Synthesis of Iron Oxide Nanoparticles from Madhuca indica Plant Extract and Assessment of Their Cytotoxic, Antioxidant, Anti-Inflammatory, and Anti-Diabetic Properties via Different Nanoinformatics Approaches.

Recently, nanobiotechnology has attracted a lot of attention, as it is a rapidly emerging field that is still growing and developing efficient and advanced therapeutic protocols under the umbrella of nanomedicine. It can revolutionize solutions to biomedical problems by developing effective treatment protocols and therapeutics. However, focus and research are still required to make these therapeutics more effective and safer to use. In this study, iron oxide nanoparticles were synthesized from Madhuca indica extract using green synthesis protocols. The nanoparticles were further characterized based on their absorption spectrum, size, structural morphology, and other related parameters. Biological assays were also performed to evaluate biological applications for the synthesized nanoparticles. In silico analysis was performed to assess the druglike properties of synthesized nanoparticles. The results proved an optimized synthesis of the iron oxide nanoparticles with the size of 56 nm confirmed by SEM. The FTIR analysis predicted the presence of nitro and carbonyl groups in the synthesized nanoparticles. The 81% DPPH inhibition confirmed the antioxidant activity, and the 96.20% inhibition of egg albumin protein confirmed the anti-inflamatory activity. Additionally, the 73.26% inhibition of α-amylase, which was more than that of the control used, confirmed the antidiabetic activity. The ADMET analysis confirmed the synthesized nanoparticles as potential therapeutic candidates as well. However, further evaluation for safety concerns is still required to use these FeONPs as potential therapeutic agents. This study can be proved as a significant contribution to the scientific community and a gateway to the future scientists who are willing to work on nanomedicine and nanobiotechnology. ADMET analysis confirmed the synthesized nanoparticles as potential therapeutic candidates as well. However, further evaluation for safety concerns is still required to use these FeONPs and potential therapeutic agents.

In Silico Investigation of a Chimeric IL24-LK6 Fusion Protein as a Potent Candidate Against Breast Cancer.

Targeted delivery of therapeutic anticancer chimeric molecules enhances the efficacy of drug by improving cellular uptake and circulation time. Engineering the molecules to facilitate the specific interaction between chimeric protein and its receptor is critical to elucidate biological mechanism as well as accuracy in modeling of complexes. A theoretically designed novel protein-protein interfaces can serve as a bottom-up method for comprehensive understanding of interacting protein residues. This study was aimed for in silico analyses of a chimeric fusion protein against breast cancer. The amino acid sequences of the interleukin 24 (IL-24) and LK-6 peptide were used to design the chimeric fusion protein via a rigid linker. The secondary and tertiary structures along with physicochemical properties by ProtParam and solubility were predicted using online software. The validation and quality of the fusion protein was confirmed by Rampage and ERRAT2. The newly designed fusion construct has a total length of 179 amino acids. The top-ranked structure from alpha fold2 showed 18.1 KD molecular weight by ProtParam, quality factor of 94.152 by ERRAT, and a valid structure by a Ramachandran plot with 88.5% residues in the favored region. Finally, the docking and simulation studies were performed using HADDOCK and Desmond module of Schrodinger. The quality, validity, interaction analysis, and stability of the fusion protein depict a functional molecule. The fusion gene IL24-LK6 after cloning and expression in a suitable prokaryotic cell might be a useful candidate for developing a novel anticancer therapy.

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.