Artificial intelligence (AI) has the potential to disrupt and revolutionize many aspects of clinical trials. One area in which it is already making an impact is patient recruitment. Mining electronic medical records (EMRs) for patients meeting inclusion and exclusion criteria can be tedious and time-consuming work but is a chore that seems ideal for an AI solution.
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.
It is certainly no surprise that risk management continues to hold the spotlight as a hot topic within the biotechnology/pharmaceutical industry. With the increased focus by regulatory authorities on an organization’s ability to identify, mitigate, and control risks, the industry remains in a state of growth, developing and evolving practices to ensure proper alignment with industry best practices and regulators’ expectations. Many organizations are finding it difficult to establish and embed risk management practices, as doing so requires a paradigm shift from a traditional risk-averse industry culture.
To control costs, improve efficiency, and successfully bring more drugs to market—life-changing, life-saving drugs—pharma and biopharma companies need to follow the lead of the financial industry and forward-thinking health systems. Here, we discuss how a data-driven approach to clinical trials—from patient recruitment to post-regulatory approval—can put an end to missed deadlines, failed studies, and wasted time and money.
Dr. El Mustapha Bahassi, Associate Director of Clinical Laboratories at Medpace gives an overview of Central laboratories and the evolving world of immuno-oncology as well as current immuno-oncology biomarkers and detection methods.
Advancements in science and technology have helped researchers develop new treatments for some of the most common diseases known to man. For rare diseases, however, patients have limited treatment options. Advocacy groups and regulatory authorities continue to encourage rare disease research, and more and more scientists are stepping up to the task.
Life sciences companies are embracing the essential roles that real world data (RWD) and the generation of real world evidence (RWE) play in the development of new treatments for patients.This paper will explore three areas where RWD and RWE are being applied to improve the design and execution of clinical studies.
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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.
At Janssen, patients are at the center of everything we do. And we’re constantly working to improve their experience.
Good recruitment and retention is critical to the success of clinical trials. Get it right, and a trial will likely achieve its primary objective; get it wrong, and the time, effort, expense, and any patient participation is likely wasted. This article discusses how a new national approach to involving patients and the public in the U.K. is helping life sciences companies get it right the first time.
For the biopharma industries specifically, AI represents an opportunity to avert the R&D productivity crisis with paradigm-shifting applications such as in-silico drug design, prediction of trial risks and big data analytics. However, with every opportunity, there are risks and challenges, and this blog discusses how pharma needs to address the opacity of AI to ensure trust and credibility with all stakeholders.
This blog discusses how specialist CROs can add value and streamline processes by providing oversight of data management services delivered by another CRO. This model helps to fulfill essential regulatory obligations for biopharma companies who may lack their own internal oversight resources.
There are no concrete instructions when it comes to RBM, and planning a successful RBM design can be challenging. Even so, we believe investing in an experienced partner to help you with RBM design is worthwhile. RBM can be an indispensable tool for your trial’s success. Following is an out of outline a criteria that could be helpful in finding the right RBM specialist.
Big data and data mining are being leveraged to improve drug development, with huge potential implications in identifying and correcting issues that arise during clinical trials. This article is a quick overview of what big data is and how we look at and define program failures and faltering trials.
Degraded samples are common and certainly problematic for investigators. Read more about common causes of degradation as well as agents and systems available to protect against freeze–thaw.
