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.
The purpose of this study was to extensively explore in vitro protein binding to [Ru (NO)(Et2NpyS4)]Br (RuNOTSP) and its ligand (TSP), correlate the findings with antibacterial and cytotoxic effects, and try to answer the question: Is the metal center always important for binding? With the aid of stopped‐flow and other spectroscopic and computational methods, the interaction between RuNOTSP and TSP and bovine serum albumin (BSA) was studied and a mechanism was proposed. From UV–Vis and fluorescence experiments, a static quenching mechanism and binding constants at a single site demonstrated the higher stability of the RuNOTSP‐BSA system at 298 K compared with TSP‐BSA. Stopped‐flow experiments indicated that binding of RuNOTSP and TSP to BSA is accomplished mainly via a two‐step mechanism: a fast second‐order binding followed by a slow first‐order isomerization process. The first reaction step is reversible with coordinate affinity Ka1 190 (RuNOTSP) and 50 M−1 (TSP). The second step for RuNOTSP is a reversible reaction with Ka2 of 76.4 M−1, whereas an irreversible step that was observed with a K2 of 0.18 M−1 for TSP indicates that TSP‐BSA is kinetically more stable than RuNOTSP‐BSA. The results revealed that the ruthenium center not only improves reaction rate through coordination affinity but also changes the binding process. Mode and strength of interaction of RuNOTSP and TSP with protein have also been supported by molecular docking and showed that RuNOTSP is located in IA pocket (Trp134) with a binding affinity (−7.27 kcal/mol), a slightly lower than TSP (−8.05 kcal/mol), and these results are in agreement with the binding constants. Furthermore, antibacterial testing revealed that RuNOTSP and TSP had high antibacterial activity against both Gram‐positive and Gram‐negative bacteria. They are also highly cytotoxic to HepG2 human liver cancer cells. Because the TSP‐BSA complex is kinetically more stable than that of RuNOTSP, the former illustrated better antibacterial and cytotoxic activities.
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.
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 was 0.952 and 0.923 for the training and testing sets, respectively, using cross-validation, while was 0.8822 using leave-one-out (LOO).
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.
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.
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