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Clinical Pharmacokinetics

, Volume 57, Issue 10, pp 1307–1323 | Cite as

Development of a Physiologically Based Pharmacokinetic Model for Sinogliatin, a First-in-Class Glucokinase Activator, by Integrating Allometric Scaling, In Vitro to In Vivo Exploration and Steady-State Concentration–Mean Residence Time Methods: Mechanistic Understanding of its Pharmacokinetics

  • Ling Song
  • Yi Zhang
  • Ji Jiang
  • Shuang Ren
  • Li Chen
  • Dongyang Liu
  • Xijing Chen
  • Pei Hu
Original Research Article

Abstract

Aim

The objective of this study was to develop a physiologically based pharmacokinetic (PBPK) model for sinogliatin (HMS-5552, dorzagliatin) by integrating allometric scaling (AS), in vitro to in vivo exploration (IVIVE), and steady-state concentration–mean residence time (Css-MRT) methods and to provide mechanistic insight into its pharmacokinetic properties in humans.

Methods

Human major pharmacokinetic parameters were analyzed using AS, IVIVE, and Css-MRT methods with available preclinical in vitro and in vivo data to understand sinogliatin drug metabolism and pharmacokinetic (DMPK) characteristics and underlying mechanisms. On this basis, an initial mechanistic PBPK model of sinogliatin was developed. The initial PBPK model was verified using observed data from a single ascending dose (SAD) study and further optimized with various strategies. The final model was validated by simulating sinogliatin pharmacokinetics under a fed condition. The validated model was applied to support a clinical drug–drug interaction (DDI) study design and to evaluate the effects of intrinsic (hepatic cirrhosis, genetic) factors on drug exposure.

Results

The two-species scaling method using rat and dog data (TS-rat,dog) was the best AS method in predicting human systemic clearance in the central compartment (CL). The IVIVE method confirmed that sinogliatin was predominantly metabolized by cytochrome P450 (CYP) 3A4. The Css-MRT method suggested dog pharmacokinetic profiles were more similar to human pharmacokinetic profiles. The estimated CL using the AS and IVIVE approaches was within 1.5-fold of that observed. The Css-MRT method in dogs also provided acceptable prediction of human pharmacokinetic characteristics. For the PBPK approach, the 90% confidence intervals (CIs) of the simulated maximum concentration (Cmax), CL, and area under the plasma concentration–time curve (AUC) of sinogliatin were within those observed and the 90% CI of simulated time to Cmax (tmax) was closed to that observed for a dose range of 5–50 mg in the SAD study. The final PBPK model was validated by simulating sinogliatin pharmacokinetics with food. The 90% CIs of the simulated Cmax, CL, and AUC values for sinogliatin were within those observed and the 90% CI of the simulated tmax was partially within that observed for the dose range of 25–200 mg in the multiple ascending dose (MAD) study. This PBPK model selected a final clinical DDI study design with itraconazole from four potential designs and also evaluated the effects of intrinsic (hepatic cirrhosis, genetic) factors on drug exposure.

Conclusions

Sinogliatin pharmacokinetic properties were mechanistically understood by integrating all four methods and a mechanistic PBPK model was successfully developed and validated using clinical data. This PBPK model was applied to support the development of sinogliatin.

Abbreviations

AS

Allometric scaling

AUC

Area under the curve

BP

Blood plasma ratio

BW

Body weight

CI

90% confidence interval

CL

Systemic clearance in central compartment

CL/F

Clearance after oral administration

CLh

Hepatic clearance

CLint

Intrinsic clearance

CLiv

Clearance after intravenous administration

Cmax

Peak concentration

CLr

Renal clearance

Css-MRT

Steady-state concentration- mean residence time

DDI

Drug–drug interactions

ER

Excretion ratio

Fa

Absorbed fraction

F

Bioavailability

FASSIF

Fasted state simulated intestinal fluid

FESSIF

Fed state simulated intestinal fluid

FIH

First in human

Fsc

Scaling factor between observed and predicted data

GK

Glucokinase

GKA

Glucokinase activator

H

Hematocrit

HLM

Human liver microsomes

IVIVE

In vitro to in vivo exploration

Ka

Absorption rate constant

Km

Michelis–Menten constant

MPPGL

mg protein per liver weight

MRT

Mean residence time

M.W

Molecular weight

OATP

Organic anion transporting polypeptide

Obs

Observed values

Papp

Apparent permeability coefficient

Peff

Jejunum effective permeability

P-gp

P-Glycoprotein

PK

Pharmacokinetics

PBPK

Physiologically based pharmacokinetic

rhCYP

Recombinant human cytochrome P450 enzyme

SAS

Simple allometric scaling method

Sim

Simulated values

SGF

Simulated gastric fluid

SSS

Single species scaling method

T2DM

Type 2 diabetic patients

Tmax

Time at the peak concentration occurs

Vc

Distribution volume of central compartment

TS

Two species scaling method

Vd

Distribution volume of peripheral compartment

Vmax

Enzyme maximum rate of metabolite formation

Vss

Steady-state distribution volume

Notes

Acknowledgements

We acknowledge Dr Bo Liu for his comments for the SimCYP® software technology support.

Funding

This study was supported by the National Natural Science Foundation of China (Nos. 81403013 and 81403015) and the ‘13th Five-Year’ National Major New Drug Projects (Nos. 2017ZX09101001-002-001 and 2017ZX09304031-001).

Compliance with Ethical Standards

Conflict of interest

Yi Zhang, Shuang Ren, and Li Chen are employees of Hua Medicine (Shanghai) Ltd., the company developing sinogliatin. Ling Song, Ji Jiang, Dongyang Liu, Xijing Chen, and Pei Hu declare no conflicts of interest relevant to the contents of this manuscript.

Supplementary material

40262_2018_631_MOESM1_ESM.docx (113 kb)
Supplementary material 1 (DOCX 113 kb)

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Clinical Pharmacokinetics Laboratory, School of Basic Medicine and Clinical PharmacyChina Pharmaceutical UniversityNanjingChina
  2. 2.Clinical Pharmacology Research CenterPeking Union Medical College Hospital and Chinese Academy of Medical SciencesBeijingChina
  3. 3.Hua Medicine (Shanghai) Ltd.ShanghaiChina

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