Chaozhuang Shen (1), Chenshuang Zhao (1), Haitang Xie (2), Xuehua Jiang (1), Ling Wang (1)*
(1) Department of Clinical Pharmacy and Pharmacy Administration, West China school of Pharmacy, Sichuan University, Chengdu 610064, China. (2) Anhui Provincial Center for Drug Clinical Evaluation, Yijishan Hospital of Wannan Medical College, Wuhu, Anhui 241001, China.
Introduction: Type 2 diabetes is considered to be one of the most significant health problems affecting the world, and prolonged hyperglycaemia predisposes to chronic damage and dysfunction of various tissues [1]. Alogliptin is a highly selective dipeptidyl peptidase-4 (DPP-4) inhibitor that elevates glucagon-like peptide-1 (GLP-1) and glucose-dependent insulinotropic peptide (GIP) levels to restore glucose-dependent insulin secretion for glucose-lowering effects [2]. Since 60-71% of the dose of alogliptin is excreted as unchanged drug in the urine, making people with renal impairment susceptible to drug accumulation in the body and increasing the risk of serious adverse effects such as hypoglycaemia, acute pancreatitis and abnormal liver function [3, 4]. Therefore, prior modelling of potential exposure and clinical outcomes of alogliptin in individuals with renal impairment may help to ensure the efficacy and safety of the treatment.
Objectives: To develop a physiologically-based quantitative systematic pharmacology (PB-QSP) blood glucose control systemic modulation model to predict alogliptin exposure levels in vivo and use blood glucose levels as a clinical endpoint to prospectively understand its therapeutic outcomes in populations with varying renal function.
Methods: A PB pharmacokinetic (PBPK) model was developed using reported physicochemical properties and clinical data of alogliptin, and the inhibitory effect of alogliptin on DPP-4 was determined based on the principle of target occupancy. The DPP-4-GLP-1-GIP-glucagon-insulin- blood glucose control system was integrated and established, including crosstalk and feedback loops between the components [5]. The models were validated using data from clinical studies with different dose ranges and different routes and intervals of administration. The fit performance of the models was assessed by comparing predicted and observed plasma concentration data, PK parameters and biomarker concentration [6]. Finally, the renal impairment population was developed by modifying parameters such as in glomerular filtration rate, kidney volume, renal perfusion, hematocrit, plasma protein concentrations, and gastrointestinal transit, and compared with in vivo exposure and changes in blood glucose levels in a healthy population [7, 8].
Results: The PB-QSP model contains five indirect response models as a “skeleton” structure and contains 12 feedback loops that adequately characterized the PK of alogliptin, the time course of DPP-4 inhibition, and the kinetics of GLP-1, GIP, glucagon, insulin, and blood glucose simultaneously in humans. Compared with clinically observed values, 97.62% of predicted plasma concentrations of alogliptin, 100% of DPP4 inhibition, 76.19% of GLP-1, 100% of glucagon, 70% of insulin, and 100% of blood glucose were within 2-fold error of the corresponding observed concentrations. In a virtual oral glucose tolerance test (OGTT) performed before and after oral administration of 25 mg of alogliptin, the AUC0-∞ in stage 3, 4, and 5 renal impairment groups was 2.05, 2.99, and 3.77 times higher than in healthy groups, and the maximum blood glucose level after 72 hours was reduced by 2.26, 3.48, and 4.18 mmol/L, respectively. But the minimum blood glucose level was not significantly different between the groups.
Conclusions: Our model provided valuable insights PK/pharmacodynamics (PD) and complicated glucose homeostasis of alogliptin in populations with different renal functions. In addition, since the glucose regulation modelling framework in the QSP model is “drug-independent”, our model can be further extended to predict the effects of other such drugs on blood glucose.
References:
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Reference: PAGE 32 (2024) Abstr 11130 [www.page-meeting.org/?abstract=11130]
Poster: Drug/Disease Modelling - Endocrine