Hematologic Abnormalities in Patients With Hidradenitis Suppurativa Receiving Adalimumab.

This post hoc analysis of PIONEER I and II randomized clinical trials assesses whether receiving adalimumab is associated with decreased hematologic abnormalities and increased clinical improvement in patients with hidradenitis suppurativa.

Changes to Registered and Pivotal Clinical Trials after the 2011 Tri-Societies Guidelines for Clinical Endpoints and Response Criteria in Mycosis Fungoides and Sézary Syndrome.

Guidelines for mycosis fungoides and Sézary syndrome clinical trials were published in 2011 to standardize endpoint criteria and trial design. Our retrospective cohort study of mycosis fungoides/Sézary syndrome clinical trials registered on ClinicalTrials.gov and pivotal trials supporting drug approvals and label extensions evaluates adherence to these guidelines. Sixty-three trials met our inclusion criteria. In a subpopulation of trials, mean adherence to the guidelines was approximately 60%. When comparing trials that began in the first 6 years after their publication with those that started after, we found no difference in mean adherence (4.12 vs 3.41) (P = .15). Among the 8 pivotal trials supporting new mycosis fungoides or Sézary syndrome systemic therapies from 1990 to 2020, systemic trials published after 2011 were more likely to randomize patients (100 vs 0%, P = .036), perform superiority testing (100 vs 0%, P = .036), and use an intention-to-treat analysis (100 vs 0%, P = .036). The design of trials registered on Clinicaltrials.gov did not change significantly between the first 6 years after the publication of the guidelines and after. This demonstrates that the guidelines are still not consistently implemented across all trials. However, registrational trials were more likely to implement the recommendations.

Market dynamics of authorized generics in Medicaid from 2014 to 2020.

OBJECTIVE: To investigate the use and timing of market introduction of authorized generics (AGs), which unlike independent generics are sold by the brand-name drug manufacturer under a generic label. DATA SOURCES: This study used public Medicaid prescription drug use data from 2014 to 2020. STUDY DESIGN: This cross-sectional study measured the percentage of filled prescriptions for AGs, compared with brand-name and independent generic versions. We also identified the frequency and characteristics of AGs marketed at least 1 year before independent generics. DATA EXTRACTION METHODS: Drugs were classified based on manufacturer-reported data to Medicaid. PRINCIPAL FINDINGS: From 2014 to 2020, 1023 AGs accounted for 175 million filled Medicaid prescriptions. These represented 4% of Medicaid prescription drug use, and 16% of medication use among products with AGs available. Among 393 AGs for drugs without generic competition before 2014, 139 (35%) were marketed at least 1 year before independent generics or had no independent generic competition through December 2020. CONCLUSIONS: AGs represented a small share of Medicaid prescription drug use from 2014 to 2020, but when AGs were available, they accounted for sizeable market share. Among the minority of cases in which AGs were marketed a year or more before independent generics, manufacturers may be using AGs to bolster brand-name drug prices or to undermine independent generic competition, meriting further attention by regulators.

Analysis of Supportive Evidence for US Food and Drug Administration Approvals of Novel Drugs in 2020.

Importance: In recent years, drug approvals have been based on fewer, smaller, and less rigorous pivotal trials. Less robust preapproval testing raises questions about the efficacy and clinical value of these drugs. Objective: To assess the regulatory context, pivotal design characteristics, and postmarket requirements (PMRs) and postmarket commitments (PMCs) of novel 2020 drug approvals to characterize the state of evidence at the time of approval. Design, Setting, and Participants: This cohort study identified novel drugs approved by the US Food and Drug Administration's (FDA) Center for Drug Evaluation and Research in 2020. The Drugs@FDA database was used to extract key characteristics of each drug's pivotal trials. Drug approval packages provided regulatory information. The prevalence of key trial design features was compared between oncology and nononcology drugs. Exposures: Drug names, date of approval, indication on labeling, and clinical and regulatory details. Main Outcomes and Measures: Number of pivotal trials, pivotal trial design (randomization, masking, groups), trial comparator, trial hypothesis, trial end points, results, number and type of expedited pathway designations, and number and type of PMRs and PMCs. Results: The 49 novel therapeutics approved in 2020 were supported by 75 pivotal trials. More than half of drugs (28 [57.1%]) were supported by a single pivotal trial. Trial sizes ranged from 19 to 2230 participants. More than three-fourths of trials (57 [76.0%]) had a randomization component, and nearly two-thirds (46 [61.3%]) were double-masked. Most used a superiority approach. Roughly half (39 [52.0%]) compared the novel therapeutic with a placebo or vehicle control; 13 (17.3%), an active control; 2 (2.7%), both a placebo and active control; and 21 (28.0%), a historical, external, or other control. Nearly half of pivotal trials (34 [45.3%]) used a surrogate measure as a primary end point. Pivotal trials supporting oncology approvals were much more likely to have historical controls than nononcology approvals (13 of 18 [72.2%] vs 8 of 57 [14.0%]; P < .001) and to use at least 1 surrogate measure as a primary end point (17 [94.4%] vs 17 [29.8%]; P < .001). Forty drugs had at least 1 PMR or PMC, accounting for 178 PMRs and PMCs across the cohort. Conclusions and Relevance: These findings suggest that the increased flexibility in the characteristics of acceptable preapproval evidence can be partially explained by the increase in trials of drugs for rare and other serious conditions that require flexible testing strategies as well as the associated regulatory changes that have accumulated over time. The FDA and consumers may benefit from a revised approach that better balances time to market with ensuring that approved drugs show evidence of efficacy.

How achievable are COVID-19 clinical trial recruitment targets? A UK observational cohort study and trials registry analysis.

OBJECTIVES: To analyse enrolment to interventional trials during the first wave of the COVID-19 pandemic in England and describe the barriers to successful recruitment in the circumstance of a further wave or future pandemics. DESIGN: We analysed registered interventional COVID-19 trial data and concurrently did a prospective observational study of hospitalised patients with COVID-19 who were being assessed for eligibility to one of the RECOVERY, C19-ACS or SIMPLE trials. SETTING: Interventional COVID-19 trial data were analysed from the clinicaltrials.gov and International Standard Randomized Controlled Trial Number databases on 12 July 2020. The patient cohort was taken from five centres in a respiratory National Institute for Health Research network. Population and modelling data were taken from published reports from the UK government and Medical Research Council Biostatistics Unit. PARTICIPANTS: 2082 consecutive admitted patients with laboratory-confirmed SARS-CoV-2 infection from 27 March 2020 were included. MAIN OUTCOME MEASURES: Proportions enrolled, and reasons for exclusion from the aforementioned trials. Comparisons of trial recruitment targets with estimated feasible recruitment numbers. RESULTS: Analysis of trial registration data for COVID-19 treatment studies enrolling in England showed that by 12 July 2020, 29 142 participants were needed. In the observational study, 430 (20.7%) proceeded to randomisation. 82 (3.9%) declined participation, 699 (33.6%) were excluded on clinical grounds, 363 (17.4%) were medically fit for discharge and 153 (7.3%) were receiving palliative care. With 111 037 people hospitalised with COVID-19 in England by 12 July 2020, we determine that 22 985 people were potentially suitable for trial enrolment. We estimate a UK hospitalisation rate of 2.38%, and that another 1.25 million infections would be required to meet recruitment targets of ongoing trials. CONCLUSIONS: Feasible recruitment rates, study design and proliferation of trials can limit the number, and size, that will successfully complete recruitment. We consider that fewer, more appropriately designed trials, prioritising cooperation between centres would maximise productivity in a further wave.

Aberrantly activated Cox-2 and Wnt signaling interact to maintain cancer stem cells in glioblastoma.

Glioblastoma recurrence after aggressive therapy typically occurs within six months, and patients inevitably succumb to their disease. Tumor recurrence is driven by a subpopulation of cancer stem cells in glioblastoma (glioblastoma stem-like cells, GSCs), which exhibit resistance to cytotoxic therapies, compared to their non-stem-cell counterparts. Here, we show that the Cox-2 and Wnt signaling pathways are aberrantly activated in GSCs and interact to maintain the cancer stem cell identity. Cox-2 stimulates GSC self-renewal and proliferation through prostaglandin E2 (PGE2), which in turn activates the Wnt signaling pathway. Wnt signaling underlies PGE2-induced GSC self-renewal and independently directs GSC self-renewal and proliferation. Inhibition of PGE2 enhances the effect of temozolomide on GSCs, but affords only a modest survival advantage in a xenograft model in the setting of COX-independent Wnt activation. Our findings uncover an aberrant positive feedback interaction between the Cox-2/PGE2 and Wnt pathways that mediates the stem-like state in glioblastoma.

Stratification bias in low signal microarray studies.

BACKGROUND: When analysing microarray and other small sample size biological datasets, care is needed to avoid various biases. We analyse a form of bias, stratification bias, that can substantially affect analyses using sample-reuse validation techniques and lead to inaccurate results. This bias is due to imperfect stratification of samples in the training and test sets and the dependency between these stratification errors, i.e. the variations in class proportions in the training and test sets are negatively correlated. RESULTS: We show that when estimating the performance of classifiers on low signal datasets (i.e. those which are difficult to classify), which are typical of many prognostic microarray studies, commonly used performance measures can suffer from a substantial negative bias. For error rate this bias is only severe in quite restricted situations, but can be much larger and more frequent when using ranking measures such as the receiver operating characteristic (ROC) curve and area under the ROC (AUC). Substantial biases are shown in simulations and on the van 't Veer breast cancer dataset. The classification error rate can have large negative biases for balanced datasets, whereas the AUC shows substantial pessimistic biases even for imbalanced datasets. In simulation studies using 10-fold cross-validation, AUC values of less than 0.3 can be observed on random datasets rather than the expected 0.5. Further experiments on the van 't Veer breast cancer dataset show these biases exist in practice. CONCLUSION: Stratification bias can substantially affect several performance measures. In computing the AUC, the strategy of pooling the test samples from the various folds of cross-validation can lead to large biases; computing it as the average of per-fold estimates avoids this bias and is thus the recommended approach. As a more general solution applicable to other performance measures, we show that stratified repeated holdout and a modified version of k-fold cross-validation, balanced, stratified cross-validation and balanced leave-one-out cross-validation, avoids the bias. Therefore for model selection and evaluation of microarray and other small biological datasets, these methods should be used and unstratified versions avoided. In particular, the commonly used (unbalanced) leave-one-out cross-validation should not be used to estimate AUC for small datasets.