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Nat Rev Neurol. 2020 Jul;16(7):381-400. doi: 10.1038/s41582-020-0362-2. Epub 2020 Jun 15.

Discovery and validation of biomarkers to aid the development of safe and effective pain therapeutics: challenges and opportunities.

Nature reviews. Neurology

Karen D Davis, Nima Aghaeepour, Andrew H Ahn, Martin S Angst, David Borsook, Ashley Brenton, Michael E Burczynski, Christopher Crean, Robert Edwards, Brice Gaudilliere, Georgene W Hergenroeder, Michael J Iadarola, Smriti Iyengar, Yunyun Jiang, Jiang-Ti Kong, Sean Mackey, Carl Y Saab, Christine N Sang, Joachim Scholz, Marta Segerdahl, Irene Tracey, Christin Veasley, Jing Wang, Tor D Wager, Ajay D Wasan, Mary Ann Pelleymounter

Affiliations

  1. Department of Surgery and Institute of Medical Science, University of Toronto, Toronto, ON, Canada. [email protected].
  2. Division of Brain, Imaging and Behaviour, Krembil Brain Institute, Toronto Western Hospital, University Health Network, Toronto, ON, Canada. [email protected].
  3. Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA.
  4. Teva Pharmaceuticals, Frazer, PA, USA.
  5. Center for Pain and the Brain, Harvard Medical School, Boston, MA, USA.
  6. Mycroft Bioanalytics, Salt Lake City, UT, USA.
  7. Teva Pharmaceutical Industries, Frazer, PA, USA.
  8. Xyzagen, Pittsboro, NC, USA.
  9. Pain Management Center, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
  10. The Vivian L. Smith Department of Neurosurgery, The University of Texas Health Science Center at Houston, McGovern Medical School, Houston, TX, USA.
  11. Department of Perioperative Medicine, Clinical Center, NIH, Rockville, MD, USA.
  12. Division of Translational Research, National Institute of Neurological Disorders and Stroke, NIH, Rockville, MD, USA.
  13. The Biostatistics Center, Milken Institute School of Public Health, The George Washington University, Washington, DC, USA.
  14. Department of Neuroscience and Department of Neurosurgery, Carney Institute for Brain Science, Brown University, Providence, RI, USA.
  15. Department of Anesthesiology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
  16. Neurocognitive Disorders, Pain and New Indications, Biogen, Cambridge, MA, USA.
  17. Asarina Pharma, Copenhagen, Denmark.
  18. Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.
  19. Chronic Pain Research Alliance, Bethesda, MD, USA.
  20. Department of Anesthesiology, Perioperative Care and Pain Medicine, NYU School of Medicine, New York, NY, USA.
  21. Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, USA.
  22. Anesthesiology and Perioperative Medicine and Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA.

PMID: 32541893 PMCID: PMC7326705 DOI: 10.1038/s41582-020-0362-2

Abstract

Pain medication plays an important role in the treatment of acute and chronic pain conditions, but some drugs, opioids in particular, have been overprescribed or prescribed without adequate safeguards, leading to an alarming rise in medication-related overdose deaths. The NIH Helping to End Addiction Long-term (HEAL) Initiative is a trans-agency effort to provide scientific solutions to stem the opioid crisis. One component of the initiative is to support biomarker discovery and rigorous validation in collaboration with industry leaders to accelerate high-quality clinical research into neurotherapeutics and pain. The use of objective biomarkers and clinical trial end points throughout the drug discovery and development process is crucial to help define pathophysiological subsets of pain, evaluate target engagement of new drugs and predict the analgesic efficacy of new drugs. In 2018, the NIH-led Discovery and Validation of Biomarkers to Develop Non-Addictive Therapeutics for Pain workshop convened scientific leaders from academia, industry, government and patient advocacy groups to discuss progress, challenges, gaps and ideas to facilitate the development of biomarkers and end points for pain. The outcomes of this workshop are outlined in this Consensus Statement.

References

  1. Treede, R. D. et al. Chronic pain as a symptom or a disease: the IASP classification of chronic pain for the International Classification of Diseases (ICD-11). Pain 160, 19–27 (2019). - PubMed
  2. Von Korff, M. et al. United States National Pain Strategy for population research: concepts, definitions, and pilot data. J. Pain. 17, 1068–1080 (2016). - PubMed
  3. GBD 2017 Disease and Injury Incidence and Prevalence Collaborators. Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet 392, 1789–1858 (2018). - PubMed
  4. Nahin, R. L., Sayer, B., Stussman, B. J. & Feinberg, T. M. Eighteen-year trends in the prevalence of, and health care use for, noncancer pain in the United States: data from the Medical Expenditure Panel Survey. J. Pain 20, 796–809 (2019). - PubMed
  5. US Institute of Medicine. Relieving Pain in America: A Blueprint for Transforming Prevention, Care, Education, and Research (National Academies, 2011). - PubMed
  6. Dahlhamer, J. et al. Prevalence of chronic pain and high-impact chronic pain among adults — United States, 2016. MMWR Morb. Mortal. Wkly. Rep. 67, 1001–1006 (2018). - PubMed
  7. Gatchel, R. J. et al. Research agenda for the prevention of pain and its impact: report of the Work Group on the Prevention of Acute and Chronic Pain of the Federal Pain Research Strategy. J. Pain 19, 837–851 (2018). - PubMed
  8. World Health Organization. Management of substance abuse. Information sheet on opioid overdose (WHO, 2018). - PubMed
  9. US Substance Abuse and Mental Health Services Administration. Facing addiction in America: the Surgeon General’s report on alcohol, drugs, and health (US Department of Health and Human Services, 2016). - PubMed
  10. Mackey, S. & Kao, M. C. Managing twin crises in chronic pain and prescription opioids. BMJ 364, l917 (2019). - PubMed
  11. Pitcher, M. H., Von Korff, M., Bushnell, M. C. & Porter, L. Prevalence and profile of high-impact chronic pain in the United States. J. Pain 20, 146–160 (2019). - PubMed
  12. FDA Center for Drug Evaluation and Research. Advancing health through innovation: 2018 new drug therapy approvals (FDA, 2019). - PubMed
  13. Fogel, D. B. Factors associated with clinical trials that fail and opportunities for improving the likelihood of success: a review. Contemp. Clin. Trials Commun. 11, 156–164 (2018). - PubMed
  14. Thomas, D. & Wessel, C. The state of innovation in highly prevalent chronic disease. BIO Ind. Anal. II, 1–15 (2018). - PubMed
  15. Ferber, G. Biomarkers and proof of concept. Methods Find. Exp. Clin. Pharmacol. 24 (Suppl. C), 35–40 (2002). - PubMed
  16. Morgan, P. et al. Impact of a five-dimensional framework on R&D productivity at AstraZeneca. Nat. Rev. Drug Discov. 17, 167–181 (2018). - PubMed
  17. Thomas, D. W. et al. Clinical development success rates 2006–2015 (BIO, 2016). - PubMed
  18. Nagakura, Y. The need for fundamental reforms in the pain research field to develop innovative drugs. Expert Opin. Drug Discov. 12, 39–46 (2017). - PubMed
  19. Niculescu, A. B. et al. Towards precision medicine for pain: diagnostic biomarkers and repurposed drugs. Mol. Psychiat. 24, 501–522 (2019). - PubMed
  20. Wideman, T. H. et al. The multimodal assessment model of pain: a novel framework for further integrating the subjective pain experience within research and practice. Clin. J. Pain. 35, 212–221 (2019). - PubMed
  21. Treede, R. D. The International Association for the Study of Pain definition of pain: as valid in 2018 as in 1979, but in need of regularly updated footnotes. Pain. Rep. 3, e643 (2018). - PubMed
  22. Bonafe, F. S. S., de Campos, L. A., Maroco, J. & Campos, J. Brief pain inventory: a proposal to extend its clinical application. Eur. J. Pain. 23, 565–576 (2019). - PubMed
  23. Main, C. J. Pain assessment in context: a state of the science review of the McGill pain questionnaire 40 years on. Pain 157, 1387–1399 (2016). - PubMed
  24. Bullock, L. et al. Pain assessment and pain treatment for community-dwelling people with dementia: a systematic review and narrative synthesis. Int. J. Geriatr. Psychiat. 34, 807–821 (2019). - PubMed
  25. Birnie, K. A., Hundert, A. S., Lalloo, C., Nguyen, C. & Stinson, J. N. Recommendations for selection of self-report pain intensity measures in children and adolescents: a systematic review and quality assessment of measurement properties. Pain 160, 5–18 (2019). - PubMed
  26. Dansie, E. J. & Turk, D. C. Assessment of patients with chronic pain. Br. J. Anaesth. 111, 19–25 (2013). - PubMed
  27. Smith, S. M. et al. Pain intensity rating training: results from an exploratory study of the ACTTION PROTECCT system. Pain 157, 1056–1064 (2016). - PubMed
  28. Vollert, J. et al. Quantitative sensory testing using DFNS protocol in Europe: an evaluation of heterogeneity across multiple centers in patients with peripheral neuropathic pain and healthy subjects. Pain 157, 750–758 (2016). - PubMed
  29. Vollert, J. et al. Stratifying patients with peripheral neuropathic pain based on sensory profiles: algorithm and sample size recommendations. Pain 158, 1446–1455 (2017). - PubMed
  30. Haanpaa, M. et al. NeuPSIG guidelines on neuropathic pain assessment. Pain 152, 14–27 (2011). - PubMed
  31. Group, B. D. W. Biomarkers and surrogate endpoints: preferred definitions and conceptual framework. Clin. Pharmacol. Ther. 69, 89–95 (2001). - PubMed
  32. US Food and Drug Administration–National Institutes of Health Biomarker Working Group. BEST (Biomarkers, EndpointS, and other Tools) Resource (FDA, 2016). - PubMed
  33. European Medicines Agency. Guideline on the clinical investigation of medicines for the treatment of Alzheimer’s disease (EMA, 2018). - PubMed
  34. FDA Center for Drug Evaluation and Research. Biomarker qualification: evidentiary framework (FDA, 2018). - PubMed
  35. Wager, T. D. et al. An fMRI-based neurologic signature of physical pain. N. Engl. J. Med. 368, 1388–1397 (2013). - PubMed
  36. Woo, C.-W. & Wager, T. D. Neuroimaging-based biomarker discovery and validation. Pain 156, 1379–1381 (2015). - PubMed
  37. Davis, K. D. et al. Brain imaging tests for chronic pain: medical, legal and ethical issues and recommendations. Nat. Rev. Neurol. 13, 624–638 (2017). - PubMed
  38. Kragel, P. A., Koban, L., Barrett, L. F. & Wager, T. D. Representation, pattern information, and brain signatures: from neurons to neuroimaging. Neuron 99, 257–273 (2018). - PubMed
  39. Woo, C.-W., Chang, L. J., Lindquist, M. A. & Wager, T. D. Building better biomarkers: brain models in translational neuroimaging. Nat. Neurosci. 20, 365–377 (2017). - PubMed
  40. Kohoutová, L. et al. Toward a unified framework for interpreting machine-learning models in neuroimaging. Nat. Protoc. 15, 1399–1435 (2020). - PubMed
  41. Varoquaux, G. Cross-validation failure: small sample sizes lead to large error bars. Neuroimage 180, 68–77 (2018). - PubMed
  42. Hastie, T., Tibshirani, R. & Friedman, J. The Elements of Statistical Learning: Data Mining, Inference, and Prediction (Springer, 2013). - PubMed
  43. Chang, L. J., Gianaros, P. J., Manuck, S. B. & Krishnan, A. A sensitive and specific neural signature for picture-induced negative affect. PLOS Biol. 13, e1002180 (2015). - PubMed
  44. Woo, C. W. et al. Quantifying cerebral contributions to pain beyond nociception. Nat. Commun. 8, 14211 (2017). - PubMed
  45. Zunhammer, M., Bingel, U. & Wager, T. D. Placebo effects on the neurologic pain signature: a meta-analysis of individual participant functional magnetic resonance imaging data. JAMA Neurol. 75, 1321–1330 (2018). - PubMed
  46. Borsook, D., Becerra, L. & Hargreaves, R. Biomarkers for chronic pain and analgesia. Part 1: the need, reality, challenges, and solutions. Discov. Med. 11, 197–207 (2011). - PubMed
  47. Borsook, D., Becerra, L. & Hargreaves, R. Biomarkers for chronic pain and analgesia. Part 2: how, where, and what to look for using functional imaging. Discov. Med. 11, 209–219 (2011). - PubMed
  48. Bair, E. et al. Identification of clusters of individuals relevant to temporomandibular disorders and other chronic pain conditions: the OPPERA study. Pain 157, 1266–1278 (2016). - PubMed
  49. Rolke, R. et al. Quantitative sensory testing in the German Research Network on Neuropathic Pain (DFNS): standardized protocol and reference values. Pain 123, 231–243 (2006). - PubMed
  50. Diatchenko, L., Fillingim, R. B., Smith, S. B. & Maixner, W. The phenotypic and genetic signatures of common musculoskeletal pain conditions. Nat. Rev. Rheumatol. 9, 340–350 (2013). - PubMed
  51. Smith, S. M. et al. The potential role of sensory testing, skin biopsy, and functional brain imaging as biomarkers in chronic pain clinical trials: IMMPACT considerations. J. Pain 18, 757–777 (2017). - PubMed
  52. Ashraf, A. B. et al. The painful face — pain expression recognition using active appearance models. Image Vis. Comput. 27, 1788–1796 (2009). - PubMed
  53. Bartlett, M. S., Littlewort, G. C., Frank, M. G. & Lee, K. Automatic decoding of facial movements reveals deceptive pain expressions. Curr. Biol. 24, 738–743 (2014). - PubMed
  54. LaChapelle, D. L., Hadjistavropoulos, T. & Craig, K. D. Pain measurement in persons with intellectual disabilities. Clin. J. Pain. 15, 13–23 (1999). - PubMed
  55. Sikka, K. et al. Automated assessment of children’s postoperative pain using computer vision. Pediatrics 136, e124–e131 (2015). - PubMed
  56. Branco, A., Fekete, S. M. W., Rugolo, L. M. S. S. & Rehder, M. I. The newborn pain cry: descriptive acoustic spectrographic analysis. Int. J. Pediatr. Otorhinolaryngol. 71, 539–546 (2007). - PubMed
  57. Cohn, J. F. et al. Detecting depression from facial actions and vocal prosody. Int. Conf. Affect. Comput. Intell. Interact. Workshops https://doi.org/10.1109/ACII.2009.5349358 (2009). - PubMed
  58. Gholami, B., Haddad, W. M. & Tannenbaum, A. R. Relevance vector machine learning for neonate pain intensity assessment using digital imaging. IEEE Trans. Biomed. Eng. 57, 1457–1466 (2010). - PubMed
  59. Yang, M. et al. A machine learning approach to assessing gait patterns for complex regional pain syndrome. Med. Eng. Phys. 34, 740–746 (2012). - PubMed
  60. Nguyen, Q. C. et al. Social media indicators of the food environment and state health outcomes. Public Health 148, 120–128 (2017). - PubMed
  61. Olausson, H., Wessberg, J., Morrison, I., McGlone, F. & Vallbo, A. The neurophysiology of unmyelinated tactile afferents. Neurosci. Biobehav. Rev. 34, 185–191 (2010). - PubMed
  62. Serra, J. Microneurography: towards a biomarker of spontaneous pain. Pain 153, 1989–1990 (2012). - PubMed
  63. Serra, J. et al. Hyperexcitable C nociceptors in fibromyalgia. Ann. Neurol. 75, 196–208 (2014). - PubMed
  64. Waxman, S. G. Chasing Men on Fire: The Story of the Search for a Pain Gene (MIT Press, 2018). - PubMed
  65. Ploner, M., Sorg, C. & Gross, J. Brain rhythms of pain. Trends Cogn. Sci. 21, 100–110 (2017). - PubMed
  66. Ploner, M. & May, E. S. Electroencephalography and magnetoencephalography in pain research — current state and future perspectives. Pain 159, 206–211 (2018). - PubMed
  67. Pinheiro, E. S. et al. Electroencephalographic patterns in chronic pain: a systematic review of the literature. PLOS ONE 11, e0149085 (2016). - PubMed
  68. Peng, W. et al. Brain oscillations reflecting pain-related behavior in freely moving rats. Pain 159, 106–118 (2018). - PubMed
  69. Nickel, M. M. et al. Brain oscillations differentially encode noxious stimulus intensity and pain intensity. Neuroimage 148, 141–147 (2017). - PubMed
  70. May, E. S. et al. Prefrontal gamma oscillations reflect ongoing pain intensity in chronic back pain patients. Hum. Brain Mapp. 40, 293–305 (2018). - PubMed
  71. Leblanc, B. W., Lii, T. R., Silverman, A. E., Alleyne, R. T. & Saab, C. Y. Cortical theta is increased while thalamocortical coherence is decreased in rat models of acute and chronic pain. Pain 155, 773–782 (2014). - PubMed
  72. LeBlanc, B. W., Bowary, P. M., Chao, Y. C., Lii, T. R. & Saab, C. Y. Electroencephalographic signatures of pain and analgesia in rats. Pain 157, 2330–2340 (2016). - PubMed
  73. LeBlanc, B. W. et al. T-type calcium channel blocker Z944 restores cortical synchrony and thalamocortical connectivity in a rat model of neuropathic pain. Pain 157, 255–263 (2016). - PubMed
  74. Koyama, S., Xia, J., Leblanc, B. W., Gu, J. W. & Saab, C. Y. Sub-paresthesia spinal cord stimulation reverses thermal hyperalgesia and modulates low frequency EEG in a rat model of neuropathic pain. Sci. Rep. 8, 7181 (2018). - PubMed
  75. Koyama, S. et al. An electroencephalography bioassay for preclinical testing of analgesic efficacy. Sci. Rep. 6, 16402 (2018). - PubMed
  76. Llinas, R. R., Ribary, U., Jeanmonod, D., Kronberg, E. & Mitra, P. P. Thalamocortical dysrhythmia: a neurological and neuropsychiatric syndrome characterized by magnetoencephalography. Proc. Natl Acad. Sci. USA 96, 15222–15227 (1999). - PubMed
  77. Sarnthein, J., Stern, J., Aufenberg, C., Rousson, V. & Jeanmonod, D. Increased EEG power and slowed dominant frequency in patients with neurogenic pain. Brain 129, 55–64 (2006). - PubMed
  78. Stern, J., Jeanmonod, D. & Sarnthein, J. Persistent EEG overactivation in the cortical pain matrix of neurogenic pain patients. Neuroimage 31, 721–731 (2006). - PubMed
  79. Saab, C. Y. & Barrett, L. F. Thalamic bursts and the epic pain model. Front. Comput. Neurosci. 10, 147 (2016). - PubMed
  80. LeBlanc, B. W. et al. Thalamic bursts down-regulate cortical theta and nociceptive behavior. Sci. Rep. 7, 2482 (2017). - PubMed
  81. Mamas, M., Dunn, W. B., Neyses, L. & Goodacre, R. The role of metabolites and metabolomics in clinically applicable biomarkers of disease. Arch. Toxicol. 85, 5–17 (2011). - PubMed
  82. Ramsden, C. E. et al. A systems approach for discovering linoleic acid derivatives that potentially mediate pain and itch. Sci. Signal. 10, eaal5241 (2017). - PubMed
  83. Dorsey, S. G. et al. Whole blood transcriptomic profiles can differentiate vulnerability to chronic low back pain. PLOS ONE 14, e0216539 (2019). - PubMed
  84. Roses, A. D. Apolipoprotein E alleles as risk factors in Alzheimer’s disease. Annu. Rev. Med. 47, 387–400 (1996). - PubMed
  85. Molinuevo, J. L. et al. Current state of Alzheimer’s fluid biomarkers. Acta Neuropathol. 136, 821–853 (2018). - PubMed
  86. Blennow, K., Mattsson, N., Scholl, M., Hansson, O. & Zetterberg, H. Amyloid biomarkers in Alzheimer’s disease. Trends Pharmacol. Sci. 36, 297–309 (2015). - PubMed
  87. Mattsson, N., Cullen, N. C., Andreasson, U., Zetterberg, H. & Blennow, K. Association between longitudinal plasma neurofilament light and neurodegeneration in patients with Alzheimer disease. JAMA Neurol. 76, 791–799 (2019). - PubMed
  88. McIntosh, A. M. et al. Genetic and environmental risk for chronic pain and the contribution of risk variants for major depressive disorder: a family-based mixed-model analysis. PLOS Med. 13, e1002090 (2016). - PubMed
  89. Gormley, P. et al. Common variant burden contributes to the familial aggregation of migraine in 1,589 families. Neuron 99, 1098 (2018). - PubMed
  90. Zorina-Lichtenwalter, K., Meloto, C. B., Khoury, S. & Diatchenko, L. Genetic predictors of human chronic pain conditions. Neuroscience 338, 36–62 (2016). - PubMed
  91. Tracey, I., Woolf, C. J. & Andrews, N. A. Composite pain biomarker signatures for objective assessment and effective treatment. Neuron 101, 783–800 (2019). - PubMed
  92. Sandoval, J., Peiro-Chova, L., Pallardo, F. V. & Garcia-Gimenez, J. L. Epigenetic biomarkers in laboratory diagnostics: emerging approaches and opportunities. Exp.Rev. Mol. Diagn. 13, 457–471 (2013). - PubMed
  93. Douglas, S. R. et al. Analgesic response to intravenous ketamine is linked to a circulating microRNA signature in female patients with complex regional pain syndrome. J. Pain 16, 814–824 (2015). - PubMed
  94. Ramanathan, S. & Ajit, S. K. MicroRNA-based biomarkers in pain. Adv. Pharmacol. 75, 35–62 (2016). - PubMed
  95. Lopez-Gonzalez, M. J., Landry, M. & Favereaux, A. MicroRNA and chronic pain: from mechanisms to therapeutic potential. Pharmacol. Ther. 180, 1–15 (2017). - PubMed
  96. Descalzi, G. et al. Epigenetic mechanisms of chronic pain. Trends Neurosci. 38, 237–246 (2015). - PubMed
  97. Raoof, R., Willemen, H. & Eijkelkamp, N. Divergent roles of immune cells and their mediators in pain. Rheumatology 57, 429–440 (2018). - PubMed
  98. Ji, R. R., Chamessian, A. & Zhang, Y. Q. Pain regulation by non-neuronal cells and inflammation. Science 354, 572–577 (2016). - PubMed
  99. Tsai, A. S. et al. A year-long immune profile of the systemic response in acute stroke survivors. Brain 142, 978–991 (2019). - PubMed
  100. Aghaeepour, N. et al. A proteomic clock of human pregnancy. Am. J. Obstet. Gynecol. 218, 347.e1–347.e14 (2018). - PubMed
  101. Ghaemi, M. S. et al. Multiomics modeling of the immunome, transcriptome, microbiome, proteome and metabolome adaptations during human pregnancy. Bioinformatics 35, 95–103 (2019). - PubMed
  102. Ganz, P. et al. Development and validation of a protein-based risk score for cardiovascular outcomes among patients with stable coronary heart disease. JAMA 315, 2532–2541 (2016). - PubMed
  103. Erez, O. et al. The prediction of late-onset preeclampsia: results from a longitudinal proteomics study. PLOS ONE 12, e0181468 (2017). - PubMed
  104. Aghaeepour, N. et al. Deep immune profiling of an arginine-enriched nutritional intervention in patients undergoing surgery. J. Immunol. 199, 2171–2180 (2017). - PubMed
  105. Gaudilliere, B. et al. Clinical recovery from surgery correlates with single-cell immune signatures. Sci. Transl. Med. 6, 255ra131 (2014). - PubMed
  106. Fragiadakis, G. K. et al. Patient-specific immune states before surgery are strong correlates of surgical recovery. Anesthesiology 123, 1241–1255 (2015). - PubMed
  107. Wallace, D. J., Gavin, I. M., Karpenko, O., Barkhordar, F. & Gillis, B. S. Cytokine and chemokine profiles in fibromyalgia, rheumatoid arthritis and systemic lupus erythematosus: a potentially useful tool in differential diagnosis. Rheumatol. Int. 35, 991–996 (2015). - PubMed
  108. LaPaglia, D. M. et al. RNA-Seq investigations of human post-mortem trigeminal ganglia. Cephalalgia 38, 912–932 (2018). - PubMed
  109. Jacob, M., Lopata, A. L., Dasouki, M. & Abdel Rahman, A. M. Metabolomics toward personalized medicine. Mass. Spectrom. Rev. 38, 221–238 (2017). - PubMed
  110. Parker, K. S. et al. Urinary metabolomics identifies a molecular correlate of interstitial cystitis/bladder pain syndrome in a Multidisciplinary Approach to the Study of Chronic Pelvic Pain (MAPP) research network cohort. EBioMedicine 7, 167–174 (2016). - PubMed
  111. Chan, D. D. et al. In vivo articular cartilage deformation: noninvasive quantification of intratissue strain during joint contact in the human knee. Sci. Rep. 6, 19220 (2016). - PubMed
  112. Lu, G. & Fei, B. Medical hyperspectral imaging: a review. J. Biomed. Opt. 19, 10901 (2014). - PubMed
  113. Marcu, L., Boppart, S. A., Hutchinson, M. R., Popp, J. & Wilson, B. C. Biophotonics: the big picture. J. Biomed. Opt. 23, 1–7 (2017). - PubMed
  114. Mackey, S., Greely, H. T. & Martucci, K. Neuroimaging-based pain biomarkers: definitions, clinical and research applications, and evaluation frameworks to achieve personalized pain medicine. Pain. Rep. 4, e762 (2019). - PubMed
  115. van der Miesen, M. M., Lindquist, M. A. & Wager, T. D. Neuroimaging-based biomarkers for pain: state of the field and current directions. Pain. Rep. 4, e751 (2019). - PubMed
  116. Vachon-Presseau, E. et al. Corticolimbic anatomical characteristics predetermine risk for chronic pain. Brain 139, 1958–1970 (2016). - PubMed
  117. Fischer, T. Z. & Waxman, S. G. Neuropathic pain in diabetes — evidence for a central mechanism. Nat. Rev. Neurol. 6, 462–466 (2010). - PubMed
  118. Kuner, R. & Flor, H. Structural plasticity and reorganisation in chronic pain. Nat. Rev. Neurosci. 18, 20–30 (2016). - PubMed
  119. Reddan, M. C. & Wager, T. D. Brain systems at the intersection of chronic pain and self-regulation. Neurosci. Lett. 702, 24–33 (2018). - PubMed
  120. O’Muircheartaigh, J. et al. Multivariate decoding of cerebral blood flow measures in a clinical model of on-going postsurgical pain. Hum. Brain Mapp. 36, 633–642 (2015). - PubMed
  121. Marshall, T. M. et al. Activation of descending pain-facilitatory pathways from the rostral ventromedial medulla by cholecystokinin elicits release of prostaglandin-E2 in the spinal cord. Pain 153, 86–94 (2012). - PubMed
  122. Xie, J. Y. et al. Cholecystokinin in the rostral ventromedial medulla mediates opioid-induced hyperalgesia and antinociceptive tolerance. J. Neurosci. 25, 409–416 (2005). - PubMed
  123. Martucci, K. T., Weber, K. A. 2nd & Mackey, S. C. Altered cervical spinal cord resting-state activity in fibromyalgia. Arthritis Rheumatol. 71, 441–450 (2019). - PubMed
  124. Weber, K. A. 2nd et al. Thermal stimulation alters cervical spinal cord functional connectivity in humans. Neuroscience 369, 40–50 (2018). - PubMed
  125. Islam, H., Law, C. S. W., Weber, K. A., Mackey, S. C. & Glover, G. H. Dynamic per slice shimming for simultaneous brain and spinal cord fMRI. Magn. Reson. Med. 81, 825–838 (2019). - PubMed
  126. Davis, K. D. & Moayedi, M. Central mechanisms of pain revealed through functional and structural MRI. J. Neuroimmune Pharmacol. 8, 518–534 (2013). - PubMed
  127. Geuter, S., et al. in Handbook of Psychophysiology (eds Cacioppo, J. T. et al.) 41–73 (Cambridge Univ. Press, 2017). - PubMed
  128. Cherry, S. R. Fundamentals of positron emission tomography and applications in preclinical drug development. J. Clin. Pharmacol. 41, 482–491 (2001). - PubMed
  129. Jones, A. K. P., Watabe, H., Cunningham, V. J. & Jones, T. Cerebral decreases in opioid receptor binding in patients with central neuropathic pain measured by [ - PubMed
  130. Zubieta, J. K. et al. Regional μ opioid receptor regulation of sensory and affective dimensions of pain. Science 293, 311–315 (2001). - PubMed
  131. Loggia, M. L. et al. Evidence for brain glial activation in chronic pain patients. Brain 138, 604–615 (2015). - PubMed
  132. Notter, T., Coughlin, J. M., Sawa, A. & Meyer, U. Reconceptualization of translocator protein as a biomarker of neuroinflammation in psychiatry. Mol. Psychiat. 23, 36–47 (2018). - PubMed
  133. Gent, Y. Y. J. et al. Macrophage positron emission tomography imaging as a biomarker for preclinical rheumatoid arthritis: findings of a prospective pilot study. Arthritis Rheum. 64, 62–66 (2012). - PubMed
  134. Brown, J. E., Chatterjee, N., Younger, J. & Mackey, S. Towards a physiology-based measure of pain: patterns of human brain activity distinguish painful from non-painful thermal stimulation. PLOS ONE 6, e24124 (2011). - PubMed
  135. Marquand, A. et al. Quantitative prediction of subjective pain intensity from whole-brain fMRI data using Gaussian processes. Neuroimage 49, 2178–2189 (2010). - PubMed
  136. López-Solà, M. et al. Towards a neurophysiological signature for fibromyalgia. Pain 158, 34–47 (2017). - PubMed
  137. Mano, H. et al. Classification and characterisation of brain network changes in chronic back pain: a multicenter study. Wellcome Open. Res. 3, 19 (2018). - PubMed
  138. Mansour, A. et al. Global disruption of degree rank order: a hallmark of chronic pain. Sci. Rep. 6, 34853 (2016). - PubMed
  139. Cheng, J. C. et al. Multivariate machine learning distinguishes cross-network dynamic functional connectivity patterns in state and trait neuropathic pain. Pain 159, 1764–1776 (2018). - PubMed
  140. Nan, J. et al. Whole-brain functional connectivity identification of functional dyspepsia. PLOS ONE 8, e65870 (2013). - PubMed
  141. Callan, D., Mills, L., Nott, C., England, R. & England, S. A tool for classifying individuals with chronic back pain: using multivariate pattern analysis with functional magnetic resonance imaging data. PLOS ONE 9, e98007 (2014). - PubMed
  142. Bagarinao, E. et al. Preliminary structural MRI based brain classification of chronic pelvic pain: a MAPP network study. Pain 155, 2502–2509 (2014). - PubMed
  143. Ung, H. et al. Multivariate classification of structural MRI data detects chronic low back pain. Cereb. Cortex 24, 1037–1044 (2014). - PubMed
  144. Baliki, M. N. et al. Corticostriatal functional connectivity predicts transition to chronic back pain. Nat. Neurosci. 15, 1117–1119 (2012). - PubMed
  145. Kutch, J. J. et al. Resting-state functional connectivity predicts longitudinal pain symptom change in urologic chronic pelvic pain syndrome: a MAPP network study. Pain 158, 1069–1082 (2017). - PubMed
  146. Hashmi, J. A. et al. Brain networks predicting placebo analgesia in a clinical trial for chronic back pain. Pain 153, 2393–2402 (2012). - PubMed
  147. Tetreault, P. et al. Brain connectivity predicts placebo response across chronic pain clinical trials. PLOS Biol. 14, e1002570 (2016). - PubMed
  148. Bosma, R. L. et al. Brain dynamics and temporal summation of pain predicts neuropathic pain relief from ketamine infusion. Anesthesiology 129, 1015–1024 (2018). - PubMed
  149. Hung, P. S., Chen, D. Q., Davis, K. D., Zhong, J. & Hodaie, M. Predicting pain relief: use of pre-surgical trigeminal nerve diffusion metrics in trigeminal neuralgia. Neuroimage Clin. 15, 710–718 (2017). - PubMed
  150. Rosa, M. J. & Seymour, B. Decoding the matrix: benefits and limitations of applying machine learning algorithms to pain neuroimaging. Pain 155, 864–867 (2014). - PubMed
  151. Davis, K. D. Is chronic pain a disease? Evaluating pain and nociception through self-report and neuroimaging. J. Pain 14, 332–333 (2013). - PubMed
  152. Hemington, K. S., Wu, Q., Kucyi, A., Inman, R. D. & Davis, K. D. Abnormal cross-network functional connectivity in chronic pain and its association with clinical symptoms. Brain Struct. Funct. 221, 4203–4219 (2016). - PubMed
  153. Marbach, D. et al. Wisdom of crowds for robust gene network inference. Nat. Methods 9, 796–804 (2012). - PubMed
  154. Aghaeepour, N. et al. Critical assessment of automated flow cytometry data analysis techniques. Nat. Methods 10, 228–238 (2013). - PubMed
  155. Aghaeepour, N. et al. A benchmark for evaluation of algorithms for identification of cellular correlates of clinical outcomes. Cytometry A 89, 16–21 (2016). - PubMed
  156. Halilaj, E., Hastie, T. J., Gold, G. E. & Delp, S. L. Physical activity is associated with changes in knee cartilage microstructure. Osteoarthr. Cartil. 26, 770–774 (2018). - PubMed
  157. Tibshirani, R. & Friedman, J. A pliable lasso. Preprint at arXiv https://arxiv.org/abs/1712.00484v4 (2018). - PubMed
  158. Choo, J. & Liu, S. Visual analytics for explainable deep learning. IEEE Comput. Graph. Appl. 38, 84–92 (2018). - PubMed
  159. Aghaeepour, N. et al. GateFinder: projection-based gating strategy optimization for flow and mass cytometry. Bioinformatics 34, 4131–4133 (2018). - PubMed
  160. Taylor, J. & Tibshirani, R. Post-selection inference for ℓ - PubMed
  161. Cagney, D. N. et al. The FDA NIH Biomarkers, EndpointS, and other Tools (BEST) resource in neuro-oncology. Neuro Oncol. 20, 1162–1172 (2018). - PubMed
  162. Dworkin, R. H. et al. Core outcome measures for chronic pain clinical trials: IMMPACT recommendations. Pain 113, 9–19 (2005). - PubMed
  163. Edwards, R. R. et al. Patient phenotyping in clinical trials of chronic pain treatments: IMMPACT recommendations. Pain 157, 1851–1871 (2016). - PubMed
  164. Bennett, M. The LANSS pain scale: the Leeds Assessment of Neuropathic Symptoms and Signs. Pain 92, 147–157 (2001). - PubMed
  165. Bennett, M. I. et al. Using screening tools to identify neuropathic pain. Pain 127, 199–203 (2007). - PubMed
  166. Bouhassira, D. et al. Neuropathic pain phenotyping as a predictor of treatment response in painful diabetic neuropathy: data from the randomized, double-blind, COMBO-DN study. Pain 155, 2171–2179 (2014). - PubMed
  167. Forstenpointner, J., Rehm, S., Gierthmuhlen, J. & Baron, R. Stratification of neuropathic pain patients: the road to mechanism-based therapy? Curr. Opin. Anaesthesiol. 31, 562–568 (2018). - PubMed
  168. Turk, D. C. et al. Identifying important outcome domains for chronic pain clinical trials: an IMMPACT survey of people with pain. Pain 137, 276–285 (2008). - PubMed
  169. Taylor, A. M. et al. Assessment of physical function and participation in chronic pain clinical trials: IMMPACT/OMERACT recommendations. Pain 157, 1836–1850 (2016). - PubMed
  170. Turk, D. C., Fillingim, R. B., Ohrbach, R. & Patel, K. V. Assessment of psychosocial and functional impact of chronic pain. J. Pain. 17, T21–T49 (2016). - PubMed
  171. Perrot, S. & Lanteri-Minet, M. Patients’ global impression of change in the management of peripheral neuropathic pain: clinical relevance and correlations in daily practice. Eur. J. Pain 23, 1117–1128 (2019). - PubMed
  172. Jamison, R. N., Dorado, K., Mei, A., Edwards, R. R. & Martel, M. O. Influence of opioid-related side effects on disability, mood, and opioid misuse risk among patients with chronic pain in primary care. Pain. Rep. 2, e589 (2017). - PubMed
  173. Lauria, G. et al. European Federation of Neurological Societies/Peripheral Nerve Society guideline on the use of skin biopsy in the diagnosis of small fiber neuropathy. Report of a joint task force of the European Federation of Neurological Societies and the Peripheral Nerve Society. Eur. J. Neurol. 17, 903–912 (2010). - PubMed
  174. Devigili, G. et al. The diagnostic criteria for small fibre neuropathy: from symptoms to neuropathology. Brain 131, 1912–1925 (2008). - PubMed
  175. Themistocleous, A. C. et al. The Pain in Neuropathy Study (PiNS): a cross-sectional observational study determining the somatosensory phenotype of painful and painless diabetic neuropathy. Pain 157, 1132–1145 (2016). - PubMed
  176. Zhou, L. et al. Correlates of epidermal nerve fiber densities in HIV-associated distal sensory polyneuropathy. Neurology 68, 2113–2119 (2007). - PubMed
  177. von Hehn, C. A., Baron, R. & Woolf, C. J. Deconstructing the neuropathic pain phenotype to reveal neural mechanisms. Neuron 73, 638–652 (2012). - PubMed
  178. Costigan, M., Scholz, J. & Woolf, C. J. Neuropathic pain: a maladaptive response of the nervous system to damage. Annu. Rev. Neurosci. 32, 1–32 (2009). - PubMed
  179. Zunhammer, M., Bingel, U., Wager, T. D. & Placebo Imaging Consortium. Placebo effects on the neurologic pain signature: a meta-analysis of individual participant functional magnetic resonance imaging data. JAMA Neurol. 75, 1321–1330 (2018). - PubMed
  180. Campbell, C. M. et al. Randomized control trial of topical clonidine for treatment of painful diabetic neuropathy. Pain 153, 1815–1823 (2012). - PubMed
  181. Rowbotham, M. C. et al. Oral and cutaneous thermosensory profile of selective TRPV1 inhibition by ABT-102 in a randomized healthy volunteer trial. Pain 152, 1192–1200 (2011). - PubMed
  182. Serra, J. et al. Effects of a T-type calcium channel blocker, ABT-639, on spontaneous activity in C-nociceptors in patients with painful diabetic neuropathy: a randomized controlled trial. Pain 156, 2175–2183 (2015). - PubMed
  183. Yarnitsky, D. et al. Remote electrical neuromodulation (REN) relieves acute migraine: a randomized, double-blind, placebo-controlled, multicenter trial. Headache 59, 1240–1252 (2019). - PubMed
  184. US Food and Drug Administration. Statement by FDA Commissioner Scott Gottlieb, MD on the agency’s ongoing work to forcefully address the opioid crisis (FDA, 2018). - PubMed
  185. Canadian Institutes of Health Research. Institute of Musculoskeletal Health and Arthritis IMHA Strategic Plan 2014–2018: enhancing musculoskeletal, skin and oral health. CIHR https://cihr-irsc.gc.ca/e/48830.html (2014). - PubMed
  186. Heath Canada. Responding to Canada’s opioid crisis (Government of Canada, 2019). - PubMed
  187. International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use. E16 biomarkers related to drug or biotechnology product develoment: context, structure and format of quantification submissions. ICH https://www.ich.org/page/efficacy-guidelines (2010). - PubMed
  188. European Medicines Agency. Essential considerations for successful qualification of novel methodologies (EMA, 2017). - PubMed
  189. US Food and Drug Administration. Table of surrogate endpoints that were the basis of drug approval or licensure. FDA https://www.fda.gov/drugs/development-resources/table-surrogate-endpoints-were-basis-drug-approval-or-licensure (2019). - PubMed
  190. Innovative Medicines Initiative. Innovative Medicines Initiative IMI1 Final project report public summary: Europain. Understanding chronic pain and improving its treatment (IMI, 2015). - PubMed
  191. Fitzgerald, M. & Walker, S. M. Infant pain management: a developmental neurobiological approach. Nat. Clin. Pract. Neurol. 5, 35–50 (2009). - PubMed
  192. Goksan, S. et al. fMRI reveals neural activity overlap between adult and infant pain. eLife 4, e06356 (2015). - PubMed
  193. Hicks, C. L., von Baeyer, C. L., Spafford, P. A., van Korlaar, I. & Goodenough, B. The faces pain scale — revised: toward a common metric in pediatric pain measurement. Pain 93, 173–183 (2001). - PubMed
  194. Zamzmi, G. et al. A review of automated pain assessment in infants: features, classification tasks, and databases. IEEE Rev. Biomed. Eng. 11, 77–96 (2018). - PubMed
  195. Boly, M. et al. Perception of pain in the minimally conscious state with PET activation: an observational study. Lancet Neurol. 7, 1013–1020 (2008). - PubMed
  196. Monti, M. M. et al. Willful modulation of brain activity in disorders of consciousness. N. Engl. J. Med. 362, 579–589 (2010). - PubMed
  197. Cole, L. J. et al. Pain sensitivity and fMRI pain-related brain activity in Alzheimer’s disease. Brain 129, 2957–2965 (2006). - PubMed
  198. de Knegt, N. & Scherder, E. Pain in adults with intellectual disabilities. Pain 152, 971–974 (2011). - PubMed
  199. de Knegt, N. C. et al. Behavioral pain indicators in people with intellectual disabilities: a systematic review. J. Pain 14, 885–896 (2013). - PubMed
  200. Fanurik, D., Koh, J. L., Dale Harrison, R., Conrad, T. M. & Tomerun, C. Pain assessment in children with cognitive impairment. Clin.Nurs. Res. 7, 103–119 (1998). - PubMed
  201. Wolff, B. B. & Langley, S. Cultural factors and the response to pain: a review. Am. Anthropol. 70, 494–501 (1968). - PubMed
  202. Zborowski, M. Cultural components in responses to pain. J. Soc. Issues 8, 16–30 (1952). - PubMed
  203. Anderson, S. R. & Reynolds Losin, E. A. A sociocultural neuroscience approach to pain. Cult. Brain 5, 14–35 (2017). - PubMed
  204. Loeb, S. et al. Overdiagnosis and overtreatment of prostate cancer. Eur. Urol. 65, 1046–1055 (2014). - PubMed
  205. Cannon, A., Kurklinsky, S., Guthrie, K. J. & Riegert-Johnson, D. L. Advanced genetic testing comes to the pain clinic to make a diagnosis of paroxysmal extreme pain disorder. Case Rep. Neurol. Med. 2016, 9212369 (2016). - PubMed
  206. Drenth, J. P. & Waxman, S. G. Mutations in sodium-channel gene SCN9A cause a spectrum of human genetic pain disorders. J. Clin. Invest. 117, 3603–3609 (2007). - PubMed
  207. Carey, T. S. & Garrett, J. M. The relation of race to outcomes and the use of health care services for acute low back pain. Spine 28, 390–394 (2003). - PubMed
  208. Quartana, P. J., Campbell, C. M. & Edwards, R. R. Pain catastrophizing: a critical review. Expert Rev. Neurother. 9, 745–758 (2009). - PubMed
  209. Clarke, T. K. et al. Low frequency genetic variants in the μ-opioid receptor (OPRM1) affect risk for addiction to heroin and cocaine. Neurosci. Lett. 542, 71–75 (2013). - PubMed
  210. Petersen, K. K., Arendt-Nielsen, L., Simonsen, O., Wilder-Smith, O. & Laursen, M. B. Presurgical assessment of temporal summation of pain predicts the development of chronic postoperative pain 12 months after total knee replacement. Pain 156, 55–61 (2015). - PubMed
  211. Lauria, G. et al. Intraepidermal nerve fiber density at the distal leg: a worldwide normative reference study. J. Peripher. Nerv. Syst. 15, 202–207 (2010). - PubMed
  212. Freeman, R., Baron, R., Bouhassira, D., Cabrera, J. & Emir, B. Sensory profiles of patients with neuropathic pain based on the neuropathic pain symptoms and signs. Pain 155, 367–376 (2014). - PubMed
  213. Reimer, M. et al. Prediction of response to tapentadol in chronic low back pain. Eur. J. Pain 21, 322–333 (2017). - PubMed
  214. Starkey Lewis, P. J. et al. Circulating microRNAs as potential markers of human drug-induced liver injury. Hepatology 54, 1767–1776 (2011). - PubMed
  215. Serra, J. et al. Microneurographic identification of spontaneous activity in C-nociceptors in neuropathic pain states in humans and rats. Pain 153, 42–55 (2012). - PubMed
  216. Ackerley, R. & Watkins, R. H. Microneurography as a tool to study the function of individual C-fiber afferents in humans: responses from nociceptors, thermoreceptors, and mechanoreceptors. J. Neurophysiol. 120, 2834–2846 (2018). - PubMed
  217. Pascal, M. M. V. et al. DOLORisk: study protocol for a multi-centre observational study to understand the risk factors and determinants of neuropathic pain. Wellcome Open. Res. 3, 63 (2019). - PubMed
  218. Levitt, J. & Saab, C. Y. What does a pain ‘biomarker’ mean and can a machine be taught to measure pain? Neurosci. Lett. 702, 40–43 (2019). - PubMed
  219. Schulman, J., Ramirez, R., Zonenshayn, M., Ribary, U. & Llinas, R. R. Thalamocortical dysrhythmia syndrome: MEG imaging of neuropathic pain. Thalamus Relat. Syst. 31, 33–39 (2005). - PubMed
  220. Juottonen, K. et al. Altered central sensorimotor processing in patients with complex regional pain syndrome. Pain 98, 315–323 (2002). - PubMed
  221. Kim, J. A. et al. Neuropathic pain and pain interference are linked to alpha-band slowing and reduced beta-band magnetoencephalography activity within the dynamic pain connectome in patients with multiple sclerosis. Pain 160, 187–197 (2019). - PubMed
  222. Scuteri, D. et al. New trends in migraine pharmacology: targeting calcitonin gene-related peptide (CGRP) with monoclonal antibodies. Front. Pharmacol. 10, 363 (2019). - PubMed
  223. Goadsby, P. J. et al. Pathophysiology of migraine: a disorder of sensory processing. Physiol. Rev. 97, 553–622 (2017). - PubMed
  224. Oaklander, A. L., Herzog, Z. D., Downs, H. M. & Klein, M. M. Objective evidence that small-fiber polyneuropathy underlies some illnesses currently labeled as fibromyalgia. Pain 154, 2310–2316 (2013). - PubMed
  225. Vlckova-Moravcova, E., Bednarik, J., Dusek, L., Toyka, K. V. & Sommer, C. Diagnostic validity of epidermal nerve fiber densities in painful sensory neuropathies. Muscle Nerve 37, 50–60 (2008). - PubMed
  226. Carragee, E. J., Alamin, T. F., Miller, J. L. & Carragee, J. M. Discographic, MRI and psychosocial determinants of low back pain disability and remission: a prospective study in subjects with benign persistent back pain. Spine J. 5, 24–35 (2005). - PubMed
  227. Szabo, N. et al. White matter microstructural alterations in migraine: a diffusion-weighted MRI study. Pain 153, 651–656 (2012). - PubMed
  228. Woodworth, D. et al. Unique microstructural changes in the brain associated with urological chronic pelvic pain syndrome (UCPPS) revealed by diffusion tensor MRI, super-resolution track density imaging, and statistical parameter mapping: a MAPP network neuroimaging study. PLOS ONE 10, e0140250 (2015). - PubMed
  229. Griebel, A. J., Trippel, S. B., Emery, N. C. & Neu, C. P. Noninvasive assessment of osteoarthritis severity in human explants by multicontrast MRI. Magn. Res. Med. 71, 807–814 (2014). - PubMed
  230. Staikopoulos, V. et al. Hyperspectral imaging of endogenous fluorescent metabolic molecules to identify pain states in central nervous system tissue. Proc. SPIE 10013, 1001306 (2016). - PubMed
  231. Aarnio, M. et al. Visualization of painful inflammation in patients with pain after traumatic ankle sprain using [ - PubMed
  232. Uceyler, N. et al. Increased cortical activation upon painful stimulation in fibromyalgia syndrome. BMC Neurol. 15, 210 (2015). - PubMed
  233. Vrana, A., Meier, M. L., Hotz-Boendermaker, S., Humphreys, B. K. & Scholkmann, F. Cortical sensorimotor processing of painful pressure in patients with chronic lower back pain — an optical neuroimaging study using fNIRS. Front. Hum. Neurosci. 10, 578 (2016). - PubMed
  234. Demant, D. T. et al. The effect of oxcarbazepine in peripheral neuropathic pain depends on pain phenotype: a randomised, double-blind, placebo-controlled phenotype-stratified study. Pain 155, 2263–2273 (2014). - PubMed
  235. Geuter, S., Gamer, M., Onat, S. & Büchel, C. Parametric trial-by-trial prediction of pain by easily available physiological measures. Pain 155, 994–1001 (2014). - PubMed
  236. Kalliomaki, J. et al. Evaluation of a novel chemokine receptor 2 (CCR2)-antagonist in painful diabetic polyneuropathy. Scand. J. Pain 4, 77–83 (2013). - PubMed
  237. Kalliomaki, J. et al. A randomized, double-blind, placebo-controlled trial of a chemokine receptor 2 (CCR2) antagonist in posttraumatic neuralgia. Pain 154, 761–767 (2013). - PubMed
  238. Quiding, H. et al. TRPV1 antagonistic analgesic effect: a randomized study of AZD1386 in pain after third molar extraction. Pain 154, 808–812 (2013). - PubMed
  239. Miller, F., Bjornsson, M., Svensson, O. & Karlsten, R. Experiences with an adaptive design for a dose-finding study in patients with osteoarthritis. Contemp. Clin. Trials 37, 189–199 (2014). - PubMed
  240. Gimbel, J. S. et al. Long-term safety and effectiveness of tanezumab as treatment for chronic low back pain. Pain 155, 1793–1801 (2014). - PubMed
  241. Juhasz, G. et al. Sumatriptan causes parallel decrease in plasma calcitonin gene-related peptide (CGRP) concentration and migraine headache during nitroglycerin induced migraine attack. Cephalalgia 25, 179–183 (2005). - PubMed
  242. Yarnitsky, D., Granot, M., Nahman-Averbuch, H., Khamaisi, M. & Granovsky, Y. Conditioned pain modulation predicts duloxetine efficacy in painful diabetic neuropathy. Pain 153, 1193–1198 (2012). - PubMed
  243. Yarnitsky, D. et al. Nonpainful remote electrical stimulation alleviates episodic migraine pain. Neurology 88, 1250–1255 (2017). - PubMed
  244. Nahman-Averbuch, H. et al. Waning of “conditioned pain modulation”: a novel expression of subtle pronociception in migraine. Headache 53, 1104–1115 (2013). - PubMed
  245. Yarnitsky, D. Conditioned pain modulation (the diffuse noxious inhibitory control-like effect): its relevance for acute and chronic pain states. Curr. Opin. Anaesthesiol. 23, 611–615 (2010). - PubMed
  246. Petropoulos, I. N. et al. Corneal confocal microscopy: ready for prime time. Clin. Exp. Optom. 103, 265–277 (2019). - PubMed

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