Artificial intelligence has developed a treatment for an aggressive form of cancer in just 30 days and has shown it can predict a patient’s survival rate using doctors’ notes.
The advances were made by separate systems, but they show how the uses of the powerful technology go far beyond generating images and text.
Researchers at the University of Toronto worked with Insilico Medicine to develop a potential treatment for hepatocellular carcinoma (HCC) using an AI drug discovery platform called Pharma.
HCC is a form of liver cancer, but the AI discovered a previously unknown treatment pathway and engineered a ‘novel hit molecule’ that could bind to that target.
The system, which can also predict survival rate, is the brainchild of scientists at the University of British Columbia and BC Cancer, who found the model to be 80 percent accurate.
AI developed the cancer treatment (stock) in just 30 days from targeting and after synthesizing only seven compounds
AI is becoming the new weapon against deadly diseases, as the technology is capable of analyzing vast amounts of data, discovering patterns and relationships, and predicting the effects of treatments.
Insilico Medicine founder and CEO Alex Zhavoronkov said in a statement: “While the world was fascinated with advances in generative AI in art and language, our generative AI algorithms succeeded in designing powerful inhibitors of a target with a structure derived from AlphaFold.”
The team used AlphaFold, an artificial intelligence (AI)-powered database of protein structures, to design and synthesize a potential drug to treat hepatocellular carcinoma (HCC), the most common type of primary liver cancer.
The feat was accomplished in just 30 days from target selection and after synthesizing just seven compounds.
In a second round of AI-powered compound generation, the researchers discovered a more potent hit molecule, though any potential drug would still have to undergo clinical trials.
Feng Ren, Chief Scientific Officer and Co-CEO of Insilico Medicine, said: ‘AlphaFold broke new scientific ground by predicting the structure of all proteins in the human body.
“At Insilico Medicine, we saw it as an incredible opportunity to take these frameworks and apply them to our end-to-end AI platform to generate novel therapies to address diseases with high unmet need. This document is an important first step in that direction.’
Another AI system identified unique characteristics of each patient, predicting survival at six months, 36 months, and 60 months with greater than 80% accuracy.
The system used to predict life expectancy used natural language processing (NLP), a branch of AI that understands complex human language, to analyze an oncologist’s notes after a patient’s initial consultation visit.
The model identified unique characteristics for each patient, predicting survival at six months, 36 months and 60 months with greater than 80 percent accuracy.
John-Jose Nunez, a psychiatrist and clinical research fellow at the UBC Center for Mood Disorders and BC Cancer, said in a statement: ‘The AI essentially reads the query document as a human would.
‘These documents have many details such as the patient’s age, type of cancer, underlying health conditions, past substance use and family history.
“AI combines all of this to paint a complete picture of patient outcomes.”
Traditionally, cancer survival rates have been calculated retrospectively and categorized by only a few generic factors, such as cancer site and tissue type.
The model, however, can capture unique clues within a patient’s initial consultation document to provide a more nuanced assessment.
The AI was trained and tested using data from 47,625 patients at BC Cancer’s six sites located in British Columbia.
“Because the model is trained on BC data, that makes it a potentially powerful tool for predicting cancer survival in the province,” Núñez said.
“(But) the great thing about NLP neural models is that they are highly scalable, portable, and don’t require structured data sets. We can quickly train these models using local data to improve performance in a new region.”
