DNA Binding and Cleavage, Stopped-Flow Kinetic, Mechanistic, and Molecular Docking Studies of Cationic Ruthenium(II) Nitrosyl Complexes Containing "NS4" Core.

This work aimed to evaluate in vitro DNA binding mechanistically of cationic nitrosyl ruthenium complex [RuNOTSP]+ and its ligand (TSPH2) in detail, correlate the findings with cleavage activity, and draw conclusions about the impact of the metal center. Theoretical studies were performed for [RuNOTSP]+, TSPH2, and its anion TSP-2 using DFT/B3LYP theory to calculate optimized energy, binding energy, and chemical reactivity. Since nearly all medications function by attaching to a particular protein or DNA, the in vitro calf thymus DNA (ctDNA) binding studies of [RuNOTSP]+ and TSPH2 with ctDNA were examined mechanistically using a variety of biophysical techniques. Fluorescence experiments showed that both compounds effectively bind to ctDNA through intercalative/electrostatic interactions via the DNA helix's phosphate backbone. The intrinsic binding constants (Kb), (2.4 ± 0.2) × 105 M-1 ([RuNOTSP]+) and (1.9 ± 0.3) × 105 M-1 (TSPH2), as well as the enhancement dynamic constants (KD), (3.3 ± 0.3) × 104 M-1 ([RuNOTSP]+) and (2.6 ± 0.2) × 104 M-1 (TSPH2), reveal that [RuNOTSP]+ has a greater binding propensity for DNA compared to TSPH2. Stopped-flow investigations showed that both [RuNOTSP]+ and TSPH2 bind through two reversible steps: a fast second-order binding, followed by a slow first-order isomerization reaction via a static quenching mechanism. For the first and second steps of [RuNOTSP]+ and TSPH2, the detailed binding parameters were established. The total binding constants for [RuNOTSP]+ (Ka = 43.7 M-1, Kd = 2.3 × 10-2 M-1, ΔG0 = -36.6 kJ mol-1) and TSPH2 (Ka = 15.1 M-1, Kd = 66 × 10-2 M, ΔG0 = -19 kJ mol-1) revealed that the relative reactivity is approximately ([RuNOTSP]+)/(TSPH2) = 3/1. The significantly negative ΔG0 values are consistent with a spontaneous binding reaction to both [RuNOTSP]+ and TSPH2, with the former being very favorable. The findings showed that the Ru(II) center had an effect on the reaction rate but not on the mechanism and that the cationic [RuNOTSP]+ was a more highly effective DNA binder than the ligand TSPH2 via strong electrostatic interaction with the phosphate end of DNA. Because of its higher DNA binding affinity, cationic [RuNOTSP]+ demonstrated higher cleavage efficiency towards the minor groove of pBR322 DNA via the hydrolytic pathway than TSPH2, revealing the synergy effect of TSPH2 in the form of the complex. Furthermore, the mode of interaction of both compounds with ctDNA has also been supported by molecular docking.

Green Synthesized Zinc Oxide Nanoparticles Based on Cestrum diurnum L. of Potential Antiviral Activity against Human Corona 229-E Virus.

SARS-CoV-2 has caused more than 596 million infections and 6 million fatalities globally. Looking for urgent medication for prevention, treatment, and rehabilitation is obligatory. Plant extracts and green synthesized nanoparticles have numerous biological activities, including antiviral activity. HPLC analysis of C. dirnum L. leaf extract showed that catechin, ferulic acid, chlorogenic acid, and syringic acid were the most major compounds, with concentrations of 1425.16, 1004.68, 207.46, and 158.95 µg/g, respectively. Zinc nanoparticles were biosynthesized using zinc acetate and C. dirnum extract. TEM analysis revealed that the particle size of ZnO-NPs varied between 3.406 and 4.857 nm. An XRD study showed the existence of hexagonal crystals of ZnO-NPs with an average size of 12.11 nm. Both ZnO-NPs (IC50 = 7.01 and CC50 = 145.77) and C. dirnum L. extract (IC50 = 61.15 and CC50 = 145.87 µg/mL) showed antiviral activity against HCOV-229E, but their combination (IC50 = 2.41 and CC50 = 179.23) showed higher activity than both. Molecular docking was used to investigate the affinity of some metabolites against the HCOV-229E main protease. Chlorogenic acid, solanidine, and catchin showed high affinity (-7.13, -6.95, and -6.52), compared to the ligand MDP (-5.66 Kcal/mol). Cestrum dinurum extract and ZnO-NPs combination should be subjected to further studies to be used as an antiviral drug.

Classification of toxicity effects of biotransformed hepatic drugs using whale optimized support vector machines.

Measuring toxicity is an important step in drug development. Nevertheless, the current experimental methods used to estimate the drug toxicity are expensive and time-consuming, indicating that they are not suitable for large-scale evaluation of drug toxicity in the early stage of drug development. Hence, there is a high demand to develop computational models that can predict the drug toxicity risks. In this study, we used a dataset that consists of 553 drugs that biotransformed in liver. The toxic effects were calculated for the current data, namely, mutagenic, tumorigenic, irritant and reproductive effect. Each drug is represented by 31 chemical descriptors (features). The proposed model consists of three phases. In the first phase, the most discriminative subset of features is selected using rough set-based methods to reduce the classification time while improving the classification performance. In the second phase, different sampling methods such as Random Under-Sampling, Random Over-Sampling and Synthetic Minority Oversampling Technique (SMOTE), BorderLine SMOTE and Safe Level SMOTE are used to solve the problem of imbalanced dataset. In the third phase, the Support Vector Machines (SVM) classifier is used to classify an unknown drug into toxic or non-toxic. SVM parameters such as the penalty parameter and kernel parameter have a great impact on the classification accuracy of the model. In this paper, Whale Optimization Algorithm (WOA) has been proposed to optimize the parameters of SVM, so that the classification error can be reduced. The experimental results proved that the proposed model achieved high sensitivity to all toxic effects. Overall, the high sensitivity of the WOA+SVM model indicates that it could be used for the prediction of drug toxicity in the early stage of drug development.

Circulating miRNA's biomarkers for early detection of hepatocellular carcinoma in Egyptian patients based on machine learning algorithms.

Liver cancer, which ranks sixth globally and third in cancer-related deaths, is caused by chronic liver disorders and a variety of risk factors. Despite therapeutic improvements, the prognosis for Hepatocellular Carcinoma (HCC) remains poor, with a 5-year survival rate for advanced cases of less than 12%. Although there is a noticeable decrease in the frequency of cases, liver cancer remains a significant worldwide health concern, with estimates surpassing one million cases by 2025. The prevalence of HCC has increased in Egypt, and it includes several neoplasms with distinctive messenger RNA (mRNA) and microRNA (miRNA) expression profiles. In HCC patients, certain miRNAs, such as miRNA-483-5P and miRNA-21, are upregulated, whereas miRNA-155 is elevated in HCV-infected people, encouraging hepatocyte proliferation. Short noncoding RNAs called miRNAs in circulation have the potential as HCC diagnostic and prognostic markers. This paper proposed a model for examining circulating miRNAs as diagnostic and predictive markers for HCC in Egyptian patients and their clinical and pathological characteristics. The proposed HCC detection model consists of three main phases: data preprocessing phase, feature selection based on the proposed Binary African Vulture Optimization Algorithm (BAVO) phase, and finally, classification as well as cross-validation phase. The first phase namely the data preprocessing phase tackle the main problems associated with the adopted datasets. In the feature selection based on the proposed BAVO algorithm phase, a new binary version of the BAVO swarm-based algorithm is introduced to select the relevant markers for HCC. Finally, in the last phase, namely the classification and cross-validation phase, the support vector machine and k-folds cross-validation method are utilized. The proposed model is evaluated on three studies on Egyptians who had HCC. A comparison between the proposed model and traditional statistical studies is reported to demonstrate the superiority of using the machine learning model for evaluating circulating miRNAs as diagnostic markers of HCC. The specificity and sensitivity for differentiation of HCC cases in comparison with the statistical-based method for the first study were 98% against 88% and 99% versus 92%, respectively. The second study revealed the sensitivity and specificity were 97.78% against 90% and 98.89% versus 92.5%, respectively. The third study reported 83.2% against 88.8% and 95.80% versus 92.4%, respectively. Additionally, the results show that circulating miRNA-483-5p, 21, and 155 may be potential new prognostic and early diagnostic biomarkers for HCC.

Boosting Sinh Cosh Optimizer and arithmetic optimization algorithm for improved prediction of biological activities for indoloquinoline derivatives.

Quantitative Structure Activity Relation (QSAR) models are mathematical techniques used to link structural characteristics with biological activities, thus considered a useful tool in drug discovery, hazard evaluation, and identifying potentially lethal molecules. The QSAR regulations are determined by the Organization for Economic Cooperation and Development (OECD). QSAR models are helpful in discovering new drugs and chemicals to treat severe diseases. In order to improve the QSAR model's predictive power for biological activities of naturally occurring indoloquinoline derivatives against different cancer cell lines, a modified machine learning (ML) technique is presented in this paper. The Arithmetic Optimization Algorithm (AOA) operators are used in the suggested model to enhance the performance of the Sinh Cosh Optimizer (SCHO). Moreover, this improvement functions as a feature selection method that eliminates superfluous descriptors. An actual dataset gathered from previously published research is utilized to evaluate the performance of the suggested model. Moreover, a comparison is made between the outcomes of the suggested model and other established methodologies. In terms of pIC50 values for different indoloquinoline derivatives against human MV4-11 (leukemia), human HCT116 (colon cancer), and human A549 (lung cancer) cell lines, the suggested model achieves root mean square error (RMSE) of 0.6822, 0.6787, 0.4411, and 0.4477, respectively. The biological application of indoloquinoline derivatives as possible anticancer medicines is predicted with a high degree of accuracy by the suggested model, as evidenced by these findings.

Experimental and Molecular Docking Studies of Cyclic Diphenyl Phosphonates as DNA Gyrase Inhibitors for Fluoroquinolone-Resistant Pathogens.

DNA gyrase and topoisomerase IV are proven to be validated targets in the design of novel antibacterial drugs. In this study, we report the antibacterial evaluation and molecular docking studies of previously synthesized two series of cyclic diphenylphosphonates (1a-e and 2a-e) as DNA gyrase inhibitors. The synthesized compounds were screened for their activity (antibacterial and DNA gyrase inhibition) against ciprofloxacin-resistant E.coli and Klebsiella pneumoniae clinical isolates having mutations (deletion and substitution) in QRDR region of DNA gyrase. The target compound (2a) that exhibited the most potent activity against ciprofloxacin Gram-negative clinical isolates was selected to screen its inhibitory activity against DNA gyrase displayed IC50 of 12.03 µM. In addition, a docking study was performed with inhibitor (2a), to illustrate its binding mode in the active site of DNA gyrase and the results were compatible with the observed inhibitory potency. Furthermore, the docking study revealed that the binding of inhibitor (2a) to DNA gyrase is mediated and modulated by divalent Mg2+ at good binding energy (-9.08 Kcal/mol). Moreover, structure-activity relationships (SARs) demonstrated that the combination of hydrazinyl moiety in conjunction with the cyclic diphenylphosphonate based scaffold resulted in an optimized molecule that inhibited the bacterial DNA gyrase by its detectable effect in vitro on gyrase-catalyzed DNA supercoiling activity.

Antiviral zinc oxide nanoparticles mediated by hesperidin and in silico comparison study between antiviral phenolics as anti-SARS-CoV-2.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which caused the coronavirus (COVID-19), is the virus responsible for over 69,613,607 million infections and over 1,582,966 deaths worldwide. All treatment measures and protocols were considered to be supportive only and not curative. During this current coronavirus pandemic, searching for pharmaceutical or traditional complementary and integrative medicine to assist with prevention, treatment, and recovery has been advantageous. These phytopharmaceuticals and nutraceuticals can be more economic, available, safe and lower side effects. This is in silico comparison study of ten phenolic antiviral agents against SARS-CoV-2, as well as isolation of the most active metabolite from natural sources. Zinc oxide nanoparticles (ZnO NPs) were also then prepared using these metabolite as a reducing agent. All tested compounds showed predicted anti-SARS-CoV-2 activity. Hesperidin showed the highest docking score, this leads us to isolate it from the orange peels and we confirmed its structure by conventenional spectroscopic analysis. In addition, synthesis of hesperidin zinc oxide nanoparticles was characterized by UV, IR, XRD and TEM. In vitro antiviral activity of hesperidin and ZnO NPs was evaluated against hepatitis A virus as an example of RNA viruses. However, ZnO NPs and hesperidin showed antiviral activity against HAV but ZnO NPs showed higher activity than hesperidin. Thus, hesperidin and its mediated ZnO nanoparticles are willing antiviral agents and further studies against SARS-CoV-2 are required to be used as a potential treatment.

Quantitative Structure-Activity Relationship Model for HCVNS5B inhibitors based on an Antlion Optimizer-Adaptive Neuro-Fuzzy Inference System.

The global prevalence of hepatitis C Virus (HCV) is approximately 3% and one-fifth of all HCV carriers live in the Middle East, where Egypt has the highest global incidence of HCV infection. Quantitative structure-activity relationship (QSAR) models were used in many applications for predicting the potential effects of chemicals on human health and environment. The adaptive neuro-fuzzy inference system (ANFIS) is one of the most popular regression methods for building a nonlinear QSAR model. However, the quality of ANFIS is influenced by the size of the descriptors, so descriptor selection methods have been proposed, although these methods are affected by slow convergence and high time complexity. To avoid these limitations, the antlion optimizer was used to select relevant descriptors, before constructing a nonlinear QSAR model based on the PIC50 and these descriptors using ANFIS. In our experiments, 1029 compounds were used, which comprised 579 HCVNS5B inhibitors (PIC50 < ~14) and 450 non-HCVNS5B inhibitors (PIC50 > ~14). The experimental results showed that the proposed QSAR model obtained acceptable accuracy according to different measures, where [Formula: see text] was 0.952 and 0.923 for the training and testing sets, respectively, using cross-validation, while [Formula: see text] was 0.8822 using leave-one-out (LOO).

A Predictive Model for Toxicity Effects Assessment of Biotransformed Hepatic Drugs Using Iterative Sampling Method.

Measuring toxicity is one of the main steps in drug development. Hence, there is a high demand for computational models to predict the toxicity effects of the potential drugs. In this study, we used a dataset, which consists of four toxicity effects:mutagenic, tumorigenic, irritant and reproductive effects. The proposed model consists of three phases. In the first phase, rough set-based methods are used to select the most discriminative features for reducing the classification time and improving the classification performance. Due to the imbalanced class distribution, in the second phase, different sampling methods such as Random Under-Sampling, Random Over-Sampling and Synthetic Minority Oversampling Technique are used to solve the problem of imbalanced datasets. ITerative Sampling (ITS) method is proposed to avoid the limitations of those methods. ITS method has two steps. The first step (sampling step) iteratively modifies the prior distribution of the minority and majority classes. In the second step, a data cleaning method is used to remove the overlapping that is produced from the first step. In the third phase, Bagging classifier is used to classify an unknown drug into toxic or non-toxic. The experimental results proved that the proposed model performed well in classifying the unknown samples according to all toxic effects in the imbalanced datasets.