In Silico Prediction of Human Organ Toxicity via Artificial Intelligence Methods

Advancing toxicity studies of per- and poly-fluoroalkyl substances (pfass) through machine learning: Models, mechanisms, and future directions

Perfluorinated and perfluoroalkyl substances (PFASs), encompassing a vast array of isomeric chemicals, are recognized as typical emerging contaminants with direct or potential impacts on human health and the ecological environment. With the complex and elusive toxicological profiles of PFASs, machine learning (ML) has been increasingly employed in their toxicity studies due to its proficiency in prediction and data analytics. This integration is poised to become a predominant trend in environmental toxicology, propelled by the swift advancements in computational technology. This review diligently examines the literature to encapsulate the varied objectives of employing ML in the toxicity studies of PFASs: (1) Utilizing ML to establish Quantitative Structure-Activity Relationship (QSAR) models for PFASs with diverse toxicity endpoints, facilitating the targeted toxicity prediction of unidentified PFASs; (2) Investigating and substantiating the Adverse Outcome Pathway (AOP) through the synergy of ML and traditional toxicological methods, with this refining the toxicity assessment framework for PFASs; (3) Dissecting and elucidating the features of established ML models to advance Open Research into the toxicity of PFASs, with a primary focus on determinants and mechanisms. The discourse extends to an in-depth examination of ML studies, segregating findings based on their distinct application trajectories. Given that ML represents a nascent paradigm within PFASs research, this review delineates the collective challenges encountered in the ML-mediated study of PFAS toxicity and proffers strategic guidance for ensuing investigations.

Bioactive Metal-Organic Frameworks as a Distinctive Platform to Diagnosis and Treat Vascular Diseases

Vascular diseases (VDs) pose the leading threat worldwide due to high morbidity and mortality. The detection of VDs is commonly dependent on individual signs, which limits the accuracy and timeliness of therapies, especially for asymptomatic patients in clinical management. Therefore, more effective early diagnosis and lesion-targeted treatments remain a pressing clinical need. Metal-organic frameworks (MOFs) are porous crystalline materials formed by the coordination of inorganic metal ions and organic ligands. Due to their unique high specific surface area, structural flexibility, and functional versatility, MOFs are recognized as highly promising candidates for diagnostic and therapeutic applications in the field of VDs. In this review, the potential of MOFs to act as biosensors, contrast agents, artificial nanozymes, and multifunctional therapeutic agents in the diagnosis and treatment of VDs from the clinical perspective, highlighting the integration between clinical methods with MOFs is generalized. At the same time, multidisciplinary cooperation from chemistry, physics, biology, and medicine to promote the substantial commercial transformation of MOFs in tackling VDs is called for.

Anti-virulence potential of patuletin, a natural flavone, against Staphylococcus aureus: In vitro and In silico investigations

Staphylococcus aureus is a highly prevalent and aggressive human pathogen causing a wide range of infections. This study aimed to explore the potential of Patuletin, a rare natural flavone, as an anti-virulence agent against S. aureus. At a sub-inhibitory concentration (1/4 MIC), Patuletin notably reduced biofilm formation by 27 % and 23 %, and decreased staphyloxanthin production by 53 % and 46 % in Staphylococcus aureus isolate SA25923 and clinical isolate SA1, respectively. In order to gain a more comprehensive understanding of the in vitro findings, several in silico analyses were conducted. Initially, a 3D-flexible alignment study demonstrated a favorable structural similarity between Patuletin and B70, the co-crystallized ligand of CrtM, an enzyme that plays a pivotal role in the biosynthesis of staphyloxanthin. Molecular docking highlighted the strong binding of Patuletin to the active site of CrtM, with a high affinity of -20.95 kcal/mol. Subsequent 200 ns molecular dynamics simulations, along with MM-GBSA, ProLIF, PLIP, and PCAT analyses, affirmed the stability of the Patuletin-CrtM complex, revealing no significant changes in CrtM's structure upon binding. Key amino acids crucial for binding were also identified. Collectively, this study showcased the effective inhibition of CrtM activity by Patuletin in silico and its attenuation of key virulence factors in vitro, including biofilm formation and staphyloxanthin production. These findings hint at Patuletin's potential as a valuable therapeutic agent, especially in combination with antibiotics, to counter antibiotic-resistant Staphylococcus aureus infections.

The Trends and Future Prospective of In Silico Models from the Viewpoint of ADME Evaluation in Drug Discovery

Drug discovery and development are aimed at identifying new chemical molecular entities (NCEs) with desirable pharmacokinetic profiles for high therapeutic efficacy. The plasma concentrations of NCEs are a biomarker of their efficacy and are governed by pharmacokinetic processes such as absorption, distribution, metabolism, and excretion (ADME). Poor ADME properties of NCEs are a major cause of attrition in drug development. ADME screening is used to identify and optimize lead compounds in the drug discovery process. Computational models predicting ADME properties have been developed with evolving model-building technologies from a simplified relationship between ADME endpoints and physicochemical properties to machine learning, including support vector machines, random forests, and convolution neural networks. Recently, in the field of in silico ADME research, there has been a shift toward evaluating the in vivo parameters or plasma concentrations of NCEs instead of using predictive results to guide chemical structure design. Another research hotspot is the establishment of a computational prediction platform to strengthen academic drug discovery. Bioinformatics projects have produced a series of in silico ADME models using free software and open-access databases. In this review, we introduce prediction models for various ADME parameters and discuss the currently available academic drug discovery platforms.