[News] Hua Medicine to Present Research Findings at CDS 2023

Time:

November 24, 16:17-16:25

Venue:

Chuxiu Room 107

Title:

Dorzagliatin, a glucokinase activator, repairs defective islet function in high-fat diet-induced diabetic mice

Main content:

The objective of this study is to assess the long-term efficacy of dorzagliatin in restoring islet function in a mouse model of diabetes. Even with a high-fat diet, islet function can recover over time, even after drug administration is discontinued. This occurrence gives biological support for additional clinical study into diabetes remission.

Speaker:

Nanjing AscendRare Pharmaceutical Technology Co., Ltd Dongnan Han

Time:

November 24, 16:57-17:05

Venue:

Chuhe Room 111

Title:

Drug development for disease caused by glutamate dehydrogenase (GDH) gain-of-function mutations

Main content:

Using an in-house new drug screening technology platform, which included virtual drug screening, activity verification of compounds on mutant proteins, and drug efficacy verification of disease models, multiple GDH inhibitors were discovered, providing new options for the treatment of rare diseases caused by GDH gain-of-function mutations.

Speaker:

Nanjing AscendRare Pharmaceutical Technology Co., Ltd, Yue Yuan

Time:

November 24, 14:24-14:32

Venue:

Chuyue Room 109

Title:

Mechanisms of GDH gain-of-function mutation-induced hyperammonemia based on knock-in mouse model studies

Main content:

In addition to the occurrence of hypoglycemia, individuals with rare disorders resulting from gain-of-function mutations in the GDH gene also exhibit hyperammonemia, the underlying causes of which remain unknown. The investigation of a mouse model with a knock-in of mutant GDH gene has revealed insights into the effects of GDH mutations on amino acid metabolism and the underlying mechanism of hyperammonemia.

Speaker:

Beijing Children's Hospital, Capital Medical University, Chang Su

Title:

Investigation into the molecular mechanisms that underlie the effects of dorzagliatin in GK rats to delay hyperglycemia and cognitive impairment.

Partners：

Hua Medicine (Shanghai) Ltd.
The Interdisciplinary Research Center on Biology and Chemistry

Main content:

Cognitive impairment in GK rats may be caused by aberrant expression of synaptic proteins in neurons as a result of insulin resistance and inadequate glucose uptake in nerve cells. This study discovered that by controlling brain glucose homeostasis, brain glucose uptake, and insulin resistance, continuous low-dose dorzagliatin treatment to GK rats from infancy can prevent GK rats from developing cognitive impairment phenotypes.

Title:

Enzymological and islet function comparison between MK-0941 and GKA dorzagliatin

Partners：

Nanjing AscendRare Pharmaceutical Technology Co., Ltd.
Hua Medicine (Shanghai) Ltd.

Main content:

The findings of the study indicate that dorzagliatin demonstrates notable glucose concentration-dependent properties in its ability to stimulate glucokinase (GK) activity. The observed phenomena indicate that the impact is limited when the glucose concentration is low, but becomes more significant as the concentration of glucose increases. The infrequent incidence of hypoglycemia documented in clinical studies of dorzagliatin can be attributed to its underlying mechanism of action.

Title:

Investigating the impact of glycine on glucose homeostasis and insulin secretion in mouse models and human subjects

Partners：

Nanjing AscendRare Pharmaceutical Technology Co., Ltd
Peking University First Hospital

Main content:

Clinical observations have indicated that the administration of oral glycine has the potential to enhance basal insulin secretion and increase glucose tolerance. However, the precise mechanism remains uncertain. This investigation employed islet perfusion tests and utilized mice with various disease models to elucidate the capacity of glycine to augment insulin secretion under distinct circumstances. The mechanism pertains to the manifestation of proteins peculiar to the central nervous system within pancreatic islets, including NMDA receptors. This finding presents an innovative pathway for further exploring the correlation between the functionality of pancreatic islets and the central nervous system.

Title:

Application of oral mini-model to evaluate the improvement effect of dorzagliatin on islet function

Partners：

Peking University Third Hospital
Hua Medicine (Shanghai) Ltd.

Main content:

This research utilizes an oral mini-model approach to quantify the beneficial impact of dorzagliatin on patients diagnosed with type 2 diabetes. Research has shown that dorzagliatin significantly enhances the function of pancreatic islet in individuals with type 2 diabetes in comparison to a placebo. The ability of GKA to increase insulin secretion in response to glucose is consistent with enhanced beta-cell function.

Title:

Investigation of a TIR prediction model based on glucose tolerance test and machine learning algorithm

Partners：

Nanjing First Hospital
Hua Medicine (Shanghai) Ltd.

Main content:

Continuous glucose monitoring (CGM) with plasma glucose time in range (TIR), one of the most important parameters for assessing blood glucose fluctuations, has been shown to be strongly associated with a variety of diabetes complications and is therefore recommended as a foundational standard for diabetes management alongside glycated hemoglobin (HbA1c). The objective of this research endeavor is to examine the correlation between fasting plasma glucose (FPG) and 2-hour postprandial plasma glucose (PPG), as measured by CGM parameters and the glucose tolerance test. Additionally, this study investigates the potential application of machine learning algorithms in developing a more precise target glycemic response (TIR) prediction model by utilizing HbA1c, FPG, and PPG. The outcomes of the TIR prediction model demonstrate that the multivariate nonlinear random forest model outperforms the TIR, thereby illuminating the abundance of plasma glucose fluctuation indicators embodied by the TIR and offering a novel perspective on the application of machine learning algorithms in diabetes management and more precise TIR prediction.