Making sure therapeutic drugs work as intended means making sure each patient receives the right dose of the right medicine at the right time.
Now multiply that simple-sounding formula by the number of drugs and the number of patients, and it quickly becomes clear why, as one expert recently put it, so-called adverse drug events (ADEs) “are some of the costliest medical complications and add more than $30 billion annually to the U.S. healthcare system.”
In a recent commentary, Sirj Goswami, CEO and co-founder of InsightRX, which provides a cloud-based precision dosing platform, described how AI is increasingly being incorporated into clinical decision platforms aimed at optimizing drug treatment.
Precision dosing, he explained, “is defined as the process of individualizing medication doses by taking into account patient-specific factors such as demographics, clinical characteristics, and genetic data.”
With the goal being to understand a patient’s particular pharmacological profile, “modern precision dosing support platforms employ pharmacology models and machine learning, operating on patient-specific data.”
Increasingly, precision dosing is applied to numerous medications and is improving patient outcomes by ensuring the patient receives the maximum effective dose while reducing the likelihood of an ADE.
One big reason for ADE’s, Goswami said, is because of the fact that “drugs are typically developed with the average patient in mind. Drugs are often studied in a few thousand patients during a clinical development program. As a result, many types of patients are not adequately studied in clinical trials, including geriatric patients, pediatric patients, and those who have end organ dysfunction. This has downstream implications since once the drug is approved, it is typically administered more broadly in patient populations not well characterized in clinical trials.”
Which can lead to a higher risk of poor clinical outcomes. Indeed, Goswami added, according to the FDA “medications across a range of therapeutic areas are only effective in 25% to 62% of patients.”
The key, he argued, is precision dosing, which can be particularly beneficial “for drugs with a narrow therapeutic window (i.e., drugs with a toxic dose that is very close to the minimum dose needed for the drug to be effective),” as well as “for drugs with high inter-patient variability in drug response.”
The upshot, Goswami noted, is that AI and other emerging technologies are bringing exciting changes to hospitals and health systems as hospital physicians and pharmacists increasingly roll precision dosing into their clinical practices to ensure patients receive the right dose of complicated medications, from intravenous antibiotics to chemotherapy.
Looking forward, the “use of more accurate, population-specific precision dosing models will help organizations improve care quality and reduce costs as they increasingly transition to value-based care and risk-based contracting.”
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