There were numerous articles in January 2017 that covered a wide range of topical issues. Here are five of the best stories you might have missed... (click the headline to finish reading the story)
Trump’s health care pledge signals shift to bring drugs to market quicker – but will that put patient safety at risk?
Over the last few months, President Trump has given little away in terms of his vision for the pharma and biotech industries. However, drip by drip, Trump has provided small granules of what he has in store for the healthcare space.
In one of his first press conferences as president, Trump gave his clearest indication yet of what he intends to do:
“We have to get our drug industry coming back. Our drug industry has been disastrous. They’re leaving left and right. They supply our drugs but they don’t make them here, to a large extent. And the other thing we have to do is create new bidding procedures for the drug industry, because they’re getting away with murder...”
21st Century studies with 21st Century tools – What is developing to increase identification, assessment and (re)use of health data?
In my previous article, Randomized clinical trials, big and real world data – 21st Century studies with 21st Century tools, I discussed the increasing role of real world data in supporting clinical development to optimize clinical trials, and specifically mentioned some of the programs that could support this, such as EMIF and EHR4CR.
The Innovative Medicine Initiative (IMI) is Europe’s largest public-private initiative, aiming to speed up the development of better and safer medicines for patients. It is a joint undertaking between the EU and the European Federation of Pharmaceutical Industries and Associations (EFPIA), and it supports collaborative research projects and builds networks of industrial and academic experts to boost pharmaceutical innovation in Europe.
Across the globe, one in five children is unable to receive vaccines, a problem that endures in much of the developing world. The challenge of ‘the fifth child’ stems from a broken cold chain and having the ability to transport vaccines to remote locations far and wide in prime condition. Harvey Rubin, a professor of medicine at the University of Pennsylvania, is the director and co-founder of Energize the Chain, a standalone, non-profit organization run by volunteers. In this compelling interview, Rubin argues the technology to improve the vaccine supply chain is there, but admits a greater effort is needed to reach other areas of the world.
Management of specimens collected in biomarker-driven Clinical Trials: Integration of datasets to drive Translational Science
Recent progress in translational and personalized medicine initiatives has created a new landscape with the requirement for innovative ways to manage clinical specimens collected in global biomarker driven trials. With such advancements, many have seen a shift in expectations from the classical bench-to-bedside approach, to one that integrates bedside-to-bench information. To truly fulfill the promise of personalized medicine, we need to incorporate, in real time, specimen metadata, clinical information, and molecular data from a trial to execute translational science. In the past, specimens were often collected from clinical trial participants, stored in a biorepository, and accessed at some later point long after the trial was completed.
The first randomized clinical trial testing a curative treatment was performed in England in 1946 by the Medical Research Council on the antibiotic drug streptomycin. In that era, striking examples of non-interventional studies had also been revealed in metabolic disorders, cancer, and rheumatic fever (Dawber, Meadors and Moore, 1951).
Driven by the evolution of ethics, safety and regulatory requirements, major steps, such as the Helsinki Declaration, the creation of Good Clinical Practices / Good Pharmacovigilance Practices, and the International Conference on Harmonization (1996) led to a high level of standardization for clinical trials. But real world data studies aiming at generating real world evidence also need a higher level of standardization. Even in the absence of intervention or randomization of participants, real world evidence needs to rely on harmonized good practices.
PHOTO CREDIT: Rob Nguyen