Model-Informed Dosing Regimen of Ticagrelor in Chinese Patients with Acute Coronary Syndrome.

The exposure to ticagrelor is higher in the East Asian population compared to the Caucasian population, thus, East Asians have an increased risk of bleeding. We developed a population pharmacokinetic model of ticagrelor based on a randomized 3 × 3 crossover study in healthy subjects. The area under concentration-time curve (AUC) of Chinese patients with acute coronary syndrome was simulated based on this model. Following this, eight machine learning methods were used to construct bleeding risk models. Variables included in the final bleeding risk model were age, hypertension, body weight, AUC, drinking status, Calcium channel blockers, antidiabetic medications, β-blockers, peripheral vascular disease, diabetes, transient ischemic attack (TIA), sex, and proton pump inhibitor (PPI). In terms of F1 scores and area under the curve of receiver operating characteristic curve (ROC-AUC), the Random Forest model performed best among all models, with an F1 score of 0.73 and ROC-AUC of 0.81. Moreover, the PPK model and machine learning algorithm were used to bridge the real-world data to build a bleeding risk prediction model based on drug exposure and clinical information. Using this model, a ticagrelor regimen that is associated with a lower risk of bleeding in individuals can be obtained. This model should be further validated prospectively in clinical settings.