Chemokine-receptor profiling for painful diabetic neuropathy in biological samples from human clinical trials

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Project Summary/Abstract Chronic pain is a major healthcare burden, representing economic costs of up to $635B per year, more than cancer, diabetes, and heart disease, according to a Johns Hopkins study published in the Journal of Pain. Pain is a highly heterogeneous condition comprising neuropathic, nociceptive and inflammatory components, and patient responses to currently available drugs vary greatly. A very promising approach to target various forms of pain is through antagonism of key players in neuro- inflammation. Specifically, the chemokine receptor system, a complex network of over 20 different receptors and over 80 ligands, is integral to neuroinflammatory processes and the pharmaceutical industry had been very active in developing compounds targeting individual receptors in the network. However, the biological complexity, ligand promiscuity, and receptor redundancy of the chemokine receptor system has precluded successful clinical development of the compounds and many pharma have exited the pain therapeutic area. A major limitation of successful targeting of this network is sufficient understanding of the molecular and cellular dynamics of the chemokine network, and how specific receptors vary in chronic pain conditions. In this study, we aim to determine the genetic expression of a chemokine receptor utilizing human clinical samples collected in a clinical trial evaluating a receptor antagonist. Further, we will utilize this understanding in conjunction with patient efficacy data from the clinical trials to continue the development of our lead compound, a drug that showed a statistically significant clinical signal in pain. Results from this study will enable patient stratification and effective clinical trial design by including patients with the appropriate genetic and phenotypic background and thus significantly increase the likelihood achieving primary endpoints of pain reduction in the clinic.