June 19, 2023
Dr. Doyle comments on the use of predictive AI and ML models to support the development of cardiovascular disease treatments
Artificial intelligence and machine learning techniques are bringing renewed capabilities to cardiovascular disease treatments, according to Exponent Group Vice President and Principal Scientist John Doyle, Dr.P.H., M.P.H.
In a recent Pharmaceutical Commerce article, "Innovation in Heart Disease: A Deep Dive," Dr. Doyle discusses advances in diagnostic tools for cardiovascular diseases such as hypertension, atrial fibrillation (Afib)/arrhythmia, stroke, and heart failure, among others.
"In the near future, it will not just be some companies — it will be most companies — that develop analytic algorithms to support their drugs. I expect that we're going to see a lot more of that — developing predictive models based on AI and ML that are approved like a companion diagnostic — to support cardiovascular disease (CVD) drug development and market access," says Dr. Doyle, who also serves as an adjunct assistant professor at Columbia University's Mailman School of Public Health.
The feature story covers the recent boom in new agents and refined drug capabilities in cardiovascular and cardiometabolic care that target the molecular basis of heart disease.
"Drug developers must strive to connect medication adherence with tangible outcomes, such as reduced hospitalizations or cardiac events, and develop comparative clinical effectiveness and cost data." — John Doyle, Dr.P.H., M.P.H., Exponent Group Vice President & Principal Scientist
Pharmaceutical companies are pairing these new drug offerings with algorithmic tools and insights culled from sensor-based devices to identify those who may develop Afib and optimize benefit-risk of treatments. According to the article, cardiovascular medications often have low adherence among patients, so pharmaceutical companies and public health researchers are seeking to develop personalized interventions that increase compliance, improve long-term outcomes, and help justify value-based pricing and coverage decisions.
"To do this, drug developers must strive to connect medication adherence with tangible outcomes, such as reduced hospitalizations or cardiac events, and develop comparative clinical effectiveness and cost data," says Dr. Doyle, noting that sensor-derived insights can help more quickly identify patients at an elevated risk for Afib, particularly those in disadvantaged communities.
"While such findings are still subject to medical follow-up to verify and validate the finding, the opportunity to accelerate diagnosis is beneficial to patients, providers, payers, and manufacturers," adds Dr. Doyle.
Read the full publication here.
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