Having seen an increasing number of gene therapy approvals, the FDA has issued draft guidance1 to help the developers of human gene therapy (GT) products for the treatment of hemophilia A & B. In this article we will be focusing our attention on what guidance has been provided about the design of human gene therapy clinical trials for hemophilia A & B, including what is needed to support an accelerated approval approach.
Food ingredient and nutraceutical manufacturers are increasingly looking to market their products by substantiating their unique health benefits.
There are many generic, widely used claims - in the EU, Article 13.1 of the European Food Safety Authority’s (EFSA) EC Regulation permits manufacturers to use some 4,6371 that pertain to vitamin and mineral content.
This proliferation is driving manufacturers to develop unique claims to use in marketing efforts, which in turn has led to a significant growth in demand for nutraceutical trials.
At the present time, the regulatory and drug development communities are adapting to a rising trend in biosimilar development in a number of therapeutic areas.
The regulatory framework for biosimilars in the US is still evolving; the number of biosimilars approved by the FDA in 2015 was 1, this rose to 4 in 2016 and is set to increase in 2017. The FDA is developing and consulting on draft guidance documents that will shape future trials, and at this early stage there are a number of legal issues to be agreed around licencing conditions, such as the period of exclusivity and the applicability of the biosimilar to all approved indications of the reference product. To put this in context with the European landscape, biosimilars have been approved and used in the EU for over a decade without highlighting any major safety concerns. As of April 2017, there were 28 approved biosimilars in the EU on 11 different biologics. However, there are aspects of the emerging FDA guidance that will almost certainly be reflected in the evolution of trial designs in the future, for products aimed at the US market.
Wearables and mobile health (Mhealth) apps collect subject/patient data from mobile or purpose built devices to record data in real time. The rationale behind this type of technology is to reduce the burden on subjects by eliminating unnecessary procedures, streamlining routine procedures and reducing time spent at clinical trial sites. It is evident from the review of a range of literature that studies integrating some form of mobile health technology can be broadly categorised into a few phases of development: studies on the development of new devices, studies on the validity of functional wearable devices, studies comparing new device and conventional endpoints, and finally those studies which trial the device as a health intervention. This article aims to briefly discuss these phases with reference to examples of recent studies demonstrating some safety or efficacy endpoint relating to a newly developed device.
Outcomes research aka health outcomes research, is the study of the end results of particular health care practices and interventions, in other words it is the study of what happens in the real world to patients when they are given a certain treatment or a certain method of care. Outcomes research studies are used to improve the quality and value of healthcare for patients.
We've all heard the hype - Big Data will solve all your storage, processing and analytic problems effortlessly! Some moving beyond the buzzwords find things really do work well, but others rapidly run into issues. The difference usually isn't the technologies or the vendors per-se, but their appropriateness to the requirements, which aren't always clear up-front.
Big Data, and the related area of NoSQL, are actually a broad range of technologies, solutions and approaches, with varying levels of overlap. Sadly it's not just enough to pick "a" Big Data solution, it needs to be the right one for your requirements. In this talk, we'll first do a whistle-stop tour of the different broad areas and approaches of the Big Data space. Then, we'll look at how Quanticate selected and built our Big Data platform for clinical data, driven by the needs and requirements. We won't tell you what Big Data platform you yourself need, but instead try to help you with the questions you need to answer to derive your own requirements and approach, from which your successful Big Data in clinical trials solution can emerge!
Today, Big Data is one of the hot topics within almost every Industry, especially in clinical trials. May saw the biggest ever European technologists conference on this, Berlin Buzzwords, while the likes of O'Reilly's Strata conference pull in huge numbers of attendees keen to learn how to adapt to this new world.
Quanticate has released several white papers around Clinical Study Design in the area of Bayesian Statistics and a focus on Phase 1 studies. Our statistical consultancy team was pleased to receive feedback and questions from our clients and piers on these papers which we would like to share with you all below.
The majority of companies within the Pharmaceutical Industry have large historical clinical databases, much of which may never be used beyond its original purpose: to prove that the drug in question is safe and efficacious. This historical data can be used to better inform future decisions in clinical trials.
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