Stochastic modeling of medication adherence

Medication adherence is the process by which patients take their medications as prescribed. Medication nonadherence is a major problem, resulting in over 100,000 preventable deaths and more than 100 billion dollars in preventable healthcare costs every year in the United States. Many fundamental and clinically important questions surround nonadherence, including (1) why some drugs are "forgiving," in that they maintain efficacy despite nonadherence (missed doses, late doses, etc.) whereas other drugs are decidedly not forgiving, and (2) how the deleterious effects of nonadherence can be mitigated by designing robust dosing regimens and identifying remedial protocols for whether patients should skip or take late doses of medication. This project uses mathematical modeling and analysis to answer these questions. This project has strong potential to improve societal well-being via improved pharmacotherapy outcomes. These improvements will arise both from nonadherence mitigation for existing medications and a new quantitative understanding of drug forgiveness allowing for identification and design of robust medications. This project will develop the STEM workforce through training junior researchers. Medication nonadherence is challenging to study because (a) clinical trials that force patients to skip doses may be unethical, (b) nonadherence is erratic (i.e. patients do not miss doses in regular, deterministic patterns), and (c) there are many competing factors to consider (adherence rates, dose timing, drug absorption, drug half-life, etc.), and it is difficult to disentangle their individual contributions. For these reasons, mathematical modeling is well suited to study medication nonadherence. Importantly, there is an extensive literature of published, empirically parameterized and validated mathematical models of how specific drugs move through and affect the body (pharmacokinetic and pharmacodynamic models). This research takes these established models and subjects them to stochastic drug input. This stochastic drug input models medication nonadherence, such as randomly missed doses or random late doses. Quantifying drug forgiveness, designing dosing regimens, and evaluating remedial protocols thus involves determining how this stochastic input flows through the deterministic dynamical system. For instance, understanding drug forgiveness requires quantifying how the topology and kinetics of a dynamical system can either dampen or magnify stochastic inputs. From the standpoint of biology and biotechnology, this project transforms the understanding of medication nonadherence and offer innovative methods to alleviate this persistent public health problem. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. NSF Award ID: 2602237 | Program: 01002627DB NSF RESEARCH & RELATED ACTIVIT | Principal Investigator: Sean Lawley | Institution: University of Utah, SALT LAKE CITY, UT | Award Amount: $385,147 View on NSF Award Search: https://www.nsf.gov/awardsearch/show-award/?AWD_ID=2602237 View on Research.gov: https://www.research.gov/awardapi-service/v1/awards/2602237.html