Artificial intelligence develops treatment for liver cancer in 30 days
Artificial intelligence could be the cutting edge technology for cancer treatment of the future, which can be applied in oncology clinics around the world, reports New York Post.
In a new study published in the journal Chemical Science, University of Toronto researchers partnered with Insilico Medicine to develop a potential treatment for hepatocellular carcinoma (HCC) using an AI drug development platform called Pharma.AI.
According to the Cleveland Clinic, HCC is the most common type of liver cancer and occurs when a tumor grows in the liver.
The researchers applied AlphaFold, an AI-based protein structure database, to Pharma.AI to uncover a new target—a previously unknown treatment route—and developed a “new molecule” that could bind to that target unaided.
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The creation of a potential drug was completed in just 30 days from the moment the target was chosen and after the synthesis of only seven compounds.
After a second round of compounding, they found a more powerful molecule, but any potential drug must pass clinical trials before being widely used.
“While the world has been fascinated by advances in generative AI in art and language, our AI algorithms have succeeded in developing powerful target inhibitors with an AlphaFold-derived structure,” Alexey Zhavoronkov, founder and CEO of Insilico Medicine, said in a statement.
AI is rapidly changing the way drugs are discovered and developed, as traditional trial and error is slow, expensive, and limits the scope of research.
“This work is further evidence of AI's ability to transform the drug discovery process with increased speed, efficiency, and accuracy,” said Michael Levitt, Nobel Laureate in Chemistry. “By combining the predictive power of AlphaFold and the targeted power and drug development capabilities of the Pharma.AI Insilico Medicine platform, we can imagine that we are on the cusp of a new era of AI-assisted drug discovery.”
In 2022, AlphaFold made a huge breakthrough in both artificial intelligence and structural biology by predicting the protein structure for the entire human genome.
"AlphaFold has opened new scientific horizons in predicting the structure of all proteins in the human body," said co-author Feng Ren, chief scientist and co-CEO of Insilico Medicine.
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“At Insilico Medicine, we saw this as an incredible opportunity to take these structures and apply them to our end-to-end AI platform to create new therapeutics for high-demand diseases. This article is an important first step in that direction,” he said.
The researchers also explained how various AI information could revolutionize healthcare.
“This article demonstrates that for healthcare, AI developments are more than the sum of their parts,” said Alan Aspuru-Guzik, professor of chemistry and computer science. “If we use a generative model that targets a protein derived from artificial intelligence, we can significantly expand the range of diseases that we can influence. If we add unmanned laboratories to this, we will find ourselves in uncharted territory.”
A separate study published in the journal JAMA Network Open found that an artificial intelligence system invented by scientists at the University of British Columbia is able to predict the survival of cancer patients using doctors' records. The model uses Natural Language Processing (NLP), which is a piece of AI capable of understanding complex human language.
NLP can analyze doctors' records after the initial visit and identify individual characteristics specifically for each patient.
She was able to predict six-month, 36-month, and 60-month survival rates with over 80% accuracy. This model can also provide indicators for all types of cancer, while previous models could only be applied to certain types of cancer.
"The AI essentially reads the consultation document in the same way that a human would read it," lead author Dr. John-Jose Nunez, a psychiatrist and researcher at UBC's Center for Mood Disorders and Cancer, said in a statement. “These documents have a lot of details, such as the age of the patient, type of cancer, underlying health conditions, past substance use, and family history. AI brings it all together to paint a more complete picture of patient outcomes.”
Cancer survival rates are traditionally calculated retrospectively and classified by only a few general factors such as tissue type and cancer location.
“Because the model is trained on cancer data, this makes it a potentially powerful tool for predicting cancer survival here in the province,” Nunes said.
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“The great thing about NLP neural models is that they are highly scalable, portable, and don’t require structured datasets,” he added. “We can quickly train these models using local data to improve performance in a new region. I suspect that these models provide a good base anywhere in the world where patients can go to an oncologist.”
“Predicting cancer survival is an important factor that can be used to improve cancer treatment,” Nunes said. – He may suggest that health care providers refer the patient to support earlier or offer a more aggressive treatment option in advance. We hope that such a tool can be used to personalize and optimize the treatment that the patient receives immediately, providing him with the best possible result.”
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