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

The exposure to ticagrelor (BRILINTA) is higher in the East Asian population compared with the White population, thus, East Asians have an increased risk of bleeding. We developed a population pharmacokinetic (PopPK) model of ticagrelor based on a randomized 3 x 3 crossover study in healthy subjects. The area under the concentration-time curve (AUC) of Chinese patients with acute coronary syndrome was simulated based on this model. Following this, eight machine learning (ML) 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, beta-blockers, peripheral vascular disease, diabetes, transient ischemic attack, sex, and proton pump inhibitor. 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 PopPK model and ML 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.

Automated summary

The exposure to ticagrelor is higher in East Asians, resulting in an increased risk of bleeding. A population pharmacokinetic model was developed and machine learning methods were used to construct bleeding risk models. The Random Forest model performed the best and a lower bleeding risk ticagrelor regimen can be obtained using this model.