Recently, there has been a lot of talk about how artificial intelligence and machine learning would change pharmaceutical research. The new technology has allowed significant progress in the development and identification of new medications. Now, technological advancements are reviving the clinical testing process. With a recent discussion on AI/ML in medication research, the US Food and Medication Administration (FDA) is laying the regulatory foundation.
According to the McKinsey Global Institute, rapid breakthroughs in AI-guided automation will transform how scientists find new medications in the laboratory. Generative AI drug discovery opportunities are the next frontier. AI will be utilized to enhance clinical trial design, management, and outcomes, allowing for better resource use while also offering more accurate findings.
Artificial Intelligence in the Clinic
AI is being suggested in a variety of methods to enhance clinical trials and medication development. According to a study of digital innovation in the life sciences, 76% of respondents are now investing in AI for clinical research. AI-enabled data gathering and management may shorten the time and effort necessary for clinical trials, speed up medication development, and assist firms in bringing innovative therapies to market more rapidly.
Artificial intelligence (AI) technology may be used to generate organized, standardized, and digitized data pieces from a variety of inputs and sources. These tools can process large amounts of data, feed downstream operating systems, and assist in populating essential reports and analyses.
- Protocol creation — AI-enabled study design might aid in the optimization and expediting of suitable targeted clinical trial procedures. AI algorithms can examine past clinical trial data to discover possible protocol improvement areas, such as choosing optimal objectives, sample sizes, and research lengths. Researchers may design more efficient and informative experiments by harnessing AI’s capacity to interpret complicated data.
- Data gathering — AI might aid in the development of new methods for gathering trial data and minimizing the need for patients to attend medical locations. Body sensors and wearable devices, such as wristbands, heart monitors, patches, and sensor-enabled clothes, may monitor vital signs and other data from the comfort of the patient’s home.
- Patient screening — AI-powered algorithms may aid in determining whether individuals are suitable for clinical trials based on their particular personal medical traits and alignment with trial enrollment requirements. Patients with subtle combinations of symptoms may be detected and diagnosed early, and alternatives for therapeutic trials may be offered. AI/ML can mine massive volumes of data, such as clinical trial databases, announcements, medical literature, registries, and organized and unstructured data in EHRs, to match people to trials.
- Dosing — AI and machine learning may be used to describe and predict pharmacokinetic (PK) patterns after medication delivery. It may also be used to investigate the link between drug exposure and response.
- Monitoring and real-time safety — AI-powered solutions may provide real-time insights into patients’ health states and probable adverse responses during clinical trials by continuously monitoring them. This may assist in guaranteeing participant safety and prompt action when necessary.
These are just some of the key applications of AI in the medical sector. And we expect that the industry will develop fast!
AI Developing the Industry
Investors are taking note of AI-enabled drug development, which has piqued the interest of scientists. Morgan Stanley estimates that “modest improvements in drug development success rates promoted by the application of AI and machine learning” might result in an extra 50 innovative medicines over a 10-year period, representing a more than $50 billion potential. Others seem to concur, with third-party investment in AI-enabled drug development more than tripling yearly for five years and expected to exceed $5.2 billion by the end of 2023. From February 2020 to April 2021, a number of players have raised their budgets to pursue their AI-driven drug discovery pipelines, including Schrödinger, AbCellera, Insitro, Relay Therapeutics, Atomwise, XtalPi, ExScientia, and Recursion Pharmaceuticals.
If present trends develop in the future, it will only be a matter of time until the pharmaceuticals we consume are created by robots rather than humans. AI-enabled drug discovery; it also has enormous potential to boost medicine accessibility and cure currently incurable illnesses. It does, however, open the door to a slew of unsolved concerns, like intellectual property rights, the potential of technical abuse, and the continuous guarantee of medicine safety and effectiveness in this new age.
