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Oral anthelmintic niclosamide has potent in vitro antiviral activity against SARS-CoV-2. Repurposed niclosamide could be a safe and efficacious COVID-19 therapy.To investigate whether niclosamide decreased SARS-CoV-2 shedding and duration of symptoms among patients with mild to moderate COVID-19.This randomized, placebo-controlled clinical trial enrolled individuals testing positive for SARS-CoV-2 by polymerase chain reaction with mild to moderate symptoms of COVID. All trial participants, investigators, staff, and laboratory personnel were kept blind to participant assignments. Enrollment was among individuals reporting at Tufts Medical Center and Wellforce Network in Massachusetts for outpatient COVID-19 testing. The trial opened to accrual on October 1, 2020; the last participant enrolled on April 20, 2021. Trial exclusion criteria included hospitalization at time of enrollment or use of any experimental treatment for COVID-19, including vaccination. Enrollment was stopped before attaining the planned sample size when COVID-19 diagnoses decreased precipitously in Massachusetts. Data were analyzed from July through September 2021.In addition to receiving current standard of care, participants were randomly assigned on a 1:1 basis to receive niclosamide 2 g by mouth daily for 7 days or identically labeled placebo at the same dosing schedule.Oropharyngeal and fecal samples were self-collected for viral shedding measured by reverse-transcriptase-polymerase-chain-reaction on days 3, 7, 10, and 14, and an additional fecal sample was collected on day 21. A telehealth platform was developed to conduct remote study visits, monitor symptoms, and coordinate sample collection via couriers. The primary end point was the proportion of participants with viral clearance in respiratory samples at day 3 based on the intention-to-treat sample. Mean times to viral clearance and symptom resolution were calculated as restricted mean survival times and accounted for censored observations.Among 73 participants, 36 individuals were enrolled and randomized to niclosamide and 37 individuals to placebo. Participant characteristics were similar across treatment groups; among 34 patients receiving placebo and 33 patients receiving niclosamide in the intention-to-treat sample, mean (SD) age was 36.0 (13.3) years vs 36.8 (12.9) years and there were 21 (61.8%) men vs 20 (60.6%) men. The overall mean (SD) age was 36.4 (13.0) years. For the primary end point, 66.67% (95% CI, 50.74% to 81.81%) of participants receiving niclosamide and 55.88% (95% CI, 40.27% to 72.73%) of participants receiving placebo had oropharyngeal SARS-CoV-2 clearance at day 3 (P = .37). Among 63 participants with symptoms, niclosamide did not significantly shorten symptom duration, which was 12.01 (95% CI, 8.82 to 15.2) days in the niclosamide group vs 14.61 (95% CI, 11.25 to 17.96) days in the placebo group (mean difference, -2.6 [95% CI, -7.23 to 2.03] days). Niclosamide was well-tolerated; the most commonly reported adverse events in the placebo and niclosamide groups were headaches (11 patients [32.4%] vs 7 patients [21.2%]; P = .31) and cough (8 patients [23.5%] vs 7 patients [21.2%]; P = .82).In this randomized clinical trial, there was no significant difference in oropharyngeal clearance of SARS-CoV-2 at day 3 between placebo and niclosamide groups. Confirmation in larger studies is warranted.ClinicalTrials.gov Identifier: NCT04399356.

Efficacy trials, designed to gain regulatory marketing approval, evaluate drugs in optimally selected patients under advantageous conditions for relatively short time periods. Effectiveness trials, designed to evaluate use in usual practice, assess treatments among more typical patients in real-world conditions with longer follow-up periods. In "efficacy-to-effectiveness (E2E) trials," if the initial efficacy trial component is positive, the trial seamlessly transitions to an effectiveness trial component to efficiently yield both types of evidence. Yet more time could be saved by simultaneously addressing efficacy and effectiveness in an "efficacy and effectiveness too (EE2) trial." Additionally, hybrids of the E2E and EE2 approaches with differing degrees of overlap of the two components could allow flexibility for specific drug development needs. In planning EE2 trials, each stakeholder's current and future needs, incentives, and perspective must be considered. Although challenging, the ultimate benefits to stakeholders, the health system, and the public should justify this effort.

Clinicians and patients often try a treatment for an initial period to inform longer-term therapeutic decisions. A more rigorous approach involves N-of-1 trials. In these single-patient crossover trials, typically conducted in patients with chronic conditions, individual patients are given candidate treatments in a double-blinded, random sequence of alternating periods to determine the most effective treatment for that patient. However, to date, these trials are rarely done outside of research settings and have not been integrated into general care where they could offer substantial benefit. Designating this classical, N-of-1 trial design as Type 1, there also are new and evolving uses of N-of-1 trials that we designate as Type 2. In these, rather than focusing on optimizing treatment for chronic diseases when multiple approved choices are available, as is typical of Type 1, a Type 2 N-of-1 trial tests treatments designed specifically for a patient with a rare diseases, to facilitate personalized medicine. While the aims differ, both types face the challenge of collecting individual-patient evidence using standard, trusted, widely accepted methods. To fulfill their potential for producing both clinical and research benefits, and to be available for wide use, N-of-1 trials will have to fit into the current healthcare ecosystem. This will require generalizable and accepted processes, platforms, methods, and standards. This also will require sustainable value-based arrangements among key stakeholders. In this article, we review opportunities, stakeholders, issues, and possible approaches that could support general use of N-of-1 trials and deliver benefit to patients and the healthcare enterprise. To assess and expand the benefits of N-of-1 trials, we propose multi-stakeholder meetings, workshops, and the generation of methods, standards, and platforms that would support wider availability and the value of N-of-1 trials.

Background:To identify potential participants for clinical trials, electronic health records (EHRs) are searched at potential sites. As an alternative, we investigated using medical devices used for real-time diagnostic decisions for trial enrollment.Methods:To project cohorts for a trial in acute coronary syndromes (ACS), we used electrocardiograph-based algorithms that identify ACS or ST elevation myocardial infarction (STEMI) that prompt clinicians to offer patients trial enrollment. We searched six hospitals’ electrocardiograph systems for electrocardiograms (ECGs) meeting the planned trial’s enrollment criterion: ECGs with STEMI or > 75% probability of ACS by the acute cardiac ischemia time-insensitive predictive instrument (ACI-TIPI). We revised the ACI-TIPI regression to require only data directly from the electrocardiograph, the e-ACI-TIPI using the same data used for the original ACI-TIPI (development set n = 3,453; test set n = 2,315). We also tested both on data from emergency department electrocardiographs from across the US (n = 8,556). We then used ACI-TIPI and e-ACI-TIPI to identify potential cohorts for the ACS trial and compared performance to cohorts from EHR data at the hospitals.Results:Receiver-operating characteristic (ROC) curve areas on the test set were excellent, 0.89 for ACI-TIPI and 0.84 for the e-ACI-TIPI, as was calibration. On the national electrocardiographic database, ROC areas were 0.78 and 0.69, respectively, and with very good calibration. When tested for detection of patients with > 75% ACS probability, both electrocardiograph-based methods identified eligible patients well, and better than did EHRs.Conclusion:Using data from medical devices such as electrocardiographs may provide accurate projections of available cohorts for clinical trials.

Dexmedetomidine–propofol pharmacodynamic interaction was evaluated in nine healthy subjects in a crossover design. Dexmedetomidine/placebo was infused using a computer‐controlled infusion pump (CCIP) to maintain a pseudo‐steady‐state plasma concentration of 0.66 ± 0.080 or 0 ng/mL, respectively. Forty‐five minutes after the dexmedetomidine/placebo infusion was started, propofol was infused using a second CCIP to achieve a stepwise logarithmically ascending propofol concentration (1.00 to 13.8 μg/mL) profile. Each propofol step lasted 10 min. Blood was sampled for plasma concentration determination, and pharmacodynamic endpoint assessments were made during the study. Propofol and dexmedetomidine/placebo infusions were terminated when three endpoints (subjects were too sedated to hold a syringe, followed by loss of eyelash reflex, followed by loss of motor response to electrical stimulation) were achieved sequentially. The concentration of propofol associated with 50% probability of achieving a pharmacodynamic endpoint in the absence of dexmedetomidine (EC 50 ; placebo treatment) was 6.63 μg/mL for motor response to electrical stimulation and ranged from 1.14 to 1.98 μg/mL for the ability to hold a syringe, eyelash reflex, and sedation scores. The apparent EC 50 values of propofol (EC 50APP ; concentration of propofol at which the probability of achieving a pharmacodynamic endpoint is 50% in the presence of dexmedetomidine concentrations observed in the current study; dexmedetomidine treatment) were 0.273, 0.544–0.643, and 3.89 μg/mL for the ability to hold a syringe, sedation scores, and motor response, respectively. Dexmedetomidine reduced propofol concentrations required for sedation and suppression of motor response. Therefore, the propofol dose required for sedation and induction of anesthesia may have to be reduced in the presence of dexmedetomidine. © 2001 Wiley‐Liss, Inc. and the American Pharmaceutical Association J Pharm Sci 90:172–181, 2001

BACKGROUND The question of the minimum number of Papanicolaou (Pap) smear slides that must be rescreened to draw statistically valid conclusions regarding the accuracy of screening often is raised. No method for generating answers in varying laboratory circumstances has achieved widespread application; standard statistical sample size calculations may represent such a resource. METHODS A series of tables was constructed to display minimum required numbers of rescreens, with each table representing differing hypothetical laboratory circumstances. To use each table, assumptions must be specified in advance as to prevalence of abnormality, definition of error, baseline false-negative proportions (FNPs) of performance, and a degree of increase in FNPs that is considered a departure from baseline warranting concern, among others. RESULTS The authors constructed four sample tables displaying minimum numbers of slides that must be rescreened in differing specified laboratory scenarios. Depending on assumed conditions and predetermined levels of satisfactory and unsatisfactory accuracy, the range of numbers is very broad (38-10,000). One example representing likely conditions indicates that 1040 slides must be reexamined; in another scenario, a sample size of 300 is sufficient. CONCLUSIONS The minimum number of rescreened slides needed to draw statistically valid conclusions regarding Pap smear screening accuracy can be calculated using standard statistical methods. However, a number of assumptions must be detailed in advance. The authors offer this as a practical guide and a continuation of a general inquiry regarding Pap smear error rate measurement and display. The use of these tables raises at least as many questions as it answers, but still may represent a significant advance. Future efforts at further numeric characterization of aspects of Pap smear screening performance are warranted to enable rational decision making when performance is examined in the course of quality assurance, and during quality control and regulatory activities. [See editorial on pages 127-9, this issue.]

Dexmedetomidine, a highly selective alpha(2)-adrenoceptor agonist, reduces the requirements for anesthetic, analgesic, sedative, and hypnotic drugs. Dexmedetomidine pharmacokinetics were characterized in healthy subjects after intravenous administration by means of a computer-controlled infusion pump. A series of seven stepwise increasing pseudo-steady-state plasma concentrations were targeted. The influence of cardiac output on the pharmacokinetics was investigated by use of a compartmental modeling approach in which the elimination clearance was characterized as being either cardiac output independent or dependent. At dexmedetomidine concentrations of 0, 0.6, and 1.2 ng/mL, mean (SD) estimated cardiac outputs were 5. 6 (0.85), 5.1 (0.67), and 4.5 (0.83) L/min, and mean (SD) clearances were 40 (10), 38 (9.0), and 35 (8.5) L/h, respectively. Dexmedetomidine V(SS) and elimination half-life were 72 (19) L and 1. 9 (0.62) h, respectively. The approximately 3 to 19% decrease in cardiac output observed within the anticipated therapeutic range of 0.3 to 1.2 ng/mL was similar to that observed for clonidine. The decrease in cardiac output with increasing plasma concentrations of dexmedetomidine resulted in a corresponding decrease in drug elimination clearance of < or =12% within the therapeutic range; however, this decrease in dexmedetomidine clearance is likely not clinically relevant.