A semi-mechanistic model to predict the effects of liver cirrhosis on drug clearance

Predicting Drug-Drug Interactions between Rifampicin and Ritonavir-Boosted Atazanavir Using PBPK Modelling

Objectives

The aim of this study was to simulate the drug–drug interaction (DDI) between ritonavir-boosted atazanavir (ATV/r) and rifampicin (RIF) using physiologically based pharmacokinetic (PBPK) modelling, and to predict suitable dose adjustments for ATV/r for the treatment of people living with HIV (PLWH) co-infected with tuberculosis.

Methods

A whole-body DDI PBPK model was designed using Simbiology 9.6.0 (MATLAB R2019a) and verified against reported clinical data for all drugs administered alone and concomitantly. The model contained the induction mechanisms of RIF and ritonavir (RTV), the inhibition effect of RTV for the enzymes involved in the DDI, and the induction and inhibition mechanisms of RIF and RTV on the uptake and efflux hepatic transporters. The model was considered verified if the observed versus predicted pharmacokinetic values were within twofold. Alternative ATV/r dosing regimens were simulated to achieve the trough concentration (C_trough) clinical cut-off of 150 ng/mL.

Results

The PBPK model was successfully verified according to the criteria. Simulation of different dose adjustments predicted that a change in regimen to twice-daily ATV/r (300/100 or 300/200 mg) may alleviate the induction effect of RIF on ATV C_trough, with > 95% of individuals predicted to achieve C_trough above the clinical cut-off.

Conclusions

The developed PBPK model characterized the induction-mediated DDI between RIF and ATV/r, accurately predicting the reduction of ATV plasma concentrations in line with observed clinical data. A change in the ATV/r dosing regimen from once-daily to twice-daily was predicted to mitigate the effect of the DDI on the C_trough of ATV, maintaining plasma concentration levels above the therapeutic threshold for most patients.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40262-021-01067-1.

Non-uniformity of Changes in Drug-Metabolizing Enzymes and Transporters in Liver Cirrhosis: Implications for Drug Dosage Adjustment

Liver cirrhosis is a chronic disease that affects the liver structure, protein expression, and overall metabolic function. Abundance data for drug-metabolizing enzymes and transporters (DMET) across all stages of disease severity are scarce. Levels of these proteins are crucial for the accurate prediction of drug clearance in hepatically impaired patients using physiologically based pharmacokinetic (PBPK) models, which can be used to guide the selection of more precise dosing. This study aimed to experimentally quantify these proteins in human liver samples and assess how they can impact the predictive performance of the PBPK models. We determined the absolute abundance of 51 DMET proteins in human liver microsomes across the three degrees of cirrhosis severity (n = 32; 6 mild, 13 moderate, and 13 severe), compared to histologically normal controls (n = 14), using QconCAT-based targeted proteomics. The results revealed a significant but non-uniform reduction in the abundance of enzymes and transporters, from control, by 30–50% in mild, 40–70% in moderate, and 50–90% in severe cirrhosis groups. Cancer and/or non-alcoholic fatty liver disease-related cirrhosis showed larger deterioration in levels of CYP3A4, 2C8, 2E1, 1A6, UGT2B4/7, CES1, FMO3/5, EPHX1, MGST1/3, BSEP, and OATP2B1 than the cholestasis set. Drug-specific pathways together with non-uniform changes of abundance across the enzymes and transporters under various degrees of cirrhosis necessitate the use of PBPK models. As case examples, such models for repaglinide, dabigatran, and zidovudine were successful in recovering disease-related alterations in drug exposure. In conclusion, the current study provides the biological rationale behind the absence of a single dose adjustment formula for all drugs in cirrhosis and demonstrates the utility of proteomics-informed PBPK modeling for drug-specific dose adjustment in liver cirrhosis.

Gene Expression and Protein Abundance of Hepatic Drug Metabolizing Enzymes in Liver Pathology

Hepatic drug metabolizing enzymes (DMEs) markedly affect drug pharmacokinetics. Because liver diseases may alter enzymatic function and in turn drug handling and clinical efficacy, we investigated DMEs expression in dependence on liver pathology and liver failure state. In 5 liver pathologies (hepatitis C, alcoholic liver disease, autoimmune hepatitis, primary biliary cholangitis and primary sclerosing cholangitis) and for the first time stratified according to the Child–Pugh score, 10 CYPs (CYP1A1, CYP1A2, CYP2B6, CYP2C8, CYP2C9, CYP2C19, CYP2D6, CYP2E1, CYP3A4 and CYP3A5) and 4 UGTs (UGT1A1, UGT1A3, UGT2B7 and UGT2B) enzymes were quantified for protein abundance (LC-MS/MS) and gene expression (qRT-PCR). CYP2E1 was the most vulnerable enzyme, and its protein levels were significantly reduced just in Child–Pugh class A livers. The protein abundance of CYP1A1, CYP2B6, CYP2C19, CYP2D6 as well as UGT1A1, UGT1A3 and UGT2B15 was relatively stable in the course of progression of liver function deterioration. Alcoholic liver disease and primary biliary cholangitis were involved in the most prominent changes in the protein abundances, with downregulation of 6 (CYP1A2, CYP2C8, CYP2D6, CYP2E1, CYP3A4, UGT2B7) and 5 (CYP1A1, CYP2B6, CYP2C8, CYP2E1, CYP3A4) significantly downregulated enzymes, respectively. The results of the study demonstrate that DMEs protein abundance is affected both by the type of liver pathology as well as functional state of the organ.

Physiologically based pharmacokinetic modelling to predict the clinical effect of CYP3A inhibitors/inducers on esaxerenone pharmacokinetics in healthy subjects and subjects with hepatic impairment

Purpose

Esaxerenone is a novel, oral, nonsteroidal treatment for hypertension. Physiologically based pharmacokinetic (PBPK) modelling was performed to predict the drug–drug interaction (DDI) effect of cytochrome P450 (CYP)3A modulators on esaxerenone pharmacokinetics in healthy subjects and subjects with hepatic impairment.

Methods

In our PBPK model, the fraction of esaxerenone metabolised by CYP3A was estimated from mass-balance data and verified and optimised by clinical DDI study results with strong CYP3A modulators. The model was also verified by the observed pharmacokinetics after multiple oral dosing and by the effect of hepatic impairment on esaxerenone pharmacokinetics. The model was applied to predict the DDI effects on esaxerenone pharmacokinetics with untested CYP3A modulators in healthy subjects and with strong CYP3A modulators in subjects with hepatic impairment.

Results

The PBPK model well described esaxerenone pharmacokinetics after multiple oral dosing. The predicted fold changes in esaxerenone plasma exposure after coadministration with strong CYP3A modulators were comparable with the observed data (1.53-fold with itraconazole and 0.31-fold with rifampicin). Predicted DDIs with untested moderate CYP3A modulators were less than the observed DDI with strong CYP3A modulators. The PBPK model also described the effect of hepatic impairment on esaxerenone plasma exposure. The predicted DDI results with strong CYP3A modulators in subjects with hepatic impairment indicate that, for concomitant use of CYP3A modulators, caution is advised for subjects with hepatic impairment, as is for healthy subjects.

Conclusion

The PBPK model developed predicted esaxerenone pharmacokinetics and DDIs and informed concurrent use of esaxerenone with CYP3A modulators.

Supplementary information

The online version contains supplementary material available at 10.1007/s00228-021-03194-x.

Proteomic Quantification of Changes in Abundance of Drug-Metabolizing Enzymes and Drug Transporters in Human Liver Cirrhosis: Different Methods, Similar Outcomes

Model-based assessment of the effects of liver disease on drug pharmacokinetics requires quantification of changes in enzymes and transporters responsible for drug metabolism and disposition. Different proteomic methods are currently used for protein quantification in tissues and in vitro systems, each with specific procedures and requirements. The outcome of quantitative proteomic assays using four different methods (one targeted and three label-free) applied to the same sample set was compared in this study. Three pooled cirrhotic liver microsomal samples corresponding to cirrhosis with nonalcoholic fatty liver disease, biliary disease, or cancer and a control microsomal pool were analyzed using quantification concatemer-based targeted proteomics, the total protein approach (TPA), high three ion intensity (Hi3) approach, and intensity-based absolute quantification (iBAQ) to determine the absolute and relative abundance in disease compared with control. The relative abundance data provided a "disease perturbation factor" (DPF) for each target protein. Absolute and relative abundances generated by standard-based label-free methods (iBAQ and Hi3) showed good agreement with targeted proteomics (limited bias and scatter), but TPA (standard-free method) overestimated absolute abundances by approximately 2-fold. The DPF was consistent between different proteomic methods but varied between enzymes and transporters, indicating discordance of effects of cirrhosis on various metabolism-related proteins. The DPF ranged from no change (e.g., for glucuronosyltransferase-1A6 in nonalcoholic fatty liver disease group) to less than 0.3 (e.g., carboxylesterases-1 in cirrhosis of biliary origin). SIGNIFICANCE STATEMENT: This study demonstrated that relative changes in enzymes and transporters (DPF) are independent of the quantitative proteomic methods used. Standard-based label-free methods, such as high three ion intensity (Hi3) and intensity-based absolute quantification (iBAQ) methods, were less biased and more precise than the total protein approach (TPA) when compared with targeted data. The DPF reconciled differences across proteomic methods observed with absolute levels. Using this approach, differences were revealed in the expression of enzymes/transporters in cirrhosis associated with different etiologies.

Review article: time to revisit Child-Pugh score as the basis for predicting drug clearance in hepatic impairment

BACKGROUND

Prescription information for many drugs entering the market lacks dosage guidance for hepatic impairment. Dedicated studies for assessing the fate of drugs in hepatic impairment commonly stratify patients using Child-Pugh score. Child-Pugh is a prognostic clinical score with limitations in reflecting the liver's metabolic capacity.

AIMS

To demonstrate the need for better drug dosing approaches in hepatic impairment, summarise the current status, identify knowledge gaps related to drug kinetic parameters in hepatic impairment, propose solutions for predicting the liver disease impact on drug exposure and discuss barriers to dosing guidance in those patients.

METHODS

Relevant reports on dosage adjustment in hepatic impairment were analysed concerning the prediction of the impairment impact on drug kinetics using physiologically-based pharmacokinetic (PBPK) modelling.

RESULTS

PBPK models are suggested as a potential framework to understand drug clearance changes in hepatic impairment. Quantifying changes in abundance and activity of drug-metabolising enzymes and transporters, understanding the impact of shunting, and accounting for interindividual variations in drug absorption could help in extending the success of these models in hepatically-impaired populations. These variables might not correlate with Child-Pugh score as a whole. Therefore, new metabolic activity markers, imaging techniques and other scoring systems are proposed to either support or substitute Child-Pugh score.

CONCLUSIONS

Many physiological changes in hepatic impairment determining the fate of drugs do not necessarily correlate with Child-Pugh score. Quantifying these changes in individual patients is essential in future hepatic impairment studies. Further studies assessing Child-Pugh alternatives are recommended to allow better prediction of drug exposure.

Using physiologically-based pharmacokinetic modeling for predicting the effects of hepatic impairment on the pharmacokinetics of olanzapine and samidorphan given as a combination tablet

A combination of olanzapine and samidorphan (OLZ/SAM) was recently approved by the US Food and Drug Administration for treatment of patients with schizophrenia or bipolar I disorder. The effects of moderate hepatic impairment on the pharmacokinetics (PKs) of olanzapine and samidorphan after a single dose of OLZ/SAM were characterized in a clinical study. Physiologically-based pharmacokinetic (PBPK) modeling was used to extend the clinical findings to predict the effects of varying degrees of hepatic impairment on the PKs of olanzapine and samidorphan. A previously developed PBPK model for OLZ/SAM was refined to recover the observed pharmacokinetic differences between individuals with moderate hepatic impairment and healthy controls. The optimized model was applied to predict changes in olanzapine and samidorphan PKs after multiple once-daily doses of OLZ/SAM in subjects with mild, moderate, and severe hepatic impairment relative to healthy controls. Modifications to model parameters, including absorption rate constant and fraction unbound to plasma protein, were made to recover the observed change in the PKs of olanzapine and samidorphan in individuals with moderate hepatic impairment. In applying the optimized model, mild, moderate, and severe hepatic impairment were predicted to increase steady-state total systemic exposures by 1.1-, 1.5-, and 1.6-fold, respectively, for olanzapine, and by 1.2-, 1.9-, and 2.3-fold, respectively, for samidorphan. PBPK modeling allowed for prediction of untested clinical scenarios of varying degrees of hepatic impairment in lieu of additional clinical studies.

Physiologically Based Pharmacokinetic Modelling to Identify Pharmacokinetic Parameters Driving Drug Exposure Changes in the Elderly

BACKGROUND

Medication use is highly prevalent with advanced age, but clinical studies are rarely conducted in the elderly, leading to limited knowledge regarding age-related pharmacokinetic changes.

OBJECTIVE

The objective of this study was to investigate which pharmacokinetic parameters determine drug exposure changes in the elderly by conducting virtual clinical trials for ten drugs (midazolam, metoprolol, lisinopril, amlodipine, rivaroxaban, repaglinide, atorvastatin, rosuvastatin, clarithromycin and rifampicin) using our physiologically based pharmacokinetic (PBPK) framework.

METHODS

PBPK models for all ten drugs were developed in young adults (20-50 years) following the best practice approach, before predicting pharmacokinetics in the elderly (≥ 65 years) without any modification of drug parameters. A descriptive relationship between age and each investigated pharmacokinetic parameter (peak concentration [C_max], time to C_max [t_max], area under the curve [AUC], clearance, volume of distribution, elimination-half-life) was derived using the final PBPK models, and verified with independent clinically observed data from 52 drugs.

RESULTS

The age-related changes in drug exposure were successfully simulated for all ten drugs. Pharmacokinetic parameters were predicted within 1.25-fold (70%), 1.5-fold (86%) and 2-fold (100%) of clinical data. AUC increased progressively by 0.9% per year throughout adulthood from the age of 20 years, which was explained by decreased clearance, while C_max, t_max and volume of distribution were not affected by human aging. Additional clinical data of 52 drugs were contained within the estimated variability of the established age-dependent correlations for each pharmacokinetic parameter.

CONCLUSION

The progressive decrease in hepatic and renal blood flow, as well as glomerular filtration, rate led to a reduced clearance driving exposure changes in the healthy elderly, independent of the drug.

Physiologically Based Pharmacokinetic Modelling of Cabozantinib to Simulate Enterohepatic Recirculation, Drug-Drug Interaction with Rifampin and Liver Impairment

Cabozantinib (CAB) is a receptor tyrosine kinase inhibitor approved for the treatment of several cancer types. Enterohepatic recirculation (EHC) of the substance is assumed but has not been further investigated yet. CAB is mainly metabolized via CYP3A4 and is susceptible for drug-drug interactions (DDI). The goal of this work was to develop a physiologically based pharmacokinetic (PBPK) model to investigate EHC, to simulate DDI with Rifampin and to simulate subjects with hepatic impairment. The model was established using PK-Sim^® and six human clinical studies. The inclusion of an EHC process into the model led to the most accurate description of the pharmacokinetic behavior of CAB. The model was able to predict plasma concentrations with low bias and good precision. Ninety-seven percent of all simulated plasma concentrations fell within 2-fold of the corresponding concentration observed. Maximum plasma concentration (C_max) and area under the curve (AUC) were predicted correctly (predicted/observed ratio of 0.9-1.2 for AUC and 0.8-1.1 for C_max). DDI with Rifampin led to a reduction in predicted AUC by 77%. Several physiological parameters were adapted to simulate hepatic impairment correctly. This is the first CAB model used to simulate DDI with Rifampin and hepatic impairment including EHC, which can serve as a starting point for further simulations with regard to special populations.

Physiologically-based pharmacokinetic model for alectinib, ruxolitinib, and panobinostat in the presence of cancer, renal impairment, and hepatic impairment

Renal (RIP) and hepatic (HIP) impairments are prevalent conditions in cancer patients. They can cause changes in gastric emptying time, albumin levels, hematocrit, glomerular filtration rate, hepatic functional volume, blood flow rates, and metabolic activity that can modify drug pharmacokinetics. Performing clinical studies in such populations has ethical and practical issues. Using predictive physiologically-based pharmacokinetic (PBPK) models in the evaluation of the PK of alectinib, ruxolitinib, and panobinostat exposures in the presence of cancer, RIP, and HIP can help in using optimal doses with lower toxicity in these populations. Verified PBPK models were customized under scrutiny to account for the pathophysiological changes induced in these diseases. The PBPK model-predicted plasma exposures in patients with different health conditions within average 2-fold error. The PBPK model predicted an area under the curve ratio (AUCR) of 1, and 1.8, for ruxolitinib and panobinostat, respectively, in the presence of severe RIP. On the other hand, the severe HIP was associated with AUCR of 1.4, 2.9, and 1.8 for alectinib, ruxolitinib, and panobinostat, respectively, in agreement with the observed AUCR. Moreover, the PBPK model predicted that alectinib therapeutic cerebrospinal fluid levels are achieved in patients with non-small cell lung cancer, moderate HIP, and severe HIP at 1-, 1.5-, and 1.8-fold that of healthy subjects. The customized PBPK models showed promising ethical alternatives for simulating clinical studies in patients with cancer, RIP, and HIP. More work is needed to quantify other pathophysiological changes induced by simultaneous affliction by cancer and RIP or HIP.

Development and Evaluation of a Physiologically Based Pharmacokinetic Drug-Disease Model of Propranolol for Suggesting Model Informed Dosing in Liver Cirrhosis Patients

AIM

The study was aimed to understand the underlying causes for the differences in propranolol pharmacokinetics (PK) between healthy and cirrhosis populations by using a systematic whole-body physiologically based pharmacokinetic (PBPK) model-building approach for suggesting model informed propranolol dosing in liver cirrhosis patients with different stages of disease severity.

METHODS

A whole-body PBPK model was developed by using population simulator PK-Sim® by using reported physicochemical and clinical data for propranolol in healthy and liver cirrhosis populations. The model evaluation was done by visual verification and comparison of PK parameters using their observed/predicted ratios (Robs/pred).

RESULTS

The developed model has effectively described the disposition of propranolol after intravenous and oral application in healthy and liver cirrhosis populations. All the model predictions were comparable to the observed clinical data and the Robs/pred for all the PK parameters were within a 2-fold range. A significant increase in plasma concentration of propranolol and decrease in drug clearance was observed in progressive stages of liver cirrhosis. The developed model after evaluation with the reported clinical PK data was used for suggesting model informed propranolol dosing in different stages of liver cirrhosis based on systemic unbound drug concentration.

CONCLUSION

The developed PBPK model has successfully described propranolol PK in healthy and cirrhosis populations after IV and oral administration. The evaluated PBPK propranolol-cirrhosis model can have many implications in predicting propranolol dosing in liver cirrhosis patients with different stages of disease severity.

Clinical Pharmacokinetics of Propranolol Hydrochloride: A Review

BACKGROUND

Nobel laureate Sir James Black's molecule, propranolol, still has broad potential in cardiovascular diseases, infantile haemangiomas and anxiety. A comprehensive and systematic review of the literature for the summarization of pharmacokinetic parameters would be effective to explore the new safe uses of propranolol in different scenarios, without exposing humans and using virtual-human modeling approaches.

OBJECTIVE

This review encompasses physicochemical properties, pharmacokinetics and drug-drug interaction data of propranolol collected from various studies.

METHODS

Clinical pharmacokinetic studies on propranolol were screened using Medline and Google Scholar databases. Eighty-three clinical trials, in which pharmacokinetic profiles and plasma time concentration were available after oral or IV administration, were included in the review.

RESULTS

The study depicts that propranolol is well absorbed after oral administration. It has dose-dependent bioavailability, and a 2-fold increase in dose results in a 2.5-fold increase in the area under the curve, a 1.3-fold increase in the time to reach maximum plasma concentration and finally, 2.2 and 1.8-fold increase in maximum plasma concentration in both immediate and long-acting formulations, respectively. Propranolol is a substrate of CYP2D6, CYP1A2 and CYP2C19, retaining potential pharmacokinetic interactions with co-administered drugs. Age, gender, race and ethnicity do not alter its pharmacokinetics. However, in renal and hepatic impairment, it needs a dose adjustment.

CONCLUSION

Physiochemical and pooled pharmacokinetic parameters of propranolol are beneficial to establish physiologically based pharmacokinetic modeling among the diseased population.

Transcriptional regulation of alcohol induced liver fibrosis in a translational porcine hepatocellular carcinoma model

Hepatocellular carcinoma (HCC) is the 5th most common and 2nd deadliest cancer worldwide. HCC risk factors include alcohol induced liver cirrhosis, which prompts hepatic inflammation, cell necrosis, and fibrosis deposition. As 25% of HCC cases are associated with alcohol induced liver disease, understanding the effects of the cirrhotic liver microenvironment on HCC tumor biology and therapeutic responses are critical. This study utilized the Oncopig Cancer Model-a transgenic pig model that recapitulates human HCC through induced expression of KRAS^G12D and TP53^R167H driver mutations-to investigate the molecular mechanisms underlying alcohol induced liver disease. Oncopigs (n = 5) underwent fibrosis induction via infusion of ethanol and ethiodized oil (1:3 v/v dosed at 0.75 mL/kg) into the hepatic arterial circulation. Eight-weeks post induction, liver tissue samples from fibrotic and age-matched control (n = 5) Oncopigs were collected for histological evaluation and transcriptional profiling. Increased hepatic inflammation and fibrosis was observed in fibrotic Oncopigs via pathological assessment. Transcriptional profiling (RNA-seq) resulted in the identification of 4387 differentially expressed genes between Oncopig fibrotic and control livers. GO term enrichment analysis identified pathway alterations associated with cirrhosis progression in humans, including cell proliferation, angiogenesis, extracellular matrix deposition, and oxidation-reduction. Key alterations include activation of hepatic stellate cells, increased matrix metalloproteinase production, and altered expression of ABC and SLC transporter genes involved in transport of anticancer drugs.These results demonstrate Oncopig liver fibrosis recapitulates transcriptional hallmarks of human cirrhosis, making the Oncopig an ideal model for studying the effects of the cirrhotic liver microenvironment on HCC tumor biology and therapeutic response.

Physiologically-Based Pharmacokinetic Modeling in Renal and Hepatic Impairment Populations: A Pharmaceutical Industry Perspective

The predictive performance of physiologically‐based pharmacokinetics (PBPK) models for pharmacokinetics (PK) in renal impairment (RI) and hepatic impairment (HI) populations was evaluated using clinical data from 29 compounds with 106 organ impairment study arms were collected from 19 member companies of the International Consortium for Innovation and Quality in Pharmaceutical Development. Fifty RI and 56 HI study arms with varying degrees of organ insufficiency along with control populations were evaluated. For RI, the area under the curve (AUC) ratios of RI to healthy control were predicted within twofold of the observed ratios for > 90% (N = 47/50 arms). For HI, > 70% (N = 43/56 arms) of the hepatically impaired to healthy control AUC ratios were predicted within twofold. Inaccuracies, typically overestimation of AUC ratios, occurred more in moderate and severe HI. PBPK predictions can help determine the need and timing of organ impairment study. It may be suitable for predicting the impact of RI on PK of drugs predominantly cleared by metabolism with varying contribution of renal clearance. PBPK modeling may be used to support mild impairment study waivers or clinical study design.

Investigating the interaction between nifedipine- and ritonavir-containing antiviral regimens: A physiologically based pharmacokinetic/pharmacodynamic analysis

AIMS

Hypertension is a common comorbidity of patients with COVID-19, SARS or HIV infection. Such patients are often concomitantly treated with antiviral and antihypertensive agents, including ritonavir and nifedipine. Since ritonavir is a strong inhibitor of CYP3A and nifedipine is mainly metabolized via CYP3A, the combination of ritonavir and nifedipine can potentially cause drug-drug interactions. This study provides guidance on nifedipine treatment during and after coadministration with ritonavir-containing regimens, using a physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) analysis.

METHODS

The PBPK/PD models for 3 formations of nifedipine were developed based on the Simcyp nifedipine model and the models were verified using published data. The effects of ritonavir on nifedipine exposure and systolic blood pressure (SBP) were assessed for instant-release, sustained-release and controlled-release formulations in patients. Various nifedipine regimens were investigated when coadministered with or without ritonavir.

RESULTS

PBPK/PD models for 3 formulations of nifedipine were successfully established. The predicted maximum concentration (C_max ), area under plasma concentration-time curve (AUC), maximum reduction in SBP and area under effect-time curve were all within 0.5-2.0-fold of the observed data. Model simulations showed that the inhibitory effect of ritonavir on CYP3A4 increased the C_max of nifedipine 17.92-48.85-fold and the AUC 63.30-84.01-fold at steady state and decreased the SBP by >40 mmHg. Thus, the combination of nifedipine and ritonavir could lead to severe hypotension.

CONCLUSION

Ritonavir significantly affects the pharmacokinetics and antihypertensive effect of nifedipine. It is not recommended for patients to take nifedipine- and ritonavir-containing regimens simultaneously.

Scaling Factors for Clearance in Adult Liver Cirrhosis

In vitro to in vivo extrapolation (IVIVE) enables prediction of in vivo clinical outcomes related to drug exposure in various populations from in vitro data. Prudent IVIVE requires scalars specific to the biologic characteristics of the system in each population. This study determined experimentally for the first time scalars in liver samples from patients with varying degrees of cirrhosis. Microsomal and cytosolic fractions were extracted from 13 noncirrhotic and 32 cirrhotic livers (six mild, 13 moderate, and 13 severe, based on Child-Pugh score). Fractional protein content was determined, and cytochrome P450 reductase activity was used to correct for microsomal protein loss. Although the median microsomal protein per gram liver (MPPGL) in mild, moderate, and severe cirrhosis (26.2, 32.4, and 30.8 mg⋅g-1, respectively) seemed lower than control livers (36.6 mg⋅g-1), differences were not statistically significant (Kruskal-Wallis test, P > 0.05). Corresponding values for cytosolic protein per gram liver were 88.2, 67.9, 62.2, and 75.4 (mg⋅g-1) for mild, moderate, and severe cirrhosis and control livers, respectively, with statistically lower values for severe versus controls (Mann-Whitney P = 0.006). Cirrhosis associated with cancer showed lower MPPGL (24.8 mg⋅g-1) than cirrhosis associated with cholestasis (38.3 mg⋅g-1, P = 0.003). Physiologically based pharmacokinetic simulations with disease-specific scalars captured cirrhosis impact on exposure to alfentanil, metoprolol, midazolam, and ethinylestradiol. These experimentally-determined scalars should alleviate the need for indirect scaling using functional liver volume. Scaling factors in cirrhosis might be a reflection of the etiology rather than the disease severity. Hence, bundling various cirrhotic conditions under the same umbrella when predicting hepatic impairment impact should be revisited. SIGNIFICANCE STATEMENT: Cirrhosis-specific scalars required for extrapolation from microsomal or cytosolic in vitro systems to liver tissue are lacking. These scalars can help in predicting drug clearance and selection of dosage regimens for cirrhosis populations. Attempts to consider potential changes have been empirical and ignored the potential impact of the cause of cirrhosis. We obtained experimental values for these scalars for the first time and assessed their impact on predicted exposure to various substrate drugs using physiologically-based pharmacokinetics simulations.

Physiologically Based Pharmacokinetic Modeling of Transdermal Selegiline and Its Metabolites for the Evaluation of Disposition Differences between Healthy and Special Populations

A physiologically based pharmacokinetic (PBPK) model of selegiline (SEL), and its metabolites, was developed in silico to evaluate the disposition differences between healthy and special populations. SEL is metabolized to methamphetamine (MAP) and desmethyl selegiline (DMS) by several CYP enzymes. CYP2D6 metabolizes the conversion of MAP to amphetamine (AMP), while CYP2B6 and CYP3A4 predominantly mediate the conversion of DMS to AMP. The overall prediction error in simulated PK, using the developed PBPK model, was within 0.5-1.5-fold after intravenous and transdermal dosing in healthy and elderly populations. Simulation results generated in the special populations demonstrated that a decrease in cardiac output is a potential covariate that affects the SEL exposure in renally impaired (RI) and hepatic impaired (HI) subjects. A decrease in CYP2D6 levels increased the systemic exposure of MAP. DMS exposure increased due to a reduction in the abundance of CYP2B6 and CYP3A4 in RI and HI subjects. In addition, an increase in the exposure of the primary metabolites decreased the exposure of AMP. No significant difference between the adult and adolescent populations, in terms of PK, were observed. The current PBPK model predictions indicate that subjects with HI or RI may require closer clinical monitoring to identify any untoward effects associated with the administration of transdermal SEL patch.

Impact of Disease on Plasma and Lung Exposure of Chloroquine, Hydroxychloroquine and Azithromycin: Application of PBPK Modeling

We use a mechanistic lung model to demonstrate that accumulation of chloroquine (CQ), hydroxychloroquine (HCQ), and azithromycin (AZ) in the lungs is sensitive to changes in lung pH, a parameter that can be affected in patients with coronavirus disease 2019 (COVID-19). A reduction in pH from 6.7 to 6 in the lungs, as observed in respiratory disease, led to 20-fold, 4.0-fold, and 2.7-fold increases in lung exposure of CQ, HCQ, and AZ, respectively. Simulations indicated that the relatively high concentrations of CQ and HCQ in lung tissue were sustained long after administration of the drugs had stopped. Patients with COVID-19 often present with kidney failure. Our simulations indicate that renal impairment (plus lung pH reduction) caused 30-fold, 8.0-fold, and 3.4-fold increases in lung exposures for CQ, HCQ, and AZ, respectively, with relatively small accompanying increases (20 to 30%) in systemic exposure. Although a number of different dosage regimens were assessed, the purpose of our study was not to provide recommendations for a dosing strategy, but to demonstrate the utility of a physiologically-based pharmacokinetic modeling approach to estimate lung concentrations. This, used in conjunction with robust in vitro and clinical data, can help in the assessment of COVID-19 therapeutics going forward.

Modelled plasma concentrations of pemafibrate with co-administered typical cytochrome P450 inhibitors clopidogrel, fluconazole or clarithromycin predicted by physiologically based pharmacokinetic modelling in virtual populations

Oral antidyslipidaemic drug pemafibrate is cleared from human plasma via hepatic uptake by organic anion transporting polypeptide (OATP) 1B1 and oxidation by cytochromes P450 (P450) 2C8, 2C9 and 3A4. The pharmacokinetic profiles of pemafibrate with virtual administrations of P450 inhibitors and/or disease interactions were generated using a physiologically based pharmacokinetic (PBPK) model previously established for co-administration of pemafibrate with OATP1B1 inhibitors. This PBPK model was validated in the current study using reported maximum pemafibrate plasma concentrations and areas under the curve from interaction studies in healthy subjects co-administered with clopidogrel (P450 2C8 inhibitor), fluconazole (P450 2C9/3A4 inhibitor) or clarithromycin (P450 3A4 inhibitor). Virtual co-administrations of pemafibrate with clopidogrel, fluconazole or clarithromycin increased the predicted plasma exposures of pemafibrate 1.4-1.7-fold, 1.2-1.4-fold and 2.9-11-fold, respectively, in subjects with or without moderate or severe renal impairment or Child-Pugh A or B liver cirrhosis. Some of the exposure-enhancing effects of clarithromycin may originate from its inhibitory potential toward OATP1B1, because the estimated effects of itraconazole (a P450 3A4 inhibitor) were only minor. Simulations using the current PBPK model in groups of virtual subjects with or without renal or hepatic impairment revealed modified pharmacokinetic profiles for pemafibrate following co-administration of typical P450 inhibitors.

Investigating the Role of Altered Systemic Albumin Concentration on the Disposition of Theophylline in Adult and Pediatric Patients with Asthma by Using the Physiologically Based Pharmacokinetic Approach

Theophylline is commonly used for the treatment of asthma and has a low hepatic clearance. The changes in plasma albumin concentration occurring in asthma may affect the exposure of theophylline. The aim of the presented work was to predict theophylline pharmacokinetics (PK) after incorporating the changes in plasma albumin concentration occurring in patients with asthma into a physiologically based pharmacokinetic (PBPK) model to see whether these changes can affect the systemic theophylline concentrations in asthma. The PBPK model was developed following a systematic model building approach using Simcyp. The predictions were performed initially in healthy adults after intravenous and oral drug administration. Only when the developed adult PBPK model had adequately predicted theophylline PK in healthy adults, the changes in plasma albumin concentrations were incorporated into the model for predicting drug exposure in patients with asthma. After evaluation of the developed model in the adult population, it was scaled to children on physiologic basis. The model evaluation was performed by using visual predictive checks and comparison of ratio of observed and predicted (Robs/Pre) PK parameters along with their 2-fold error range. The developed PBPK model has effectively described theophylline PK in both healthy and diseased populations, as Robs/Pre for all the PK parameters were within the 2-fold error limit. The predictions in patients with asthma showed that there were no significant changes in PK parameters after incorporating the changes in serum albumin concentration. The mechanistic nature of the developed asthma-PBPK model can facilitate its extension to other drugs. SIGNIFICANCE STATEMENT: Exposure of a low hepatic clearance drug like theophylline may be susceptible to plasma albumin concentration changes that occur in asthma. These changes in systemic albumin concentrations can be incorporated into a physiologically based pharmacokinetic model to predict theophylline pharmacokinetics in adult and pediatric asthma populations. The presented work is focused on predicting theophylline absorption, distribution, metabolism, and elimination in adult and pediatric asthma populations after incorporating reported changes in serum albumin concentrations to see their impact on the systemic theophylline concentrations.

Modelling Age-Related Changes in the Pharmacokinetics of Risperidone and 9-Hydroxyrisperidone in Different CYP2D6 Phenotypes Using a Physiologically Based Pharmacokinetic Approach

PURPOSE

Dose-optimization strategies for risperidone are gaining in importance, especially in the elderly. Based on the genetic polymorphism of cytochrome P 450 (CYP) 2D6 genetically and age-related changes cause differences in the pharmacokinetics of risperidone and 9-hydroxyrisperidone. The goal of the study was to develop physiologically based pharmacokinetic (PBPK) models for the elderly aged 65+ years. Additionally, CYP2D6 phenotyping using metabolic ratio were applied and different pharmacokinetic parameter for different age classes predicted.

METHODS

Plasma concentrations of risperidone and 9-hydroxyrisperidone were used to phenotype 17 geriatric inpatients treated under naturalistic conditions. For this purpose, PBPK models were developed to examine age-related changes in the pharmacokinetics between CYP2D6 extensive metabolizer, intermediate metabolizer, poor metabolizer, (PM) and ultra-rapid metabolizer.

RESULTS

PBPK-based metabolic ratio was able to predict different CYP2D6 phenotypes during steady-state. One inpatient was identified as a potential PM, showing a metabolic ratio of 3.39. About 88.2% of all predicted plasma concentrations of the inpatients were within the 2-fold error range. Overall, age-related changes of the pharmacokinetics in the elderly were mainly observed in Cmax and AUC. Comparing a population of young adults with the oldest-old, Cmax of risperidone increased with 24-44% and for 9-hydroxyrisperidone with 35-37%.

CONCLUSIONS

Metabolic ratio combined with PBPK modelling can provide a powerful tool to identify potential CYP2D6 PM during therapeutic drug monitoring. Based on genetic, anatomical and physiological changes during aging, PBPK models ultimately support decision-making regarding dose-optimization strategies to ensure the best therapy for each patient over the age of 65 years.

PBPK modeling of impact of nonalcoholic fatty liver disease on toxicokinetics of perchloroethylene in mice

BACKGROUND

Nonalcoholic fatty liver disease (NAFLD), a major cause of chronic liver disease in the Western countries with increasing prevalence worldwide, may substantially affect chemical toxicokinetics and thereby modulate chemical toxicity.

OBJECTIVES

This study aims to use physiologically-based pharmacokinetic (PBPK) modeling to characterize the impact of NAFLD on toxicokinetics of perchloroethylene (perc).

METHODS

Quantitative measures of physiological and biochemical changes associated with the presence of NAFLD induced by high-fat or methionine/choline-deficient diets in C57B1/6 J mice are incorporated into a previously developed PBPK model for perc and its oxidative and conjugative metabolites. Impacts on liver fat and volume, as well as blood:air and liver:air partition coefficients, are incorporated into the model. Hierarchical Bayesian population analysis using Markov chain Monte Carlo simulation is conducted to characterize uncertainty, as well as disease-induced variability in toxicokinetics.

RESULTS

NAFLD has a major effect on toxicokinetics of perc, with greater oxidative and lower conjugative metabolism as compared to healthy mice. The NAFLD-updated PBPK model accurately predicts in vivo metabolism of perc through oxidative and conjugative pathways in all tissues across disease states and strains, but underestimated parent compound concentrations in blood and liver of NAFLD mice.

CONCLUSIONS

We demonstrate the application of PBPK modeling to predict the effects of pre-existing disease conditions as a variability factor in perc metabolism. These results suggest that non-genetic factors such as diet and pre-existing disease can be as influential as genetic factors in altering toxicokinetics of perc, and thus are likely contribute substantially to population variation in its adverse effects.

The Effect of Liver and Kidney Disease on the Pharmacokinetics of Clozapine and Sildenafil: A Physiologically Based Pharmacokinetic Modeling

Background and Objectives

Physiologically based pharmacokinetic (PBPK) modeling permits clinical scientists to reduce practical constraints for clinical trials on patients with special diseases. In this study, simulations were carried out to validate the pharmacokinetic parameters of clozapine and sildenafil using Simcyp^® simulator in young male adults and compare the effect of renal or hepatic impairment on the pharmacokinetic parameters of clozapine and sildenafil. Also, the effect of age on pharmacokinetic parameters of both drugs was investigated in healthy population and in patients with renal and hepatic impairment.

Methods

A full PBPK model was built in the simulator for clozapine and sildenafil based on physicochemical properties and observed clinical results. The model used was Advanced, Dissolution, Absorption and Metabolism (ADAM) for both drugs.

Results

The PBPK model adequately predicted the pharmacokinetic parameters of clozapine and sildenafil for the healthy adult population. In the simulation results, the bioavailability of both drugs was remarkably raised in both renal and hepatic impairment in young and elderly populations.

Conclusion

PBPK modeling could be helpful in the investigation and comparison of the pharmacokinetics in populations with specific disease conditions.

Pharmacokinetics and safety of olaparib in patients with advanced solid tumours and mild or moderate hepatic impairment

AIMS

Olaparib, a potent oral poly(ADP-ribose) polymerase inhibitor, is partially hepatically cleared. We investigated the pharmacokinetics (PK) and safety of olaparib in patients with mild or moderate hepatic impairment to provide dosing recommendations.

METHODS

This Phase I open-label study assessed the PK, safety and tolerability of single doses of olaparib 300-mg tablets in patients with advanced solid tumours. Patients had normal hepatic function (NHF), or mild (MiHI; Child-Pugh class A) or moderate (MoHI; Child-Pugh class B) hepatic impairment. Blood was collected for PK assessments for 96 hours. Patients could continue taking olaparib 300 mg twice daily for long-term safety assessment.

RESULTS

Thirty-one patients received ≥1 dose of olaparib and 30 were included in the PK assessment. Patients with MiHI had an area under the curve geometric least-squares mean (GLSmean) ratio of 1.15 (90% confidence interval 0.72, 1.83) and a GLSmean maximum plasma concentration ratio of 1.13 (0.82, 1.56) vs those with NHF. In patients with MoHI, GLSmean ratio for area under the curve was 1.08 (0.66, 1.74) and for maximum plasma concentration was 0.87 (0.63, 1.22) vs those with NHF. For patients with mild or moderate hepatic impairment, no new safety signals were detected.

CONCLUSION

Patients with MiHI or MoHI had no clinically significant changes in exposure to olaparib compared with patients with NHF. The safety profile of olaparib did not differ from a clinically relevant extent between cohorts. No olaparib tablet or capsule dose reductions are required for patients with MiHI or MoHI.

Liver Bioreactor Design Issues of Fluid Flow and Zonation, Fibrosis, and Mechanics: A Computational Perspective

Tissue engineering, with the goal of repairing or replacing damaged tissue and organs, has continued to make dramatic science-based advances since its origins in the late 1980’s and early 1990’s. Such advances are always multi-disciplinary in nature, from basic biology and chemistry through physics and mathematics to various engineering and computer fields. This review will focus its attention on two topics critical for tissue engineering liver development: (a) fluid flow, zonation, and drug screening, and (b) biomechanics, tissue stiffness, and fibrosis, all within the context of 3D structures. First, a general overview of various bioreactor designs developed to investigate fluid transport and tissue biomechanics is given. This includes a mention of computational fluid dynamic methods used to optimize and validate these designs. Thereafter, the perspective provided by computer simulations of flow, reactive transport, and biomechanics responses at the scale of the liver lobule and liver tissue is outlined, in addition to how bioreactor-measured properties can be utilized in these models. Here, the fundamental issues of tortuosity and upscaling are highlighted, as well as the role of disease and fibrosis in these issues. Some idealized simulations of the effects of fibrosis on lobule drug transport and mechanics responses are provided to further illustrate these concepts. This review concludes with an outline of some practical applications of tissue engineering advances and how efficient computational upscaling techniques, such as dual continuum modeling, might be used to quantify the transition of bioreactor results to the full liver scale.

Physiologically-Based Pharmacokinetic Modeling for Optimal Dosage Prediction of Quinine Coadministered With Ritonavir-Boosted Lopinavir

The coformulated lopinavir/ritonavir significantly reduces quinine concentration in healthy volunteers due to potential drug-drug interactions (DDIs). However, DDI information in malaria and HIV coinfected patients are lacking. The objective of the study was to apply physiologically-based pharmacokinetic (PBPK) modeling to predict optimal dosage regimens of quinine when coadministered with lopinavir/ritonavir in malaria and HIV coinfected patients with different conditions. The developed model was validated against literature. Model verification was evaluated using the accepted method. The verified PBPK models successfully predicted unbound quinine disposition when coadministered with lopinavir/ritonavir in coinfected patients with different conditions. Suitable dose adjustments to counteract with the DDIs have identified in patients with various situations (i.e., a 7-day course at 1,800 mg t.i.d. in patients with malaria with HIV infection, 648 mg b.i.d. in chronic renal failure, 648 mg t.i.d. in hepatic insufficiency except for severe hepatic insufficiency (324 mg b.i.d.), and 648 mg t.i.d. in CYP3A4 polymorphism).

Overview of organic anion transporters and organic anion transporter polypeptides and their roles in the liver

Organic anion transporters (OATs) and organic anion transporter polypeptides (OATPs) are classified within two SLC superfamilies, namely, the SLC22A superfamily and the SLCO superfamily (formerly the SLC21A family), respectively. They are expressed in many tissues, such as the liver and kidney, and mediate the absorption and excretion of many endogenous and exogenous substances, including various drugs. Most are composed of 12 transmembrane polypeptide chains with the C-terminus and the N-terminus located in the cell cytoplasm. OATs and OATPs are abundantly expressed in the liver, where they mainly promote the uptake of various endogenous substrates such as bile acids and various exogenous drugs such as antifibrotic and anticancer drugs. However, differences in the locations of glycosylation sites, phosphorylation sites, and amino acids in the OAT and OATP structures lead to different substrates being transported to the liver, which ultimately results in their different roles in the liver. To date, few articles have addressed these aspects of OAT and OATP structures, and we study further the similarities and differences in their structures, tissue distribution, substrates, and roles in liver diseases.

Development and Evaluation of Physiologically Based Pharmacokinetic Drug-Disease Models for Predicting Rifampicin Exposure in Tuberculosis and Cirrhosis Populations

The physiologically based pharmacokinetic (PBPK) approach facilitates the construction of novel drug–disease models by allowing incorporation of relevant pathophysiological changes. The aim of the present work was to explore and identify the differences in rifampicin pharmacokinetics (PK) after the application of its single dose in healthy and diseased populations by using PBPK drug–disease models. The Simcyp^® simulator was used as a platform for modeling and simulation. The model development process was initiated by predicting rifampicin PK in healthy population after intravenous (i.v) and oral administration. Subsequent to successful evaluation in healthy population, the pathophysiological changes in tuberculosis and cirrhosis population were incorporated into the developed model for predicting rifampicin PK in these populations. The model evaluation was performed by using visual predictive checks and the comparison of mean observed/predicted ratios (ratio_(Obs/pred)) of the PK parameters. The predicted PK parameters in the healthy population were in adequate harmony with the reported clinical data. The incorporation of pathophysiological changes in albumin concentration in the tuberculosis population revealed improved prediction of clearance. The developed PBPK drug–disease models have efficiently described rifampicin PK in tuberculosis and cirrhosis populations after administering single drug dose, as the ratio_(Obs/pred) for all the PK parameters were within a two-fold error range. The mechanistic nature of the developed PBPK models may facilitate their extension to other diseases and drugs.

A Comprehensive Framework for Physiologically-Based Pharmacokinetic Modeling in Matlab

Physiologically-based pharmacokinetic (PBPK) models are useful tools to predict clinical scenarios for special populations for whom there are high hurdles to conduct clinical trials such as children or the elderly. However, the coding of a PBPK model in a mathematical programming language can be challenging. This tutorial illustrates how to build a whole-body PBPK model in Matlab to answer specific pharmacological questions involving drug disposition and magnitudes of drug-drug interactions in different patient populations.

The nested enzyme-within-enterocyte (NEWE) turnover model for predicting dynamic drug and disease effects on the gut wall

Physiologically-based pharmacokinetic (PBPK) models provide a framework for in vitro-in vivo extrapolation of metabolic drug clearance. Many of the concepts in PBPK can have consequential impact on more mechanistic systems pharmacology models. In the gut wall, turnover of enzymes and enterocytes are typically lumped into one rate constant that describes the time dependent enzyme activity. This assumption may influence predictability of any sustained and dynamic effects such as mechanism-based inhibition (MBI), particularly when considering translation from healthy to gut disease. A novel multi-level systems PBPK model was developed. This model comprised a 'nested enzyme-within enterocyte' (NEWE) turnover model to describe levels of drug-metabolising enzymes. The ability of the model to predict gut metabolism following MBI and gut disease was investigated and compared to the conventional modelling approach. For MBI, the default NEWE model performed comparably to the conventional model. However, when drug-specific spatial crypt-villous absorption was considered, up to approximately 50% lower impact of MBI was simulated for substrates highly metabolised by cytochrome P450 (CYP) 3A4, interacting with potent inhibitors. Further, the model showed potential in predicting the disease effect of gastrointestinal mucositis and untreated coeliac disease when compared to indirect clinical pharmacokinetic parameters. Considering the added complexity of the NEWE model, it does not provide an attractive solution for improving upon MBI predictions in healthy individuals. However, nesting turnover may enable extrapolation to gut disease-drug interactions. The principle detailed herein may be useful for modelling drug interactions with cellular targets where turnover is significant enough to affect this process.

Prediction of Ticagrelor and its Active Metabolite in Liver Cirrhosis Populations Using a Physiologically Based Pharmacokinetic Model Involving Pharmacodynamics

Ticagrelor, a P2Y₁₂ receptor antagonist, has been highly recommended for use in acute coronary syndrome. The major active metabolite (AM) is similar to the parent drug, which exhibits antiplatelet activity. The inhibition of platelet aggregation (IPA) is used as an assay to demonstrate the anticoagulant efficacy of ticagrelor. In this study, we developed a physiologically based pharmacokinetic (PBPK) model to predict the pharmacokinetics of ticagrelor and its AM and combined this model with a pharmacodynamics model to reflect potential pharmacodynamic alterations in liver cirrhosis populations. The simulated results obtained using the PBPK model were validated by fold error values, which were all smaller than 2. Comparisons of exposure in different classifications of liver cirrhosis indicated that exposure to ticagrelor increased significantly with an increase in the degree of cirrhosis severity, whereas exposure to AM was decreased. The total concentration of ticagrelor and AM was related to the IPA included in the Sigmoid E_max model. The PBPK model of ticagrelor and AM could predict the pharmacokinetics of all populations, and a combination of PD models was used to extrapolate for predicting unknown scenarios. Liver cirrhosis may result in prolonged IPA, depending on the severity degree of this disease. The combined PBPK model including IPA can reveal changes in pharmacokinetics and pharmacodynamics in populations affected by liver cirrhosis and indicate the risk potential.

Complex Drug-Drug-Gene-Disease Interactions Involving Cytochromes P450: Systematic Review of Published Case Reports and Clinical Perspectives

Drug pharmacokinetics (PK) is influenced by multiple intrinsic and extrinsic factors, among which concomitant medications are responsible for drug-drug interactions (DDIs) that may have a clinical relevance, resulting in adverse drug reactions or reduced efficacy. The addition of intrinsic factors affecting cytochromes P450 (CYPs) activity and/or expression, such as genetic polymorphisms and diseases, may potentiate the impact and clinical relevance of DDIs. In addition, greater variability in drug levels and exposures has been observed when such intrinsic factors are present in addition to concomitant medications perpetrating DDIs. This variability results in poor predictability of DDIs and potentially dramatic clinical consequences. The present review illustrates the issue of complex DDIs using systematically searched published case reports of DDIs involving genetic polymorphisms, renal impairment, cirrhosis, and/or inflammation. Current knowledge on the impact of each of these factors on drug exposure and DDIs is summarized and future perspectives for the management of such complex DDIs in clinical practice are discussed, including the use of advanced Computerized Physician Order Entry (CPOE) systems, the development of model-based dose optimization strategies, and the education of healthcare professionals with respect to personalized medicine.

Physiologically based pharmacokinetic modelling to guide drug delivery in older people

Older patients are generally not included in Phase 1 clinical trials despite being the population group who use the largest number of prescription medicines. Physiologically based pharmacokinetic (PBPK) modelling provides an understanding of the absorption and disposition of drugs in older patients. In this review, PBPK models used for the prediction of absorption and exposure of drugs after parenteral, oral and transdermal administration are discussed. Comparisons between predicted drug pharmacokinetics (PK) and observed PK are presented to illustrate the accuracy of the predictions by the PBPK models and their potential use in informing clinical trial design and dosage adjustments in older patients. In addition, a case of PBPK modelling of a bioequivalence study on two controlled release products is described, where PBPK predictions reproduced the study showing bioequivalence in healthy volunteers but not in older subjects with achlorhydria, indicating further utility in prospectively identifying challenges in bioequivalence studies.

Effect of Hepatic Impairment on the Pharmacokinetics of Alectinib

Alectinib is approved and recommended as the preferred first‐line treatment for patients with anaplastic lymphoma kinase (ALK)‐positive non–small cell lung cancer. The effect of hepatic impairment on the pharmacokinetics (PK) of alectinib was assessed with physiologically based PK modeling prospectively and in a clinical study. An open‐label study (NCT02621047) investigated a single 300‐mg dose of alectinib in moderate (n = 8) and severe (n = 8) hepatic impairment (Child‐Pugh B/C), and healthy subjects (n = 12) matched for age, sex, and body weight. Physiologically based PK modeling was conducted prospectively to inform the clinical study design and support the use of a lower dose and extended PK sampling in the study. PK parameters were calculated for alectinib, its major similarly active metabolite, M4, and the combined exposure of alectinib and M4. Unbound concentrations were assessed at 6 and 12 hours postdose. Administration of alectinib to subjects with hepatic impairment increased the area under the plasma concentration–time curve from time 0 to infinity of the combined exposure of alectinib and M4 to 136% (90% confidence interval [CI], 94.7‐196) and 176% (90%CI 98.4‐315), for moderate and severe hepatic impairment, respectively, relative to matched healthy subjects. Unbound concentrations for alectinib and M4 did not appear substantially different between hepatic‐impaired and healthy subjects. Moderate hepatic impairment had only a modest, not clinically significant effect on alectinib exposure, while the higher exposure observed in severe hepatic impairment supports a dose adjustment in this population.

Abundance of Phase 1 and 2 Drug-Metabolizing Enzymes in Alcoholic and Hepatitis C Cirrhotic Livers: A Quantitative Targeted Proteomics Study

To predict the impact of liver cirrhosis on hepatic drug clearance using physiologically based pharmacokinetic (PBPK) modeling, we compared the protein abundance of various phase 1 and phase 2 drug-metabolizing enzymes (DMEs) in S9 fractions of alcoholic (n = 27) or hepatitis C (HCV, n = 30) cirrhotic versus noncirrhotic (control) livers (n = 25). The S9 total protein content was significantly lower in alcoholic or HCV cirrhotic versus control livers (i.e., 38.3 ± 8.3, 32.3 ± 12.8, vs. 51.1 ± 20.7 mg/g liver, respectively). In general, alcoholic cirrhosis was associated with a larger decrease in the DME abundance than HCV cirrhosis; however, only the abundance of UGT1A4, alcohol dehydrogenase (ADH)1A, and ADH1B was significantly lower in alcoholic versus HCV cirrhotic livers. When normalized to per gram of tissue, the abundance of nine DMEs (UGT1A6, UGT1A4, CYP3A4, UGT2B7, CYP1A2, ADH1A, ADH1B, aldehyde oxidase (AOX)1, and carboxylesterase (CES)1) in alcoholic cirrhosis and five DMEs (UGT1A6, UGT1A4, CYP3A4, UGT2B7, and CYP1A2) in HCV cirrhosis was <25% of that in control livers. The abundance of most DMEs in cirrhotic livers was 25% to 50% of control livers. CES2 abundance was not affected by cirrhosis. Integration of UGT2B7 abundance in cirrhotic livers into the liver cirrhosis (Child Pugh C) model of Simcyp improved the prediction of zidovudine and morphine PK in subjects with Child Pugh C liver cirrhosis. These data demonstrate that protein abundance data, combined with PBPK modeling and simulation, can be a powerful tool to predict drug disposition in special populations.

Physiologically Based Pharmacokinetic Modelling and Prediction of Metformin Pharmacokinetics in Renal/Hepatic-Impaired Young Adults and Elderly Populations

BACKGROUND AND OBJECTIVES

Physiologically based pharmacokinetic (PBPK) modelling and simulation enable researchers to overcome practical limitations for clinical trials on special populations. This study was conducted to investigate how the PBPK model describes the pharmacokinetics of metformin in young adult and elderly populations and to predict the pharmacokinetics of metformin in patients with renal or hepatic impairment in both populations.

METHODS

A first-order absorption/PBPK model for metformin was built in the Simcyp simulator version 14 release 1. A full PBPK model was constructed for metformin based on physicochemical properties and clinical observations. The model was refined and validated using clinical plasma concentration data obtained in healthy young adults and elderly after the oral administration of metformin. Metformin pharmacokinetics in patients with renal or hepatic impairment were then investigated and compared by simulation.

RESULTS

The PBPK model reasonably predicted the pharmacokinetic profiles of metformin for both young adults and the elderly. The predicted pharmacokinetic parameters, including maximum concentration, area under the time-concentration curve, and apparent oral clearance values, were within 1.5-fold of the observed data of metformin. In the simulation results, the systemic exposure of metformin was expected to be markedly increased not only with a decrease in renal function but also with severe hepatic impairments.

CONCLUSIONS

The PBPK model adequately characterised the pharmacokinetics of metformin in both young adult and elderly populations. PBPK modelling and simulation can be used as a useful tool to investigate and compare the pharmacokinetics in geriatric populations incorporating various disease conditions.

Elucidating the Plasma and Liver Pharmacokinetics of Simeprevir in Special Populations Using Physiologically Based Pharmacokinetic Modelling

The disposition of simeprevir (SMV) in humans is characterised by cytochrome P450 3A4 metabolism and hepatic uptake by organic anion transporting polypeptide 1B1/3 (OATP1B1/3). This study was designed to investigate SMV plasma and liver exposure upon oral administration in subjects infected with hepatitis C virus (HCV), in subjects of Japanese or Chinese origin, subjects with organ impairment and subjects with OATP genetic polymorphisms, using physiologically based pharmacokinetic modelling. Simulations showed that compared with healthy Caucasian subjects, SMV plasma exposure was 2.4-, 1.7-, 2.2- and 2.0-fold higher, respectively, in HCV-infected Caucasian subjects, in healthy Japanese, healthy Chinese and subjects with severe renal impairment. Further simulations showed that compared with HCV-infected Caucasian subjects, SMV plasma exposure was 1.6-fold higher in HCV-infected Japanese subjects. In subjects with OATP1B1 genetic polymorphisms, no noteworthy changes in SMV pharmacokinetics were observed. Simulations suggested that liver concentrations in Caucasians with HCV are 18 times higher than plasma concentrations.

Using a Vancomycin PBPK Model in Special Populations to Elucidate Case-Based Clinical PK Observations

Simultaneous changes in several physiological factors may contribute to the large pharmacokinetic (PK) variability of vancomycin. This study was designed to systematically characterize the effects of multiple physiological factors to the altered PK of vancomycin observed in special populations. A vancomycin physiologically based pharmacokinetic (PBPK) model was developed as a PK simulation platform to quantitatively assess the effects of changes in physiologies to the PK profiles. The developed model predicted the concentration-time profiles in healthy adults and diseased patients. The implementation of developmental changes in both renal and non-renal elimination pathways to the pediatric model improved the predictability of vancomycin clearance. Simulated PK profiles with a 50% decrease in cardiac output (peak plasma concentration (C_max ), 59.9 ng/mL) were similar to those observed in patients before bypass surgery (C_max , 55.1 ng/mL). The PBPK modeling of vancomycin demonstrated its potential to provide mechanistic insights into the altered disposition observed in patients who have changes in multiple physiological factors.

Physiologically Based Pharmacokinetic Model Qualification and Reporting Procedures for Regulatory Submissions: A Consortium Perspective

This work provides a perspective on the qualification and verification of physiologically based pharmacokinetic (PBPK) platforms/models intended for regulatory submission based on the collective experience of the Simcyp Consortium members. Examples of regulatory submission of PBPK analyses across various intended applications are presented and discussed. European Medicines Agency (EMA) and US Food and Drug Administration (FDA) recent draft guidelines regarding PBPK analyses and reporting are encouraging, and to advance the use and acceptability of PBPK analyses, more clarity and flexibility are warranted.

Changes in cytochrome P450s-mediated drug clearance in patients with hepatocellular carcinoma in vitro and in vivo: a bottom-up approach

Hepatocellular carcinoma (HCC) accompanied by severe liver dysfunction is a serious disease, which results in altered hepatic clearance. Generally, maintenance doses depend upon drug clearance, so individual dosage regimens should be customized for HCC patients based on the condition of patients. Based on clearance of CYP isoform-specific substrates at the microsomal level (CL_M), microsomal protein per gram of liver (MPPGL), liver weight, hepatic blood flow, hepatic clearance values (CL_H) for 10 CYPs in HCC patients (n=102) were extrapolated using a predictive bottom-up pharmacokinetic model. Compared with controls, the CL_M values for CYP2C9, 2D6, 2E1 were significantly increased in HCC patients. Additionally, CYP1A2, 2C8, 2C19 CL_M values decreased while the values for CYP2A6, 2B6, 3A4/5 were unchanged. The MPPGL values in HCC tissues were significantly reduced. CL_H values of HCC patients for CYP1A2, 2A6, 2B6, 2C8, 2C19, and 3A4/5 were significantly reduced, while this for CYP2E1 were markedly increased and those for CYP2C9 and 2D6 did not change. Moreover, disease (fibrosis and cirrhosis) and polymorphisms of the CYP genes have influenced the CL_H for some CYPs. Prediction of the effects of HCC on drug clearance may be helpful for the design of clinical studies and the clinical management of drugs in HCC patients.

Physiologically Based Pharmacokinetic Model of the CYP2D6 Probe Atomoxetine: Extrapolation to Special Populations and Drug-Drug Interactions

Physiologically based pharmacokinetic (PBPK) modeling of drug disposition and drug-drug interactions (DDIs) has become a key component of drug development. PBPK modeling has also been considered as an approach to predict drug disposition in special populations. However, whether models developed and validated in healthy populations can be extrapolated to special populations is not well established. The goal of this study was to determine whether a drug-specific PBPK model validated using healthy populations could be used to predict drug disposition in specific populations and in organ impairment patients. A full PBPK model of atomoxetine was developed using a training set of pharmacokinetic (PK) data from CYP2D6 genotyped individuals. The model was validated using drug-specific acceptance criteria and a test set of 14 healthy subject PK studies. Population PBPK models were then challenged by simulating the effects of ethnicity, DDIs, pediatrics, and renal and hepatic impairment on atomoxetine PK. Atomoxetine disposition was successfully predicted in 100% of healthy subject studies, 88% of studies in Asians, 79% of DDI studies, and 100% of pediatric studies. However, the atomoxetine area under the plasma concentration versus time curve (AUC) was overpredicted by 3- to 4-fold in end stage renal disease and hepatic impairment. The results show that validated PBPK models can be extrapolated to different ethnicities, DDIs, and pediatrics but not to renal and hepatic impairment patients, likely due to incomplete understanding of the physiologic changes in these conditions. These results show that systematic modeling efforts can be used to further refine population models to improve the predictive value in this area.

Critical Issues and Optimized Practices in Quantification of Protein Abundance Level to Determine Interindividual Variability in DMET Proteins by LC-MS/MS Proteomics

Protein quantification data on drug metabolizing enzymes and transporters (collectively referred as DMET proteins) in human tissues are useful in predicting interindividual variability in drug disposition. While targeted proteomics is an emerging technique for quantification of DMET proteins, the methodology involves significant technical challenges especially when multiple samples are analyzed in a single study over a long period of time. Therefore, it is important to thoroughly address the critical variables that could affect DMET protein quantification.

Prediction of inter-individual variability on the pharmacokinetics of CYP2C8 substrates in human

Inter-individual variability in pharmacokinetics can lead to unexpected side effects and treatment failure, and is therefore an important factor in drug development. CYP2C8 is a major drug-metabolizing enzyme known to be involved in the metabolism of over 100 drugs. In this study, we predicted the inter-individual variability in AUC/Dose of CYP2C8 substrates in healthy volunteers using the Monte Carlo simulation. Inter-individual variability in the hepatic intrinsic clearance of CYP2C8 substrates (CL_int,h,2C8) was estimated from the inter-individual variability in pharmacokinetics of pioglitazone, which is a major CYP2C8 substrate. The coefficient of variation (CV) of CL_int,h,2C8 was estimated to be 40%. Using this value, the CVs of AUC/Dose of other major CYP2C8 substrates, rosiglitazone and amodiaquine, were predicted to validate the estimated CV of CL_int,h,2C8. As a result, the reported CVs of both substrates were within the 2.5-97.5 percentile range of the predicted CVs. Furthermore, the CVs of AUC/Dose of the CYP2C8 substrates loperamide and chloroquine, which are affected by renal clearance, were also successfully predicted. Combining this value with previously reported CVs of other CYPs, we were able to successfully predict the inter-individual variability in pharmacokinetics of various drugs in clinical.