Towards personalized treatment of pain using a quantitative systems pharmacology approach
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...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Goulooze, Sebastiaan C. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2017transfer abstract |
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| Umfang: |
7 |
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| Übergeordnetes Werk: |
Enthalten in: The face of wrongdoing? An expectancy violations perspective on CEO facial characteristics and media coverage of misconducting firms - Heyden, Mariano L.M. ELSEVIER, 2022, official journal of the European Federation for Pharmaceutical Sciences, New York, NY [u.a.] |
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| Übergeordnetes Werk: |
volume:109 ; year:2017 ; day:15 ; month:11 ; pages:32-38 ; extent:7 |
| Links: |
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| DOI / URN: |
10.1016/j.ejps.2017.05.027 |
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ELV04106335X |
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| 520 | |a 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. | ||
| 520 | |a 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. | ||
| 700 | 1 | |a Krekels, Elke H.J. |4 oth | |
| 700 | 1 | |a van Dijk, Monique |4 oth | |
| 700 | 1 | |a Tibboel, Dick |4 oth | |
| 700 | 1 | |a van der Graaf, Piet H. |4 oth | |
| 700 | 1 | |a Hankemeier, Thomas |4 oth | |
| 700 | 1 | |a Knibbe, Catherijne A.J. |4 oth | |
| 700 | 1 | |a van Hasselt, J.G. Coen |4 oth | |
| 773 | 0 | 8 | |i Enthalten in |n Elsevier |a Heyden, Mariano L.M. ELSEVIER |t The face of wrongdoing? An expectancy violations perspective on CEO facial characteristics and media coverage of misconducting firms |d 2022 |d official journal of the European Federation for Pharmaceutical Sciences |g New York, NY [u.a.] |w (DE-627)ELV009954198 |
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10.1016/j.ejps.2017.05.027 doi GBV00000000000043A.pica (DE-627)ELV04106335X (ELSEVIER)S0928-0987(17)30255-5 DE-627 ger DE-627 rakwb eng 610 610 DE-600 300 330 360 VZ 85.05 bkl 85.06 bkl 89.52 bkl Goulooze, Sebastiaan C. verfasserin aut Towards personalized treatment of pain using a quantitative systems pharmacology approach 2017transfer abstract 7 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier 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. 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. Krekels, Elke H.J. oth van Dijk, Monique oth Tibboel, Dick oth van der Graaf, Piet H. oth Hankemeier, Thomas oth Knibbe, Catherijne A.J. oth van Hasselt, J.G. Coen oth Enthalten in Elsevier Heyden, Mariano L.M. ELSEVIER The face of wrongdoing? An expectancy violations perspective on CEO facial characteristics and media coverage of misconducting firms 2022 official journal of the European Federation for Pharmaceutical Sciences New York, NY [u.a.] (DE-627)ELV009954198 volume:109 year:2017 day:15 month:11 pages:32-38 extent:7 https://doi.org/10.1016/j.ejps.2017.05.027 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 85.05 Betriebssoziologie Betriebspsychologie VZ 85.06 Unternehmensführung VZ 89.52 Politische Psychologie Politische Soziologie VZ AR 109 2017 15 1115 32-38 7 109.2017, S32-, (7 S.) 045F 610 |
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10.1016/j.ejps.2017.05.027 doi GBV00000000000043A.pica (DE-627)ELV04106335X (ELSEVIER)S0928-0987(17)30255-5 DE-627 ger DE-627 rakwb eng 610 610 DE-600 300 330 360 VZ 85.05 bkl 85.06 bkl 89.52 bkl Goulooze, Sebastiaan C. verfasserin aut Towards personalized treatment of pain using a quantitative systems pharmacology approach 2017transfer abstract 7 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier 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. 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. Krekels, Elke H.J. oth van Dijk, Monique oth Tibboel, Dick oth van der Graaf, Piet H. oth Hankemeier, Thomas oth Knibbe, Catherijne A.J. oth van Hasselt, J.G. Coen oth Enthalten in Elsevier Heyden, Mariano L.M. ELSEVIER The face of wrongdoing? An expectancy violations perspective on CEO facial characteristics and media coverage of misconducting firms 2022 official journal of the European Federation for Pharmaceutical Sciences New York, NY [u.a.] (DE-627)ELV009954198 volume:109 year:2017 day:15 month:11 pages:32-38 extent:7 https://doi.org/10.1016/j.ejps.2017.05.027 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 85.05 Betriebssoziologie Betriebspsychologie VZ 85.06 Unternehmensführung VZ 89.52 Politische Psychologie Politische Soziologie VZ AR 109 2017 15 1115 32-38 7 109.2017, S32-, (7 S.) 045F 610 |
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10.1016/j.ejps.2017.05.027 doi GBV00000000000043A.pica (DE-627)ELV04106335X (ELSEVIER)S0928-0987(17)30255-5 DE-627 ger DE-627 rakwb eng 610 610 DE-600 300 330 360 VZ 85.05 bkl 85.06 bkl 89.52 bkl Goulooze, Sebastiaan C. verfasserin aut Towards personalized treatment of pain using a quantitative systems pharmacology approach 2017transfer abstract 7 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier 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. 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. Krekels, Elke H.J. oth van Dijk, Monique oth Tibboel, Dick oth van der Graaf, Piet H. oth Hankemeier, Thomas oth Knibbe, Catherijne A.J. oth van Hasselt, J.G. Coen oth Enthalten in Elsevier Heyden, Mariano L.M. ELSEVIER The face of wrongdoing? An expectancy violations perspective on CEO facial characteristics and media coverage of misconducting firms 2022 official journal of the European Federation for Pharmaceutical Sciences New York, NY [u.a.] (DE-627)ELV009954198 volume:109 year:2017 day:15 month:11 pages:32-38 extent:7 https://doi.org/10.1016/j.ejps.2017.05.027 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 85.05 Betriebssoziologie Betriebspsychologie VZ 85.06 Unternehmensführung VZ 89.52 Politische Psychologie Politische Soziologie VZ AR 109 2017 15 1115 32-38 7 109.2017, S32-, (7 S.) 045F 610 |
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10.1016/j.ejps.2017.05.027 doi GBV00000000000043A.pica (DE-627)ELV04106335X (ELSEVIER)S0928-0987(17)30255-5 DE-627 ger DE-627 rakwb eng 610 610 DE-600 300 330 360 VZ 85.05 bkl 85.06 bkl 89.52 bkl Goulooze, Sebastiaan C. verfasserin aut Towards personalized treatment of pain using a quantitative systems pharmacology approach 2017transfer abstract 7 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier 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. 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. Krekels, Elke H.J. oth van Dijk, Monique oth Tibboel, Dick oth van der Graaf, Piet H. oth Hankemeier, Thomas oth Knibbe, Catherijne A.J. oth van Hasselt, J.G. Coen oth Enthalten in Elsevier Heyden, Mariano L.M. ELSEVIER The face of wrongdoing? An expectancy violations perspective on CEO facial characteristics and media coverage of misconducting firms 2022 official journal of the European Federation for Pharmaceutical Sciences New York, NY [u.a.] (DE-627)ELV009954198 volume:109 year:2017 day:15 month:11 pages:32-38 extent:7 https://doi.org/10.1016/j.ejps.2017.05.027 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 85.05 Betriebssoziologie Betriebspsychologie VZ 85.06 Unternehmensführung VZ 89.52 Politische Psychologie Politische Soziologie VZ AR 109 2017 15 1115 32-38 7 109.2017, S32-, (7 S.) 045F 610 |
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10.1016/j.ejps.2017.05.027 doi GBV00000000000043A.pica (DE-627)ELV04106335X (ELSEVIER)S0928-0987(17)30255-5 DE-627 ger DE-627 rakwb eng 610 610 DE-600 300 330 360 VZ 85.05 bkl 85.06 bkl 89.52 bkl Goulooze, Sebastiaan C. verfasserin aut Towards personalized treatment of pain using a quantitative systems pharmacology approach 2017transfer abstract 7 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier 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. 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. Krekels, Elke H.J. oth van Dijk, Monique oth Tibboel, Dick oth van der Graaf, Piet H. oth Hankemeier, Thomas oth Knibbe, Catherijne A.J. oth van Hasselt, J.G. Coen oth Enthalten in Elsevier Heyden, Mariano L.M. ELSEVIER The face of wrongdoing? An expectancy violations perspective on CEO facial characteristics and media coverage of misconducting firms 2022 official journal of the European Federation for Pharmaceutical Sciences New York, NY [u.a.] (DE-627)ELV009954198 volume:109 year:2017 day:15 month:11 pages:32-38 extent:7 https://doi.org/10.1016/j.ejps.2017.05.027 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 85.05 Betriebssoziologie Betriebspsychologie VZ 85.06 Unternehmensführung VZ 89.52 Politische Psychologie Politische Soziologie VZ AR 109 2017 15 1115 32-38 7 109.2017, S32-, (7 S.) 045F 610 |
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Enthalten in The face of wrongdoing? An expectancy violations perspective on CEO facial characteristics and media coverage of misconducting firms New York, NY [u.a.] volume:109 year:2017 day:15 month:11 pages:32-38 extent:7 |
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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. |
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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. |
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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. |
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