Predicting and Preventing Adverse Maternal and Child Outcomes of Opioid Use Disorder in Pregnancy

预测和预防妊娠期阿片类药物使用障碍的不良母婴结局

• 批准号: 10683849
• 负责人: Steven R. Plump
• 金额: $ 32.52万
• 依托单位: OPALGENIX, INC.
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-05-01 至 2025-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10683849
• 关键词: ABCB1 gene; ADRB2 gene; Adopted; Adult; Adverse event; Age; Algorithms; American; Anxiety; Biological; Buprenorphine; COVID-19 pandemic; CYP2B6 gene; CYP2D6 gene; CYP3A4 gene; Candidate Disease Gene; Child; Clinical; Clinical Trials; Computer software; Data; Devices; Dose; Electronic Health Record; Future; Genes; Genetic; Genetic Predisposition to Disease; Genetic Risk; Genetic Variation; Genotype; Head; Hospital Costs; Hospitalization; Incidence; Individual; Infant; Institutional Review Boards; Intelligence; Length of Stay; Logistic Regressions; Long-Term Effects; Machine Learning; Medical Device; Medication Management; Mental Depression; Metabolic; Metabolism; Methadone; Morphine; Neonatal Abstinence Syndrome; Newborn Infant; Opioid; Opioid Receptor; Outcome; Overdose; Pathway interactions; Patients; Performance; Pharmaceutical Preparations; Pharmacogenomics; Phase; Physicians; Physiological; Postpartum Period; Predictive Analytics; Pregnancy; Pregnant Women; Prevention; Prospective Studies; Public Health; Publishing; Race; Relapse; Reporting; Risk; Risk Assessment; Risk Factors; Safety; Sensitivity and Specificity; Severities; Small Business Innovation Research Grant; Smoking; Technology; Time; Up-Regulation; Variant; Weight; Withdrawal; Withdrawal Symptom; Woman; adverse outcome; algorithm development; behavioral outcome; candidate validation; care costs; clinical development; clinical practice; clinical predictors; clinical risk; combinatorial; commercialization; cost; craving; economic outcome; experience; fetal opioid exposure; genetic predictors; genetic signature; genetic variant; high risk; improved; innovation; inter-individual variation; intrapartum; maternal anxiety; maternal depression; maternal opioid use; maternal risk; meetings; opioid epidemic; opioid use; opioid use disorder; outcome prediction; personalized intervention; personalized risk prediction; phase 2 study; polysubstance use; prediction algorithm; predictive tools; pregnant; prevent; primary outcome; programs; response; risk prediction; risk prediction model; secondary outcome; standard of care; unpublished works

PROJECT SUMMARY: Opioid Use Disorder (OUD) in pregnant women, Neonatal Opioid Withdrawal Syndrome (NOWS) and associated hospital costs have dramatically increased in the past decade, with an American child is born suffering from NOWS every 15 minutes. The ongoing opioid epidemic is further worsened by the COVID- 19 pandemic. Despite medication treatment for OUD with buprenorphine or methadone, the pregnant women continue to be at high risk for early relapse, polysubstance use, overdose, depression, poor outcomes, and associated high costs of longer hospital stays, and their significant negative long-term effects in women and their children. Maternal depression and anxiety increase the risks for OUD, maternal relapse and NOWS. Children with NOWS also experience poor long-term neurodevelopmental and behavioral outcomes. Genetic factors influence opioid-related adverse events and outcomes including OUD and NOWS. The current clinical practices for OUD treatment in pregnant women and NOWS do not include proactive risk prediction and prevention. There is no comprehensive and polygenetic clinically adoptable risk prediction tool to proactively identify risks for maternal relapse, NOWS and costly care. There is an urgent and unmet clinical need for a reliable technology to proactively predict maternal relapse, NOWS, and improve the safety of pregnant women with OUD and their children. We have shown that opioid related poor clinical outcomes vary significantly based on underlying genetic predisposition. Single gene variations are independently associated with OUD, NOWS and inter-individual variations in responses to buprenorphine, methadone and morphine, the commonly used opioids to treat OUD in pregnant women and NOWS in infants. OpalGenix will build on our extensive prior prospective studies of genetic and clinical predictors of opioid-related adverse outcomes to develop and commercialize a transformative device, OpalGenix’s Genotype-guided Physician Support for Opioids, GPS-OpioidTM. GPS-Opioid will be a 510(k) cleared predictive analytic software as a medical device consisting of polygenetic, clinical risk factors and electronic health record-integrated intelligent analytics to provide personalized risk analysis to proactively predict risk for OUD-related risks including maternal relapse and NOWS with high accuracy (>80%) to enable personalized interventions, significantly improve clinical outcomes while reducing costs of care. In this Phase I proposal, OpalGenix will build on these studies to develop and validate GPS Opioid as a 510(k) cleared algorithm (medical device) that innovatively integrates polygenetic and clinical risks (e.g., maternal depression) to proactively predict personalized risk for maternal relapse and NOWS. We will leverage our team’s expertise with opioid pharmacogenomics, maternal OUD, NOWS, combinatorial risk predictive algorithms and commercialization to reduce OUD related burden in pregnant women and their children. Completion of this Phase I program will demonstrate value for GPS Opioid to improve clinical and economic outcomes, support an FDA Investigational Device Exemption (IDE) application, and de-risk a Phase II head-to-head clinical trial.

项目概述：孕妇阿片类药物使用障碍（OUD），新生儿阿片类药物戒断综合征