Dorzagliatin: First Approval

Genetically proxied glucokinase activation and risk of diabetic complications: Insights from phenome-wide and multi-omics mendelian randomization

AIMS

This study aims to assess the benefits and adverse effects of long-term glucokinase (GK) activation from a genetic perspective.

METHODS

We identified genetic variants in the GCK gene associated with glycated hemoglobin (HbA1c) levels from a genome-wide association study (GWAS) involving 146,806 individuals, which served as proxies for glucokinase activation. To assess the effects and potential pathways of GK activation on a range of diabetic complications and safety outcomes, we integrated drug-target Mendelian randomization (MR), lipidome-wide and proteome-wide MR, phenome-wide MR, and colocalization analyses.

RESULTS

Genetically proxied GK activation was associated with reduced risks of several predefined diabetic complications, including cardiovascular diseases, stroke and diabetic retinopathy. No kidney-related benefits were observed. Safety analysis revealed a relationship between GK activation and elevated AST levels, while impaired interaction between GK and glucokinase regulatory protein (GKRP) was associated with dyslipidemia, increased liver fat content, AST, systolic blood pressure, and uric acid. Phenome-wide MR suggested that GK activation may have potential benefits for lung function and fluid intelligence score.

CONCLUSIONS

Our genetic evidence supports GK as a promising target for reducing the risk of specific diabetic complications. These findings require further validation through cohort studies and randomized controlled trials in patients with diabetes.

Modulating Phosphorylation by Proximity-Inducing Modalities for Cancer Therapy

Abnormal phosphorylation of proteins can lead to various diseases, particularly cancer. Therefore, the development of small molecules for precise regulation of protein phosphorylation holds great potential for drug design. While the traditional kinase/phosphatase small-molecule modulators have shown some success, achieving precise phosphorylation regulation has proven to be challenging. The emergence of heterobifunctional molecules, such as phosphorylation-inducing chimeric small molecules (PHICSs) and phosphatase recruiting chimeras (PHORCs), with proximity-inducing modalities is expected to lead to a breakthrough by specifically recruiting kinase or phosphatase to the protein of interest. Herein, we summarize the drug targets with aberrant phosphorylation in cancer and underscore the potential of correcting phosphorylation in cancer therapy. Through reported cases of heterobifunctional molecules targeting phosphorylation regulation, we highlight the current design strategies and features of these molecules. We also provide a systematic elaboration of the link between aberrantly phosphorylated targets and cancer as well as the existing challenges and future research directions for developing heterobifunctional molecular drugs for phosphorylation regulation.

Current status and challenges of model-informed drug discovery and development in China

In the past decade, biopharmaceutical research and development in China has been notably boosted by government policies, regulatory initiatives and increasing investments in life sciences. With regulatory agency acting as a strong driver, model-informed drug development (MIDD) is transitioning rapidly from an academic pursuit to a critical component of innovative drug discovery and development within the country. In this article, we provided a cross-sectional summary on the current status of MIDD implementations across early and late-stage drug development in China, illustrated by case examples. We also shared insights into regulatory policy development and decision-making. Various modeling and simulation approaches were presented across a range of applications. Furthermore, the challenges and opportunities of MIDD in China were discussed and compared with other regions where these practices have a more established history. Through this analysis, we highlighted the potential of MIDD to enhance drug development efficiency and effectiveness in China's evolving pharmaceutical landscape.

Clinical investigation of glucokinase activators for the restoration of glucose homeostasis in diabetes

As a sensor, glucokinase (GK) controls glucose homeostasis, which progressively declines in patients with diabetes. GK maintains the equilibrium of glucose levels and regulates the homeostatic system set points. Endocrine and hepatic cells can both respond to glucose cooperatively when GK is activated. GK has been under study as a therapeutic target for decades due to the possibility that cellular GK expression and function can be recovered, hence restoring glucose homeostasis in patients with type 2 diabetes. Five therapeutic compounds targeting GK are being investigated globally at the moment. They all have distinctive molecular structures and have been clinically shown to have strong antihyperglycemia effects. The mechanics, classification, and clinical development of GK activators are illustrated in this review. With the recent approval and marketing of the first GK activator (GKA), dorzagliatin, GKA's critical role in treating glucose homeostasis disorder and its long-term benefits in diabetes will eventually become clear.

Tetrahydropalmatine from medicinal plants activates human glucokinase to regulate glucose homeostasis

Many synthetic glucokinase activators (GKAs), modulating glucokinase (GK), an important therapeutic target in diabetes have failed to clear clinical trials. In this study, an in silico structural similarity search with differing scaffolds of reference GKAs have been used to identify derivatives from natural product databases. Ten molecules with good binding score and similar interactions to that in the co-crystallized GK as well good activation against recombinant human GK experimentally were identified. Tetrahydropalmatine, an alkaloid present in formulations and drugs from medicinal plants, has not been explored as an antidiabetic agent and no information regarding its mechanism of action or GK activation exists. Tetrahydropalmatine activates GK with EC50 value of 71.7 ± 17.9 μM while lowering the S0.5 (7.1 mM) and increasing Vmax (9.22 μM/min) as compared to control without activator (S0.5 = 10.37 mM; Vmax = 4.8 μM/min). Kinetic data (α and β values) suggests it to act as mixed, nonessential type activator. Using microscale thermophoresis, Kd values of 3.8 μM suggests a good affinity for GK. In HepG2 cell line, the compound potentiated the uptake of glucose and maintained glucose homeostasis by increasing the expression of GK, glycogen synthase, and insulin receptor genes and lowering the expression of glucokinase regulatory protein (GKRP) and glucagon. Tetrahydropalmatine at low concentrations could elicit a good response by reducing expression of GKRP, increasing expression of GK while also activating it. Thus, it could be used alone or in combination as therapeutic drug as it could effectively modulate GK and alter glucose homeostasis.

Perspectives on genetic studies of type 2 diabetes from the genome-wide association studies era to precision medicine

Genome-wide association studies (GWAS) have facilitated a substantial and rapid increase in the number of confirmed genetic susceptibility variants for complex diseases. Approximately 700 variants predisposing individuals to the risk for type 2 diabetes have been identified through GWAS until 2023. From 2018 to 2022, hundreds of type 2 diabetes susceptibility loci with smaller effect sizes were identified through large-scale GWAS with sample sizes of 200,000 to >1 million. The clinical translation of genetic information for type 2 diabetes includes the development of novel therapeutics and risk predictions. Although drug discovery based on loci identified in GWAS remains challenging owing to the difficulty of functional annotation, global efforts have been made to identify novel biological mechanisms and therapeutic targets by applying multi-omics approaches or searching for disease-associated coding variants in isolated founder populations. Polygenic risk scores (PRSs), comprising up to millions of associated variants, can identify individuals with higher disease risk than those in the general population. In populations of European descent, PRSs constructed from base GWAS data with a sample size of approximately 450,000 have predicted the onset of diseases well. However, European GWAS-derived PRSs have limited predictive performance in non-European populations. The predictive accuracy of a PRS largely depends on the sample size of the base GWAS data. The results of GWAS meta-analyses for multi-ethnic groups as base GWAS data and cross-population polygenic prediction methodology have been applied to establish a universal PRS applicable to small isolated ethnic populations.

Synthetic Approaches to the New Drugs Approved During 2022

In 2022, 23 new small molecule chemical entities were approved as drugs by the United States FDA, European Union EMA, Japan PMDA, and China NMPA. This review describes the synthetic approach demonstrated on largest scale for each new drug based on patent or primary literature. The synthetic routes highlight practical methods to construct molecules, sometimes on the manufacturing scale, to access the new drugs. Ten additional drugs approved in 2021 and one approved in 2020 are included that were not covered in the previous year's review.

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.

Advances in Research on Type 2 Diabetes Mellitus Targets and Therapeutic Agents

Diabetes mellitus is a chronic multifaceted disease with multiple potential complications, the treatment of which can only delay and prolong the terminal stage of the disease, i.e., type 2 diabetes mellitus (T2DM). The World Health Organization predicts that diabetes will be the seventh leading cause of death by 2030. Although many antidiabetic medicines have been successfully developed in recent years, such as GLP-1 receptor agonists and SGLT-2 inhibitors, single-target drugs are gradually failing to meet the therapeutic requirements owing to the individual variability, diversity of pathogenesis, and organismal resistance. Therefore, there remains a need to investigate the pathogenesis of T2DM in more depth, identify multiple therapeutic targets, and provide improved glycemic control solutions. This review presents an overview of the mechanisms of action and the development of the latest therapeutic agents targeting T2DM in recent years. It also discusses emerging target-based therapies and new potential therapeutic targets that have emerged within the last three years. The aim of our review is to provide a theoretical basis for further advancement in targeted therapies for T2DM.

iPADD: A Computational Tool for Predicting Potential Antidiabetic Drugs Using Machine Learning Algorithms

Diabetes mellitus is a chronic metabolic disease, which causes an imbalance in blood glucose homeostasis and further leads to severe complications. With the increasing population of diabetes, there is an urgent need to develop drugs to treat diabetes. The development of artificial intelligence provides a powerful tool for accelerating the discovery of antidiabetic drugs. This work aims to establish a predictor called iPADD for discovering potential antidiabetic drugs. In the predictor, we used four kinds of molecular fingerprints and their combinations to encode the drugs and then adopted minimum-redundancy-maximum-relevance (mRMR) combined with an incremental feature selection strategy to screen optimal features. Based on the optimal feature subset, eight machine learning algorithms were applied to train models by using 5-fold cross-validation. The best model could produce an accuracy (Acc) of 0.983 with the area under the receiver operating characteristic curve (auROC) value of 0.989 on an independent test set. To further validate the performance of iPADD, we selected 65 natural products for case analysis, including 13 natural products in clinical trials as positive samples and 52 natural products as negative samples. Except for abscisic acid, our model can give correct prediction results. Molecular docking illustrated that quercetin and resveratrol stably bound with the diabetes target NR1I2. These results are consistent with the model prediction results of iPADD, indicating that the machine learning model has a strong generalization ability. The source code of iPADD is available at https://github.com/llllxw/iPADD.

Enhancing the Cardiovascular Safety of Hemodialysis Care Using Multimodal Provider Education and Patient Activation Interventions: Protocol for a Cluster Randomized Controlled Trial

BACKGROUND

End-stage kidney disease (ESKD) is treated with dialysis or kidney transplantation, with most patients with ESKD receiving in-center hemodialysis treatment. This life-saving treatment can result in cardiovascular and hemodynamic instability, with the most common form being low blood pressure during the dialysis treatment (intradialytic hypotension [IDH]). IDH is a complication of hemodialysis that can involve symptoms such as fatigue, nausea, cramping, and loss of consciousness. IDH increases risks of cardiovascular disease and ultimately hospitalizations and mortality. Provider-level and patient-level decisions influence the occurrence of IDH; thus, IDH may be preventable in routine hemodialysis care.

OBJECTIVE

This study aims to evaluate the independent and comparative effectiveness of 2 interventions-one directed at hemodialysis providers and another for patients-in reducing the rate of IDH at hemodialysis facilities. In addition, the study will assess the effects of interventions on secondary patient-centered clinical outcomes and examine factors associated with a successful implementation of the interventions.

METHODS

This study is a pragmatic, cluster randomized trial to be conducted in 20 hemodialysis facilities in the United States. Hemodialysis facilities will be randomized using a 2 × 2 factorial design, such that 5 sites will receive a multimodal provider education intervention, 5 sites will receive a patient activation intervention, 5 sites will receive both interventions, and 5 sites will receive none of the 2 interventions. The multimodal provider education intervention involved theory-informed team training and the use of a digital, tablet-based checklist to heighten attention to patient clinical factors associated with increased IDH risk. The patient activation intervention involves tablet-based, theory-informed patient education and peer mentoring. Patient outcomes will be monitored during a 12-week baseline period, followed by a 24-week intervention period and a 12-week postintervention follow-up period. The primary outcome of the study is the proportion of treatments with IDH, which will be aggregated at the facility level. Secondary outcomes include patient symptoms, fluid adherence, hemodialysis adherence, quality of life, hospitalizations, and mortality.

RESULTS

This study is funded by the Patient-Centered Outcomes Research Institute and approved by the University of Michigan Medical School's institutional review board. The study began enrolling patients in January 2023. Initial feasibility data will be available in May 2023. Data collection will conclude in November 2024.

CONCLUSIONS

The effects of provider and patient education on reducing the proportion of sessions with IDH and improving other patient-centered clinical outcomes will be evaluated, and the findings will be used to inform further improvements in patient care. Improving the stability of hemodialysis sessions is a critical concern for clinicians and patients with ESKD; the interventions targeted to providers and patients are predicted to lead to improvements in patient health and quality of life.

TRIAL REGISTRATION

ClinicalTrials.gov NCT03171545; https://clinicaltrials.gov/ct2/show/NCT03171545.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

PRR1-10.2196/46187.