Israeli researchers develop AI-based software to track progression of ALS

Software will later be adapted to other neurodegenerative diseases, Ben-Gurion University technology transfer arm says; BGN Tech seeks partner to further develop the technology

Shoshanna Solomon is The Times of Israel's Startups and Business reporter

Prof. Boaz Lerner of Ben-Gurion University of the Negev has developed an AI-based software to monitor and predict the progression of neurodegenerative diseases. (Dani Machlis)
Prof. Boaz Lerner of Ben-Gurion University of the Negev has developed an AI-based software to monitor and predict the progression of neurodegenerative diseases. (Dani Machlis)

Researchers at Israel’s Ben-Gurion University of the Negev have developed a new, artificial-intelligence based software to monitor and predict the progression of neurodegenerative diseases, BGN Technologies, the technology transfer company of the university said.

The idea is to identify markers in diseases such amyotrophic lateral sclerosis (ALS), also known as Lou Gehrig’s disease, and other neurodegenerative diseases, such as Parkinson’s and Alzheimer’s, to help develop personalized patient care and improve drug development, BGN Technologies said in a statement.

The technology, developed by Prof. Boaz Lerner of the Department of Industrial Engineering and Management at BGU and his team, will initially focus on ALS and at a later stage will be adapted to various other neurodegenerative diseases. BGN Technologies has filed for a patent for the technology. The method was presented by the researchers at a number of scientific conferences, and now that the patent application has been filed, they are gearing up to publish their work in scientific journals, a spokeswoman for BGN Technologies said.

ALS is a motor neuron disease that causes the death of neurons controlling voluntary muscles and almost invariably progresses with time. It affects people of all races and backgrounds. In 2016, the Centers for Disease Control and Prevention, estimated that between 14,000 to 15,000 Americans have ALS.

Illustrative. A woman in a wheelchair. (Halfpoint; iStock by Getty Images)

Research and drug development of this condition are complicated by the heterogeneity of the ALS population leading to variety of  symptoms at the onset of the illness, in the disease progression rate and pattern, and survival.

“One of the big challenges of designing and managing clinical trials for ALS stems from the fact that not only is it a rare disease, but also the ALS population is very heterogeneous,” Lerner said in emailed comments to The Times of Israel. This makes it hard to identify common markers and to tailor specific treatments to all patients. “As a result, after decades of research, there is still no real cure for ALS and other neurodegenerative diseases, such as Alzheimer’s disease,” he said.

“For example, a certain treatment could suit some patients, while it may lead to side effects for patients of other groups. Similarly, another treatment will suit only another group of patients,” Lerner explained.

The new software is able to divide the ALS patient population to “small homogeneous sub-groups of patients that are similar to each other in some aspect. This can help to tailor a personalized treatment as well as to design and manage clinical trials for patients of a specific homogeneous sub-group.”

The platform analyzes demographic data such age and sex, together with clinical data such as vital signs (blood pressure and the location in the body in which the disease started), and lab test results, and then uses machine learning and data mining algorithms to identify factors that enable the software to produce models that can predict the rate and pattern of ALS progression, and stratify homogeneous sub-groups from the heterogeneous ALS population. As clinical data are added for each patient, the algorithms, and thus the disease progression prediction, improve, Lerner said.

This will improve patient care and quality of life and also help improve the design of clinical trials and the ability to assess the influence of treatment in clinical studies by identifying markers of various patient sub-populations for which treatment is beneficial, thus improving success rate of the studies, he said.

“ALS lacks a cure, and our system could help in expediting the clinical trials needed to develop a drug, reducing its cost, and making it targeted to specific patient populations,” Lerner said. “Our platform can also improve the quality of life of patients and caregivers. If we can predict, for example, that the patient’s walking or speech ability will deteriorate in six months, he or she can organize the home to address their needs or move to a more appropriate environment, or start looking for a specific device to communicate with people. This will also enable physicians to know where to begin specific treatment, whether and when to focus on the respiratory system or physiotherapy.”

The researchers have received funding from the Israel Innovation Authority that will enable them to create a system that can be implemented on PCs, the cloud, and cellular applications for the personalized monitoring and prediction of ALS progression for patients, caregivers and health organizations to use, said Itzik Mashiach, in charge of business development at BGN Technologies in a statement.

“We are now seeking an industry partner for the further development and commercialization of this innovative patent-pending technology.”

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