Optimizing the resveratrol fragments for novel in silico hepatocellular carcinoma de novo drug design.

Hepatocellular carcinoma (HCC) incidence varies widely around the world and is impacted by factors such as the prevalence of chronic hepatitis B and C infections, alcohol intake, and access to healthcare. The proteins (BRAF_human, VGFR3_human, EGFR_human and UFO_human) play a vital role in hepatocellular carcinoma prognosis, which involves cell proliferation, cell growth, transmission of extracellular signals to the cell nucleus and consequently regulating many other cellular processes. Fostamatinib has been studied for its possible use in the treatment of hepatocellular cancer because it is a more convenient therapy choice for patients and has minor side effects on the human body. However, resveratrol phytochemical has been investigated for its potential use in the prevention and treatment of a wide range of disorders, including cancer, cardiovascular disease, diabetes, and neurological problems due to its frequently antioxidant, anti-inflammatory, and immune-modulating characteristics, which can aid in the prevention of chronic illnesses. This study developed de novo-based fragment-optimized resveratrol (FOR), enhancing therapeutic potential and lowering toxicity. The docking study was performed with four target proteins, and the findings reveal that the vascular endothelial growth factor receptor 3 protein possessed the highest binding energy values of -7.6 kcal/mol with FOR. Additionally, it completely fulfills the criteria of drug-likeliness rules. Thus, FOR proves to be an efficient drug candidate for future in-vivo studies against hepatocellular carcinoma.

Investigating the anti-cancer compounds from Calliandra harrisii for precision medicine in pancreatic cancer via in-silico drug design and GC-MS analysis.

Pancreatic cancer is a fatal illness caused by mutations in multiple genes. Pancreatic cancer damages the organ that helps in digestion, resulting in symptoms including fatigue, bloating, and nausea. The use of medicinal plants has been crucial in the treatment of numerous disorders. The medicinal plant Calliandra Harrisi has been widely exploited for its possibilities in biology and medicine. The current study aimed to assess the biopotential of biologically active substances against pancreatic cancer. The GC-MS data of these phytochemicals from Calliandra Harrisi were further subjected to computational approaches with pancreatic cancer genes to evaluate their potential as therapeutic candidates. Molecular docking analysis revealed that N-[Carboxymethyl] maleamic acid is the leading molecule responsible for protein denaturation inhibition, having the highest binding affinity of 6.8 kJ/mol among all other compounds with KRAS inflammatory proteins. Furthermore, ADMET analysis and Lipinski's rule validation were also performed revealing its higher absorption in the gastrointestinal tract. The results of the hepatotoxicity test demonstrated that phytochemicals are non-toxic, safe to use, and do not cause necrosis, fibrosis, or vacuolar degeneration even at excessive levels. Calliandra Harrisi has phytoconstituents that have a variety of pharmacological uses in consideration.

Artificial Intelligence Assisted Pharmacophore Design for Philadelphia Chromosome-Positive Leukemia with Gamma-Tocotrienol: A Toxicity Comparison Approach with Asciminib.

BCR-ABL1 is a fusion protein as a result of a unique chromosomal translocation (producing the so-called Philadelphia chromosome) that serves as a clinical biomarker primarily for chronic myeloid leukemia (CML); the Philadelphia chromosome also occurs, albeit rather rarely, in other types of leukemia. This fusion protein has proven itself to be a promising therapeutic target. Exploiting the natural vitamin E molecule gamma-tocotrienol as a BCR-ABL1 inhibitor with deep learning artificial intelligence (AI) drug design, this study aims to overcome the present toxicity that embodies the currently provided medications for (Ph+) leukemia, especially asciminib. Gamma-tocotrienol was employed in an AI server for drug design to construct three effective de novo drug compounds for the BCR-ABL1 fusion protein. The AIGT's (Artificial Intelligence Gamma-Tocotrienol) drug-likeliness analysis among the three led to its nomination as a target possibility. The toxicity assessment research comparing AIGT and asciminib demonstrates that AIGT, in addition to being more effective nonetheless, is also hepatoprotective. While almost all CML patients can achieve remission with tyrosine kinase inhibitors (such as asciminib), they are not cured in the strict sense. Hence it is important to develop new avenues to treat CML. We present in this study new formulations of AIGT. The docking of the AIGT with BCR-ABL1 exhibited a binding affinity of -7.486 kcal/mol, highlighting the AIGT's feasibility as a pharmaceutical option. Since current medical care only exclusively cures a small number of patients of CML with utter toxicity as a pressing consequence, a new possibility to tackle adverse instances is therefore presented in this study by new formulations of natural compounds of vitamin E, gamma-tocotrienol, thoroughly designed by AI. Even though AI-designed AIGT is effective and adequately safe as computed, in vivo testing is mandatory for the verification of the in vitro results.