Discovery of Cobimetinib as a novel A-FABP inhibitor using machine learning and molecular docking-based virtual screening

Food-derived DPP4 inhibitors: Drug discovery based on high-throughput virtual screening and deep learning

Dipeptidyl peptidase-4 (DPP-4) is a critical target for the treatment of type 2 diabetes. This study outlines the development of six compounds derived from food sources and modified to create promising candidates for the treatment of diabetes. These compounds were identified through a combination of virtual screening, deep learning algorithms, ADMET characterization assessment, and molecular dynamics simulations. Furthermore, a taste prediction model was used to assess the flavor of these DPP-4 inhibiting compounds. After thorough evaluation, we concluded that the six food-derived DPP-4 inhibitors identified have significant potential for therapeutic success. This study has greatly contributed to the discovery of novel dietary supplements for the management of type 2 diabetes.

Exploring putative enteric methanogenesis inhibitors using molecular simulations and a graph neural network

Atmospheric methane (CH4) acts as a key contributor to global warming. As CH4 is a short-lived climate forcer (12 years atmospheric lifespan), its mitigation represents the most promising means to address climate change in the short term. Enteric CH4 (the biosynthesized CH4 from the rumen of ruminants) represents 5.1% of total global greenhouse gas (GHG) emissions, 23% of emissions from agriculture, and 27.2% of global CH4 emissions. Therefore, it is imperative to investigate methanogenesis inhibitors and their underlying modes of action. We hereby elucidate the detailed biophysical and thermodynamic interplay between anti-methanogenic molecules and cofactor F430 of methyl coenzyme M reductase and interpret the stoichiometric ratios and binding affinities of sixteen inhibitor molecules. We leverage this as prior in a graph neural network to first functionally cluster these sixteen known inhibitors among ~54,000 bovine metabolites. We subsequently demonstrate a protocol to identify precursors to and putative inhibitors for methanogenesis, based on Tanimoto chemical similarity and membrane permeability predictions. This work lays the foundation for computational and de novo design of inhibitor molecules that retain/ reject one or more biochemical properties of known inhibitors discussed in this study.

The Role of Adipocytes Recruited as Part of Tumor Microenvironment in Promoting Colorectal Cancer Metastases

Adipose tissue dysfunction, which is associated with an increased risk of colorectal cancer (CRC), is a significant factor in the pathophysiology of obesity. Obesity-related inflammation and extracellular matrix (ECM) remodeling promote colorectal cancer metastasis (CRCM) by shaping the tumor microenvironment (TME). When CRC occurs, the metabolic symbiosis of tumor cells recruits adjacent adipocytes into the TME to supply energy. Meanwhile, abundant immune cells, from adipose tissue and blood, are recruited into the TME, which is stimulated by pro-inflammatory factors and triggers a chronic local pro-inflammatory TME. Dysregulated ECM proteins and cell surface adhesion molecules enhance ECM remodeling and further increase contractibility between tumor and stromal cells, which promotes epithelial-mesenchymal transition (EMT). EMT increases tumor migration and invasion into surrounding tissues or vessels and accelerates CRCM. Colorectal symbiotic microbiota also plays an important role in the promotion of CRCM. In this review, we provide adipose tissue and its contributions to CRC, with a special emphasis on the role of adipocytes, macrophages, neutrophils, T cells, ECM, and symbiotic gut microbiota in the progression of CRC and their contributions to the CRC microenvironment. We highlight the interactions between adipocytes and tumor cells, and potential therapeutic approaches to target these interactions.

The Effects of FABP4 on Cardiovascular Disease in the Aging Population

PURPOSE OF REVIEW

Fatty acid-binding protein 4 (FABP4) plays a role in lipid metabolism and cardiovascular health. In this paper, we cover FABP4 biology, its implications in atherosclerosis from observational studies, genetic factors affecting FABP4 serum levels, and ongoing drug development to target FABP4 and offer insights into future FABP4 research.

RECENT FINDINGS

FABP4 impacts cells through JAK2/STAT2 and c-kit pathways, increasing inflammatory and adhesion-related proteins. In addition, FABP4 induces angiogenesis and vascular smooth muscle cell proliferation and migration. FABP4 is established as a reliable predictive biomarker for cardiovascular disease in specific at-risk groups. Genetic studies robustly link PPARG and FABP4 variants to FABP4 serum levels. Considering the potential effects on atherosclerotic lesion development, drug discovery programs have been initiated in search for potent inhibitors of FABP4. Elevated FABP4 levels indicate an increased cardiovascular risk and is causally related to acceleration of atherosclerotic disease, However, clinical trials for FABP4 inhibition are lacking, possibly due to concerns about available compounds' side effects. Further research on FABP4 genetics and its putative causal role in cardiovascular disease is needed, particularly in aging subgroups.

Discovery of novel SOS1 inhibitors using machine learning

Overactivation of the rat sarcoma virus (RAS) signaling is responsible for 30% of all human malignancies. Son of sevenless 1 (SOS1), a crucial node in the RAS signaling pathway, could modulate RAS activation, offering a promising therapeutic strategy for RAS-driven cancers. Applying machine learning (ML)-based virtual screening (VS) on small-molecule databases, we selected a random forest (RF) regressor for its robustness and performance. Screening was performed with the L-series and EGFR-related datasets, and was extended to the Chinese National Compound Library (CNCL) with more than 1.4 million compounds. In addition to a series of documented SOS1-related molecules, we uncovered nine compounds that have an unexplored chemical framework and displayed inhibitory activity, with the most potent achieving more than 50% inhibition rate in the KRAS G12C/SOS1 PPI assay and an IC50 value in the proximity of 20 μg mL-1. Compared with the manner that known inhibitory agents bind to the target, hit compounds represented by CL01545365 occupy a unique pocket in molecular docking. An in silico drug-likeness assessment suggested that the compound has moderately favorable drug-like properties and pharmacokinetic characteristics. Altogether, our findings strongly support that, characterized by the distinctive binding modes, the recognition of novel skeletons from the carboxylic acid series could be candidates for developing promising SOS1 inhibitors.

Levofloxacin alleviates blood-brain barrier disruption following cerebral ischemia and reperfusion via directly inhibiting A-FABP

Reperfusion therapy is currently the most effective treatment for acute ischemic stroke, but often results in secondary brain injury. Adipocyte fatty acid-binding protein (A-FABP, FABP4, or aP2) was shown to critically mediate cerebral ischemia/reperfusion (I/R) injury by exacerbating blood-brain barrier (BBB) disruption. However, no A-FABP inhibitors have been approved for clinical use due to safety issues. Here, we identified the therapeutic effect of levofloxacin, a widely used antibiotic displaying A-FABP inhibitory activity in vitro, on cerebral I/R injury and determined its target specificity and action mechanism in vivo. Using molecular docking and site-directed mutagenesis, we showed that levofloxacin inhibited A-FABP activity through interacting with the amino acid residue Asp76, Gln95, Arg126 of A-FABP. Accordingly, levofloxacin significantly inhibited A-FABP-induced JNK phosphorylation and expressions of proinflammatory factors and matrix metalloproteinase 9 (MMP-9) in mouse primary macrophages. In wild-type mice with transient middle cerebral artery occlusion, levofloxacin substantially mitigated BBB disruption and neuroinflammation, leading to reduced cerebral infarction, alleviated neurological outcomes, and improved survival. Mechanistically, levofloxacin decreased MMP-9 expression and activity, and thus reduced degradation of extracellular matrix and endothelial tight junction proteins. Importantly, the BBB- and neuro-protective effects of levofloxacin were abolished in A-FABP or MMP-9 knockout mice, suggesting that the therapeutic effects of levofloxacin highly depended on specific targeting of the A-FABP-MMP-9 axis. Overall, our study demonstrates that levofloxacin alleviates A-FABP-induced BBB disruption and neural tissue injury following cerebral I/R, and unveils its therapeutic potential for the treatment of ischemic stroke.

Sequential virtual screening collaborated with machine-learning strategies for the discovery of precise medicine against non-small cell lung cancer

Dysregulation of MAPK pathway receptors are crucial in causing uncontrolled cell proliferation in many cancer types including non-small cell lung cancer. Due to the complications in targeting the upstream components, MEK is an appealing target to diminish this pathway activity. Hence, we have aimed to discover potent MEK inhibitors by integrating virtual screening and machine learning-based strategies. Preliminary screening was conducted on 11,808 compounds using the cavity-based pharmacophore model AADDRRR. Further, seven ML models were accessed to predict the MEK active compounds using six molecular representations. The LGB model with morgan2 fingerprints surpasses other models ensuing 0.92 accuracy and 0.83 MCC value versus test set and 0.85 accuracy and 0.70 MCC value with external set. Further, the binding ability of screened hits were examined using glide XP docking and prime-MM/GBSA calculations. Note that we have utilized three ML-based scoring functions to predict the various biological properties of the compounds. The two hit compounds such as DB06920 and DB08010 resulted excellent binding mechanism with acceptable toxicity properties against MEK. Further, 200 ns of MD simulation combined with MM-GBSA/PBSA calculations confirms that DB06920 may have stable binding conformations with MEK thus step forwarded to the experimental studies in the near future.Communicated by Ramaswamy H. Sarma.

TCMBank: bridges between the largest herbal medicines, chemical ingredients, target proteins, and associated diseases with intelligence text mining

Traditional Chinese Medicine (TCM) has long been viewed as a precious source of modern drug discovery. AI-assisted drug discovery (AIDD) has been investigated extensively. However, there are still two challenges in applying AIDD to guide TCM drug discovery: the lack of a large amount of standardized TCM-related information and AIDD is prone to pathological failures in out-of-domain data. We have released TCM Database@Taiwan in 2011, and it has been widely disseminated and used. Now, we developed TCMBank, the largest systematic free TCM database, which is an extension of TCM Database@Taiwan. TCMBank contains 9192 herbs, 61 966 ingredients (unduplicated), 15 179 targets, 32 529 diseases, and their pairwise relationships. By integrating multiple data sources, TCMBank provides 3D structure information of ingredients and provides a standard list and detailed information on herbs, ingredients, targets and diseases. TCMBank has an intelligent document identification module that continuously adds TCM-related information retrieved from the literature in PubChem. In addition, driven by TCMBank big data, we developed an ensemble learning-based drug discovery protocol for identifying potential leads and drug repurposing. We take colorectal cancer and Alzheimer's disease as examples to demonstrate how to accelerate drug discovery by artificial intelligence. Using TCMBank, researchers can view literature-driven relationship mapping between herbs/ingredients and genes/diseases, allowing the understanding of molecular action mechanisms for ingredients and identification of new potentially effective treatments. TCMBank is available at https://TCMBank.CN/.

Exploring natural products as multi-target-directed drugs for Parkinson's disease: an in-silico approach integrating QSAR, pharmacophore modeling, and molecular dynamics simulations

Parkinson's disease is a neurodegenerative disorder characterized by the progressive loss of dopaminergic neurons in the midbrain. Current treatments provide limited symptomatic relief without halting disease progression. A multi-targeting approach has shown potential benefits in treating neurodegenerative diseases. In this study, we employed in silico approaches to explore the COCONUT natural products database and identify novel drug candidates with multi-target potential against relevant Parkinson's disease targets. QSAR models were developed to screen for potential bioactive molecules, followed by a hybrid virtual screening approach involving pharmacophore modeling and molecular docking against MAO-B, AA2AR, and NMDAR. ADME evaluation was performed to assess drug-like properties. Our findings revealed 22 candidates that exhibited the desired pharmacophoric features. Particularly, two compounds: CNP0121426 and CNP0242698 exhibited remarkable binding affinities, with energies lower than -10 kcal/mol and promising interaction profiles with the chosen targets. Furthermore, all the ligands displayed desirable pharmacokinetic properties for brain-targeted drugs. Lastly, molecular dynamics simulations were conducted on the lead candidates, belonging to the dihydrochalcone and curcuminoid class, to evaluate their stability over a 100 ns timeframe and compare their dynamics with reference complexes. Our findings revealed the curcuminoid CNP0242698 to have an overall better stability with the three targets compared to the dihydrochalcone, despite the high ligand RMSD, the curcuminoid CNP0242698 showed better protein stability, implying ligand exploration of different orientations. Similarly, AA2AR exhibited higher stability with CNP0242698 compared to the reference complex, despite the high initial ligand RMSD due to the bulkier active site. In NMDAR, CNP0242698 displayed good stability and less fluctuations implying a more restricted conformation within the smaller active site of NMDAR. These results may serve as lead compounds for the development and optimization of natural products as multi-target disease-modifying natural remedies for Parkinson's disease patients. However, experimental assays remain necessary to validate these findings.Communicated by Ramaswamy H. Sarma.

Discovery of the First-in-Class Intestinal Restricted FXR and FABP1 Dual Modulator ZLY28 for the Treatment of Nonalcoholic Fatty Liver Disease

The prevalence of nonalcoholic steatohepatitis (NASH) is increasing rapidly worldwide, and NASH has become a serious problem for human health. Recently, the selective activation of the intestinal farnesoid X receptor (FXR) was considered as a more promising strategy for the treatment of NASH with lesser side effects due to reduced systemic exposure. Moreover, the inhibition of intestinal fatty acid binding protein 1 (FABP1) alleviated obesity and NASH by reducing dietary fatty acid uptake. In this study, the first-in-class intestinal restricted FXR and FABP1 dual-target modulator ZLY28 was discovered by comprehensive multiparameter optimization studies. The reduced systemic exposure of ZLY28 might provide better safety by decreasing the on- and off-target side effects in vivo. In NASH mice, ZLY28 exerted robust anti-NASH effects by inhibiting FABP1 and activating the FXR-FGF15 signaling pathway in the ileum. With the above attractive efficacy and preliminary safety profiles, ZLY28 is worthy of further evaluation as a novel anti-NASH agent.

Fatty acid-binding protein 4 in kidney diseases: From mechanisms to clinics

Considerable evidence indicated the relationship between fatty acid-binding protein 4 (FABP4) and kidney diseases. FABP4, a small molecular lipid chaperone, is identified to regulate fatty acid oxidation, inflammation, apoptosis, endoplasmic reticulum stress and macrophage-to-myofibroblast transition in kidney diseases. Many studies have shown that circulating FABP4 level is related to proteinuria, renal function decline, cardiovascular complications of end-stage renal disease and even the prognosis of kidney transplanted patients. Notably, pharmacological or genetic inhibition of FABP4 attenuated renal injury in the various experimental models of kidney diseases, making it promising to develop potential therapeutic strategies targeting FABP4 in kidney diseases. In this study, we updated and reviewed the mechanisms and clinical significance of FABP4 in kidney diseases.

Adipocyte fatty acid binding protein 4 (FABP4) inhibitors. An update from 2017 to early 2022

The fatty acid binding protein 4 (FABP4) is a protein predominantly expressed in macrophages and adipose tissue, where it regulates fatty acids storage and lipolysis and is an essential mediator of inflammation. Small molecule inhibitors of FABP4 have attracted interest following the recent publications of beneficial pharmacological effects of these compounds for the treatment of metabolic syndrome and, more recently, for other pathologies. Since the synthesis of the BMS309403, one of the first selective and effective FABP4 inhibitors, hundreds of other inhibitors have been synthesized (i.e., derivatives of niacin, quinoxaline, aryl-quinoline, bicyclic pyridine, urea, aromatic compounds and other novel heterocyclic compounds). This review updates the recently reported (2017 to early 2022) molecules as adipocyte fatty acid binding protein 4 inhibitors.