New PK/PD model directly links diabetes drug dose to blood glucose level for personalized care

The management of diabetes mellitus focuses on close monitoring of a patient's blood glucose level while the clinician experiments with a dosing strategy using clinical guidelines and his/her own experience. We propose a pharmacokinetic and pharmacodynamics model that characterizes the dose-response of patients receiving anti-diabetic drug therapy. We derive and establish a direct relationship between drug dosage and blood glucose level. This new drug-dose drug-effect model, combined with a linear disease progression model, is used to fit the patient's daily self-monitored blood glucose (SMBG) data to obtain the personalized treatment effect for each patient. The model predicts the long-term drug effect using the prescribed dose, thus allowing for dose optimization. The model is evaluated on patients with gestational diabetes mellitus. SMBG data collected during the first month of treatment is used to train the model. The model is able to characterize the personalized dose-response and disease progression. Moreover, when compared to a descriptive autoregression model, our model gives a better long-term prediction of the drug effect on the trend of the blood glucose level. This mechanism-based treatment effect model utilizes daily recorded blood glucose data to estimate and predict a patient's personalized dose-response and disease progression. Such evidence can be used by clinicians to individualize and optimize dose regimens to achieve better treatment outcomes.