How Life Sciences Companies Stay Focused on Drug Development

Looking to technology and data for clinical development and commercialization

Four ways to move forward during times of uncertainty

This year hasn’t gone as planned. But that doesn't mean progress and innovation can stop. In fact, now is when life sciences companies must find innovative ways to move work forward — and experts believe that leading technology and real-world data (RWD) can help life sciences companies get treatments to market faster.

provider wearing mask

Here are ways that organizations have turned to these methods and how they may be able to help you during this uncertain time.

Approach data in a different way

As the amount of RWD grows, it’s important to know that it is ultimately messy data, so considerations and trade offs may be required as the industry works on harnessing the power of RWD.

Additional challenges include data quality and data relevancy as well as understanding the different approaches for quantifying data. Yet those challenges are worth overcoming because of the potential to use RWD for understanding patient populations. However, this is also why experts believe the industry must shift from trying ways to harness the vast amount of RWD that exists and using data that’s fit for purpose.

Keep a pulse on artificial intelligence

In clinical development, there are opportunities for artificial intelligence (AI) and machine learning to change data capture and to help clean clinical data. Using AI to improve other parts of the clinical trial process, including site selection, patient recruitment and monitoring, is also being explored. Although real-world examples of AI in clinical development are still emerging, it’s worth paying attention to the few players in the market that provide AI technology in their offerings.

Look for opportunities to connect technologies

Until recently, companies have used multiple clinical development solutions for each part of their development process. Now the industry is looking for smoother connections between technologies and collaborators. This is why life sciences leaders are turning to products that provide a unified technology experience through integrations, partnerships and thoughtful design.

Build studies around the patient

Many life sciences professionals are motivated by patient stories or their own personal connections to illness. Therefore, it’s no surprise the industry is looking for more ways to be patient centered. For recruiting patients, it’s important to consider how they could be engaged throughout the process. Technology that helps patients conveniently and securely share important information is key for making it easier for them to participate in studies.

Explore IBM Watson Health® life sciences solutions →

COVID-19 presents a new path for clinical trials

provider using a device

Given the large investment that the life sciences industry makes in drug development, and the regulatory complexity of the work, life sciences companies are often hesitant to rock the boat and adopt a new approach to clinical trials. But the COVID-19 pandemic has changed everything and a paradigm shift for clinical trials is occurring as more organizations weigh the pros and cons of decentralized (that is, siteless, hybrid, virtual) trials.

Rather than transitioning to this model as a broad strategy, many organizations are making changes out of necessity. Due to the impact of COVID-19, space within hospitals is being saved for emergencies, and there are heightened concerns around the safety of patients and healthcare workers. Alternative approaches are needed to move clinical trials forward, and fortunately there is technology available to help researchers and their sponsors pivot to a new model that gives them the flexibility they need.

I feel very optimistic that we’ll see a move to more decentralized trial methods powered by associated data and technologies in the next 12 to 18 months.

- Mary Varghese Presti, VP, Life Sciences, IBM Watson Health

Real-world data can drive clinical trials forward

Incorporating technologies that many people use daily into clinical trials could help make trials more efficient. Recently, synthetic control arms driven by RWD have emerged as an option. These unique arms aren’t a fit for every clinical trial, but they may work if there’s an abundance of preexisting data about the control group’s clinical attributes or circumstances that make a traditional trial protocol prohibitive.

Oncology, for example, is a research area that might find synthetic control arms useful because randomizing patients tends to prolong recruitment. Furthermore,recruitment for oncology trials can be a challenge because patients know they could be put on a placebo. A synthetic control arm could help speed up recruitment and be more efficient than a traditional randomized trial because the number of patients needed for the trial could be cut in half.

connected device capturing data
connected device capturing data

Decentralized trials are happening

Data is at the crux of this shift, and it’s important to have systems in place that can help leverage data to design clinical trials well. Just think about what it takes to amend a protocol. There’s resubmission to the FDA as well as retraining sites and additional recruitment. That’s why ways to design smarter protocols using existin greal-world data are needed to help set meaningful, practical inclusion and exclusion criteria.

There’s a lot on the line, and for some organizations, it mightfeel safer to stick with familiar technologies and trial structures. But with worldwide challenges like COVID-19, the only way the life sciences industry will have to develop an appetite for alternative approaches. Mary Varghese Presti, VP, Life Sciencesat IBM Watson Health®, predicts that new models will emerge relatively soon. She says, I feel very optimistic that we’ll see a move to more decentralized trial methods powered by associated data and technologies in the next 12 to 18 months.”

Virtual visits are becoming the new normal

A survey launched on March 26, 2020 by Greenphire, a provider of financial solutions for clinical trials, asked site staff around the globe about how their operations have been affected by COVID-19. Within 24 hours, more than 1,700 people responded. Roughly 70% of respondents indicated that they’re using technology (virtual visits and telemedicine) to support patients who cannot get to a study clinic due to social distancing. The respondents were concerned though that not every patient had the systems needed or was comfortable using the technology.

An electronic data capture (EDC) solution or a clinical data management system (CDMS) built for older data sources can make it hard to address those technology challenges.With data being generated by everything from wearables to electronic health records (EHR) to mobile applications, sponsors and CROs need to find ways to make sense of it all.

To help achieve that goal, the use of artificial intelligence(AI) and machine learning could increase. However, there is one stumbling block to achieving seamless data flow that can’t be overlooked — interoperability.

visual graphic dots

Roughly 70% of respondents indicated that they’re using technology (virtual visits and telemedicine) to support patients who cannot get to a study clinic due to social distancing.

visual graphic dots

Roughly 70% of respondents indicated that they’re using technology (virtual visits and telemedicine) to support patients who cannot get to a study clinic due to social distancing.

Interconnectivity is key to success

The healthcare industry experienced a turning point years ago with the introduction of EHRs, but progress was hampered amid interoperability and integration issues. The result was a transition from paper charts to basic digital paper charts that were not easy to access or connected to other systems, which trapped data inside each proprietary system.

It’s critical to avoid those mistakes when building decentralized trials. Due to the coronavirus crisis, making a process, product, or system patient-centrichas never been so critical. For example, patients using ePRO systems are more likely to participate and comply if they can use their own devices.

Data should be gathered through intuitive and easy-to-use interfaces. Patients expect this technology to have the same look and feel as when they’re ordering food on their phone or capturing their workout data. And the device they’re expected to use should already connect seamlessly with all applications and systems involved in the trial. In fact, it’s imperative that all clinical trial software solutions are built on anopen platformthat easily integrates with various applications (for example, clinical trial management [CTMS] or trial master file [eTMF]) used during a trial.

Of course, the value of a streamlined and intuitive design also applies when implementing a CDMS. Seeking to shorten its drug development life cycle, Biorasi implemented IBM® Clinical Development, a unified, cloud-based platform. The ease of use and efficient workflows helped the Biorasi team reduce its database build time by 25% and the full life cycle cost by 50%.

Why real-world insights should be part of your clinical trials

The use of real-world data (RWD) and real-world evidence (RWE) is a major imperative for the life sciences industry. More efficient clinical trials, expedited pathways to fulfilling regulatory requirements and new ways togain treatment approvals are driving life science companies to develop RWD and RWE capabilities as they seek to successfully leverage data and insights in their pursuit of therapies.

women wearing masks and looking at computer

Understanding RWD and RWE

For those not familiar with RWD , the real-world moniker includes data from patient electronic medical records, claims and billing data, data from drug and disease registries, patient-generated medical data, and more.

RWE could be derived from RWD. For example, it could be used to examine how people respond to drugs. Personal genomics can account for most of a patient's response to a drug, but researchers don't fully understand yet how human genomes work. So with RWDthat includes hundreds of thousands (and, in some cases, millions) of disease-specific patient records, advanced analytics could be used to identify common traits among patients without needing to identify individuals. Then researchers could see which patient populations responded well to a treatment and which populations didn't respond at all or had a negative reaction to the drug.

How clinical trials can benefit from real-world data

RWD offers practical insights that can help drive productivity for pharma, biotech and medical device companies. For example, being able to look through millions of patient records and identify only patients who would be well-suited to participate in a clinical trial could speed up patient recruitment.

Similarly, with previous data on patients who have participated in and completed drug clinical trials, researchers would haveclear expectations of how those patients (as a population) would respond if they were tested again.This research approach has been described as synthetic control arms, and althoughcompanies will likely move some what slowly in shifting to this approach to ensure that regulators approve, this approach will likely become more common over the next several years.

Another potential benefit of RWD would be touse it for post-approval studies, which are also known as Phase 4 non-interventional trials, to quickly determine drug performance in the field and satisfy regulatory compliance requirements.

metaphoric visual depicting data
metaphoric visual depicting data

Discovering new uses for treatments with real-world data

The FDA has signaled a willingness to accept real-world data to expand the labels for approved drugs. By leveraging RWE, a currently approved drug could be approved for a new use. This approach, if empirically proven, would mean that companies can adapt an existing drug for new uses very quickly, which could save much of the USD 2 billion that’s routinely spent on development.

The evolution of real-world data

RWD is not a static resource. It continues to grow rapidly as new technological innovations are used to figure out new ways to measure meaningful medical insights. Continued innovation in genomics makes genomics data easier, faster and cheaper to obtain, which will likely make genomics a common element in current and future real-world analyses.

RWD is a game-changer for the life sciences industry, and progress in developing new treatments and identifying disease cures will only become better with RWD.

See how IBM’s RWD databases are different from the competition. →