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New artificial intelligence (AI) and deep learning-assisted projects aim to accelerate the multiple stages of the drug research and development (R&D) process by using drug repurposing to enable treatments to be approved faster. The Defense Threat Reduction Agency’s (DTRA) Chemical and Biological Technologies Department in its role as the Joint Science and Technology Office (JSTO) for the Chemical and Biological Defense Program is investing in research at the Massachusetts Institute of Technology (MIT) to develop new AI and deep-learning technologies capable of automating scientific data analysis and interpretations in the early stages of drug discovery.
Recently, the U.S. Government supported accelerated COVID-19 therapeutic drug trials, and investigators began a rigorous selection process of the vast number of candidate drugs with potential benefits. Scientists have made significant progress in treating presymptomatic and early stage COVID-19 cases using repurposed drugs, such as molnupiravir that DTRA-JSTO had invested in for development as a Venezuelan equine encephalitis virus treatment; however, more advanced tools are needed to prioritize drugs and predict repurposed drug combinations that can be beneficial across all disease stages.
Advanced AI combined with deep learning will automate repurposed drug prioritization with increasing sophistication and reduce the need for hands-on, time-consuming experimentation. The drug-development process normally lasts years, and sometimes decades, beginning with a discovery phase, progressing to R&D, testing in human clinical trials, and then achieving FDA approval if the drug is shown to be safe and effective. The ongoing COVID-19 pandemic illustrates how critical it is to reduce timelines at each stage of drug-development from decades or years to months or weeks.
For medical personnel, an indication—a sign, symptom, or medical condition—leads to their recommendation for a treatment, test or procedure. Researchers explore drug repurposing to discover new disease indications for pharmaceuticals that have already been tested in clinical trials, which enables them to skip most of the safety and toxicity testing already completed. The MIT research team has gone a step further than others in this field by developing a new AI-assisted computerized approach for discovering drugs that work together with increased effectiveness, or synergism, to alleviate COVID-19 symptoms. They described their approach in a recent article, “Deep learning identifies synergistic drug combinations for treating COVID-19.”
Synergistic drug combinations are frequently used to treat many diseases, including cancers, asthma, diabetes, hypertension, cardiovascular disease, and some infectious diseases like HIV, viral hepatitis, and tuberculosis. The “one bug, one drug” development approach has a low historical success rate for treating infectious diseases. By contrast, discovering synergistic drugs that act on the infected human, rather than just on the bug, has potential for treating more than one infectious disease.
Synergistic therapies have many advantages and can:
| Date Taken: | 05.13.2022 |
| Date Posted: | 05.13.2022 16:48 |
| Story ID: | 420715 |
| Location: | FT. BELVOIR, VIRGINIA, US |
| Web Views: | 311 |
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