Structural Investigation for Optimization of Anthranilic Acid Derivatives as Partial FXR Agonists by in Silico Approaches

The Molecular Mechanism of Farnesoid X Receptor Alleviating Glucose Intolerance in Turbot (Scophthalmus maximus)

To explore the molecular targets for regulating glucose metabolism in carnivorous fish, the turbot (Scophthalmus maximus) was selected as the research object to study. Farnesoid X receptor (FXR; NR1H4), as a ligand-activated transcription factor, plays an important role in glucose metabolism in mammals. However, the mechanisms controlling glucose metabolism mediated by FXR in fish are not understood. It was first found that the protein levels of FXR and its target gene, small heterodimer partner (SHP), were significantly decreased in the high-glucose group (50 mM, HG) compared with those in the normal glucose group (15 mM, CON) in primary hepatocytes of turbot. By further exploring the function of FXR in turbot, the full length of FXR in turbot was cloned, and its nuclear localization function was characterized by subcellular localization. The results revealed that the FXR had the highest expression in the liver, and its capability to activate SHP expression through heterodimer formation with retinoid X receptor (RXR) was proved, which proved RXR could bind to 15 binding sites of FXR by forming hydrogen bonds. Activation of FXR in both the CON and HG groups significantly increased the expression of glucokinase (gk) and pyruvate kinase (pk), while it decreased the expression of cytosolic phosphoenolpyruvate carboxykinase (cpepck), mitochondrial phosphoenolpyruvate carboxykinase (mpepck), glucose-6-phosphatase 1 (g6pase1) and glucose-6-phosphatase 2 (g6pase2), and caused no significant different in glycogen synthetase (gs). ELISA experiments further demonstrated that under the condition of high glucose with activated FXR, it could significantly decrease the activity of PEPCK and G6PASE in hepatocytes. In a dual-luciferase reporter assay, the FXR could significantly inhibit the activity of G6PASE2 and cPEPCK promoters by binding to the binding site 'ATGACCT'. Knockdown of SHP after activation of FXR reduced the inhibitory effect on gluconeogenesis. In summary, FXR can bind to the mpepck and g6pase2 promoters to inhibit their expression, thereby directly inhibiting the gluconeogenesis pathway. FXR can also indirectly inhibit the gluconeogenesis pathway by activating shp. These findings suggest the possibility of FXR as a molecular target to regulate glucose homeostasis in turbot.

Development and validation of a machine learning model for predicting drug-drug interactions with oral diabetes medications

Diabetes management is often complicated by comorbidities, requiring complex medication regimens that increase the risk of drug-drug interactions (DDIs), potentially compromising treatment outcomes or causing toxicity. Although machine learning (ML) models have made strides in DDI prediction, existing approaches lack specificity for oral diabetes medications and face challenges in interpretability. To address these limitations, we propose a novel ML-based framework utilizing the Simplified Molecular Input Line Entry System (SMILES) to encode structural information of oral diabetes drugs. Using this representation, we developed an XGBoost model, selecting molecular features through LASSO. Our dataset, sourced from DrugBank, included 42 oral diabetes drugs and 1,884 interacting drugs, divided into training, validation, and testing sets. The model identified 606 optimal features, achieving an F1-score of 0.8182. SHAP analysis was employed for feature interpretation, enhancing model transparency and clinical relevance. By predicting adverse DDIs, our model offers a valuable tool for clinical decision-making, aiding safer prescription practices. The 606 critical features provide insights into atomic-level interactions, linking computational predictions with biological experiments. We present a classification model specifically designed for predicting DDIs associated with oral diabetes medications, with an openly accessible web application to support diabetes management in multi-drug regimens and comorbidity settings.

Development and Validation of an Explainable Machine Learning-Based Prediction Model for Drug-Food Interactions from Chemical Structures

Possible drug-food constituent interactions (DFIs) could change the intended efficiency of particular therapeutics in medical practice. The increasing number of multiple-drug prescriptions leads to the rise of drug-drug interactions (DDIs) and DFIs. These adverse interactions lead to other implications, e.g., the decline in medicament's effect, the withdrawals of various medications, and harmful impacts on the patients' health. However, the importance of DFIs remains underestimated, as the number of studies on these topics is constrained. Recently, scientists have applied artificial intelligence-based models to study DFIs. However, there were still some limitations in data mining, input, and detailed annotations. This study proposed a novel prediction model to address the limitations of previous studies. In detail, we extracted 70,477 food compounds from the FooDB database and 13,580 drugs from the DrugBank database. We extracted 3780 features from each drug-food compound pair. The optimal model was eXtreme Gradient Boosting (XGBoost). We also validated the performance of our model on one external test set from a previous study which contained 1922 DFIs. Finally, we applied our model to recommend whether a drug should or should not be taken with some food compounds based on their interactions. The model can provide highly accurate and clinically relevant recommendations, especially for DFIs that may cause severe adverse events and even death. Our proposed model can contribute to developing more robust predictive models to help patients, under the supervision and consultants of physicians, avoid DFI adverse effects in combining drugs and foods for therapy.

Multi-model in silico characterization of 3-benzamidobenzoic acid derivatives as partial agonists of Farnesoid X receptor in the management of NAFLD

Non-alcoholic fatty liver disease (NAFLD) is a pathological condition which is strongly correlated with fat accumulation in the liver that has become a major health hazard globally. So far, limited treatment options are available for the management of NAFLD and partial agonism of Farnesoid X receptor (FXR) has proven to be one of the most promising strategies for treatment of NAFLD. In present work, a range of validated predictive cheminformatics and molecular modeling studies were performed with a series of 3-benzamidobenzoic acid derivatives in order to recognize their structural requirements for possessing higher potency towards FXR. 2D-QSAR models were able to extract the most significant structural attributes determining the higher activity towards the receptor. Ligand-based pharmacophore model was created with a novel and less-explored open access tool named QPhAR to acquire information regarding important 3D-pharmacophoric features that lead to higher agonistic potential towards the FXR. The alignment of the dataset compounds based on pharmacophore mapping led to 3D-QSAR models that pointed out the most crucial steric and electrostatic influence. Molecular dynamics (MD) simulation performed with the most potent and the least potent derivatives of the current dataset helped us to understand how to link the structural interpretations obtained from 2D-QSAR, 3D-QSAR and pharmacophore models with the involvement of specific amino acid residues in the FXR protein. The current study revealed that hydrogen bond interactions with carboxylate group of the ligands play an important role in the ligand receptor binding but higher stabilization of different helices close to the binding site of FXR (e.g., H5, H6 and H8) through aromatic scaffolds of the ligands should lead to higher activity for these ligands. The present work affords important guidelines towards designing novel FXR partial agonists for new therapeutic options in the management of NAFLD. Moreover, we relied mainly on open-access tools to develop the in-silico models in order to ensure their reproducibility as well as utilization.

An in silico and in vitro integrated analysis method to reveal the curative mechanisms and pharmacodynamic substances of Bufei granule on chronic obstructive pulmonary disease

Chronic obstructive pulmonary disease (COPD) is a common respiratory disease with high disability and mortality. Clinical studies have shown that the Traditional Chinese Medicine Bufei Granule (BFG) has conspicuous effects on relieving cough and improving lung function in patients with COPD and has a reliable effect on the treatment of COPD, whereas the therapeutic mechanism is vague. In the present study, the latent bronchodilators and mechanism of BFG in the treatment of COPD were discussed through the method of network pharmacology. Then, the molecular docking and molecular dynamics simulation were performed to calculate the binding efficacy of corresponding compounds in BFG to muscarinic receptor. Finally, the effects of BFG on bronchial smooth muscle were validated by in vitro experiments. The network pharmacology results manifested the anti-COPD effect of BFG was mainly realized via restraining airway smooth muscle contraction, activating cAMP pathways and relieving oxidative stress. The results of molecular docking and molecular dynamics simulation showed alpinetin could bind to cholinergic receptor muscarinic 3. The in vitro experiment verified both BFG and alpinetin could inhibit the levels of CHRM3 and acetylcholine and could be potential bronchodilators for treating COPD. This study provides an integrating network pharmacology method for understanding the therapeutic mechanisms of traditional Chinese medicine, as well as a new strategy for developing natural medicines for treating COPD.

Farnesoid X receptor (FXR): Structures and ligands

Farnesoid X receptor (FXR) is a bile acid activated nuclear receptor (BAR) and is mainly expressed in the liver and intestine. Upon ligand binding, FXR regulates key genes involved in the metabolic process of bile acid synthesis, transport and reabsorption and is also involved in the metabolism of carbohydrates and lipids. Because of its important functions, FXR is considered as a promising drug target for the therapy of bile acid-related liver diseases. With the approval of obeticholic acid (OCA) as the first small molecule to target FXR, many other small molecules are being evaluated in clinical trials. This review summarizes the structures of FXR, especially its ligand binding domain, and the development of small molecules (including agonists and antagonists) targeting FXR.

Fast Identification of Adverse Drug Reactions (ADRs) of Digestive and Nervous Systems of Organic Drugs by In Silico Models

This study aimed to discover concurrences of adverse drug reactions (ADRs) and derive models of the most frequent items of ADRs based on the SIDER database, which included 1430 marketed drugs and 5868 ADRs. First, common ADRs of organic drugs were manually reclassified according to side effects in the human system and followed by an association rule analysis, which found ADRs of digestive and nervous systems often occurred at the same time with a good association rule. Then, three algorithms, linear discriminant analysis (LDA), support vector machine (SVM) and deep learning, were used to derive models of ADRs of digestive and nervous systems based on 497 organic monomer drugs and to identify key structural features in defining these ADRs. The statistical results indicated that these kinds of QSAR models were good tools for screening ADRs of digestive and nervous systems, which gave the ROC AUC values of 81.5%, 98.9%, 91.5%, 69.5%, 78.4% and 78.8%, respectively. Then, these models were applied to investigate ADRs of 1536 organic compounds with four phase and zero rule-of-five (RO5) violations from the ChEMBL database. Based on the consensus ADRs’ predictions of models, 58.1% and 42.6% of compounds were predicted to cause these two ADRs, respectively, indicating the significance of initial assessment of ADRs in early drug discovery.

Identfication of Potent LXRβ-Selective Agonists without LXRα Activation by In Silico Approaches

Activating Liver X receptors (LXRs) represents a promising therapeutic option for dyslipidemia. However, activating LXRα may cause undesired lipogenic effects. Discovery of highly LXRβ-selective agonists without LXRα activation were indispensable for dyslipidemia. In this study, in silico approaches were applied to develop highly potent LXRβ-selective agonists based on a series of newly reported 3-(4-(2-propylphenoxy)butyl)imidazolidine-2,4-dione-based LXRα/β dual agonists. Initially, Kohonen and stepwise multiple linear regression SW-MLR were performed to construct models for LXRβ agonists and LXRα agonists based on the structural characteristics of LXRα/β dual agonists, respectively. The obtained LXRβ agonist model gave a good predictive ability (R²_train = 0.837, R²_test = 0.843, Q²_LOO = 0.715), and the LXRα agonist model produced even better predictive ability (R²_train = 0.968, R²_test = 0.914, Q²_LOO = 0.895). Also, the two QSAR models were independent and can well distinguish LXRβ and LXRα activity. Then, compounds in the ZINC database met the lower limit of structural similarity of 0.7, compared to the 3-(4-(2-propylphenoxy)butyl)imidazolidine-2,4-dione scaffold subjected to our QSAR models, which resulted in the discovery of ZINC55084484 with an LXRβ prediction value of pEC₅₀ equal to 7.343 and LXRα prediction value of pEC₅₀ equal to −1.901. Consequently, nine newly designed compounds were proposed as highly LXRβ-selective agonists based on ZINC55084484 and molecular docking, of which LXRβ prediction values almost exceeded 8 and LXRα prediction values were below 0.

Discovery of molecular mechanism of a clinical herbal formula upregulating serum HDL-c levels in treatment of metabolic syndrome by in vivo and computational studies

Decreased HDL cholesterol (HDL-c) is considered as an independent risk factor of cardiovascular disease in metabolic syndrome (Mets). Wendan decoction (WDD), a famous clinical traditional Chinese medicine formula in Mets in China, which can obviously up-regulate serum HDL-c levels in Mets. However, till now, the molecular mechanism of up-regulation still remained unclear. In this study, an integrated approach that combined serum ABCA1 in vivo assay, QSAR modeling and molecular docking was developed to explore the molecular mechanism and chemical substance basis of WDD upregulating HDL-c levels. Compared with Mets model group, serum ABCA1 and HDL-c levels intervened by two different doses of WDD for two weeks were significantly up-regulated. Then, kohonen and LDA were applied to develop QSAR models for ABCA1 up-regulators based flavonoids. The derived QSAR model produced the overall accuracy of 100%, a very powerful tool for screening ABCA1 up-regulators. The QSAR model prediction revealed 67 flavonoids in WDD were ABCA1 up-regulators. Finally, they were subjected to the molecular docking to understand their roles in up-regulating ABCA1 expression, which led to discovery of 23 ABCA1 up-regulators targeting LXR beta. Overall, QSAR modeling and docking studies well accounted for the observed in vivo activities of ABCA1 affected by WDD.

Metabolomic investigation into molecular mechanisms of a clinical herb prescription against metabolic syndrome by a systematic approach

This study provided an effective and comprehensive approach for understanding the pathophysiological mechanisms of Mets and therapeutic mechanisms of WDD in treatment of Mets.

Systematic Understanding of Mechanisms of a Chinese Herbal Formula in Treatment of Metabolic Syndrome by an Integrated Pharmacology Approach

Metabolic syndrome (MS) is becoming a worldwide health problem. Wendan decoction (WDD)—a famous traditional Chinese medicine formula—has been extensively employed to relieve syndromes related to MS in clinical practice in China. However, its pharmacological mechanisms still remain vague. In this study, a comprehensive approach that integrated chemomics, principal component analysis, molecular docking simulation, and network analysis was established to elucidate the multi-component and multi-target mechanism of action of WDD in treatment of MS. The compounds in WDD were found to possess chemical diversity, complexity and drug-likeness compared to MS drugs. Six nuclear receptors were obtained to have strong binding affinity with 217 compounds of five herbs in WDD. The importance roles of targets and herbs were also identified due to network parameters. Five compounds from Radix Glycyrrhizae Preparata can hit all six targets, which can assist in screening new MS drugs. The pathway network analysis demonstrated that the main pharmacological effects of WDD might lie in maintaining lipid and glucose metabolisms and anticancer activities as well as immunomodulatory and hepatoprotective effects. This study provided a comprehensive system approach for understanding the multi-component, multi-target and multi-pathway mechanisms of WDD during the treatment of MS.

Data Analysis in Chemistry and Bio-Medical Sciences

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