Human response to drugs learned to predict

Drug development takes a long time. From finding the right compound to FDA approval

 sanitary supervision of qualityFood and Drugs) could take more than ten years and cost a billion dollars. A research team at the City University of New York Graduate Center has created an artificial intelligence model that reduces the time and cost of the drug development process.

The model was called CODE-AE and it tests newdrug compounds to predict how they will affect people and their effectiveness. During the tests, scientists, albeit theoretically, found personalized medicines for more than 9,000 patients that would better treat the diseases of the subjects. The authors of the development hope that their method will significantly speed up the discovery of drugs and precision medicine.

Accurate and reliable reaction predictionA specific patient response to a new chemical compound is critical to the discovery of safe and effective therapeutics and the selection of an existing drug for a specific patient. However, early testing of the effectiveness of the drug directly on people is not possible.

As analogues, scientists use cellularor fabric models. The goal is to evaluate the therapeutic effect of a drug molecule. But the drug's effect in the model often does not correlate with the drug's efficacy and toxicity in human patients. This knowledge gap is a major factor in the high cost and low productivity of drug development.

Read more:

Living organisms have made Mars uninhabitable

The most powerful black hole collision in the universe proved Einstein's theory

The bones in the Midnight Terror Cave were carefully examined and found inexplicable footprints.