Quantified ‘immune age’ could transform drug development, disease management: CytoReason

By Melissa Fassbender

- Last updated on GMT

(Image: Getty/Danor_a)
(Image: Getty/Danor_a)

Related tags Drug development Clinical trials Natural Language Processing Vaccine

Researchers describe a technique to quantify a person’s “immune age,” information which could help design and analyze clinical trials – improving the drug development process, among other “impressive” possibilities, says industry expert.

“The immune system varies greatly between individuals – especially with age – but there was no clear understanding of how it varies, that is, if there was some regularity to it or not,”​ said Professor Shai Shen-Orr, head of systems immunology and precision medicine lab at Technion and co-founder and chief scientist at CytoReason.

“It was clear that identifying metrics of immune health may provide significant benefits for general disease prediction and management, and also in the way we go about developing new therapies and vaccines.”

CytoReason’s technology uses a proprietary data and machine learning model to reconstruct cellular information from bulk tissue, train an immune-specific natural language processing (NLP) engine, and integrate multi-omics data, according to the company.

Using this technology, Scientists from CytoReason, Technion, and Stanford recently published data in Nature Medicine​ describing a technique to quantify a person’s “immune age.”

Shen-Orr told us one’s immune age could in the future be used to better inform drug and vaccine development, “both of which are key contributors to improved health long-term.”

The researchers followed a group of healthy volunteers both young and old for nine years, tracking changes to their immune system through annual blood samples which were profiled against several ‘omics technologies. This included cell subset phenotyping, functional responses of cells to cytokine stimulations, and whole blood gene expression.

The data was analyzed using a suite of statistical and machine learning algorithms, said Shen-Orr, and was validated against a cohort of more than 2,000 patients from the Framingham Heart Study​.

Shen-Orr said the researchers are presently able to calculate immune age in one of two ways: “Via cell-subset composition nearest neighbor approach; or based on a gene expression signature where the genes are predictive of the cell-subsets composition and we test for their enrichment in the gene expression pattern of the sample.”

“We do so, by using the rich immune profiles of individuals to construct a trajectory of immune changes based on a semi-supervised machine learning methodology deployed on cell-subset data,”​ he explained.

According to the researchers, the data to date suggests a high-immune age increases the risk of cardiovascular disease and the overall chances of dying. Even when taking all regular clinical co-variates into account, Shen-Orr said the age is especially predictive in the first 500 days post-assessment.

“What this means is that knowing a person’s immune age in the context of their overall medical picture enables a more individual preventative care and health management program to be put in place with the likelihood of better health outcomes,”​ he explained.

A potential paradigm shift in drug development?

Fueled by advances in technology and science, Shen-Orr said we now have the potential for a blood-based metric for monitoring immune health in healthy adults.

“We also understand this is a huge driver of variation in the data and hence needs to be taken into account when designing and analyzing clinical trials,”​ he said, and the researchers believe this will help “unmask stronger or previously unseen signals.”

All of this could have significant implications for clinical trial design and patient selection, Shen-Orr noted.

“The aim of a clinical trial is to demonstrate efficacy and safety of a given drug in a given population – you do not need to try and prove it is a panacea. So, if you can tailor the inclusion and exclusion of certain populations for a given trial you can increase the likelihood of success for that trial.”

For all those concerned, Shen-Orr said this is good news and will reduce the inherent risks of clinical trials, to both patients and developers. It also could accelerate the time to market for new therapies.

“More specifically, at least for immunotherapies, it would suggest that ones' response would be dependent on one's immune age, which may open new research and development directions that could lead to more tailored treatment,”​ he added.

While the data is new and more development is needed, Shen-Orr said: “the possibilities are impressive.”

The researchers are currently exploring the implications of immune-age in disease conditions and special patient populations, as well as looking at a way to reduce the cost of testing, making it more easily available in the clinic.

Looking to the future, Shen-Orr described the “holy grail”​ as finding ways to reverse immune-aging and “giving people a better chance of healthier longevity.”

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