Integrated Network Pharmacology, ADME Profiling and Molecular Dcking of persea americana seed metabolites for Potential Anti-prostate cancer activity

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dc.contributor.authorEyoku Emmanuel
dc.date.accessioned2026-08-26T12:22:29Z
dc.date.available2026-08-26T12:22:29Z
dc.date.issued2026-08-26
dc.description.abstractABSTRACT This study investigates the potential anti-prostate cancer activity of bioactive metabolites derived from Persea americana (avocado) seeds using an integrated computational approach combining network pharmacology, ADME (absorption, distribution, metabolism, and excretion) profiling, and molecular docking. Initially, metabolites were identified through literature and phytochemical databases, followed by screening based on drug-likeness and ADME properties to ensure appropriate pharmacokinetic profiles. The selected compounds were subjected to target prediction using network pharmacology tools to construct a compound-target-disease interaction network, revealing key protein targets implicated in prostate cancer pathways. Subsequent pathway enrichment analysis identified critical signalling pathways involved in cancer progression, such as PI3K-Akt and androgen receptor signalling. Molecular docking simulations were performed against major prostate cancer-related protein targets to assess binding affinity and interaction patterns. The top-ranked metabolites demonstrated strong binding energies and key residue interactions indicative of potential inhibitory activity. Overall, this integrative workflow combined phytochemical profiling, computational pharmacokinetics, network analysis, and molecular docking to prioritize Persea americana seed metabolites as promising candidates for further experimental validation in prostate cancer therapeutics. Workflow Breakdown 1. Metabolite Identification • Collect and curate Persea americana seed metabolites from scientific literature and phytochemical databases. 2. ADME Profiling • Evaluate absorption, distribution, metabolism, and excretion properties using computational tools. • Screen metabolites for drug-likeness based on standard criteria (e.g., Lipinski’s Rule of Five). • Eliminate compounds with poor pharmacokinetic profiles to focus on candidates with high oral bioavailability and low toxicity. 3. Target Prediction & Network Construction • Use computational target prediction tools to identify potential molecular targets of the selected metabolites. • Construct a compound-target-disease interaction network visualizing metabolite connections to prostate cancer-related proteins. • Analyse network topological metrics (e.g., degree centrality, betweenness) to identify hub targets highly relevant to prostate cancer biology. 4. Pathway and Functional Enrichment Analysis • Perform pathway enrichment analysis on the predicted targets to uncover involved biological pathways. • Highlight key cancer-related pathways such as PI3K-Akt, androgen receptor signalling, and apoptosis-regulating mechanisms. 5. Molecular Docking Simulation • Dock selected metabolites onto key prostate cancer protein targets to estimate binding affinities. • Analyse interaction patterns, such as hydrogen bonds and hydrophobic contacts, with critical active site residues. • Rank compounds based on docking scores to prioritize the strongest binders. Major Computational Outputs • ADME Results: • Number of metabolites passing drug-likeness filters (e.g., % complying with Lipinski's rules). • Predicted oral bioavailability and clearance rates enabling selection of pharmacokinetically viable compounds. • Network Analysis: • Identification of top hub targets, e.g., AR (androgen receptor), AKT1, or other critical proteins implicated in prostate cancer. • Visual network maps showing metabolite-target and target-disease interactions. • Key network metrics quantifying target centrality and relevance. • Pathway Enrichment: • List of statistically significant pathways with associated genes targeted by metabolites. • Insight into mechanisms like cell proliferation, apoptosis, and hormone signalling affected by the metabolites. • Molecular Docking: • Binding energy values (e.g., kcal/mol) for each metabolite against target proteins. • Description of interaction types and residues involved in binding, identifying potential inhibitory action. • Ranking of metabolites by docking affinity to highlight the most promising candidates.
dc.identifier.urihttps://bdears.busitema.ac.ug/handle/123456789/9159
dc.language.isoen_US
dc.publisherBusitema University
dc.titleIntegrated Network Pharmacology, ADME Profiling and Molecular Dcking of persea americana seed metabolites for Potential Anti-prostate cancer activity
dc.typeThesis
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