Comprehensive Physiologically Based Pharmacokinetic Model to Assess Drug-Drug Interactions of Phenytoin

Regulatory agencies worldwide expect that clinical pharmacokinetic drug-drug interactions (DDIs) between an investigational new drug and other drugs should be conducted during drug development as part of an adequate assessment of the drug's safety and efficacy. However, it is neither time nor cost efficient to test all possible DDI scenarios clinically. Phenytoin is classified by the Food and Drug Administration as a strong clinical index inducer of CYP3A4, and a moderate sensitive substrate of CYP2C9. A physiologically based pharmacokinetic (PBPK) platform model was developed using GastroPlus® to assess DDIs with phenytoin acting as the victim (CYP2C9, CYP2C19) or perpetrator (CYP3A4). Pharmacokinetic data were obtained from 15 different studies in healthy subjects. The PBPK model of phenytoin explains the contribution of CYP2C9 and CYP2C19 to the formation of 5-(4'-hydroxyphenyl)-5-phenylhydantoin. Furthermore, it accurately recapitulated phenytoin exposure after single and multiple intravenous and oral doses/formulations ranging from 248 to 900 mg, the dose-dependent nonlinearity and the magnitude of the effect of food on phenytoin pharmacokinetics. Once developed and verified, the model was used to characterize and predict phenytoin DDIs with fluconazole, omeprazole and itraconazole, i.e., simulated/observed DDI AUC ratio ranging from 0.89 to 1.25. This study supports the utility of the PBPK approach in informing drug development.

Population Pharmacokinetic/Pharmacodynamic Modelling of Daptomycin for Schedule Optimization in Patients with Renal Impairment

The aims of this study are (i) to develop a population pharmacokinetic/pharmacodynamic model of daptomycin in patients with normal and impaired renal function, and (ii) to establish the optimal dose recommendation of daptomycin in clinical practice. Several structural PK models including linear and non-linear binding kinetics were evaluated. Monte Carlo simulations were conducted with a fixed combination of creatinine clearance (30–90 mL/min/1.73 m²) and body weight (50–100 kg). The final dataset included 46 patients and 157 daptomycin observations. A two-compartment model with first-order peripheral distribution and elimination kinetics assuming non-linear protein-binding kinetics was selected. The bactericidal effect for Gram+ strains with MIC ≤ 0.5 mg/L could be achieved with 5–12 mg/kg daily daptomycin based on body weight and renal function. The administration of 10–17 mg/kg q48 h daptomycin allows to achieve bactericidal effect for Gram+ strains with MIC ≤ 1 mg/L. Four PK samples were selected as the optimal sampling strategy for an accurate AUC estimation. A quantitative framework has served to characterize the non-linear binding kinetics of daptomycin in patients with normal and impaired renal function. The impact of different dosing regimens on the efficacy and safety outcomes of daptomycin treatment based on the unbound exposure of daptomycin and individual patient characteristics has been evaluated.

Protein Binding in Translational Antimicrobial Development-Focus on Interspecies Differences

Background/Introduction: Plasma protein binding (PPB) continues to be a key aspect of antibiotic development and clinical use. PPB is essential to understand several properties of drug candidates, including antimicrobial activity, drug-drug interaction, drug clearance, volume of distribution, and therapeutic index. Focus areas of the review: In this review, we discuss the basics of PPB, including the main drug binding proteins i.e., Albumin and α-1-acid glycoprotein (AAG). Furthermore, we present the effects of PPB on the antimicrobial activity of antibiotics and the current role of PPB in in vitro pharmacodynamic (PD) models of antibiotics. Moreover, the effect of PPB on the PK/PD of antibiotics has been discussed in this review. A key aspect of this paper is a concise evaluation of PPB between animal species (dog, rat, mouse, rabbit and monkey) and humans. Our statistical analysis of the data available in the literature suggests a significant difference between antibiotic binding in humans and that of dogs or mice, with the majority of measurements from the pre-clinical species falling within five-fold of the human plasma value. Conversely, no significant difference in binding was found between humans and rats, rabbits, or monkeys. This information may be helpful for drug researchers to select the most relevant animal species in which the metabolism of a compound can be studied for extrapolating the results to humans. Furthermore, state-of-the-art methods for determining PPB such as equilibrium dialysis, ultracentrifugation, microdialysis, gel filtration, chromatographic methods and fluorescence spectroscopy are highlighted with their advantages and disadvantages.

Risk factors for biliary stent infections in malignant biliary obstruction secondary to unresectable malignancies

BACKGROUND

Palliative biliary drainage in patients with unresectable malignant biliary obstruction (MBO) frequently leads to biliary stent infection (BI), which could impact medical care. The aim of this study was to assess the risk factors for BI occurrence in patients after stenting procedure and the impact of BI on patient survival.

METHODS

All consecutive patients hospitalized from 2014 to 2018 for MBO and biliary stenting were retrospectively included. Demographic, clinical, and microbiological characteristics of each BI episode during a 1-year follow-up were described. Documented BI was defined as the association of BI episode and confirmed blood stream infection (BSI). Univariate and multivariate analyses were performed to evaluate risk factors for the first BI occurrence.

RESULTS

Among 180 patients, 56% were men (mean age of 69±12), and 54% have pancreatic cancer, 16% biliary cancer, 2% hepatic cancer, and 28% lymph node or metastatic compression; metallic stent was placed in 92%. A total of 113 BI episodes occurred in 74 patients, 55% of the first episodes occurring within 3 months after stenting. BI was documented in 56% of the episodes. Enterobacteriaceae were the most frequent pathogens found, while no yeasts were documented. Mortality rate in patients with BI was 64%. Multivariate analysis showed a significant difference in BI occurrence for two criteria: WHO score 3-4 (OR=8.79 [1.79-42.89]; p=0.007) and transpapillary stenting location (OR=3.72 [1.33-10.44]; p=0.013).

CONCLUSION

Since transpapillary stenting is a risk factor for BI, preserving the papilla as much as possible is a priority so as to avoid BI.

Comparison of liquid-liquid extraction, microextraction and ultrafiltration for measuring free concentrations of testosterone and phenytoin

Aim: The purpose of the study was to find methods suitable for measuring the free concentrations of testosterone and phenytoin. Materials & methods: Sample solutions of the compounds in buffer and human albumin were processed using liquid-liquid extraction, microextraction and ultrafiltration and analyzed by LC-MS/MS. Results: Liquid-liquid extraction with dibutyl phthalate provided complete extraction from buffer solutions and partial extraction from albumin samples. Spintip C18 devices provided exhaustive extraction from buffer and albumin samples. Spintip C8 devices offered complete extraction from buffer and approximately 50% recovery from albumin samples. Centrifree ultrafiltration devices showed high recovery of free concentrations from all the samples, while Amicon and Nanosep devices provided partial recovery. Conclusion: Spintip C8 and Centrifree devices proved useful for measuring free concentrations.

Impact of Changes in Free Concentrations and Drug-Protein Binding on Drug Dosing Regimens in Special Populations and Disease States

Over the last few decades, scientists and clinicians have often focused their attention on the unbound fraction of drugs as an indicator of efficacy and the eventual outcome of drug treatments for specific illnesses. Typically, the total drug concentration (bound and unbound) in plasma is used in clinical trials to assess a compound's efficacy. However, the free concentration of a drug tends to be more closely related to its activity and interaction with the body. Thus far, measuring the unbound concentration has been a challenge. Several mechanistic models have attempted to solve this problem by estimating the free drug fraction from available data such as total drug and binding protein concentrations. The aims of this review are first, to give an overview of the methods that have been used to date to calculate the unbound drug fraction. Second, to assess the pharmacokinetic parameters affected by changes in drug protein binding in special populations such as pediatrics, the elderly, pregnancy, and obesity. Third, to review alterations in drug protein binding in some selected disease states and how these changes impact the clinical outcomes for the patients; the disease states include critical illnesses, transplantation, renal failure, chronic kidney disease, and epilepsy. And finally, to discuss how various disease states shift the ratio of unbound to total drug and the consequences of such shifts on dosing adjustments and reaching the therapeutic target.

Probability of pharmacological target attainment with flucloxacillin in Staphylococcus aureus bloodstream infection: a prospective cohort study of unbound plasma and individual MICs

Objectives

MSSA bloodstream infections (BSIs) are associated with considerable mortality. Data regarding therapeutic drug monitoring (TDM) and pharmacological target attainment of the β-lactam flucloxacillin are scarce.

Patients and methods

We determined the achievement of pharmacokinetic/pharmacodynamic targets and its association with clinical outcome and potential toxicity in a prospective cohort of 50 patients with MSSA-BSI. Strain-specific MICs and unbound plasma flucloxacillin concentrations (at five different timepoints) were determined by broth microdilution and HPLC–MS, respectively.

Results

In our study population, 48% were critically ill and the 30 day mortality rate was 16%. The median flucloxacillin MIC was 0.125 mg/L. The median unbound trough concentration was 1.7 (IQR 0.4–9.3), 1.9 (IQR 0.4–6.2) and 1.0 (IQR 0.6–3.4) mg/L on study day 1, 3 and 7, respectively. Optimal (100% fT_>MIC) and maximum (100% fT_>4×MIC) target attainment was achieved in 45 (90%) and 34 (68%) patients, respectively, throughout the study period. Conversely, when using the EUCAST epidemiological cut-off value instead of strain-specific MICs, target attainment was achieved in only 13 (26%) patients. The mean unbound flucloxacillin trough concentration per patient was associated with neurotoxicity (OR 1.12 per 1 mg/L increase, P = 0.02) and significantly higher in deceased patients (median 14.8 versus 1.7 mg/L, P = 0.01).

Conclusions

Flucloxacillin pharmacological target attainment in MSSA-BSI patients is frequently achieved when unbound flucloxacillin concentrations and strain-specific MICs are considered. However, currently recommended dosing regimens may expose patients to excessive flucloxacillin concentrations, potentially resulting in drug-related organ damage.

Consideration of the Unbound Drug Concentration in Enzyme Kinetics

The study of enzyme kinetics in drug metabolism involves assessment of rates of metabolism and inhibitory potencies over a suitable concentration range. In all but the very simplest in vitro system, these drug concentrations can be influenced by a variety of nonspecific binding reservoirs that can reduce the available concentration to the enzyme system(s) under investigation. As a consequence, the apparent kinetic parameters, such as K_m or K_i, that are derived can deviate from the true values. There are a number of sources of these nonspecific binding depots or barriers, including membrane permeation and partitioning, plasma or serum protein binding, and incubational binding. In the latter case, this includes binding to the assay apparatus as well as biological depots, depending on the characteristics of the in vitro matrix being used. Given the wide array of subcellular, cellular, and recombinant enzyme systems utilized in drug metabolism, each of these has different components which can influence the free drug concentration. The physicochemical properties of the test compound are also paramount in determining the influential factors in any deviation between true and apparent kinetic behavior. This chapter describes the underlying mechanisms determining the free drug concentration in vitro and how these factors can be accounted for in drug metabolism studies, illustrated with case studies from the literature.

Method for Simultaneous Determination of Free Concentration, Total Concentration, and Plasma Binding Capacity in Clinical Samples

Most quantitative research methods are based on measuring either the total or the free concentration of an analyte in a sample. However, this is often insufficient for the study of complex biological systems. The main objective of this research was to develop new methods for providing more information from samples: the free concentration (C_f), the total concentration (C_t), and the plasma binding capacity (PBC). Samples were processed using microextraction and ultrafiltration. For each of these techniques, two quantification procedures were used: addition of isotopically labeled standard and repeated analysis of the same sample. The new methods were validated by analyzing clinical samples and samples with known concentrations. Methods based on addition of labeled compound were found to be the fastest, and most reproducible. For analysis of clinical samples, methods based on microextraction were more sensitive and more accurate than those based on ultrafiltration. For analysis of pooled plasma samples, the overall accuracy of all approaches to determine PBC, testosterone C_f, and testosterone C_t was between 94 and 109%, 87-113%, and 94-122% respectively. The new approach goes beyond a simple concentration measurement, giving more information from clinical samples, with great potential for personalizing drug dosage and therapy to the needs of individual patients.

Comparisons of Four Protein-Binding Models Characterizing the Pharmacokinetics of Unbound Phenytoin in Adult Patients Using Non-Linear Mixed-Effects Modeling

Background and objective

Phenytoin is extensively protein bound with a narrow therapeutic range. The unbound phenytoin is pharmacologically active, but total concentrations are routinely measured in clinical practice. The relationship between free and total phenytoin has been described by various binding models with inconsistent findings. Systematic comparison of these binding models in a single experimental setting is warranted to determine the optimal binding behaviors.

Methods

Non-linear mixed-effects modeling was conducted on retrospectively collected data (n = 37 adults receiving oral or intravenous phenytoin) using a stochastic approximation expectation–maximization algorithm in MonolixSuite-2019R2. The optimal base structural model was initially developed and utilized to compare four binding models: Winter–Tozer, linear binding, non-linear single-binding site, and non-linear multiple-binding site. Each binding model was subjected to error and covariate modeling. The final model was evaluated using relative standard errors (RSEs), goodness-of-fit plots, visual predictive check, and bootstrapping.

Results

A one-compartment, first-order absorption, Michaelis–Menten elimination, and linear protein-binding model best described the population pharmacokinetics of free phenytoin at typical clinical concentrations. The non-linear single-binding-site model also adequately described phenytoin binding but generated larger RSEs. The non-linear multiple-binding-site model performed the worst, with no identified covariates. The optimal linear binding model suggested a relatively high binding capacity using a single albumin site. Covariate modeling indicated a positive relationship between albumin concentration and the binding proportionality constant.

Conclusions

The linear binding model best described the population pharmacokinetics of unbound phenytoin in adult subjects and may be used to improve the prediction of free phenytoin concentrations.

Electronic supplementary material

The online version of this article (10.1007/s40268-020-00323-2) contains supplementary material, which is available to authorized users.

Total flucloxacillin plasma concentrations poorly reflect unbound concentrations in hospitalized patients with Staphylococcus aureus bacteraemia

AIMS

Flucloxacillin dosing may be guided by measurement of its total plasma concentrations. Flucloxacillin is highly protein bound with fraction unbound in plasma (f_u ) of around 0.04 in healthy individuals. The utility of measuring unbound flucloxacillin concentrations for patients outside the intensive care unit (ICU) is not established. We aimed to compare flucloxacillin f_u in non-ICU hospitalised patients against healthy volunteers, and to examine the performance of a published model for predicting unbound concentrations, using total flucloxacillin and plasma albumin concentrations.

METHODS

Data from 12 healthy volunteers (248 samples) and 47 hospitalized patients (61 samples) were examined. Plasma flucloxacillin concentrations were measured using a validated liquid chromatography-tandem mass spectrometry method. Flucloxacillin f_u for the two groups was compared using a generalized estimating equation model to account for clustered observations. The performance of the single protein binding site prediction model in hospitalized patients was compared with measured unbound concentrations using Bland-Altman plots.

RESULTS

The median (range) flucloxacillin f_u for healthy (median albumin 45 g l^-1 ) and hospitalized individuals (median albumin 30 g l^-1 ) were 0.04 (0.02-0.07) and 0.10 (0.05-0.37), respectively (P < 0.0001). The prediction model underpredicted unbound flucloxacillin concentrations with a mean bias (95% limits of agreement) of -54% (-137%, +30%).

CONCLUSIONS

The flucloxacillin f_u values observed in our cohort of hospitalized patients had a wide range and were greater than those of healthy individuals. Unbound flucloxacillin plasma concentrations were predicted poorly by the model. Instead, unbound concentrations should be measured to guide dosing.

Normalized vitamin D metabolite concentrations are better correlated to pharmacological effects than measured concentrations

Background:

Vitamin D deficiency has been associated with a multitude of diseases, ranging from fractures to cancer. Nearly 99% of vitamin D metabolites are bound to proteins, altering the relationship between concentration and activity.

Methods & results:

Normalized concentrations were calculated and validated using published data regarding the correlation of 25-hydroxyvitamin D with bone mineral density. In addition, healthy and kidney disease subjects were recruited for preliminary investigations. Use of the normalizing equations resulted in statistically significant improvements in the relationship between vitamin D metabolites and several markers of health status.

Conclusion:

Normalized concentrations are similar to clinically reported values and are easier to interpret than free or bioavailable concentrations, since their values match the range of measured total concentrations.

Lay abstract: Vitamin D deficiency has been associated with a multitude of diseases, ranging from fractures to cancer. Most vitamin D metabolites are bound to various body components, altering the relationship between reported concentration and biological effects. To compensate for differences in binding between individuals, normalized concentrations were calculated. Use of the normalizing equations resulted in significant improvements in the relationship between the concentration of vitamin D metabolites and health status. The newly developed normalized concentrations are therefore better indicators of vitamin D status and are easier to interpret than free or bioavailable concentrations, since their values match the range of measured total concentrations.

Impact of hypoalbuminemia on voriconazole pharmacokinetics in critically ill adult patients

Setting the adequate dose for voriconazole is challenging due to its variable pharmacokinetics. We investigated the impact of hypoalbuminemia (<35 g/liter) on voriconazole pharmacokinetics in adult intensive care unit (ICU) patients treated with voriconazole (20 samples in 13 patients) as well as in plasma samples from ICU patients that had been spiked with voriconazole at concentrations of 1.5 mg/liter, 2.9 mg/liter, and 9.0 mg/liter (66 samples from 22 patients). Plasma albumin concentrations ranged from 13.8 to 38.7 g/liter. Total voriconazole concentrations in adult ICU patients treated with voriconazole ranged from 0.5 to 8.7 mg/liter. Unbound and bound voriconazole concentrations were separated using high-throughput equilibrium dialysis followed by liquid chromatography-tandem mass spectrometry (LC-MSMS). Multivariate analysis revealed a positive relationship between voriconazole plasma protein binding and plasma albumin concentrations (P < 0.001), indicating higher unbound voriconazole concentrations with decreasing albumin concentrations. The correlation is more pronounced in the presence of elevated bilirubin concentrations (P = 0.05). We therefore propose to adjust the measured total voriconazole concentrations in patients with abnormal plasma albumin and total serum bilirubin plasma concentrations who show adverse events potentially related to voriconazole via a formula that we developed. Assuming 50% protein binding on average and an upper limit of 5.5 mg/liter for total voriconazole concentrations, the upper limit for unbound voriconazole concentrations is 2.75 mg/liter. Alterations in voriconazole unbound concentrations caused by hypoalbuminemia and/or elevated bilirubin plasma concentrations cannot be countered immediately, due to the adult saturated hepatic metabolism. Consequently, increased unbound voriconazole concentrations can possibly cause adverse events, even when total voriconazole concentrations are within the reference range.

Visualization of plasma and tissue binding using dose fractions parameter

A novel concept of dose fractions, based on the distribution of total bioavailable dose between the six combinations of location and binding state in Øie-Tozer's model is suggested as a way to visualize the distribution pharmacokinetics of a drug. The concept of dose fractions provides a sharper terminology in discussions of drug distribution allowing for a more precise description of the state and location of a drug within a system. In medicinal chemistry literature, the free fraction of a drug in plasma is a commonly discussed factor affecting the exposure to free drug while tissue binding is less well addressed. The free dose fraction, defined as the fraction of the bioavailable dose existing in free form, is suggested as a potentially valuable term for such discussions. Presently, drugs with high (>95%) plasma protein binding are viewed with skepticism, the rational behind which is questioned. The plasma protein bound dose fraction defined as the fraction of the total available dose, which is bound to plasma proteins, is suggested as a measure of the risk for problems related to fluctuations in free drug exposure due to variations in the concentration of drug binding plasma protein.

Consideration of the unbound drug concentration in enzyme kinetics

The study of enzyme kinetics in drug metabolism involves assessment of rates of metabolism and inhibitory potencies over a suitable concentration range. In all but the very simplest in vitro system, these drug concentrations can be influenced by a variety of nonspecific binding reservoirs that can reduce the available concentration to the enzyme system under investigation. As a consequence, the apparent kinetic parameters that are derived, such as K m or K i, can deviate from the true values. There are a number of sources of these nonspecific binding depots or barriers, including membrane permeation and partitioning, plasma or serum protein binding, and incubational binding. In the latter case, this includes binding to the assay apparatus, as well as biological depots, depending on the characteristics of the in vitro matrix being used. Given the wide array of subcellular, cellular, and recombinant enzyme systems utilized in drug metabolism, each of these has different components that can influence the free drug concentration. The physicochemical properties of the test compound are also paramount in determining the influential factors in any deviation between true and apparent kinetic behavior. This chapter describes the underlying mechanisms determining the free drug concentration in vitro and how these factors can be accounted for in drug metabolism studies, illustrated with case studies from the literature.

Importance of hematocrit for a tacrolimus target concentration strategy

Purpose

To identify patient characteristics that influence tacrolimus individual dose requirement in kidney transplant recipients.

Methods

Data on forty-four 12-h pharmacokinetic profiles from 29 patients and trough concentrations in 44 patients measured during the first 70 days after transplantation (1,546 tacrolimus whole blood concentrations) were analyzed. Population pharmacokinetic modeling was performed using NONMEM 7.2®.

Results

Standardization of tacrolimus whole blood concentrations to a hematocrit value of 45 % improved the model fit significantly (p < 0.001). Fat-free mass was the best body size metric to predict tacrolimus clearance and volume of distribution. Bioavailability was 49 % lower in expressers of cytochrome P450 3A5 (CYP3A5) than in CYP3A5 nonexpressers. Younger females (<40 years) showed a 35 % lower bioavailability than younger males. Bioavailability increased with age for both males and females towards a common value at age >55 years that was 47 % higher than the male value at age <40 years. Bioavailability was highest immediately after transplantation, decreasing steeply thereafter to reach its nadir at day 5, following which it increased during the next 55 days towards an asymptotic value that was 28 % higher than that on day 5.

Conclusions

Hematocrit predicts variability in tacrolimus whole blood concentrations but is not expected to influence unbound (therapeutically active) concentrations. Fat-free mass, CYP3A5 genotype, sex, age and time after transplant influence the tacrolimus individual dose requirement. Because hematocrit is highly variable in kidney transplant patients and increases substantially after kidney transplantation, hematocrit is a key factor in the interpretation of tacrolimus whole blood concentrations.

Electronic supplementary material

The online version of this article (doi:10.1007/s00228-013-1584-7) contains supplementary material, which is available to authorized users.

Approaches for dosage individualisation in critically ill patients

INTRODUCTION

Pharmacokinetic variability in critically ill patients is the result of the overlapping of multiple pathophysiological and clinical factors. Unpredictable exposure from standard dosage regimens may influence the outcome of treatment. Therefore, strategies for dosage individualisation are recommended in this setting.

AREAS COVERED

The authors focus on several approaches for dosage individualisation that have been developed, ranging from the well-established therapeutic drug monitoring (TDM) up to the innovative application of pharmacogenomics criteria. Furthermore, the authors summarise the specific population pharmacokinetic models for different drugs developed for critically ill patients to improve the initial dosage selection and the Bayesian forecasting of serum concentrations. The authors also consider the use of Monte Carlo simulation for the selection of dosage strategies.

EXPERT OPINION

Pharmacokinetic/pharmacodynamics (PK/PD) modelling and dosage individualisation methods based on mathematical and statistical criteria will contribute in improving pharmacologic treatment in critically ill patients. Moreover, substantial effort will be necessary to integrate pharmacogenomics criteria into critical care practice. The lack of availability of target biomarkers for dosage adjustment emphasizes the value of TDM which allows a large part of treatment outcome variability to be controlled.

Clinical pharmacology in old persons

The epidemiological transition, with a rapid increase in the proportion in the global population aged over 65 years from 11% in 2010 to 22% in 2050 and 32% in 2100, represents a challenge for public health. More and more old persons have multimorbidities and are treated with a large number of medicines. In advanced age, the pharmacokinetics and pharmacodynamics of many drugs are altered. In addition, pharmacotherapy may be complicated by difficulties with obtaining drugs or adherence and persistence with drug regimens. Safe and effective pharmacotherapy remains one of the greatest challenges in geriatric medicine. In this paper, the main principles of geriatric pharmacology are presented.