Summary
Chromatin plays a central role in orchestrating gene regulation at the transcriptional level. However, our understanding of how chromatin states are altered in response to environmental and ...developmental cues, and then maintained epigenetically over many cell divisions, remains poor. The floral repressor gene FLOWERING LOCUS C (FLC) in Arabidopsis thaliana is a useful system to address these questions. FLC is transcriptionally repressed during exposure to cold temperatures, allowing studies of how environmental conditions alter expression states at the chromatin level. FLC repression is also epigenetically maintained during subsequent development in warm conditions, so that exposure to cold may be remembered. This memory depends on molecular complexes that are highly conserved among eukaryotes, making FLC not only interesting as a paradigm for understanding biological decision‐making in plants, but also an important system for elucidating chromatin‐based gene regulation more generally. In this review, we summarize our understanding of how cold temperature induces a switch in the FLC chromatin state, and how this state is epigenetically remembered. We also discuss how the epigenetic state of FLC is reprogrammed in the seed to ensure a requirement for cold exposure in the next generation.
Significance Statement
FLOWERING LOCUS C (FLC) regulation provides a paradigm for understanding how chromatin can be modulated to determine gene expression in a developmental context. This review describes our current mechanistic understanding of how FLC expression is genetically specified and epigenetically regulated throughout the plant life cycle, and how this determines plant life‐history strategy.
Background:
A “platform trial” is a clinical trial with a single master protocol in which multiple treatments are evaluated simultaneously. Adaptive platform designs offer flexible features such as ...dropping treatments for futility, declaring one or more treatments superior, or adding new treatments to be tested during the course of a trial.
Methods:
A simulation study explores the efficiencies of various platform trial designs relative to a traditional two-arm strategy.
Results:
Platform trials can find beneficial treatments with fewer patients, fewer patient failures, less time, and with greater probability of success than a traditional two-arm strategy.
Conclusion:
In an era of personalized medicine, platform trials provide the innovation needed to efficiently evaluate modern treatments.
Platform and basket trial have the potential to alter the landscape of drug development in many disease areas. Although these trials create many efficiencies compared with simple trials, they also ...create new statistical challenges for regulators. I highlight some of these new issues and discuss the regulatory challenges emerging from these trials. I highlight some critical points made by Collignon et al.,1 while providing some conflicting views on these statistical challenges.
Lecanemab (BAN2401), an IgG1 monoclonal antibody, preferentially targets soluble aggregated amyloid beta (Aβ), with activity across oligomers, protofibrils, and insoluble fibrils. BAN2401-G000-201, a ...randomized double-blind clinical trial, utilized a Bayesian design with response-adaptive randomization to assess 3 doses across 2 regimens of lecanemab versus placebo in early Alzheimer's disease, mild cognitive impairment due to Alzheimer's disease (AD) and mild AD dementia.
BAN2401-G000-201 aimed to establish the effective dose 90% (ED90), defined as the simplest dose that achieves ≥90% of the maximum treatment effect. The primary endpoint was Bayesian analysis of 12-month clinical change on the Alzheimer's Disease Composite Score (ADCOMS) for the ED90 dose, which required an 80% probability of ≥25% clinical reduction in decline versus placebo. Key secondary endpoints included 18-month Bayesian and frequentist analyses of brain amyloid reduction using positron emission tomography; clinical decline on ADCOMS, Clinical Dementia Rating-Sum-of-Boxes (CDR-SB), and Alzheimer's Disease Assessment Scale-Cognitive Subscale (ADAS-Cog14); changes in CSF core biomarkers; and total hippocampal volume (HV) using volumetric magnetic resonance imaging.
A total of 854 randomized subjects were treated (lecanemab, 609; placebo, 245). At 12 months, the 10-mg/kg biweekly ED90 dose showed a 64% probability to be better than placebo by 25% on ADCOMS, which missed the 80% threshold for the primary outcome. At 18 months, 10-mg/kg biweekly lecanemab reduced brain amyloid (-0.306 SUVr units) while showing a drug-placebo difference in favor of active treatment by 27% and 30% on ADCOMS, 56% and 47% on ADAS-Cog14, and 33% and 26% on CDR-SB versus placebo according to Bayesian and frequentist analyses, respectively. CSF biomarkers were supportive of a treatment effect. Lecanemab was well-tolerated with 9.9% incidence of amyloid-related imaging abnormalities-edema/effusion at 10 mg/kg biweekly.
BAN2401-G000-201 did not meet the 12-month primary endpoint. However, prespecified 18-month Bayesian and frequentist analyses demonstrated reduction in brain amyloid accompanied by a consistent reduction of clinical decline across several clinical and biomarker endpoints. A phase 3 study (Clarity AD) in early Alzheimer's disease is underway.
Clinical Trials.gov NCT01767311 .
The development of a prognostic mortality risk model for hospitalized COVID-19 patients may facilitate patient treatment planning, comparisons of therapeutic strategies, and public health ...preparations. We retrospectively reviewed the electronic health records of patients hospitalized within a 13-hospital New Jersey USA network between March 1, 2020 and April 22, 2020 with positive polymerase chain reaction results for SARS-CoV-2, with follow-up through May 29, 2020. With death or hospital discharge by day 40 as the primary endpoint, we used univariate followed by stepwise multivariate proportional hazard models to develop a risk score on one-half the data set, validated on the remainder, and converted the risk score into a patient-level predictive probability of 40-day mortality based on the combined dataset. The study population consisted of 3123 hospitalized COVID-19 patients; median age 63 years; 60% were men; 42% had >3 coexisting conditions. 713 (23%) patients died within 40 days of hospitalization for COVID-19. From 22 potential candidate factors 6 were found to be independent predictors of mortality and were included in the risk score model: age, respiratory rate greater than or equal to25/minute upon hospital presentation, oxygenation <94% on hospital presentation, and pre-hospital comorbidities of hypertension, coronary artery disease, or chronic renal disease. The risk score was highly prognostic of mortality in a training set and confirmatory set yielding in the combined dataset a hazard ratio of 1.80 (95% CI, 1.72, 1.87) for one unit increases. Using observed mortality within 20 equally sized bins of risk scores, a predictive model for an individual's 40-day risk of mortality was generated as -14.258 + 13.460*RS + 1.585*(RS-2.524)^2-0.403*(RS-2.524)^3. An online calculator of this 40-day COVID-19 mortality risk score is available at www.HackensackMeridianHealth.org/CovidRS. A risk score using six variables is able to prognosticate mortality within 40-days of hospitalization for COVID-19.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Background
Recurrent
Clostridioides difficile
infection, associated with dysbiosis of gut microbiota, has substantial disease burden in the USA. RBX2660 is a live biotherapeutic product consisting of ...a broad consortium of microbes prepared from human stool that is under investigation for the reduction of recurrent
C. difficile
infection.
Methods
A randomized, double-blind, placebo-controlled, phase III study, with a Bayesian primary analysis integrating data from a previous phase IIb study, was conducted. Adults who had one or more
C. difficile
infection recurrences with a positive stool assay for
C. difficile
and who were previously treated with standard-of-care antibiotics were randomly assigned 2:1 to receive a subsequent blinded, single-dose enema of RBX2660 or placebo. The primary endpoint was treatment success, defined as the absence of
C. difficile
infection diarrhea within 8 weeks of study treatment.
Results
Of the 320 patients screened, 289 were randomly assigned and 267 received blinded treatment (
n
= 180, RBX2660;
n
= 87, placebo). Original model estimates of treatment success were 70.4% versus 58.1% with RBX2660 and placebo, respectively. However, after aligning the data to improve the exchangeability and interpretability of the Bayesian analysis, the model-estimated treatment success rate was 70.6% with RBX2660 versus 57.5% with placebo, with an estimated treatment effect of 13.1% and a posterior probability of superiority of 0.991. More than 90% of the participants who achieved treatment success at 8 weeks had sustained response through 6 months in both the RBX2660 and the placebo groups. Overall, RBX2660 was well tolerated, with manageable adverse events. The incidence of treatment-emergent adverse events was higher in RBX2660 recipients compared with placebo and was mostly driven by a higher incidence of mild gastrointestinal events.
Conclusions
RBX2660 is a safe and effective treatment to reduce recurrent
C. difficile
infection following standard-of-care antibiotics with a sustained response through 6 months.
Clinical Trial Registration
NCT03244644; 9 August, 2017.
Infographic
AKN5i_vZYNJMF_jRRaL_sV
Video abstract:
(MP4 62,291 KB)
Plain Language Summary
Clostridioides difficile
is a diarrhea-causing bacterium that is associated with potentially serious and fatal consequences. Antibiotics used to treat or prevent infections have a side effect of damaging the healthy protective gut bacteria (microbiota). Damage to the gut microbiota can allow
C. difficile
to over-grow and produce toxins that injure the colon. Paradoxically, the standard of care treatment of
C. difficile
infection (CDI) is antibiotics. Although initially effective for the control of diarrhea, antibiotics can leave a patient at risk for CDI recurrence after antibiotic treatment is stopped. Live biotherapeutic products are microbiota-based treatments used to repair the gut microbiota. These products have been shown to reduce the recurrence of CDI. RBX2660 is an investigational microbiota-based live biotherapeutic. RBX2660 contains a diverse set of microorganisms. RBX2660 has been developed to reduce CDI recurrence in adults following antibiotic treatment for recurrent CDI. This study was conducted to demonstrate that RBX2660 is effective and safe in treating patients with recurrent CDI. Treatment was considered successful in participants who did not experience CDI recurrence within 8 weeks after administration. Overall, statistical modeling demonstrated that 70.6% of participants treated with RBX2660 and 57.5% of participants treated with placebo remained free of CDI recurrence through 8 weeks. A 13.1 percentage point increase in treatment success was observed with RBX2660 treatment compared with placebo. In participants who achieved treatment success at 8 weeks, more than 90% remained free of CDI recurrence through 6 months. The most common side effects with RBX2660 treatment were abdominal pain and diarrhea. No serious treatment-related side effects were reported. The current data from the comprehensive clinical development program support a positive benefit-risk profile for RBX2660 in the reduction of CDI recurrence in adults following antibiotic therapy for recurrent CDI.
Despite promising preclinical and early human trials, there have been numerous negative phase 3 trials of treatments for Alzheimer disease and more than 40 negative phase 3 trials of neuroprotectants ...for stroke. Effective treatments for such diseases will likely require combining treatments to affect multiple targets in complex cellular pathways and, perhaps, tailoring treatments to subgroups defined by genetic, proteomic, metabolomic, or other markers. Here, Berry et al comment on platform trial, an efficient strategy for evaluating multiple treatments.
Robertson et al. provide an elegant, thorough and accessible overview of the rich theoretical background of response adaptive randomization. Many completed and ongoing clinical trials utilize these ...methods with demonstrated success. We provide a summary of multiple real world examples of response adaptive randomization, and a discussion of themes that arise in planning and executing response adaptive trials.
Background
In oncology, the treatment paradigm is shifting toward personalized medicine, where the goal is to match patients to the treatments most likely to deliver benefit. Treatment effects in ...various subpopulations may provide some information about treatment effects in other subpopulations.
Purpose
We compare different approaches to Phase II trial design where a new treatment is being investigated in several groups of patients. We compare considering each group in an independent trial to a single trial with hierarchical modeling of the patient groups.
Methods
We assume four patient groups with different background response rates and simulate operating characteristics of three trial designs, Simon’s Optimal Two-Stage design, a Bayesian adaptive design with frequent interim analyses, and a Bayesian adaptive design with frequent interim analyses and hierarchical modeling across patient groups.
Results
Simon’s designs are based on 10% Type I and Type II error rates. The independent Bayesian designs are tuned to have similar error rates, but may have a slightly smaller mean sample size due to more frequent interim analyses. Under the null, the mean sample size is 2–4 patients smaller. A hierarchical model across patient groups can provide additional power and a further reduction in mean sample size. Under the null, the addition of the hierarchical model decreases the mean sample size an additional 4–7 patients in each group. Under the alternative hypothesis, power is increased to at least 98% in all groups.
Limitations
Hierarchical borrowing can make finding a single group in which the treatment is promising, if there is only one, more difficult. In a scenario where the treatment is uninteresting in all but one group, power for that one group is reduced to 65%. When the drug appears promising in some groups and not in others, there is potential for borrowing to inflate the Type I error rate.
Conclusions
The Bayesian hierarchical design is more likely to correctly conclude efficacy or futility than the other two designs in many scenarios. The Bayesian hierarchical design is a strong design for addressing possibly differential effects in different groups.
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