Recent Developments in 3D QSAR and Molecular Docking Studies of Organic and Nanostructures. In: Leszczynski J, Kaczmarek-kedziera A, Puzyn T, G. Papadopoulos M, Reis H, K. Shukla M, editors. Handbook of Computational Chemistry. Cham: Springer International Publishing; 2017. pp. 2133-61. [DOI: 10.1007/978-3-319-27282-5_54]

Computational identification of PDL1 inhibitors and their cytotoxic effects with silver and gold nanoparticles

Immunotherapy is a promising treatment for cancer that aims to boost the immune system's response to cancer cells. This can be achieved by blocking Programmed cell death protein 1/Programmed death-ligand 1 (PD1/PDL1), which activates T cells. In this work, the aim was to find high-affinity drugs against PDL1 using computational tools and conjugate nanoparticles with them. The cytotoxic activity of the nanoparticle conjugated drugs was then tested. The screening of 100,000 drugs from the ZINC database and FDA-approved drugs was done computationally. The physicochemical properties and toxicity of the drugs were analyzed using SwissADME and ProTox-II, respectively. Silver nanoparticles (AgNPs) and gold nanoparticles (AuNPs) were synthesized using extracts of Catharanthus roseus flowers and Juglans regia shells, respectively. The characterization of AgNPs and AuNPs was performed using UV-Vis spectroscopy, X-ray diffraction (XRD), and Fourier transform infrared spectroscopy (FTIR). Their conjugation with the drugs Irinotecan, Imatinib, and Methotrexate was also confirmed using UV-Vis, FTIR, and Dynamic light scattering (DLS). The top screened drugs were ZINC1098661 and 3 FDA-approved drugs (Irinotecan, Imatinib, and Methotrexate). Docking studies revealed that Irinotecan had the highest binding affinity towards PDL1 when conjugated with silver nanoparticles (AgNPs) and gold nanoparticles (AuNPs). The Irinotecan-PDL1 complex was confirmed as the most stable through molecular dynamics simulations. The result of the methylthiazol tetrazolium (MTT) assay showed that conjugated AgNPs and AuNPs with Irinotecan had a higher toxic effect on the A549 cancer cell line than AgNPs and AuNPs conjugated with Imatinib. This study provides a promising avenue for further investigation and development of nanoparticle-drug conjugates as a potential cancer immunotherapy strategy.

Updating the Pharmacological Effects of α-Mangostin Compound and Unraveling Its Mechanism of Action: A Computational Study Review

α-Mangostin, initially identified in 1855, is a xanthone derivative compound predominantly located in the pericarp of the mangosteen fruit (Garcinia mangostana L). This compound is known for its beneficial properties as an antioxidant and anti-inflammatory agent, still holding promise for potential benefits in other related pathologies. In the investigative process, computational studies have proven highly valuable in providing evidence and initial screening before progressing to preclinical and clinical studies. This review aims to present the pharmacological findings and mechanisms of action of α-mangostin based on computational studies. The compilation of this review is founded on the analysis of relevant articles obtained from PubMed, Scopus, and ScienceDirect databases. The study commences with an elucidation of the physicochemical characteristics, drug-likeness, pharmacokinetics, and toxicity profile of α-mangostin, which demonstrates that α-mangostin complies with the Lipinski's Rule of Five, exhibits favorable profiles of absorption, distribution, metabolism, and excretion, and presents low toxicity. Subsequent investigations have revealed that computational studies employing various software tools including ArgusLab, AutoDock, AutoDock Vina, Glide, HEX, and MOE, have been pivotal to comprehend the pharmacology of α-mangostin. Beyond its well established roles as an antioxidant and anti-inflammatory agent, α-mangostin is now recognized for its pharmacological effects in Alzheimer's disease, diabetes, cancer, chronic kidney disease, chronic periodontitis, infectious diseases, and rheumatoid arthritis. Moreover, α-mangostin is projected to have applications in pain management and as a potent mosquito larvicide. All of these findings are based on the attainment of adequate binding affinity to specific target receptors associated with each respective pathological condition. Consequently, it is anticipated that these findings will serve as a foundation for future scientific endeavours, encompassing both in vitro and in vivo studies, as well as clinical investigations, to better understand the pharmacological effects of α-mangostin.

Identification of novel curcumin derivatives against pancreatic cancer: a comprehensive approach integrating 3D-QSAR pharmacophore modeling, virtual screening, and molecular dynamics simulations

Pancreatic cancer, known as the "silent killer," poses a daunting challenge in cancer therapy. The dysregulation of the PI3Kα signaling pathway in pancreatic cancer has attracted considerable interest as a promising target for therapeutic intervention. In this regard, the use of curcumin derivatives as inhibitors of PI3Kα has emerged, providing a novel and promising avenue for developing effective treatments for this devastating disease. Computational approaches were employed to explore this potential and investigate 58 curcumin derivatives with cytotoxic activity against the Panc-1 cell line. Our approach involved ligand-based pharmacophore modeling and atom-based 3D-QSAR analysis. The resulting QSAR model derived from the best-fitted pharmacophore hypothesis (AAHRR_1) demonstrated remarkable performance with high correlation coefficients (R2) of 0.990 for the training set and 0.977 for the test set. The cross-validation coefficient (Q2) of 0.971 also validated the model's predictive power. Tropsha's recommended criteria, including the Y-randomization test, were employed to ensure its reliability. Furthermore, an enrichment study was conducted to evaluate the model's performance in identifying active compounds. AAHRR_1 was used to screen a curated PubChem database of curcumin-related compounds. Two molecules (CID156189304 and CID154728220) exhibited promising pharmacokinetic properties and higher docking scores than Alpelisib, warranting further investigation. Extensive molecular dynamics simulations provided crucial insights into the conformational dynamics within the binding site, validating their stability and behavior. These findings contribute to our understanding of the potential therapeutic effectiveness of these compounds as PI3Kα inhibitors in pancreatic cancer.Communicated by Ramaswamy H. Sarma.

Updates on drug designing approach through computational strategies: a review

The drug discovery and development (DDD) process in pursuit of novel drug candidates is a challenging procedure requiring lots of time and resources. Therefore, computer-aided drug design (CADD) methodologies are used extensively to promote proficiency in drug development in a systematic and time-effective manner. The point in reference is SARS-CoV-2 which has emerged as a global pandemic. In the absence of any confirmed drug moiety to treat the infection, the science fraternity adopted hit and trial methods to come up with a lead drug compound. This article is an overview of the virtual methodologies, which assist in finding novel hits and help in the progression of drug development in a short period with a specific medicinal solution.

3D-QSAR Studies of 1,2,4-Oxadiazole Derivatives as Sortase A Inhibitors

Sortase A (SrtA) is an enzyme that catalyzes the attachment of proteins to the cell wall of Gram-positive bacterial membrane, preventing the spread of pathogenic bacterial strains. Here, one class of oxadiazole compounds was distinguished as an efficient inhibitor of SrtA via the "S. aureus Sortase A" substrate-based virtual screening. The current study on 3D-QSAR was done by utilizing preparation of the structure in the Schrödinger software suite and an assessment of 120 derivatives with the crystal structure of 1,2,4-oxadiazole which was extracted from the PDB data bank. The docking operation of the best compound in terms of pMIC (pMIC = 2.77) was done to determine the drug likeliness and binding form of 1,2,4-oxadiazole derivatives as antibiotics in the active site. Using the kNN-MFA way, seven models of 3D-QSAR were created and amongst them, and one model was selected as the best. The chosen model based on q 2 (pred_r 2) and R 2 values related to the sixth factor of PLS illustrates better and more acceptable external and internal predictions. Values of crossvalidation (pred_r 2), validation (q 2), and F were observed 0.5479, 0.6319, and 179.0, respectively, for a test group including 24 molecules and the training group including 96 molecules. The external reliability outcomes showed that the acceptable and the selective 3D-QSAR model had a high predictive potential (R 2 = 0.9235) which was confirmed by the Y-randomization test. Besides, the model applicability domain was described successfully to validate the estimation of the model.

Detection of macromolecular inversion-induced structural changes in osteosarcoma cells by FTIR microspectroscopy

Fourier transform infrared (FTIR) microspectroscopy provides a biochemical fingerprint of the cells. In this study, chemical changes in 143B osteosarcoma cells were investigated using FTIR analysis of cancer cells after their treatment with polymeric invertible micellar assemblies (IMAs) and curcumin-loaded IMAs and compared with untreated osteosarcoma cells. A comprehensive principal component analysis (PCA) was applied to analyze the FTIR results and confirm noticeable changes in cell surface chemical structures in the fingerprint regions of 1480-900 cm^-1. The performed clustering shows visible differences for all investigated groups of cancer cells. It is demonstrated that a combination of FTIR microspectroscopy with PCA can be an efficient approach in determining interactions of osteosarcoma cells and drug-loaded polymer micellar assemblies. Graphical abstract.