A mechanistic physiologically based pharmacokinetic-enzyme turnover model involving both intestine and liver to predict CYP3A induction-mediated drug-drug interactions

Pharmacokinetics-Pharmacodynamics Modeling for Evaluating Drug-Drug Interactions in Polypharmacy: Development and Challenges

Polypharmacy is commonly employed in clinical settings. The potential risks of drug-drug interactions (DDIs) can compromise efficacy and pose serious health hazards. Integrating pharmacokinetics (PK) and pharmacodynamics (PD) models into DDIs research provides a reliable method for evaluating and optimizing drug regimens. With advancements in our comprehension of both individual drug mechanisms and DDIs, conventional models have begun to evolve towards more detailed and precise directions, especially in terms of the simulation and analysis of physiological mechanisms. Selecting appropriate models is crucial for an accurate assessment of DDIs. This review details the theoretical frameworks and quantitative benchmarks of PK and PD modeling in DDI evaluation, highlighting the establishment of PK/PD modeling against a backdrop of complex DDIs and physiological conditions, and further showcases the potential of quantitative systems pharmacology (QSP) in this field. Furthermore, it explores the current advancements and challenges in DDI evaluation based on models, emphasizing the role of emerging in vitro detection systems, high-throughput screening technologies, and advanced computational resources in improving prediction accuracy.

Omeprazole Induces CYP3A4 mRNA Expression but Not CYP3A4 Protein Expression in HepaRG Cells

Unknown interactions between drugs remain the limiting factor for clinical application of drugs, and the induction and inhibition of drug-metabolizing CYP enzymes are considered the key to examining the drug-drug interaction (DDI). In this study, using human HepaRG cells as an in vitro model system, we analyzed the potential DDI based on the expression levels of CYP3A4 and CYP1A2. Rifampicin and omeprazole, the potent inducers for CYP3A4 and CYP1A2, respectively, induce expression of the corresponding CYP enzymes at both the mRNA and protein levels. We noticed that, in addition to inducing CYP1A2, omeprazole induced CYP3A4 mRNA expression in HepaRG cells. However, unexpectedly, CYP3A4 protein expression levels were not increased after omeprazole treatment. Concurrent administration of rifampicin and omeprazole showed an inhibitory effect of omeprazole on the CYP3A4 protein expression induced by rifampicin, while its mRNA induction remained intact. Cycloheximide chase assay revealed increased CYP3A4 protein degradation in the cells exposed to omeprazole. The data presented here suggest the potential importance of broadening the current DDI examination beyond conventional transcriptional induction and enzyme-activity inhibition tests to include post-translational regulation analysis of CYP enzyme expression.

Effect of CYP3A4 Inhibitors and Inducers on Pharmacokinetics and Pharmacodynamics of Saxagliptin and Active Metabolite M2 in Humans Using Physiological-Based Pharmacokinetic Combined DPP-4 Occupancy

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.

A Whole-Body Physiologically Based Pharmacokinetic Model Characterizing Interplay of OCTs and MATEs in Intestine, Liver and Kidney to Predict Drug-Drug Interactions of Metformin with Perpetrators

Transmembrane transport of metformin is highly controlled by transporters including organic cation transporters (OCTs), plasma membrane monoamine transporter (PMAT), and multidrug/toxin extrusions (MATEs). Hepatic OCT1, intestinal OCT3, renal OCT2 on tubule basolateral membrane, and MATE1/2-K on tubule apical membrane coordinately work to control metformin disposition. Drug–drug interactions (DDIs) of metformin occur when co-administrated with perpetrators via inhibiting OCTs or MATEs. We aimed to develop a whole-body physiologically based pharmacokinetic (PBPK) model characterizing interplay of OCTs and MATEs in the intestine, liver, and kidney to predict metformin DDIs with cimetidine, pyrimethamine, trimethoprim, ondansetron, rabeprazole, and verapamil. Simulations showed that co-administration of perpetrators increased plasma exposures to metformin, which were consistent with clinic observations. Sensitivity analysis demonstrated that contributions of the tested factors to metformin DDI with cimetidine are gastrointestinal transit rate > inhibition of renal OCT2 ≈ inhibition of renal MATEs > inhibition of intestinal OCT3 > intestinal pH > inhibition of hepatic OCT1. Individual contributions of transporters to metformin disposition are renal OCT2 ≈ renal MATEs > intestinal OCT3 > hepatic OCT1 > intestinal PMAT. In conclusion, DDIs of metformin with perpetrators are attributed to integrated effects of inhibitions of these transporters.

Physiologically-Based Pharmacokinetic-Pharmacodynamics Model Characterizing CYP2C19 Polymorphisms to Predict Clopidogrel Pharmacokinetics and Its Anti-Platelet Aggregation Effect Following Oral Administration to Coronary Artery Disease Patients With or Without Diabetes

Background and Objective: Clopidogrel (CLOP) is commonly used in coronary artery disease (CAD) patients with or without diabetes (DM), but these patients often suffer CLOP resistance, especially those with diabetes. This study was aimed to develop a physiologically-based pharmacokinetic-pharmacodynamic (PBPK-PD) model to describe the pharmacokinetics and pharmacodynamics of clopidogrel active metabolite (CLOP-AM) in CAD patients with or without DM.

Methods: The PBPK-PD model was first established and validated in healthy subjects and then in CAD patients with or without DM. The influences of CYP2C19, CYP2C9, CYP3A4, carboxylesterase 1 (CES1), gastrointestinal transit rates (K_t,i) and platelets response to CLOP-AM (k_irre) on predicted pharmacokinetics and pharmacodynamics were investigated, followed with their individual and integrated effects on CLOP-AM pharmacokinetics due to changes in DM status.

Results: Most predictions fell within 0.5–2.0 folds of observations, indicating successful predictions. Sensitivity analysis showed that contributions of interested factors to pharmacodynamics were CES1> k_irre> K_t,i> CYP2C19 > CYP3A4> CYP2C9. Mimicked analysis showed that the decreased exposure of CLOP-AM by DM was mainly attributed to increased CES1 activity, followed by decreased CYP2C19 activity.

Conclusion: The pharmacokinetics and pharmacodynamics of CLOP-AM were successfully predicted using the developed PBPK-PD model. Clopidogrel resistance by DM was the integrated effects of altered K_t,i, CYP2C19, CYP3A4, CES1 and k_irre.

Prediction of Cyclosporin-Mediated Drug Interaction Using Physiologically Based Pharmacokinetic Model Characterizing Interplay of Drug Transporters and Enzymes

Uptake transporter organic anion transporting polypeptides (OATPs), efflux transporters (P-gp, BCRP and MRP2) and cytochrome P450 enzymes (CYP450s) are widely expressed in the liver, intestine or kidney. They coordinately work to control drug disposition, termed as "interplay of transporters and enzymes". Cyclosporine A (CsA) is an inhibitor of OATPs, P-gp, MRP2, BCRP and CYP3As. Drug-drug interaction (DDI) of CsA with victim drugs occurs via disordering interplay of transporters and enzymes. We aimed to establish a whole-body physiologically-based pharmacokinetic (PBPK) model which predicts disposition of CsA and nine victim drugs including atorvastatin, cerivastatin, pravastatin, rosuvastatin, fluvastatin, simvastatin, lovastatin, repaglinide and bosentan, as well as drug-drug interactions (DDIs) of CsA with nine victim drugs to investigate the integrated effect of enzymes and transporters in liver, intestinal and kidney on drug disposition. Predictions were compared with observations. Most of the predictions were within 0.5-2.0 folds of observations. Atorvastatin was represented to investigate individual contributions of transporters and CYP3As to atorvastatin disposition and their integrated effect. The contributions to atorvastatin disposition were hepatic OATPs >> hepatic CYP3A > intestinal CYP3As ≈ efflux transporters (P-gp/BCRP/MRP2). The results got the conclusion that the developed PBPK model characterizing the interplay of enzymes and transporters was successfully applied to predict the pharmacokinetics of 10 OATP substrates and DDIs of CsA with 9 victim drugs.

Physiologically Based Pharmacokinetic Model of the DPP-4 Inhibitor Linagliptin to Describe its Nonlinear Pharmacokinetics in Humans

Linagliptin, a dipeptidyl peptidase (DPP)-4 inhibitor, for type 2 diabetes exhibits nonlinear plasma protein binding in the therapeutic concentration range due to its high affinity binding to the pharmacological target DPP-4, and its pharmacokinetics both in plasma and urine is also nonlinear. The purpose of the present study was to explain the nonlinear pharmacokinetic profiles using a physiologically based pharmacokinetic (PBPK) model with saturable binding of linagliptin to soluble and membrane-bound DPP-4 in blood and organs including kidneys. The model was first fitted to previously reported full-scale plasma concentrations and urinary excretion data at 4 intravenous (iv) dose levels. Additional fitting to the data from 4 oral (po) dose levels was then performed to yield the final iv-po based model including gastrointestinal absorption-associated parameters. Data from [¹⁴C]linagliptin mass balance study were also used for optimizing parameters related to enterohepatic circulation. The PBPK model was thus constructed and well describes the nonlinear pharmacokinetic profiles of linagliptin in both plasma and urine, demonstrating that the nonlinear pharmacokinetics are fully explained by its specific binding to target protein. The present study thus introduces the involvement of target-mediated disposition for linagliptin in humans.

Physiologically based pharmacokinetic-pharmacodynamic modeling for prediction of vonoprazan pharmacokinetics and its inhibition on gastric acid secretion following intravenous/oral administration to rats, dogs and humans

Vonoprazan is characterized as having a long-lasting antisecretory effect on gastric acid. In this study we developed a physiologically based pharmacokinetic (PBPK)-pharmacodynamic (PD) model linking to stomach to simultaneously predict vonoprazan pharmacokinetics and its antisecretory effects following administration to rats, dogs, and humans based on in vitro parameters. The vonoprazan disposition in the stomach was illustrated using a limited-membrane model. In vitro metabolic and transport parameters were derived from hepatic microsomes and Caco-2 cells, respectively. We found the most predicted plasma concentrations and pharmacokinetic parameters of vonoprazan in rats, dogs and humans were within twofold errors of the observed data. Free vonoprazan concentrations (f_u × C₂) in the stomach were simulated and linked to the antisecretory effects of the drug (I) (increases in pH or acid output) using the fomula dI/dt = k × f_u × C₂ × (I_max − I) − k_d × I. The vonoprazan dissociation rate constant k_d (0.00246 min⁻¹) and inhibition index K_I (35 nM) for H⁺/K⁺-ATPase were obtained from literatures. The vonoprazan-H⁺/K⁺-ATPase binding rate constant k was 0.07028 min⁻¹· μM⁻¹ using ratio of k_d to K_I. The predicted antisecretory effects were consistent with the observations following intravenous administration to rats (0.7 and 1.0 mg/kg), oral administration to dogs (0.3 and 1.0 mg/kg) and oral single dose or multidose to humans (20, 30, and 40 mg). Simulations showed that vonoprazan concentrations in stomach were 1000-fold higher than those in the plasma at 24 h following administration to human. Vonoprazan pharmacokinetics and its antisecretory effects may be predicted from in vitro data using the PBPK-PD model of the stomach. These findings may highlight 24-h antisecretory effects of vonoprazan in humans following single-dose or the sustained inhibition throughout each 24-h dosing interval during multidose administration.

The Segregated Intestinal Flow Model (SFM) for Drug Absorption and Drug Metabolism: Implications on Intestinal and Liver Metabolism and Drug-Drug Interactions

The properties of the segregated flow model (SFM), which considers split intestinal flow patterns perfusing an active enterocyte region that houses enzymes and transporters (<20% of the total intestinal blood flow) and an inactive serosal region (>80%), were compared to those of the traditional model (TM), wherein 100% of the flow perfuses the non-segregated intestine tissue. The appropriateness of the SFM model is important in terms of drug absorption and intestinal and liver drug metabolism. Model behaviors were examined with respect to intestinally (M1) versus hepatically (M2) formed metabolites and the availabilities in the intestine (F_I) and liver (F_H) and the route of drug administration. The %contribution of the intestine to total first-pass metabolism bears a reciprocal relation to that for the liver, since the intestine, a gateway tissue, regulates the flow of substrate to the liver. The SFM predicts the highest and lowest M1 formed with oral (po) and intravenous (iv) dosing, respectively, whereas the extent of M1 formation is similar for the drug administered po or iv according to the TM, and these values sit intermediate those of the SFM. The SFM is significant, as this drug metabolism model explains route-dependent intestinal metabolism, describing a higher extent of intestinal metabolism with po versus the much reduced or absence of intestinal metabolism with iv dosing. A similar pattern exists for drug–drug interactions (DDIs). The inhibitor or inducer exerts its greatest effect on victim drugs when both inhibitor/inducer and drug are given po. With po dosing, more drug or inhibitor/inducer is brought into the intestine for DDIs. The bypass of flow and drug to the enterocyte region of the intestine after intravenous administration adds complications to in vitro–in vivo extrapolations (IVIVE).

Theoretical consideration of the properties of intestinal flow models on route-dependent drug removal: Segregated Flow (SFM) vs. Traditional (TM)

The intestine is endowed with a plethora of enzymes and transporters and regulates the flow of substrate to the liver. Physiologically-based pharmacokinetic models have surfaced to describe intestinal removal. The traditional model (TM) describes the intestinal flow as a whole perfusing the entire tissue that contains the intestinal transporters and enzymes. The segregated flow model (SFM) describes that only a fraction (f_Q  < 0.2) of the intestinal blood flow perfuses the enterocyte region where the intestinal enzymes and transporters are housed, rendering a lower drug distribution/intestinal clearance when drug enters via the circulation than from the gut lumen. As shown by simulations, a higher intestinal clearance and extraction ratio (E_I,iv ) exists for the TM than for SFM after iv dosing. By contrast, the E_I,po after po dosing is higher for the SFM, due to the smaller volume of distribution for the enterocyte region and a lower flow rate that result in increased mean residence time and higher drug extraction. Under MBI (mechanism-based inhibition), the AUC_R,po after oral bolus is the highest for drug when inhibitor is given orally, with SFM > TM. Competitive inhibition of intestinal enzymes leads to higher liver metabolism; again, when both drug and inhibitor are given orally, changes in the SFM > TM. However, less definitive patterns result with inhibition of both intestinal and liver enzymes. In conclusion, differences exist for E_I and drug-drug interaction (DDI) between the TM and SFM. The fractional intestinal blood flow (f_Q ) is a key factor affecting different extents of intestinal/liver metabolism of the drug after oral as well as intravenous administration.

Short-chain fatty acids oppositely altered expressions and functions of intestinal cytochrome P4503A and P-glycoprotein and affected pharmacokinetics of verapamil following oral administration to rats

OBJECTIVES

To investigate effects of short-chain fatty acids (SCFAs) on expressions and functions of intestinal cytochrome P4503A (Cyp3a) and P-glycoprotein (P-gp). To develop a semi-physiologically based pharmacokinetic (semi-PBPK) model for assessing their contributions.

METHODS

Verapamil pharmacokinetics was investigated following oral administration to rats receiving water containing 150 mm SCFAs for 3 weeks. Cyp3a activities in intestinal and liver mircosomes were assessed by norverapamil formation. In-situ single-pass perfusion was used to evaluate intestinal transport of verapamil and P-gp function. Functions and expressions of Cyp3a and P-gp were measured in mouse primary enterocytes following 48-h exposure to SCFAs. Contributions of intestinal P-gp and Cyp3a to verapamil pharmacokinetics were assessed using a semi-PBPK model.

KEY FINDINGS

Short-chain fatty acids significantly increased oral plasma exposures of verapamil and norverapamil. SCFAs upregulated Cyp3a activity and expression, but downregulated P-gp function and expression in rat intestine, which were repeated in mouse primary enterocytes. PBPK simulation demonstrated contribution of intestinal Cyp3a to oral plasma verapamil exposure was minor, and the increased oral plasma verapamil exposure was mainly attributed to downregulation of intestinal P-gp.

CONCLUSIONS

Short-chain fatty acids oppositely regulated functions and expressions of intestinal Cyp3a and P-gp. The downregulation of P-gp mainly contributed to the increased oral plasma verapamil exposure by SCFAs.

Transporter-Mediated Drug-Drug Interactions and Their Significance

Drug transporters are considered to be determinants of drug disposition and effects/toxicities by affecting the absorption, distribution, and excretion of drugs. Drug transporters are generally divided into solute carrier (SLC) family and ATP binding cassette (ABC) family. Widely studied ABC family transporters include P-glycoprotein (P-GP), breast cancer resistance protein (BCRP), and multidrug resistance proteins (MRPs). SLC family transporters related to drug transport mainly include organic anion-transporting polypeptides (OATPs), organic anion transporters (OATs), organic cation transporters (OCTs), organic cation/carnitine transporters (OCTNs), peptide transporters (PEPTs), and multidrug/toxin extrusions (MATEs). These transporters are often expressed in tissues related to drug disposition, such as the small intestine, liver, and kidney, implicating intestinal absorption of drugs, uptake of drugs into hepatocytes, and renal/bile excretion of drugs. Most of therapeutic drugs are their substrates or inhibitors. When they are comedicated, serious drug-drug interactions (DDIs) may occur due to alterations in intestinal absorption, hepatic uptake, or renal/bile secretion of drugs, leading to enhancement of their activities or toxicities or therapeutic failure. This chapter will illustrate transporter-mediated DDIs (including food drug interaction) in human and their clinical significances.

Physiologically-Based Pharmacokinetic Modeling Approach to Predict Rifampin-Mediated Intestinal P-Glycoprotein Induction

Physiologically‐based pharmacokinetic (PBPK) modeling is a powerful tool to quantitatively describe drug disposition profiles in vivo, thereby providing an alternative to predict drug–drug interactions (DDIs) that have not been tested clinically. This study aimed to predict effects of rifampin‐mediated intestinal P‐glycoprotein (Pgp) induction on pharmacokinetics of Pgp substrates via PBPK modeling. First, we selected four Pgp substrates (digoxin, talinolol, quinidine, and dabigatran etexilate) to derive in vitro to in vivo scaling factors for intestinal Pgp kinetics. Assuming unbound Michaelis‐Menten constant (K_m) to be intrinsic, we focused on the scaling factors for maximal efflux rate (J_max) to adequately recover clinically observed results. Next, we predicted rifampin‐mediated fold increases in intestinal Pgp abundances to reasonably recover clinically observed DDI results. The modeling results suggested that threefold to fourfold increases in intestinal Pgp abundances could sufficiently reproduce the DDI results of these Pgp substrates with rifampin. Hence, the obtained fold increases can potentially be applicable to DDI prediction with other Pgp substrates.

Prediction of Atorvastatin Pharmacokinetics in High-Fat Diet and Low-Dose Streptozotocin-Induced Diabetic Rats Using a Semiphysiologically Based Pharmacokinetic Model Involving Both Enzymes and Transporters

Atorvastatin is a substrate of cytochrome P450 3a (CYP3a), organic anion-transporting polypeptides (OATPs), breast cancer-resistance protein (BCRP), and P-glycoprotein (P-gp). We aimed to develop a semiphysiologically based pharmacokinetic (semi-PBPK) model involving both enzyme and transporters for predicting the contributions of altered function and expression of CYP3a and transporters to atorvastatin transport in diabetic rats by combining high-fat diet feeding and low-dose streptozotocin injection. Atorvastatin metabolism and transport parameters comes from in situ intestinal perfusion, primary hepatocytes, and intestinal or hepatic microsomes. We estimated the expressions and functions of these proteins and their contributions. Diabetes increased the expression of hepatic CYP3a, OATP1b2, and P-gp but decreased the expression of intestinal CYP3a, OATP1a5, and P-gp. The expression and function of intestinal BCRP were significantly decreased in 10-day diabetic rats but increased in 22-day diabetic rats. Based on alterations in CYP3a and transporters by diabetes, the developed semi-PBPK model was successfully used to predict atorvastatin pharmacokinetics after oral and intravenous doses to rats. Contributions to oral atorvastatin PK were intestinal OATP1a5 < intestinal P-gp < intestinal CYP3a < hepatic CYP3a < hepatic OATP1b2 < intestinal BRCP. Contributions of decreased expression and function of intestinal CYP3a and P-gp by diabetes to oral atorvastatin plasma exposure were almost attenuated by increased expression and function of hepatic CYP3a and OATP1b2. Opposite alterations in oral plasma atorvastatin exposure in 10- and 22-day diabetic rats may be explained by altered intestinal BCRP. In conclusion, the altered atorvastatin pharmacokinetics by diabetes was the synergistic effects of altered intestinal or hepatic CYP3a and transporters and could be predicted using the developed semi-PBPK.

Simultaneously predict pharmacokinetic interaction of rifampicin with oral versus intravenous substrates of cytochrome P450 3A/P‑glycoprotein to healthy human using a semi-physiologically based pharmacokinetic model involving both enzyme and transporter turnover

Several reports demonstrated that rifampicin affected pharmacokinetics of victim drugs following oral more than intravenous administration. We aimed to establish a semi-physiologically based pharmacokinetic (semi-PBPK) model involving both enzyme and transporter turnover to simultaneously predict pharmacokinetic interaction of rifampicin with oral versus intravenous substrates of cytochrome P450 (CYP) 3A4/P‑glycoprotein (P-GP) in human. Rifampicin was chosen as the CYP3A /P-GP inducer. Thirteen victim drugs including P-GP substrates (digoxin and talinolol), CYP3A substrates (alfentanil, midazolam, nifedipine, ondansetron and oxycodone), dual substrates of CYP3A/P-GP (quinidine, cyclosporine A, tacrolimus and verapamil) and complex substrates (S-ketamine and tramadol) were chosen to investigate drug-drug interactions (DDIs) with rifampicin. Corresponding parameters were cited from literatures. Before and after multi-dose of oral rifampicin, the pharmacokinetic profiles of victim drugs for oral or intravenous administration to human were predicted using the semi-PBPK model and compared with the observed values. Contribution of both CYP3A and P-GP induction in intestine and liver by rifampicin to pharmacokinetic profiles of victim drugs was investigated. The predicted pharmacokinetic profiles of drugs before and after rifampicin administration accorded with the observations. The predicted pharmacokinetic parameters and DDIs were successful, whose fold-errors were within 2. It was consistent with observations that the DDIs of rifampicin with oral victim drugs were larger than those with intravenous victim drugs. DDIs of rifampicin with CYP3A or P-GP substrates following oral versus intravenous administration to human were successfully predicted using the developed semi-PBPK model.

PBPK Models for CYP3A4 and P-gp DDI Prediction: A Modeling Network of Rifampicin, Itraconazole, Clarithromycin, Midazolam, Alfentanil, and Digoxin

According to current US Food and Drug Administration (FDA) and European Medicines Agency (EMA) guidance documents, physiologically based pharmacokinetic (PBPK) modeling is a powerful tool to explore and quantitatively predict drug‐drug interactions (DDIs) and may offer an alternative to dedicated clinical trials. This study provides whole‐body PBPK models of rifampicin, itraconazole, clarithromycin, midazolam, alfentanil, and digoxin within the Open Systems Pharmacology (OSP) Suite. All models were built independently, coupled using reported interaction parameters, and mutually evaluated to verify their predictive performance by simulating published clinical DDI studies. In total, 112 studies were used for model development and 57 studies for DDI prediction. 93% of the predicted area under the plasma concentration‐time curve (AUC) ratios and 94% of the peak plasma concentration (C_max) ratios are within twofold of the observed values. This study lays a cornerstone for the qualification of the OSP platform with regard to reliable PBPK predictions of enzyme‐mediated and transporter‐mediated DDIs during model‐informed drug development. All presented models are provided open‐source and transparently documented.

[Dynamic Simulation of Drug-Drug Interactions by Using Multi-level Physiological Modeling & Simulation Platforms]

　Drug-drug interactions mediated by drug metabolizing enzymes are serious, clinically relevant issues. Prediction and evaluation of the probability and consequences of drug-drug interactions are essential during drug development, as well as during clinical application. A physiologically based pharmacokinetic (PBPK) model, which considers the hierarchical structure of the physiological behavior of drugs, has been demonstrated to be effective for in vitro-in vivo extrapolation of the phenomena of drug-drug interaction (DDI). While commercial software that implements PBPK models is now available, increasing attention has been given to developing similar models on open platforms for systems biology modeling and simulation. Open simulation model development environments, including CellDesigner and PhysioDesigner, have been developed and improved with the advent of research fields associated with systems biology or synthetic biology. Model developers implement their models using the platform, then publish the models in public databases. Through sharing and reuse among researchers, these models can become more generalized and sophisticated. This review article aims to discuss the attractive features and potential of these open platforms, and to evaluate the prediction effectiveness for enzyme induction-based drug-drug interactions via integrating the PBPK models of inducers and substrates and the dynamic models of enzyme induction kinetics.

Application of physiologically based pharmacokinetic modeling to the prediction of drug-drug and drug-disease interactions for rivaroxaban

PURPOSE

Rivaroxaban is a direct oral anticoagulant with a large inter-individual variability. The present study is to develop a physiologically based pharmacokinetic (PBPK) model to predict several scenarios in clinical practice.

METHODS

A whole-body PBPK model for rivaroxaban, which is metabolized by the cytochrome P450 (CYP) 3A4/5, 2J2 pathways and excreted via kidneys, was developed to predict the pharmacokinetics at different doses in healthy subjects and patients with hepatic or renal dysfunction. Hepatic clearance and drug-drug interactions (DDI) were estimated by in vitro in vivo extrapolation (IVIVE) based on parameters obtained from in vitro experiments. To validate the model, observed concentrations were compared with predicted concentrations, and the impact of special scenarios was investigated.

RESULTS

The PBPK model successfully predicted the pharmacokinetics for healthy subjects and patients as well as DDIs. Sensitivity analysis shows that age, renal, and hepatic clearance are important factors affecting rivaroxaban pharmacokinetics. The predicted fold increase of rivaroxaban AUC values when combined administered with the inhibitors such as ketoconazole, ritonavir, and clarithromycin were 2.3, 2.2, and 1.3, respectively. When DDIs and hepatic dysfunction coexist, the fold increase of rivaroxaban exposure would increase significantly compared with one factor alone.

CONCLUSIONS

Our study using PBPK modeling provided a reasonable approach to evaluate exposure levels in special patients under special scenarios. Although further clinical study or real-life experience would certainly merit the current work, the modeling work so far would at least suggest caution of using rivaroxaban in complicated clinical settings.

A quantitative systems pharmacology approach, incorporating a novel liver model, for predicting pharmacokinetic drug-drug interactions

All pharmaceutical companies are required to assess pharmacokinetic drug-drug interactions (DDIs) of new chemical entities (NCEs) and mathematical prediction helps to select the best NCE candidate with regard to adverse effects resulting from a DDI before any costly clinical studies. Most current models assume that the liver is a homogeneous organ where the majority of the metabolism occurs. However, the circulatory system of the liver has a complex hierarchical geometry which distributes xenobiotics throughout the organ. Nevertheless, the lobule (liver unit), located at the end of each branch, is composed of many sinusoids where the blood flow can vary and therefore creates heterogeneity (e.g. drug concentration, enzyme level). A liver model was constructed by describing the geometry of a lobule, where the blood velocity increases toward the central vein, and by modeling the exchange mechanisms between the blood and hepatocytes. Moreover, the three major DDI mechanisms of metabolic enzymes; competitive inhibition, mechanism based inhibition and induction, were accounted for with an undefined number of drugs and/or enzymes. The liver model was incorporated into a physiological-based pharmacokinetic (PBPK) model and simulations produced, that in turn were compared to ten clinical results. The liver model generated a hierarchy of 5 sinusoidal levels and estimated a blood volume of 283 mL and a cell density of 193 × 106 cells/g in the liver. The overall PBPK model predicted the pharmacokinetics of midazolam and the magnitude of the clinical DDI with perpetrator drug(s) including spatial and temporal enzyme levels changes. The model presented herein may reduce costs and the use of laboratory animals and give the opportunity to explore different clinical scenarios, which reduce the risk of adverse events, prior to costly human clinical studies.

Prediction of pharmacokinetics and drug-drug interaction potential using physiologically based pharmacokinetic (PBPK) modeling approach: A case study of caffeine and ciprofloxacin

Over the last decade, physiologically based pharmacokinetics (PBPK) application has been extended significantly not only to predicting preclinical/human PK but also to evaluating the drug-drug interaction (DDI) liability at the drug discovery or development stage. Herein, we describe a case study to illustrate the use of PBPK approach in predicting human PK as well as DDI using in silico, in vivo and in vitro derived parameters. This case was composed of five steps such as: simulation, verification, understanding of parameter sensitivity, optimization of the parameter and final evaluation. Caffeine and ciprofloxacin were used as tool compounds to demonstrate the “fit for purpose” application of PBPK modeling and simulation for this study. Compared to caffeine, the PBPK modeling for ciprofloxacin was challenging due to several factors including solubility, permeability, clearance and tissue distribution etc. Therefore, intensive parameter sensitivity analysis (PSA) was conducted to optimize the PBPK model for ciprofloxacin. Overall, the increase in C_max of caffeine by ciprofloxacin was not significant. However, the increase in AUC was observed and was proportional to the administered dose of ciprofloxacin. The predicted DDI and PK results were comparable to observed clinical data published in the literatures. This approach would be helpful in identifying potential key factors that could lead to significant impact on PBPK modeling and simulation for challenging compounds.

Prediction of drug disposition in diabetic patients by means of a physiologically based pharmacokinetic model

BACKGROUND AND OBJECTIVE

Accumulating evidence has shown that diabetes mellitus may affect the pharmacokinetics of some drugs, leading to alteration of pharmacodynamics and/or toxic effects. The aim of this study was to develop a novel physiologically based pharmacokinetic (PBPK) model for predicting drug pharmacokinetics in patients with type 2 diabetes mellitus quantitatively.

METHODS

Contributions of diabetes-induced alteration of physiological parameters including gastric emptying rates, intestinal transit time, drug metabolism in liver and kidney functions were incorporated into the model. Plasma concentration-time profiles and pharmacokinetic parameters of seven drugs (antipyrine, nisoldipine, repaglinide, glibenclamide, glimepiride, chlorzoxazone, and metformin) in non-diabetic and diabetic patients were predicted using the developed model. The PBPK model coupled with a Monte-Carlo simulation was also used to predict the means and variability of pharmacokinetic parameters.

RESULTS

The predicted area under the plasma concentration-time curve (AUC) and maximum (peak) concentration (C max) were reasonably consistent (<2-fold errors) with the reported values. Sensitivity analysis showed that gut transit time, hepatic enzyme activity, and renal function affected the pharmacokinetic characteristics of these drugs. Shortened gut transit time only decreased the AUC of controlled-released drugs and drugs with low absorption rates. Impairment of renal function markedly altered pharmacokinetics of drugs mainly eliminated via the kidneys.

CONCLUSION

All of these results indicate that the developed PBPK model can quantitatively predict pharmacokinetic alterations induced by diabetes.

Physiologically Based Pharmacokinetic (PBPK) Modeling and Simulation Approaches: A Systematic Review of Published Models, Applications, and Model Verification

Modeling and simulation of drug disposition has emerged as an important tool in drug development, clinical study design and regulatory review, and the number of physiologically based pharmacokinetic (PBPK) modeling related publications and regulatory submissions have risen dramatically in recent years. However, the extent of use of PBPK modeling by researchers, and the public availability of models has not been systematically evaluated. This review evaluates PBPK-related publications to 1) identify the common applications of PBPK modeling; 2) determine ways in which models are developed; 3) establish how model quality is assessed; and 4) provide a list of publically available PBPK models for sensitive P450 and transporter substrates as well as selective inhibitors and inducers. PubMed searches were conducted using the terms "PBPK" and "physiologically based pharmacokinetic model" to collect published models. Only papers on PBPK modeling of pharmaceutical agents in humans published in English between 2008 and May 2015 were reviewed. A total of 366 PBPK-related articles met the search criteria, with the number of articles published per year rising steadily. Published models were most commonly used for drug-drug interaction predictions (28%), followed by interindividual variability and general clinical pharmacokinetic predictions (23%), formulation or absorption modeling (12%), and predicting age-related changes in pharmacokinetics and disposition (10%). In total, 106 models of sensitive substrates, inhibitors, and inducers were identified. An in-depth analysis of the model development and verification revealed a lack of consistency in model development and quality assessment practices, demonstrating a need for development of best-practice guidelines.

Applications of physiologically based pharmacokinetic modeling for the optimization of anti-infective therapies

INTRODUCTION

The pharmacokinetic properties of anti-infective drugs are a determinant part of treatment success. Pathogen replication is inhibited if adequate drug levels are achieved in target sites, whereas excessive drug concentrations linked to toxicity are to be avoided. Anti-infective distribution can be predicted by integrating in vitro drug properties and mathematical descriptions of human anatomy in physiologically based pharmacokinetic models. This method reduces the need for animal and human studies and is used increasingly in drug development and simulation of clinical scenario such as, for instance, drug-drug interactions, dose optimization, novel formulations and pharmacokinetics in special populations.

AREAS COVERED

We have assessed the relevance of physiologically based pharmacokinetic modeling in the anti-infective research field, giving an overview of mechanisms involved in model design and have suggested strategies for future applications of physiologically based pharmacokinetic models.

EXPERT OPINION

Physiologically based pharmacokinetic modeling provides a powerful tool in anti-infective optimization, and there is now no doubt that both industry and regulatory bodies have recognized the importance of this technology. It should be acknowledged, however, that major challenges remain to be addressed and that information detailing disease group physiology and anti-infective pharmacodynamics is required if a personalized medicine approach is to be achieved.