Benzimidazole Compounds Research Articles

Current status of drug therapy for alveolar echinococcosis

Alveolar echinococcosis (AE) is a chronic zoonotic parasitic disease caused by infection with Echinococcus multilocularis . AE is associated with a high mortality rate and poses a significant threat to human health. The primary treatment for AE is surgical resection of the lesions; however, owing to its long incubation period and insidious disease progression, many patients are diagnosed only after the onset of complications such as liver cirrhosis, jaundice, and portal hypertension, which preclude curative surgical intervention. For patients who are unwilling or unable to undergo surgery, lifelong administration of anti-AE medications is necessary. Benzimidazole compounds, such as albendazole and mebendazole, are the current mainstays of treatment, offering good efficacy. Nevertheless, these medications primarily inhibit parasite proliferation rather than eradicate the infection, and their long-term use can lead to significant drug-related toxic effects. Consequently, there is an urgent need to develop new therapeutic strategies that convey better efficacy and reduce the adverse effects associated with current treatments. Recent advancements in AE therapy include novel synthetic compounds such as antiviral agents, antibiotics, antineoplastic agents, immunosuppressants, and antiangiogenic agents, as well as natural compounds derived from traditional Chinese and Tibetan medicine. These new drugs show promising clinical potential because they interfere with parasitic metabolic pathways and cellular structures. This review aims to discuss recent research on AE drug therapy, including mechanisms of action, dosing regimens, signalling pathways, and therapeutic outcomes, with a goal of providing new insights and directions for the development of anti-AE drugs and summarizing current advancements in AE pharmacotherapy.

Novel Substituted Benzimidazole Compounds as TLR9 Inhibitors for Treating Fibrotic Diseases, Particularly Idiopathic Pulmonary Fibrosis

Novel Substituted Benzimidazole Compounds as TLR9 Inhibitors for Treating Fibrotic Diseases, Particularly Idiopathic Pulmonary Fibrosis

Adjuvants restore colistin sensitivity in mouse models of highly colistin-resistant isolates, limiting bacterial proliferation and dissemination.

Antimicrobial resistance (AMR) has led to a marked reduction in the effectiveness of many antibiotics, representing a substantial and escalating concern for global health. Particularly alarming is resistance in Gram-negative bacteria due to the scarcity of therapeutic options for treating infections caused by these pathogens. This challenge is further compounded by the rising incidence of resistance to colistin, an antibiotic traditionally considered a last resort for the treatment of multi-drug resistant (MDR) Gram-negative bacterial infections. In this study, we demonstrate that adjuvants restore colistin sensitivity in vivo. We previously reported that the salicylanilide kinase inhibitor IMD-0354, which was originally developed to inhibit the human kinase IKKβ in the NFκB pathway, is a potent colistin adjuvant. Subsequent analog synthesis using an amide isostere approach led to the creation of a series of novel benzimidazole compounds with enhanced colistin adjuvant activity. Herein, we demonstrate that both IMD-0354 and a lead benzimidazole effectively restore colistin susceptibility in mouse models of highly colistin-resistant Klebsiella pneumoniae and Acinetobacter baumannii-induced peritonitis. These novel adjuvants show low toxicity in vivo, significantly reduce bacterial load, and prevent dissemination that could otherwise result in systemic infection.

Sustainable synthesis of benzimidazoles catalyzed by recyclable functionalized organosilica

A highly efficient protocol is herein reported for the preparation of benzimidazole compounds from o-phenylenediamine and various benzaldehyde derivatives in the presence of heterogeneous pyridinium protic ionic liquids supported on periodic mesoporous organosilica (PMO) in water. The synthesis of three novel benzimidazole derivatives is also reported. The proposed approach successfully afforded products in good yield under mild reaction conditions. PMO-Py-IL exhibited excellent catalyst activity and reusability for at least ten reaction cycles with no significant activity loss.

Oral Fenbendazole for Cancer Therapy in Humans and Animals.

Fenbendazole is a benzimidazole anthelmintic agent commonly used to treat animal parasitic infections. In humans, other benzimidazoles, such as mebendazole and albendazole, are used as antiparasitic agents. Since fenbendazole is not currently approved by the FDA or EMA, its pharmacokinetics and safety in humans have yet to be well-documented in medical literature. Despite this, insights can be drawn from existing in vitro and in vivo animal studies on its pharmacokinetics. Given the low cost of fenbendazole, its high safety profile, accessibility, and unique anti-proliferative activities, fenbendazole would be the preferred benzimidazole compound to treat cancer. To ensure patient safety in the repurposing use of fenbendazole, it is crucial to perform clinical trials to assess its potential anticancer effects, optimal doses, therapeutic regimen, and tolerance profiles. This review focuses on the pharmacokinetics of orally administered fenbendazole and its promising anticancer biological activities, such as inhibiting glycolysis, down-regulating glucose uptake, inducing oxidative stress, and enhancing apoptosis in published experimental studies. Additionally, we evaluated the toxicity profile of fenbendazole and discussed possibilities for improving the bioavailability of the drug, enhancing its efficacy, and reducing potential toxicity.

Machine learning models and computational simulation techniques for prediction of anti-corrosion properties of novel benzimidazole derivatives

The present paper delves into the development of predictive models for the optimum prediction of inhibition efficiencies and anti-corrosion properties of newly designed benzimidazole compounds in an HCl medium. Density functional theory (DFT) was used to obtain the molecular descriptors. 17 descriptors considered as input variables were reduced to 9 after redundant variables were eliminated using the variance inflation factor (VIF). Machine learning models such as random forest regression (Rf), K-nearest neighbor (KNN), and gradient boosting (GB) were used to develop a predictive model using data from 50 benzimidazole derivatives whose inhibition efficiencies for steel alloy in HCl medium have been determined experimentally and are available in the literature. The cross-validation (CV) technique was used to evaluate the predictive capability of the models. KNN gave the best result with a coefficient of correlation (R2) of 0.654, compared to GB (0.591) and RF (0.512). Subsequently, the three models were used to predict the inhibition efficiencies of three novel benzimidazole compounds. The corrosion inhibition efficiency of three newly developed benzimidazole compounds was 91 % - 98.5 % when assessed with the best-performing model, K-nearest neighbor (KNN) with MSBAH recording the highest value of 98.5 %. Fukui indices parameters fk+k,fk−k,andf2(r) showed that both the electrophilic and nucleophilic sites are concentrated around specific atoms most of whom are heteroatoms that participate actively in electron-donor and acceptor interaction with the metal atom leading to the formation of coordinate covalent bonds. The results of electron distribution and Fukui dual descriptors support the results of the predictive model. The study also revealed that machine learning algorithms in conjunction with DFT and MD simulation is an innovative technique for the prediction of anti-corrosion properties of newly designed molecules such as corrosion inhibition efficiencies, and mechanism of inhibitor adsorption on metal/alloy surfaces exposed to an aggressive environment. This technique can be harnessed in proffering solutions to acid corrosion especially those that occur during descaling operation.

Synthesis and Anticancer Evaluation of Disubstituted Benzimidazoles via One-Pot Telescopic Grinding Approach.

Benzimidazole compounds are known for their broad spectrum therapeutic potentials. A small library of benzimidazole derivatives were designed and synthesized via a one-pot telescopic grinding approach. The ability of these molecules as proposed anticancer agents were evaluated by their potential to bind to two important cancer pathway protein targets, human estrogen receptors and cyclin dependant kinases, 3ERT and 5FGK respectively. Further nucleic acid binding and reactive oxygen species (ROS) scavenging capacity being in the scope for anticancer potential evaluations, the ability of these molecules have been evaluated for the same. Further, to support the experimental and computational results, AI-assisted tools were employed to predict the anticancer activity (PASS) as well as to identify false positives (PAINS). Also, the druggability of the proposed compounds was evaluated by following their pharmacokinetic parameters - ADME.

Evaluation of the antitumor activity of albendazole using Langmuir-Blodgett monolayers as surface mediated drug delivery system

This study demonstrates the application of Langmuir and Langmuir-Blodgett films as biomimetic drug reservoirs and delivery systems to investigate the effect of an anthelmintic on cancer cell culture. The repurposing of benzimidazole anthelmintics for cancer therapy due to their microtubule-inhibiting properties has gained attention, showing promising anticancer effects and tumor-suppressive properties. Although widely used in medicine, the low aqueous solubility of benzimidazole compounds poses challenges for studying their effects on cancer cells, requiring incorporation into various formulations. Our study demonstrates that incorporating albendazole into stable Palmitic Acid Langmuir monolayers, forming Langmuir-Blodgett films, significantly affects the proliferation of liver carcinoma cells. This report presents the initial findings of the effect of an antitumoral drug on cancer cell culture using a simple and repeatable methodology.

Synthesis of new two 1,2-disubstituted benzimidazole compounds: their in vitro anticancer and in silico molecular docking studies

In this study, two new molecules were synthesized from the reaction of 2-methyl-1H-benzo[d]imidazole with aryl halides in the presence of a strong base. The structures newly of synthesized 1,2-disubstituted benzimidazole compounds were characterized using spectroscopic techniques (FT-IR, 1HNMR, 13CNMR) and chromatographic technique (LC/MS). For discovering an effective anticancer drug, the developed heterocyclic compounds were screened against three different human cancer cell lines (A549, DLD-1, and L929). The results demonstrated that of IC50 values of compound 2a were higher as compared to cisplatin for the A549 and DLD-1 cell lines. The frontier molecular orbital (FMO), and molecular electrostatic potential map (MEP) analyses were studied by using DFT (density functional theory) calculations at B3LYP/6-31G** level of theory. The molecular docking studies of the synthesized compound with lung cancer protein, PDB ID: 1M17, and colon cancer antigen proteins, PDB ID: 2HQ6 were performed to compare with experimental and theoretical data. Compound 2a had shown the best binding affinity with -6.6 kcal/mol. It was observed that the theoretical and experimental studies carried out supported each other.

Evaluating some new benzimidazole derivative: Insilico docking studies for anthelmintic drug development

The Heterocyclic compounds used as medicinal agents in the field of study since long time. Benzo-fused heterocyclic nucleus benzimidazole founds significant class for new drug development. Molecular docking study is a technique in Computer Aided Drug Designing for studying ligand and protein interaction. The main motto of this work is to perform Preliminary screening using SAR studies, OSIRIS molecular property explorer, PASS Prediction Activity spectra and Rule of Five and to check the proposed benzimidazole compounds are potentially active as Anthelminthic agent by Docking studies with beta tubulin protein (PDB ID 1OJ0) using Auto dock 1.5.6 and VINA. Among all proposed benzimidazole derivatives, 4-((1H-benzimidazol-1-ylimino) methyl) phenol (5d) is the most effective and showed maximum binding energy (−7.4 K/Cal) when compared with standard drug albendazole (−7.1 K/Cal).

PH-tailored delivery of a multitarget anticancer benzimidazole derivative using a PEGylated β-cyclodextrin-curcumin functionalized nanocomplex

In this study, we aimed to enhance the bioavailability of a benzimidazole derivative with potent anticancer potential through a nano-based approach. Benzimidazole-loaded polyethylene glycol-β-cyclodextrin–functionalized curcumin nanocomplex (BMPE-Cur) was prepared and characterized for its physicochemical properties and drug release profiles under different pH conditions. In addition, the biological activities of the nanocomplex including antioxidant potentials and pro-apoptogenic properties, against HepG2, PC3, and the chemo-resistant MCF-7-ADR cell lines relative to the normal Wi-38 cell line were in vitro assessed and compared with those of the free benzimidazole compound. In addition to FTIR, XRD, and NMR spectral studies, a polymeric nanocomplex with an average particle size of 467.7 nm and high stability was successfully developed, as indicated by the negative zeta potential (−28.24 mV). The nanocomplex also showed prolonged pH-sensitive sustained drug release under conditions that replicated the tumor's extra/intracellular pH. The formulated nanocomplex also demonstrated potent radical scavenging capacity owing to the inclusion of curcumin, a known radical quencher. In addition, compared with the free compound, BMPE-Cur induced DNA fragmentation-driven cell cycle arrest in HepG2, PC3, and MCF-7-ADR cells at the G1/S, G1 & S phases; respectively, with remarkable selectivity. In conclusion, the newly formulated BMPE-Cur nanocomplex represents an attractive multitarget anticancer candidate.

XGBoost performance in predicting corrosion inhibition efficiency of Benzimidazole Compounds

In this study, we compare the performance of the XGBoost model with a Support Vector Machine (SVM) model from the literature in predicting a given task. Performance metrics such as the coefficient of determination (R2), root mean squared error (RMSE), and mean absolute error (MAE) were utilized to evaluate and compare the models. The XGBoost model achieved an R² of 0.99, an RMSE of 2.54, and an MAE of 1.96, significantly outperforming the SVM model, which recorded an R² of 0.96 and an RMSE of 6.79. The scatter plot for the XGBoost model further illustrated its superior performance, showing a tight clustering of points around the ideal line (y = x), indicating high accuracy and low prediction errors. These findings suggest that the XGBoost model is highly effective for the given prediction task, likely due to its ability to capture complex patterns and interactions within the data.

Dynamic and structural response of a multispecies biofilm to environmental perturbations induced by the continuous increase of benzimidazole fungicides in a permeable reactive biobarrier.

This work explores the dynamics of spatiotemporal changes in the taxonomic structure of biofilms and the degradation kinetics of three imidazole group compounds: carbendazim (CBZ), methyl thiophanate (MT), and benomyl (BN) by a multispecies microbial community attached to a fixed bed horizontal tubular reactor (HTR). This bioreactor mimics a permeable reactive biobarrier, which helps prevent the contamination of water bodies by pesticides in agricultural wastewater. To rapidly quantify the microbial response to crescent loading rates of benzimidazole compounds, a gradient system was used to transiently raise the fungicide volumetric loading rates, measuring the structural and functional dynamics response of a microbial community in terms of the volumetric removal rates of the HTR entering pollutants. The loading rate gradient of benzimidazole compounds severely impacts the spatiotemporal taxonomic structure of the HTR biofilm-forming microbial community. Notable differences with the original structure in HTR stable conditions can be noted after three historical contingencies (CBZ, MT, and BN gradient loading rates). It was evidenced that the microbial community did not return to the composition prior to environmental disturbances; however, the functional similarity of microbial communities after steady state reestablishment was observed. The usefulness of the method of gradual delivery of potentially toxic agents for a microbial community immobilized in a tubular biofilm reactor was shown since its functional and structural dynamics were quickly evaluated in response to fungicide composition and concentration changes. The rapid adjustment of the contaminants' removal rates indicates that even with changes in the taxonomic structure of a microbial community, its functional redundancy favors its adjustment to gradual environmental disturbances.

Synthesis, Characterization, and Antifungal Activity of Benzimidazole-Grafted Chitosan against Aspergillus flavus.

Aspergillus flavus contamination in agriculture and food industries poses threats to human health, leading to a requirement of a safe and effective method to control fungal contamination. Chitosan-based nitrogen-containing derivatives have attracted much attention due to their safety and enhanced antimicrobial applications. Herein, a new benzimidazole-grafted chitosan (BAC) was synthesized by linking the chitosan (CS) with a simple benzimidazole compound, 2-benzimidazolepropionic acid (BA). The characterization of BAC was confirmed by Fourier transform infrared (FTIR) spectroscopy and nuclear magnetic resonance spectroscopy (1H and 13C NMR). Then, the efficiency of BAC against A. flavus ACCC 32656 was investigated in terms of spore germination, mycelial growth, and aflatoxin production. BAC showed a much better antifungal effect than CS and BA. The minimum inhibitory concentration (MIC) value was 1.25 mg/mL for BAC, while the highest solubility of CS (16.0 mg/mL) or BA (4.0 mg/mL) could not completely inhibit the growth of A. flavus. Furthermore, results showed that BAC inhibited spore germination and elongation by affecting ergosterol biosynthesis and the cell membrane integrity, leading to the permeabilization of the plasma membrane and leakage of intracellular content. The production of aflatoxin was also inhibited when treated with BAC. These findings indicate that benzimidazole-derived natural CS has the potential to be used as an ideal antifungal agent for food preservation.

Development of a Machine Learning Model to Predict the Corrosion Inhibition Ability of Benzimidazole Compounds

The purpose of this study is to use quantitative structure-property relationship (QSPR)-based machine learning (ML) to examine the corrosion inhibition capabilities of benzimidazole compounds. The primary difficulty in ML development is creating a model with a high degree of precision so that the predictions are correct and pertinent to the material's actual attributes. We assess the comparison between the extra trees regressor (EXT) as an ensemble model and the decision tree regressor (DT) as a basic model. It was discovered that the EXT model had better predictive performance in predicting the corrosion inhibition performance of benzimidazole compounds based on the coefficient of determination (R2) and root mean square error (RMSE) metrics compared DT model. This method provides a fresh viewpoint on the capacity of ML models to forecast potent corrosion inhibitors.

Quantitation of three benzimidazole compounds in milk combining effervescence-assisted dispersive liquid–liquid microextraction with electrochemical detection using carbon nanotube-electropolymerized L-DOPA modified sensor

Benzimidazoles constitute a group of compounds known for their applications in pharmacy, agriculture and food industry. In recent decades, researchers have dedicated significant efforts to improving microextraction and detection methods that align with the principles of green analytical chemistry. In the present study, we have devised an effervescence-assisted dispersive liquid–liquid microextraction method and built an electrochemical sensor for the detection of albendazole, fenbendazole, and thiabendazole using GCEs (glassy carbon electrode) modified with multiple walled carbon nanotubes (MWCNTs) and poly (L-Dopa) film, allowing benzimidazole detection in complex matrices such as milk. Optimization of the microextraction procedure and electrochemical performance of the sensor was done by response surface methodology. The electrochemical sensor enabled the detection of albendazole, fenbendazole, and thiabendazole in a range of 0.37 to 100 μg mL−1 with a detection limit ranged between 0.14 to 0.28 μg mL−1. The developed sensor was tested in commercial milk. Results show satisfactory analytical performance, achieving a sensitive and selective quantification, especially considering the complexity of the matrix in which it was applied. The newly created sensor facilitates the simultaneous quantitation of the desired analytes, delivering an exceptionally praiseworthy analytical performance with detection limits lower than most existing methods.

2-Pyran-4-Ylidene Malononitrile Based Conjugated Microporous Polymers as Metal-Free Heterogeneous Photocatalysts for Organic Synthesis.

Photoactive conjugated microporous polymers (CMPs) as heterogeneous photocatalysts provide a sustainable alternative to classical metal-based semiconductor photosensitizers. However, previously reported CMPs are typically synthesized through metal catalyzed coupling reactions, which bears product separation, but also increases the price of materials. Herein, a new type of sp2 carbon linked DCM-CMPs are successfully designed and synthesized by organic base catalyzed Knoevenagel reaction using 2,6-Dimethyl-4H-pyran-4-ylidene-malononitrile and aromatic polyaldehydes as monomers. The new polymers feature inherent porosity, excellent stability, and fully π-conjugated skeleton with broad visible-light absorption. They effectively induce the synthesis of benzimidazole compounds under light irradiation, and exhibit wide substrate adaptability with outstanding recyclability.

Investigasi Model Machine Learning Terbaik untuk Memprediksi Kemampuan Penghambatan Korosi oleh Senyawa Benzimidazole

This research aims to investigate the corrosion inhibition performance of Benzimidazole compounds using a machine learning (ML) approach. The main challenge in developing ML is to obtain a model with high accuracy so that the prediction results are relevant and accurate to the actual properties of a material. In this research, we evaluate various linear and non-linear algorithms to obtain the best model. Based on the coefficient of determination (R2) and root mean square error (RMSE) metrics, it was found that the AdaBoost Regressor (ADA) model was the model with the best predictive performance in predicting the corrosion inhibition performance of benzimidazole compounds. This approach offers a new perspective on the ability of ML models to predict effective corrosion inhibitors.

Hexanuclear nickel-substituted silicotungstate as heterogeneous catalyst for construction of benzimidazoles

The development of heterogeneous catalysts is very important for the chemical industry. [Ni0.5(en)] [Ni6(μ3-OH)3(en)3(H2O)6(B-α-SiW9O34)]·3.5H2O (Ni6en, en = ethylenediamine) is one of the representatives of those semi-open Ni6-cluster based polyoxometalates that could be easily synthesized and shows potential catalytic activity in heterogeneous catalytic system. In this work, Ni6en exhibited high stability and excellent catalytic activity in the condensation reaction of aldehydes and o-phenylenediamines at room temperature. A range of benzimidazole compounds could be isolated in good to excellent yields. Furthermore, this heterogeneous catalytic system could be scaled up to gram reaction, and Ni6en could be reused 5 times with no significant decrease in activity, showing its potential application prospect.

Molecular docking, pharmacokinetic profiling and toxicity studies of 2-substituted benzimidazole derivatives against breast cancer

The most prevalent malignancy among women is breast cancer, which had almost 1.3 million new cases in 2020. It is the second most common cancer in the world, followed by lung cancer. The survival rate would be 99% if the cancerous tumour was limited to the breast. If the cancer migrated to neighbouring lymph nodes, the survival percentage would be 85% and it would drop to 27% if it spread to distant regions. In fact, the most prevalent breast cancer subtype is that caused by excessive estrogen levels. The enhancement of pertinent treatment techniques depends on the estrogen receptors (ER) in both healthy and pathological conditions. There are two primary types of ER, ERα and ERβ, which are each encoded by a different gene. ER status is the most important indicator of breast cancer prediction. To develop novel therapeutics for breast cancer, 30 newly designed benzimidazole compounds targeting the ER were docked. Among them, a compound with a glide score of −9.293 was discovered to be the leading compound. ADME investigations provided additional validation of the docking results. The pyrazole fused benzimidazole nucleus is therefore suggested as a potential pharmacophore for the development of innovative anticancer treatment for breast cancer.