Abstract
Pain 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.
Highlights
Pain has been defined as an “unpleasant sensory and emotional experience associated with actual or potential tissue damage” (Merskey and Bogduk, 1994), and involves a complex interplay of neurophysiology (Vardeh et al, 2016), psychosocial factors (Mao, 2012), and inflammatory processes (Ji et al, 2016)
Pain and treatment response for both acute and chronic pain is associated with substantial interindividual variability (Aubrun et al, 2012; Gilron et al, 2013; HinrichsRocker et al, 2009)
We discuss the use of empirical and mechanism-based pharmacokinetic-pharmacodynamic modelling of clinical pain. We propose how these concepts can be extended to a quantitative systems pharmacology (QSP) approach that integrates the components and biomarkers of clinical pain to enable personalized treatment
Summary
Pain has been defined as an “unpleasant sensory and emotional experience associated with actual or potential tissue damage” (Merskey and Bogduk, 1994), and involves a complex interplay of neurophysiology (Vardeh et al, 2016), psychosocial factors (Mao, 2012), and inflammatory processes (Ji et al, 2016). For chronic pain there is a need to predict both the type of drugs and their dosage regimen that will optimally treat the individual patient. Nents of pain, biomarkers for pain or treatment response could contribute to personalized treatment in several ways: i) pain monitoring in patients where self-report is not possible ii) diagnosis of pain conditions iii) a priori prediction of optimal treatment (Backryd, 2015; Beger et al, 2016). Characterizing the inter-individual variability in the underlying components of pain might support the personalized treatment of pain. An approach that integrates these components would support the use and development of biomarkers to guide personalized treatment of pain. We propose how these concepts can be extended to a quantitative systems pharmacology (QSP) approach that integrates the components and biomarkers of clinical pain to enable personalized treatment
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