Front. Pharmacol.Frontiers in PharmacologyFront. Pharmacol.1663-9812Frontiers Media S.A.74659410.3389/fphar.2021.746594PharmacologyOriginal ResearchEffect of CYP3A4 Inhibitors and Inducers on Pharmacokinetics and Pharmacodynamics of Saxagliptin and Active Metabolite M2 in Humans Using Physiological-Based Pharmacokinetic Combined DPP-4 OccupancyLi et al.Saxagliptin PK and PD predictionLiGang1†YiBowen2†LiuJingtong3JiangXiaoquan3PanFulu3YangWenning3LiuHaibo4*LiuYang3*WangGuopeng5
Beijing Adamadle Biotech Co, Ltd., Beijing, China
Xiyuan Hospital, China Academy of Chinese Medical Sciences, Beijing, China
School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, China
Chinese Academy of Medical Sciences and Peking Union Medical College, Institute of Medicinal Plant Development, Beijing, China
Zhongcai Health (Beijing) Biological Technology Development Co, Ltd., Beijing, China
Edited by:Sabina Passamonti, University of Trieste, Italy
Reviewed by:Zhihao Liu, United States Department of Agriculture, United States
Jinyao Li, Xinjiang University, China
*Correspondence: Haibo Liu, hbliu@implad.ac.cn; Yang Liu, liuyang@bucm.edu.cn
These authors have contributed equally to this work
This article was submitted to Drug Metabolism and Transport, a section of the journal Frontiers in Pharmacology
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
We aimed to develop a physiological-based pharmacokinetic and dipepidyl peptidase 4 (DPP-4) occupancy model (PBPK-DO) characterized by two simultaneous simulations to predict pharmacokinetic (PK) and pharmacodynamic changes of saxagliptin and metabolite M2 in humans when coadministered with CYP3A4 inhibitors or inducers. Ketoconazole, delavirdine, and rifampicin were selected as a CYP3A4 competitive inhibitor, a time-dependent inhibitor, and an inducer, respectively. Here, we have successfully simulated PK profiles and DPP-4 occupancy profiles of saxagliptin in humans using the PBPK-DO model. Additionally, under the circumstance of actually measured values, predicted results were good and in line with observations, and all fold errors were below 2. The prediction results demonstrated that the oral dose of saxagliptin should be reduced to 2.5 mg when coadministrated with ketoconazole. The predictions also showed that although PK profiles of saxagliptin showed significant changes with delavirdine (AUC 1.5-fold increase) or rifampicin (AUC: a decrease to 0.19-fold) compared to those without inhibitors or inducers, occupancies of DPP-4 by saxagliptin were nearly unchanged, that is, the administration dose of saxagliptin need not adjust when there is coadministration with delavirdine or rifampicin.
Patients undergoing multiple comorbidities are usually treated by complicated polypharmacy schemes and long-term administration, which means that they will be at the risk of drug–drug interactions (DDIs). In DDIs, one drug may affect the PK or PD behavior of another drug, thereby resulting in a series of side effects and even a withdrawal of approved pharmaceuticals from the market, for instance, mibefradil and terfenadine (Valicherla et al., 2019; Pelkonen et al., 2020). Therefore, prospective assessment of a potential risk of DDIs is significant within the pharmaceutical industry, such as guaranteeing the safety and reducing unnecessary consumption of new drugs. Cytochrome P450, which is the most comprehensive metabolizing enzyme in the gut and the liver, plays an important role in pharmacokinetic (PK) interaction-related DDIs through the interfering metabolism of most drugs (Dmitriev et al., 2019). Currently, PK-related DDIs mediated by CYP have become a research focus over the past decades (Conner et al., 2018; Peng et al., 2021). However, only focusing on PK-related DDIs is far from enough, and pharmacodynamic (PD)-related DDIs should receive more attention, particularly on dose adjustment for patients. Although PD-related DDIs recorded are roughly 1.9-fold higher than PK-related DDIs (Spanakis et al., 2019), unfortunately, simulation study on PD-related DDIs is far less compared to those on PK-related DDIs.
Ketoconazole and delavirdine are strongly competitive and time-dependent inhibitors (TDIs) against the CYP3A4 enzyme, and rifampicin is a strong CYP3A4 inducer. It is well known that ketoconazole and rifampicin are often recommended to assess potential DDIs for other drugs mainly metabolized by the CYP3A4 enzyme (Rytkönen et al., 2020; Xu et al., 2021). To our knowledge, owing to irreversible loss of P450 enzyme functions, TDIs are regarded to have a longer persistent time on the drug metabolic enzyme and consequently to cause clinically more significant DDIs in contrast to competitive inhibitors (Kosaka et al., 2017; Eng et al., 2020; Tanna et al., 2021). Therefore, ketoconazole, delavirdine, and rifampicin were chosen for potential DDI assessments.
Saxagliptin is an orally potent competitive DPP-4 inhibitor and utilized for the treatment of type 2 diabetes at a daily dose of 5 mg.1. Saxagliptin is metabolized extensively in humans via CYP3A4 to be converted into many metabolites, of which 5-hydroxy saxagliptin (M2) is the most major active metabolite. M2 in vivo is roughly in a 2-fold higher amount (44.1 versus 24.0%) and binds to DPP-4 with a ∼2-fold lower affinity than saxagliptin (1.3 versus 2.6 nm)1 (Su et al., 2012). Hence, when coadministered with inhibitors or inducers of CYP3A4, significant effects of them on PK and PD of saxagliptin should be taken into account.
We aimed to develop a mathematical model to assess the dynamic effect of ketoconazole, delavirdine, and rifampicin on the PK and PD of saxagliptin tablets in humans, when coadministered. More precisely, the physiological-based pharmacokinetic (PBPK) model and the DPP-4 occupancy (DO) model were incorporated into a new mathematical model, termed as the PBPK-DO model, which enables the changes of PK and PD of saxagliptin and M2 in humans to be quantified simultaneously with coadministration of CYP3A4 inhibitors or inducers.
MethodsData Collection
Clinical PK studies of ketoconazole, delavirdine, and rifampicin were taken from the published literature2, 3(Hanke et al., 2018), which could be used in their respective PBPK model establishment and verification. Clinical PK and PD studies of saxagliptin and metabolite M2 were collected from published data (Upreti et al., 2011), which could be used in the PBPK-DO model development and verification for saxagliptin and M2. Physicochemical properties of four drugs, binding kinetics of saxagliptin and M2, and physiological parameters in humans required in developing the PBPK-DO model were obtained from published scientific studies, and the built-in libraries of Gastroplus software were used in this study, including Berkeley Madonna (Version 10.2.8, Berkeley Madonna, Inc Albany, CA, United States) and ADMET Predictor (Version 9.0.0.0, Simulation Plus, Inc Lancaster, CA, United States).
Development of the PBPK-DO Model
A PBPK-DO model was developed to simulate PK and DPP-4 occupancy time profiles of saxagliptin and metabolite M2 simultaneously after oral coadministration with ketoconazole, delavirdine, and rifampicin. The PBPK-DO model was composed of three key simulation processes, that is, PK prediction of saxagliptin and M2, simulation of DPP-4 occupancy by saxagliptin and M2, and interaction prediction with inhibitors/inducers of CYP3A4. In this PBPK-DO model, first, the simulation of saxagliptin and M2 concentration changes over time was enabled simultaneously by the PBPK model, which consisted of a stomach–gut compartment, a enterocytes compartment, a portal vein compartment, a blood compartment (arterial and venous blood), eliminating tissues (the liver, the kidney), non-eliminating tissues (adipose, the bone, the brain, the heart, muscle, the skin, and the spleen), and the lung. Next, the interactions between inhibitors/inducers and CYP3A4 were calculated through inhibition or inducing parameters (Ki, Kinact, Emax, and EC50), CYP3A4 expression amount, and free saxagliptin concentration in the gut and liver. Eventually, time courses of DPP-4 occupancy were characterized by two key rate constants of on-rate (kon) and off-rate (koff) combined with free drug concentration around the DPP-4. In addition, we assumed that DPP-4 was located in the venous blood compartment in the present model. The overall framework of the PBPK-DO model is represented in Figure 1.
Schematic representation of the PBPK-DO model for saxagliptin in humans. The PBPK-DO model includes the gut lumen, enterocytes, the portal vein, the liver, blood (arterial supply and venous return), the lung, and other eliminating tissues (kidney) and non-eliminating tissues (11 compartments). The CYP3A metabolism enzyme was set into enterocytes and liver compartments. DPP-4 was assumed to only reside in the venous return compartment.
Stomach–Gut Compartment
Assuming that the drug in the stomach was neither absorbed nor metabolized, the drug amount in the stomach (A0) is only governed by the gastric emptying rate (K0). The change in mass within the stomach is described as follows (Qian et al., 2019):dA0dt=−K0×A0.
The gut lumen consists of the duodenum, jejunum, and ileum, and the amount of drug in the gut lumen (Ai) is controlled by the gut transit rate constant (Kt,i) and absorption rate constant (Ka,i). The change in mass within each gut lumen was described as follows (Qian et al., 2019):dAidt=Kt,i−1×Ai−1−Kt,i×Ai−Ka,i×Ai,where i = 1, 2, and 3 corresponds to the duodenum, jejunum, and ileum, respectively. The calculation of the Ka,i value is given by the following:4Ka,i=Ka,i0×Ai×fa,where Ka,i0 represents the initial absorption rate constant and fa represents the absorption adjustment factor. The calculation of the Ka,i0 value is given by the following:6Ka,i0=2×Peff,human×ASFri,where Peff, human is the effective permeability in humans and ri is the average radius of different intestinal segments (the duodenum, jejunum, and ileum). ASF is the absorption scale factor.
Enterocyte Compartment
Each different intestinal segment is linked with its enterocytes compartment. The amount of drug in each enterocyte compartment (Aent,i) was calculated according to the following equations (Qian et al., 2019):dAent,idt=Ka,i×Ai+Qent,i×ApvVpv−(Qent,i+fugut×CLint,ent(t))×Aent,iVent,i×Kp,ent,CLint,ent,i(t)=Vmax×Abundanceent,i(t)×ISEFKm+fugut×Aent,i(t)Vent,i×Kp,ent×M,where Qent,i and Vent,i represent the blood flow rate and the volume of each enterocyte compartment, respectively. Vpv is the volume of the portal vein compartment, fugut is the free drug concentration in the enterocyte compartment, and Kp,ent is the ratio of intestine over blood drug concentration.
CLint,ent (t) and Abundanceent,i (t) represent the intrinsic metabolic clearance and CYP3A content in the enterocyte compartment with inhibitors/inducers or without inhibitors/inducers, respectively. Vmax and Km are metabolic parameters of the drug in each different enterocyte compartment. According to the reported result, saxagliptin is a weak P-glycoprotein substrate (Boulton, 2017); consequently, the efflux effect of P-glycoprotein on the amount of saxaliptin in the gut lumen was not considered in this study.
Portal Vein Compartment
The drug from the enterocyte compartment enters the liver via the portal vein, and hence, the change of drug amount over time in the portal vein compartment (Apv) is illustrated according to the following equations (Li et al., 2012; Qian et al., 2019):dApvdt=Qpv×Cab+∑i=13Qent,i×Aent,iVent,i×Kp,ent−∑i=13Qent,i×ApvVpv−Qpv×ApvVpv,where Cab is the drug concentration in arterial blood.
Liver Compartment
The liver is a main eliminating tissue of saxagliptin, and the amount of drug in the liver is described as follows6 (Li et al., 2012):dAlidt=Qpv×ApvVpv+Qlia×Cab−Qli×Ali×RbpVli×Kp,li−CLint,li(t)×Ali×fupVli×Kp,li,CLint,li(t)=Vmax×Abundanceli(t)×ISEFKm+Ali(t)×fupVli×M×Kp,li,where Ali, Vli, Qli, and Qlia are the drug amount in the liver, the volume and hepatic blood flow, and the hepatic artery blood flow rate of the liver, respectively. Rbp is the blood-to-plasma concentration ratio, fup is the fraction of free drug in the plasma, and Kp,li is liver-to-plasma partition coefficient. M is molecular weight of the drug.
CLint,li(t) represents the intrinsic metabolic clearance of a drug in the liver. Abundanceli(t) represents the hepatic CYP3A amount with inhibitors/inducers or without inhibitors/inducers. ISEF is the intersystem extrapolation factor. Owing to the finding that saxagliptin is not almost eliminated through biliary excretion (Fura et al., 2009), biliary clearance of saxagliptin is not incorporated into this PBPK model.
The amount of metabolite in the enterocyte compartment (Amet,ent) is illustrated by the following:dAmet,entdt=∑i=13fscale×Vmax×Abundanceent,i(t)×Aent,i(t)×fugutVent,i×Kp,entKm+Aent,i(t)×fugutVent,i×M×Kp,ent.
The amount of metabolite in the liver (Amet,li) is illustrated by the following:dAmet,lidt=fscale×Vmax×Abundanceli(t)×Ali(t)×fupVli×Kp,liKm+Ali(t)×fupVli×M×Kp,li,where fscale is the scale factor of metabolite conversion. The total amount of metabolite (Amet) enters the venous blood compartment directly without consideration of gut absorption and is calculated by the following:dAmetdt=Amet,ent(t)+Amet,li(t)−Ka,met×Amet,Ka,met=Ka,met0×Amet×fa,met,where Ka,met0 and Ka,met are the initial absorption rate constant and absorption rate constant of metabolite M2, respectively. fa,met represents the absorption adjustment factor of the metabolite.
Kidney Compartment
The change of drug amount with time within the kidney (Aki) is described by the following (Li et al., 2012):dAkidt=Qki×Cab−Qki×Aki×RbpVki×Kp,ki−Aki×fup×CLrVki×Kp,ki,where Qki and Vki are the blood flow and the volume of the kidney, respectively. Kp,ki is the kidney-to-plasma partition coefficient. CLr is the kidney elimination rate of the drug.
Lung Compartment
The amount of drug in the lung (Alu) is described as follows (Li et al., 2012):dAludt=Qlu×Cvb−Qlu×Alu×RbpVlu×Kp,lu,where Qlu and Vlu are the blood flow and the volume of the lung, respectively. Kp,lu is lung-to-plasma partition coefficient.
Other Non-eliminating Tissue Compartments
Overall, the amount of drug within non-eliminating tissue compartments (Ant) (the adipose, the bone, the brain, the heart, the muscle, the skin, the spleen, the red marrow, the yellow marrow, reproductive organs, and the rest of the body), except for the lung, follows the following equation (Li et al., 2012):dAntdt=Qnt×Cab−Qnt×Ant×RbpVnt×Kp,nt,where Qnt and Vnt are the blood flow sum and the mean volume of other non-eliminating tissues, respectively. Kp,nt is the partition coefficient sum of other non-eliminating over plasma.
Arterial Blood Compartment
The drug concentration within the arterial blood (Cab) is described as follows (Li et al., 2012; Fu et al., 2019):dCabdt=1Vab×(Qlu×Alu×RbpVlu×Kp,lu−∑iQi×Cab),where Vab is the volume of the arterial blood compartment. i represents all eliminating and non-eliminating tissues except for the lung tissue.
Venous Blood Compartment
The drug concentration within the venous blood (Cvb) is described as follows (Li et al., 2012; Fu et al., 2019):dCvbdt=1Vvb×(∑iQi×Ai×RbpVi×Kp,i−Qlu×Cvb),where Vvb is the volume of the venous blood compartment. i represents all eliminating and non-eliminating tissues except for the lung tissue.
CYP3A Dynamics of Inhibition and Induction
Here, the inhibition and induction model of CYP3A were developed assuming that intestinal inhibition and induction dynamic parameters were identical to hepatic dose. The CYP3A reversible competition inhibition dynamics is described as follows (Baneyx et al., 2014):dAbundanceidt=−INli×fup,in×AbundanceiKp,li,in×Min×(Ki+fup,in×INliKp,in×Min)+kdeg×(Abundance0−Abundancei),
The CYP3A time-dependent inhibition dynamics is described as follows (Baneyx et al., 2014):dAbundanceidt=−kinact×Ili×fup,in×AbundanceiKp,li,in×Min×(Ki+fup,in×IliKp,in×Min)+kdeg×(Abundance0−Abundancei),
The CYP3A induction dynamics is described by the following (Qian et al., 2019):dAbundanceidt=kdeg×Emax×Ili×fup,in×AbundanceiKp,li,in×Min×(EC50+fup,in×IliKp,in×Min)+kdeg×(Abundance0−Abundancei),(21)where Ili is the concentration of the inhibitor/inducer within the targeted tissue (the gut and liver). fup,in is the fraction of the free inhibitor/inducer in the plasma. Kp,in is the partition coefficient of the inhibitor/inducer of the targeted tissue (the gut and liver) to plasma. Min is the molecular weight of the inhibitor/inducer. Ki and kinact are inactivation parameters of the inhibitor against CYP3A4. ECmax and EC50 are inductive parameters of the inducer on CYP3A4. kdeg is the degradation rate constant of CYP3A4, and in this study, it was assumed that the kdeg value in the liver is identical to that in the gut.
DPP-4 Engagement Dynamics by Saxagliptin and Metabolite M2
The DPP-4 engagement time course by saxagliptin and metabolite M2 is described by a series of equations as follows (de Witte et al., 2016):dTCdt=∑i=12(kon,iCvb,i×fup,iM,i−koff,i×TC,i),dTfreedt=∑i=12(−kon,iCvb,i×fup,i×TfreeM,i+koff,i×TC,i),Ttotal=Tfree+TC,TO=∑i=12TCiTtotal×100,where i = 1 and 2 corresponds to saxagliptin and active metabolite M2, respectively. TC is the concentration of saxagliptin/M2-DPP-4 complex formed. Tfree is the concentration of free DPP-4. Ttotal is the sum of TC plus Tfree. Cvb,i, kon,i, and koff,i are the drug concentration within the venous blood and the on-rate and off-rate of saxagliptin and active metabolite M2, respectively.
DDI Prediction Through the PBPK-DO Model
The DDI predictions were conducted to evaluate the parameter changes of PD (TOAUC, area under the occupancy–time curve; TOmax, maximum occupancy; and DTO>60% duration of >60% TO) and PK (AUC, area under the concentration–time curve; and Cmax, peak concentration) on saxagliptin and M2 with and without inhibitors/inducers.
ResultsData Collection of the PBPK-DO Model
The input parameters of saxagliptin and metabolite M2 for the PBPK-DO model are given in Table 1 (Rodgers et al., 2005; Kim et al., 2006; Rodgers and Rowland, 2006; Su et al., 2012; Qian et al., 2019). The corresponding parameters of inhibitors/inducers are listed in Supplementary Tables S1–S3 (Rodgers et al., 2005; Zhou et al., 2005; Rodgers and Rowland, 2006; Usach et al., 2013; Qian et al., 2019; Motiei et al., 2021; Lanni et al., 2021). The physiological parameters in humans are presented in Supplementary Table S4 (Li et al., 2012 and Qian et al., 2019)6.
Summary of input parameters for saxagliptin and metabolite M2 in the PBPK-DO model.
Property
Saxagliptin
Metabolite M2
Source
Values
Values
Molecular weight (M)
315.42 g mol−1
331.42 g mol−1
Su et al. (2012)
PKa
7.3 (base)
7.6 (base)
Obtained from the literature9 and Chemspider, respectively
LogP
−1.82 (@pH1.2)
−1.44
Obtained from the literature9 and Chemspider, respectively
Effective permeability in humans (Peff)
19 × 10–5 cm s−1
19 × 10–5 cm s−1
Calculated by ADMET Predictor 7.0
Fraction of free drug (fup)
0.95
0.95
Plasma protein binding of both compounds was very low14 and hence assigned at 5%
Blood-to-plasma concentration ratio (Rbp)
0.83
0.83
Calculated by ADMET Predictor 7.0
Initial absorption rate constant (Ka,i0/Ka,met0)
1.51 h−1 (duodenum)
0.25
Calculated based on Eq. 4 for saxagliptin; optimized by PK curves of M2 for the metabolite
2.25 h−1 (jejunum)
2.46 h−1 (ileum)
Absorption adjustment factor (fa/fa,met)
0.005 μg−1
0.00015 μg−1
Optimized by matching observed Tmax
Scale factor of metabolite conversion (fscale)
_
2
Adjusted based on the PK profile of M2 in humans
Vmax for 3A4
Vmax = 31.7 pmol M2/pmol CYP/min
_
Su et al. (2012)
Km for 3A4
Km = 81.7 μM
_
Free drug concentration in the enterocyte compartment (fugut)
1.0
1.0
Defaulted according to the literature Qian et al. (2019)
Intestine/blood concentration ratio (Kp,ent)
1.03
_
Estimated by Rodgers’ model Rodgers et al. (2005); Rodgers and Rowland (2006)
Obtained from the literature Kim et al. (2006) for saxagliptin, calculated with koff/Ki for M2 (Ki = 0.7 μM)
Off-rate (koff) from DPP-4
0.2 h−1
1.8 h−1
Obtained from the literature Kim et al. (2006) for saxagliptin and9 for M2
Development of the PBPK-DO Model for Saxagliptin and Metabolite M2
The PBPK-DO model for saxagliptin has been established by a massive number of input parameters from Table 1 and Supplementary Table S4. Figures 2A,B show the predictions and observations of the human PK and DPP-4 occupancy for saxagliptin and metabolite M2, respectively, after oral administration of a 5 mg saxagliptin. Comparison of observed PK and DPP-4 occupancy parameters with simulated parameters of saxagliptin and M2 is summarized in Supplementary Table S5. It is clearly indicated that human PK simulation corresponds closely to observed values for saxagliptin and M2 (Upreti et al., 2011) and that simulation of time course of DPP-4 occupancy in humans by saxagliptin could also be matched with experimentally determined values very well (Upreti et al., 2011). The simulation results have displayed that the developed PBPK-DO model could accurately predict PK profiles and DPP-4 time profiles in humans for saxagliptin and M2.
Simulations of pharmacokinetics and DPP-4 occupancy in humans. (A) Predicted and observed human plasma concentration–time curves of saxagliptin and M2 after oral administration at a dose of 5 mg. Black (∩) and red (∩) solid lines represent plasma concentration–time curves of saxagliptin and M2, respectively. The blue squares (.) and blue up-triangles (△) refer to experimentally measured pharmacokinetic data of saxagliptin and M2, respectively. (B) Predicted and observed time course of DPP-4 occupancy by saxagliptin in humans following oral administration of 5 mg. Black (∩), green (∩), and red (∩) solid lines represent human DPP-4 occupancy curves of saxagliptin + M2, saxagliptin, and M2, respectively. The blue circles (◯) refer to experimentally measured DPP-4 occupancy data.
Effect of Inhibitors/Inducers on PK and PD of Saxagliptin in Humans
The PK profile predictions of inhibitors/inducers have been shown in Supplementary Figure S1, and the predicted and observed data are listed in Supplementary Table S6. The accuracies of PK prediction using the developed PBPK model have been verified by comparing predicted and observed PK of the inhibitors/inducers. The comparison displayed that predicted PK profiles matched observed profiles well (Supplementary Figure S1) and that all fold errors were less than 2 between predicted and observed PK data (Supplementary Table S6). The result indicated that the established PBPK model could accurately simulate the PK process in humans of inhibitors and inducers.
The PK profiles and DPP-4 occupancy profiles of saxagliptin in humans at a dose of 5 mg were simulated using the developed PBPK-DO model after coadministration of ketoconazole (200°mg, twice daily), delavirdine (400°mg, twice daily), and rifampicin (600°mg, once daily) for 10°days, respectively (Figures 3, 4). The Cmax and AUC0-t of saxagliptin coadministrated with multidose ketoconazole on the sixth day increased by 1.89- and 3.42-fold of those without ketoconazole, respectively, (Supplementary Table S7). Slight underestimation was found in Cmax and AUC of saxagliptin for DDI predictions (Cmax ratio: 1.89; AUC0-t ratio: 3.42) versus DDI observations (Cmax ratio: 2.44; AUC0-t ratio: 3.67) after coadministration of ketoconazole7. It was observed that Cmax and AUC of saxagliptin increased by 1.33- and 1.50-fold with delavirdine compared to those without the inhibitor, respectively (Supplementary Table S8). The PK profiles and parameters of saxagliptin were strongly influenced following coadministration of rifampicin, while PK profiles and parameters of metabolite M2 were nearly unchanged (Figure 3D and Supplementary Table S9). Slight overestimation was observed in Cmax and AUC of saxagliptin compared to actually experimentally determined values, with DDI predictions of a Cmax ratio of 0.31 and an AUC0-t ratio of 0.19 versus DDI observations of a Cmax ratio of 0.42 and an AUC0-t ratio of 0.24 (Upreti et al., 2011). The predicted AUC0-t ratio of M2 was slightly below clinical experiment data (0.78 versus 0.91) (Upreti et al., 2011), while the Cmax ratio of M2 had medium differences between predicted and experimentally determined values (0.88 versus 1.38) (Upreti et al., 2011).
Human plasma level of saxagliptin and M2 following coadministration of inhibitors or inducers. Human plasma profiles of saxagliptin with ketoconazole (A, 200°mg, twice daily), with delavirdine (B, 400°mg, twice daily) and rifampicin (C, 600°mg, once daily) and of M2 with rifampicin (D). The blue squares, blue up-triangles, and red up-triangles refer to observed PK data of saxagliptin without inhibitors/inducers (□) and observed PK data of saxagliptin (△) and M2 (△) with rifampicin on the sixth day, respectively.
Human DPP-4 occupancy by saxagliptin and M2 following coadministration of inhibitors or inducers. (A) Human DPP-4 by saxagliptin of 5 mg (∩) and 2.5 mg (∩) with ketoconazole (200 mg, twice daily). (B) Human DPP-4 by saxagliptin of 5 mg (∩) with delavirdine (400°mg, twice daily). (C) Human DPP-4 by saxagliptin of 5 mg (∩) with rifampicin (600°mg, once daily). The black lines (∩) represent human DPP-4 by saxagliptin of 5 mg without inhibitors/inducers. The blue squares (□) and blue up-triangles (△) refer to observed DPP-4 occupancy data of saxagliptin and M2 without inhibitors/inducers and observed DPP-4 occupancy data of saxagliptin and M2 with rifampicin on the sixth day, respectively.
The levels of DPP-4 occupancy by saxagliptin (5 mg) and M2 coadministrated with multidose ketoconazole have been significantly enhanced compared to that without ketoconazole, with the lowest DPP-4 occupancy being >80% (Figure 4A, red line). Owing to significant improvement of DPP-4 occupancy, next, the time course of DPP-4 occupancy was simulated at a lower dose of 2.5 mg (Figure 4A, green line). The comparison between the DPP-4 occupancy profile at 5 mg of saxagliptin without ketoconazole (black line in Figure 4A) and that at 2.5 mg of saxagliptin with ketoconazole (green line in Figure 4A) demonstrated that oral saxagliptin should be decreased to 2.5 mg when there is coadministration with ketoconazole. The result was in good agreement with reported data in the literature7. Although AUC of saxagliptin increased 1.5-fold with delavirdine, however, the DPP-4 occupancy time profile by saxagliptin and M2 coadministrated with multidose delavirdine almost coincided with that without delavirdine (Figure 4B). In contrast, while Cmax and AUC of saxagliptin with coadministration of rifampicin had a considerable decrease, the percent occupancy of DPP-4 by saxagliptin and M2 with coadministration of rifampicin closely resembled that without rifampicin (Figure 4C). This prediction was in line with the published result in the study (Upreti et al., 2011). The similar occupancy of DPP-4 indicated that M2 was likely the main contributor to human DPP-4 occupancy under the circumstance of coadministration with rifampicin.
Discussion
In this study, we have developed a PBPK-DO mathematical model characterized by two simultaneous simulations (parent/metabolite and PK/PD), which was first utilized to quantify the impacts of CYP3A4 inhibitors and inducers on the PK and PD of saxagliptin and M2 in humans simultaneously. Ketoconazole and rifampicin are recommended as the standard CYP3A4 competitive inhibitor and inducer for the potential clinical DDI study, respectively. In addition, we also assessed the effect of a TDI (delavirdine) on the PK and PD of saxagliptin and M2, which is a distinct inhibition type from ketoconazole.
Although some studies have showed that P450 metabolic activity of a drug could be frequently different in the gut and the liver (Choe et al., 2017; Kapetas et al., 1208; Kurucz et al., 2019; Klomp et al., 2020), the metabolic parameter of saxagliptin by intestinal CYP3A4 was considered comparable to hepatic CYP3A4 in this PBPK-DO model. fa and ASF were incorporated into this model to optimize PK peak time of saxagliptin and scale the effective permeability4, 6, which greatly improved prediction performance. According to the literature (Upreti et al., 2011; Boulton, 2017), here, we assumed that all DPP-4 enzymes were located in the blood compartment in the present model. Across published studies (Rowland Yeo et al., 2011; Bolleddula et al., 2021), kdeg values of CYP3A4 ranged from 0.0077–1 to 0.03 h−1. However, a recent study has confirmed that it was a more reasonable value at 0.03 h−1 for the most accurate prediction (Rowland Yeo et al., 2011); hence, the kdeg value was set at 0.03 h−1 in this PBPK-DO model. The induction activity (Emax and EC50) of rifampicin suggested major individual variability between different literature studies (Yamazaki et al., 2015; Almond et al., 2016; Asaumi et al., 2018; Hanke et al., 2018). To minimize the variation, we used mean values of Emax (6.2 μm) and EC50 (0.6 μm) in the current model (Qian et al., 2019). Use of rifampicin induction parameters could have been rationalized indirectly that the predicted expression amount of mean intestinal CYP3A4 after induction by rifampicin was close to the experimentally determined value in humans8 (6.4-fold change versus 4.4-fold change). The content of human microsomal protein was set at 38 mg/g in the liver6 rather than 45 mg/g liver in some studies (Guo et al., 2013; Qian et al., 2019) because the former parameter had been proved to be a more reasonable value in more studies and built-in business software6.
All predictions were within 2-fold of observations among them, and the highest fold error was 1.5-fold, which occurred between the predicted and observed Cmax ratios of M2 with rifampicin. In accordance with FDA clinical DDI guidance (Sudsakorn et al., 2020), if the AUC ratio of a drug with and without the inhibitor is ≥1.25 or ≤0.8 with and without the inducer, clinically relevant DDI should be considered. In our simulations, the AUC ratio of saxagliptin was found to be 1.50 with delavirdine and to be 0.19 with rifampicin, which could occur in significant clinical DDIs based predominantly on PK comparisons. Nevertheless, DPP-4 occupancy by saxagliptin with delavirdine or rifampicin was almost unchanged, and PD simulations displayed that delavirdine or rifampicin would not cause human DDIs for saxagliptin, which was in good agreement in clinical experiments (Upreti et al., 2011). The simulation results also have further demonstrated the importance of two simultaneous simulations in the present model.
It was reported that the Cmax and AUC0-t of saxagliptin increased by less than 2-fold in patients with severe hepatic impairment but by more than 2-fold in patients with severe renal impairment, respectively (Boulton, 2017). PK simulation in humans with hepatic or renal impairment is performed by modulating many physiological parameters based on healthy humans (Malik et al., 2020). However, currently, this simulation model cannot simulate PK of patients with hepatic or renal impairment yet. Recent literature reported that catalytic activities of 27 CYP3A4 variants on the in vitro metabolism of saxagliptin were evaluated (Liu et al., 2021). CYP3A4 variants showed decreased activities ranging from 1.9 to 77.1% as compared to the wild type. Hence, we also preliminarily evaluated the effect of genetic variations in metabolizing enzymes on PK and PD of saxagliptin using this model. Here, we only simulated the PK and PD of saxagliptin in humans with variant CYP3A4*22. In the simulation, expression and activity of CYP3A4 were replaced with 59 and 40% of the wild type (Alqahtani and Kaddoumi, 2016). The results displayed that Cmax and AUC0-t of saxagliptin increased by about 2-fold, and DPP-4 occupancies by saxagliptin between three oral doses (5, 2.5, and 1 mg) have a slight difference (Supplementary Table S10 and Supplementary Figure S2). Of note, due to clinical data unavailability, prediction accuracy need be proven with further in vivo studies.
Conclusion
Taken together, this mathematic model is characterized by two simultaneous simulations (parent/metabolite and PK/PD), describing two interaction processes between inhibitors/inducer-CYP3A4 and saxagliptin/M2-DPP-4. We conceive that compared to most current single PK-DDI predictions, the wide application of the PBPK-DO model has the power to improve the predictions of potential clinical DDIs for victim drugs metabolized by CYP3A4.
Data Availability Statement
The original contributions presented in the study are included in the article/Supplementary Material further inquiries can be directed to the corresponding authors.
Author Contributions
Conceptualization, HL, YL, and GW; methodology, GL, BY, and GW; software, GL and FP; validation, BY and JL; formal analysis, GW, GL, and BY; investigation, JL; resources, XJ; data curation, WY; writing—original draft preparation, GW, GL, and BY; writing—review and editing, GW, GL, and BY; visualization, GL and BY; supervision, GL and BY; project administration, HL, YL, and GW. All authors have read and agreed to the published version of the article.
Conflict of Interest
The authors GL and GW were employed by the companies Beijing Adamadle Biotech Co., Ltd. and Zhongcai Health (Beijing) Biological Technology Development Co., Ltd., respectively.
The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisher’s Note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations or those of the publisher, the editors, and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
Supplementary Material
The Supplementary Material for this article can be found online at https://www.frontiersin.org/articles/10.3389/fphar.2021.746594/full#supplementary-material
Saxagliptin.pdf
Clinical Pharmacokinetics of Ketoconazole.pdf
Delavirdine mesylate and didanosine
PBPK book.pdf
Drug–drug interaction of.pdf
GastroPlusManual-9.7.pdf
Saxagliptin instruction.pdf
ClinPharmR_P1.pdf
The role of intestinal P-glycoprotein.pdf
Clinical Pharmacokinetics of Systemically.pdf
Disposition of Azole Antifungal Agents. I.pdf
metabolism of dalavirdine.pdf
An Examination of IC50 and IC50-Shift Experiments in Assessing.pdf
IF.pdf
ReferencesAlmondL. M.MukadamS.GardnerI.OkialdaK.WongS.HatleyO. (2016). Prediction of Drug-Drug Interactions Arising from CYP3A Induction Using a Physiologically Based Dynamic Model. Drug Metab. Dispos44, 821–832. 10.1124/dmd.115.066845AlqahtaniS.KaddoumiA. (2016). Development of a Physiologically Based Pharmacokinetic/Pharmacodynamic Model to Predict the Impact of Genetic Polymorphisms on the Pharmacokinetics and Pharmacodynamics Represented by Receptor/Transporter Occupancy of Central Nervous System Drugs. Clin. Pharmacokinet.55, 957–969. 10.1007/s40262-016-0367-6AsaumiR.ToshimotoK.TobeY.HashizumeK.NunoyaK. I.ImawakaH. (2018). Comprehensive PBPK Model of Rifampicin for Quantitative Prediction of Complex Drug-Drug Interactions: CYP3A/2C9 Induction and OATP Inhibition Effects. CPT Pharmacometrics Syst. Pharmacol.7, 186–196. 10.1002/psp4.12275Author AnonymousClinical Pharmacokinetics of Ketoconazole.Pdf>.BaneyxG.ParrottN.MeilleC.IliadisA.LavéT. (2014). Physiologically Based Pharmacokinetic Modeling of CYP3A4 Induction by Rifampicin in Human: Influence of Time between Substrate and Inducer Administration. Eur. J. Pharm. Sci.56, 1–15. 10.1016/j.ejps.2014.02.002BolleddulaJ.KeA.YangH.PrakashC. (2021). PBPK Modeling to Predict Drug-Drug Interactions of Ivosidenib as a Perpetrator in Cancer Patients and Qualification of the Simcyp Platform for CYP3A4 Induction. CPT Pharmacometrics Syst. Pharmacol.10, 577–588. 10.1002/psp4.12619BoultonD. W. (2017). Clinical Pharmacokinetics and Pharmacodynamics of Saxagliptin, a Dipeptidyl Peptidase-4 Inhibitor. Clin. Pharmacokinet.56, 11–24. 10.1007/s40262-016-0421-4ChoeA.HaS. K.ChoiI.ChoiN.SungJ. H. (2017). Microfluidic Gut-Liver Chip for Reproducing the First Pass Metabolism. Biomed. Microdevices19, 4. 10.1007/s10544-016-0143-2ConnerT. M.NikolianV. C.GeorgoffP. E.PaiM. P.AlamH. B.SunD. (2018). Physiologically Based Pharmacokinetic Modeling of Disposition and Drug-Drug Interactions for Valproic Acid and Divalproex. Eur. J. Pharm. Sci.111, 465–481. 10.1016/j.ejps.2017.10.009de WitteW. E. A.DanhofM.van der GraafP. H.de LangeE. C. M. (2016). In Vivo Target Residence Time and Kinetic Selectivity: The Association Rate Constant as Determinant. Trends Pharmacol. Sci.37, 831–842. 10.1016/j.tips.2016.06.008DmitrievA. V.FilimonovD. A.RudikA. V.PogodinP. V.KarasevD. A.LaguninA. A. (2019). Drug-drug Interaction Prediction Using PASS. SAR QSAR Environ. Res.30, 655–664. 10.1080/1062936X.2019.1653966EngH.TsengE.CernyM. A.GoosenT. C.ObachR. S. (2020). Cytochrome P450 3A Time-dependent Inhibition Assays Are Too Sensitive for Identification of Drugs Causing Clinically Significant Drug-Drug Interactions: A Comparison of Human Liver Microsomes and Hepatocytes and Definition of Boundaries for Inactivation Rate Constants. Drug Metab. Dispos49, 442–450. 10.1124/dmd.121.000356FuQ.SunX.LustburgM. B.SparreboomA.HuS. (2019). Predicting Paclitaxel Disposition in Humans with Whole-Body Physiologically-Based Pharmacokinetic Modeling. CPT Pharmacometrics Syst. Pharmacol.8, 931–939. 10.1002/psp4.12472FuraA.KhannaA.VyasV.KoplowitzB.ChangS. Y.CaporuscioC. (2009). Pharmacokinetics of the Dipeptidyl Peptidase 4 Inhibitor Saxagliptin in Rats, Dogs, and Monkeys and Clinical Projections. Drug Metab. Dispos37, 1164–1171. 10.1124/dmd.108.026088GuoH.LiuC.LiJ.ZhangM.HuM.XuP. (2013). A Mechanistic Physiologically Based Pharmacokinetic-Enzyme Turnover Model Involving Both Intestine and Liver to Predict CYP3A Induction-Mediated Drug-Drug Interactions. J. Pharm. Sci.102, 2819–2836. 10.1002/jps.23613HankeN.FrechenS.MojD.BritzH.EissingT.WendlT. (2018). PBPK Models for CYP3A4 and P-Gp DDI Prediction: A Modeling Network of Rifampicin, Itraconazole, Clarithromycin, Midazolam, Alfentanil, and Digoxin. CPT Pharmacometrics Syst. Pharmacol.7, 647–659. 10.1002/psp4.12343KapetasA. J.SorishA. J.RodriguesA. D. Guidance for Rifampin and Midazolam Dosing Protocols to Study Intestinal and Hepatic Cytochrome P450 (CYP) 3A4 Induction and De-induction. AAPS J.10.1208/s12248-019-0341-yKimY. B.KopchoL. M.KirbyM. S.HamannL. G.WeigeltC. A.MetzlerW. J. (2006). Mechanism of Gly-Pro-pNA Cleavage Catalyzed by Dipeptidyl Peptidase-IV and its Inhibition by Saxagliptin (BMS-477118). Arch. Biochem. Biophys.445, 9–18. 10.1016/j.abb.2005.11.010KlompF.WenzelC.DrozdzikM.OswaldS. (2020). Drug-Drug Interactions Involving Intestinal and Hepatic CYP1A Enzymes. Pharmaceutics12. 10.3390/pharmaceutics12121201KosakaM.KosugiY.HirabayashiH. (2017). Risk Assessment Using Cytochrome P450 Time-dependent Inhibition Assays at Single Time and Concentration in the Early Stage of Drug Discovery. J. Pharm. Sci.106, 2839–2846. 10.1016/j.xphs.2017.04.077KuruczÁ.OrbánK.MackeiM.FébelH.NeográdyZ.MátisG. (2019). Investigations on Hepatic and Intestinal Drug-Metabolizing Cytochrome P450 Enzymes in Wild Boar Compared to Domestic Swine. Eur. J. Wildl. Res.66. 10.1007/s10344-019-1345-8LanniF.BurtonN.HarrisD.FotheringhamS.ClarkS.SkinnerO. (2021). The Potential of Microdialysis to Estimate Rifampicin Concentrations in the Lung of guinea Pigs. PLoS One16, e0245922. 10.1371/journal.pone.0245922LiG. F.WangK.ChenR.ZhaoH. R.YangJ.ZhengQ. S. (2012). Simulation of the Pharmacokinetics of Bisoprolol in Healthy Adults and Patients with Impaired Renal Function Using Whole-Body Physiologically Based Pharmacokinetic Modeling. Acta Pharmacol. Sin33, 1359–1371. 10.1038/aps.2012.103LiuQ.Ou‐YangQ. G.LinQ. M.LuX. R.MaY. Q.LiY. H. (2021). Effects of 27 CYP3A4 Protein Variants on Saxagliptin Metabolism In Vitro. Fundam. Clin. Pharmacol.10.1111/fcp.12693MalikP. R. V.YeungC. H. T.IsmaeilS.AdvaniU.DjieS.EdgintonA. N. (2020). A Physiological Approach to Pharmacokinetics in Chronic Kidney Disease. J. Clin. Pharmacol.60 Suppl 1 (Suppl. 1), S52–S62. 10.1002/jcph.1713MotieiM.Pleno de GouveiaL.ŠopíkT.VíchaR.ŠkodaD.CísařJ. (2021). Nanoparticle-Based Rifampicin Delivery System Development. Molecules26. 10.3390/molecules26072067PelkonenO.HakkolaJ.HukkanenJ.TurpeinenM. (2020). CYP-associated Drug-Drug Interactions: A mission Accomplished. Arch. Toxicol.94, 3931–3934. 10.1007/s00204-020-02912-1PengY.ChengZ.XieF. (2021). Evaluation of Pharmacokinetic Drug-Drug Interactions: A Review of the Mechanisms, In Vitro and In Silico Approaches. Metabolites11, 75. 10.3390/metabo11020075QianC. Q.ZhaoK. J.ChenY.LiuL.LiuX. D. (2019). Simultaneously Predict Pharmacokinetic Interaction of Rifampicin with Oral versus Intravenous Substrates of Cytochrome P450 3A/Pglycoprotein to Healthy Human Using a Semi-physiologically Based Pharmacokinetic Model Involving Both Enzyme and Transporter Turnover. Eur. J. Pharm. Sci.134, 194–204. 10.1016/j.ejps.2019.04.026RodgersT.LeahyD.RowlandM. (2005). Physiologically Based Pharmacokinetic Modeling 1: Predicting the Tissue Distribution of Moderate-To-strong Bases. J. Pharm. Sci.94, 1259–1276. 10.1002/jps.20322RodgersT.RowlandM. (2006). Physiologically Based Pharmacokinetic Modelling 2: Predicting the Tissue Distribution of Acids, Very Weak Bases, Neutrals and Zwitterions. J. Pharm. Sci.95, 1238–1257. 10.1002/jps.20502Author AnonymousThe Role of Intestinal P-glycoprotein.Rowland YeoK.WalskyR. L.JameiM.Rostami-HodjeganA.TuckerG. T. (2011). Prediction of Time-dependent CYP3A4 Drug-Drug Interactions by Physiologically Based Pharmacokinetic Modelling: Impact of Inactivation Parameters and Enzyme Turnover. Eur. J. Pharm. Sci.43, 160–173. 10.1016/j.ejps.2011.04.008RytkönenJ.RantaV. P.KokkiM.KokkiH.HautajärviH.RinneV. (2020). Physiologically Based Pharmacokinetic Modelling of Oxycodone Drug-Drug Interactions. Biopharm. Drug Dispos41, 72–88. 10.1002/bdd.2215SpanakisM.SfakianakisS.KallergisG.SpanakisE. G.SakkalisV. (2019). PharmActa: Personalized Pharmaceutical Care eHealth Platform for Patients and Pharmacists. J. Biomed. Inform.100, 103336. 10.1016/j.jbi.2019.103336SuH.BoultonD. W.BarrosA.WangL.CaoK.BonacorsiS. J. (2012). Characterization of the In Vitro and In Vivo Metabolism and Disposition and Cytochrome P450 Inhibition/induction Profile of Saxagliptin in Human. Drug Metab. Dispos40, 1345–1356. 10.1124/dmd.112.045450SudsakornS.BahadduriP.FretlandJ.LuC. (2020). 2020 FDA Drug-Drug Interaction Guidance: A Comparison Analysis and Action Plan by Pharmaceutical Industrial Scientists. Curr. Drug Metab.21, 403–426. 10.2174/1389200221666200620210522TannaR. S.TianD. D.CechN. B.OberliesN. H.RettieA. E.ThummelK. E. (2021). Refined Prediction of Pharmacokinetic Kratom-Drug Interactions: Time-dependent Inhibition Considerations. J. Pharmacol. Exp. Ther.376, 64–73. 10.1124/jpet.120.000270UpretiV. V.BoultonD. W.LiL.ChingA.SuH.LacretaF. P. (2011). Effect of Rifampicin on the Pharmacokinetics and Pharmacodynamics of Saxagliptin, a Dipeptidyl Peptidase-4 Inhibitor, in Healthy Subjects. Br. J. Clin. Pharmacol.72, 92–102. 10.1111/j.1365-2125.2011.03937.xUsachI.MelisV.PerisJ. E. (2013). Non-nucleoside Reverse Transcriptase Inhibitors: a Review on Pharmacokinetics, Pharmacodynamics, Safety and Tolerability. J. Int. AIDS Soc.16, 1–14. 10.7448/IAS.16.1.18567ValicherlaG. R.MishraA.LenkalapellyS.JillelaB.FrancisF. M.RajagopalanL. (2019). Investigation of the Inhibition of Eight Major Human Cytochrome P450 Isozymes by a Probe Substrate Cocktail In Vitro with Emphasis on CYP2E1. Xenobiotica49, 1396–1402. 10.1080/00498254.2019.1581301XuR.TangH.ChenL.GeW.YangJ. (2021). Developing a Physiologically Based Pharmacokinetic Model of Apixaban to Predict Scenarios of Drug-Drug Interactions, Renal Impairment and Paediatric Populations. Br. J. Clin. Pharmacol.87, 3244–3254. 10.1111/bcp.14743YamazakiS.JohnsonT. R.SmithB. J. (2015). Prediction of Drug-Drug Interactions with Crizotinib as the CYP3A Substrate Using a Physiologically Based Pharmacokinetic Model. Drug Metab. Dispos43, 1417–1429. 10.1124/dmd.115.064618ZhouR.MoenchP.HeranC.LuX.MathiasN.FariaT. N. (2005). pH-dependent Dissolution In Vitro and Absorption In Vivo of Weakly Basic Drugs: Development of a Canine Model. Pharm. Res.22, 188–192. 10.1007/s11095-004-1185-3