Personalized regimens and medication dosing
Our approach to medication dosing is about to change.
Some quick thoughts about personalization of regimens and medication optimization:
Historically, we've mostly studied and prescribed medications for their average effects in a population, rather than how they perform in a given individual in daily life.
Even with clinical guidelines that encourage adjustment to achieve intended outcomes, few people ever see their regimen optimized once a medication is prescribed.
In asthma, for example, as few as 10 percent had their dosage of inhaled steroids reduced despite a year of stable control. See this ERJ paper, for example.
But we're heading toward a lot more variability and optimization in dosing. Often this will mean less medication and fewer side effects to achieve similar outcomes.
We can expect much more personalization of medication regimens, like these results announced at ASCO, in which some piece of technology determined chemotherapy doses as much as 20 percent lower.
We don't necessarily need new technology (eg AI/ML models) to do this.
In respiratory, we just need simple assessments of symptom frequency and a feedback system to adjust dosing until control is achieved.
The trick will be how to adjust treatment without adding hassle and uncertainty to the experience of the disease and its therapy.
And, whether we can create positive effects on adherence by encouraging the perception that one is now getting the ideal, personal dose.