• Sebastiaan C Goulooze, Krekels Elke H J EHJ Division of Pharmacology, Cluster Systems Pharmacology, Leiden Academic Centre for Drug Research, Leiden University, Leiden, The Netherlands., Monique van Dijk, Dick Tibboel, Piet H van der Graaf, Thomas Hankemeier, Knibbe Catherijne A J CAJ Division of Pharmacology, Cluster Systems Pharmacology, Leiden Academic Centre for Drug Research, Leiden University, Leiden, The Netherlands; De, and van Hasselt J G Coen JGC Division of Pharmacology, Cluster Systems Pharmacology, Leiden Academic Centre for Drug Research, Leiden University, Leiden, The Netherlands. Ele.
• Division of Pharmacology, Cluster Systems Pharmacology, Leiden Academic Centre for Drug Research, Leiden University, Leiden, The Netherlands.
• Eur J Pharm Sci. 2017 Nov 15; 109S: S32-S38.

AbstractPain is a complex biopsychosocial phenomenon of which the intensity, location and duration depends on various underlying components. Treatment of pain is associated with considerable inter-individual variability, and as such, requires a personalized approach. However, a priori prediction of optimal analgesic treatment for individual patients is still challenging. Another challenge is the assessment and treatment of pain in patients unable to self-report pain. In this mini-review, we first provide a brief overview of the various components underlying pain, and their associated biomarkers. These include clinical, psychosocial, neurophysiological, and biochemical components. We then discuss the use of empirical and mechanism-based pharmacokinetic-pharmacodynamic modelling to support personalized treatment of pain. Finally, we propose how these concepts can be extended to a quantitative systems pharmacology (QSP) approach that integrates the components of clinical pain and treatment response. This integrative approach can support predictions of optimal pharmacotherapy of pain, compared with approaches that focus on single components of pain. Moreover, combination of QSP modelling with state-of-the-art metabolomics approaches may offer unique possibilities to identify novel pain biomarkers. Such biomarkers could support both the personalized treatment of pain and translational drug development of novel analgesic agents. In conclusion, a QSP approach will likely improve our ability to predict pain and treatment response, paving the way for personalized treatment of pain.Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

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