Suzetrigine (Journavx), from a new class of non-opioid analgesics, is the first approved agent indicated for adults with moderate to severe pain in almost 3 decades. Reviewed are the drug's unique pharmacology, history of development, clinical evidence of safety and effectiveness, with an emphasis on implications for nursing practice. Literature describing the basic pharmacology as well as the results from extant clinical trials are integrated in this narrative educational review.

Opioid use disorder (OUD) remains a global public health concern, exerting a significant economic burden on societies worldwide. To combat the rise of substance use disorder, a complementary vaccine-based intervention was developed to support and strengthen the existing treatment modalities. Hapten-protein conjugate vaccines are employed as a promising novel strategy for neutralizing nonimmunogenic small-molecule drugs such as opioids by eliciting drug-specific antibodies that sequester opioids in circulation before reaching the brain. In this study, multiple parameters to optimize the efficacy of the TT-6-AmHap heroin conjugate vaccine were investigated in mice and rats, focusing on dose optimization, hapten density, booster timing, and cross-reactivity with other prescription opioids. Binding antibody responses were quantified, and vaccine efficacy was assessed using nociceptive assays following a drug challenge. Immunization with TT-6-AmHap vaccine in combination with adjuvants ALF43 and Alhydrogel[®] (ALFA), demonstrated a robust in-vivo efficacy across multiple opioid challenges including heroin, hydrocodone, and hydromorphone. Both antibody titers to 6-AmHap and protective efficacy were maintained following repeated heroin challenges up to 54 weeks. Notably, these repeat heroin challenges did not induce any measurable decline in antibody titers. Further characterization of the vaccine responses indicated a strong correlation between hapten density on the carrier protein and both binding antibody titers and overall vaccine efficacy. These findings support the continued development of hapten-based conjugate vaccines for OUD and demonstrate that an optimized vaccine can provide long-lasting protection against the antinociceptive effects of opioids in animals, laying the groundwork for future translational studies.

Chronic low back pain (LBP) is a major cause of disability around the world, and pharmaceutical treatments like opioids are often used to treat it. However, the efficacy and safety of opioids in LBP continue to be controversial due to inconsistent results and associated side effects. This systematic review assesses the effectiveness and safety of opioid medication in the treatment of LBP. A systematic search was performed utilising various electronic databases, including PubMed, Scopus, Google Scholar, MEDLINE, the Cochrane Library, and Web of Science, following a structured methodology in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) principles. Randomised controlled trials that were published in English within the past two decades and evaluated the use of opioids in adults with LBP were included. The Cochrane Risk of Bias tool and the Critical Appraisal Skills Programme (CASP) checklist were used to screen for bias, and a narrative synthesis was done because the studies were not all the same. There were six randomised, double-blind, placebo-controlled trials involving between 83 and 905 people. Opioid formulations, including buprenorphine, hydrocodone, oxycodone/naloxone, and oxymorphone, exhibited overall efficacy in diminishing pain intensity relative to placebo, especially in opioid-naïve individuals. But the magnitude and consistency of the advantage varied across studies, and some treatments showed only a small statistical difference. There were only a few reports of functional improvement, and they were not always constant. Nausea, constipation, and drowsiness were common side effects, although long-term safety outcomes were not fully evaluated. In general, opioids may help with short-term pain relief in people with LBP, but they aren't very useful in the long term because they aren't very effective and very safe and there isn't much evidence for their long-term use. It is advisable to select patients and use them with caution. More high-quality research is needed to understand their role in long-term management better.

Morbilliform drug eruptions in palliative care are often misattributed to active drugs, leading to inappropriate "opioid allergy" labels. Generic medications contain varying inactive excipients that can trigger delayed hypersensitivity. A man in his 70s with a prior history of ibuprofen-associated angioedema and metoclopramide-associated rash developed a pruritic morbilliform eruption after switching from a Mallinckrodt hydrocodone-acetaminophen product to a Camber oxycodone-acetaminophen formulation (National Drug Code [NDC] 31722-0951-05). During a washout period, the patient successfully tolerated SpecGx-manufactured morphine (NDC 00406-5118-01) and Aurobindo potassium chloride (NDC 65862-0987-99) while the rash resolved. Comparison of NDC data identified croscarmellose sodium as the likely culprit, as it was unique to the offending formulation. Clinicians should document the NDC rather than broad drug classes when reactions occur after a generic substitution to preserve future therapeutic options and ensure continued access to essential analgesia.

The development of inexpensive, portable, and rapid opioid screening techniques is urgently needed because of the ongoing opioid crisis in the United States, primarily driven by illicit fentanyl. In this work, a nanoelectronic detection method was developed for opioids using machine learning. Fentanyl and three other opioids (codeine, hydrocodone, and morphine) were tested with field-effect transistor (FET) sensor array composed of single-walled carbon nanotubes (SWCNTs) decorated with gold and platinum nanoparticles. Hundreds of opioid samples were tested manually and with an automated electrolyte-gated FET test system capable of testing up to 96 sensors. Fifteen features were extracted from experimental FET data to train conventional supervised machine learning models. Optimized linear discriminant analysis (LDA) and random forest (RF) models were applied for opioid classification, achieving 82.6% accuracy. Deep learning methods were subsequently applied to the full FET dataset to eliminate human bias in feature selection. A convolutional neural network (CNN) achieved 98.3% accuracy for the manual test group and revealed the significant role of gate-source current in distinguishing opioid molecules. This insight guided improvements to conventional models, uncovering unexpected mechanisms of molecular sensing with liquid-gated FETs and demonstrating the broader potential of combining nanoelectronics with machine learning for chemical detection.