Unlearn to use APST clinical data to create ‘digital twins’ of ALS patients
Clinical information from more than 8,000 patients will be used
The technology company Unlearn is teaming up with APST Research to create “digital twins” to support clinical trials for amyotrophic lateral sclerosis (ALS).
Clinical data from more than 8,000 ALS patients in APST’s database will be fed into Unlearn’s Digital Twin Generator (DTG), an artificial intelligence (AI) platform that creates digital duplicates of each patient.
Digital twins support clinical trials from design and planning to execution and analysis. By using large volumes of completed clinical data to train machine learning models, the company can predict at the individual level the digital twins’ outcomes if they were given the control treatment. This can increase statistical power with smaller control groups than traditional trial designs, identify subgroups with different treatment responses, and add simulated comparator groups.
“Our ability to improve on and deliver digital twins of clinical trial participants relies on the quality of our data,” Steve Herne, CEO of Unlearn, said in a company press release that was sent to ALS News Today. “This collaboration with APST not only establishes a long-term partnership but also equips us with the most robust ALS dataset available. Together, we’re advancing the fight against ALS by significantly reducing research timelines.”
ALS is a progressive neurological disorder marked by the loss of motor neurons, the specialized nerve cells that control muscle movement, speaking, swallowing, and breathing. The hallmark ALS symptom is muscle weakness, which usually first affects one part of the body then tends to spread and become more severe. Patients lose the ability to perform everyday tasks early on, but paralysis can occur over time and affect the ability to breathe.
While therapies are available that can slow disease progression and extend survival, their benefits are limited and more research is needed to improve the long-term outlook for people with the disease.
Utilizing the ALS dataset
Collaborating with ALS centers in Europe, APST has access to a community of more than 8,000 people with ALS who share their clinical data.
APST’s dataset includes standard ALS clinical assessments, such as the ALS Functional Rating Scale Revised (ALSFRS-R), lung function, and muscle strength. It also features patient self-assessments and biomarker analyses that includes neurofilament light chain (NfL), an indicator of nerve damage commonly used to monitor ALS progression and treatment response.
The dataset also records individuals’ ALS progression rates and holds real-world healthcare data on symptomatic drug treatments, assistive technology devices, nutritional support, and ventilation therapies.
“This collaboration marks the first time APST’s dataset has been licensed, creating a unique opportunity to positively impact ALS clinical trials,” said Thomas Meyer, MD, founder of APST. “By working with Unlearn, we can accelerate research projects and clinical trials, bringing us closer to a holistic understanding of ALS and potential breakthroughs for people with ALS.”
In addition to integrating APST data into the DTG, Unlearn and APST plan to publish studies that report on advancements in digital twin technology and its applications. Unlearn will also provide APST with digital twins of study participants to support further research and enhance the dataset’s utility.
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