Discovery of multitarget-directed small molecule inhibitors from Andrographis paniculata for Nipah virus disease therapy: Molecular docking, molecular dynamics simulation and ADME-Tox profiling

Chemical Physics Impact, Journal Year: 2024, Volume and Issue: 8, P. 100493 - 100493

Published: Jan. 24, 2024

Nipah virus (NiV), an emerging highly infectious agent, causes fatal infection to humans through zoonosis, particularly in Asian countries including India. No specific treatment or vaccine is available cure combat the NiV infection. Targetable plant-based computer-assisted drug discovery a novel approach disease. The purpose of current silico study was evaluate potential Andrographis paniculata phytochemicals act as molecules disease by targeting multiple proteins: attachment glycoprotein (NiV-G), nucleoprotein (NiV-N) and phosphoprotein (NiV-P). Six from A. paniculata: andrograpanin (AGNN), andrographidine E (AGDN), andrographin (AGPN), andrographolide (AGLD), deoxyandrographolide (DGLD) neoandrographolide (NGLD) were docked against NiV-G, NiV-N NiV-P. Outcomes molecular docking approved anti-NiV capacity compounds with top binding affinity NGLD all targets NiV-P energies -8.1, -7.7 -6.0 kcal/mol, respectively. dynamic simulation results further validated protein-ligand complexes' stability. Compared phytochemicals, standard antivirals, favipiravir ribavirin, had low efficacy target proteins. Furthermore, phytocompounds displayed acceptable drug-likeness ADME-Tox (absorption, distribution, metabolism, excretion, toxicity) profiles. Overall, present findings can be translated into preclinical clinical investigations inspect mainstream drugs for

Language: Английский

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Discovering potential ERK1 inhibitors from natural products for therapeutic targeting of Alzheimer's disease

Journal of Alzheimer s Disease, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 15, 2025

Background Extracellular signal-regulated kinase 1 (ERK1) belongs to mitogen-activated protein kinases, which are essential for memory formation, cognitive function, and synaptic plasticity. During Alzheimer's disease (AD), ERK1 phosphorylates tau at 15 phosphorylation sites, leading the formation of neurofibrillary tangles. The overactivation in microglia promotes release pro-inflammatory cytokines, results neuroinflammation. Additionally, elevated oxidative stress during AD stimulates pathway, neuronal loss. Objective Because signaling plays a significant role phosphorylation, targeting may be therapeutically beneficial by either preventing excessive activation pathway or altering its enhance neuroprotective effects AD. Methods This study employed structure-based virtual screening phytoconstituents from IMPPAT library. Subsequently, in-depth docking molecular dynamics (MD) simulation studies were implemented identify potential inhibitors with desirable pharmacological properties. Results Silandrin Hydroxytuberosone found higher affinity specificity than control molecule Tizaterkib. These compounds specifically bind substrate binding pocket interact crucial residues. Finally, elucidated evaluated using an all-atom MD analyze structural dynamics, compactness, hydrogen bond principal component analysis, free energy landscape. Conclusions suggested that can further exploited as lead molecules therapeutic development against ERK1-mediated

Language: Английский

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Machine Learning‐Enabled Drug‐Induced Toxicity Prediction

Advanced Science, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 3, 2025

Abstract Unexpected toxicity has become a significant obstacle to drug candidate development, accounting for 30% of discovery failures. Traditional assessment through animal testing is costly and time‐consuming. Big data artificial intelligence (AI), especially machine learning (ML), are robustly contributing innovation progress in toxicology research. However, the optimal AI model different types usually varies, making it essential conduct comparative analyses methods across domains. The diverse sources also pose challenges researchers focusing on specific studies. In this review, 10 categories drug‐induced examined, summarizing characteristics applicable ML models, including both predictive interpretable algorithms, striking balance between breadth depth. Key databases tools used prediction highlighted, toxicology, chemical, multi‐omics, benchmark databases, organized by their focus function clarify roles prediction. Finally, strategies turn into opportunities analyzed discussed. This review may provide with valuable reference understanding utilizing available resources bridge mechanistic insights, further advance application drugs‐induced

Language: Английский

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Modern drug discovery using ethnobotany: A large-scale cross-cultural analysis of traditional medicine reveals common therapeutic uses

iScience, Journal Year: 2023, Volume and Issue: 26(9), P. 107729 - 107729

Published: Aug. 25, 2023

For millennia, numerous cultures and civilizations have relied on traditional remedies derived from plants to treat a wide range of conditions ailments. Here, we systematically analyzed ethnobotanical patterns across taxonomically related plants, demonstrating that congeneric medicinal are more likely be used for treating similar indications. Next, reconstructed the phytochemical space covered by reveal (i) cover space, (ii) chemical similarity correlates with therapeutic usage. Lastly, present several case scenarios illustrating how mining this information can drug discovery applications, including: investigating taxonomic hotspots around particular indications, exploring shared located in different geographic areas, but which been same (iii) showing concordance between among non-taxonomically presence bioactive phytochemicals.

Language: Английский

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Hepatoprotective effects of Juglans regia on carbon tetrachloride‐induced hepatotoxicity: In silico/in vivo approach

Food Science & Nutrition, Journal Year: 2024, Volume and Issue: 12(9), P. 6482 - 6497

Published: June 18, 2024

Abstract Juglans regia L. is a well‐known therapeutic plant in Nepal, employed traditional medicine for treating liver ailments. This study aimed to evaluate the vivo and silico liver‐protective effects of J. extract using carbon tetrachloride (CCl 4 )‐induced hepatic damage rat model. Healthy male rats were randomly divided into six groups: normal control (distilled water 10 mL/kg), toxic standard test (silymarin 100 mg/kg), three groups receiving oral extracts (125, 250, 500 mg/kg/day) seven days. On eighth day, ) was administered intraperitoneally (i.p.) (1.5 mL/kg 1:1 olive oil ratio all groups, except control). Rats sacrificed on ninth blood collected retro‐orbitally injury tests histopathological studies. Molecular docking performed against cytochrome P450 2E1 (CYP450 2E1) enzyme 16 selected phytoconstituents. , at doses 125, mg/kg, significantly reduced levels (alanine aminotransferase, alkaline phosphatase, direct bilirubin, total bilirubin), while increasing serum albumin. Histological analysis revealed mitigation injury, reducing fatty degeneration necrosis. supported findings, with Beta‐sitosterol Betulinic acid exhibiting best binding affinity −9.2 −9.1 kcal/mol, respectively. In conclusion, result suggests that showed dose‐dependent hepatoprotective activity CCl ‐induced hepatotoxicity it could be utilized as promising agent. potential bark extracts, emphasizing need further clinical validation.

Language: Английский

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DCABM-TCM: A Database of Constituents Absorbed into the Blood and Metabolites of Traditional Chinese Medicine

Journal of Chemical Information and Modeling, Journal Year: 2023, Volume and Issue: 63(15), P. 4948 - 4959

Published: July 24, 2023

Traditional Chinese medicine (TCM) not only maintains the health of Asian people but also provides a great resource active natural products for modern drug development. Herein, we developed Database Constituents Absorbed into Blood and Metabolites TCM (DCABM-TCM), first database systematically collecting blood constituents prescriptions herbs, including prototypes metabolites experimentally detected in blood, together with corresponding detailed detection conditions through manual literature mining. The DCABM-TCM has collected 1816 chemical structures 192 194 herbs integrated their related annotations, physicochemical, absorption, distribution, metabolism, excretion, toxicity properties, associated targets, pathways, diseases. Furthermore, supported two constituent-based analysis functions, network pharmacology molecular mechanism elucidation, target/pathway/disease-based screening candidate constituents, or TCM-based discovery. is freely accessible at http://bionet.ncpsb.org.cn/dcabm-tcm/. will contribute to elucidation effective TCMs discovery TCM-derived drug-like compounds that are both bioactive bioavailable.

Language: Английский

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Identifying potential inhibitors of phosphatidylinositol 4,5-bisphosphate 3-kinase: Molecular dynamic insights into the interaction and inhibitory mechanism

Chemical Physics Impact, Journal Year: 2024, Volume and Issue: 8, P. 100458 - 100458

Published: Jan. 2, 2024

Phosphatidylinositol 4,5-bisphosphate 3-kinase catalytic subunit alpha (PIK3CA) plays a crucial role in signaling pathways and has emerged as an attractive target for anticancer therapy. Developing small-molecule inhibitors targeting PIK3CA promising therapeutic strategy. Here, we employed in-silico approaches to identify potential of from pool bioactive phytoconstituents available IMPPAT 2.0 database. The initial screening was based on their drug-like properties docking score followed by interaction analysis, 100 nanoseconds (ns) molecular dynamics (MD) simulations evaluate conformational stability with PIK3CA. MD simulation studies suggested the formation stable complexes between elucidated compounds, Apollinine Isoglycyrol. These compounds exhibited exceptional properties, binding efficiency, remarkable stability. findings suggest that Isoglycyrol could serve leads development cancer. Moreover, insights gained shed light mechanisms inhibition facilitate rational design future inhibitors.

Language: Английский

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HTINet2: herb–target prediction via knowledge graph embedding and residual-like graph neural network

Briefings in Bioinformatics, Journal Year: 2024, Volume and Issue: 25(5)

Published: July 25, 2024

Abstract Target identification is one of the crucial tasks in drug research and development, as it aids uncovering action mechanism herbs/drugs discovering new therapeutic targets. Although multiple algorithms herb target prediction have been proposed, due to incompleteness clinical knowledge limitation unsupervised models, accurate for targets still faces huge challenges data models. To address this, we proposed a deep learning-based framework termed HTINet2, which designed three key modules, namely, traditional Chinese medicine (TCM) graph embedding, residual representation learning, supervised prediction. In first module, constructed large-scale that covers TCM properties treatment herbs, component embedding learn herbs remaining two residual-like convolution network capture interactions among targets, Bayesian personalized ranking loss conduct training Finally, comprehensive experiments, comparison with baselines indicated excellent performance HTINet2 (HR@10 increased by 122.7% NDCG@10 35.7%), ablation experiments illustrated positive effect our modules case study demonstrated reliability predicted Artemisia annua Coptis chinensis based on base, literature, molecular docking.

Language: Английский

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Chemical Multiverse and Diversity of Food Chemicals

Journal of Chemical Information and Modeling, Journal Year: 2024, Volume and Issue: 64(4), P. 1229 - 1244

Published: Feb. 15, 2024

Food chemicals have a fundamental role in our lives, with an extended impact on nutrition, disease prevention, and marked economic implications the food industry. The number of chemical compounds public databases has substantially increased past few years, which can be characterized using chemoinformatics approaches. We other groups explored libraries containing up to 26,500 compounds. This study aimed analyze contents, diversity, coverage space additives and, from here on, components. approach components addressed this is database more than 70,000 compounds, including those predicted via

Language: Английский

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Network pharmacology and molecular docking analysis of Catharanthus roseus compounds: Implications for non-small cell lung cancer treatment

Journal of King Saud University - Science, Journal Year: 2024, Volume and Issue: 36(4), P. 103134 - 103134

Published: Feb. 19, 2024

The prevalence of non-small cell lung cancer (NSCLC), particularly in the Eastern Region Saudi Arabia, is concerning and highlights need for novel treatments, despite recent progress NSCLC treatment. Plant-derived natural compounds, such as those found Catharanthus roseus, have shown promising results research. By employing cutting-edge computational analysis techniques, molecular docking network pharmacology, study aimed to understand mechanism action compounds isolated from C. roseus. identified several bioactive drug-like roseus that displayed diverse interactions with important proteins associated cancer. PubChem CIDs 5316490, 425978, 261578 were form significant within binding site B-cell-lymphoma-2 (BCL-2) protein. Compound particular, interacted critical residues target protein, highlighting its potential a therapeutic agent against understanding targets obtained this provides hope improving current treatment options. In conclusion, findings provide additional details on evidence supporting suggest further research could lead development innovative, potent therapies.

Language: Английский

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