Tech for Clinical Trials

Making purchases online has never been easier, with shoppers moving quickly along a streamlined path from browsing to buying.

“Every piece of the funnel is optimized so that you will end up buying something,” said Yael Elish, founder and CEO of StuffThatWorks, a crowdsourced platform aimed at helping patients find treatments for their chronic illnesses. 

But the opposite can be true for those in the market for a clinical trial, she said. In fact, a StuffThatWorks survey showed that participating in a clinical trial can be an obstacle-studded pathway that’s driving interested patients away.

The online poll of more than 6,000 adults with a range of chronic illnesses — from fibromyalgia to high blood pressure and depression — found that while more than 93% said they would be interested in learning more about participating in a research study, nearly 96% said they’d gotten little or no information from providers about clinical trials in the last six months. 

“There is also a perception by patients that pharma is not doing a very good job in communicating, or has no interest or some disinterest, in engaging with patients and communicating about clinical trials,” Elish said. 

Patients told about a trial are often dissuaded by transportation costs, reluctance to miss work and inconvenient schedules. While 23% of people said they wanted to participate because they had no other treatment options, financial factors weighed heavily on their decision-making. About 41% of those polled faced financial stress, and more than 31% said finances would factor into their willingness to participate. But 19% said having their treatment covered or receiving an honorarium could pave the way.

Signs of change

While trial enrollment challenges persist, progress has been moving in the right direction over the past two to three years, said Xoli Belgrave, executive director of drug development services at Parexel, in an email. Clinical trial participation has ticked up thanks to industry efforts to draw in patients. 

“The regulatory landscape around the world supported this with guidance that encourages sponsors to think about patients earlier on in the drug development process,” she said.

Despite improved measures to engage underserved groups and initiatives by patient-advocacy organizations, enrollment remains a struggle and troubles typically begin at the outset of their treatment journey, Elish said. 

“People rely on their doctors as a source of information or for guidance,” she said. 

But the survey found that just 13% of patients’ doctors explained the potential benefits of a clinical trial. Doctors often have limited time with patients and providers have a disincentive to recommend trials — if the patient accepts, they lose the patient, Elish said.

Once a patient learns about a trial, additional barriers arise.  

“Time required to attend visits continues to be a patient and care partner burden as does the cost of lost income and/or travel,” Belgrave said. “As an industry we still have work to do to reduce such burdens.”  

Patients also balk at trials due to the complexity of participating. 

“Patient groups and communities are asking us to simplify the language for all patient interactions beginning with the patient information leaflet and informed consent form,” Belgrave said. “When patients are overwhelmed, they can either pull away or sign without being informed.”

Where pharma can improve 

New strategies are slowly making a difference, Belgrave said, including the rise of concierge services, specialized support services that help keep patients on track as they move through trials, and patient apps that ease check-ins, providing reminders and instructional videos. 

“Flexible clinic times — early morning, late evening or weekends — are opening up trial participation options for people who work shifts or who cannot attend a 9 a.m. to 5 p.m. clinic visit for other reasons,” Belgrave said. 

Still, the industry needs to make wholesale structural changes to the trial pathway as they seek to bridge the enrollment disconnect, Elish said. Survey data shows that patients want to join trials, but numbers will remain low until trials provide better information, outreach and protocols to make it possible.

Recursion Pharmaceuticals teamed up with AI biotech Exscientia last year in a deal worth almost $700 million. The matchup marked the biggest life sciences AI merger to date and comes amid a “surge in AI partnerships and acquisitions in the past five years [that] signals the opportunities the technology offers to life sciences companies,” according to an EY report. 

Now, successfully integrating these companies into the more traditional drugmaking processes will take creative thinking and a full commitment to the changes ahead, said Recursion CFO and president of Recursion UK, Ben Taylor, who compared the current moment to “when biologics were growing and coming of age” about a decade ago.

“You had every different strategy come out of pharma, from going big to acquire assets, or going smaller to grow it from there, or not acquiring at all to grow entirely in house,” Taylor said. “You’re seeing the same thing happen, because most companies haven’t had a lot of validation coming through on AI platforms yet, and that’ll be a triggering part for a lot of pharma companies to start thinking about more.”

While Exscientia was struggling under the weight of losing a big Bayer partnership and leadership changes in the last few years, Recursion was building up its pipeline of AI-discovered drugs and securing high-profile partnerships. But the companies had a lot in common, and together they now have 10 programs in the pipeline and hundreds of millions of dollars in potential milestones lined up with Big Pharma partners like Sanofi and Roche, Taylor said.

While the new Recursion’s technology is promising and backed by world-leading computer chip maker NVIDIA, the company’s pipeline is early in clinical stages. Its most advanced program is in the rare disease cerebral cavernous malformation and has crossed the phase 2 threshold, while its programs in oncology and other rare diseases are earlier in the process or preclinical.

Although the supersized AI marriage is still a novel concept, Taylor believes life sciences leaders will start to see more of these deals down the road, bringing new technologies into the fold and changing the way drugs are made. 

Here, Taylor discusses the AI disruption in biopharma, what companies should be looking for as they explore partnerships and acquisitions, as well as the challenges associated with collecting and using big data to drive further shifts in the industry.

This interview has been edited for brevity and style.

PHARMAVOICE: AI is clearly becoming a disruptor in the life sciences — why is that happening at this point in time?

BEN TAYLOR: The right way to think about AI is as a far better tool than [what] has been available previously that gives you functionality that wasn’t there before. I would liken it to when tools like Excel were coming all of a sudden, and it was so much easier to do something on a spreadsheet rather than using a calculator. We’re seeing a similar level jump in computation, going from the traditional methods we’ve been using for the last 20 to 30 years to something where we can perform a highly efficient, multi-parameter function. 

In biology and chemistry, there are no simple problems — everything is a complex, multi-parameter problem. If, for instance, NVIDIA weren’t able to provide the world with their processors, you couldn’t even do the analysis, but beyond that, the architecture of the analysis is fundamentally changed to allow us to perform deep learning on big data problems. We’re finding a needle in a haystack, and there’s a huge variety of algorithms to optimize and, for example, figure out where this atom should be placed on this molecule. You’re not going to solve all of the different aspects to create a new molecule with a single algorithm — you need to integrate them.

Speaking of integration, why did the Recursion and Exscientia deal make sense from your perspective?

Both companies were founded about the same time 12 years ago with a very similar mission, which is drug discovery. At the time, there were new technologies but they weren’t predictive, and so even with some of the smartest people in the world, we still haven’t been able to solve the very basic, fundamental problem of a failure rate that’s over 95%. Why is that happening? It’s because we didn’t have predictive ways of understanding what’s going to happen later on in a clinical trial. You actually have to predict how a molecule should work, and both companies had that vision with Recursion focusing on the biology and Excientia focusing on the chemistry. It’s been fascinating to watch the integration because it’s been much easier than you would imagine. Now the chemists have all these amazing tools to play with in biology, and the biologists have these amazing tools to play with in chemistry, and it came together so powerfully.

What should drugmakers be looking for as they scan the horizon for AI partners or acquisitions?

Look for the use case. Look for the validation. We’re in an industry where lots of people have lots of ideas on how to do both, and the ideas all sound like they’re going toward the same goal, or they use the same language behind it. So the only way you can really differentiate is by defining the actual product you produce with a platform or technology. It’s funny, because I’ve been deep in this for years, and I read a press release, and I’m like, ‘Oh, it sounds like they do exactly what we do. And then I dig in, and it’s like, ‘Oh, they do nothing like what we do.’ The only way you can really get your arms around that is to see what’s being produced.

On the flipside, what should AI companies be looking at when they team up with the life sciences?

The No. 1 thing is commitment, because it’s always easy to find a partner or even some money around new and exciting technologies. In fact, I think a lot of partners have a part of their budget that’s set aside to say, ‘Hey, go and spend this on new, crazy ideas.’ The partners that make a difference — if you look at our Roche and Sanofi partnerships, for example, with multiple programs each — their side comes in as committed as we are, and that comes from the top of the organization all the way through. When they’ve had relationships that haven’t progressed as quickly, it’s almost always because you just don’t have that commitment and that sense of doing whatever it takes to get this done. You’ve got to invest the time. If you’re not bought in on the process changing, it’s like taking an analog cable and putting it in the middle of a fiber optic — either way, you’re limited by whichever is the lowest quality point. So if a large pharma is saying they’re going to run everything traditionally, then insert AI for three months and go back to the traditional method. You’re going to lose most of the benefits of the more novel platform that way. In our relationship with Sanofi and Roche, and all of our relationships, we’re able to break down a lot of silos into an AI-first environment.

If you think about the pharma decision making process, it takes a lot to move through it. There are a lot of different signoffs, committees, groups — all sorts of things. I can always tell the companies that are committed to AI because you see that smoothness of decision making, and that engagement comes from all levels inside the organization. If you don’t have that, getting the milestone across can be really tough, because it always requires another level of approval.

If we look at data as currency in the life sciences, is there a limit to the availability for AI systems to make use of?

I don’t think there’s a limit. But most of the data isn’t worth anything. A lot of the data that has been collected historically generally isn’t in the right format or is very specific and not that useful. We use public datasets, or when we’re in a partnership we use their data and dig into everything we can, but honestly it only gets us to a very rough starting position. More useful is creating a fit-for-purpose dataset to be able to answer questions — and it’s fit directly for the purpose of the questions you’re asking. Since we work in proprietary drugs, and if you actually want to come up with novel concepts, you have to start with something that’s going to answer very specific questions.

So you're going to have to create your own data on that as well, right? That's the No. 1 concern. We once had a discussion with a pharma partner who was asking us to come in and take a look at their old data and help them build models with it. The conclusion we came to was that it was going to be far more efficient and probably predictive to actually recreate all of the data that they had been doing in a much smaller context. You could get to it without trying to recreate 30 years of data, right? Because all of their data was in different formats and based on very specific projects, you would have had an army of people working night and day to try and wrangle that into shape to where you could actually use it. So that's something I think a lot of people miss.

Sponsored content

By PPD, part of Thermo Fisher Scientific

Clinical trials are critical for advancing medical research and bringing new treatments to market. However, a significant challenge drug developers face is that of non-enrolling sites. These trial locations, despite being activated, fail to enroll participants, leading to increased costs, delays, and potentially compromised study outcomes. As clinical research becomes more complex and timelines accelerate, efficiently managing and optimizing site performance is increasingly critical.

Traditional methods of addressing non-enrollment are reactive and often too late to prevent negative impacts on trials. These inefficiencies require a shift to proactive strategies. Innovative solutions like machine learning offer a transformative approach.

Proactively Navigating Non-Enrolling Sites

Non-enrolling sites are a common issue in clinical trials, leading to inefficiencies, high costs, delays, and a lack of detailed insights into site-specific problems. Key obstacles in addressing non-enrolling sites include early identification of at-risk sites, limited resources, and ineffective communication and support strategies.

It is crucial to understand the root causes of non-enrollment and develop targeted mitigation strategies to enhance trial efficiency and outcomes. Historically, clinical trial sponsors have relied on retrospective data analysis and manual intervention to identify and address issues at non-enrolling sites. These traditional approaches often fall short due to their reactive nature. By the time issues are identified, the damage is already done. Moreover, these methods are labor-intensive and lack the precision needed to address the root causes of non-enrollment effectively.

To tackle the challenges of non-enrolling sites, forethought is key. This involves anticipating potential issues before they arise and implementing targeted interventions. Proactive strategies leverage advanced technologies and data-driven insights to identify sites at risk of non-enrollment early in the process.

Machine Learning Offers a Transformative Solution

Machine learning (ML) revolutionizes clinical trials by analyzing vast amounts of data to predict non-enrollment issues before they occur. Utilizing sophisticated algorithms, ML evaluates numerous factors influencing site performance and delivers actionable insights.

Here are some examples of ML solutions:

• Proactive Site Identification: One of the key applications of ML in clinical trials is proactive site identification. By analyzing historical data and identifying patterns, ML models can predict which sites are likely to face enrollment challenges. This allows sponsors to intervene early, providing the necessary support to these sites to improve their enrollment performance.
• Personalized Engagement Strategies: ML also enables personalized engagement strategies. Instead of a one-size-fits-all approach, ML can tailor interventions based on the specific needs of each site. For instance, if a site struggles with patient recruitment due to a lack of community engagement, the sponsor can implement targeted community outreach programs. Similarly, sites facing data management issues can receive additional training and resources to enhance their data collection processes.
• Continuous Monitoring and Adjustment: Continuous monitoring and adjustment are vital components of a proactive approach. ML models can continuously analyze site performance data, providing real-time insights. This allows sponsors to make timely adjustments to their strategies, ensuring sustained improvements in site performance throughout the trial.

Our Expertise Leads to Your Trial’s Success

The PPD™ clinical research business of Thermo Fisher Scientific excels in using ML and advanced technologies for site management. Our ML platform, built on advanced algorithms and robust data infrastructure, enables our data scientists and clinical experts to analyze large datasets quickly and accurately. Custom solutions tailored to sponsors' needs provide insights and tools to optimize site performance and ensure trial success. Our effective outreach strategies and commitment to continuous improvement make us an ideal partner for addressing site non-enrollment issues.

Real-world data shows the effectiveness of our strategies. In a study in Taiwan, predictive analytics identified high-risk non-enrolling sites during the pre-site initiation phase. ML models analyzed various data points to identify high-risk sites early, enabling proactive enrollment measures. Continuous monitoring allowed timely adjustments, significantly improving site performance and trial success.

Innovative Approaches and Data-Driven Insights Help Address Challenges of Non-Enrolling Sites

Non-enrolling sites present significant risk for clinical trials with increased costs and delays. Traditional reactive methods are often inadequate. Proactive ML-driven strategies can identify at-risk sites early and implement personalized engagement, improving site performance and trial success. Our expertise in ML has shown substantial improvements in trial outcomes, enabling sponsors to anticipate and mitigate challenges effectively.

The split between longtime life sciences software partners Veeva and Salesforce is getting down to crunch time, and pharmas and biotechs need to confront what that change will entail in an evolving healthcare market.

Soon, clients who depend on the customer relationship management platforms that connect sales reps with buyers will need to choose which path to take as the divorce finalizes. And the decision could be more impactful than it seems on the surface, according to Nancy Phelan, head of customer engagement at industry consulting firm Trinity Life Sciences.

“Sometimes this is being seen as just an IT migration — but no, if you’re just migrating, you’re missing the strategic importance of this,” Phelan said. “A lot of leaders are starting to realize this is a much bigger decision, and they need to think through the choices, the timing and the approach to set up for a very different world.”

The contract between the two companies expires this September, and drugmakers will have until September 2030 to make the call. Veeva announced the intended split in December 2022.

Veeva’s departure from the Salesforce platform stems from mounting risks and roadblocks that made going solo a better option for the company’s tailored life sciences position, according to a report from Everest Group. And while the separation will diminish Salesforce’s market presence, the firm’s larger size could allow for quicker development of next-generation technologies.

With Salesforce being the largest CRM provider in the world across sectors and Veeva still a particular favorite of the life sciences, this upcoming fork in the road demands some attention from the industry to understand the implications of each choice, Phelan said.

Honeymoon is over?

The two companies have been intertwined since Veeva launched in 2007 based on the Salesforce CRM platform. Neither one has worked in the life sciences sector without the other, but each has been developing new products that will carry on past the split.

Almost two decades later, that’s where the paths diverge, Phelan said.

“Large companies like Pfizer or Novartis last made this decision more than 15 years ago, and the business model was very different then,” she said. “You didn’t have the specialty pharmacy complexity, the need for some of the field reimbursement, issues with patient affordability and the kinds of teams you have out in the field right now.”

With both companies rolling out new CRM platforms, it’s time for drugmakers to reflect on the changes to the industry in that time, Phelan said.

“It’s time for companies to think differently about what’s happening in the market and what’s happening with their customers,” she said, adding that CRM software companies like Oracle, SAP and others exist in the space, as well. But Salesforce and Veeva are the dominant players serving the biggest companies, Phelan said.

When the partnership began, digital marketing activity was still getting off the ground — but now, AI and big data analytics play a role that didn’t exist at the time. And that makes the decision between providers a much different choice than it was back then, according to Phelan.

Two roads

The main difference between Salesforce and Veeva moving forward will be what drugmakers are looking to get out of the platforms, Phelan said, noting that Trinity doesn’t encourage one over the other unilaterally.

Fundamentally, Salesforce is betting on bigger, connected AI while Veeva is playing to its strengths of rep interface and content. Each one will still have flavors of the other, though, and it will come down to individual companies’ needs and preferences, Phelan said.

Salesforce will be working with IQVIA’s orchestrated customer engagement platform to develop the Life Sciences Cloud, which will employ AI to give biotech and pharma leaders “greater cohesion,” Phelan said. The platform has launched as of last year, but sales automation functions won’t be offered until September this year due to the existing contract with Veeva.

Veeva announced in December last year the newest iteration of its Vault CRM Suite, which will include AI components and industry-specific programming. For life sciences companies, this is the user experience they’re most accustomed to, and Phelan said Veeva has promised not much will change with that interface.

In some ways, the divorce has gotten a little messy with the two companies scrambling to convince clients to pick their platform. At the end of December, Salesforce had reportedly poached some big customers from Veeva, which held about 80% market share in the life sciences.

Both companies are adapting to drastic changes in the healthcare landscape including an abundance of data, more complex therapies and evolving customer needs.

From what Phelan has seen so far, drugmakers aren’t pooling to one side or the other based on characteristics like size, pipeline or core focus area. Each of the diverging roads isn’t designed for one particular pharma or biotech in mind, but they could shape the experience in multi-dimensional ways, she said.

Meanwhile, both Veeva and Salesforce are putting their best foot forward ahead of the split, and pharma companies still have time to come to terms with their preferred path.

“How we’re advising companies is, you’ve got a window of time and a future that is radically different from the last time you made this decision,” Phelan said. “You need to strategically assess the pieces that are important to you.”

Strategic R&D cuts have been a high priority for many pharma companies lately, from Moderna slashing R&D by 20% and discontinuing five clinical programs to Incyte eliminating several pipeline programs after a “strategic review.”

Although reevaluating and deemphasizing clinical programs is nothing new in biopharma, signs point to it being more widespread than a handful of companies doing business as usual. Analysts point to Charles River Laboratories’ staffing cuts and site closures as a sign of a decrease in demand for drug development services and a bellwether for the industry as a whole.

For the R&D programs that survive, the goal is to make them more efficient and cost effective than ever.

“There’s huge spend there,” said Bill Coyle, global head of biopharma at ZS, a healthcare consulting firm.

R&D efficiencies go beyond streamlining the pipeline to only include the assets that are most likely to succeed in the marketplace or using AI to boost efficiencies in drug discovery and trial design. This strategy should also include more practical, brass-tacks factors that impact the entire development process. Here are a few tactics that Coyle said will help companies get products to market more quickly.

Embrace human-centered trial design

Participating in a clinical trial can be burdensome for patients, which is why sponsors should design their trials with people’s real lives in mind and remove barriers to participation.

This could include compensating patients for transportation services and child and elder care, or connecting them with financial support groups. When looking to boost diversity in its clinical trials, Johnson & Johnson employed these kinds of tactics, Denise Bronner, the company’s director of diversity, equity and inclusion, told PharmaVoice earlier this year.

However, this approach can also extend to the way the trial is designed by using digitization and decentralization when possible and reducing the frequency of patient visits for assessments.

“If it's a blood draw, can you do it at home versus having to come in to the site?” Coyle said.

Human-centered trial design can also help boost ROI by making trial enrollment faster.

“If we increase the speed of enrollment, that is a big driver of the cost and the time to market, the time to which we get the data to submit,” he said.

Human-centered trial design also reduces patient dropout. Researchers estimate that it costs more than $6,500 to recruit just one patient, and replacing them adds to that expense.

Speed up decision making

One of the biggest inefficiencies in R&D is slow decision making, Coyle said.

Sponsors should decide quickly whether next steps are a go or no-go after a data readout. But that doesn't always happen. Instead, they take too long moving from proof-of-concept to phase 2 and phase 3.

“They're big, important, expensive decisions,” Coyle acknowledged. “But I think there's a little bit of time lag there sometimes. How do we more rapidly make those decisions?”

One way is by thinking through decisions early. As trials move into later stages, companies must determine whether the data supports a marketable product, as well as shore up their phase 3 trial design and study endpoints. Instead of waiting for data readouts to start that decision-making process, companies can think ahead and map out different actions based on possible phase 2 outcomes.

“Being very clear on those as you're going through phase 2 [is important] so when you see the readout, you're able to act quickly,” Coyle said.

He also warns executives against fighting too hard for a lagging asset because they’re wedded to a therapeutic category or were involved in the asset’s identification or purchase.

“Some of the human elements do get involved in these decisions, which make them slower to negotiate and move through,” he said.

Be smart about site selection

Sponsors often choose trial sites they have an existing relationship with. But making that the sole or most important criteria is the wrong strategy. Instead, companies should choose the most productive trial sites by leveraging data about prior performance, speed of enrollment and whether they hit their enrollment targets, Coyle said.

Sponsors should also look for sites that will provide the appropriate clinical trial diversity and that are geographically relevant to the therapeutic category based on patient data.

“We have to be thinking about the prevalence of the diseases,” Coyle said. “Different diseases might have different prevalence drivers and be more prevalent in different areas.”

The pharma industry has long been keyed into the excitement about AI. And the predictions for its potential impact are staggering. 

AI tools could generate $60 billion to $110 billion in value for pharma per year thanks to boosts in operating efficiencies, R&D speed and data insights, according to an analysis published by The McKinsey Global Institute in January.  

PwC also illustrated the potential returns from AI adoption on a per-company basis in a report this year.

“Overall, pharma companies that industrialize AI use cases completely across their organizations have the potential to double today’s operating profits by boosting revenues and reducing costs. We expect that this industrialization process will begin to be fully realized by companies prioritizing AI by 2030,” the PwC analysts wrote.

Although AI adoption has historically been slower in pharma than in other industries such as retail, PwC noted that “pharma [is] closing the AI value gap in an accelerated way.”

As more pharma companies leverage AI tools, what unique insights are being revealed? Here’s what several industry leaders using and developing AI tools told us. 

The factors that truly impact trial outcomes 

Dave Latshaw is CEO of Biophy, an AI company with solutions designed to predict clinical trial outcomes and speed drug development tasks.  

“For any given trial phase, [when] you can look at all [what’s] impactful to the outcome, if [there were] just a few smoking guns that would always dictate whether or not a trial succeeds, they would be very overweight factors in the model and the rest wouldn’t matter. But the truth — and why it is complex — is that when you look at a massive number of features and the magnitude of the impact over all of them, it doesn’t reveal any one or two factors that are absolutely critical. They are all interdependent and all have the same magnitude of impact. That means you have to have to do a lot of things right to have a successful trial. 

Here’s an example of what could be more important: Endpoint selection. A lot of times there will be sets of endpoints that are typically used, and some have to be for regulators, but sometimes the selection is a company’s decision. Sometimes people are surprised to understand the variability of performance for certain endpoints and that it’s very different from what they expected. The common wisdom is to use what everyone else uses. 

But there might be a better endpoint that could be used [to] capture the information at the same level of detail to provide the evidence you need but is maybe more consistent and doesn’t have the risk of some inherent mechanism for variability that could throw off results. For example, some therapeutic areas, like psychiatric indications, often have a really enhanced placebo effect that can undersell the efficacy of an individual drug. But that variability with the placebo — not the drug — is the factor that sinks the trial. And when you choose a placebo you’re subjecting yourself to more randomness than you may want.”

Predicting patient outcomes

Miruna Sasu is CEO of COTA, which specializes in solutions that leverage real-world data to support oncology research. COTA also recently introduced a suite of AI-powered solutions called CAILIN designed to generate insights from RWD and datasets for research. 

“You treat [patients but] you want to understand what’s going to happen. Is the treatment going to stop the disease? Or just lessen the progression? We’re finding that we have the ability to predict that, which is further analysis than we thought we would get. With clinical trial data, you only have a point in time. Here you get more of the patient journey. 

We also enhanced some of our data points with genomics, and [then] you have the ability to see which genes are turned on and off before and after the treatment. And if you do that again and again you understand that patient’s pattern. Doing this manually would consume a whole person’s career, but with an algorithm you can tease apart which genes are turned on and off and which cancer, for example, could be optimally treated with which biomarker inhibitor. You have the ability to almost look into the future.

In the long run, a drug candidate would come out of discovery and go right into a real-world model and be tested against patient populations to see if it’s going to work and by how much. The technology is there to allow for that.”

Deeper insights into when genes are expressed

Craig Thompson is CEO of Cerevance, a biotech leveraging a large collection of brain tissue samples along with AI/ML tools to develop drug candidates for the CNS space. 

“What we have been more surprised by is looking at certain genes by cell type and seeing when some of them are intrinsically upregulated or downregulated. And we always like to test what we’re seeing with our machine learning capabilities.

Some companies are looking at genes that we see using our machine learning are only transiently upregulated or downregulated at a certain point in disease progression, so you scratch your head about why they’re going after that target. In certain cases our technology has shown some pharma companies going after targets that aren’t expressed in the human brain and they’ve gone into phase 2 or 3 trials and then failed. At the other end, in the [neurodegenerative] space you see some targets that companies are pursuing that you don’t [understand] because the gene is extremely broadly expressed. Our anticipation is given the broad expression you would see high rates of adverse events and some of those compounds are being advanced in the clinic.”

AI solutions need to be supported by digital infrastructure

Dr. Mert Aral is the chief medical officer of Huma Therapeutics, which develops remote patient monitoring solutions used in decentralized clinical trials that is also aiming to create a platform any company can use to build their own healthcare apps. 

“We’re looking at how we can enhance engagement and compliance and how we can provide patient education and awareness. We know these AI solutions work. We know they accelerate time to treatment change. We know we’re delivering data and real-world evidence to pharma. But how can we increase scale?  

Right now, [solutions such as digital health monitoring] still rely on clinical teams. You still have a nurse that monitors the data and calls or works with patients. The generative AI is allowing that nurse to more efficiently monitor data and trends. But our thinking is: How can we replace that nurse with a GenAI nurse so we don’t need to deploy so much staff? 

Of course, you would have to train these models. This is a high-risk environment and you can’t have a GenAI nurse giving the wrong advice to the patient. We still haven’t fully solved for that. But from a technology perspective, I don't think that’s the limiting step. We’ll get there and I don’t think it’s far off. 

So when we look at GenAI it’s not just the many applications of it. We need to also have the right digital infrastructure to launch and train and deploy these solutions at scale, and our health systems are not quite there.”

AI platforms are revealing deeper levels of biology and chemistry, while creating long-term value

Ayman AlAbdallah is a partner with Mubadala Capital, a venture firm that manages over $24 billion in assets, including about 45 life sciences companies. 

“We’ve seen the application of AI in drug discovery and development over the past 10 years and different waves of the technology have built on top of each other. One observation is that the value of AI companies will continue to accrue in the pipelines they generate. 

The intention is not to replace the scientist but to supercharge the scientists with a better understanding of biology and chemistry to develop drugs in a much more efficient and effective manner. We’re also generating insights that were otherwise not possible using conventional assays or analytics. The examples are many. 

One of our investments — Iambic Therapeutics — is leveraging physics-informed AI to propel the field forward in how we use computational biology and chemistry to discover and develop drugs. Another company Vevo Therapeutics is developing large language models for biology and moving in vivo drug discovery from a downstream step that is limited in terms of its representation of tumor heterogeneity into early-stage development to uncover misunderstood or novel mechanisms of action and new drug combinations that can result in high efficacy.”

When you’re looking to make changes to biopharma clinical trials, one of the top minds in the industry could help you get there — by welcoming Robert Langer to its advisory board, that’s exactly what Lindus Health aims to do.

Langer, an MIT professor and co-founder of Moderna (in addition to a long list of successful biotechs), is the author of more than 1,500 scientific articles, holds more than 1,400 patents issued and pending, and is widely considered the most cited engineer in history.

Bringing Langer to the advisory board of a contract research organization like Lindus, which calls itself “the anti-CRO,” sets the company up to address some of the most pressing concerns in clinical research. Langer’s mind for biotech business also contributes to the ways he can help the company innovate to mitigate some of the risks associated with the CRO model.

Since the COVID-19 pandemic, traditional clinical trials have shifted to a more flexible approach, according to industry research from WCG. With clinical trial starts expected to rise in 2024 compared to the year before and new technologies emerging at a rapid clip, CROs are exploring ways to stay ahead of the curve.

A change is gonna come

Overall, Langer sees where CROs often fall short and where Lindus could make an impact in the industry.

“I think CROs haven’t changed much [in the last decade],” Langer said in an email interview. “There are some recent attempts to incorporate AI and virtual clinical trial techniques to increase efficiencies and expand reach of studies [but] in general CRO processes have remained unchanged.”

With massive success in the science and business of biopharma, Langer is notoriously modest and focused on the work. At Lindus, that work centers around removing bottlenecks in the clinical trial process with an end-to-end business model.

Langer isn’t the only big name with a hand in Lindus’ path forward — the company has raised more than $24 million from Peter Thiel and other tech and biopharma investors.

And one of the changes Lindus is trying to bring to the CRO table is a new financial model that positions drugmakers to share the risk of the clinical process.

“The incentives of CROs are currently totally misaligned with sponsors and with patients — CROs typically earn more money from projects that are delayed, due to their billable hour business model,” said Meri Beckwith, co-founder of Lindus, in an email.

Founded in 2021, Lindus gained insight from Langer on what would become the company’s driving business model. Beckwith said Langer was critical in shaping a model based on hitting clinical milestones so the CRO and the drugmaker are both locked in on the same goal. Milestones are also a way to share the risk since the contractor and the client are both in the same boat, Beckwith said.

“Sponsors are understandably risk-averse,” Beckwith said. “No one gets fired for hiring [CRO giant] IQVIA.”

Under one roof

Beyond a milestone-based financial model, Langer also sees the end-to-end nature of the clinical trial model as something that sets Lindus apart.

“By building their own robust tech platform and internal site capabilities, they have been able to streamline clinical trial execution,” Langer said.

For example, software that allows for quicker study configuration can help get a trial started. And since recruitment is often a narrow bottleneck for most studies, with most not recruiting on time, according to Langer, expanding the reach through virtual assistance and access to electronic medical records can grow the patient pool faster.

Lindus has also developed software that uses machine learning to write study protocols and capture data with AI monitors from beginning to end.

Langer’s role will be that of a biotech industry guru who can speak to many different facets and disease areas where Lindus is looking to innovate.

“[Langer]’s involvement will help us better design clinical trials, drawing on his wealth of experience from having been involved with dozens of pioneering life science companies,” Beckwith said, noting that Lindus is identifying market segments that work best with its model. “He has consistently been at the forefront of new modalities like gene therapy, tissue engineering and transdermal drug delivery.”

Diseases areas like diabetes where pharma companies have had a particularly difficult time developing novel drugs is where Langer could bring his expertise to find new solutions.

“I take a lot of what I’ve learned in the biotech and drug delivery space about the needs of these companies when looking to establish clinical evidence,” Langer said. “There are a lot of common threads with the types of issues that come up during clinical development that I am able to share.”

The clinical trials industry is up against howling headwinds.

Novel therapies are more complicated to work with, staffing has been strained for years and the need to move efficiently and quickly without impacting quality is ceaseless.

Even positive developments can make work more complex. The regulatory push for DEI, for example, has the industry rethinking trial protocols and design. And new tech aimed at boosting efficiency brings the added challenge of training and preparing staff. 

On top of that, geopolitical issues in the Middle East and Russia’s war with Ukraine pose an ongoing threat to trials in those regions.  

“A lot of people assumed those [challenges] would stop, but they’ve gone on,” said Peyton Howell, CEO of global contract research organization Parexel, which runs trials in Israel and Ukraine. “It is concerning.”

Howell took the helm of Parexel in May and is eager to boast about its recent accomplishments, including DEI initiatives and industry awards. But Howell is also forthcoming about the challenges Parexel and other research organizations are up against. 

Tech’s promise and perils

The No. 1 question Parexel receives from pharma partners is about opportunities to use AI, Howell said.

“These tools might sound mundane, but they are the most exciting because they affect how a person spends time in their career,” Howell said, adding she’s particularly excited about tech “focused on study startup, patient recruitment and trial design. 

Still, the new platforms have to be tested and reviewed, and when they’re applied to operations, staff members have to be trained. AI platforms add another layer of tech to new demands already established by wearable devices for remote patient monitoring. 

In a study Parexel conducted with the Tufts Center for the Study of Drug Development, Howell said sites “expressed frustration with the number of different systems imposed on them and the complexity that tech burden creates.”

And those criticisms are being taken seriously, down to the letter.

“We are asking sites to do more. Some staff at sites say they feel like they’re in tech support right now,” Howell said, adding, “We’ve heard loud and clear from sites that we as an industry need to do a better job reducing the tech burden.”

The staffing crunch

The tech burden has also compounded ongoing staffing issues in clinical research. 

Although turnover has dropped significantly since the peak of the Great Resignation, and so far, the staffing crunch has yet to delay any trial starts at Parexel, recruiting new talent remains a challenge, Howell said. Research professionals for oncology, ophthalmology and neuroscience therapeutics are often in higher demand but have a smaller, more specialized talent pool.

 “The market remains competitive … meaning most new hires have more than one job offer,” Howell said.

To more quickly onboard and prepare research assistants, Parexel joined fellow CROs by launching a training program in 2022 for new hires. Called APEX, the program teaches the ins and outs of clinical work, such as navigating the required systems and patient monitoring. 

The Walgreens factor

Patient-guided trial design is a “biggie” in the industry, Howell said, but implementing it can be tough. And although many sponsors worry that gathering patient input will slow the trial process, Howell said it can speed the whole thing up. 

“Do it early and you’ll be more successful with recruitment and retention,” she said. “And it can reduce the cost burden because you are pressure testing the data you’ll need and the number of site visits you’ll need … which is a win-win.”

Going into the recruitment phase, Howell said patients need to remain at the center of protocol design to help retention.

“Clinical research has a burden on patients we need to understand,” Howell said.

Clinical research needs to meet patients where they are, Howell noted. 

“They want access to healthcare providers, but travel adds another burden,” she pointed out.

To bridge the access gap while improving diversity in clinical trials, pharmacy retail giants moved into the research arena — with mixed results.

Two years after launching a clinical trials division, CVS Health walked away in 2023.

CVS’ departure left more room in the market for Walgreens, which launched its clinical trials arm in 2022. Now, Walgreens said it’s reached 5 million patients to “potentially recruit into clinical trials.” 

Partnerships have also been key to the Walgreens strategy. Most recently, it struck a $100 million pact with the Biomedical Advanced Research and Development Authority to “increase innovation in decentralized clinical trials.” And earlier this year, Walgreens announced it’s teaming up with Boehringer Ingelheim to connect patients to phase 3 trials for the drugmaker’s GLP-1 candidate in obesity. 

Because of Walgreens’ capacity to recruit patients and provide trial sites, its foray into the space is a potential boon for research organizations like Parexel, Howell said.

“Seeing big companies like Walgreens in the arena is fantastic,” she said. 

Although Parexel doesn’t have an official partnership with Walgreens, Howell said the company is an “active supporter” of the pharmacy giant’s efforts.

Earlier this week, Walgreens announced it’s shuttering 1,200 pharmacies over the next three years in the wake of a $14 billion operating loss in fiscal year 2024. In an earnings report, the company didn’t indicate it plans to scale back the clinical trials business.  

Going forward with clinical trials, Howell said her advice to the pharmacy behemoth would be to “keep it simple.” 

“Focus on recruiting and bringing patients in,” she said. “I want patients to know about trials in a community setting, and I think that’s going to bring in a more diverse population — and that’s where we have strong alignment with Walgreens.”