Deciphering potential inhibitors targeting THI4 of Fusarium solani sp. to combat fungal keratitis: An integrative computational approach

How can machine learning and multiscale modeling benefit ocular drug development?

The eyes possess sophisticated physiological structures, diverse disease targets, limited drug delivery space, distinctive barriers, and complicated biomechanical processes, requiring a more in-depth understanding of the interactions between drug delivery systems and biological systems for ocular formulation development. However, the tiny size of the eyes makes sampling difficult and invasive studies costly and ethically constrained. Developing ocular formulations following conventional trial-and-error formulation and manufacturing process screening procedures is inefficient. Along with the popularity of computational pharmaceutics, non-invasive in silico modeling & simulation offer new opportunities for the paradigm shift of ocular formulation development. The current work first systematically reviews the theoretical underpinnings, advanced applications, and unique advantages of data-driven machine learning and multiscale simulation approaches represented by molecular simulation, mathematical modeling, and pharmacokinetic (PK)/pharmacodynamic (PD) modeling for ocular drug development. Following this, a new computer-driven framework for rational pharmaceutical formulation design is proposed, inspired by the potential of in silico explorations in understanding drug delivery details and facilitating drug formulation design. Lastly, to promote the paradigm shift, integrated in silico methodologies were highlighted, and discussions on data challenges, model practicality, personalized modeling, regulatory science, interdisciplinary collaboration, and talent training were conducted in detail with a view to achieving more efficient objective-oriented pharmaceutical formulation design.

Discovery of novel potential inhibitors of TMPRSS2 and Mpro of SARS-CoV-2 using E-pharmacophore and docking-based virtual screening combined with molecular dynamic and quantum mechanics

The pandemic of coronavirus disease is caused by the SARS-CoV-2 which is considered a global health issue. The main protease of COVID 19 (Mpro) has an important role in viral multiplication in the host cell. Inhibiting Mpro is a novel approach to drug discovery and development. Also, transmembrane serine proteases (TMPSS2) facilitate viral activation by cleavage S glycoproteins, thus considered one of the essential host factors for COVID-19 pathogenicity. Computational tools were widely used to reduce time and costs in search of effective inhibitors. A chemical library that contains over two million molecules was virtually screened against TMPRSS2. Also, XP docking for the top hits was screened against (Mpro) to identify dual-target inhibitors. Furthermore, MM-GBSA and predictive ADMET were performed. The top hits were further studied through density functional theory (DFT) calculation and showed good binding to the active sites. Moreover, molecular dynamics (MD) for the top hits were performed which gave information about the stability of the protein-ligand complex during the simulation period. This study has led to the discovery of potential dual-target inhibitors Z751959696, Z751954014, and Z56784282 for COVID-19 with acceptable pharmacokinetic properties. The outcome of this study can participate in the development of novel inhibitors to defeat SARS-CoV-2.Communicated by Ramaswamy H. Sarma.

New 24-Membered Macrolactins Isolated from Marine Bacteria Bacillus siamensis as Potent Fungal Inhibitors against Sugarcane Smut

Sugarcane smut, caused by Sporisorium scitamineum, is one of the most devastating fungal diseases affecting sugarcane worldwide. To develop a potent sugarcane smut fungicide, secondary metabolites of marine-derived Bacillus siamensis were isolated and screened for inhibitory activities, which led to the discovery of five new 24-membered macrolactins, bamemacrolactins A-E (1-5), with 3 being the most potent inhibitor. The antifungal mechanism of 3 was studied by assessing its effects on mycelial morphology and the cell wall. Differential proteomics were used to analyze proteins in S. scitamineum upon treatment with bamemacrolactin C and to elucidate its antifungal mechanism. A total of 533 differentially expressed proteins were found. After the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses, eight target proteins were selected, and their functions were discussed. Six of the eight proteins were reported as antifungal targets. The target proteins are involved in the oxidative phosphorylation pathway. Therefore, the potent inhibition of S. scitamineum by compound 3 is most likely through oxidative phosphorylation and targeting a series of enzymes.

Membrane dynamics simulation and virtual screening reveals potential dual natural inhibitors of endothelin receptors for targeting glaucomatous condition

Glaucoma is the second leading cause of blindness in the world and is characterized by the loss of retinal ganglion cells (RGC) over a period of time, leading to complete blindness. Recently, endothelin has been identified as an important factor that influences intraocular pressure IOP, OBF, and direct RGC damage. Targeting the endothelin receptor signaling pathway in glaucoma is considered to be highly beneficial, as it can effectively modulate IOP, OBF, and RGC damage, the key factors which are essential to modulate the disease progression holistically. Currently, synthetic drugs like Bosentan, BQ-123, and prostaglandin analogues are available as endothelin receptor antagonists, which are extensively used in the treatment of cardiovascular and other conditions like systemic hypertension. However, the usage of these drugs in glaucoma is limited due to toxicity and poor bioavailability in the ocular milieu. Thus, there is a need for potential natural compounds as endothelin receptor antagonists that acts as dual inhibitors by targeting both ETA and ETB and are highly efficient with the least toxicity. Hence, this study is intended to prioritize endothelin receptor antagonists by structural bioinformatics approaches involving molecular modeling, molecular dynamics, and molecular docking studies. Subsequently, High throughput virtual screening (HTVS) vs. Natural compound databases targeting the optimal binding sites of both ETA and ETB. Following this, the common hits were subjected to binding free energy calculations (MMGBSA) and ADMETox analysis. Finally, the most potential hits were analyzed for MD based binding stability analysis and binding free energy. Similarly, the known synthetic inhibitors were also docked to the receptors and the results were analyzed. From this study, it was inferred that among the natural compounds dataset (8929 compounds), only 4 common compounds were identified as hits. Among these, only one compound ST075640 surpassed all the prioritization criteria including MMGBSA, ADMETox prediction, dual inhibitory potential (ETA & ETB), and also in structural comparative analysis with bosentan it showed similar efficiency. Thus, the validated hit shall prove to be effective in modulating endothelin mediated IOP, OBF, and RGC damage in glaucomatous condition.