The new AI model CODE-AE from CUNY researchers aims to improve drug discovery by accurately predicting human responses to drugs based on cell models.
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 accuracy and reduce the time and cost of the drug development process.
As described in a paper to be published today (October 17) in Nature Machine Intelligence, the new model, called CODE-AE, can screen novel drug compounds to accurately predict efficacy in humans. In tests, it was also able to theoretically identify personalized drugs for over 9,000 patients that could better treat their conditions. Scientists expect the technique to significantly accelerate drug discovery and precision medicine.
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.
Biology-Inspired Design and Machine Learning Innovations
“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” by Di He, Qiao Liu, You Wu and Lei Xie, 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.
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