Drugs are notoriously costly and time-consuming to produce and distribute. Small businesses are in a unique position to alter that.
Clinical trials should ideally be quick, dependable, and affordable in order to enable the approval of essential new medicines. Regretfully, the clinical trial procedures used today are quite costly, time-consuming, and prone to errors. The COVID-19 pandemic brought to light how critical it is to accelerate this process because delayed procedures might result in fatalities and longer trial periods incur larger costs that are eventually borne by the customer.
Throughout the 20 years I worked in clinical testing positions at prestigious pharmaceutical companies and CROs, I witnessed this personally. I founded Yonalink, which offers automated data streaming for clinical trials, as a result of that experience.
It’s safe to conclude that technology is no longer the problem because a number of other businesses are now providing comparable services. The sector as a whole is being held back by Big Pharma’s reluctance to adopt technological solutions in certain domains, which is the true obstacle. Notwithstanding this obstacle, I think that small pharmaceutical and biotech businesses may steer the industry in a different direction that will allow data streaming to become widely used and transform the way clinical trials are conducted by researchers.
There is a Data Issue with Clinical Trials
The effectiveness of clinical trial data management is completely lacking. McKinsey reports that between 2011 and 2021, the average length of a Phase II clinical trial increased from 37 to 41 months, while for Phase III studies, the average length increased from 41 to 44 months. A U.S. According to a report by the Congressional Budget Office, the development of an approved drug typically costs between $1 and $2 billion, and the FDA’s examination of manual data verification due to the high frequency of errors drives up additional expenses. Millions of dollars were spent by American taxpayers on these in-person data inspections conducted by the FDA, but there is still no assurance that the information utilized to approve pharmaceuticals is entirely accurate. When so much is on the line in clinical trials, why should we tolerate errors that we wouldn’t accept on our bank account statements?
The primary reason for lost time, higher expenses, and a higher number of errors is manual data handling. Clinical studies require vast amounts of data; for example, a Phase III cancer study typically needs 3.6 million data points. Employees manually copy most data points from patients’ electronic health records (EHRs) into the clinical trial database, also referred to as an EDC (electronic data capture).
When data are put into a patient’s electronic health record (EHR) at a medical center, it typically takes 6-8 weeks for the data to reach the clinical trial’s EDC and be prepared for analysis. Errors in manual data transfer impede the trial procedure as a whole. Additionally, it is quite expensive for individuals to copy data across screens, manually verify it, and then double-check the verification.
The Memorable Element: Conventional Thinking
We developed an AI-based solution that delivers patient data from EHRs directly to the EDC, creating dependable, correct records within a day and at a fraction of the expense of manual entry, driven by the twin needs of expediting clinical trials and obtaining more accurate data. Rivals such as Flatiron and Ignite Data have also created solutions using various methods to tackle the issue.
Despite the fact that data transfer for clinical trials is a difficult and complicated issue, pharmaceutical executives’ attitudes continue to stand in the way of progress. Throughout the clinical trial process, the industry welcomes innovation and AI, but not when it comes to data transfer and capture.
Trial managers naturally go toward methods that have been shown to be effective in the past, even if they aren’t flawless. The dangers of altering established procedures are significant because trial sponsors must invest an average of $100 million before the trial phase can begin. I have personal experience as a trial manager, therefore I am aware of how difficult it is to implement change in the data management space.
In contrast to other drug development domains, artificial intelligence (AI) in data capture is not just augmenting the current method but completely changing it. Trial managers find this an unsettling possibility, particularly since statistics are the cornerstone of every effective medication. Pharma businesses are today mired in this mud: the need for change vs the fear of change.
The Pioneers: Biotech and Small Pharma
In the pharmaceutical industry, innovation typically starts with the larger players and spreads to the smaller ones. However, in this instance, tiny biopharma firms need to seize the initiative since Big Pharma is stifled by its reluctance to try something novel. Present data management techniques unfairly affect smaller businesses. They cannot afford to squander money on human data entry, nor can they take the chance of having to wait weeks or even months to discover errors. They must be prepared to break new ground in order to reach their goals of dependable, affordable, and quick data handling.
Positive trend in this direction is beginning to emerge as tiny biopharma enterprises come to understand the advantages of innovation. Recently, biopharma businesses and companies like SAS, Nucleai, and Datacubed announced collaborations to integrate their technologies into the clinical trial process.
The mindset of large pharmaceutical firms is the only thing preventing this technology from being widely used. Small biopharma is emerging as the first to adopt these tools since they can’t afford to pass up the advantages of streaming data transfer. Small businesses will play a crucial role in guiding the sector toward significant changes in the data domain in this way, but I believe that all pharmaceutical companies will be utilizing automated data streaming solutions in five years.
