As any drug developer knows, clinical trials generate a lot of raw and electronic data from multiple sources. Yet tracking progress and reviewing results from each separate database can be cumbersome in traditional environments. This “rear-view” mirror approach to monitoring doesn’t support preventative planning to mitigate future risks and can account for 20-30% of a trial’s costs.
Recognizing the opportunity increase efficiency and deliver information faster, Covance created Xcellerate® Monitoring, a platform that integrates clinical trial data to help sponsors proactively decrease the inherent risks associated with clinical trials.
At a recent clinical seminar in China, Dimitris Agrafiotis, PhD, Vice President, Chief Data Officer discussed how Xcellerate Monitoring tracks quality, patient safety and protocol compliance in clinical trials. Continue reading
What to Expect When Submitting Your First SEND Dataset to the FDA
With the December 17, 2016* requirement for the FDA Standard for the Exchange of Nonclinical Data (SEND*) fast-approaching, our Covance SEND action team prepared a dataset for test submission to the FDA. This helped us to better understand the FDA’s SEND submission requirements, build experience and confirm our readiness to help clients submit their SEND datasets.
During this process, we uncovered a couple of significant learnings:
- Allow for adequate time to prepare and submit to the FDA
- The process to deliver our first test submission took more than two months from kickoff to FDA notification
- It’s important to start early to understand the preparation time needed for submissions
- For test submissions only, the dataset must be submitted on a physical CD and sent to the FDA via postal mail
- This came as a surprise to us, since SEND is a streamlined electronic format of the data
- (Note: In a real submission, the SEND datasets will appear in a specific location labeled “tabulations” in the submission folder structure as described in section 7 of the FDA CDER/CBER Study Data Technical Conformance Guide).
(This is part 3 of a 3-part series on Inflammatory Disorders Studies. View part 1 here.View the complete series in our Inflammation eBook.)
Patient-reported outcomes, compliance and retention are key components of success.
Recent research contends some underlying immune system response mechanisms are common to inflammation-related diseases, such as asthma, COPD, psoriasis, rheumatoid arthritis, lupus and inflammatory bowel disease. These diseases are referred to as Immune-Mediated Inflammatory Disorders (IMIDs). There is a significant shift in the approach to managing traditional inflammatory diseases from organ-based symptom relief to tackling common underlying pathways of immune dysregulation which offers the hope of disease modification. Continue reading
(This is part 2 of a 3-part series on Inflammatory Disorders Studies. View part 1 here.View the complete series in our Inflammation eBook.)
Ensure your ROI and keep inflammation clinical trials on track.
The good news: The surge in the number and size of industry-sponsored trials in inflammation presents opportunity. The not-so-good news: The surge also presents challenge. Clinical trials for Immune-Mediated Inflammatory Disorders (IMIDs) present certain pressures for even the most committed investigators and sites: IMID trials frequently have longer than usual duration and enrollment can be highly competitive. Additionally, patients whose disease is well-managed by the new treatments available may not be motivated to try something different. Continue reading
(This is part 1 of a 3-part series on Inflammatory Disorders Studies. View part 2 here.View the complete series in our Inflammation eBook.)
Placebo response rates can obscure treatment effects, putting effective drugs at risk
One of the confounding factors in clinical studies that can contribute to difficulty in discriminating an active treatment effect versus placebo is subject eligibility creep when subjects (e.g. with milder forms of disease severity at baseline) may get enrolled inappropriately by sites when struggling to meet recruitment targets and timelines. Baselines are skewed and misrepresented since subjects initially may be assessed as suffering from the more severe disease grades required to meet inclusion criteria. Continue reading
To drive change, medicine requires hard data to supply evidence of clinical benefit. However, the studies we rely on to make decisions about a drug’s efficacy are often statistically underpowered – that is, therapeutic trials may fail to show the benefit of agents or devices when a benefit does, in fact, exist. This is due to limited data from smallpatient populations or too much variability in the data.
We performed analyses of studies of anticoagulation in electrical cardioversion to examine this problem more clearly. We also show how proactive data pooling could help to mitigate limitations in statistical power. Continue reading
Companion Diagnostics: The New Engine
Companion diagnostics’ impact on pharmaceutical development is like dropping a new engine into a classic car. Faster speed. Better performance. More efficiency. Companion diagnostics is changing the way we develop, test and market new therapies—with full-throttle power.
Today, we often have the ability to test a patient to see what drugs will work—or not work—and watch for mutations and triggers down the road. But back up a bit: we can also design drug trials to include subjects with the correct biomarkers for the treatment. And back up a bit more: we can develop drugs and biomarker tests together for the most effective combinations of disease targets, drugs and patients. Continue reading
Despite the growing use of flow cytometry, there are currently no official regulatory guidance documents governing its validation. Having recognized the gap, stakeholders from the pharmaceutical industry and clinical testing laboratories have proactively published recommendations.
Scientists helping scientists with guidelines
In 2005, biomarker research was gaining momentum but the lack of clear validation guidelines made biomarker data difficult to interpret, hampering its usage. Existing validation paradigms applied only to PK data. Scientists from the American Association of Pharmaceutical Scientists (AAPS) realized that one set of rules could not fit all and that new standards were needed. They issued Fit-for-Purpose papers, addressing the need for accuracy, compliance and fitness for intended use and introducing the concept of iterative method validation to track biomarker development phases.
In the effort to reduce attrition rates and improve approval rates of new molecular entities by regulatory agencies, there’s no doubt that biomarkers can make a big impact. But it’s not as simple as tacking on additional studies. Biomarker development requires an insightful strategy and consideration of specific opportunities and needs throughout the drug development pipeline.
A quality biomarker starts at the source—the sample itself. Sample collection and handling protocols must be standardized to specify the minimum volume requirement in the proper container along with the most optimal temperature during transportation and storage. These requirements should be backed and driven by validated processes. To further ensure biomarker stability, it’s equally critical to include the maximum allowed time in transportation. Continue reading
Treatments that are safe and effective for adults may be ineffective or even dangerous for children. But infants and children are often prescribed medications with “off-label” use, where the treatment’s safety, dosage and efficacy are based solely on adult studies. To address this issue, both drug developers and regulators are working to boost clinical trials in children and include this underserved market in their studies.
Challenges with pediatric trials
A number of factors work against studying pediatric populations. As a highly fragmented and dynamic population, children and infants undergo rapid developmental changes over time, complicating study design and interpretation.
In addition, small sample sizes and potentially low incidence rates can make it difficult to find a treatment group—as well as a suitable control group with an approved active control. Finally, ethical considerations, such as informed consent can be more complex in pediatric trials. Continue reading