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.

Background Opioid abuse in chronic pain patients is a major public health issue, with rapidly increasing addiction rates and deaths from unintentional overdose more than quadrupling since 1999. Purpose This study seeks to determine the predictability of aberrant behavior to opioids using a comprehensive scoring algorithm incorporating phenotypic risk factors and neuroscience-associated single-nucleotide polymorphisms (SNPs). Patients and methods The Proove Opioid Risk (POR) algorithm determines the predictability of aberrant behavior to opioids using a comprehensive scoring algorithm incorporating phenotypic risk factors and neuroscience-associated SNPs. In a validation study with 258 subjects with diagnosed opioid use disorder (OUD) and 650 controls who reported using opioids, the POR successfully categorized patients at high and moderate risks of opioid misuse or abuse with 95.7% sensitivity. Regardless of changes in the prevalence of opioid misuse or abuse, the sensitivity of POR remained >95%. Conclusion The POR correctly stratifies patients into low-, moderate-, and high-risk categories to appropriately identify patients at need for additional guidance, monitoring, or treatment changes.

Background Fibromyalgia (FM) is a complex, centralized pain condition that is often difficult to diagnose and treat. FM is considered to have a genetic background due to its familial aggregation and due to findings from multiple candidate-gene studies implicating catecholaminergic and serotonergic neurotransmitter systems in chronic pain. However, a multi-factorial analysis of both genetic and environmental risk factors is lacking. A better characterization of the interplay of risk factors may assist in understanding the pathophysiology of FM, its clinical course, and assist in early diagnosis and treatment of the disorder. Methods This retrospective study included 60,367 total participants from 237 clinics across the USA. Of those, 2713 had been diagnosed with fibromyalgia, as indicated by ICD code. Logistic regression was used to test for associations of diagnosed FM in study subjects with COMT SNPs and COMT haplotypes, which were previously found to be linked with pain sensitivity, as well as demographics such as age, sex, and ethnicity. The minor allele frequencies of COMT SNPs in the FM population were compared with 1000 Genomes data using a χ2 test to determine significant deviations from the estimated population allelic frequencies. Results FM diagnosis was strongly associated with sex, age, and ethnicity. Females, those between 49 and 63 years, and non-Caucasians were at higher risk of FM. Females had 1.72 increased odds of FM ( p = 1.17 × 10 − 30 ). African-Americans were 1.52 times more likely to have a diagnosis of FM compared to Caucasians ( p = 3.11 × 10 − 12 ). Hispanics were less likely to have a diagnosis of FM compared to Caucasians ( p = 3.95 × 10 − 7 ). After adjusting for sex and ethnicity, those in the low age group and mid age group had 1.29 (p = 1.02 × 10 − 5 ) and 1.60 ( p = 1.93 × 10 − 18 ) increased odds of FM, respectively, compared to the high age group, where age was categorized by tertile (low (< 49), mid (49–63), and high (> 63)). The COMT haplotypes associated with pain sensitivity were not associated with FM, but African-Americans were 11.3 times more likely to have a high pain sensitivity COMT diplotype, regardless of FM diagnosis. However, the minor alleles of COMT SNPs rs4680 , rs4818 , rs4633 and rs6269 were overrepresented in the FM population overall, and varied when compared with ethnically-similar populations from 1000 Genomes. Conclusions ...

Introduction: Physicians prescribing opioids are at the front lines of the opioid abuse epidemic, battling to tip the scale between rising abuse rates and adequate pain control. This study evaluates the performance of an algorithm that incorporates genetic and non-genetic risk factors in accurately predicting patients at risk of Opioid Use Disorder (OUD).

Many genetic markers have been associated with variations in treatment response to analgesics, but none have been assessed in the context of combination therapies. In this study, the treatment effects of nortriptyline and morphine were tested for an association with genetic markers relevant to pain pathways. Treatment effects were determined for single and combination therapies. A total of 24 functional single nucleotide polymorphisms were tested within the gene loci of mu-opioid receptor (OPRM1) gene locus, ATP-Binding Cassette B1 Transporter (ABCB1), Cytochrome P450 gene family (CYP2C19 and CYP2D6), catecholamine inactivator Catechol-O-Methyl Transferase (COMT), and serotonin receptor 2A (HTR2A). Genotyping was performed in a population of neuropathic pain patients who previously participated in a clinical trial. For monotherapy, neither nortriptyline nor morphine responses were associated with single nucleotide polymorphisms. However, for nortriptyline + morphine combination therapy, the single nucleotide polymorphism rs1045642 within the drug efflux pump ABCB1 transporter significantly predicted analgesic response. The presence of the C allele accounted for 51% of pain variance in this subgroup in response to combination treatment. The T-allele homozygotes demonstrated only 20% improvement in pain scores, whereas the C-allele homozygotes 88%. There was no significant contribution of rs1045642 to the medication side effects under all treatment conditions. The UK Biobank data set was then used to validate this genetic association. Here, patients receiving similar combination therapy (opioid + tricyclic antidepressant) carrying the C allele of rs1045642 displayed 33% fewer body pain sites than patients without that allele, suggesting better pain control. In all, our results show a robust effect of the rs1045642 polymorphism in response to chronic pain treatment with a nortriptyline + morphine combination.