Molecular docking, dynamics simulation and ADMET prediction of Acetaminophen and its modified derivatives based on quantum calculations

Intensifying the sonochemical degradation of hydrophilic organic contaminants by organic and inorganic additives

The sonochemical system is highly effective at degrading hydrophobic substances but has limitations when it comes to eliminating hydrophilic compounds. This study examines the impact of organic and inorganic additives on improving the sonochemical degradation of hydrophilic pollutants in water. The effects of adding an organic substance (CCl4) and two inorganic ions (Fe2+ and HCO3-) were tested. The treatment was focused on a representative hydrophilic antibiotic, cefadroxil (CDX). Initially, the sonodegradation of CDX without additives was assessed and compared with two reference pollutants more hydrophobic than CDX: dicloxacillin (DCX) and methyl orange (MO). The results highlighted the limitations of ultrasound alone in degrading CDX. Subsequently, the impact of the additives on enhancing the removal of this recalcitrant pollutant was evaluated at two frequencies (375 and 990 kHz). A significant improvement in the CDX degradation was observed with the presence of CCl4 and Fe2+ at both frequencies. Increasing CCl4 concentration led to greater CDX elimination, whereas a high Fe2+ concentration had detrimental effects. To identify the reactive sites on CDX towards the species generated with the additives, theoretical calculations (i.e. Fukui indices and HOMO-LUMO gaps) were performed. These analyses indicated that the β-lactam and dihydrothiazine rings on CDX are highly reactive towards oxidizing species. This research enhances our understanding of the relationship between the structural characteristics of contaminants and the sonochemical frequency in the action of additives having diverse nature.

Computational Exploration of Potential Pharmacological Inhibitors Targeting the Envelope Protein of the Kyasanur Forest Disease Virus

The limitations of the current vaccination strategy for the Kyasanur Forest Disease virus (KFDV) underscore the critical need for effective antiviral treatments, highlighting the crucial importance of exploring novel therapeutic approaches through in silico drug design. Kyasanur Forest Disease, caused by KFDV, is a tick-borne disease with a mortality of 3-5% and an annual incidence of 400 to 500 cases. In the early stage of infection, the envelope protein plays a crucial role by facilitating host-virus interactions. The objective of this research is to develop effective antivirals targeting the envelope protein to disrupt the virus-host interaction. In line with this, the 3D structure of the envelope protein was modeled and refined through molecular modeling techniques, and subsequently, ligands were designed via de novo design and pharmacophore screening, yielding 12 potential hits followed by ADMET analysis. The top five candidates underwent geometry optimization and molecular docking. Notably, compounds L4 (SA28) and L3 (CNP0247967) are predicted to have significant binding affinities of -8.91 and -7.58 kcal/mol, respectively, toward the envelope protein, based on computational models. Both compounds demonstrated stability during 200 ns molecular dynamics simulations, and the MM-GBSA binding free-energy values were -85.26 ± 4.63 kcal/mol and -66.60 ± 2.92 kcal/mol for the envelope protein L3 and L4 complexes, respectively. Based on the computational prediction, it is suggested that both compounds have potential as drug candidates for controlling host-virus interactions by targeting the envelope protein. Further validation through in-vitro assays would complement the findings of the present in silico investigations.

Modification of ibuprofen to improve the medicinal effect; structural, biological, and toxicological study

Ibuprofen is classified as a non-steroidal anti-inflammatory drug (NSAID) that is employed as an initial treatment option for its non-steroidal anti-inflammatory, pain-relieving, and antipyretic properties. However, Ibuprofen is linked to specific well-known gastrointestinal adverse effects like ulceration and gastrointestinal bleeding. It has been linked to harmful effects on the liver, kidney, and heart. The purpose of the study is to create novel and potential IBU analogue with reduced side effects with the enhancement of their medicinal effects, so as to advance the overall safety profile of the drug. The addition of some novel functional groups including CH3, F, CF3, OCF3, Cl, and OH at various locations in its core structure suggestively boost the chemical as well as biological action. The properties of these newly designed structures were analyzed through chemical, physical, and spectral calculations using Density Functional Theory (DFT) and time-dependent DFT through B3LYP/6-31 g (d,p) basis set for geometry optimization. Molecular docking and non-bonding interaction studies were conducted by means of the human prostaglandin synthase protein (PDB ID: 5F19) to predict binding affinity, interaction patterns, and the stability of the protein-drug complex. Additionally, ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) and PASS (Prediction of Activity Spectra for Substances) predictions were employed to evaluate the pharmacokinetic and toxicological properties of these structures. Importantly, most of the analogues displayed reduced hepatotoxicity, nephrotoxicity, and carcinogenicity in comparison to the original drug. Moreover, molecular docking analyses indicated improved medicinal outcomes, which were further supported by pharmacokinetic calculations. Together, these findings suggest that the modified structures have reduced adverse effects along with improved therapeutic action compared to the parent drug.

Simple Fluorescence Sensing Approach for Selective Detection of Fe3+ Ions: Live-Cell Imaging and Logic Gate Functioning

A pyrene-based fluorescent chemosensor APSB [N-(pyrene-1-ylmethylene) anthracen-2-amine] was designed and developed by a simple condensation reaction between pyrene carboxaldehyde and 2-aminoanthracene. The APSB fluorescent sensor selectively binds Fe³⁺ in the presence of other metal ions. Apart from this, APSB shows high selectivity and sensitivity toward Fe³⁺ ion detection. The detection limit for APSB was 1.95 nM, and the binding constant (K _b) was obtained as 8.20 × 10⁵ M^-1 in DMSO/water (95/5, v/v) medium. The fluorescence quantum yields for APSB and APSB-Fe³⁺ were calculated as 0.035 and 0.573, respectively. The function of this fluorescent sensor APSB can be explained through the photo-induced electron transfer mechanism which was further proved by density functional theory studies. Finally, a live-cell image study of APSB in HeLa cells was also carried out to investigate the cell permeability of APSB and its efficiency for selective detection of Fe³⁺ in living cells.

N-Formylsaccharin: A Sweet(able) Formylating Agent in Mechanochemistry

The acylation of amines has always attracted a deep interest as a synthetic route due to its high versatility in organic chemistry and biochemical processes. The purpose of this article is to present a mechanochemical acylation procedure based on the use of acyl-saccharin derivatives, namely N-formylsaccharin, N-acetylsaccharin, and N-propionylsaccharin. This protocol furnishes a valuable solvent-free alternative to the existing processes and aims to be highly beneficial in multi-step procedures due to its rapid and user-friendly workup.

Characterization, molecular modeling and pharmacology of some 2́-hydroxychalcone derivatives as SARS-CoV-2 inhibitor

This work presented the microwave assisted synthesis of six new 2́-hydroxychalcones and their characterization based on FTIR, UV-Vis, ¹H NMR, and mass spectral analysis. Quantum chemical studies confirmed the structures of prepared chalcones. Antioxidant, in vitro antimicrobial and in silico antiviral studies have been performed to evaluate their biological performance. Results of molecular docking of prepared 2́-hydroxychalcones against SARS-CoV-2 (7BQY) main protease disclosed their inhibition which is comparable to standard, remdesivir and better than hydroxychloroquine (HCQ). ADMET prediction revealed them to be non-carcinogenic and relatively safe.

Artificial Intelligence, Big Data and Machine Learning Approaches in Precision Medicine & Drug Discovery

Artificial Intelligence revolutionizes the drug development process that can quickly identify potential biologically active compounds from millions of candidate within a short period. The present review is an overview based on some applications of Machine Learning based tools, such as GOLD, Deep PVP, LIB SVM, etc. and the algorithms involved such as support vector machine (SVM), random forest (RF), decision tree and Artificial Neural Network (ANN), etc. at various stages of drug designing and development. These techniques can be employed in SNP discoveries, drug repurposing, ligand-based drug design (LBDD), Ligand-based Virtual Screening (LBVS) and Structure- based Virtual Screening (SBVS), Lead identification, quantitative structure-activity relationship (QSAR) modeling, and ADMET analysis. It is demonstrated that SVM exhibited better performance in indicating that the classification model will have great applications on human intestinal absorption (HIA) predictions. Successful cases have been reported which demonstrate the efficiency of SVM and RF models in identifying JFD00950 as a novel compound targeting against a colon cancer cell line, DLD-1, by inhibition of FEN1 cytotoxic and cleavage activity. Furthermore, a QSAR model was also used to predict flavonoid inhibitory effects on AR activity as a potent treatment for diabetes mellitus (DM), using ANN. Hence, in the era of big data, ML approaches have been evolved as a powerful and efficient way to deal with the huge amounts of generated data from modern drug discovery to model small-molecule drugs, gene biomarkers and identifying the novel drug targets for various diseases.

Structural modification of aspirin to design a new potential cyclooxygenase (COX-2) inhibitors

Aspirin (Asp) is one of the most important and ancient member of nonsteroidal anti-inflammatory drugs (NSAID), commonly used in medication of fever, pain and inflammation. It can inhibit the synthesis of prostaglandin by blocking the cyclooxygenase (COX). Attempts have been taken to analyze aspirin together with some of its modified derivatives applying quantum mechanical calculations in order to compare their physicochemical and biochemical properties. Density functional theory (DFT) with B3LYP/6-31G (d, p) basis set has been employed to elucidate their thermal, molecular orbital, equilibrium geometrical properties in gas phase. Molecular docking and nonbonding interactions have been performed against human cyclooxygenase-2 protein 5F1A to investigate the binding affinity and mode(s) of newly designed aspirin derivatives. ADMET prediction has been utilized to compare the absorption, metabolism, and carcinogenic properties of new derivatives with parent drug (Asp). Thermal and geometrical results support the thermochemical stability and equilibrium geometry of all the structures. From the molecular docking simulation, most of the derivatives exhibited better binding affinity than parent drug (Asp) with the receptor protein (5F1A). ADMET prediction disclosed the improved pharmacokinetic properties with lower acute oral toxicity of some derivatives. Based on quantum chemical, molecular docking and ADMET analysis, this investigation can be useful to understand the physicochemical and biochemical/biological activities of Asp and its modified derivatives to search a new antipyretic analgesic drug.