Explore the vast range of titles available.

Torsades de Pointes (TdP) is a type of ventricular arrhythmia which could be observed as an unwanted drug-induced cardiac side effect, and it is associated with repolarization abnormalities in single cells. The pharmacological evaluations of TdP risk in previous years mainly focused on the hERG channel due to its vital role in the repolarization of cardiomyocytes. However, only considering drug effects on hERG led to false positive predictions since the drug action on other ion channels can also have crucial regulatory effects on repolarization. To address the limitation of only evaluating hERG, the Comprehensive in Vitro Proarrhythmia Assay initiative has proposed to systematically integrate drug effects on multiple ion channels into drug trial to improve TdP risk assessment. It is not clear how many ion channels are sufficient for reliable TdP risk predictions, and whether differences in IC and Hill coefficient values from independent sources can lead to divergent prediction outcomes. The rationale of this work is to investigate the above two questions using a computationally efficient population of human ventricular cells optimized to favor repolarization abnormality. Our blinded results based on two independent data sources confirm that simulations with the optimized population of human ventricular cell models enable efficient in silico drug screening, and also provide direct observation and mechanistic analysis of repolarization abnormality. Our results show that 1) the minimum set of ion channels required for reliable TdP risk predictions are Nav1.5 (peak), Cav1.2, and hERG; 2) for drugs with multiple ion channel blockage effects, moderate IC variations combined with variable Hill coefficients can affect the accuracy of predictions.

The drug discovery process is a rocky path that is full of challenges, with the result that very few candidates progress from hit compound to a commercially available product, often due to factors, such as poor binding affinity, off-target effects, or physicochemical properties, such as solubility or stability. This process is further complicated by high research and development costs and time requirements. It is thus important to optimise every step of the process in order to maximise the chances of success. As a result of the recent advancements in computer power and technology, computer-aided drug design (CADD) has become an integral part of modern drug discovery to guide and accelerate the process. In this review, we present an overview of the important CADD methods and applications, such as in silico structure prediction, refinement, modelling and target validation, that are commonly used in this area.

The pharmaceutical industry is progressively operating in an era where development costs are constantly under pressure, higher percentages of drugs are demanded, and the drug-discovery process is a trial-and-error run. The profit that flows in with the discovery of new drugs has always been the motivation for the industry to keep up the pace and keep abreast with the endless demand for medicines. The process of finding a molecule that binds to the target protein using in silico tools has made computational chemistry a valuable tool in drug discovery in both academic research and pharmaceutical industry. However, the complexity of many protein-ligand interactions challenges the accuracy and efficiency of the commonly used empirical methods. The usefulness of quantum mechanics (QM) in drug-protein interaction cannot be overemphasized; however, this approach has little significance in some empirical methods. In this review, we discuss recent developments in, and application of, QM to medically relevant biomolecules. We critically discuss the different types of QM-based methods and their proposed application to incorporating them into drug-design and -discovery workflows while trying to answer a critical question: are QM-based methods of real help in drug-design and -discovery research and industry?

Despite the virulence and high fatality of coronavirus disease 2019 (COVID-19), no specific antiviral treatment exists until the current moment. Natural agents with immune-promoting potentials such as bee products are being explored as possible treatments. Bee honey and propolis are rich in bioactive compounds that express strong antimicrobial, bactericidal, antiviral, anti-inflammatory, immunomodulatory, and antioxidant activities. This review examined the literature for the anti-COVID-19 effects of bee honey and propolis, with the aim of optimizing the use of these handy products as prophylactic or adjuvant treatments for people infected with severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2). Molecular simulations show that flavonoids in propolis and honey (e.g., rutin, naringin, caffeic acid phenyl ester, luteolin, and artepillin C) may inhibit viral spike fusion in host cells, viral-host interactions that trigger the cytokine storm, and viral replication. Similar to the potent antiviral drug remdesivir, rutin, propolis ethanolic extract, and propolis liposomes inhibited non-structural proteins of SARS-CoV-2 in vitro, and these compounds along with naringin inhibited SARS-CoV-2 infection in Vero E6 cells. Propolis extracts delivered by nanocarriers exhibit better antiviral effects against SARS-CoV-2 than ethanolic extracts. In line, hospitalized COVID-19 patients receiving green Brazilian propolis or a combination of honey and Nigella sativa exhibited earlier viral clearance, symptom recovery, discharge from the hospital as well as less mortality than counterparts receiving standard care alone. Thus, the use of bee products as an adjuvant treatment for COVID-19 may produce beneficial effects. Implications for treatment outcomes and issues to be considered in future studies are discussed.

Peptides have positively impacted the pharmaceutical industry as drugs, biomarkers, or diagnostic tools of high therapeutic value. However, only a handful have progressed to the market. Toxicity is one of the main obstacles to translating peptides into clinics. Hemolysis or hemotoxicity, the principal source of toxicity, is a natural or disease-induced event leading to the death of vital red blood cells. Initial screenings for toxicity have been widely evaluated using erythrocytes as the gold standard. More recently, many online databases filled with peptide sequences and their biological meta-data have paved the way toward hemolysis prediction using user-friendly, fast-access machine learning-driven programs. This review details the growing contributions of in silico approaches developed in the last decade for the large-scale prediction of erythrocyte lysis induced by peptides. After an overview of the pharmaceutical landscape of peptide therapeutics, we highlighted the relevance of early hemolysis studies in drug development. We emphasized the computational models and algorithms used to this end in light of historical and recent findings in this promising field. We benchmarked seven predictors using peptides from different data sets, having 7–35 amino acids in length. According to our predictions, the models have scored an accuracy over 50.42% and a minimal Matthew’s correlation coefficient over 0.11. The maximum values for these statistical parameters achieved 100.0% and 1.00, respectively. Finally, strategies for optimizing peptide selectivity were described, as well as prospects for future investigations. The development of in silico predictive approaches to peptide toxicity has just started, but their important contributions clearly demonstrate their potential for peptide science and computer-aided drug design. Methodology refinement and increasing use will motivate the timely and accurate in silico identification of selective, non-toxic peptide therapeutics.

Osteoporosis is characterized by loss of bone mineral density and increased fracture risk. Reduction of hip fracture incidence is of major clinical importance. Hip protectors aim to attenuate the impact force transmitted to the femur upon falling, however different conclusions on their efficacy have been reported; some authors suggest this may be due to differences in compliance. The aim of this study was to apply an In Silico trial methodology to predict the effectiveness of hip protectors and its dependence on compliance. A cohort of 1044 virtual patients (Finite Element models of proximal femur) were generated. A Markov chain process was implemented to predict fracture incidence with and without hip protectors, by simulating different levels of compliance. At each simulated follow-up year, a Poisson distribution was randomly sampled to determine the number of falls sustained by each patient. Impact direction and force were stochastically sampled from a range of possible scenarios. The effect of wearing a hip protector was simulated by applying attenuation coefficients to the impact force (12.9 %, 19 % and 33.8 %, as reported for available devices). A patient was considered fractured when impact force exceeded the femur strength. Without hip protector, virtual patients experienced 66 ± 5 fractures in 10 years. Wearing the three devices, fracture incidence was reduced to 43 ± 4, 35 ± 4 and 17 ± 2 respectively, at full compliance. As expected, effectiveness was dependent on compliance. This In Silico trial technology can be applied in the future to test multiple interventions, optimise intervention strategies, improve clinical trial design and drug development.

Despite rapid advances in the human healthcare, the infection caused by certain viruses results in high morbidity and mortality accentuate the importance for development of new antivirals. The existing antiviral drugs are limited, due to their inadequate response, increased rate of resistance and several adverse side effects. Therefore, one of the newly emerging field “peptide-based therapeutics” against viruses is being explored and seems promising. Over the last few years, a lot of scientific effort has been made for the identification of novel and potential peptide-based therapeutics using various advanced technologies. Consequently, there are more than 60 approved peptide drugs available for sale in the market of United States, Europe, Japan, and some Asian countries. Moreover, the number of peptide drugs undergoing the clinical trials is rising gradually year by year. The peptide-based antiviral therapeutics have been approved for the Human immunodeficiency virus (HIV), Influenza virus and Hepatitis virus (B and C). This review enlightens the various peptide sources and the different approaches that have contributed to the search of potential antiviral peptides. These include computational approaches, natural and biological sources (library based high throughput screening) for the identification of lead peptide molecules against their target. Further the applications of few advanced techniques based on combinatorial chemistry and molecular biology have been illustrated to measure the binding parameters such as affinity and kinetics of the screened interacting partners. The employment of these advanced techniques can contribute to investigate antiviral peptide therapeutics for emerging infections.

The newly synthesized quinoline–benzimidazole hybrids containing two types of triazole-methyl-phenoxy linkers were characterized via NMR and elemental analysis. Additional derivatization was achieved by introducing bromine at the C-2 position of the phenoxy core. These novel hybrids were tested for their effects on the growth of the non-tumor cell line MRC-5 (human fetal lung fibroblasts), leukemia and lymphoma cell lines: Hut78, THP-1 and HL-60, and carcinoma cell lines: HeLa and CaCo-2. The results obtained, presented as the concentration that achieves 50% inhibition of cell growth (IC50 value), show that the compounds tested affect tumor cell growth differently depending on the cell line and the dose applied (IC50 ranged from 0.2 to >100 µM). The quinoline–benzimidazole hybrids tested, including 7-chloro-4-(4-[4-(5-methoxy-1H-1,3-benzo[d]imidazol-2-yl)phenoxy]methyl-1H-1,2,3-triazol-1-yl)quinoline 9c, 2-(3-bromo-4-[1-(7-chloroquinolin-4-yl)-1H-1,2,3-triazol-4-yl]methoxyphenyl)-N-propyl-1H-benzo[d]imidazol-5-carboximidamide trihydrochloride 10e, 2-4-[(1-2-[(7-chloroquinolin-4-yl)amino]ethyl-1H-1,2,3-triazol-4-yl)methoxy]phenyl-N-propyl-1H-benzo[d]imidazol-5-carboximidamide trihydrochloride 14e and 2-3-bromo-4-[(1-2-[(7-chloroquinolin-4-yl)amino]ethyl-1H-1,2,3-triazol-4-yl)methoxy]phenyl-N-propyl-1H-benzo[d]imidazol-5-carboximidamide trihydrochloride 15e, arrested the cell cycle of lymphoma (HuT78) cells. The calculated ADMET properties showed that the synthesized compounds violated at most two of Lipinski’s rules, making them potential drug candidates, but mainly for parenteral use due to low gastrointestinal absorption. The quinoline–benzimidazole hybrid 14e, which was shown to be a potent and selective inhibitor of lymphoma cell line growth, obtained the highest binding energy (−140.44 kcal/mol), by docking to the TAO2 kinase domain (PDB: 2GCD).

Arsenic and its various forms have been in use in ancient Chinese medicine for more than 2000 years. Arsenicals have gained importance for having remedial effects for various diseases from syphilis to cancer thus highlighting its role as a therapeutic agent even though it has been labelled as a potential ‘poison’. The ability of arsenic, specifically arsenic trioxide, to treat acute promyelocytic leukaemia has radically changed the perception of this poison and has been the main factor for the re-emergence of this candidate to Western medicine for the treatment of leukaemia and other solid tumours. This review highlights the glorious history of arsenic and its various forms with major emphasis on arsenic trioxide as a therapeutic agent. The mechanism of action, pathogenesis, pharmacokinetic profile, safety concerns, ongoing clinical trials and various new forms of arsenic trioxide are discussed. The review also outlines the therapeutic ability of this drug, discusses the latest developments and recent investigations and potential advancement of arsenic trioxide as nanoformulations that has made it emerge as a potential remedial agent.

Virtual clinical trials (VCTs) are growing in popularity as a tool for quantitatively predicting heterogeneous treatment responses across a population. In the context of a VCT, a plausible patient is an instance of a mathematical model with parameter (or attribute) values chosen to reflect features of the disease and response to treatment for that particular patient. A number of techniques have been introduced to determine the set of model parametrizations to include in a virtual patient cohort. These methodologies generally start with a prior distribution for each model parameter and utilize some criteria to determine whether a parameter set sampled from the priors should be included or excluded from the plausible population. No standard technique exists, however, for generating these prior distributions and choosing the inclusion/exclusion criteria. In this work, we rigorously quantify the impact that VCT design choices have on VCT predictions. Rather than use real data and a complex mathematical model, a spatial model of radiotherapy is used to generate simulated patient data and the mathematical model used to describe the patient data is a two-parameter ordinary differential equations model. This controlled setup allows us to isolate the impact of both the prior distribution and the inclusion/exclusion criteria on both the heterogeneity of plausible populations and on predicted treatment response. We find that the prior distribution, rather than the inclusion/exclusion criteria, has a larger impact on the heterogeneity of the plausible population. Yet, the percent of treatment responders in the plausible population was more sensitive to the inclusion/exclusion criteria utilized. This foundational understanding of the role of virtual clinical trial design should help inform the development of future VCTs that use more complex models and real data.