Project description:We compare and contrast the expected duration and number of infections and deaths averted among several designs for clinical trials of COVID-19 vaccine candidates, including traditional and adaptive randomized clinical trials and human challenge trials. Using epidemiological models calibrated to the current pandemic, we simulate the time course of each clinical trial design for 756 unique combinations of parameters, allowing us to determine which trial design is most effective for a given scenario. A human challenge trial provides maximal net benefits-averting an additional 1.1M infections and 8,000 deaths in the U.S. compared to the next best clinical trial design-if its set-up time is short or the pandemic spreads slowly. In most of the other cases, an adaptive trial provides greater net benefits.
Project description:Management of glioblastoma is a clinical challenge since very few systemic treatments have shown clinical efficacy in recurrent disease. Thanks to an increased knowledge of the biological and molecular mechanisms related to disease progression and growth, promising novel treatment strategies are emerging. The expanding availability of innovative compounds requires the design of a new generation of clinical trials, testing experimental compounds in a short time and tailoring the sample cohort based on molecular and clinical behaviors. In this review, we focused our attention on the assessment of promising novel treatment approaches, discussing novel trial design and possible future fields of development in this setting.
Project description:Development of oncologic therapies has traditionally been performed in a sequence of clinical trials intended to assess safety (phase I), preliminary efficacy (phase II), and improvement over the standard of care (phase III) in homogeneous (in terms of tumor type and disease stage) patient populations. As cancer has become increasingly understood on the molecular level, newer "targeted" drugs that inhibit specific cancer cell growth and survival mechanisms have increased the need for new clinical trial designs, wherein pertinent questions on the relationship between patient biomarkers and response to treatment can be answered. Herein, we review the clinical trial design literature from initial to more recently proposed designs for targeted agents or those treatments hypothesized to have enhanced effectiveness within patient subgroups (e.g., those with a certain biomarker value or who harbor a certain genetic tumor mutation). We also describe a number of real clinical trials where biomarker-based designs have been utilized, including a discussion of their respective advantages and challenges. As cancers become further categorized and/or reclassified according to individual patient and tumor features, we anticipate a continued need for novel trial designs to keep pace with the changing frontier of clinical cancer research.
Project description:The Cape-Covid trial is an embedded clinical trial within the ongoing Cape-Cod trial. The Covid-19 pandemic appeared while we were conducting a randomized trial assessing the effectiveness of corticosteroids in severe community-acquired pneumonia. We took advantage of this ongoing trial to embed a sub-trial assessing hydrocortisone in SARS-CoV-2 infected patients. In this manuscript, we wish to share our experience when we needed to make fast and robust methodological decisions during the Covid-19 pandemic in a two weeks period of time.
Project description:The COVID-19 pandemic has led to an unprecedented response in terms of clinical research activity. An important part of this research has been focused on randomized controlled clinical trials to evaluate potential therapies for COVID-19. The results from this research need to be obtained as rapidly as possible. This presents a number of challenges associated with considerable uncertainty over the natural history of the disease and the number and characteristics of patients affected, and the emergence of new potential therapies. These challenges make adaptive designs for clinical trials a particularly attractive option. Such designs allow a trial to be modified on the basis of interim analysis data or stopped as soon as sufficiently strong evidence has been observed to answer the research question, without compromising the trial's scientific validity or integrity. In this article, we describe some of the adaptive design approaches that are available and discuss particular issues and challenges associated with their use in the pandemic setting. Our discussion is illustrated by details of four ongoing COVID-19 trials that have used adaptive designs.
Project description:Social scientists have a robust history of contributing to better understandings of and responses to disease outbreaks. The implementation of qualitative research in the context of infectious epidemics, however, continues to lag behind in the delivery, credibility, and timeliness of findings when compared with other research designs. The purpose of this article is to reflect on our experience of carrying out three research studies (a rapid appraisal, a qualitative study based on interviews, and a mixed-methods survey) aimed at exploring health care delivery in the context of COVID-19. We highlight the importance of qualitative data to inform evidence-based public health responses and provide a way forward to global research teams who wish to implement similar rapid qualitative studies. We reflect on the challenges of setting up research teams, obtaining ethical approval, collecting and analyzing data in real-time and sharing actionable findings.
Project description:BackgroundChildren develop symptomatic coronavirus disease 2019 (COVID-19) more rarely than adults upon infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Pediatric oncology and hematology patients may be at increased risk of severe COVID-19 due to their underlying disease or treatment. We investigated COVID-19 and seroprevalence of anti-SARS-CoV-2 antibodies, respectively, in a Swedish cohort of pediatric oncology and hematology patients.ProcedurePatients (n = 136) were recruited between June 2020 and September 2021 at Uppsala University Children's Hospital, Sweden. Up to six consecutive blood samples per patient were analyzed for wild-type anti-S1 IgM and IgG antibodies (including after vaccination, n = 4). Clinical data on COVID-19 (including polymerase chain reaction [PCR] test results) were collected from electronic medical records. A questionnaire was completed at recruitment.ResultsA cumulative seroprevalence (IgM and IgG) of 33% (45/136 patients, 95% confidence interval: 25%-41%) was observed in this patient cohort, of whom 66% (90/136 patients) were under severe immunosuppressive treatment during the study period. Increasing patient age (p = .037) and PCR test results (p < .002) were associated with seropositivity in nonvaccinated cases. Most seropositive, nonvaccinated cases (32/43, 74%) were never PCR-verified for SARS-CoV-2 infection. Of the 13 patients with PCR-verified infection, nine (69%) reported mild disease. A majority (63%) reported continued school attendance during the pandemic.ConclusionsSwedish pediatric oncology and hematology patients developed antibodies against SARS-CoV-2, despite their diagnosis and/or treatment, and the observed seroprevalence was similar to that in national pediatric outpatients. PCR-verified cases underestimate the true incidence of COVID-19 in this patient cohort.
Project description:A novel bioinformatic approach for drug repurposing against emerging viral epidemics like Covid-19 is described. It exploits the COMPARE algorithm, a public program from the National Cancer Institute (NCI) to sort drugs according to their patterns of growth inhibitory profiles from a diverse panel of human cancer cell lines. The data repository of the NCI includes the growth inhibitory patterns of more than 55,000 molecules. When candidate drug molecules with ostensible anti-SARS-CoV-2 activities were used as seeds (e.g., hydroxychloroquine, ritonavir, and dexamethasone) in COMPARE, the analysis uncovered several molecules with fingerprints similar to the seeded drugs. Interestingly, despite the fact that the uncovered drugs were from various pharmacological classes (antiarrhythmic, nucleosides, antipsychotic, alkaloids, antibiotics, and vitamins), they were all reportedly known from published literature to exert antiviral activities via different modes, confirming that COMPARE analysis is efficient for predicting antiviral activities of drugs from various pharmacological classes. Noticeably, several of the uncovered drugs can be readily tested, like didanosine, methotrexate, vitamin A, nicotinamide, valproic acid, uridine, and flucloxacillin. Unlike pure in silico methods, this approach is biologically more relevant and able to pharmacologically correlate compounds regardless of their chemical structures. This is an untapped resource, reliable and readily exploitable for drug repurposing against current and future viral outbreaks.