A machine learning-integrated stepwise method to discover novel anti-obesity phytochemicals that antagonize the glucocorticoid receptor

Artificial intelligence in anti-obesity drug discovery: unlocking next-generation therapeutics

Obesity, a multifactorial disease linked to severe health risks, requires innovative treatments beyond lifestyle changes and current medications. Existing anti-obesity drugs face limitations regarding efficacy, side effects, weight regain and high costs. Artificial intelligence (AI) is emerging as a pivotal tool in drug discovery, expediting the identification of novel drug candidates and optimizing treatment strategies. This review examines AI's potential in developing next-generation anti-obesity therapeutics, with a focus on glucagon-like peptide-1 receptor agonists (GLP-1 RAs) and their role in discovering anti-obesity peptides. Additionally, it explores integration challenges and offers future perspectives on leveraging AI to reshape the landscape of anti-obesity drug discovery.

Phytochemicals in Drug Discovery-A Confluence of Tradition and Innovation

Phytochemicals have a long and successful history in drug discovery. With recent advancements in analytical techniques and methodologies, discovering bioactive leads from natural compounds has become easier. Computational techniques like molecular docking, QSAR modelling and machine learning, and network pharmacology are among the most promising new tools that allow researchers to make predictions concerning natural products' potential targets, thereby guiding experimental validation efforts. Additionally, approaches like LC-MS or LC-NMR speed up compound identification by streamlining analytical processes. Integrating structural and computational biology aids in lead identification, thus providing invaluable information to understand how phytochemicals interact with potential targets in the body. An emerging computational approach is machine learning involving QSAR modelling and deep neural networks that interrelate phytochemical properties with diverse physiological activities such as antimicrobial or anticancer effects.

Comparison of the Glucocorticoid Receptor Binding and Agonist Activities of Typical Glucocorticoids: Insights into Their Endocrine Disrupting Effects

Over the past decades, the synthetic glucocorticoids (GCs) have been widely used in clinical practice and animal husbandry. Given the health hazard of these toxic residues in food, it is necessary to explore the detailed interaction mechanisms of typical GCs and their main target glucocorticoid receptor (GR). Hence, this work compared the GR binding and agonist activities of typical GCs. Fluorescence polarization assay showed that these GCs were potent ligands of GR. Their GR binding affinities were in the order of methylprednisolone>betamethasone≈prednisolone>dexamethasone, with IC50 values of 1.67, 2.94, 2.95, and 5.58 nM. Additionally, the limits of detection of dexamethasone, betamethasone, prednisolone, and methylprednisolone were 0.32, 0.14, 0.19, and 0.09 μg/kg in fluorescence polarization assay. Reporter gene assay showed that these GCs induced GR transactivation in a dose-dependent manner, confirming their GR agonist activities. Among which, dexamethasone at the concentration of 100 nM produced a maximal induction of more than 11-fold over the blank control. Molecular docking and molecular dynamics simulations suggested that hydrogen-bonding and hydrophobic interactions played an important role in stabilizing the GC-GR-LBD complexes. In summary, this work might help to understand the GR-mediated endocrine disrupting effects of typical GCs.

Nutrient synergy: definition, evidence, and future directions

Nutrient synergy refers to the concept that the combined effects of two or more nutrients working together have a greater physiological impact on the body than when each nutrient is consumed individually. While nutrition science traditionally focuses on isolating single nutrients to study their effects, it is recognized that nutrients interact in complex ways, and their combined consumption can lead to additive effects. Additionally, the Dietary Reference Intakes (DRIs) provide guidelines to prevent nutrient deficiencies and excessive intake but are not designed to assess the potential synergistic effects of consuming nutrients together. Even the term synergy is often applied in different manners depending on the scientific discipline. Considering these issues, the aim of this narrative review is to investigate the potential health benefits of consuming different nutrients and nutrient supplements in combination, a concept we define as nutrient synergy, which has gained considerable attention for its impact on overall well-being. We will examine how nutrient synergy affects major bodily systems, influencing systemic health. Additionally, we will address the challenges associated with promoting and conducting research on this topic, while proposing potential solutions to enhance the quality and quantity of scientific literature on nutrient synergy.

R software for QSAR analysis in phytopharmacological studies

INTRODUCTION

In recent decades, quantitative structure-activity relationship (QSAR) analysis has become an important method for drug design and natural product research. With the availability of bioinformatic and cheminformatic tools, a vast number of descriptors have been generated, making it challenging to select potential independent variables that are accurately related to the dependent response variable.

OBJECTIVE

The objective of this study is to demonstrate various descriptor selection procedures, such as the Boruta approach, all subsets regression, the ANOVA approach, the AIC method, stepwise regression, and genetic algorithm, that can be used in QSAR studies. Additionally, we performed regression diagnostics using R software to test parameters such as normality, linearity, residual histograms, PP plots, multicollinearity, and homoscedasticity.

RESULTS

The workflow designed in this study highlights the different descriptor selection procedures and regression diagnostics that can be used in QSAR studies. The results showed that the Boruta approach and genetic algorithm performed better than other methods in selecting potential independent variables. The regression diagnostics parameters tested using R software, such as normality, linearity, residual histograms, PP plots, multicollinearity, and homoscedasticity, helped in identifying and diagnosing model errors, ensuring the reliability of the QSAR model.

CONCLUSION

QSAR analysis is vital in drug design and natural product research. To develop a reliable QSAR model, it is essential to choose suitable descriptors and perform regression diagnostics. This study offers an accessible, customizable approach for researchers to select appropriate descriptors and diagnose errors in QSAR studies.