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

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.

A functional polymorphism in the ATP-Binding Cassette B1 transporter predicts pharmacologic response to combination of nortriptyline and morphine in neuropathic pain patients.

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.

A Predictive Algorithm to Detect Opioid Use Disorder: What Is the Utility in a Primary Care Setting?

PURPOSE: The purpose of this study was to determine the clinical utility of an algorithm-based decision tool designed to assess risk associated with opioid use in the primary care setting. METHODS: A prospective, longitudinal study was conducted to assess the utility of precision medicine testing in 1822 patients across 18 family medicine/primary care clinics in the United States. Using the profile, patients were categorized into low, moderate, and high risk for opioid use. Physicians who ordered testing were asked to complete patient evaluations and document their actions, decisions, and perceptions regarding the utility of the precision medicine tests. RESULTS: Approximately 47% of primary care physicians surveyed used the profile to guide clinical decision-making. These physicians rated the benefit of the profile on patient care an average of 3.6 on a 5-point scale (1 indicating no benefit and 5 indicating significant benefit). Eighty-eight percent of all clinicians surveyed felt the test exhibited some benefit to their patient care. The most frequent utilization for the profile was to guide a change in opioid prescribed. Physicians reported greater benefit of profile utilization for minority patients. Patients whose treatment was guided by the profile had pain levels that were reduced, on average, 2.7 levels on the numeric rating scale. CONCLUSIONS: The profile provided primary care physicians with a useful tool to stratify the risk of opioid use disorder and was rated as beneficial for decision-making and patient improvement by the majority of physicians surveyed. Physicians reported the profile resulted in greater clinical improvement for minorities, highlighting the objective use of this profile to guide judicial use of opioids in high-risk patients. Significantly, when physicians used the profile to guide treatment decisions, patient-reported pain was greatly reduced.

A prospective, longitudinal study to evaluate the clinical utility of a predictive algorithm that detects risk of opioid use disorder.

PURPOSE: The purpose of this study was to determine the clinical utility of an algorithm-based decision tool designed to assess risk associated with opioid use. Specifically, we sought to assess how physicians were using the profile in patient care and how its use affected patient outcomes. PATIENTS AND METHODS: A prospective, longitudinal study was conducted to assess the utility of precision medicine testing in 5,397 patients across 100 clinics in the USA. Using a patent-protected, validated algorithm combining specific genetic risk factors with phenotypic traits, patients were categorized into low-, moderate-, and high-risk patients for opioid abuse. Physicians who ordered precision medicine testing were asked to complete patient evaluations and document their actions, decisions, and perceptions regarding the utility of the precision medicine tests. The patient outcomes associated with each treatment action were carefully documented. RESULTS: Physicians used the profile to guide treatment decisions for over half of the patients. Of those, guided treatment decisions for 24.5% of the patients were opioid related, including changing the opioid prescribed, starting an opioid, or titrating a patient off the opioid. Treatment guidance was strongly influenced by profile-predicted opioid use disorder (OUD) risk. Most importantly, patients whose physicians used the profile to guide opioid-related treatment decisions had improved clinical outcomes, including better pain management by medication adjustments, with an average pain decrease of 3.4 points on a scale of 1-10. CONCLUSION: Patients whose physicians used the profile to guide opioid-related treatment decisions had improved clinical outcomes, as measured by decreased pain levels resulting from better pain management with prescribed medications. The clinical utility of the profile is twofold. It provides clinically actionable recommendations that can be used to 1) prevent OUD through limiting initial opioid prescriptions and 2) reduce pain in patients at low risk of developing OUD.

Association of Catechol-O-methyltransferase single nucleotide polymorphisms, ethnicity, and sex in a large cohort of fibromyalgia patients.

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: This is the largest study, to date, that examines demographic and genetic associations of FM in a diverse population. While pain sensitivity-associated COMT haplotypes were not found to be directly associated with FM diagnosis, the minor alleles that make up the COMT haplotypes were overrepresented in the FM population, suggesting a role of COMT in FM. Future studies are needed to elucidate the exact role of COMT variation in widespread pain conditions, such as FM. Clinically, this information can be used to provide insight into the pathways underlying FM and to identify those at greater risk of developing FM.

Validation Study of a Predictive Algorithm to Evaluate Opioid Use Disorder in a Primary Care Setting.

BACKGROUND: Opioid abuse in chronic pain patients is a major public health issue. Primary care providers are frequently the first to prescribe opioids to patients suffering from pain, yet do not always have the time or resources to adequately evaluate the risk of opioid use disorder (OUD). PURPOSE: This study seeks to determine the predictability of aberrant behavior to opioids using a comprehensive scoring algorithm ("profile") incorporating phenotypic and, more uniquely, genotypic risk factors. METHODS AND RESULTS: In a validation study with 452 participants diagnosed with OUD and 1237 controls, the algorithm successfully categorized patients at high and moderate risk of OUD with 91.8% sensitivity. Regardless of changes in the prevalence of OUD, sensitivity of the algorithm remained >90%. CONCLUSION: The algorithm correctly stratifies primary care patients into low-, moderate-, and high-risk categories to appropriately identify patients in need for additional guidance, monitoring, or treatment changes.

Observational study to calculate addictive risk to opioids: a validation study of a predictive algorithm to evaluate opioid use disorder.

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.

A precision medicine approach to a patient with unresolved pain following orthopedic surgery: a case report.

BACKGROUND: Precision medicine is a promising technology in patient care that combines genetic analysis with clinical data, such as health, behavioral, functional, environment, and lifestyle information. Here we present the case of a 54-year old woman who, following an accident, had uncontrolled chronic pain and was subsequently labeled a drug seeker. CASE PRESENTATION: A 54-year-old white woman who was experiencing severe calf pain was referred for treatment. Her pain was insufficiently controlled immediately following knee arthroplasty with multiple opioid medications, as well as non-opioids. Precision medicine testing was ordered for her so that we could assess her pain sensitivity objectively to determine if the pill seeker designation was correct and to determine the best medications for her. Based on the Proove profiles, we determined that she had moderately low pain sensitivity, which means that clinically she may underreport pain and may have decreased medication needs. This result suggested that her continued reporting of unresolved pain was probably due to a condition unresolved by her right knee arthroplasty. In addition, she was found to be at low risk of opioid addiction, based on the Proove Opioid Risk Profile. Taken together, along with the high levels of pain she described, we determined that her pain was not properly controlled and that the designation of pill seeker was incorrect. The next step was to determine which medications and which doses would result in the most favorable outcomes for our patient. To determine this, we used the results of the Proove Opioid Response, Proove Drug Metabolism, and Proove Non-Opioid Profiles to guide her treatment. We reduced her pain medications to a single opioid, Vicodin (acetaminophen and hydrocodone), which also eliminated the adverse side effects she experienced. CONCLUSIONS: Precision medicine offers an important health care decision tool which can reduce emotional and physical costs to patients and may reduce the economic health care burden of unnecessary surgeries and ineffective medication. The information provided by these profiles can be used clinically to guide treatment decisions and evaluate patient pain.

An observational study of the impact of genetic testing for pain perception in the clinical management of chronic non-cancer pain.

OBJECTIVE: Pain levels are a key metric in clinical care. However, the assessment of pain is limited to basic questionnaires and physician interpretation, which yield subjective data. Genetic markers of pain sensitivity, such as single nucleotide polymorphisms in the catechol-O-methyltransferase gene, have been shown to be associated with pain perception and have been used to provide objective information about a patient's pain. The goal of this study was to determine if physician treatment adjustments based on genetic tests of pain perception resulted in improved outcomes for patients. MATERIAL AND METHODS: A prospective, longitudinal study was conducted with 134 chronic non-cancer pain patients genotyped for pain perception-related catechol-O-methyltransferase haplotypes. Physicians were provided with patients' results and asked to document 1) their assessment of benefit of the genetic test; 2) treatment changes made based on the genetic test; and 3) patient clinical responses to changes implemented. RESULTS: Based on genetic testing results, physicians adjusted treatment plans for 40% of patients. When medication changes were made based on genetic testing results, 72% of patients showed improvement in clinical status. When non-pharmacological actions were performed, 69% of physicians felt their patients' clinical status improved. Moreover, physicians believed the genetic test results were consistent with patient pain levels in 85% of cases. CONCLUSIONS: These results demonstrate that providing personalized medicine with genetic information related to pain perception affected physician clinical decision-making for a substantial proportion of patients in this study, and that the availability and utilization of this information was a contributing factor in clinical improvement.

Engineered single nucleotide polymorphisms in the mosquito MEK docking site alter Plasmodium berghei development in Anopheles gambiae.

BACKGROUND: Susceptibility to Plasmodium infection in Anopheles gambiae has been proposed to result from naturally occurring polymorphisms that alter the strength of endogenous innate defenses. Despite the fact that some of these mutations are known to introduce non-synonymous substitutions in coding sequences, these mutations have largely been used to rationalize knockdown of associated target proteins to query the effects on parasite development in the mosquito host. Here, we assay the effects of engineered mutations on an immune signaling protein target that is known to control parasite sporogonic development. By this proof-of-principle work, we have established that naturally occurring mutations can be queried for their effects on mosquito protein function and on parasite development and that this important signaling pathway can be genetically manipulated to enhance mosquito resistance. METHODS: We introduced SNPs into the A. gambiae MAPK kinase MEK to alter key residues in the N-terminal docking site (D-site), thus interfering with its ability to interact with the downstream kinase target ERK. ERK phosphorylation levels in vitro and in vivo were evaluated to confirm the effects of MEK D-site mutations. In addition, overexpression of various MEK D-site alleles was used to assess P. berghei infection in A. gambiae. RESULTS: The MEK D-site contains conserved lysine residues predicted to mediate protein-protein interaction with ERK. As anticipated, each of the D-site mutations (K3M, K6M) suppressed ERK phosphorylation and this inhibition was significant when both mutations were present. Tissue-targeted overexpression of alleles encoding MEK D-site polymorphisms resulted in reduced ERK phosphorylation in the midgut of A. gambiae. Furthermore, as expected, inhibition of MEK-ERK signaling due to D-site mutations resulted in reduction in P. berghei development relative to infection in the presence of overexpressed catalytically active MEK. CONCLUSION: MEK-ERK signaling in A. gambiae, as in model organisms and humans, depends on the integrity of conserved key residues within the MEK D-site. Disruption of signal transmission via engineered SNPs provides a purposeful proof-of-principle model for the study of naturally occurring mutations that may be associated with mosquito resistance to parasite infection as well as an alternative genetic basis for manipulation of this important immune signaling pathway.