is the main vector of dengue fever transmission, yellow fever, Zika, and chikungunya in tropical and subtropical regions and it is considered to cause health risks to millions of people in the world. In this study, we search to obtain new molecules with insecticidal potential against Ae. aegypti via virtual screening. Pyriproxyfen was chosen as a template compound to search molecules in the database Zinc_Natural_Stock (ZNSt) with structural similarity using ROCS (rapid overlay of chemical structures) and EON (electrostatic similarity) software, and in the final search, the top 100 were selected. Subsequently, in silico pharmacokinetic and toxicological properties were determined resulting in a total of 14 molecules, and these were submitted to the PASS online server for the prediction of biological insecticide and acetylcholinesterase activities, and only two selected molecules followed for the molecular docking study to evaluate the binding free energy and interaction mode. After these procedures were performed, toxicity risk assessment such as LD50 values in mg/kg and toxicity class using the PROTOX online server, were undertaken. Molecule ZINC00001624 presented potential for inhibition for the acetylcholinesterase enzyme (insect and human) with a binding affinity value of -10.5 and -10.3 kcal/mol, respectively. The interaction with the juvenile hormone was -11.4 kcal/mol for the molecule ZINC00001021. Molecules ZINC00001021 and ZINC00001624 had excellent predictions in all the steps of the study and may be indicated as the most promising molecules resulting from the virtual screening of new insecticidal agents.

Toward of Safer Phenylbutazone Derivatives by Exploration of Toxicity Mechanism

A drug design for safer phenylbutazone was been explored by reactivity and docking studies involving single electron transfer mechanism, as well as toxicological predictions. Several approaches about its structural properties were performed through quantum chemistry calculations at the B3LYP level of theory, together with the 6-31+G(d,p) basis sets. Molecular orbital and ionization potential were associated to electron donation capacity. The spin densities contribution showed a preferential hydroxylation at the para-positions of phenyl ring when compared to other positions. In addition, on electron abstractions the aromatic hydroxylation has more impact than alkyl hydroxylation. Docking studies indicate that six structures 1, 7, 8 and 13–15 have potential for inhibiting human as well as murine COX-2, due to regions showing similar intermolecular interactions to the observed for the control compounds (indomethacin and refecoxib). Toxicity can be related to aromatic hydroxylation. In accordance to our calculations, the derivatives here proposed are potentially more active as well safer than phenylbutazone and only structures 8 and 13–15 were the most promising. Such results can explain the biological properties of phenylbutazone and support the design of potentially safer candidates.

HUME: large-scale detection of causal genetic factors of adverse drug reactions

Motivation

Adverse drug reactions are one of the major factors that affect the wellbeing of patients and financial costs of healthcare systems. Genetic variations of patients have been shown to be a key factor in the occurrence and severity of many ADRs. However, the large number of confounding drugs and genetic biomarkers for each adverse reaction case demands a method that evaluates all potential genetic causes of ADRs simultaneously.

Results

To address this challenge, we propose HUME, a multi-phase algorithm that recommends genetic factors for ADRs that are causally supported by the patient record data. HUME consists of the construction of a network from co-prevalence between significant genetic biomarkers and ADRs, a link score phase for predicting candidate relations based on the Adamic-Adar measure, and a causal refinement phase based on multiple hypothesis testing of quasi experimental designs for evaluating evidence and counter evidence of candidate relations in the patient records.

Supplementary information

Supplementary data are available at Bioinformatics online.

Formation of a Toxic Quinoneimine Metabolite from Diclofenac: A Quantum Chemical Study

BACKGROUND

Diclofenac is a non-steroidal antiinflammatory drug. It is predominantly metabolized by CYP2C9. 4'-hydroxydiclofenac and its quinoneimine are the metabolites of diclofenac. However, few numbers of serious cases of idiosyncratic hepatotoxicity due to diclofenac metabolism were reported. The formation of the quinoneimine metabolite was found to be responsible for this idiosyncratic toxicity. Quinoneimine is an over-oxidized metabolite of diclofenac.

METHOD

In this work, computational studies were conducted to detail the formation of a quinoneimine metabolite from diclofenac. Further, the idiosyncratic toxicity of quinoneimine due to its reactivity was also investigated by quantum chemical analysis.

RESULTS & CONCLUSION

The results demonstrate the possibility of formation of quinoneimine metabolite due to various factors that are involved in the metabolism of diclofenac. The present study may provide the structural in-sights during the drug development processes to avoid the metabolism directed idiosyncratic toxicity.

Predicting Aromatic Amine Mutagenicity with Confidence: A Case Study Using Conformal Prediction

The occurrence of mutagenicity in primary aromatic amines has been investigated using conformal prediction. The results of the investigation show that it is possible to develop mathematically proven valid models using conformal prediction and that the existence of uncertain classes of prediction, such as both (both classes assigned to a compound) and empty (no class assigned to a compound), provides the user with additional information on how to use, further develop, and possibly improve future models. The study also indicates that the use of different sets of fingerprints results in models, for which the ability to discriminate varies with respect to the set level of acceptable errors.

Oil from the fruits of Pterodon emarginatus Vog.: A traditional anti-inflammatory. Study combining in vivo and in silico

ETHNOBOTANICAL RELEVANCE

The oil obtained from the fruits of Pterodon emarginatus Vog. (OPe) is used orally and topically, in traditional medicine for some purposes, such as acute and chronic inflammatory states as rheumatoid arthritis.

MATERIALS AND METHODS

In this work, the anti-inflammatory activity of the OPe was demonstrated based on several animal models and presented an in silico study based on the 6α,7β-dihydroxy-vouacapan-17β-oic acid (DHVA) majority compound of the OPe to evaluate the interaction this compound, with cyclooxygenase-2 (COX-2) in 4COX (Mus musculus) and 5KIR (Homo sapiens) and molecular dynamics simulation.

RESULTS

The OPe (498 mg/kg, p.o) significantly inhibited (p < 0.05, Student t-test) the primary and secondary reactions of arthritis by Freund's Complete Adjuvant (FCA) and in dermatitis induced by croton oil in mice, OPe inhibited peak of edema. In vascular permeability test in rats, the treatment with OPe was able to block the response to PGE₂, serotonin, and bradykinin (p < 0.05, Student t-test). In the writhing test in mice, the OPe at doses of 498 and 980 mg/kg (p.o) produced inhibition of 73% and 92%, respectively, and was not significantly effective in the hot plate test. In the evaluation of the potency in relation to gastric injury (gastric ulcer induced by stress) and combined assay in the assessment of anti-inflammatory potency and gastric damage, it was observed that indomethacin (10 mg/kg, p.o.) inhibited carrageenan edema by 51% and produced a higher number of gastric lesions when compared to the group treated with OPe, where only areas of hyperemia were observed, without the occurrence of ulcerative lesion, and which inhibited the edema by 47%. In the in silico study, it was found that the DHVA is capable of binding to two organisms (4COX - Mus musculus and 5KIR - Homo sapiens), however, with higher binding affinity to the organism Homo sapiens.

CONCLUSIONS

As expected, all tested ligands were capable of forming hydrogen interactions with residues at their respective binding sites, but the DHVA ligand was capable of creating slightly more hydrogen bonds when docked to either 4COX or 5KIR than the other tested ligands, thus demonstrating the participation of this compound in the anti-inflammatory and antialgic responses observed in the in vivo assays as a COX-2 inhibitor. Therefore, the results obtained support the traditional use of OPe for inflammatory and gastric problems.

Computational approaches to chemical hazard assessment

Computational prediction of toxicity has reached new heights as a result of decades of growth in the magnitude and diversity of biological data. Public packages for statistics and machine learning make model creation faster. New theory in machine learning and cheminformatics enables integration of chemical structure, toxicogenomics, simulated and physical data in the prediction of chemical health hazards, and other toxicological information. Our earlier publications have characterized a toxicological dataset of unprecedented scale resulting from the European REACH legislation (Registration Evaluation Authorisation and Restriction of Chemicals). These publications dove into potential use cases for regulatory data and some models for exploiting this data. This article analyzes the options for the identification and categorization of chemicals, moves on to the derivation of descriptive features for chemicals, discusses different kinds of targets modeled in computational toxicology, and ends with a high-level perspective of the algorithms used to create computational toxicology models.

Identification of Novel Protein Kinase Receptor Type 2 Inhibitors Using Pharmacophore and Structure-Based Virtual Screening

The Protein Kinase Receptor type 2 (RIPK2) plays an important role in the pathogenesis of inflammatory diseases; it signals downstream of the NOD1 and NOD2 intracellular sensors and promotes a productive inflammatory response. However, excessive NOD2 signaling has been associated with various diseases, including sarcoidosis and inflammatory arthritis; the pharmacological inhibition of RIPK2 is an affinity strategy that demonstrates an increased expression of pro-inflammatory secretion activity. In this study, a pharmacophoric model based on the crystallographic pose of ponatinib, a potent RIPK2 inhibitor, and 30 other ones selected from the BindingDB repository database, was built. Compounds were selected based on the available ZINC compounds database and in silico predictions of their pharmacokinetic, toxicity and potential biological activity. Molecular docking was performed to identify the probable interactions of the compounds as well as their binding affinity with RIPK2. The compounds were analyzed to ponatinib and WEHI-345, which also used as a control. At least one of the compounds exhibited suitable pharmacokinetic properties, low toxicity and an interesting binding affinity and high fitness compared with the crystallographic pose of WEHI-345 in complex with RIPK2. This compound also possessed suitable synthetic accessibility, rendering it a potential and very promising RIPK2 inhibitor to be further investigated in regards to different diseases, particularly inflammatory ones.

The Need for, and the Role of the Toxicological Chemist in the Design of Safer Chemicals

During the past several decades, there has been an ever increasing emphasis for designers of new commercial (nonpharmaceutical) chemicals to include considerations of the potential impacts a planned chemical may have on human health and the environment as part of the design of the chemical, and to design chemicals such that they possess the desired use efficacy while minimizing threats to human health and the environment. Achievement of this goal would be facilitated by the availability of individuals specifically and formally trained to design such chemicals. Medicinal chemists are specifically trained to design and develop safe and clinically efficacious pharmaceutical substances. No such formally trained science hybrid exists for the design of safer commercial (nonpharmaceutical) chemicals. This article describes the need for and role of the "toxicological chemist," an individual who is formally trained in synthetic organic chemistry, biochemistry, physiology, toxicology, environmental science, and in the relationships between structure and commercial use efficacy, structure and toxicity, structure and environmental fate and effects, and global hazard, and trained to integrate this knowledge to design safer commercially efficacious chemicals. Using examples, this article illustrates the role of the toxicological chemist in designing commercially efficacious, safer chemical candidates.

In Silico Methods for Carcinogenicity Assessment

Screening compounds for potential carcinogenicity is of major importance for prevention of environmentally induced cancers. A large sequence of alternative predictive models, ranging from short-term biological assays (e.g. mutagenicity tests) to theoretical models, have been attempted in this field. Theoretical approaches such as (Q)SAR are highly desirable for identifying carcinogens, since they actively promote the replacement, reduction, and refinement of animal tests. This chapter reports and describes some of the most noted (Q)SAR models based on the human expert knowledge and statistically approach, aiming at predicting the carcinogenicity of chemicals. Additionally, the performance of the selected models has been evaluated and the results are interpreted in details by applying these prediction models to some pharmaceutical molecules.

Identification and characterization of fluvastatin metabolites in rats by UHPLC/Q-TOF/MS/MS and in silico toxicological screening of the metabolites

The present study reports the in vivo and in vitro identification and characterization of metabolites of fluvastatin, the 3-hydroxy-3-methyl-glutaryl-coenzyme A reductase inhibitor, using liquid chromatography-mass spectrometry (LC-MS). In vitro studies were conducted by incubating the drug with human liver microsomes and rat liver microsomes. In vivo studies were carried out by administration of the drug in the form of suspension to the Sprague-Dawley rats followed by collection of urine, faeces and blood at different time points up to 24 h. Further, samples were prepared by optimized sample preparation method, which includes freeze liquid extraction, protein precipitation and solid phase extraction. The extracted and concentrated samples were analysed using ultrahigh-performance liquid chromatography-quadruple time-of-flight tandem mass spectrometry. A total of 15 metabolites were observed in urine, which includes hydroxyl, sulphated, desisopropyl, dehydrogenated, dehydroxylated and glucuronide metabolites. A few of the metabolites were also present in faeces and plasma samples. In in vitro studies, a few metabolites were observed that were also present in in vivo samples. All the metabolites were characterized using ultrahigh-performance liquid chromatography-quadruple time-of-flight tandem mass spectrometry in combination with accurate mass measurement. Finally, in silico toxicity studies indicated that some of the metabolites show or possess carcinogenicity and skin sensitization. Several metabolites that were identified in rats are proposed to have toxicological significance on the basis of in silico evaluation. However, these metabolites are of no human relevance. Copyright © 2017 John Wiley & Sons, Ltd.

Fragment Prioritization on a Large Mutagenicity Dataset

The identification of structural alerts is one of the simplest tools used for the identification of potentially toxic chemical compounds. Structural alerts have served as an aid to quickly identify chemicals that should be either prioritized for testing or for elimination from further consideration and use. In the recent years, the availability of larger datasets, often growing in the context of collaborative efforts and competitions, created the raw material needed to identify new and more accurate structural alerts. This work applied a method to efficiently mine large toxicological dataset for structural alert showing a strong statistical association with mutagenicity. In details, we processed a large Ames mutagenicity dataset comprising 14,015 unique molecules obtained by joining different data sources. After correction for multiple testing, we were able to assign a probability value to each fragment. A total of 51 rules were identified, with p-value < 0.05. Using the same method, we also confirmed the statistical significance of several mutagenicity rules already present and largely recognized in the literature. In addition, we have extended the application of our method by predicting the mutagenicity of an external data set.

Skin Sensitization: Modeling Based on Skin Metabolism Simulation and Formation of Protein Conjugates

A quantitative structure-activity relationship (QSAR) system for estimating skin sensitization potency has been developed that incorporates skin metabolism and considers the potential of parent chemicals and/or their activated metabolites to react with skin proteins. A training set of diverse chemicals was compiled and their skin sensitization potency assigned to one of three classes. These three classes were, significant, weak, or nonsensitizing. Because skin sensitization potential depends upon the ability of chemicals to react with skin proteins either directly or after appropriate metabolism, a metabolic simulator was constructed to mimic the enzyme activation of chemicals in the skin. This simulator contains 203 hierarchically ordered spontaneous and enzyme controlled reactions. Phase I and phase II metabolism were simulated by using 102 and 9 principal transformations, respectively. The covalent interactions of chemicals and their metabolites with skin proteins were described by 83 reactions that fall within 39 alerting groups. The SAR/QSAR system developed was able to correctly classify about 80% of the chemicals with significant sensitizing effect and 72% of nonsensitizing chemicals. For some alerting groups, three-dimensional (3D)-QSARs were developed to describe the multiplicity of physicochemical, steric, and electronic parameters. These 3D-QSARs, so-called pattern recognition-type models, were applied each time a latent alerting group was identified in a parent chemical or its generated metabolite(s). The concept of the mutual influence amongst atoms in a molecule was used to define the structural domain of the skin sensitization model. The utility of the structural model domain and the predictability of the model were evaluated using sensitization potency data for 96 chemicals not used in the model building. The TIssue MEtabolism Simulator (TIMES) software was used to integrate a skin metabolism simulator and 3D-QSARs to evaluate the reactivity of chemicals thus predicting their likely skin sensitization potency.