26 November 2024
Aitia Bio’s Colin Hill, writing in the November 2024 issue of DIA’s Global Forum magazine outlines why “digital twins” – virtual models of patients, systems, or devices that simulate real-world data to predict outcomes, optimize treatments, and improve patient care – have the potential to reshape the entire drug development process.
Few things in medicine seem crueler than giving a placebo to a patient in need. Yet despite ethical concerns, the so-called control arm has been the cornerstone of medical advancement since the advent of modern clinical trials in the 1950s. Without it, the scientific community, patients, and regulatory agencies will lack the data to know whether a new treatment truly outperforms a placebo or existing therapies.
Beyond the ethical concerns, practical matters have also caused problems. The latest wave of new treatments, cell and gene therapies, promise incredible advancements for patients, but restrictions on where these medicines can be administered limit their availability to large medical centers in major cities, making clinical trial recruitment more difficult. Not to mention the struggles to achieve diverse patient participation, an issue the US Food and Drug Administration (FDA) is focusing on with new guidelines.
It’s fortuitous then that technology has advanced to virtual representations of patients, or digital twins, that can be used across all aspects of drug development, from discovery of new drug candidates to clinical trial testing. By utilizing newly generated genomic, proteomic, and clinical data, digital twins are being used to improve success rates in translating research results into viable drug targets, reduce the number of people needed in clinical trials, and replace placebo-controlled cohorts with virtual representations of humans.
The Evolution of Digital Twins, from Moonshots to Molecules
Digital twins aren’t new: NASA conceived the idea in the 1960s and used a “living model” to help astronauts on the Apollo 13 mission after its oxygen tank explosion damaged the main engine. And manufacturers have used digital twins to model their own experiments and ideas with physical products. For instance, Unilever used digital twins in its soap and detergent to reduce false alerts that require attention by 90%. In China, Citic Heavy Industries used digital twins and 3D modeling to predict equipment failures and provide operations and maintenance solutions for its cement business, saving more than 30% in costs. KINEXON achieved a 5% increase in their automotive assembly line speed and significantly reduced manual errors and product recalls.
But in healthcare and biomedicine, digital twins have only recently become possible due to the rapid decline of the cost to sequence a person’s DNA—from the $2.7 billion that the Human Genome Project spent by 2003, to Illumina’s $200 price in 2023, combined with the rapid, low-cost generation of high-throughput molecular data measuring the activity of each gene product (multi-omic data), and the exponential rise in computing power. With more genetic and multi-omic data available and generative causal AI running on supercomputers able to rapidly sift through the information, scientists now can experiment in silico (in the computer) before in vivo (in a living animal organism, typically mice) and prior to clinical trials (in humans). And unlike real-world experiments that can take years to determine whether a single experimental drug is safe and effective, with digital twins, researchers can test billions of ideas in hours and days.
Regulatory agencies are working towards adopting new policies and guidelines to allow the use of digital twins in drug development and clinical trials. The FDA released a document detailing current and future uses of the technology, saying they have the potential “to enhance drug development in many ways, including to help bring safe and effective drugs to patients faster; provide broader access to drugs and thereby improve health equity.” In addition, the agency said digital twins “could potentially provide a comprehensive, longitudinal, and computationally generated clinical record that describes what may have happened to that specific participant if they had received a placebo.”
The European Medicines Agency (EMA) has also begun exploring and implementing frameworks that incorporate digital twins, starting with their broader “AI Action Plan,” and a favorable qualification opinion for the application of digital twins in phase 2 and 3 clinical trials. The EMA’s approval marks the first time a regulatory body has endorsed a machine learning-based approach for pivotal trials.
Collaborations between pharmaceutical companies and artificial intelligence (AI) firms are already yielding promising results. Unlearn, an AI company, partnered with Johnson & Johnson to show that digital twins could reduce control arm sizes by up to 33% in phase 3 Alzheimer’s trials. And clinical development data analytics company Phesi demonstrated in June that AI-powered digital twins could replace standard-of-care control arms in trials for chronic graft-versus-host disease.
“Digital twin technology allows sponsors to ‘meet the patients’ before starting a clinical trial; eliminate costly protocol amendments through better alignment between trial design and the target patient population; and improve commercial viability,” wrote Gen Li, PhD, MBA, CEO and founder of Phesi, in a September 2024 article in Applied Clinical Trials.
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