Using Quantitative Systems Pharmacology for Novel Drug Discovery

Expert Opin Drug Discov. 2015 Dec;10(12):1315-31. doi: 10.1517/17460441.2015.1082543. Epub 2015 Aug 25.

Abstract

Introduction: Over the past three decades, the predominant paradigm in drug discovery was designing selective ligands for a specific target to avoid unwanted side effects. However, in the last 5 years, the aim has shifted to take into account the biological network in which they interact. Quantitative and Systems Pharmacology (QSP) is a new paradigm that aims to understand how drugs modulate cellular networks in space and time, in order to predict drug targets and their role in human pathophysiology.

Areas covered: This review discusses existing computational and experimental QSP approaches such as polypharmacology techniques combined with systems biology information and considers the use of new tools and ideas in a wider 'systems-level' context in order to design new drugs with improved efficacy and fewer unwanted off-target effects.

Expert opinion: The use of network biology produces valuable information such as new indications for approved drugs, drug-drug interactions, proteins-drug side effects and pathways-gene associations. However, we are still far from the aim of QSP, both because of the huge effort needed to model precisely biological network models and the limited accuracy that we are able to reach with those. Hence, moving from 'one molecule for one target to give one therapeutic effect' to the 'big systems-based picture' seems obvious moving forward although whether our current tools are sufficient for such a step is still under debate.

Keywords: adverse polypharmacology; complex cellular network; drug action; pathophysiology; personalized medicine; pharmacokinetics/pharmacodynamics; quantitative and systems pharmacology; side effects; systems biology; systems pharmacology; therapeutic polypharmacology; virtual screening.

Publication types

  • Review

MeSH terms

  • Animals
  • Drug Design*
  • Drug Discovery / methods*
  • Drug Interactions
  • Drug-Related Side Effects and Adverse Reactions / prevention & control
  • Humans
  • Ligands
  • Models, Biological
  • Molecular Targeted Therapy
  • Pharmacology
  • Systems Biology / methods*

Substances

  • Ligands