Asprin Dosing Estimator in Healthy Adults

Overview

Understanding sources of variability in human drug dosing is important to the beneficial and safe use of any drug. Understanding and applying the science of individualizing a drug dose to a patient is called precision medicine. Aspirin is one of the oldest most utilized medications for its ability to lower fever, relieve pain, and to reduce the stickiness of platelets (tiny blood cells that help your body form clots to stop bleeding. Aspirin dosing is currently the same for all patients and is not individualized. In the last century, aspirin has shown benefit in reducing cancer, stroke, and preventing cardiovascular events after one has already had a heart attack or stroke. Previous human studies have not found consistent positive effects of aspirin when dosed by body weight. Therefore, how should aspirin be dosed in 2019? Aspirin resistance is the failure of aspirin to reduce platelet stickiness and thin the blood and most importantly, is associated with higher risk of heart attacks and strokes. Aspirin resistance may occur due to not taking aspirin on a regular basis, differences in how platelets behave in some persons, use of over the counter pain medicines like Motrin®, reduced amount of drug in the body, and/or a lack of being able to predict a dose for a certain individual. To find out the best way to dose aspirin, the investigators propose to study healthy volunteers (persons without any known disease) with different ages and body sizes to see if aspirin blood levels are tied to platelet stickiness. This information will be used to mathematically build a computer-based picture of aspirin dosing that will help physicians pick the best dose of aspirin for each patient. The investigators will then extend studies for the aspirin dose estimator to be used in other countries in people with heart problems and stroke, recording future events in a randomized (i.e., coin toss) manner, to determine if the ability of the aspirin dose estimator to prevent future heart attacks and stroke compared to people receiving aspirin doses that were chosen without the estimator.

AIM 1: Determine urine TXB2, platelet aggregation function testing (VerifyNow® ASA Test), salicylate level, CBC with differential, and hs-CRP, in 18 healthy volunteers across BMI classes of 22-25 (Normal Weight), \>25-30 (Overweight), and \> 30 kg/m2 (Obese).Total enrolled cohort: 60 patients and planned treatment cohort: 54 completed patients (anticipated dropout rate of 10% = 6 patients). The investigators have powered this sample size based on estimates of effect sizes from published studies examining platelet activation in patients across a range of BMIs and assuming an alpha = 0.05, with 80% power. In addition, height and weight as predictors will be evaluated independently of BMI. BMI patient groups (22-25, \>25-30, and \> 30 kg/m2) will be randomized to low-dose ASA (81mg standard-release), moderate dose ASA (325mg) or high dose ASA (500mg) (6 patients/each dose). All patients will have a CBC with differential (to measure blood cell counts including platelets) and hs-CRP at baseline, serial urine TXB2 (-1, and 2 and 5 hours post ASA dose), platelet aggregation function testing using VerifyNow® ASA Test 15 min post ASA dose, serial salicylate levels (0, 15", 2 hours post-ASA dose) and again 10-14 days after chronic dosing (urine TXB2 2 hours post ASA dose and platelet aggregation function testing using VerifyNow® Test 15 min post ASA dose only). AIM 2: Model associations between construct variables (BMI and aspirin dose) with predictive variables as collected in AIM 1. Multiple and Linear Regression with backward selection will be used. In addition, a Structured Equation Model will be applied to the data. Statistical assessment of model fit will be conducted for all models. AIM 3: Build an Aspirin Dose Estimator to predict aspirin dosing. Model associations from AIM 2 will create demand estimates that will feed into a user-friendly aspirin dosage estimator. The simulator will comprise: 1) Entry: An entry screen. In this screen the user will enter the features of patient clinical information attributes. The user then clicks a 'run' button. 2) Demand Output: The simulator will then create an output screen that will show graphically aspirin dosing options.