Digital twins are revolutionizing clinical trials and so much more

Clinical trials offer pharma and biotech companies incredibly valuable information. A cornerstone of drug development, the results of clinical trials often have a significant impact on a company’s decision-making moving forward.  

However, clinical trials are often criticized for being costly, slow to conduct, and burdensome for patients. Very few clinical trials succeed, and many fail because not enough suitable patients are enrolled. 

What is a digital twin in healthcare? 

Enter digital twins. Imagine this: a virtual version of yourself, right down to the cellular level. This copy can mimic everything happening inside your body—and simulate what might happen in the future. In clinical trials, digital twins are virtual replicas of patients, tissues, and organs designed to predict specific treatment outcomes.  

In this blog, we’ll explore how healthcare organizations use digital twins to solve the “people problem” of clinical trials and why this relatively new technology warrants your attention in 2024.  

What’s the problem with clinical trials? 

Companies pour large quantities of money, time, and expertise into planning and executing clinical trials. Yet, despite all these investments, clinical trials don’t always succeed. Why? 

There are many reasons that could explain why good clinical trials go bad. Potential culprits could be a failure to keep enrolled patients safe, a lack of funding, or an inability to demonstrate the efficacy of the drug that the trial is testing.   

The challenge of identifying and enrolling a full panel of suitable patients (and keeping them enrolled throughout the trial) is often regarded as the biggest barrier to success. Difficulty in enrolling patients can result in costly delays or even the termination of the trial. In fact, about 80% of all clinical trials fail to meet their original enrollment deadline and 55% of trials are terminated for failure to achieve full enrollment.  

Here are a few common reasons why this occurs: 

1. Screening candidates is slow and time-consuming. Traditional methods of screening clinical trial candidates usually involve analyzing textual data like electronic health records. This is often a bottleneck for researchers, as they must manually review records and other paperwork, which is time that could be spent on other important tasks. 
2. Difficulty meeting inclusion/exclusion parameters. It’s not easy to find patients who check all the boxes researchers are looking for. Criteria such as patients needing to be on certain medications or having to wean off others to be eligible can disqualify what would otherwise be a good candidate. 
3. Researchers are fighting against time. The longer a study is, the more difficult it is to keep track of patients as life happens. They may move, experience changes in their health, or simply lose interest. Equally important is keeping clinical sites excited and interested in your clinical trial. Requests from the FDA or difficulty getting enough sites on board may result in delays. And some clinical sites may reprioritize their daily practice or shift to other studies. 
4. Studies fail to spread awareness. Some clinical trials fail to meet their goals simply because no one knew they existed. According to one national survey, 17% of respondents noted they didn’t know what clinical trials were. And, of the respondents who were aware, only 13% reported actually participating in a trial. A lack of good communication that helps alleviate fears or misconceptions a patient may have about participating in a clinical trial can also be a factor. 

Digital twins can help solve this critical problem. Researchers can use virtual replicas of patients to predict how the human body—or certain systems—may respond to experimental treatments. Scenarios could even be crafted, refined, and tweaked in order to produce more specific results.  

How can digital twins make clinical trials more successful? 

There are several key benefits digital twins bring to the table.  

First, researchers can use digital twins to accelerate and streamline drug development. The underlying theory of digital twins stands at the intersection of AI, machine learning (ML), and predictive analytics. Using these principles, researchers can use patient data to help identify and sift out the wrong formulas before test samples are developed, making the development process—and clinical trial process—faster, more precise, and potentially less costly.  

Researchers can also use digital twins to improve patient safety and reduce the likelihood of adverse side effects. Before a real human takes a drug, scientists can test treatments in a risk-free, virtual environment and measure the effects of the therapy. For example, researchers could create a virtual model of a post-stroke brain, showing the same characteristics and complications a real traumatized brain would have. Researchers could try different substances, mixes of chemicals, and dosages to determine the most beneficial treatments. The result? Fewer real-world complications.  

Digital twins also offer clinical trial researchers greater flexibility and efficiency than human patients. This is made possible by being able to run multiple tests on copies of digital twins concurrently. Should researchers want to change a variable mid-test or try an entirely new approach, they can make these on-the-fly modifications without the need to start over with human subjects.  

Digital twins have the potential to revolutionize all healthcare 

Digital twins can be much more than just virtual replicas of patients in clinical trials.  

Simulations of biological entities like tissues and organs can be made to help doctors better understand how diseases impact certain systems. Digital twins can be made of medical devices to help engineers and other specialists see in detail how components function. Then, they can evaluate how the device will perform under various conditions, such as network overload, power cuts, hacking attempts, etc. Virtual models of entire medical facilities could even be made to see how certain scenarios may affect a hospital’s bed count, or to identify areas where workflows can be improved.

Risks of digital twins 

The possibilities of digital twins are seemingly sky-high, but there’s one major challenge healthcare companies must grapple with before these use cases can be realized.  

To squeeze the most value out of digital twins, companies need massive amounts of patient data, including medical histories, genomics, lifestyle factors, etc. Human beings are complex—so it tracks that digitally recreating one requires a comprehensive set of health data.  

The problem is that digital twins are most effective when used at scale. Not only does the amount of data required balloon in size, but so does the need for robust IT infrastructure and equipment. Not to mention the challenges involved in keeping all this sensitive patient information secure and private. 

 Altogether, implementing digital twin technology can be a daunting and costly venture for many healthcare facilities and companies. It’s likely that only the biggest players in the healthcare market can make the most out of digital twins for now.  

Despite the hefty costs these roadblocks pose, digital twin technology promises a brighter future for clinical trials and the broader healthcare industry. As companies strategize for 2024 and beyond, they should consider adopting the digital twin concept to bring more drugs or devices to market, cut down on R&D costs, and ultimately improve patient care. 

Learn more

This blog is the third in a series of posts exploring the 2024 healthcare trends you should care about. In case you missed our second trend, I recommend reading our post on how GLP-1 drugs are reshaping bodies—and the economy. Or you can jump back to the beginning and read up on the trends you’re most interested in.  

For more on the latest trends, and how healthcare commercial intelligence can help you prepare and compete in the shifting healthcare market, sign up for a free trial with Definitive Healthcare.