Improvement of early-phase insulin secretion is an independent factor for achieving glycaemic control: A pooled analysis of SEED and DAWN study.

AIM: To investigate the effect of improving early phase insulin secretion function for glycaemic control in patients with type 2 diabetes mellitus treated with a new class of antidiabetic drug dorzagliatin. MATERIALS AND METHODS: Early insulin secretion function was studied in 726 participants of which 414 were treated with dorzagliatin in the SEED and DAWN study. The early insulinogenic index (IGI30min ) and disposition index (DI) were used to assess early-phase insulin secretion function in this study. Logistic regression analysis was performed to verify the importance of IGI30min and DI indices for achieving effective glycaemic control. RESULTS: The reduction in HbA1c has a significant correlation with the improvement of IGI30min for patients that received 24 weeks of dorzagliatin treatment (p < .001), and this correlation was not observed in the placebo group (p = .364). In the dorzagliatin treatment group, the responders showed significant improvements in homeostasis model assessment 2-ß, IGI30min and DI compared with the non-responders. Logistic regression analysis revealed that the odds ratio (OR) for achieving glycaemic control was 1.28 (95% CI 1.14-1.43) for baseline IGI30min , and 1.24 (95% CI 1.14-1.35) for the 24-week incremental IGI30min from baseline. The OR for baseline DI and 24-week changes in DI from baseline were 1.39 (95% CI 1.2-1.6) and 1.30 (95% CI 1.19-1.43) respectively. The timing of insulin secretion analysis showed the significant contribution of early-phase insulin secretion, rather than late-phase insulin secretion, to postprandial glucose control with the OR for the incremental IGI30min and IGI2h to postprandial glucose control were 1.3 (95% CI 1.19-1.42) and 1 (95% CI 1-1.01) respectively. CONCLUSIONS: Restoring the impaired early-phase insulin secretion function in patients with type 2 diabetes mellitus is a critical factor for improving the glycaemic control by dorzagliatin treatment.

Population Pharmacokinetic Analysis of Dorzagliatin in Healthy Subjects and Patients with Type 2 Diabetes Mellitus.

BACKGROUND AND OBJECTIVES: Dorzagliatin is a first-in-class small molecule glucokinase activator (GKA) that improves pancreatic insulin secretion behavior and regulates hepatic glucose conversion in a glucose concentration-dependent manner. The primary objective of this study was to develop a population pharmacokinetic model of dorzagliatin to evaluate the influence of covariates, such as demographic characteristics and liver and kidney function, on the pharmacokinetics of dorzagliatin and provide a basis for medication guidance. METHOD: The pharmacokinetic data of dorzagliatin in this study came from six clinical trials. Based on the combined data, a population pharmacokinetic model of dorzagliatin was established using NONMEM software (ICON, MD, USA, version 7.4.3). The algorithm used was first-order conditional estimation with interaction (FOCEI). The dorzagliatin population pharmacokinetic modeling analysis included 1062 subjects and 7686 observable concentrations. Covariates, including age (AGE), sex (GEND), body weight (TBW), body mass index (BMI), body surface area (BSA), albumin (ALB), alanine aminotransferase (ALT), aspartate aminotransferase (AST), serum creatinine (CR), creatinine clearance (CRCL), and total bilirubin (TBIL), were screened using the forward-backward method. Model evaluation was performed using goodness-of-fit plots, prediction corrected visual prediction check (pcVPC), and bootstrap. RESULTS: Concentration data of dorzagliatin in the dose range were best characterized by a two-compartment model with sequential zero-order then first-order absorption and first-order elimination. The final model estimated dorzagliatin data for typical male subjects (69 kg body weight, 18 U/L AST and 55 years old); the apparent total clearance (CL/F) was 10.4 L/h, apparent volume of central compartment distribution (Vc/F) was 80.6 L, inter-compartmental clearance (Q/F) was 3.02 L/h, apparent volume of peripheral compartment distribution (Vp/F) was 26.5 L, absorption rate constant (Ka) was 3.29 h-1, and duration of zero-order absorption (D1) was 0.418 h. The inter-individual variation of CL/F, Vc/F, Vp/F, and D1 was 22.5%, 14.9%, 48.8%, and 82.8%, respectively. CONCLUSION: The two-compartment linear pharmacokinetic model with zero- and first-order sequential absorption adequately described the pharmacokinetic characteristics of dorzagliatin. Body weight, aspartate aminotransferase, and age had a statistically significant effect on the CL/F of dorzagliatin. Body weight and sex had a statistically significant effect on Vc/F. However, considering the clinically insignificant changes in the magnitude of steady-state exposure caused by these covariates, as well as the minimal changes in the steady-state exposure for individuals with mild and moderate impaired hepatic function and all stages of renal impairment, dose adjustments based on the tested covariates or for specific populations are deemed unnecessary.

Time in Range Estimation in Patients with Type 2 Diabetes is Improved by Incorporating Fasting and Postprandial Glucose Levels.

INTRODUCTION: Time in range (TIR) as assessed by continuous glucose monitoring (CGM) measures an individual's glucose fluctuations within set limits in a time period and is increasingly used together with HbA1c in patients with diabetes. HbA1c indicates the average glucose concentration but provides no information on glucose fluctuation. However, before CGM becomes available for patients with type 2 diabetes (T2D) worldwide, especially in developing nations, fasting plasma glucose (FPG) and postprandial plasma glucose (PPG) are still the common biomarkers used for monitoring diabetes conditions. We investigated the importance of FPG and PPG to glucose fluctuation in patients with T2D. We used machine learning to provide a new estimate of TIR based on the HbA1c, together with FPG and PPG. METHODS: This study included 399 patients with T2D. (1) Univariate and (2) multivariate linear regression models and (3) random forest regression models were developed to predict the TIR. Subgroup analysis was performed in the newly diagnosed T2D population to explore and optimize the prediction model for patients with different disease history. RESULTS: Regression analysis suggests that FPG was strongly linked to minimum glucose, while PPG was strongly correlated with maximum glucose. After FPG and PPG were incorporated into the multivariate linear regression model, the prediction performance of TIR was improved compared with the univariate correlation between HbA1c and TIR, and the correlation coefficient (95% CI) increased from 0.62 (0.59, 0.65) to 0.73 (0.72, 0.75) (p < 0.001). The random forest model significantly outperformed the linear model (p < 0.001) in predicting TIR through FPG, PPG and HbA1c, with a stronger correlation coefficient 0.79 (0.79, 0.80). CONCLUSIONS: The results offered a comprehensive understanding of glucose fluctuations through FPG and PPG compared to HbA1c alone. Our novel TIR prediction model based on random forest regression with FPG, PPG, and HbA1c provides a better prediction performance than the univariate model with solely HbA1c. The results indicate a nonlinear relationship between TIR and glycaemic parameters. Our results suggest that machine learning may have the potential to be used in developing better models for understanding patients' disease status and providing necessary interventions for glycaemic control.

A phase I open-label clinical trial to study drug-drug interactions of Dorzagliatin and Sitagliptin in patients with type 2 diabetes and obesity.

This is a phase 1, open-label, single-sequence, multiple-dose, single-center trial conducted in the US (NCT03790839), to evaluate the clinical pharmacokinetics, safety and pharmacodynamics of dorzagliatin co-administered with sitagliptin in patients with T2D and obesity. The trial has completed. 15 patients with T2D and obesity were recruited and treated with sitagliptin 100 mg QD on Day 1-5, followed by a combination of sitagliptin 100 mg QD with dorzagliatin 75 mg BID at second stage on Day 6-10 and the third stage of dorzagliatin 75 mg BID alone on Day 11-15. Primary outcomes include pharmacokinetic geometric mean ratio (GMR), safety and tolerability. Secondary outcomes include the incremental area under the curve for 4 hours post oral glucose tolerance test (iAUC) of pharmacodynamic biomarkers and glucose sensitivity. GMR for AUC0-24h and Cmax were 92.63 (90% CI, 85.61, 100.22) and 98.14 (90% CI, 83.73, 115.03) in combination/sitagliptin, and 100.34 (90% CI, 96.08, 104.79) and 102.34 (90% CI, 86.92, 120.50) in combination/dorzagliatin, respectively. Combination treatment did not increase the adverse events and well-tolerated in T2D patients. Lack of clinically meaningful pharmacokinetic interactions between dorzagliatin and sitagliptin, and an improvement of glycemic control under combination potentially support their co-administration for diabetes management.