Patient-facing digital technologies — also referred to as patient technology (PT) — have the potential to make a significant impact on the clinical research environment. PT includes any digital technology patients interact with to participate in clinical trial activities. When implemented effectively, these technologies can enhance patients’ experiences and engagement, streamline clinical trial processes, and enable better, more robust data collection in clinical trials.
Electronic consent to clinical research — “eConsent” — is a hot topic within the clinical research world as more sponsors and sites explore what solutions are available and appropriate for them. Yet many clinical trials are stuck in paper-based consent, either underutilizing the technology tools at their disposal or not using them at all. Understanding the reasons for a transition to eConsent and the hesitancies to make these changes gives insight into one of the last bastions of the clinical research industry to embrace the digital revolution.
As part of our ongoing series detailing an approach for the rapid assessment and prototyping of digital and other clinical trial technologies, we introduce here the use of artificial intelligence (AI) and machine learning (ML) to optimize clinical study design and execution.
Clinical research is sometimes viewed as a box-checking exercise, where we run fairly cut-and-dried randomized, placebo-controlled studies based on long-established protocols. But things are actually changing very rapidly in the world of clinical development, which is by far the largest part of pharma R&D spending. We are on the threshold of a transformation that will allow us to dramatically accelerate the acquisition of useful knowledge, get new medicines to patients sooner, and expand their usage to new indications more quickly.
Three decades ago when I entered the profession of clinical research, our workplace equipment extended to typewriters, white-out, mimeographs, hand-written documents, rubber erasers, pens, label makers, and fax and copy machines. Rows of massive, locked, fire-proof filing cabinets storing millions of papers for a nationally funded research program lined record rooms, hallways, and every spare corner of the offices. Most of us felt rewarded when we could use a typewriter with a correction key despite one’s typing skills being firmly judged by the illegible mistakes in the carbon copies.
When you’re diagnosed with cancer, diabetes, an autoimmune disorder, chronic pain, a neurological condition such as Alzheimer’s disease, or another illness, having the safest, most effective medicine is paramount. Knowing a promising therapy is “in the pipeline” doesn’t bring you a whole lot of comfort. You want access to the most advanced medicines now.
A Q&A with Sudip Parikh, PhD, senior VP and managing director, DIA Americas
In research related to many rare genetic diseases, availability of long-term benchmark data to support the development of accurate assessments is limited. This can have a detrimental effect on new product development. To address this challenge and ensure that clinical development efforts render meaningful results, there is a need to develop outcome measures that assess the entire characteristic clinical phenotype of any given rare genetic condition.
The pharmaceutical industry as we know it today has roots that reach back to the apothecaries and pharmacies of the Middle Ages, when drug discovery largely involved sourcing plants and herbs for natural remedies and drug development and testing was an unstructured concept that relied on the hit-or-miss reactions patients experienced.1 Failure was arguably more common than success and, unfortunately for the patients, treatment courses often did not yield optimal results.
More than two years have passed since the 21st Century Cures Act was signed into law in December 2016. According to the FDA, the Cures Act is designed to help accelerate medical product development and bring new innovation and advances to patients who need them faster and more efficiently.
|
|
|
|
|
|
|
|
|