Artificial intelligence & machine learning

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.”

First, there was the Human Genome Project. The turn-of-the-century, international research program led to the sequencing and mapping of humanity’s entire genetic makeup at a fundamental level, paving the way for thousands of discoveries and treatments to address the world’s trickiest diseases.

Now, chemical and biological “maps” have advanced far beyond what the Human Genome Project achieved, digging deeper into the inner workings of physics and the human body. And new technology like machine learning and AI has sparked a revolution in how complex and voluminous these roadmaps can be.

Last week, the Nobel Prize in chemistry went to three scientists who developed and used the neural network AI program AlphaFold to predict the intricate folding patterns of proteins and create proteins of their own. The researchers — Demis Hassabis and John Jumper of Google’s DeepMind in the U.K., and the University of Washington’s David Baker — have touted the program’s potential for unprecedented drug discovery and development.

AlphaFold is far from the only use of machine learning to unveil the mysteries of biology. The life sciences industry is awash with companies leveraging big data and predictive analytics to decipher critical pathways and catalog them as an advanced tool for drugmakers. With the range of capabilities the mapping tools offer — from revealing the complexities of the immune system and how drugs interact — these technologies could change the way drug development is done.

Mapping the immune system

Immunology is a hot target in biotech and pharma, and with the success of megablockbusters like Humira as a guiding light, venture capital investments in autoimmune conditions reached $1.7 billion in the first half of 2024 alone.

But when it comes to the complexities of the immune system and what causes it to function improperly, the data is so complicated that vast swaths of it are still misunderstood. Developing new treatments that improve upon their predecessors becomes more challenging and expensive over time, said Noam Solomon, CEO of Immunai, which boasts the largest knowledge base of cell-level immunology in the world.

The goal of Immunai’s AI platform, called Amica, is to create a map of the immune system at the single-cell level and demonstrate how those on and off switches respond to different drugs, Solomon said. Because of the breadth of the immune system’s involvement with human disease, the implications could span from oncology to neuroscience and many other areas. And Immunai wants to make the drug development process more directed toward the end outcome.

“The reason you have such poor response rates and approval rates for therapeutics is because different people’s immune systems behave differently, and the way biotech and pharma companies are developing the drugs is from very simplistic and reductionist preclinical data,” Solomon said. “There are a lot of decisions for a drug, from mechanism of action to indications to patient selection and combinations — we leverage big data engineering and an AI model to answer and explain these questions.”

What that map provides is not just a view of all the connections within the immune system but also a path through the drug development process — like a robotic assistant who can point researchers and developers in the right direction.

Immunai recently expanded a partnership with pharma giant AstraZeneca to guide clinical decision-making using the immune system map. The companies have worked together since 2022 to bring compounds through development, and the two will focus the collaboration on oncology and immunology.

If Immunai’s platform is the GPS to navigate the immune system, Solomon said, then a drugmaker like AstraZeneca provides the car — the clinical trial infrastructure. Still, it can take a couple years to build the trust of a more traditional pharma company and demonstrate that a new platform is viable.

And drugmakers need to understand the limitations of technology as much as the possibilities, Solomon said.

“We need to view AI as an augmentation of capabilities,” he said. “Precision medicine right now is essentially built on the premise that there are a lot of different types of data, very high-dimensional, that the human brain can’t cope with or make sense of, but computers are exactly geared toward that.”

Down the road, maps like Immunai’s will just be how the job is done, Solomon said. DeepMind’s Nobel recognition is a testament to that.

“A tech company winning the Nobel Prize in chemistry is the dawn of a new era,” Solomon said. “And while they map the chemical structure of proteins, what we are trying to do is map immune systems in living organisms — so it’s more complex.”

Applying physics

Ramy Farid, CEO of the physics-based software company Schrödinger, views the DeepMind Nobel Prize as a step in the right direction for transformative drug development advances, but he has a bigger question on his mind: “Now what?”

Like the Human Genome Project, AlphaFold solves a specific problem by exploring the existing structures governing druggable targets. But it’s still just a step in the right direction, Farid said.

“Knowing the sequence of the whole human proteome tells us some information, and it’s a real accomplishment — but you can’t design drug molecules with this technology,” Farid said. “Knowing the structure is important, but we also need technology to understand how molecules bind to proteins.”

Every achievement begets more paths forward, and Schrödinger has for years been building its own drug discovery software that provides a molecular understanding of diseases and their potential treatments. Among other capabilities, their physics-based approach applies mathematical constants to molecular structures to create a “map” of how they will bind to one another, which is an important step in drug discovery and development. The company now has its own pipeline of candidates across oncology, immunology and neurology, as well as partnerships with Bristol Myers Squibb, Gilead Sciences and Eli Lilly.

Leading the charge with the company’s drug development arm is Karen Akinsanya, president of R&D and a former research leader at Merck & Co, where she worked with a program that was faltering until Schrödinger’s software helped pave the way.

“Essentially, we had hit a brick wall, and Schrödinger showed how powerful the technology was to go beyond the chemical space that the industry had all been working on and expand it,” Akinsanya said. “And as a value growth story, the idea was that with unlimited access to the platform, we should be able to at least attempt to solve some of the biggest challenges in drug discovery.”

But Schrödinger, as well as DeepMind and other companies in the space, needs to understand the limitations of this kind of technology, Farid said.

“AlphaFold has sparked a lot of interest in methods like this,” Farid said, “And while computational structures are great, you’re going to keep needing experimental structures, too, because it’s not perfect.”

Circling back to the Human Genome Project, Akinsanya views current advances like AlphaFold and Schrödinger’s platform as necessary steps in a long journey.

“The Human Genome Project was an incredible moment in history, and a lot of things have come out of that — but it wasn’t the solution to curing every disease we thought it would be,” Akinsanya said. “But so much has happened in the last 25 years that did come from that project, like mRNA and rapid sequencing — these are breakthroughs as a result of that breakthrough, and that’s what we’re all excited about in this space, as well.”

AI is making its mark on the pharma market, presenting opportunities for Big Pharma to cut down on drug development costs and fast-track new discoveries. But AI’s capabilities also open the door for smaller biotechs to remain independent rather than leaning on deep-pocketed, established pharmas, according to a report from S&P Global Ratings.

Big Pharma has been investing in AI and partnering with tech companies in recent years, and the area’s value could jump from $1 billion in 2022 to a projected $22 billion in 2027, according to data from Boston Consulting Group. 

Many of the early investments in the space were focused on leveraging machine learning. For example, Pfizer teamed up with IBM Watson in 2016 to leverage the company’s machine learning system in drug discovery, while Merck & Co. and AstraZeneca partnered with Amazon Web Services in 2017 to develop a cloud-based drug discovery platform. 

But the collaborations between pharmas, tech companies and biotechs have now “given rise to a new breed of AI-driven [biotech] entities,” according to the report.

This new breed has AI at the center of their platforms, but are still partnering with established pharmas to overcome high development costs. Independent AI biotechs will likely seek more partnerships and collaborations as a result, the report predicted, but also have the potential to break out on their own with treatments that could alter the commercial landscape. 

“The relationship between AI biotechs and big pharma won't necessarily be symbiotic,” the report stated. “The formers' significant potential for innovation means they could yet emerge as competitors to pharmaceutical incumbents (and Big Pharma's R&D functions).”

Already, several biotechs have drug assets in the clinic that were discovered through AI — both with and without Big Pharma contributions. 

Lantern Pharma has several drugs in clinical trials that were discovered through its AI platform. Insilico Medicine has 31 programs and out-licensing agreements for a handful of candidates generated by AI.

AI’s big impact

While S&P didn’t expect that AI will cause an influx of blockbuster drugs, the ratings company forecasted AI biotechs to have a “considerable” impact on shortening the drug development process. 

Instead of spending four to seven years in the discovery and preclinical stage, AI could shorten the timeline to two to three years, according to the report. Clinical development could be only three to five years long, shaved down from the current range of seven to nine.

The benefits of speed could help independent biotechs overcome the huge cost challenge of developing new drugs. Between 2022 and 2023,  the cost of moving a new drug from discovery to launch averaged $2.3 billion, according to a report from Deloitte.

With an 85% compound annual growth rate projected for the global pharma-AI market,  the technology will continue to expand its role across the industry. Still, investments in the space will take time to be fully realized. Instead, the gains are likely to be “incremental,” with efficiencies being slight, the report predicted.

“Drug discovery, with or without AI, will remain a complex and time-consuming practice characterized by experimentation, false starts and failures,” the report stated. 

If AI platforms are able to help pharma companies pursue treatments for more complex diseases, working in those complicated arenas could also bog down drug development timelines and increase resource demand, S&P noted.

Novo Nordisk got a major boost in March when the FDA approved its GLP-1 diabetes and weight loss drug Wegovy to also prevent heart attacks and strokes in patients with certain risk factors. The decision opened the market up to more than six million people and broadened Medicare coverage for the blockbuster drug, now on track to become one of the best-sellers of 2024.

And that may only be the tip of the iceberg, according to a sweeping AI study from Dandelion Health, which identified an additional 44 million lower-risk cardiovascular patients who could benefit from a GLP-1 treatment, hinting at the potential to greatly expand insurance coverage.

Putting AI to work

Dandelion leaders ran the study as a proof-of-concept test of their real-world data platform, which uses commercial and academic AI programs to analyze patient information obtained through revenue-sharing agreements with health systems, including Sanford Health, Sharp HealthCare and Texas Health Resources. The platform could be a faster and less expensive way to deliver results than traditional clinical trials. Studies could take weeks, not years, and incorporate larger and more diverse patient groups, according to the company’s co-founder and CEO, Elliott Green.

“No one ever does preventative trials because they're just too big,” he said. “But this AI proof-of-concept started to show that there's a path forward to being able to achieve that.”

Mining real-world data

For the observational study, Dandelion examined whether GLP-1 use might reduce the risk of major adverse cardiac events such as heart attack or stroke in overweight and obese patients. The study used inclusion criteria similar to the Novo Nordisk-sponsored trial that helped convince the FDA that Wegovy had heart benefits. But, unlike SELECT, Dandelion’s patient group didn’t have severe preexisting cardiovascular disease.

Researchers applied an AI algorithm from Pheiron trained to spot cardiovascular risk indicators on patient electrocardiograms. The AI assigned patients a risk score and compared predicted risk for heart attacks and strokes in GLP-1 users versus non-users. The results expanded on the findings of Novo’s trial, which found Wegovy reduced the risk of major cardiac events in the study group as much as 20%. 

Dandelion’s study, seven times the size of Novo’s, determined that people in the lower-risk group who took GLP-1 drugs had 15% to 20% lower risk scores for major adverse cardiovascular events after taking the drug for three years. Treating these additional patients could head off 17,300 heart attacks and 16,700 strokes a year, according to study authors.

“With further validation, this AI-driven approach could potentially unlock a new biomarker or surrogate endpoint that measures [major adverse cardiovascular event] risk, enabling shorter and smaller clinical trials by predicting cardiovascular outcomes earlier without waiting for [adverse events] to occur,” they stated.

Speeding research

Real-world data-based AI could validate GLP-1s for broader applications, from neurological and liver diseases to addiction and arthritis. 

Green said Dandelion already has another GLP-1 study in the works, using AI to assess muscle changes on patient abdominal CT scans to determine whether GLP-1 drugs also cause muscle to wither during weight loss. The platform could also help pharma companies explore and validate label expansion opportunities and perform other types of research. Dandelion is working with several companies, Green said.

“Clinical trials are very focused. You come in with a very specific question, and you get a very specific patient population,” Green said, noting that a broader approach is often too challenging due to time and cost constraints. 

With AI, instead of looking at a limited area, similar to what you might see under a single street light, Green said, you can illuminate the whole neighborhood.

The promise of artificial intelligence in the biotech and pharma sector is vast, from drug discovery to patient enrollment to clinical trial design. But the industry is also approaching the future with caution, and addressing the technology’s shortcomings is essential to making it a useful tool in the long run.

“Hallucinations” represent one of the challenges to widespread use of AI and machine learning. In a healthcare setting, the introduction of false information into the algorithms can be especially harmful, said Wael Salloum, co-founder and chief science officer at natural language processing company Mendel AI.

Currently, large language models are used to decipher patient records and guide physicians and other caretakers to the correct interventions, including drugs. If a patient is taking one drug, for example, the LLM will not push for a second treatment that can lead to complications. But when that output is designed to be trustworthy, false information is dangerous, Salloum said.

“What these LLMs are designed to do is produce really good English and convince you that’s the truth — when you’re summarizing a medical record for a doctor to make a decision, any potential mistake can be a matter of life and death,” Salloum said.

Hallucinations are defined by the more generalist LLM company ChatGPT as the “generation of content that is not based on real or existing data but is instead produced by a machine learning model's extrapolation or creative interpretation of its training data.” Many of these can be small in nature but represent bigger problems down the road, according to IBM — from the spread of misinformation to enabling bad actors to formulate a cyber attack.

Mendel’s platform, called Hypercube, combines an LLM with a logic-based AI to cut the rate of hallucinations in medical research even at large scale. The company this month joined a Google Cloud partnership giving pharmas and other healthcare companies access to the platform via the tech giant’s marketplace.

And while AI has become a massive game-changer in the life sciences, Salloum hopes platforms like Hypercube can improve the trust between AI and the researchers, scientists and doctors who use it.

“It’s very important that everything a system produces be explainable and traceable — and so many of the doctors we speak to say they spend more time verifying a summary from an original record than reading the record themselves,” Salloum said. “Any overhype of AI might cause damage to the whole industry, because once you lose trust, you don’t get it back.”

Building a trustworthy AI

Joining the Google Cloud marketplace is no accident — in truth, Hypercube works much like the Google search engine that has come to be so ubiquitous in many people’s online lives. Every time a user searches for something, the engine has already created an internal referential index rather than combing through the whole internet, Salloum said.

Similarly, Hypercube builds a knowledge base of millions of patient records that traces back to the original record, cutting the risk of hallucination. In this sense, Hypercube is more of a “research engine” than a search engine, Salloum said.

Like many AI applications, Hypercube works better when used as a tool.

“We are there to retrieve literature for review, but at the end of the day, you need human intelligence to lead, and you can use technology and AI as a support for humans, but not as a replacement,” Salloum said. “We’re nowhere close to that, and it’s important not to overhype.”

Many of Mendel’s customers are companies with data that’s ready to be used for scientific research but still too raw, Salloum said. Mendel has also helped pharma companies make connections between the data and the patient population.

Hypercube is also infused with a “world model,” or fundamental definitions within medicine that are almost philosophical in nature, Salloum said.

“What is medicine? What is a treatment? What is cancer? What is cell differentiation? These get distilled into the LLM until it’s forced to memorize that there is a certain structure that’s then fine-tuned with tasks,” Salloum said. “So the system itself is producing it and also justifying it.”

The larger purpose of AI in a healthcare setting is to provide access to information that might otherwise be out of reach, Salloum said. And making that information more trustworthy by mitigating the presence of hallucination is at the heart of that access.

“Our mission is to democratize healthcare by creating a centralized medical knowledge where everything we learn from a patient journey can be understood, from every success to every failure of a treatment,” Salloum said. “And the applications, ultimately, are infinite.”

AI’s ability to bring down the costs of drug development has allowed smaller companies to do much more with fewer resources. One 2023 study predicted AI-driven R&D efforts from discovery up to preclinical could create time and cost savings of at least 25% to 50%.

Small biotechs developing drugs can benefit from AI’s computing power and efficiency, says Panna Sharma, CEO of Lantern Pharma, a biotech that uses a proprietary AI platform to develop oncology drugs. 

The company currently has three lead drug candidates in early-stage clinical trials and an antibody-drug conjugate program in the preclinical stage. The company also collaborates with other biotech companies that want to use its AI technology platform Response Algorithm for Drug Positioning and Rescue (RADR), which aims to predict potential patient response to drugs. 

“If you look at the new chipsets that are being created, we're going to approach being able to do computations that [three years ago] probably would have cost $100,000 … and sucked up a month or two of machine time …  to [doing] that in a day [for] not even $1,000 to $2,000,” Sharma said. “It totally changes the ability for individual researchers and small companies to be meaningful developers. And this is going to keep changing. The curve only goes one way.”

As more pharmas and biotechs leverage AI platforms to develop drugs, some risks still loom large.

AI hurdles ahead

As many have long noted, AI and machine learning models are trained on available data sets, which can have gaps in patient demographics.

“There are risks that your data sets are incomplete, and we always worry about that,” Sharma said. “I think that's one of the biggest challenges — incomplete data sets that lead you to bad or incomplete conclusions.”

Incomplete or biased data sets are in the purview of the FDA, which noted its concerns last year regarding ethical considerations and generalizability of findings extrapolated outside testing environments with incomplete data.

Data also needs to reflect patient populations, which can be a challenge if data sets reflect a smaller group of specific patients, Sharma said. Companies may also be training AI algorithms on the data that is available to them, which may not reflect the true patient population.

“You have to question, is that the biology that I'm going to really see and target in their real world?” Sharma said.

Disease complexity

Another challenge in computational biology is the complexity of diseases, according to Sharma. AI models are not always trained on the specifics of diseases in cancer or neuroscience, for example, which can vary widely patient by patient.

“One of the biggest challenges I see as I talk to other AI companies is that you have a new generation of drug developers that don't appreciate the full complexity of the biology of their disease,” he said. “You can get a lot of junky early-stage molecules, to be honest.”

Relying too heavily on AI can lose the forest for the trees, he said.

“Sometimes a lot of AI [focus] tends to be too much on the software and data side and not enough on the complexities of the disease and biology side,” Sharma said. You're going to have a lot of people stub their toes in the AI drug development space as a result.”

As more drugs are developed using AI technology platforms, patient skepticism can also present a challenge and a risk to companies aiming to get into clinical trials. 

“There may be an era in which patients are going to ask questions about whether these compounds are from AI, and they may not feel good about taking AI-developed medicines,” Sharma said.

Given that risk, biotechs may have to assess how patients perceive AI drugs when enrolling in clinical trials. 

Meanwhile, the FDA is still determining how it will regulate AI in drug development and expects to release guidance on the topic. At the same time, the industry continues to increase its AI adoption, and the FDA received more than 100 submissions drug and biologic application submissions using AI or machine learning components in 2021. 

“[With] the pace at which AI and software is developing, we may, in certain instances, learn about the need for regulations later than we should have,” particularly for medical devices that may use AI, Sharma said.

But after development, there may be less of a need for regulations because the clinical trial and drug approval pathway still hold up as the gold standard of quality despite AI’s involvement, Sharma said.

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.”