Spectroscopic, quantum chemical and molecular docking studies on 1-amino-5-chloroanthraquinone: A targeted drug therapy for thyroid cancer

Toxicity of anthraquinone derivatives in relation to non-linear optical properties and electron correlation

1,4-Dialkylamino -5,8-dihydroxy anthraquinones are investigated using density functional theory (DFT) and time-dependent DFT (TD-DFT) for their growth inhibitory potential. The frontier molecular orbital shows that the electron density is located at the anthraquinone core and at the substituents NH and OH in both HOMO as well as in LUMO. The chemical potential and electrophilicity index showed a direct relation, while hardness and hyperhardness had an inverse association with an energy gap. The results of the molecular docking analysis revealed that the anthraquinone molecules have a high affinity for the primary targets of the DNA topoisomerase IIα enzyme. The docking results showed good binding ability with extremely energetically stable scores ranging from -8.9 to -7.6 kcal/mol. Electron correlation descriptors showed a direct link with NLO properties and toxicity.

Microstructure and digestive behaviors of inner, middle, and outer layers of pork during heating

To investigate the effects of heat treatment on the microstructure and digestive behaviors of pork, meat samples were subjected to a 100 °C water bath for 26 min. The inner, medium, and outer layers were assigned and analyzed according to the temperature gradient. Compared to the raw samples, significant changes were observed in the microscopic structure of pork. As the temperature increased, the myofibrillar structure of pork underwent increasingly severe damage and the moisture content decreased significantly (P < 0.05). Moreover, differential peptides were identified in digested products of the inner, middle, and outer layers of cooked pork, which are mainly derived from the structural proteins of pork. The outcomes of molecular docking indicated that a greater number of hydrogen bonds were formed between myosin and the digestive enzyme in the inner layer, rather than other parts, contributing to the transformation of digestive behaviors.

Synthesis, crystal structure, biological and docking studies of 5-hydroxy-2-{[(2-methylpropyl)iminio]methyl}phenolate

Background: Schiff base compounds are potential drugs.Results: A Schiff base compound prepared by condensing 2,4-dihydroxy benzaldehyde and isobutylamine was characterized for structure, thermal, physicochemical and biological properties. The keto-enol tautomerism and azomethine functionality enhances electron delocaliZation and biological activity. The compound showed good antibacterial and antifungal activity at 40 μg/ml against bacteria such as Escherichia coli and Staphylococcus aureus and fungi like Candida albicans and Candida tropicalis. The docking study exhibits a moderate binding affinity for the GyrB protein in E. coli with a binding energy of -4.26 kcal/mol.Conclusion: The compound exhibits enhanced biological activity and suppression of cell growth at concentrations as low as 30 μg/ml. The IC50 for MFC-7 was found to be 41.5 μg/ml.

Artificial intelligence for small molecule anticancer drug discovery

INTRODUCTION

The transition from conventional cytotoxic chemotherapy to targeted cancer therapy with small-molecule anticancer drugs has enhanced treatment outcomes. This approach, which now dominates cancer treatment, has its advantages. Despite the regulatory approval of several targeted molecules for clinical use, challenges such as low response rates and drug resistance still persist. Conventional drug discovery methods are costly and time-consuming, necessitating more efficient approaches. The rise of artificial intelligence (AI) and access to large-scale datasets have revolutionized the field of small-molecule cancer drug discovery. Machine learning (ML), particularly deep learning (DL) techniques, enables the rapid identification and development of novel anticancer agents by analyzing vast amounts of genomic, proteomic, and imaging data to uncover hidden patterns and relationships.

AREA COVERED

In this review, the authors explore the important landmarks in the history of AI-driven drug discovery. They also highlight various applications in small-molecule cancer drug discovery, outline the challenges faced, and provide insights for future research.

EXPERT OPINION

The advent of big data has allowed AI to penetrate and enable innovations in almost every stage of medicine discovery, transforming the landscape of oncology research through the development of state-of-the-art algorithms and models. Despite challenges in data quality, model interpretability, and technical limitations, advancements promise breakthroughs in personalized and precision oncology, revolutionizing future cancer management.

Structure relationship of non-covalent interactions between lotus seedpod oligomeric procyanidins and glycated casein hydrolysate during digestion

Procyanidin-amino acid interactions during transmembrane transport cause changes in the structural and physical properties of peptides, which limits further absorption of oligopeptide-advanced glycation end products (AGEs). In this study, glycated casein hydrolysates (GCSHs) were employed to investigate the structure and interaction mechanism of GCSH with lotus seedpod oligomeric procyanidin (LSOPC) complexes in an intestinal environment. LSOPC can interact with GCSH under certain conditions to form hydrogen bonds and hydrophobic interactions to form GCSH-LSOPC complexes. Results showed that procyanidin further leads to the transformation of a GCSH secondary structure and the increase of surface hydrophobicity (H0). The strongest non-covalent interaction between GCSH and (-)-epigallocatechin gallate (EGCG) was due to the polyhydroxy structure of EGCG. Binding site analysis showed that EGCG binds to the internal cavity of P1 to maintain the relative stability of the binding conformation. The antioxidant capacity of GCSH was remarkably elevated by GCSH-LSOPC. This study will provide a new reference for the accurate control of oligopeptide-AGEs absorption by LSOPC in vivo.

Application of biosensors in cancers, an overview

The deadliest disease in the world, cancer, kills many people every year. The early detection is the only hope for the survival of malignant cancer patients. As a result, in the preliminary stages of, the diagnosis of cancer biomarkers at the cellular level is critical for improving cancer patient survival rates. For decades, scientists have focused their efforts on the invention of biosensors. Biosensors, in addition to being employed in other practical scenarios, can essentially function as cost effective and highly efficient devices for this purpose. Traditional cancer screening procedures are expensive, time-consuming, and inconvenient for repeat screenings. Biomarker-based cancer diagnosis, on the other hand, is rising as one of the most potential tools for early detection, disease progression monitoring, and eventual cancer treatment. As Biosensor is an analytical device, it allows the selected analyte to bind to the biomolecules being studied (for example RNA, DNA, tissue, proteins, and cells). They can be divided based on the kind of biorecognition or transducer elements on the sensor. Most biosensor analyses necessitate the analyte being labeled with a specific marker. In this review article, the application of distinct variants of biosensors against cancer has been described.

Virtual screening, molecular docking, molecular dynamics and quantum chemical studies on (2-methoxy-4-prop-2-enylphenyl) N-(2-methoxy-4-nitrophenyl) carbamate: a novel inhibitor of hepatocellular carcinoma

HDAC protein is associated with hepatocellular carcinoma. Different medicinal plants were selected for this study to analyze the inhibitory efficacy against the target protein, HDAC. Using virtual screening, we filtered out the best compounds, and molecular docking (XP) was carried out for the top compounds which filtered out. The molecular docking results showed that the title compound (2-methoxy-4-prop-2-enylphenyl) N-(2-methoxy-4-nitrophenyl) carbamate (MEMNC) has the highest docking score of about -7.7 kcal/mol against the targeted protein histone deacetylase (HDAC) compared with the other selected phytocompounds. From the molecular dynamics analysis, the RMSD and RMSF plots depicted the overall stability of the protein-ligand complex. Toxicity properties show the acceptable range of various kinds of toxicity that were predicted using the ProTox-II server. In addition, DFT quantum chemical and physicochemical properties of the MEMNC molecule were reported. Initially, the molecular structure of the MEMNC molecule was optimized and harmonic vibrational frequencies were calculated using DFT/B3LYP method with a cc-pVTZ basis set using Gaussian 09 program. The calculated vibrational wavenumber values were assigned based on Potential Energy Distribution calculations using the VEDA 4.0 program and correlated well with the previous literature values. The molecule has bioactivity as a result of intramolecular charge transfer interactions, as demonstrated by frontier molecular orbital analysis. Molecular electrostatic potential surface and Mulliken atomic charge distribution analyses validate the reactive sites of the molecule. Thus, the title compound can be used as a potential inhibitor of HDAC protein, which paves the way for designing novel drugs to treat Hepatocellular carcinoma.Communicated by Ramaswamy H. Sarma.

Synthesis, spectroscopic characterization, molecular docking and in vitro cytotoxicity investigations on 8-Amino-6-Methoxy Quinolinium Picrate: a novel breast cancer drug

Density Functional Theory (DFT) studies of the 8-Amino-6-Methoxy Quinolinium Picrate (8A6MQP) molecule have been carried out with extensive and accurate investigations of detailed vibrational and spectroscopic investigations as well as validated experimentally. The 8A6MQP sample was synthesized and characterized using FT-IR, FT-Raman, FT-NMR and UV-Vis spectroscopic techniques. Subsequently, the optimized molecular structure and harmonic resonance frequencies of the molecule were computed based on DFT/B3LYP method with a 6-311G++(d,p) basis set using the Gaussian 09 program. The experimental and calculated vibrational wavenumbers were assigned. The absorption spectrum of the molecule was computed in the liquid phase (ethanol), which exhibits n to л* electronic transition and compared with the observed UV-Vis spectrum. Frontier molecular orbital analysis shows the molecular reactivity and kinetic stability of the molecule. The Mulliken atomic charge distribution and molecular electrostatic potential surface analysis of the molecule validate the reactive site of the molecule. The natural bond orbital analysis proves the bioactivity of the molecule. Molecular docking analysis indicates that the 8A6MQP molecule inhibits the action of DNA topoisomerase 2-alpha protein, which is associated with breast cancer. In addition, the in vitro cytotoxicity analysis of the 8A6MQP molecule against human cervical cancer cell lines (ME180) and human breast cancer cell lines (MDA MB 231) were determined by MTT assay, which evidences that the title molecule exhibits higher inhibition against the breast cancer cell lines compared to that of cervical cancer cell lines. Hence, the present study paves the way for the development of novel drugs in the treatment of breast cancer.Communicated by Ramaswamy H. Sarma.

A comprehensive review on recent approaches for cancer drug discovery associated with artificial intelligence

Through the revolutionization of artificial intelligence (AI) technologies in clinical research, significant improvement is observed in diagnosis of cancer. Utilization of these AI technologies, such as machine and deep learning, is imperative for the discovery of novel anticancer drugs and improves existing/ongoing cancer therapeutics. However, building a model for complicated cancers and their types remains a challenge due to lack of effective therapeutics that hinder the establishment of effective computational tools. In this review, we exploit recent approaches and state-of-the-art in implementing AI methods for anticancer drug discovery, and discussed how advances in these applications need to be considered in the current cancer therapeutics. Considering the immense potential of AI, we explore molecular docking and their interactions to recognize metabolic activities that support drug design. Finally, we highlight corresponding strategies in applying machine and deep learning methods to various types of cancer with their pros and cons.

Anti-cancer activity of human gastrointestinal bacteria

Malignant neoplasm is one of the most incurable diseases among inflammatory diseases. Researchers have been studying for decades to win over this lethal disease and provide the light of hope to humankind. The gastrointestinal bacteria of human hold a complex ecosystem and maintain homeostasis. One hundred trillion microbes are residing in the gastrointestinal tract of human. Disturbances in the microbiota of human's gastrointestinal tract can create immune response against inflammation and also can develop diseases, including cancer. The bacteria of the gastrointestinal tract of human can secrete a variety of metabolites and bioproducts which aid in the preservation of homeostasis in the host and gut. During pathogenic dysbiosis, on the other hand, numerous microbiota subpopulations may increase and create excessive levels of toxins, which can cause inflammation and cancer. Furthermore, the immune system of host and the epithelium cell can be influenced by gut microbiota. Probiotics, which are bacteria that live in the gut, have been protected against tumor formation. Probiotics are now studied to see if they can help fight dysbiosis in cancer patients undergoing chemotherapy or radiotherapy because of their capacity to maintain gut homeostasis. Countless numbers of gut bacteria have demonstrated anti-cancer efficiency in cancer treatment, prevention, and boosting the efficiency of immunotherapy. The review article has briefly explained the anti-cancer immunity of gut microbes and their application in treating a variety of cancer. This review paper also highlights the pre-clinical studies of probiotics against cancer and the completed and ongoing clinical trials on cancers with the two most common and highly effective probiotics Lactobacillus and Bacillus spp.