Physiology-based simulations of a pathological condition: prediction of pharmacokinetics in patients with liver cirrhosis

Physiologically based pharmacokinetic model of brivaracetam to predict the exposure and dose exploration in hepatic impairment and elderly populations

Brivaracetam (BRV) is a new third-generation antiseizure medication for the treatment of focal epileptic seizures. Its use has been increasing among epileptic populations in recent years, but pharmacokinetic (PK) behavior may change in hepatic impairment and the elderly populations. Due to ethical constraints, clinical trials are difficult to conduct and data are limited. This study used PK-Sim® to develop a physiologically based pharmacokinetic (PBPK) model for adults and extrapolate it to hepatic impairment and the elderly populations. The model was evaluated with clinical PK data, and dosage explorations were conducted. For the adult population with mild hepatic impairment, the dose is recommended to be adjusted to 70 % of the recommended dose, and to 60 % for moderate and severe hepatic impairment. For the elderly population with mild hepatic impairment under 80 years old, it is recommended that the dose be adjusted to 60 % of the recommended dose and to 50 % for moderate and severe conditions. The elderly population with hepatic impairment over 80 years old is adjusted to 50 % of the recommended dose for all stages. Healthy elderly do not need to adjust. The BRV PBPK model was successfully developed, studying exposure in hepatic impairment and elderly populations and optimizing dosing regimens.

Physiologically-based pharmacokinetic modeling predicts the drug interaction potential of GLS4 in co-administered with ritonavir

GLS4 is a first-in-class hepatitis B virus (HBV) capsid assembly modulator (class I) that is co-administered with ritonavir to maintain the anticipated concentration required for the effective antiviral activity of GLS4. In this study, the first physiologically-based pharmacokinetic (PBPK) model for GLS4/ritonavir was successfully developed. The predictive performance of the PBPK model was verified using data from 39 clinical studies, including single-dose, multiple-dose, food effects, and drug-drug interactions (DDI). The PBPK model accurately described the PK profiles of GLS4 and ritonavir, with predicted values closely aligning with observed data. Based on the verified GLS4/ritonavir model, it prospectively predicts the effect of hepatic impairment (HI) and DDI on its pharmacokinetics (PK). Notably, CYP3A4 inducers significantly influenced GLS4 exposure when co-administered with ritonavir; co-administered GLS4 and ritonavir significantly influenced the exposure of CYP3A4 substrates. Additionally, with the severity of HI increased, there was a corresponding increase in the exposure to GLS4 when co-administered with ritonavir. The GLS4/ritonavir PBPK model can potentially be used as an alternative to clinical studies or guide the design of clinical trial protocols.

Mathematical Modeling of the Gastrointestinal System for Preliminary Drug Absorption Assessment

The objective of this study is to demonstrate the potential of a multicompartmental mathematical model to simulate the activity of the gastrointestinal system after the intake of drugs, with a limited number of parameters. The gastrointestinal system is divided into five compartments, modeled as both continuous systems with discrete events (stomach and duodenum) and systems with delay (jejunum, ileum, and colon). The dissolution of the drug tablet occurs in the stomach and is described through the Noyes-Whitney equation, with pH dependence expressed through the Henderson-Hasselbach relationship. The boluses resulting from duodenal activity enter the jejunum, ileum, and colon compartments, where drug absorption takes place as blood flows countercurrent. The model includes only three parameters with assigned physiological meanings. It was tested and validated using data from in vivo experiments. Specifically, the model was tested with the concentration profiles of nine different drugs and validated using data from two drugs with varying initial concentrations. Overall, the outputs of the model are in good agreement with experimental data, particularly with regard to the time of peak concentration. The primary sources of discrepancy were identified in the concentration decay. The model's main strength is its relatively low computational cost, making it a potentially excellent tool for in silico assessment and prediction of drug adsorption in the intestine.

Physiologically Based Pharmacokinetic Model for Predicting Omadacycline Pharmacokinetics and Pharmacodynamics in Healthy and Hepatic Impairment Populations

PURPOSE

Omadacycline is a new broad-spectrum aminomethylcycline antibiotic. However, there have been limited pharmacokinetic and pharmacodynamic (PK/PD) studies of omadacycline in patients with hepatic impairment. The aim of this study was to explore the PK/PD of omadacycline intravenous administration in healthy and hepatically impaired populations.

METHODS

A physiologically based pharmacokinetic (PBPK) model of omadacycline was developed and validated based on published demographic data and the physiochemical properties of omadacycline. The PK processes in healthy adults were simulated and then extrapolated to a hepatically impaired population. Monte Carlo simulations were performed for PD evaluation by calculating the probability of target attainment (PTA) and the cumulative fraction of response (CFR) of the approved dosages.

FINDINGS

In the hepatically impaired population, there was no significant difference in the maximum concentration (Cmax) compared with the healthy population, while the area under the plasma concentration-time curve from the first data point extrapolated to infinity (AUC_inf) showed a slight increase. Monte Carlo simulations indicated that the dosage of 200 mg once daily or 100 mg twice daily intravenously (loading dose) and 100 mg once daily intravenously (maintenance dose) could cover the common pathogens of community-acquired bacterial pneumonia (CABP) and acute bacterial skin and skin structure infections (ABSSSI) : Streptococcus pneumoniae, Haemophilus influenzae, and Staphylococcus aureus.

IMPLICATIONS

Hepatic impairment exerts little impact on the PK properties of omadacycline, and no dosage adjustments are necessary for patients with mild and moderate hepatic impairment. Current dosing regimens are predicted to produce satisfactory therapeutic effects against non-drug-resistant strains of Staphylococcus aureus, Streptococcus pneumoniae, and Haemophilus influenzae but may not produce the desired AUC/MIC ratios in patients with Escherichia coli or Klebsiella pneumoniae.

Personalized Dosing of Medicines for Children: A Primer on Pediatric Pharmacometrics for Clinicians

The widespread use of drugs for unapproved purposes remains common in children, primarily attributable to practical, ethical, and financial constraints associated with pediatric drug research. Pharmacometrics, the scientific discipline that involves the application of mathematical models to understand and quantify drug effects, holds promise in advancing pediatric pharmacotherapy by expediting drug development, extending applications, and personalizing dosing. In this review, we delineate the principles of pharmacometrics, and explore its clinical applications and prospects. The fundamental aspect of any pharmacometric analysis lies in the selection of appropriate methods for quantifying pharmacokinetics and pharmacodynamics. Population pharmacokinetic modeling is a data-driven method ('top-down' approach) to approximate population-level pharmacokinetic parameters, while identifying factors contributing to inter-individual variability. Model-informed precision dosing is increasingly used to leverage population pharmacokinetic models and patient data, to formulate individualized dosing recommendations. Physiologically based pharmacokinetic models integrate physicochemical drug properties with biological parameters ('bottom-up approach'), and is particularly valuable in situations with limited clinical data, such as early drug development, assessing drug-drug interactions, or adapting dosing for patients with specific comorbidities. The effective implementation of these complex models hinges on strong collaboration between clinicians and pharmacometricians, given the pivotal role of data availability. Promising advancements aimed at improving data availability encompass innovative techniques such as opportunistic sampling, minimally invasive sampling approaches, microdialysis, and in vitro investigations. Additionally, ongoing research efforts to enhance measurement instruments for evaluating pharmacodynamics responses, including biomarkers and clinical scoring systems, are expected to significantly bolster our capacity to understand drug effects in children.

Investigating clinical pharmacokinetics of brivaracetam by using a pharmacokinetic modeling approach

The development of technology and the processing speed of computing machines have facilitated the evaluation of advanced pharmacokinetic (PK) models, making modeling processes simple and faster. The present model aims to analyze the PK of brivaracetam (BRV) in healthy and diseased populations. A comprehensive literature review was conducted to incorporate the BRV plasma concentration data and its input parameters into PK-Sim software, leading to the creation of intravenous (IV) and oral models for both populations. The developed physiologically based pharmacokinetic (PBPK) model of BRV was then assessed using the visual predictive checks, mean observed/predicted ratios (Robs/pre), and average fold error for PK parameters including the maximum systemic concentration (Cmax), the area under the curve at time 0 to t (AUC0-∞), and drug clearance (CL). The PBPK model of BRV demonstrated that mean Robs/pre ratios of the PK parameters remained within the acceptable limits when assessed against a twofold error margin. Furthermore, model predictions were carried out to assess how AUC0-∞ is affected following the administration of BRV in individuals with varying degrees of liver cirrhosis, ranging from different child-pugh (CP) scores like A, B, and C. Moreover, dose adjustments were recommended by considering the variations in Cmax and CL in various kidney disease stages (mild to severe).

Development of a Physiologically Based Pharmacokinetic Population Model for Diabetic Patients and its Application to Understand Disease-drug-drug Interactions

INTRODUCTION

The activity changes of cytochrome P450 (CYP450) enzymes, along with the complicated medication scenarios in diabetes mellitus (DM) patients, result in the unanticipated pharmacokinetics (PK), pharmacodynamics (PD), and drug-drug interactions (DDIs). Physiologically based pharmacokinetic (PBPK) modeling has been a useful tool for assessing the influence of disease status on CYP enzymes and the resulting DDIs. This work aims to develop a novel diabetic PBPK population model to facilitate the prediction of PK and DDI in DM patients.

METHODS

First, mathematical functions were constructed to describe the demographic and non-CYP physiological characteristics specific to DM, which were then incorporated into the PBPK model to quantify the net changes in CYP enzyme activities by comparing the PK of CYP probe drugs in DM versus non-DM subjects.

RESULTS

The results show that the enzyme activity is reduced by 32.3% for CYP3A4/5, 39.1% for CYP2C19, and 27% for CYP2B6, while CYP2C9 activity is enhanced by 38% under DM condition. Finally, the diabetic PBPK model was developed through integrating the DM-specific CYP activities and other parameters and was further used to perform PK simulations under 12 drug combination scenarios, among which 3 combinations were predicted to result in significant PK changes in DM, which may cause DDI risks in DM patients.

CONCLUSIONS

The PBPK modeling applied herein provides a quantitative tool to assess the impact of disease factors on relevant enzyme pathways and potential disease-drug-drug-interactions (DDDIs), which may be useful for dosing regimen optimization and minimizing the DDI risks associated with the treatment of DM.

Simultaneously Predicting the Pharmacokinetics of CES1-Metabolized Drugs and Their Metabolites Using Physiologically Based Pharmacokinetic Model in Cirrhosis Subjects

Hepatic carboxylesterase 1 (CES1) metabolizes numerous prodrugs into active ingredients or direct-acting drugs into inactive metabolites. We aimed to develop a semi-physiologically based pharmacokinetic (semi-PBPK) model to simultaneously predict the pharmacokinetics of CES1 substrates and their active metabolites in liver cirrhosis (LC) patients. Six prodrugs (enalapril, benazepril, cilazapril, temocapril, perindopril and oseltamivir) and three direct-acting drugs (flumazenil, pethidine and remimazolam) were selected. Parameters such as organ blood flows, plasma-binding protein concentrations, functional liver volume, hepatic enzymatic activity, glomerular filtration rate (GFR) and gastrointestinal transit rate were integrated into the simulation. The pharmacokinetic profiles of these drugs and their active metabolites were simulated for 1000 virtual individuals. The developed semi-PBPK model, after validation in healthy individuals, was extrapolated to LC patients. Most of the observations fell within the 5th and 95th percentiles of simulations from 1000 virtual patients. The estimated AUC and Cmax were within 0.5-2-fold of the observed values. The sensitivity analysis showed that the decreased plasma exposure of active metabolites due to the decreased CES1 was partly attenuated by the decreased GFR. Conclusion: The developed PBPK model successfully predicted the pharmacokinetics of CES1 substrates and their metabolites in healthy individuals and LC patients, facilitating tailored dosing of CES1 substrates in LC patients.

A Physiologically Based Pharmacokinetic Model to Predict Systemic Ondansetron Concentration in Liver Cirrhosis Patients

INTRODUCTION

Ondansetron is a drug that is routinely prescribed for the management of nausea and vomiting associated with cancer, radiation therapy, and surgical operations. It is mainly metabolized in the liver, and it might accumulate in patients with hepatic impairment and lead to unwanted adverse events.

METHODS

A physiologically based pharmacokinetic (PBPK) model was developed to predict the exposure of ondansetron in healthy and liver cirrhosis populations. The population-based PBPK simulator PK-Sim was utilized for simulating ondansetron exposure in healthy and liver cirrhosis populations.

RESULTS

The developed model successfully described the pharmacokinetics of ondansetron in healthy and liver cirrhosis populations. The predicted area under the curve, maximum systemic concentration, and clearance were within the allowed twofold range. The exposure of ondansetron in the population of Child-Pugh class C has doubled in comparison to Child-Pugh class A. The dose has to be adjusted for liver cirrhosis patients to ensure comparable exposure to a healthy population.

CONCLUSION

In this study, the developed PBPK model has described the pharmacokinetics of ondansetron successfully. The PBPK model has been successfully evaluated to be used as a tool for dose adjustments in liver cirrhosis patients.

Incorporation and Performance Verification of Hepatic Portal Blood Flow Shunting in Minimal and Full PBPK Models of Liver Cirrhosis

Patho-physiological changes in liver cirrhosis create portacaval shunts that allow blood flow to bypass the hepatic portal vein into the systemic circulation affecting drug pharmacokinetics (PKs). The objectives of this work were to implement a physiologically-based pharmacokinetic (PBPK) framework describing shunted blood flows in virtual patients with differing degrees of liver cirrhosis; and to assess the minimal and full PBPK model's performance using drugs with intermediate to high hepatic extraction. Single dose concentration-time profiles and PK parameters for oral ibrutinib, midazolam, propranolol, and buspirone were simulated in healthy volunteers (HVs) and subjects with cirrhosis (Child-Pugh severity score (CP-A, CP-B, or CP-C)). Model performance was verified by comparing predicted to observed fold-changes in PK parameters between HVs and cirrhotic subjects. The verified model was used to simulate the PK changes for simvastatin in patients with cirrhosis. The predicted area under the curve ratios (AUCCirr :AUCHV ) for ibrutinib were 3.38, 6.87, and 11.46 using the minimal PBPK model with shunt and 1.61, 2.58, and 4.33 without the shunt, these compared with observed values of 4.33, 8.14, and 9.04, respectively. For ibrutinib, propranolol, and buspirone, including a shunt in the PBPK model improved the prediction of the AUCCirr :AUCHV and maximum plasma concentration ratios (CmaxCirr :CmaxHV ). For midazolam, an intermediate extraction drug, the differences were less clear. Simulated simvastatin dose adjustments in cirrhosis suggested that 20 mg in CP-A and 10 mg in CP-B could be used clinically. A mechanistic model-informed understanding of the anatomic and pathophysiology of cirrhosis will facilitate improved dose prediction and adjustment in this vulnerable population.

Physiologically Based Pharmacokinetics Modeling in the Neonatal Population-Current Advances, Challenges, and Opportunities

Physiologically based pharmacokinetic (PBPK) modeling is an approach to predicting drug pharmacokinetics, using knowledge of the human physiology involved and drug physiochemical properties. This approach is useful when predicting drug pharmacokinetics in under-studied populations, such as pediatrics. PBPK modeling is a particularly important tool for dose optimization for the neonatal population, given that clinical trials rarely include this patient population. However, important knowledge gaps exist for neonates, resulting in uncertainty with the model predictions. This review aims to outline the sources of variability that should be considered with developing a neonatal PBPK model, the data that are currently available for the neonatal ontogeny, and lastly to highlight the data gaps where further research would be needed.

The Application of a Physiologically Based Toxicokinetic Model in Health Risk Assessment

Toxicokinetics plays a crucial role in the health risk assessments of xenobiotics. Classical compartmental models are limited in their ability to determine chemical concentrations in specific organs or tissues, particularly target organs or tissues, and their limited interspecific and exposure route extrapolation hinders satisfactory health risk assessment. In contrast, physiologically based toxicokinetic (PBTK) models quantitatively describe the absorption, distribution, metabolism, and excretion of chemicals across various exposure routes and doses in organisms, establishing correlations with toxic effects. Consequently, PBTK models serve as potent tools for extrapolation and provide a theoretical foundation for health risk assessment and management. This review outlines the construction and application of PBTK models in health risk assessment while analyzing their limitations and future perspectives.

Application of physiologically based pharmacokinetics modeling in the research of small-molecule targeted anti-cancer drugs

INTRODUCTION

Physiologically based pharmacokinetics (PBPK) models are increasingly used in the drug research and development, especially in anti-cancer drugs. Between 2001 and 2020, a total of 89 small-molecule targeted antitumor drugs were approved in China and the United States, some of which already included PBPK modeling in their application or approval packages. This article intended to review the prevalence and application of PBPK model in these drugs.

METHOD

Article search was performed in the PubMed to collect English research articles on small-molecule targeted anti-cancer drugs using PBPK modeling. The selected articles were classified into nine categorizes according to the application areas and further analyzed.

RESULT

From 2001 to 2020, more than 60% of small-molecule targeted anti-cancer drugs (54/89) were studied using PBPK model with a wide range of application. Ninety research articles were included, of which 48 involved enzyme-mediated drug-drug interaction (DDI). Of these retrieved articles, Simcyp, GastroPlus, and PK-Sim were the most widely model building platforms, which account for 63.8%, 15.2%, and 8.6%, respectively.

CONCLUSION

PBPK modeling is commonly and widely used to research small-molecule targeted anti-cancer drugs.

Physiologically Based Pharmacokinetic Modeling for Prediction of 5-FU Pharmacokinetics in Cancer Patients with Hepatic Impairment After 5-FU and Capecitabine Administration

PURPOSE

5-fluorouracil (5-FU) and its prodrug capecitabine are commonly prescribed anti-tumor medications. We aimed to establish physiologically based pharmacokinetic (PBPK) models of capecitabine-metabolites and 5-FU-metabolites to describe their pharmacokinetics in tumor and plasma of cancer patients with liver impairment.

METHODS

Models including the cancer compartment were developed in PK-Sim® and MoBi® and evaluated by R programming language with 25 oral capecitabine and 18 intravenous 5-FU studies for cancer patients with and without liver impairment.

RESULTS

The PBPK models were constructed successfully as most simulated Cmax and AUClast were within two-fold error of observed values. The simulated alterations of tumor 5-FU Cmax and AUClast in cancer patients with severe liver injury compared with normal liver function were 1.956 and 3.676 after oral administration of capecitabine, but no significant alteration was observed after intravenous injection of 5-FU. Besides, 5-FU concentration in tumor tissue increases with higher tumor blood flow but not tumor size. Sensitivity analysis revealed that dihydropyrimidine dehydrogenase (DPD) and other metabolic enzymes' activity, capecitabine intestinal permeability and plasma protein scale factor played a vital role in tumor and plasma 5-FU pharmacokinetics.

CONCLUSIONS

PBPK model prediction suggests no dosage adaption of capecitabine or 5-FU is required for cancer patients with hepatic impairment but it would be reduced when the toxic reaction is observed. Furthermore, tumor blood flow rate rather than tumor size is critical for 5-FU concentration in tumor. In summary, these models could predict pharmacokinetics of 5-FU in tumor in cancer patients with varying characteristics in different scenarios.

Progressive tools and critical strategies for development of best fit PBPK model aiming better in vitro-in vivo correlation

Nowadays, conducting discriminative dissolution experiments employing physiologically based pharmacokinetic modeling (PBPK) or physiologically based biopharmaceutical modeling (PBBM) is gaining significant importance in quantitatively predicting oral absorption of drugs. Mechanistic understanding of each process involved in drug absorption and its impact on the performance greatly facilitates designing a formulation with high confidence. Unfortunately, the biggest challenge scientists are facing in current days is the lack of standardized protocol for integrating dissolution experiment data during PBPK modeling. However, in vitro-in vivo drug release interrelation can be improved with the consideration and development of appropriate biorelevant dissolution media that closely mimic physiological conditions. Multiple reported dissolution models have described nature and functionality of different regions of the gastrointestinal tract (GI) to more accurately design discriminative dissolution media. Dissolution experiment data can be integrated either mechanistically or without a mechanism depending primarily on the formulation type, biopharmaceutics classification system (BCS) class and particle size of the drug substance. All such parameters are required to be considered for selecting the appropriate functions during PBPK modeling to produce a best fit model. The primary focus of this review is to critically discuss various progressive dissolution models and tools, existing challenges and approaches for establishing best fit PBPK model aiming better in vitro-in vivo correlation (IVIVC). Strategies for proper selection of dissolution models as an input function in PBPK/PBBM modeling have also been critically discussed. Logical and scientific pathway for selection of different type of functions and integration events in the commercially available in silico software has been described through case studies.

Physiologically based pharmacokinetic modeling of levetiracetam to predict the exposure in hepatic and renal impairment and elderly populations

Levetiracetam (LEV) is an anti-epileptic drug approved for use in various populations. The pharmacokinetic (PK) behavior of LEV may be altered in the elderly and patients with renal and hepatic impairment. Thus, dosage adjustment is required. This study was conducted to investigate how the physiologically-based PK (PBPK) model describes the PKs of LEV in adult and elderly populations, as well as to predict the PKs of LEV in patients with renal and hepatic impairment in both populations. The whole-body PBPK models were developed using the reported physicochemical properties of LEV and clinical data. The models were validated using data from clinical studies with different dose ranges and different routes and intervals of administration. The fit performance of the models was assessed by comparing predicted and observed blood concentration data and PK parameters. It is recommended that the doses be reduced to ~70%, 60%, and 45% of the adult dose for the mild, moderate, and severe renal impairment populations and ~95%, 80%, and 57% of the adult dose for the Child Pugh-A (CP-A), Child Pugh-B (CP-B), and Child Pugh-C (CP-C) hepatic impairment populations, respectively. No dose adjustment is required for the healthy elderly population, but dose reduction is required for the elderly with organ dysfunction accordingly, on a scale similar to that of adults. A PBPK model of LEV was successfully developed to optimize dosing regimens for special populations.

Predicting Hydroxychloroquine Clearance in Healthy and Diseased Populations Using a Physiologically Based Pharmacokinetic Approach

Hydroxychloroquine (HCQ), a congener of chloroquine, is widely used in prophylaxis and the treatment of malaria, and also as a cure for rheumatoid arthritis, systemic lupus erythematosus, and various other diseases. Physiologically based pharmacokinetic modeling (PBPK) has attracted great interest in the past few years in predicting drug pharmacokinetics (PK). This study focuses on predicting the PK of HCQ in the healthy population and extrapolating it to the diseased populations, i.e., liver cirrhosis and chronic kidney disease (CKD), utilizing a systematically built whole-body PBPK model. The time vs. concentration profiles and drug-related parameters were obtained from the literature after a laborious search and in turn were integrated into PK-Sim software for designing healthy intravenous, oral, and diseased models. The model's evaluation was performed using observed-to-predicted ratios (Robs/Rpre) and visual predictive checks within a 2-fold error range. The healthy model was then extrapolated to liver cirrhosis and CKD populations after incorporating various disease-specific pathophysiological changes. Box-whisker plots showed an increase in AUC_0-t in liver cirrhosis, whereas a decrease in AUC_0-t was seen in the CKD population. These model predictions may assist clinicians in adjusting the administered HCQ doses in patients with different degrees of hepatic and renal impairment.

Physiologically Based Pharmacokinetic Modelling to Predict Pharmacokinetics of Enavogliflozin, a Sodium-Dependent Glucose Transporter 2 Inhibitor, in Humans

Enavogliflozin is a sodium-dependent glucose cotransporter 2 (SGLT2) inhibitor approved for clinical use in South Korea. As SGLT2 inhibitors are a treatment option for patients with diabetes, enavogliflozin is expected to be prescribed in various populations. Physiologically based pharmacokinetic (PBPK) modelling can rationally predict the concentration–time profiles under altered physiological conditions. In previous studies, one of the metabolites (M1) appeared to have a metabolic ratio between 0.20 and 0.25. In this study, PBPK models for enavogliflozin and M1 were developed using published clinical trial data. The PBPK model for enavogliflozin incorporated a non-linear urinary excretion in a mechanistically arranged kidney model and a non-linear formation of M1 in the liver. The PBPK model was evaluated, and the simulated pharmacokinetic characteristics were in a two-fold range from those of the observations. The pharmacokinetic parameters of enavogliflozin were predicted using the PBPK model under pathophysiological conditions. PBPK models for enavogliflozin and M1 were developed and validated, and they seemed useful for logical prediction.

An Overview of Physiologically-Based Pharmacokinetic Models for Forensic Science

A physiologically-based pharmacokinetic (PBPK) model represents the structural components of the body with physiologically relevant compartments connected via blood flow rates described by mathematical equations to determine drug disposition. PBPK models are used in the pharmaceutical sector for drug development, precision medicine, and the chemical industry to predict safe levels of exposure during the registration of chemical substances. However, one area of application where PBPK models have been scarcely used is forensic science. In this review, we give an overview of PBPK models successfully developed for several illicit drugs and environmental chemicals that could be applied for forensic interpretation, highlighting the gaps, uncertainties, and limitations.

Predicting changes in the pharmacokinetics of CYP3A-metabolized drugs in hepatic impairment and insights into factors driving these changes

Physiologically based pharmacokinetic models, populated with drug-metabolizing enzyme and transporter (DMET) abundance, can be used to predict the impact of hepatic impairment (HI) on the pharmacokinetics (PK) of drugs. To increase confidence in the predictive power of such models, they must be validated by comparing the predicted and observed PK of drugs in HI obtained by phenotyping (or probe drug) studies. Therefore, we first predicted the effect of all stages of HI (mild to severe) on the PK of drugs primarily metabolized by cytochrome P450 (CYP) 3A enzymes using the default HI module of Simcyp Version 21, populated with hepatic and intestinal CYP3A abundance data. Then, we validated the predictions using CYP3A probe drug phenotyping studies conducted in HI. Seven CYP3A substrates, metabolized primarily via CYP3A (fraction metabolized, 0.7-0.95), with low to high hepatic availability, were studied. For all stages of HI, the predicted PK parameters of drugs were within twofold of the observed data. This successful validation increases confidence in using the DMET abundance data in HI to predict the changes in the PK of drugs cleared by DMET for which phenotyping studies in HI are not available or cannot be conducted. In addition, using CYP3A drugs as an example, through simulations, we identified the salient PK factors that drive the major changes in exposure (area under the plasma concentration-time profile curve) to drugs in HI. This theoretical framework can be applied to any drug and DMET to quickly determine the likely magnitude of change in drug PK due to HI.

The LEAP Process: Streamlining the Development of Long-Acting Products and Formulations for Infectious Diseases

Developing long-acting products and formulations for infectious diseases is a nontrivial undertaking that is frequently classified as high risk and low reward by the pharmaceutical industry. The Long-Acting/Extended Release Antiretroviral Research Resource Program (LEAP) was founded in 2015 with the support of the National Institutes of Health to encourage, promote, and accelerate the development of such products. Assessment methodology for any new proposal brought to this group is part of a framework-the LEAP Process-that includes a landscape analysis of what is currently available in the public domain. This is followed by in silico modeling and simulation offered as a service to the relevant scientific community. A variety of preclinical and clinical outcome metrics are applied to each new agent as part of a continuous feedback loop to improve product characteristics. This allows us to catalog knowledge gaps and barriers that can be addressed by engaged stakeholders. Results are communicated in scientific articles, reviews, and position papers. This undertaking serves to de-risk discovery, development, and implementation by bridging the gaps between academic, regulatory, and industrial investigators, and by engaging those in the community who will be the eventual users of these medicines. The LEAP Process has supported formulations now approved for human immunodeficiency virus, as well as products in clinical and preclinical development for tuberculosis and hepatitis viruses B and C.

Physiologically Based Pharmacokinetic (PBPK) Modeling of Lornoxicam: Exploration of doses for CYP2C9 Genotypes and Patients with Cirrhosis

Lornoxicam physiologically based pharmacokinetic (PBPK) models were developed and validated on the basis of clinical pharmacokinetic results obtained by considering CYP2C9 genetic polymorphisms in healthy adult populations. PBPK models were extended to predict lornoxicam pharmacokinetics for patients with cirrhosis by quantitatively examining the pathophysiological information associated with cirrhosis. The predicted plasma exposure to lornoxicam was approximately 1.12-2.83 times higher in the CYP2C9*1/*3 and *1/*13 groups than in the CYP2C9*1/*1 group of healthy adult populations and patients with cirrhosis. The predicted plasma exposure to lornoxicam was approximately 1.28-3.61 times higher in patients with cirrhosis than in healthy adult populations. If the relationship between lornoxicam exposure in plasma and drug efficacy was proportional, then the proposed adjusted doses of lornoxicam for each group varied from 1.25 mg to 8 mg. As the severity of cirrhosis increased, or when the CYP2C9 genotype was *1 heterozygous, the dose adjustment range of lornoxicam increased. Therefore, the effect of pathophysiological factors (cirrhosis severity) on the pharmacokinetics of lornoxicam might be more important than that of CYP2C9 genetic factors. These results could be useful for broadening the scope of clinical application of lornoxicam by enabling dose selection based on CYP2C9 genotypes and liver cirrhosis degree.

Innovations in Genomics and Big Data Analytics for Personalized Medicine and Health Care: A Review

Big data in health care is a fast-growing field and a new paradigm that is transforming case-based studies to large-scale, data-driven research. As big data is dependent on the advancement of new data standards, technology, and relevant research, the future development of big data applications holds foreseeable promise in the modern day health care revolution. Enormously large, rapidly growing collections of biomedical omics-data (genomics, proteomics, transcriptomics, metabolomics, glycomics, etc.) and clinical data create major challenges and opportunities for their analysis and interpretation and open new computational gateways to address these issues. The design of new robust algorithms that are most suitable to properly analyze this big data by taking into account individual variability in genes has enabled the creation of precision (personalized) medicine. We reviewed and highlighted the significance of big data analytics for personalized medicine and health care by focusing mostly on machine learning perspectives on personalized medicine, genomic data models with respect to personalized medicine, the application of data mining algorithms for personalized medicine as well as the challenges we are facing right now in big data analytics.

An Approach to the Management of Diabetes Mellitus in Cirrhosis: A Primer for the Hepatologist

The management of diabetes in cirrhosis and liver transplantation can be challenging. There is difficulty in diagnosis and monitoring of diabetes as fasting blood sugar values are low and glycosylated hemoglobin may not be a reliable marker. The challenges in the management of diabetes in cirrhosis include the likelihood of cognitive impairment, risk of hypoglycemia, altered drug metabolism, frequent renal dysfunction, risk of lactic acidosis, and associated malnutrition and sarcopenia. Moreover, calorie restriction and an attempt to lose weight in obese diabetics may be associated with a worsening of sarcopenia. Many commonly used antidiabetic drugs may be unsafe or be associated with a high risk of hypoglycemia in cirrhotics. Post-transplant diabetes is common and may be contributed by immunosuppressive medication. There is inadequate clinical data on the use of antidiabetic drugs in cirrhosis, and the management of diabetes in cirrhosis is hampered by the lack of guidelines focusing on this issue. The current review aims at addressing the practical management of diabetes by a hepatologist.

PBPK Modelling of Dexamethasone in Patients With COVID-19 and Liver Disease

The aim of the study was to apply Physiologically-Based Pharmacokinetic (PBPK) modelling to predict the effect of liver disease (LD) on the pharmacokinetics (PK) of dexamethasone (DEX) in the treatment of COVID-19. A whole-body PBPK model was created to simulate 100 adult individuals aged 18–60 years. Physiological changes (e.g., plasma protein concentration, liver size, CP450 expression, hepatic blood flow) and portal vein shunt were incorporated into the LD model. The changes were implemented by using the Child-Pugh (CP) classification system. DEX was qualified using clinical data in healthy adults for both oral (PO) and intravenous (IV) administrations and similarly propranolol (PRO) and midazolam (MDZ) were qualified with PO and IV clinical data in healthy and LD adults. The qualified model was subsequently used to simulate a 6 mg PO and 20 mg IV dose of DEX in patients with varying degrees of LD, with and without shunting. The PBPK model was successfully qualified across DEX, MDZ and PRO. In contrast to healthy adults, the simulated systemic clearance of DEX decreased (35%–60%) and the plasma concentrations increased (170%–400%) in patients with LD. Moreover, at higher doses of DEX, the AUC ratio between healthy/LD individuals remained comparable to lower doses. The exposure of DEX in different stages of LD was predicted through PBPK modelling, providing a rational framework to predict PK in complex clinical scenarios related to COVID-19. Model simulations suggest dose adjustments of DEX in LD patients are not necessary considering the low dose administered in the COVID-19 protocol.

Computational Models for Clinical Applications in Personalized Medicine—Guidelines and Recommendations for Data Integration and Model Validation

The future development of personalized medicine depends on a vast exchange of data from different sources, as well as harmonized integrative analysis of large-scale clinical health and sample data. Computational-modelling approaches play a key role in the analysis of the underlying molecular processes and pathways that characterize human biology, but they also lead to a more profound understanding of the mechanisms and factors that drive diseases; hence, they allow personalized treatment strategies that are guided by central clinical questions. However, despite the growing popularity of computational-modelling approaches in different stakeholder communities, there are still many hurdles to overcome for their clinical routine implementation in the future. Especially the integration of heterogeneous data from multiple sources and types are challenging tasks that require clear guidelines that also have to comply with high ethical and legal standards. Here, we discuss the most relevant computational models for personalized medicine in detail that can be considered as best-practice guidelines for application in clinical care. We define specific challenges and provide applicable guidelines and recommendations for study design, data acquisition, and operation as well as for model validation and clinical translation and other research areas.

Drug-Drug Interactions in People Living with HIV at Risk of Hepatic and Renal Impairment: Current Status and Future Perspectives

Despite the advancement of antiretroviral therapy (ART) for the treatment of human immunodeficiency virus (HIV), drug–drug interactions (DDIs) remain a relevant clinical issue for people living with HIV receiving ART. Antiretroviral (ARV) drugs can be victims and perpetrators of DDIs, and a detailed investigation during drug discovery and development is required to determine whether dose adjustments are necessary or coadministrations are contraindicated. Maintaining therapeutic ARV plasma concentrations is essential for successful ART, and changes resulting from potential DDIs could lead to toxicity, treatment failure, or the emergence of ARV‐resistant HIV. The challenges surrounding DDI management are complex in special populations of people living with HIV, and often lack evidence‐based guidance as a result of their underrepresentation in clinical investigations. Specifically, the prevalence of hepatic and renal impairment in people living with HIV are between five and 10 times greater than in people who are HIV‐negative, with each condition constituting approximately 15% of non‐AIDS‐related mortality. Therapeutic strategies tend to revolve around the treatment of risk factors that lead to hepatic and renal impairment, such as hepatitis C, hepatitis B, hypertension, hyperlipidemia, and diabetes. These strategies result in a diverse range of potential DDIs with ART. The purpose of this review was 2‐fold. First, to summarize current pharmacokinetic DDIs and their mechanisms between ARVs and co‐medications used for the prevention and treatment of hepatic and renal impairment in people living with HIV. Second, to identify existing knowledge gaps surrounding DDIs related to these special populations and suggest areas and techniques to focus upon in future research efforts.

Hepatectomy-Induced Alterations in Hepatic Perfusion and Function - Toward Multi-Scale Computational Modeling for a Better Prediction of Post-hepatectomy Liver Function

Liver resection causes marked perfusion alterations in the liver remnant both on the organ scale (vascular anatomy) and on the microscale (sinusoidal blood flow on tissue level). These changes in perfusion affect hepatic functions via direct alterations in blood supply and drainage, followed by indirect changes of biomechanical tissue properties and cellular function. Changes in blood flow impose compression, tension and shear forces on the liver tissue. These forces are perceived by mechanosensors on parenchymal and non-parenchymal cells of the liver and regulate cell-cell and cell-matrix interactions as well as cellular signaling and metabolism. These interactions are key players in tissue growth and remodeling, a prerequisite to restore tissue function after PHx. Their dysregulation is associated with metabolic impairment of the liver eventually leading to liver failure, a serious post-hepatectomy complication with high morbidity and mortality. Though certain links are known, the overall functional change after liver surgery is not understood due to complex feedback loops, non-linearities, spatial heterogeneities and different time-scales of events. Computational modeling is a unique approach to gain a better understanding of complex biomedical systems. This approach allows (i) integration of heterogeneous data and knowledge on multiple scales into a consistent view of how perfusion is related to hepatic function; (ii) testing and generating hypotheses based on predictive models, which must be validated experimentally and clinically. In the long term, computational modeling will (iii) support surgical planning by predicting surgery-induced perfusion perturbations and their functional (metabolic) consequences; and thereby (iv) allow minimizing surgical risks for the individual patient. Here, we review the alterations of hepatic perfusion, biomechanical properties and function associated with hepatectomy. Specifically, we provide an overview over the clinical problem, preoperative diagnostics, functional imaging approaches, experimental approaches in animal models, mechanoperception in the liver and impact on cellular metabolism, omics approaches with a focus on transcriptomics, data integration and uncertainty analysis, and computational modeling on multiple scales. Finally, we provide a perspective on how multi-scale computational models, which couple perfusion changes to hepatic function, could become part of clinical workflows to predict and optimize patient outcome after complex liver surgery.

A Physiological Approach to Pharmacokinetics in Chronic Kidney Disease

The conventional approach to approximating the pharmacokinetics of drugs in patients with chronic kidney disease (CKD) only accounts for changes in the estimated glomerular filtration rate. However, CKD is a systemic and multifaceted disease that alters many body systems. Therefore, the objective of this exercise was to develop and evaluate a whole-body mechanistic approach to predicting pharmacokinetics in patients with CKD. Physiologically based pharmacokinetic models were developed in PK-Sim v8.0 (www.open-systems-pharmacology.org) to mechanistically represent the disposition of 7 compounds in healthy human adults. The 7 compounds selected were eliminated by glomerular filtration and active tubular secretion by the organic cation transport system to varying degrees. After a literature search, the healthy adult models were adapted to patients with CKD by numerically accounting for changes in glomerular filtration rate, kidney volume, renal perfusion, hematocrit, plasma protein concentrations, and gastrointestinal transit. Literature-informed interindividual variability was applied to the physiological parameters to facilitate a population approach. Model performance in CKD was evaluated against pharmacokinetic data from 8 clinical trials in the literature. Overall, integration of the CKD parameterization enabled exposure predictions that were within 1.5-fold error across all compounds and patients with varying stages of renal impairment. Notable improvement was observed over the conventional approach to scaling exposure, which failed in all but 1 scenario in patients with advanced CKD. Further research is required to qualify its use for first-in-CKD dose selection and clinical trial planning for a wider selection of renally eliminated compounds, including those subject to anion transport.

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.

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.

Silybin Restored CYP3A Expression through the Sirtuin 2/Nuclear Factor κ-B Pathway in Mouse Nonalcoholic Fatty Liver Disease

Silybin is widely used as a hepatoprotective agent in various liver disease therapies and has been previously identified as a CYP3A inhibitor. However, little is known about the effect of silybin on CYP3A and the regulatory mechanism during high-fat-diet (HFD)-induced liver inflammation. In our study, we found that silybin restored CYP3A expression and activity that were decreased by HFD and conditioned medium (CM) from palmitate-treated Kupffer cells. Moreover, silybin suppressed liver inflammation in HFD-fed mice and inhibited nuclear factor κ-B translocation into the nucleus through elevation of SIRT2 expression and promotion of p65 deacetylation. This effect was confirmed by overexpression of SIRT2, which suppressed p65 nuclear translocation and restored CYP3A transcription affected by CM. The hepatic NAD⁺ concentration markedly decreased in HFD-fed mice and CM-treated hepatocytes/HepG2 cells but increased after silybin treatment. Supplementing nicotinamide mononucleotide as an NAD⁺ donor inhibited p65 acetylation, decreased p65 nuclear translocation, and restored cyp3a transcription in both HepG2 cells and mouse hepatocytes. These results suggest that silybin regulates metabolic enzymes during liver inflammation by a mechanism related to the increase in NAD⁺ and SIRT2 levels. In addition, silybin enhanced the intracellular NAD⁺ concentration by decreasing poly-ADP ribosyl polymerase-1 expression. In summary, silybin increased NAD⁺ concentration, promoted SIRT2 expression, and lowered p65 acetylation both in vivo and in vitro, which supported the recovery of CYP3A expression. These findings indicate that the NAD⁺/SIRT2 pathway plays an important role in CYP3A regulation during nonalcoholic fatty liver disease. SIGNIFICANCE STATEMENT: This research revealed the differential regulation of CYP3A by silybin under physiological and fatty liver pathological conditions. In the treatment of nonalcoholic fatty liver disease, silybin restored, not inhibited, CYP3A expression and activity through the NAD⁺/ sirtuin 2 pathway in accordance with its anti-inflammatory effect.

Data-driven personalization of a physiologically based pharmacokinetic model for caffeine: A systematic assessment

Physiologically based pharmacokinetic (PBPK) models have been proposed as a tool for more accurate individual pharmacokinetic (PK) predictions and model‐informed precision dosing, but their application in clinical practice is still rare. This study systematically assesses the benefit of using individual patient information to improve PK predictions. A PBPK model of caffeine was stepwise personalized by using individual data on (1) demography, (2) physiology, and (3) cytochrome P450 (CYP) 1A2 phenotype of 48 healthy volunteers participating in a single‐dose clinical study. Model performance was benchmarked against a caffeine base model simulated with parameters of an average individual. In the first step, virtual twins were generated based on the study subjects' demography (height, weight, age, sex), which implicated the rescaling of average organ volumes and blood flows. The accuracy of PK simulations improved compared with the base model. The percentage of predictions within 0.8‐fold to 1.25‐fold of the observed values increased from 45.8% (base model) to 57.8% (Step 1). However, setting physiological parameters (liver blood flow determined by magnetic resonance imaging, glomerular filtration rate, hematocrit) to measured values in the second step did not further improve the simulation result (59.1% in the 1.25‐fold range). In the third step, virtual twins matching individual demography, physiology, and CYP1A2 activity considerably improved the simulation results. The percentage of data within the 1.25‐fold range was 66.15%. This case study shows that individual PK profiles can be predicted more accurately by considering individual attributes and that personalized PBPK models could be a valuable tool for model‐informed precision dosing approaches in the future.

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.

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.

A systematic review on pharmacokinetics, cardiovascular outcomes and safety profiles of statins in cirrhosis

Background/Aims

There is increased interest in the therapeutic use of statins in cirrhosis, but preferred statin and safety outcomes are still not well known. In this systematic review we aimed to address pharmacokinetics (PK), safety, and effects on cardiovascular (CV) outcomes of statins in cirrhosis.

Methods

Our systematic search in several electronic databases and repositories of two regulatory bodies up to 2020-06-11 yielded 22 articles and 2 drug monographs with relevant data.

Results

Rosuvastatin and pitavastatin showed minimal PK changes in Child–Pugh A cirrhosis. Only rosuvastatin was assessed in a repeated dosing PK study. Atorvastatin showed pronounced PK changes in cirrhosis. No PK data was found for simvastatin, the most commonly used statin in cirrhosis trials. There was insufficient data to assess CV effects of statins in cirrhosis. Clinical trials in cirrhosis were limited to simvastatin, atorvastatin, and pravastatin. In patients taking simvastatin 40 mg, pooled frequency of rhabdomyolysis was 2%, an incidence 40-fold higher than that reported in non-cirrhosis patients, while this was no rhabdomyolysis observed in patients on simvastatin 20 mg, atorvastatin 20 mg, or pravastatin 40 mg. Drug-induced liver injury was of difficult interpretation due to co-existence of muscle damage. No overt liver failure was reported.

Conclusions

Simvastatin 40 mg should be avoided in decompensated cirrhosis. Safety data on simvastatin 20 mg or other statins are based on small study sample size. This rarity of evidence combined with lack of data in dose adjustment methods in cirrhosis is a barrier for using statins for CV indications or for investigational use for liver indications.

Supplementary information

The online version contains supplementary material available at 10.1186/s12876-021-01704-w.

Predicting budesonide performance in healthy subjects and patients with Crohn's disease using biorelevant in vitro dissolution testing and PBPK modeling

OBJECTIVES

Drug product performance might be affected in Crohn's disease (CD) patients compared to healthy subjects due to pathophysiological changes. Since a low number of clinical studies is performed in this patient population, physiologically-based pharmacokinetic (PBPK) models with integrated results from biorelevant in vitro dissolution studies could be used to assess differences in the bioavailability of drugs. Using this approach, budesonide was used as model drug and its performance in healthy subjects and CD patients was predicted and compared against observed pharmacokinetic data. The in vitro release tests, under healthy versus CD conditions, revealed a similar extent of drug release from a controlled-release budesonide formulation in the fasted state, whereas in the fed state a lower extent was observed with CD. Differences in the physiology of CD patients were identified in literature and their impact on budesonide performance was investigated with a PBPK model, revealing the highest impact on the simulated bioavailability for the reduced hepatic CYP3A4 enzyme abundance and lower human serum albumin concentration. For CD patients, a higher budesonide exposure compared to healthy subjects was predicted with a PBPK population adapted to CD physiology and in agreement with observed pharmacokinetic data. Budesonide performance in the fasted and fed state was successfully predicted in healthy subjects and CD patients using PBPK modeling and in vitro release testing. Following this approach, predictions of the direction and magnitude of changes in bioavailability due to CD could be made for other drugs and guide prescribers to adjust dosage regimens for CD patients accordingly.

Applications of Physiologically Based Pharmacokinetic Modeling of Rivaroxaban-Renal and Hepatic Impairment and Drug-Drug Interaction Potential

The non–vitamin K antagonist oral anticoagulant rivaroxaban is used in several thromboembolic disorders. Rivaroxaban is eliminated via both metabolic degradation and renal elimination as unchanged drug. Therefore, renal and hepatic impairment may reduce rivaroxaban clearance, and medications inhibiting these clearance pathways could lead to drug‐drug interactions. This physiologically based pharmacokinetic (PBPK) study investigated the pharmacokinetic behavior of rivaroxaban in clinical situations where drug clearance is impaired. A PBPK model was developed using mass balance and bioavailability data from adults and qualified using clinically observed data. Renal and hepatic impairment were simulated by adjusting disease‐specific parameters, and concomitant drug use was simulated by varying enzyme activity in virtual populations (n = 1000) and compared with pharmacokinetic predictions in virtual healthy populations and clinical observations. Rivaroxaban doses of 10 mg or 20 mg were used. Mild to moderate renal impairment had a minor effect on area under the concentration‐time curve and maximum plasma concentration of rivaroxaban, whereas severe renal impairment caused a more pronounced increase in these parameters vs normal renal function. Area under the concentration‐time curve and maximum plasma concentration increased with severity of hepatic impairment. These effects were smaller in the simulations compared with clinical observations. AUC and C_max increased with the strength of cytochrome P450 3A4 and P‐glycoprotein inhibitors in simulations and clinical observations. This PBPK model can be useful for estimating the effects of impaired drug clearance on rivaroxaban pharmacokinetics. Identifying other factors that affect the pharmacokinetics of rivaroxaban could facilitate the development of models that approximate real‐world pharmacokinetics more accurately.

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.

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 (R_obs/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 R_obs/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.

Targeting of Hepatic Macrophages by Therapeutic Nanoparticles

Hepatic macrophage populations include different types of cells with plastic properties that can differentiate into diverse phenotypes to modulate their properties in response to different stimuli. They often regulate the activity of other cells and play an important role in many hepatic diseases. In response to those pathological situations, they are activated, releasing cytokines and chemokines; they may attract circulating monocytes and exert functions that can aggravate the symptoms or drive reparation processes. As a result, liver macrophages are potential therapeutic targets that can be oriented toward a variety of aims, with emergent nanotechnology platforms potentially offering new perspectives for macrophage vectorization. Macrophages play an essential role in the final destination of nanoparticles (NPs) in the organism, as they are involved in their uptake and trafficking in vivo. Different types of delivery nanosystems for macrophage recognition and targeting, such as liposomes, solid-lipid, polymeric, or metallic nanoparticles, have been developed. Passive targeting promotes the accumulation of the NPs in the liver due to their anatomical and physiological features. This process is modulated by NP characteristics such as size, charge, and surface modifications. Active targeting approaches with specific ligands may also be used to reach liver macrophages. In order to design new systems, the NP recognition mechanism of macrophages must be understood, taking into account that variations in local microenvironment may change the phenotype of macrophages in a way that will affect the uptake and toxicity of NPs. This kind of information may be applied to diseases where macrophages play a pathogenic role, such as metabolic disorders, infections, or cancer. The kinetics of nanoparticles strongly affects their therapeutic efficacy when administered in vivo. Release kinetics could predict the behavior of nanosystems targeting macrophages and be applied to improve their characteristics. PBPK models have been developed to characterize nanoparticle biodistribution in organs of the reticuloendothelial system (RES) such as liver or spleen. Another controversial issue is the possible toxicity of non-degradable nanoparticles, which in many cases accumulate in high percentages in macrophage clearance organs such as the liver, spleen, and kidney.

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.

Current trends in drug metabolism and pharmacokinetics

Pharmacokinetics (PK) is the study of the absorption, distribution, metabolism, and excretion (ADME) processes of a drug. Understanding PK properties is essential for drug development and precision medication. In this review we provided an overview of recent research on PK with focus on the following aspects: (1) an update on drug-metabolizing enzymes and transporters in the determination of PK, as well as advances in xenobiotic receptors and noncoding RNAs (ncRNAs) in the modulation of PK, providing new understanding of the transcriptional and posttranscriptional regulatory mechanisms that result in inter-individual variations in pharmacotherapy; (2) current status and trends in assessing drug-drug interactions, especially interactions between drugs and herbs, between drugs and therapeutic biologics, and microbiota-mediated interactions; (3) advances in understanding the effects of diseases on PK, particularly changes in metabolizing enzymes and transporters with disease progression; (4) trends in mathematical modeling including physiologically-based PK modeling and novel animal models such as CRISPR/Cas9-based animal models for DMPK studies; (5) emerging non-classical xenobiotic metabolic pathways and the involvement of novel metabolic enzymes, especially non-P450s. Existing challenges and perspectives on future directions are discussed, and may stimulate the development of new research models, technologies, and strategies towards the development of better drugs and improved clinical practice.

Predicting Drug Binding to Human Serum Albumin and Alpha One Acid Glycoprotein in Diseased and Age Patient Populations

Plasma protein binding, namely the fraction unbound (f_u), can be an important determinant of the disposition and response of drugs. The primary objective of this study was to predict f_u values of 183 drugs utilizing either a single binding protein model, where the predominant binding protein had been established, or a multiple binding protein model (MBPM), where the relative binding contribution of human serum albumin (HSA) or alpha 1 acid glycoprotein (AAG) is known. Mean protein concentrations, dependent on disease or age, were used to account for changes in f_u. A simple scaling approach for binding protein concentration was employed to account for quantitative changes in molar concentrations of either HSA or AAG in their respective conditions. The MBPM predictive model works best if the relative binding contribution of HSA and AAG is known, and a scaler for the change in protein concentration can be adjusted accordingly. The value of MBPM was most evident when considering reported changes in lidocaine binding because of increasing AAG concentration in response to trauma. The present approach enhances the ability to predict f_u in diseased and age populations because of quantitative changes in major binding proteins.

Prioritization of Therapeutic Targets and Trial Design in Cirrhotic Portal Hypertension

Portal hypertension (PH) is the main driver of cirrhosis decompensation, the main determinant of death in patients with cirrhosis. PH results initially from increased intrahepatic vascular resistance. Subsequently, increased inflow from splanchnic vasodilation and increased cardiac output lead to a further increase in portal pressure (PP). Reducing PP in cirrhosis results in better outcomes. Removing the cause of cirrhosis might improve PP. However, this is a slow process and patients may continue to be at risk of decompensation. Additionally, for some chronic liver diseases, such as nonalcoholic fatty liver disease (NAFLD), etiological treatments are not yet available. Therefore, there is a need to develop better therapies specifically aimed at reducing PP. For over 35 years, the mainstay of such therapy has been the use of nonselective beta-blockers (NSBBs) that act by reducing portal venous inflow. Recently, many drugs (mainly targeting intrahepatic mechanisms) have shown promise in preclinical and early clinical studies and may act alone or synergistically with NSBBs in reducing PP in cirrhosis. The objective of this position paper is to propose a novel framework for the design of clinical trials (phase 1, 2, and 3) in patients with cirrhosis and PH and to prioritize targets and pharmacological therapies in this setting. We have focused the discussion on patients with compensated cirrhosis. The paper summarizes discussions held at The American Association for the Study of Liver Diseases (AASLD) Industry Colloquium in January 2018, with the participation of clinical and translational investigators, regulatory professionals, and industry partners.

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.