Model-informed target identification and validation through combining quantitative systems pharmacology with network-based analysis.
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ABSTRACT:
SUBMITTER: Chelliah V
PROVIDER: S-EPMC9007604 | biostudies-literature | 2022 Apr
REPOSITORIES: biostudies-literature
Publications
Model-informed target identification and validation through combining quantitative systems pharmacology with network-based analysis.
Chelliah Vijayalakshmi V van der Graaf Piet H PH
CPT: pharmacometrics & systems pharmacology 20220217 4
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Project description:Increased availability of bioinformatics resources is creating opportunities for the application of network pharmacology to predict drug effects and toxicity resulting from multi-target interactions. Here we present a high-precision computational prediction approach that combines two elaborately built machine learning systems and multiple molecular docking tools to assess binding potentials of a test compound against proteins involved in a complex molecular network. One of the two machine learning systems is a re-scoring function to evaluate binding modes generated by docking tools. The second is a binding mode selection function to identify the most predictive binding mode. Results from a series of benchmark validations and a case study show that this approach surpasses the prediction reliability of other techniques and that it also identifies either primary or off-targets of kinase inhibitors. Integrating this approach with molecular network maps makes it possible to address drug safety issues by comprehensively investigating network-dependent effects of a drug or drug candidate.
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Project description:BackgroundPreeclampsia, characterized by hypertension and proteinuria during pregnancy, poses significant risks to both mother and fetus. The complement system's aberrant activation, notably the C3AR1, is important to the pathogenesis of preeclampsia, although the precise mechanisms are not fully understood.Materials and methodsUtilizing the Comparative Toxicogenomics Database (CTD) and Molecular Signatures Database (MSigDB), we identified complement system targets associated with preeclampsia and environmental pollutants. Expression validation was conducted through the Gene Expression Omnibus (GEO) database. Molecular docking predicted interactions between BPA, PFOS, and C3AR1. Immunohistochemical staining of 80 placental tissues (40 early-onset preeclampsia and 40 healthy controls) confirmed C3AR1 expression and its clinical correlation. Integrated bioinformatics analyses revealed C3AR1's role in preeclampsia's molecular mechanisms. Functional verification was assessed by knocking down C3AR1 in HTR-8/Svneo cells, including cell proliferation, invasion, and apoptosis.ResultsNetwork pharmacology established connections between pollutants and preeclampsia, with C3AR1 as a key target. Molecular docking confirmed BPA and PFOS binding to C3AR1. Reduced C3AR1 in preeclamptic placentas correlated with maternal blood pressure, and showed high diagnostic potential (AUC = 0.95). C3AR1's involvement in preeclampsia was linked to Jak-STAT, TGF-β, and HIF-1 pathways, and associated with NK cell and M1 macrophage activity. C3AR1 knockdown in HTR-8/Svneo cells decreased proliferation and invasion, and increased apoptosis.ConclusionC3AR1 expression is diminished in preeclampsia placental tissues, correlating with disease severity, suggesting its potential as a biomarker. It is crucial for cellular functions and inflammation, with future studies aiming to leverage this for novel preeclampsia treatments.Clinical trialNot applicable.
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Project description:Despite recent advance of therapeutic development, coronary artery disease (CAD) remains one of the major issues to public health. The use of genomics and systems biology approaches to inform drug discovery and development have offered the possibilities for new target identification and in silico drug repurposing. In this study, we propose a network-based, systems pharmacology framework for target identification and drug repurposing in pharmacologic treatment and chemoprevention of CAD. Specifically, we build in silico models by integrating known drug-target interactions, CAD genes derived from the genetic and genomic studies, and the human protein-protein interactome. We demonstrate that the proposed in silico models can successfully uncover approved drugs and novel natural products in potentially treating and preventing CAD. In case studies, we highlight several approved drugs (e.g., fasudil, parecoxib, and dexamethasone) or natural products (e.g., resveratrol, luteolin, daidzein and caffeic acid) with new mechanism-of-action in chemical intervention of CAD by network analysis. In summary, this study offers a powerful systems pharmacology approach for target identification and in silico drug repurposing on CAD.
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