Emergence of Quantitative and Systems Pharmacology: A White Paper

Our interest in quantitative and systems pharmacology (QSP) began in 2007 as a question about why we were seeing so little integration between two fields we fund: systems biology and pharmacology. We recognized that connecting them could improve our understanding of drug action and speed drug discovery and development while also increasing our scientific understanding of biology.

To examine the potential of quantitative, systems approaches to pharmacology research, we sponsored two workshops in this area, one in 2008 and the other in 2010. After the second meeting, a committee of external scientists who were also workshop participants began drafting a white paper to assess the state of the science and enumerate the opportunities, needs and challenges for QSP as an emerging discipline.

The committee recently issued the white paper.

The paper makes the case that this post-genomic era is the right time to develop and employ quantitative, systems approaches to understand drug action more predictively, and that the need and excitement for doing so is building. Already we are starting to see evidence of this field emerging—the University of California, San Francisco, has started a Center for Quantitative Pharmacology Exit icon, and Harvard Medical School just announced an Initiative in Systems Pharmacology Exit icon. Also, the American Association of Pharmaceutical Sciences annual meeting Exit icon this month will include a session called, “Achieving the Quantitative and Systems Pharmacology Vision.”

The overall recommendation of the workshop committee is for pharmacology to move beyond characterizing drug/target interactions to a holistic quantitative understanding of drug action across many levels—from drug-receptor interactions to drug response in humans. As stated in the paper, this will require the participation of scientists from academia and industry who work in diverse areas, including traditional pharmacology, clinical pharmacology, pharmacodynamics/pharmacokinetic modeling, systems biology, chemistry, bioinformatics, multiscale modeling and computer science. Training new and established investigators also will be a critical element.

We encourage you to read the paper and let us know what you think about its recommendations for research and training in QSP.

6 comments on “Emergence of Quantitative and Systems Pharmacology: A White Paper

  1. Now seems to be a propitious time for integration of systems biology not only with pharmacology, but also with physiology (the original “systems” bioscience!).

    • The white paper committee agreed. There are many references to physiology in the white paper. An early reference is at the bottom of page 5: “QSP draws on existing ideas and established concepts from traditional pharmacology, physiology and target-based drug discovery and will therefore serve as a link between pharmacology/physiology and new systems-level and “omics” approaches.”

  2. Under the Innovation subsection, the report states, “Biochemical networks targeted by drugs are qualitatively similar but quantitatively different in different tissues, genetic backgrounds, development stages and disease states, and the operation of these networks is profoundly impacted by patient lifestyle and history.”

    The transition from qualitative to quantitative similarity is currently informal and must be made more formal and rigorous.  Early steps toward that goal are discussed in PMCID: PMC3200146.

  3. Achieving modularity, multi-scale models, & pathway composition

    The QSP objectives can be unified by a methodology for quantifying multi-scale attributes of biological systems across research and clinical contexts, achieving modularity, multi-scale models, & pathway composition.  This effort is long overdue!  Multiplex measurement and multi-factorial modeling are efficiently enabled through the use of modular, composite models, as described in the vertical integration Appendix 3.  Quantitative systemic understanding of a system is achieved through the integration of qualitative ‘word models’ with quantitative multi-factorial data at various scales, continuous and discrete.  Their integration composes a quantitative systems model, which facilitates quantitative representations of the biological system and its environment.  With such a systems model, translation (between wet-lab models and between models and a clinical context) becomes a process of quantitative comparison between variables, parameters, and observations. An early example is provided in PMID: 20406856.

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