A Quantitative Systems Pharmacology Approach to Infer Pathways Involved in Complex Disease Phenotypes

  • Mark E. Schurdak
  • Fen Pei
  • Timothy R. Lezon
  • Diane Carlisle
  • Robert Friedlander
  • D. Lansing Taylor
  • Andrew M. Stern
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1787)

Abstract

Designing effective therapeutic strategies for complex diseases such as cancer and neurodegeneration that involve tissue context-specific interactions among multiple gene products presents a major challenge for precision medicine. Safe and selective pharmacological modulation of individual molecular entities associated with a disease often fails to provide efficacy in the clinic. Thus, development of optimized therapeutic strategies for individual patients with complex diseases requires a more comprehensive, systems-level understanding of disease progression. Quantitative systems pharmacology (QSP) is an approach to drug discovery that integrates computational and experimental methods to understand the molecular pathogenesis of a disease at the systems level more completely. Described here is the chemogenomic component of QSP for the inference of biological pathways involved in the modulation of the disease phenotype. The approach involves testing sets of compounds of diverse mechanisms of action in a disease-relevant phenotypic assay, and using the mechanistic information known for the active compounds, to infer pathways and networks associated with the phenotype. The example used here is for monogenic Huntington’s disease (HD), which due to the pleiotropic nature of the mutant phenotype has a complex pathogenesis. The overall approach, however, is applicable to any complex disease.

Key words

Quantitative systems pharmacology QSP Chemogenomics Heterogeneity Pittsburgh Heterogeneity Index PHI Huntington’s disease Precision medicine 

Notes

Acknowledgments

The authors wish to thank Laura Vollmer and Seia Comsa for their technical assistance in developing the HD propidium iodide assay, and Tongying Shun for her assistance with the compound combination analysis. This work was supported by funds from the University of Pittsburgh Brain Institute (Taylor/Stern), PA Commonwealth grants SAP#4100054875 (Taylor) and SAP#4100068731 (Stern), and NIH R01NS039324 (Friedlander).

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Mark E. Schurdak
    • 1
  • Fen Pei
    • 1
  • Timothy R. Lezon
    • 1
  • Diane Carlisle
    • 2
  • Robert Friedlander
    • 2
  • D. Lansing Taylor
    • 1
  • Andrew M. Stern
    • 1
  1. 1.Department of Computational and Systems BiologyUniversity of Pittsburgh Drug Discovery Institute, University of PittsburghPittsburghUSA
  2. 2.Department of Neurological SurgeryUniversity of PittsburghPittsburghUSA

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