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A Quantitative Systems Pharmacology Platform Reveals NAFLD Pathophysiological States and Targeting Strategies.
Lefever, Daniel E; Miedel, Mark T; Pei, Fen; DiStefano, Johanna K; Debiasio, Richard; Shun, Tong Ying; Saydmohammed, Manush; Chikina, Maria; Vernetti, Lawrence A; Soto-Gutierrez, Alejandro; Monga, Satdarshan P; Bataller, Ramon; Behari, Jaideep; Yechoor, Vijay K; Bahar, Ivet; Gough, Albert; Stern, Andrew M; Taylor, D Lansing.
Afiliação
  • Lefever DE; Drug Discovery Institute, University of Pittsburgh, Pittsburgh, PA 15261, USA.
  • Miedel MT; Drug Discovery Institute, University of Pittsburgh, Pittsburgh, PA 15261, USA.
  • Pei F; Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15260, USA.
  • DiStefano JK; Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15260, USA.
  • Debiasio R; Diabetes and Fibrotic Disease Unit, Translational Genomics Research Institute TGen, Phoenix, AZ 85004, USA.
  • Shun TY; Drug Discovery Institute, University of Pittsburgh, Pittsburgh, PA 15261, USA.
  • Saydmohammed M; Drug Discovery Institute, University of Pittsburgh, Pittsburgh, PA 15261, USA.
  • Chikina M; Drug Discovery Institute, University of Pittsburgh, Pittsburgh, PA 15261, USA.
  • Vernetti LA; Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15260, USA.
  • Soto-Gutierrez A; Pittsburgh Liver Research Center, University of Pittsburgh, Pittsburgh, PA 15261, USA.
  • Monga SP; Drug Discovery Institute, University of Pittsburgh, Pittsburgh, PA 15261, USA.
  • Bataller R; Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15260, USA.
  • Behari J; Drug Discovery Institute, University of Pittsburgh, Pittsburgh, PA 15261, USA.
  • Yechoor VK; Pittsburgh Liver Research Center, University of Pittsburgh, Pittsburgh, PA 15261, USA.
  • Bahar I; Department of Pathology, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15261, USA.
  • Gough A; McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, PA 15203, USA.
  • Stern AM; Drug Discovery Institute, University of Pittsburgh, Pittsburgh, PA 15261, USA.
  • Taylor DL; Pittsburgh Liver Research Center, University of Pittsburgh, Pittsburgh, PA 15261, USA.
Metabolites ; 12(6)2022 Jun 07.
Article em En | MEDLINE | ID: mdl-35736460
Non-alcoholic fatty liver disease (NAFLD) has a high global prevalence with a heterogeneous and complex pathophysiology that presents barriers to traditional targeted therapeutic approaches. We describe an integrated quantitative systems pharmacology (QSP) platform that comprehensively and unbiasedly defines disease states, in contrast to just individual genes or pathways, that promote NAFLD progression. The QSP platform can be used to predict drugs that normalize these disease states and experimentally test predictions in a human liver acinus microphysiology system (LAMPS) that recapitulates key aspects of NAFLD. Analysis of a 182 patient-derived hepatic RNA-sequencing dataset generated 12 gene signatures mirroring these states. Screening against the LINCS L1000 database led to the identification of drugs predicted to revert these signatures and corresponding disease states. A proof-of-concept study in LAMPS demonstrated mitigation of steatosis, inflammation, and fibrosis, especially with drug combinations. Mechanistically, several structurally diverse drugs were predicted to interact with a subnetwork of nuclear receptors, including pregnane X receptor (PXR; NR1I2), that has evolved to respond to both xenobiotic and endogenous ligands and is intrinsic to NAFLD-associated transcription dysregulation. In conjunction with iPSC-derived cells, this platform has the potential for developing personalized NAFLD therapeutic strategies, informing disease mechanisms, and defining optimal cohorts of patients for clinical trials.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Risk_factors_studies Idioma: En Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Risk_factors_studies Idioma: En Ano de publicação: 2022 Tipo de documento: Article