According to the Journal of the American Medical Association (JAMA), the median cost of bringing a new drug to market is $985 million and the average cost is $1.3 billion. However, those in health care hoping to harness promising new treatments, and patients facing steep prescription costs, see this as progress. After all, previous studies estimated the average price for developing a drug to be as high as $2.8 billion, a cost usually recouped at the expense of consumers. While savings are being realized, trials still come with quite a price tag—but the reasons why are understandable. First, clinical trials are complex and, simply put, time is money. The final stage alone, the Phase 3 clinical trial, can involve tens of thousands of participants, each with hundreds or thousands of data points that need to be tracked over a number of years. This can produce hundreds of millions of data points that must be protected, secured and blinded, and all of which must be analyzed by numerous applications.
Many cite the fast development of a COVID-19 vaccine as proof that the process can be expedited. While the vetting of these vaccines was thorough, the processes benefited from unprecedented resources, industry and government cooperation and global pressure to quickly find a way to slow a highly infectious disease. Even so, one can’t help but wonder why clinical trials typically take six to seven years? Haven’t we learned how to speed up trials without jeopardizing health or draining budgets?
The short answer is yes—there is a way to reduce the time and resources needed for clinical trials. Pharmaceutical companies use dozens of software platforms from an array of vendors in their trials. As a result, crucial data gets trapped in unconnected silos, adding complexity and stalling progress. However, by unifying apps and data stores in one integrated system, trial efficiency can be raised and time-to-completion reduced by over 20%.
Raising a Platform
Deloitte noted that COVID-19 revealed health care’s need for structural and technology transformation, adding that its future will be “enabled by radically interoperable data, artificial intelligence (AI) and open, secure platforms.” The same holds true for clinical trial data software platforms; however, creating a unified system is an enormous undertaking for software developers and software engineers. Most importantly, any unified system must be able to provide the following four capabilities.
● Security: Data in platforms must not only be protected from external threats such as hackers and ransomware but it must also be blinded. The ideal is a double-blind clinical trial in which neither participants nor their physicians know who received the actual drug and who received the placebo.
● Modeling: A platform must be capable of extending a data model for each clinical trial to prevent the need to rebuild every time. The models, and the platform overall, must be flexible but still capable of managing hundreds of millions of records across multiple trials.
● Extensibility: Over the course of years, data collected—even in a single trial—may change. The ability to extend a scheme on the fly to fit evolving needs can save an enormous amount of time versus building a platform and transferring data. This is especially true when you have millions upon millions of data points.
● Ease: A platform must be easy to modify by non-programmers. If a trial designer can implement rules without having to bring in a programmer, the trial will move ahead much more quickly.
Key to being able to raise an effective clinical trial platform are software engineers and developers. According to the Bureau of Labor Statistics, software developer jobs alone will grow more than 22% by 2029—faster than all other occupations combined. While this is good news for software pros, demand will create a shortage exceeding 1.2 million by 2026, which isn’t so great for the pharmaceutical companies that need talent.
Then again, there are purpose-built platforms available that provide data, software, services and an ecosystem of partners to support functions from research and development through commercial availability. They’re designed to bring such products to market faster and more efficiently while maintaining complete compliance. The platforms can not only be comprehensive, but they can also mitigate reliance on developers and engineers, with regular provider updates future-proofing operations and decreasing investments.
Programming for Success
First and foremost, pharmaceutical companies are achieving faster trial goals by leveraging the cloud and DevOps. In doing so, all data can reside in a central repository that’s accessible from anywhere. There’s no hardware requiring time-consuming manual configuration. What’s more, modern cloud infrastructures are resilient, secure and robust. And all of this is largely being made possible by software-as-a-service (SaaS) platforms.
Despite this progress, the need to technologically bolster trials continues. AI is now being applied to auto-classify documentation. Naturally, these types of developments require the implementation of programmability and extensibility at scale. The best minds in software engineering have made considerable gains. It’s now up to pharma to ensure their trials and operations continue to be programmed for success.