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A novel artificial intelligence model could significantly improve the accuracy and reduce the time and cost of the drug development process.
Between identifying a potential therapeutic compound and U. S. Food and Drug Administration (FDA) approval of a new drug is an arduous journey that can take well over a decade and cost upwards of a billion dollars. A team of researchers at the CUNY Graduate Center has developed a novel artificial intelligence model that could significantly improve the
Accurate and robust prediction of patient-specific responses to a new chemical compound is critical to discovering safe and effective therapeutics and selecting an existing drug for a specific patient. However, it is unethical and infeasible to do early efficacy testing of a drug in humans directly. Cell or tissue models are often used as a surrogate of the human body to evaluate the therapeutic effect of a drug molecule. Unfortunately, the drug effect in a disease model often does not correlate with the drug efficacy and toxicity in human patients. This knowledge gap is a major factor in the high costs and low productivity rates of drug discovery.
An illustration of personalized drug responses. Credit: CODE-AE illustration
“Our new machine learning model can address the translational challenge from disease models to humans,” said Lei Xie, a professor of computer science, biology and biochemistry at the CUNY Graduate Center and Hunter College and the paper’s senior author. “CODE-AE uses biology-inspired design and takes advantage of several recent advances in machine learning. For example, one of its components uses similar techniques in Deepfake image generation.”
The new model can provide a workaround to the problem of having sufficient patient data to train a generalized machine learning model, said You Wu, a CUNY Graduate Center Ph.D. student and co-author of the paper. “Although many methods have been developed to utilize cell-line screens for predicting clinical responses, their performances are unreliable due to data incongruity and discrepancies,” Wu said. “CODE-AE can extract intrinsic biological signals masked by noise and confounding factors and effectively alleviated the data-discrepancy problem.”
As a result, CODE-AE significantly improves accuracy and robustness over state-of-the-art methods in predicting patient-specific drug responses purely from cell-line compound screens.
The research team’s next challenge in advancing the technology’s use in drug discovery is developing a way for CODE-AE to reliably predict the effect of a new drug’s concentration and metabolization in human bodies. The researchers also noted that the AI model could potentially be tweaked to accurately predict the human side effects of drugs.
Reference: “A Context-aware Deconfounding Autoencoder for Robust Prediction of Personalized Clinical Drug Response From Cell Line Compound Screening” 17 October 2022, Nature Machine Intelligence.
DOI: 10.1038/s42256-022-00541-0
This work was supported by the National Institute of General Medical Sciences and the National Institute on Aging.
A novel artificial intelligence model could significantly improve the accuracy and reduce the time and cost of the drug development process.
Between identifying a potential therapeutic compound and U. S. Food and Drug Administration (FDA) approval of a new drug is an arduous journey that can take well over a decade and cost upwards of a billion dollars. A team of researchers at the CUNY Graduate Center has developed a novel artificial intelligence model that could significantly improve the
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