Quantitative Estimation of Plasma Free Drug Fraction in Patients With Varying Degrees of Hepatic Impairment: A Methodological Evaluation

Facing the Facts of Altered Plasma Protein Binding: Do Current Models Correctly Predict Changes in Fraction Unbound in Special Populations?

Accounting for variability in plasma protein binding of drugs is an essential input to physiologically-based pharmacokinetic (PBPK) models of special populations. Prediction of fraction unbound in plasma (fu) in such populations typically considers changes in plasma protein concentration while assuming that the binding affinity remains unchanged. A good correlation between predicted vs observed fu data reported for various drugs in a given special population is often used as a justification for such predictive methods. However, none of these analyses evaluated the prediction of the fold-change in fu in special populations relative to the reference population. This would be a more appropriate assessment of the predictivity, analogous to drug-drug interactions. In this study, predictive performance of the single protein binding model was assessed by predicting fu for alpha-1-acid glycoprotein and albumin bound drugs in hepatic impairment, renal impairment, paediatric, elderly, patients with inflammatory disease, and in different ethnic groups for a dataset of >200 drugs. For albumin models, the concordance correlation coefficients for predicted fu were >0.90 for 16 out of 17 populations with sub-groups, indicating strong agreement between predicted and observed values. In contrast, concordance correlation coefficients for predicted fold-change in fu for the same dataset were <0.38 for all populations and sub-groups. Trends were similar for alpha-1-acid glycoprotein models. Accordingly, the predictions of fu solely based on changes in protein concentrations in plasma cannot explain the observed values in some special populations. We recommend further consideration of the impact of changes in special populations to endogenous substances that competitively bind to plasma proteins, and changes in albumin structure due to posttranslational modifications. PBPK models of special populations for highly bound drugs should preferably use measured fu data to ensure reliable prediction of drug exposure or compare predicted unbound drug exposure between populations knowing that these will not be sensitive to changes in fu.

Predicting the Effect of Renal Function on Systemic Clearance: Is a simple scaling method sufficient?

PURPOSE

To employ a simple scaling method to predict systemic or oral clearance for drugs that are primarily renally cleared knowing the fraction eliminated in urine (f_e) and a patient's renal function relative to healthy controls (S_GFR).

METHODS

Observations evaluating drug clearance as a function of creatinine clearance for renally cleared drugs (f_e >0.3) were obtained from literature sources. The analysis comprised of 82 unique drugs from 124 studies including 31 drugs with replicate studies. A simple scaler for renal function was employed and compared to the linear regression of available data. For drugs in which replicate studies were available, the ability of the linear regression (Cl vs Cl_CR) from one pharmacokinetic study was used to predict observations from an assigned replicate and compared to the scaling approach.

RESULTS

For patients categorized as severe kidney disease (Cl_CR fixed at 20 ml/min), the scalar tended to over predict some observations, but 92% of the predictions were within 50 - 200% of the observed data. For drugs with available replicates, the scalar was as good or better in predicting the influence of Cl_CR on systemic clearance from a separate study when comparing against the linear regression approach.

CONCLUSION

A scaling approach to account for alterations in drug clearance appears to have its advantages and represents a simple and generalizable method for guiding dose adjustments in patients with decreased renal function for drugs that are renally cleared (f_e >0.3). In addition to its use in clinical practice, validation of this approach may have implications in facilitating more efficient drug development processes for designing dose-adjusted pharmacokinetic studies in patients with renal disease.

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.

Torsemide Pharmacometrics in Healthy Adult Populations Including CYP2C9 Genetic Polymorphisms and Various Patient Groups through Physiologically Based Pharmacokinetic-Pharmacodynamic Modeling

Torsemide is a widely used diuretic in clinical practice. In this study, pharmacokinetic (PK) and pharmacodynamic (PD) simulations of torsemide for various population groups and exposure scenarios were performed through human-scale physiologically-based PK-PD (PBPK-PD) modeling of torsemide. For PBPK-PD modeling of torsemide, invitro and clinical data of torsemide reported previously were used. After exposure to clinical doses of torsemide, observed plasma (or serum) concentration and urine torsemide excretion profiles were used as PK-data, and observed urinary sodium excretion rate was used as PD-data. The model was then extended to take into account physiological and biochemical factors according to different CYP2C9 phenotypes or patient populations. The established model captured various torsemide clinical results well. Differences in torsemide PKs and PDs between patient groups or CYP2C9 genetic polymorphisms were modelologically identified. It was confirmed that degrees of differences in torsemide PKs and PDs by disease groups were greater than those according to different CYP2C9 phenotypes. According to torsemide administration frequency or dose change, it was confirmed that although the difference in plasma PKs between groups (healthy adult and patient groups) could increase to 14.80 times, the difference in PDs was reduced to 1.01 times. Results of this study suggested that it is very important to consider disease groups in the setting of torsemide clinical therapy and that it is difficult to predict PD proportionally with only differences in PKs of torsemide between population groups. The PBPK-PD model established in this study is expected to be utilized for various clinical cases involving torsemide application in the future, enabling optimal drug therapy.

Altered intravenous drug disposition in people living with cystic fibrosis: A meta‐analysis integrating top‐down and bottom‐up data

Cystic fibrosis (CF) has been linked to altered drug disposition in various studies. However, the magnitude of these changes, influencing factors, and underlying mechanisms remain a matter of debate. The primary aim of this work was therefore to quantify changes in drug disposition (top‐down) and the pathophysiological parameters known to affect pharmacokinetics (PKs; bottom‐up). This was done through meta‐analyses and meta‐regressions in addition to theoretical PK simulations. Volumes of distribution and clearances were found to be elevated in people living with CF. These increases were larger in studies which included patients with pulmonary exacerbations. Differences in clearance were smaller in more recent studies and when results were normalized to body surface area or lean body mass instead of body weight. For the physiological parameters investigated, measured glomerular filtration rate and serum cytokine concentrations were found to be elevated in people living with CF, whereas serum albumin and creatinine levels were decreased. Possible pathophysiological mechanisms for these alterations relate to renal hyperfiltration, increases in free fraction, and inflammation. No differences were detected for cardiac output, body fat, fat free mass, hematocrit, creatinine clearance, and the activity of drug metabolizing enzymes. These findings imply that, in general, lower total plasma concentrations of drugs can be expected in people living with CF, especially when pulmonary exacerbations are present. Given the potential effect of CF on plasma protein binding and the variability in outcome observed between studies, the clinical relevance of adapting existing dosage regimens should be evaluated on a case‐by‐case basis.

Pooled Analysis of Gastric Emptying in Patients With Obesity: Implications for Oral Absorption Projection

PURPOSE

Gastric emptying time is one of limiting factors that determines the pharmacokinetic properties of drugs administered by mouth. Despite the high prevalence of obesity worldwide, modifications in gastric emptying time have not been systematically addressed in this set of patients. The current analysis aims to quantitatively address obesity-related changes in gastric emptying time of solids, semisolids, and liquids compared with lean individuals, highlighting the relevant pharmacokinetic implications of oral drug absorption in patients with obesity.

METHODS

We searched the Cochrane Library, PubMed, Web of Science, and Embase for all relevant articles published until November 1, 2020. Differences in gastrointestinal variables in relation to gastric emptying between obese and lean individuals were quantified by weighted mean difference (WMD) and ratio of means (RoM). Robustness of the analyses was evaluated by subgroup analysis and publication bias test.

FINDINGS

A total of 17 studies with 906 participants were included. The gastric half-emptying time of solids (WMD, -10.4 minutes; P = 0.001; RoM, 0.90; P = 0.01) and liquids (WMD, -6.14 minutes; P < 0.001; RoM, 0.83, P = 0.03) was significantly shorter in individuals with obesity compared with lean individuals. These findings were confirmed by the subgroup analyses and publication bias tests.

IMPLICATIONS

Our pooled analysis systemically quantifies the differences in gastric half-emptying time between individuals with obesity and lean individuals, facilitating better understanding and prediction of drug absorption in individuals with obesity through physiologically based pharmacokinetic approaches. Obesity is associated with a faster transit of both solids and liquids through the stomach.

An update on the importance of plasma protein binding in drug discovery and development

Introduction: Plasma protein binding (PPB) remains a controversial topic in drug discovery and development. Fraction unbound (f_u) is a critical parameter that needs to be measured accurately, because it has significant impacts on the predictions of drug-drug interactions (DDI), estimations of therapeutic indices (TI), and developments of PK/PD relationships.  However, it is generally not advisable to change PPB through structural modifications, because PPB on its own has little relevance for in vivo efficacy.Areas covered: PPB fundamentals are discussed including the three main classes of drug binding proteins (i.e., albumin, alpha1-acid glycoprotein, and lipoproteins) and their physicochemical properties, in vivo half-life, and synthesis rate.  State-of-the-art methodologies for PPB are highlighted. Applications of PPB in drug discovery and development are presented.Expert opinion: PPB is an old topic in pharmacokinetics, but there are still many misconceptions. Improving the accuracy of PPB for highly bound compounds is an ongoing effort in the field with high priority. As the field continues to generate high quality data, the regulatory agencies will increase their confidence in our ability to accurately measure PPB of highly bound compounds, and experimental fu values below 0.01 will more likely be used for DDI predictions in the future.

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.

The Combination of Cell Cultured Technology and In Silico Model to Inform the Drug Development

Human-derived in vitro models can provide high-throughput efficacy and toxicity data without a species gap in drug development. Challenges are still encountered regarding the full utilisation of massive data in clinical settings. The lack of translated methods hinders the reliable prediction of clinical outcomes. Therefore, in this study, in silico models were proposed to tackle these obstacles from in vitro to in vivo translation, and the current major cell culture methods were introduced, such as human-induced pluripotent stem cells (hiPSCs), 3D cells, organoids, and microphysiological systems (MPS). Furthermore, the role and applications of several in silico models were summarised, including the physiologically based pharmacokinetic model (PBPK), pharmacokinetic/pharmacodynamic model (PK/PD), quantitative systems pharmacology model (QSP), and virtual clinical trials. These credible translation cases will provide templates for subsequent in vitro to in vivo translation. We believe that synergising high-quality in vitro data with existing models can better guide drug development and clinical use.

Evaluation of Quantitative Structure Property Relationship Algorithms for Predicting Plasma Protein Binding in Humans

The extent of plasma protein binding is an important compound-specific property that influences a compound's pharmacokinetic behavior and is a critical input parameter for predicting exposure in physiologically based pharmacokinetic (PBPK) modeling. When experimentally determined fraction unbound in plasma (fup) data are not available, quantitative structure-property relationship (QSPR) models can be used for prediction. Because available QSPR models were developed based on training sets containing pharmaceutical-like compounds, we compared their prediction accuracy for environmentally relevant and pharmaceutical compounds. Fup values were calculated using Ingle et al., Watanabe et al. and ADMET Predictor (Simulation Plus). The test set included 818 pharmaceutical and environmentally relevant compounds with fup values ranging from 0.01 to 1. Overall, the three QSPR models resulted in over-prediction of fup for highly binding compounds and under-prediction for low or moderately binding compounds. For highly binding compounds (0.01≤ fup ≤ 0.25), Watanabe et al. performed better with a lower mean absolute error (MAE) of 6.7% and a lower mean absolute relative prediction error (RPE) of 171.7 % than other methods. For low to moderately binding compounds, both Ingle et al. and ADMET Predictor performed better than Watanabe et al. with superior MAE and RPE values. The positive polar surface area, the number of basic functional groups and lipophilicity were the most important chemical descriptors for predicting fup. This study demonstrated that the prediction of fup was the most uncertain for highly binding compounds. This suggested that QSPR-predicted fup values should be used with caution in PBPK modeling.

Abundance and Associated Variations of Cytochrome P450 Drug-Metabolizing Enzymes in the Liver of East Asian Adults: A Meta-Analysis

BACKGROUND

Cytochrome P450 (CYP) enzymes are one of the main sources of variability in drug metabolic clearance. Information on their abundance levels is therefore crucial to optimize scaling factors for in vitro-in vivo extrapolation (IVIVE) to predict metabolic clearance.

OBJECTIVE

This study aims to quantify the abundance data of hepatic drug-metabolizing CYP enzymes in East Asian subjects reported from various sources in the literature using meta-analysis.

METHOD

We conducted a meta-analysis on the abundance of drug-metabolizing CYP enzymes in the liver of East Asian adults. Eligible reports were identified based on predefined criteria-(1) individual liver microsomal samples, and (2) absolute protein abundance data from normal tissues of East Asian adult subjects. Subgroup and sensitivity analyses were also performed.

RESULTS

Among the 11 CYP isoforms analyzed in East Asian subjects, CYP3A5 and CYP3A4 had the highest protein levels. In particular, the number of studies and the liver sample used to quantify the abundance of CYP3A4 were the largest. Of the isoforms involved, CYP2J2 and CYP2B6 had the lowest abundance level, i.e., <5 pmol/ mg of microsomal protein. For enzymes with abundance values available in both Chinese and Japanese subjects (CYP1A2, CYP2C9, CYP3A4, and CYP3A5), the abundance level of each CYP isoform appeared to be higher in Chinese than in Japanese subjects. The most distinct difference was observed in CYP3A5 abundance.

CONCLUSION

The current meta-analysis shows that the abundance levels of CYP enzymes appear to vary greatly among different East Asian individuals who have similar ethnic backgrounds and food habits. The pooled data of CYP abundance can be used as preliminary reference values along with the associated variations for the projections of pharmacokinetics through physiologically based pharmacokinetic (PBPK) approaches.

Impact of Ethnicity-Specific Hepatic Microsomal Scaling Factor, Liver Weight, and Cytochrome P450 (CYP) 1A2 Content on Physiologically Based Prediction of CYP1A2-Mediated Pharmacokinetics in Young and Elderly Chinese Adults

BACKGROUND

The vast majority of physiological and biological data required for physiologically based predictions are primarily available in Caucasians rather than other ethnic populations, which leads to a lack of confidence in the application of physiologically based pharmacokinetic (PBPK) modeling for ethnicity-specific prediction of pharmacokinetics in the Chinese population.

OBJECTIVES

In this study we recalibrate the system parameters of Chinese-specific PBPK modeling and explore for the first time the relative importance of ethnicity-specific microsomal protein per gram of liver (MPPGL), liver weight, and cytochrome P450 (CYP) 1A2 abundance to the projection of drug disposition mediated by CYP1A2 in young and elderly Chinese adults.

METHODS

Chinese MPPGL levels and associated variability were parameterized and incorporated for the first time into ethnicity-specific PBPK models for the Chinese adults. Parameterization of Chinese liver weights was also recalibrated on the basis of autopsy data from Chinese individuals (n = 4081) across the entire adult age range. Uncertainty surrounding the Chinese-specific CYP1A2 content has also been explored and clarified by conducting ethnicity-related PBPK simulations under different scenarios. Various ethnicity-related or 'what-if' scenarios for PBPK modeling were implemented to assess the predictive performance and explore the relative importance of ethnicity-specific MPPGL and liver weight to the projection of drug disposition mediated by CYP1A2 in terms of two typical CYP1A2 substrates, caffeine and theophylline, in young and elderly Chinese adults by comparing the predicted concentration-time data and associated pharmacokinetic parameter estimates with observations.

RESULTS

Compared with 0.85, the liver scalar of 0.9 generally produced more accurate liver weight levels in virtual Chinese peers. Additionally, simulated MPPGL levels on the basis of Caucasian data were not able to reflect the age-independent pattern observed in Chinese adults, dissimilar to that on the basis of Chinese-specific adult MPPGL data. The modeling Scenarios A and B provided similar predictions for theophylline pharmacokinetics in young Chinese adults across different age groups, while Scenario B provided the most accurate prediction for theophylline pharmacokinetics in elderly Chinese adults. However, the use of a stratified value of CYP1A2 content derived from a Han Chinese cohort with a small sample size instead of the pooled value of all Chinese cohorts involved regardless of Chinese sub-ethnicity resulted in inadequate prediction of CYP1A2-mediated pharmacokinetics in terms of caffeine and theophylline in either young or elderly Chinese subjects. Additionally, the impact of ethnic-specific MPPGL on predictive accuracy of theophylline pharmacokinetics in elderly Chinese subjects is more evident than that of liver weight.

CONCLUSION

We provided quantitative information pertaining to Chinese-specific levels of liver weight and MPPGL, and recalibrated these system parameters for PBPK modeling for young and elderly Chinese subjects. Uncertainty surrounding the Chinese-specific CYP1A2 content has also been clarified. PBPK modeling based on the recalibrated system parameters can accurately simulate CYP1A2-mediated pharmacokinetics in both young and elderly Chinese adults, particularly in elderly individuals.

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.