The new generation of technology delivers enormous health benefits, much more than body monitoring technologies, the arduousness of drug development would soon be less tasking as the City University of New York Graduate research center creates an Artificial Intelligence model (CODE-AE) that can accurately predict human responses to new drug compounds.
The conventional approach to finding the most suitable therapeutic active pharmaceutical ingredient that meets the standard of the relevant agencies takes quite a lot of time, sometimes taking over a decade and costing billions of dollars.
CODE-AE has been proven reliable in predicting drug efficacy in humans – this was asserted in the Nature Machine Intelligence review. CODE-AE was also used to predict specific medications for more than 9,000 patients with different ailments.
With this, researchers are optimistic about technologically based medicine precision and prescription for quality medication development. While it may be praiseworthy for an accurate and detailed response to a new chemical molecule, it is unethical and counterproductive to directly study drug efficacy on humans.
On the other hand, though the therapeutic effect can be assessed by utilizing cell or tissue models, it is well known that the therapeutic effect in a disease model may not coincide with its efficacy and toxicity in reality.
This, therefore, is the reason for the high costs and low production rates of drug development. Hence, the new technology could solve the problems of long time experimentation and the high cost of medication and drug development.
Despite the numerous methods adopted to predict a concise clinical response, it has ended in a low performance due to data mismatch. However, CODE-AE has the capacity to extract intrinsic bio-signals to generate data, thus solving the problem of data – discrepancies.
CODE-AE is a significant milestone in health technology, as it delivers yet another health advantage, one would anticipate that soon there would be a surge of medical treatment for most incurable illnesses. If convincingly so, then what is your next innovative expectation?
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Photo by Ramón Salinero
