Preclinical Testing of Nalfurafine as an Opioid-sparing Adjuvant that Potentiates Analgesia by the Mu Opioid Receptor-targeting Agonist Morphine

Mu opioid receptor (MOR)-targeting analgesics are efficacious pain treatments, but notorious for their abuse potential. In preclinical animal models, coadministration of traditional kappa opioid receptor (KOR)-targeting agonists with MOR-targeting analgesics can decrease reward and potentiate analgesia. However, traditional KOR-targeting agonists are well known for inducing antitherapeutic side effects (psychotomimesis, depression, anxiety, dysphoria). Recent data suggest that some functionally selective, or biased, KOR-targeting agonists might retain the therapeutic effects of KOR activation without inducing undesirable side effects. Nalfurafine, used safely in Japan since 2009 for uremic pruritus, is one such functionally selective KOR-targeting agonist. Here, we quantify the bias of nalfurafine and several other KOR agonists relative to an unbiased reference standard (U50,488) and show that nalfurafine and EOM-salvinorin-B demonstrate marked G protein-signaling bias. While nalfurafine (0.015 mg/kg) and EOM-salvinorin-B (1 mg/kg) produced spinal antinociception equivalent to 5 mg/kg U50,488, only nalfurafine significantly enhanced the supraspinal analgesic effect of 5 mg/kg morphine. In addition, 0.015 mg/kg nalfurafine did not produce significant conditioned place aversion, yet retained the ability to reduce morphine-induced conditioned place preference in C57BL/6J mice. Nalfurafine and EOM-salvinorin-B each produced robust inhibition of both spontaneous and morphine-stimulated locomotor behavior, suggesting a persistence of sedative effects when coadministered with morphine. Taken together, these findings suggest that nalfurafine produces analgesic augmentation, while also reducing opioid-induced reward with less risk of dysphoria. Thus, adjuvant administration of G protein-biased KOR agonists like nalfurafine may be beneficial in enhancing the therapeutic potential of MOR-targeting analgesics, such as morphine

Impact of SARS-CoV-2 vaccination of children ages 5–11 years on COVID-19 disease burden and resilience to new variants in the United States, November 2021–March 2022: A multi-model study

Background: The COVID-19 Scenario Modeling Hub convened nine modeling teams to project the impact of expanding SARS-CoV-2 vaccination to children aged 5–11 years on COVID-19 burden and resilience against variant strains. Methods: Teams contributed state- and national-level weekly projections of cases, hospitalizations, and deaths in the United States from September 12, 2021 to March 12, 2022. Four scenarios covered all combinations of 1) vaccination (or not) of children aged 5–11 years (starting November 1, 2021), and 2) emergence (or not) of a variant more transmissible than the Delta variant (emerging November 15, 2021). Individual team projections were linearly pooled. The effect of childhood vaccination on overall and age-specific outcomes was estimated using meta-analyses. Findings: Assuming that a new variant would not emerge, all-age COVID-19 outcomes were projected to decrease nationally through mid-March 2022. In this setting, vaccination of children 5–11 years old was associated with reductions in projections for all-age cumulative cases (7.2%, mean incidence ratio [IR] 0.928, 95% confidence interval [CI] 0.880–0.977), hospitalizations (8.7%, mean IR 0.913, 95% CI 0.834–0.992), and deaths (9.2%, mean IR 0.908, 95% CI 0.797–1.020) compared with scenarios without childhood vaccination. Vaccine benefits increased for scenarios including a hypothesized more transmissible variant, assuming similar vaccine effectiveness. Projected relative reductions in cumulative outcomes were larger for children than for the entire population. State-level variation was observed. Interpretation: Given the scenario assumptions (defined before the emergence of Omicron), expanding vaccination to children 5–11 years old would provide measurable direct benefits, as well as indirect benefits to the all-age U.S. population, including resilience to more transmissible variants. Funding: Various (see acknowledgments)

Evaluation of the US COVID-19 Scenario Modeling Hub for informing pandemic response under uncertainty

Our ability to forecast epidemics far into the future is constrained by the many complexities of disease systems. Realistic longer-term projections may, however, be possible under well-defined scenarios that specify the future state of critical epidemic drivers. Since December 2020, the U.S. COVID-19 Scenario Modeling Hub (SMH) has convened multiple modeling teams to make months ahead projections of SARS-CoV-2 burden, totaling nearly 1.8 million national and state-level projections. Here, we find SMH performance varied widely as a function of both scenario validity and model calibration. We show scenarios remained close to reality for 22 weeks on average before the arrival of unanticipated SARS-CoV-2 variants invalidated key assumptions. An ensemble of participating models that preserved variation between models (using the linear opinion pool method) was consistently more reliable than any single model in periods of valid scenario assumptions, while projection interval coverage was near target levels. SMH projections were used to guide pandemic response, illustrating the value of collaborative hubs for longer-term scenario projections