Comparison of daptomycin and glycopeptide efficacy and safety for the treatment of Gram-positive infections: a systematic review and meta-analysis.

BACKGROUND: The indications of daptomycin have been extended to off-label indications including prosthesis-related infection, and bone and joint infection (BJI). However, efficacy and safety have not been thoroughly demonstrated compared with the standard of care. This systematic review and meta-analysis aimed to compare the treatment effect of daptomycin and glycopeptides for complicated infections. MATERIALS AND METHODS: MEDLINE, Embase and Web of Science were searched for randomized controlled trials (RCTs) comparing daptomycin and standard of care for Gram-positive infections, published until 30 June 2021. The primary outcome was defined as all-cause mortality. Secondary outcomes were clinical and microbiological success. The main safety outcome was any severe adverse event (SAE) (grade  ≥3). RESULTS: Overall, eight RCTs were included in the meta-analysis, totalling 1095 patients. Six (75%) were in complicated skin and soft-structure infections, one (12.5%) in bacteraemia and one (12.5%) in a BJI setting. Six RCTs used vancomycin as a comparator and two used either vancomycin or teicoplanin. All-cause mortality and clinical cure were not different between groups. The microbiological cure rate was superior in patients who received daptomycin [risk ratio (RR) = 1.17 (95% CI: 1.01-1.35)]. The risk of SAEs [RR = 0.57 (95% CI: 0.36-0.90)] was lower in the daptomycin arm. CONCLUSIONS: While daptomycin is associated with a significantly lower risk of SAEs and a better microbiological eradication, substantial uncertainty remains about the best treatment strategy in the absence of good-quality evidence, especially in bacteraemia and endocarditis where further RCTs should be conducted.

Daptomycin Exposure as a Risk Factor for Daptomycin-Induced Eosinophilic Pneumonia and Muscular Toxicity.

BACKGROUND: High-dose daptomycin is increasingly used in patients with bone and joint infection (BJI). This raises concerns about a higher risk of adverse events (AEs), including daptomycin-induced eosinophilic pneumonia (DIEP) and myotoxicity. We aimed to examine pharmacokinetic and other potential determinants of DIEP and myotoxicity in patients with BJI receiving daptomycin. METHODS: All patients receiving daptomycin for BJI were identified in a prospective cohort study. Cases were matched at a 1:3 ratio, with controls randomly selected from the same cohort. Bayesian estimation of the daptomycin daily area under the concentration-time curve over 24 hours (AUC24h) was performed with the Monolix software based on therapeutic drug monitoring (TDM) data. Demographic and biological data were also collected. Risk factors of AEs were analyzed using Cox proportional hazards model. RESULTS: From 1130 patients followed over 7 years, 9 with DIEP, 26 with myotoxicity, and 106 controls were included in the final analysis. Daptomycin AUC24h, C-reactive protein, and serum protein levels were associated with the risk of AEs. The adjusted hazard ratio of DIEP or myotoxicity was 3.1 (95% confidence interval [CI], 1.48-6.5; P < .001) for daptomycin AUC24h > 939 mg/h/L, 9.8 (95% CI, 3.94-24.5; P < .001) for C-reactive protein > 21.6 mg/L, and 2.4 (95% CI, 1.02-5.65; P = .04) for serum protein <72 g/L. CONCLUSIONS: We identified common determinants of DIEP and myotoxicity in patients with BJI. Because the risk of AEs was associated with daptomycin exposure, daptomycin TDM and model-informed precision dosing may help optimize the efficacy and safety of daptomycin treatment in this setting. A target AUC24h range of 666 to 939 mg/h/L is suggested.

Pharmacokinetic/Pharmacodynamic Simulations of Cost-Effective Dosage Regimens of Ceftolozane-Tazobactam and Ceftazidime-Avibactam in Patients with Renal Impairment.

The pharmacokinetics of ceftolozane-tazobactam (TOL-TAZ) and ceftazidime-avibactam (CEF-AVI) is influenced by renal function. Application of recommended dosages in patients with renal impairment requires the use of fractions of the full dose, as only one dosage is available for both antibiotics. The objective of this study was to evaluate the adequacy of alternative dosage regimens based on the full dose. We performed pharmacokinetic/pharmacodynamic (PK/PD) simulations of recommended and alternative dosage regimens in patients with various degrees of renal impairment by using the Pmetrics program. Alternative regimens included longer dosage interval and prolonged infusions of the full dose for both drugs. Probabilities of target attainment (PTA) were assessed considering PK/PD targets defined for cephalosporins and beta-lactamase inhibitors as well as MIC breakpoints. The risk of overexposure was also assessed. Results showed that alternative dosage regimens based on a full dose of TOL-TAZ and CEF-AVI administered every 12 or 24 h were associated with PTA similar to that of recommended dosages, especially when administered as prolonged infusion. The alternative dosage regimens were not associated with overexposure in most cases. In addition, those regimens could reduce dosing errors, drug cost, and nurse labor. Clinical investigation ovf those alternative dosage regimens would be required before implementation.

Model-Based Comparative Analysis of Rifampicin and Rifabutin Drug-Drug Interaction Profile.

Rifamycins are widely used for treating mycobacterial and staphylococcal infections. Drug-drug interactions (DDI) caused by rifampicin (RIF) are a major issue. We used a model-based approach to predict the magnitude of DDI with RIF and rifabutin (RBT) for 217 cytochrome P450 (CYP) substrates. On average, DDI caused by low-dose RIF were twice as potent as those caused by RBT. Contrary to RIF, RBT appears unlikely to cause severe DDI, even with sensitive CYP substrates.

Population Pharmacokinetic Modeling and Dosing Simulations of Tobramycin in Pediatric Patients with Cystic Fibrosis.

Initial dosing and dose adjustment of intravenous tobramycin in children with cystic fibrosis (CF) is challenging. The objectives of this study were to develop nonparametric population pharmacokinetic (PK) models of tobramycin in children with CF to be used for dosage design and model-guided therapeutic drug monitoring. We performed a retrospective analysis of tobramycin PK data in our children's CF center. The Pmetrics package was used for nonparametric population PK analysis and dosing simulations. Both the ratios of maximal concentration to the MIC (Cmax/MIC) and daily area under the concentration-time curve to the MIC (AUC24/MIC) were considered efficacy targets. Trough concentration (Cmin) was considered the safety target. A total of 2,884 tobramycin concentrations collected in 195 patients over 9 years were analyzed. A two-compartment model including total body weight, body surface area, and creatinine clearance as covariates best described the data. A simpler model was also derived for implementation in the BestDose software to perform Bayesian dose adjustment. Both models were externally validated. PK/pharmacodynamics (PD) simulations with the final model suggest that an initial dose of tobramycin of 15 to 17.5 mg/kg/day was necessary to achieve Cmax/MICs of ≥10 for MICs up to 2 mg/liter in most patients. The AUC24/MIC target was associated with higher dosage requirements and higher Cmin. A daily dose of 12.5 mg/kg would optimize both efficacy and safety target attainment. We recommend performing tobramycin therapeutic drug monitoring (TDM), model-based dose adjustment, and MIC determination to individualize intravenous tobramycin therapy in children with CF.

Population pharmacokinetics of daptomycin in patients with bone and joint infection: minimal effect of rifampicin co-administration and confirmation of a sex difference.

BACKGROUND: Daptomycin is increasingly used in the treatment of bone and joint infection (BJI), but its pharmacokinetics (PK) and dosage requirements have not been thoroughly investigated in this indication. Daptomycin may be co-administered with rifampicin, which raises questions about a potential drug interaction. OBJECTIVES: To investigate the population PK and dosage requirements of daptomycin in patients with BJI, and examine the influence of rifampicin co-administration. METHODS: A population approach was used to analyse PK data from patients who received daptomycin in our regional reference for BJI. We examined the influence of available covariates, including rifampicin co-administration on daptomycin PK. Simulations performed with the final model investigated the influence of dosages and covariates on PTA for both efficacy and safety. RESULTS: A total of 1303 daptomycin concentrations from 183 patients were analysed. A two-compartment model best described the data. Significant intra-individual variability was observed. Daptomycin clearance was influenced by renal function and sex, with females having a 26% lower typical clearance than males. Central volume of distribution (V1) was influenced by body weight, age, sex and rifampicin co-administration. Typical V1 was 11% lower in patients who were co-administered rifampicin. In PK/PD simulations, sex influenced the probability of AUC24/MIC target attainment, while rifampicin had a marginal effect. CONCLUSIONS: A daptomycin dosage of 8 mg/kg/24 h in women and 10 mg/kg/24 h in men should optimize efficacy but may lead to excessive trough concentrations in many patients, especially in women. Therapeutic drug monitoring appears necessary for precision dosing of daptomycin.

Pharmacogenetic-guided glimepiride therapy in type-2 diabetes mellitus: a cost-effectiveness study.

The demonstration of the link between certain genetic variations and drug response has allowed the emergence of pharmacogenetics, which offers many opportunities to improve patient care. Type-2 diabetes mellitus is a disease for which several gene polymorphisms have been reported to be associated with drug response. Sulfonylureas are commonly used for the management of this disease. Genetic polymorphisms of CYP2C9, the main enzyme involved in the metabolism of sulfonylureas, have been associated with the risk of severe hypoglycaemia, particularly in poor metabolizers carrying CYP2C9 *3/*3 genotype, and especially in the case of patients treated with glimepiride. The objectives of the present study were to evaluate the potential clinical and economic outcomes of using CYP2C9 genotype data to guide the management of SU regimen in patients initiating glimepiride therapy, and to identify factors affecting the cost-effectiveness of this treatment scheme. The analysis was conducted using a decision tree, considering a 1-year time horizon, and taking as perspective that of the French national health insurance system. With pharmacogenetic-guided therapy, the cost to avoid an episode of severe hypoglycaemia event per 100 000 patients treated was €421 834. Genotyping cost was the most influential factor on the incremental cost-effectiveness ratio. In conclusion, the potential cost of CYP2C9 genotype-guided dosing for glimepiride therapy is relatively high, and associated with modest improvements with respect to the number of hypoglycaemia avoided, as compared with standard dosing. Additional economic studies are required to better specify the usefulness of CYP2C9 genotyping prior to glimepiride regimen initiation.

To Estimate or to Forecast? Lessons From a Comparative Analysis of Four Bayesian Fitting Methods Based on Nonparametric Models.

ABSTRACT: Using pharmacokinetic (PK) models and Bayesian methods in dosing software facilitates the analysis of individual PK data and precision dosing. Several Bayesian methods are available for computing Bayesian posterior distributions using nonparametric population models. The objective of this study was to compare the performance of the maximum a posteriori (MAP) model, multiple model (MM), interacting MM (IMM), and novel hybrid MM(HMM) in estimating past concentrations and predicting future concentrations during therapy. Amikacin and vancomycin PK data were analyzed in older hospitalized patients using 2 strategies. First, the entire data set of each patient was fitted using each of the 4 methods implemented in BestDose software. Then, the 4 methods were used in each therapeutic drug monitoring occasion to estimate the past concentrations available at this time and to predict the subsequent concentrations to be observed on the next occasion. The bias and precision of the model predictions were compared among the methods. A total of 406 amikacin concentrations from 96 patients and 718 vancomycin concentrations from 133 patients were available for analysis. Overall, significant differences were observed in the predictive performance of the 4 Bayesian methods. The IMM method showed the best fit to past concentration data of amikacin and vancomycin, whereas the MM method was the least precise. However, MM best predicted the future concentrations of amikacin. The MAP and HMM methods showed a similar predictive performance and seemed to be more appropriate for the prediction of future vancomycin concentrations than the other models were. The richness of the prior distribution may explain the discrepancies between the results of the 2 drugs. Although further research with other drugs and models is necessary to confirm our findings, these results challenge the widely accepted assumption in PK modeling that a better data fit indicates better forecasting of future observations.

Pharmacokinetic/Pharmacodynamic Dosage Individualization of Cefepime in Critically Ill Patients: A Case Study.

OBJECTIVE: The authors report on a case of a 59-year-old man hospitalized in the intensive care unit because of severe SARS-COV-2 infection (COVID-19). BACKGROUND: The patient had several comorbidities, including liver cirrhosis. He developed ventilation-associated bacterial pneumonia for which he was administered cefepime at an initial dose of 2 g/8 hours. Therapeutic drug monitoring was performed, showing overexposure with an initial trough concentration of >60 mg/L. METHODS: Analysis of pharmacokinetic data and model-based dose adjustment was performed using BestDose software. RESULTS: The patient had unexpected pharmacokinetic parameter values. Serum creatinine was only moderately increased, whereas measured creatinine clearance based on urine collection showed impaired renal function. Bacterial minimum inhibitory concentration was also considered in the dosing decisions. After dose reduction to 0.5 g/8 hours, the cefepime trough concentration progressively declined and reached the target values by the end of the therapy. A post-hoc analysis provided a different interpretation of drug overexposure. CONCLUSION: This case report illustrates how physiological, microbiological, and drug concentration data can be used for model-based dosage individualization of cefepime in intensive care unit patients.

Amikacin Initial Dose in Critically Ill Patients: a Nonparametric Approach To Optimize A Priori Pharmacokinetic/Pharmacodynamic Target Attainments in Individual Patients.

Amikacin is commonly used for probabilistic antimicrobial therapy in critically ill patients with sepsis. Its narrow therapeutic margin makes it challenging to determine the right individual dose that ensures the highest efficacy target attainment rate (TAR) in this setting. This study aims to develop a new initial dosing approach for amikacin by optimizing the a priori TAR in this population. A population pharmacokinetic model was built with a learning data set from critically ill patients who received amikacin. It was then used to design an initial dosing approach maximizing a priori TAR for a target ratio of ≥8 for the peak concentration to the MIC (Cmax/MIC) or of ≥75 for the ratio of the area under the concentration-time curve from 0 to 24 h to the MIC (AUC0-24/MIC). In the 166 patients included, 53% had amikacin Cmax of ≥64 mg/liter with a median dose of 23.4 mg/kg. A two-compartment model with creatinine clearance and body surface area as covariates best described the data and showed good predictive performance. Our dosing approach was successful in optimizing TAR for Cmax/MIC, with a rate of 92.9% versus 67.9% using a 30-mg/kg regimen, based on an external subset of data and assuming a MIC of 8 mg/liter. Mean optimal doses were higher (3.5 ± 0.5 g) than with the 30-mg/kg regimen (2.1 ± 0.3 g). Suggested doses varied with the MIC, the target index, and desired TAR threshold. A dosing algorithm based on the method is proposed for a large range of patient covariates. Clinical studies are necessary to confirm efficacy and safety of this optimized dosing approach.

Population pharmacokinetics of teicoplanin administered by subcutaneous or intravenous route and simulation of optimal loading dose regimen.

Objectives: To investigate the population pharmacokinetics of teicoplanin in patients treated by the subcutaneous (sc) and/or intravenous (iv) route. Patients and methods: Non-linear mixed-effects modelling described teicoplanin concentrations from 98 patients with infection caused by Gram-positive cocci. Monte Carlo simulations were performed to evaluate the probability of target attainment (PTA) of various dosage regimens. Results: Teicoplanin concentrations were best described by a two-compartment model with clearance predicted by estimated glomerular filtration rate. Estimated absorption rate constant (between-subject variability) was 0.039 h-1 (77%), clearance was 0.305 L/h (28%), central volume was 10.3 L (49%), inter-compartmental clearance was 4.42 L/h (66%) and peripheral volume was 97.4 L (51%). The sc route was associated with lower initial Cmin and AUC (day 3: loading phase) compared with the iv route. This difference appeared to vanish after 14 days, with comparable simulated PTAs based on the Cmin and AUC for all tested dosages (400, 600, 800 and 1000 mg every 12 h). However, a loading dose regimen with five administrations of either 400 or 600 mg was not sufficient to achieve the target Cmin (≥15 mg/L) for both routes. Also, PTAs for higher MIC (≥1.0 mg/L) were poor with all regimens for both routes. Conclusions: This is the first study examining the pharmacokinetic/pharmacodynamic implications of using the sc route for teicoplanin. Subcutaneous administration is associated with lower Cmin and AUC values after the loading phase compared with iv administration. Therefore, iv administration should be preferred in the first few days of therapy. This study also shows that loading doses of teicoplanin higher than currently recommended should be used to improve PTA.

Is Trough Concentration of Vancomycin Predictive of the Area Under the Curve? A Clinical Study in Elderly Patients.

BACKGROUND: Current guidelines suggest that vancomycin trough concentrations (Cmin) between 15 and 20 mg/L should be achieved to optimize vancomycin exposure and effect. The objective of this study was to analyze the correlation between vancomycin Cmin and the area under the concentration-time curve (AUC) and assess the ability to predict an AUC target of 400 mg·h/L based on Cmin. METHODS: A retrospective analysis of vancomycin therapeutic drug monitoring data collected in 95 elderly patients treated with intermittent intravenous vancomycin was performed. For each patient, individual pharmacokinetic parameters of vancomycin and AUC24 were estimated from concentration measurements using a Bayesian approach. The relationship between vancomycin Cmin and AUC was studied using global and local correlation analysis as well as logistic regression with Receiver Operating Characteristic curve analysis. RESULTS: The overall correlation between AUC24 and Cmin was significant but moderate (R = 0.51). When vancomycin Cmin was greater than 15 mg/L, the corresponding AUC24 was >400 mg·h/L in 95% of cases. However, AUC24 values >400 mg·h/L were obtained with Cmin < 15 mg/L in more than 30% of the cases. The logistic regression analysis identified a Cmin value of 10.8 mg/L as the optimal predictor of AUC24 > 400 mg·h/L. CONCLUSIONS: The results of this study indicate that the recommended target range of 15-20 mg/L for vancomycin Cmin seems acceptable for controlling vancomycin exposure, although a value of approximately 11 mg/L appears to be optimal and may be safer.

Pharmacokinetics of Vancomycin in Elderly Patients Aged over 80 Years.

Since the 1950s, vancomycin has remained a reference treatment for severe infections caused by Gram-positive bacteria, including methicillin-resistant Staphylococcus aureus Vancomycin is a nephrotoxic and ototoxic drug mainly eliminated through the kidneys. It has a large interindividual pharmacokinetic variability, which justifies monitoring its plasma concentrations in patients. This is especially important in patients aged over 80 years, who frequently have renal impairment. However, the pharmacokinetics of vancomycin in this population is very poorly described in the literature. The objective of this work was to propose a model able to predict the pharmacokinetics of vancomycin in very elderly people. First, a population pharmacokinetic model was carried out using the algorithm NPAG (nonparametric adaptive grid) on a database of 70 hospitalized patients aged over 80 years and treated with vancomycin. An external validation then was performed on 41 patients, and the predictive capabilities of the model were assessed. The model had two compartments and six parameters. Body weight and creatinine clearance significantly influenced vancomycin volume of distribution and body clearance, respectively. The means (± standard deviations) of vancomycin volume of distribution and clearance were 36.3 ± 15.2 liter and 2.0 ± 0.9 liter/h, respectively. In the validation group, the bias and precision were -0.75 mg/liter and 8.76 mg/liter for population predictions and -0.39 mg/liter and 2.68 mg/liter for individual predictions. In conclusion, a pharmacokinetic model of vancomycin in a very elderly population has been created and validated for predicting plasma concentrations of vancomycin.

Mathematical modeling and systems pharmacology of tuberculosis: Isoniazid as a case study.

Tuberculosis (TB) treatment needs to be optimized as it is currently long and associated with increasing drug resistance. The antimycobacterial effect of isoniazid (INH) is characterized by a biphasic kill curve, whose causes are still debated. In this work, we developed a complete mathematical model describing the time-course of TB infection and its treatment by INH in human lung. This model was based on a pharmacokinetic model, a pharmacodynamic model and a pathophysiological model. It was used to simulate the antibacterial effect of INH during the first days of therapy. This full model adequately reproduced some qualitative and quantitative properties of the early bactericidal activity of INH observed in TB patients. The kill curves simulated with the model reproduced the biphasic killing effect of INH and the predicted declines in extracellular bacteria were comparable to clinical data. A sensitivity analysis provided interesting insights regarding the biphasic kill curve. The first phase appeared to be essentially driven by the drug effect. In the second phase, while drug pharmacology was the major determinant of the antibacterial effect, a slight influence of the dynamics of infected macrophages was also observed. This work permits to formulate hypotheses for optimizing the efficacy of TB drug candidates and confirms the utility of mathematical modeling to generate new assumptions for TB research.

Influence of renal function estimation on pharmacokinetic modeling of vancomycin in elderly patients.

Vancomycin is a renally excreted drug, and its body clearance correlates with creatinine clearance. However, the renal function estimation equation that best predicts vancomycin clearance has not been established yet. The objective of this study was to compare the abilities of different renal function estimation equations to describe vancomycin pharmacokinetics in elderly patients. The NPAG algorithm was used to perform population pharmacokinetic analysis of vancomycin concentrations in 78 elderly patients. Six pharmacokinetic models of vancomycin clearance were built, based on the following equations: Cockcroft-Gault (CG), Jelliffe (JEL), Modification of Diet in Renal Disease (MDRD), Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) (both in milliliters per minute per 1.73 m(2)), and modified MDRD and CKD-EPI equations (both in milliliters per minute). Goodness-of-fit and predictive performances of the six PK models were compared in a learning set (58 subjects) and a validation set (20 patients). Final analysis was performed to estimate population parameters in the entire population. In the learning step, the MDRD-based model best described the data, but the CG- and JEL-based models were the least biased. The mean weighted errors of prediction were significantly different between the six models (P = 0.0071). In the validation group, predictive performances were not significantly different. However, the use of a renal function estimation equation different from that used in the model building could significantly alter predictive performance. The final analysis showed important differences in parameter distributions and AUC estimation across the six models. This study shows that methods used to estimate renal function should not be considered interchangeable for pharmacokinetic modeling and model-based estimation of vancomycin concentrations in elderly patients.

Exposure to anticholinergic and sedative drugs, risk of falls, and mortality: an elderly inpatient, multicenter cohort.

This study aimed to assess whether exposure to anticholinergic and sedative medications and its evolution was associated with increased risk of in-hospital falls and all-cause mortality. Furthermore, results were compared with 2 definitions of drug burden index (DBI) against the outcomes.This observational, multicentric, and longitudinal study was conducted among patients aged 65 years or older, in 3 geriatric hospitals, in Francheville, Lyon, and Villeurbanne, France (duration of follow-up, 11.6 months). The exposure to anticholinergic and sedative medications was quantified using a DBI, at admission and at the end of observation for 337 patients. The evolution of exposure was the absolute difference between the index at admission and at the end of observation. The outcomes were in-hospital falls and all-cause mortality.Overall, 5.9% of patients experienced a fall. The risk of fall was nearly 3-fold in patients whose DBI increased during hospital stay compared to those with stable or decreased DBI (hazard ratio, 2.9 [1.14-7.12]; P = 0.03), after adjustment for comorbidities.The overall proportion of mortality was 6.5%. The evolution of DBI during hospital stay was not related to the risk of mortality (hazard ratio, 1.9 [0.8-4.4]; P = 0.14). Results were similar with the 2 definitions of DBI.Increased exposure to anticholinergic and sedative medications during hospital stay is associated with a higher risk of in-hospital falls but not with mortality. The DBI could be implemented in hospital, to guide prescription and reduce anticholinergic and sedative drug exposure.

Quantitative analysis of nonsteroidal anti-inflammatory drugs in dried blood spot from mountain ultra-trail runners. Contribution of pharmacokinetic models for the interpretation of the results.

Monitoring of drug use in athletes is of interest both for health and competition-related issues. Considering the advantages of Dried Blood Sampling (low invasiveness, easy sampling, long term storage), we have validated a quantitative LC-MS/HRMS method for the screening of 16 nonsteroidal anti-inflammatory drugs. For all drugs, accuracy and imprecision were within 15% for the 3 levels of quality control and lower than 20% for the lower limit of quantification. Application was performed from samples obtained for Ultra-Trail du Mont-Blanc® 2021 and 2022. A focus on ibuprofen and its metabolites (hydroxyibuprofen, carboxyibuprofen, ibuprofen glucuronide and hydroxyibuprofen glucuronide) was made because the results showed that it was the most detected nonsteroidal anti-inflammatory drug. Further, an interpretation of the ibuprofen concentrations was proposed either from experimental data obtained after an intake of ibuprofen by 10 control subjects, or from a pharmacokinetic modelling and simulations. Depending on the analytical performances of the method, we proposed possible detection windows for ibuprofen in runners. The pharmacokinetic model made it possible to consider two scenarios with and without modification of the total clearance of ibuprofen linked to a modification of the pharmacokinetics of the drugs due to the practice of a long and intense physical activity.

Evaluation of Renal Impairment Influence on Metabolic Drug Clearance using a Modelling Approach.

BACKGROUND AND OBJECTIVE: Chronic kidney disease (CKD) may alter drug renal elimination but is also known for interacting with hepatic metabolism via multiple uremic components. However, few global models, considering the five major cytochromes, have been published, and none specifically address the decrease in cytochrome P450 (CYP450) activity. The aim of our study was to estimate the possibility of quantifying residual cytochrome activity as a function of filtration rate, according to the data available in the literature. METHODS: For each drug in the DDI-predictor database, we collected available pharmacokinetic data comparing drug exposition in the healthy patient and in various stages of CKD, before building a model capable of predicting the variation of exposure according to the degree of renal damage. We followed an In vivo Mechanistic Static Model (IMSM) approach, previously validated for predicting change in liver clearance. We estimated the remaining fraction parameters at glomerular filtration rate (GFR) = 0 and the alpha value of GFR to 50% impairment for the 5 major cytochromes using a non-linear constrained regression using Matlab software. RESULTS: Thirty-one compounds had usable pharmacokinetic data, with 51 AUC ratios between healthy and renal impaired patients. The remaining CYP3A4 activity was estimated to be 0.4 when CYP2D6, 2C9, 2C19 and 1A2 activity was estimated to be 0.43; 1; 0.73 and 0.7, respectively. The alpha value was estimated to be at 6.62; 25; 9.8; 1.38 and 11.04 for each cytochrome. In comparison with published data, all estimates but one were correctly predicted in the range of 0.5-2. CONCLUSION: Our approach was able to describe the impact of CKD on metabolic elimination. Modelling this process makes it possible to anticipate changes in clearance and drug exposure in CKD patients, with the advantage of greater simplicity than approaches based on physiologically-based pharmacokinetic modelling. However, a precise estimation of the impact of renal failure is not possible with an IMSM approach due to the large variability of the published data, and thus should rely on specific pharmacokinetic modelling for narrow therapeutic margin drugs.

Comparison of four renal function estimation equations for pharmacokinetic modeling of gentamicin in geriatric patients.

Most aminoglycoside pharmacokinetic models include an index of renal function, such as creatinine clearance, to describe drug clearance. However, the best clinical descriptor of renal function for the pharmacokinetic modeling of aminoglycosides has not been established. This analysis was based on 412 gentamicin concentrations from 92 geriatric patients who received intravenous gentamicin for various infectious diseases. Four two-compartment population models were fitted to gentamicin concentrations in a learning set of 64 patients using the nonparametric adaptive grid (NPAG) algorithm. Each model included an index of renal function, namely, the Cockcroft-Gault (CG), Jelliffe (JEL), modification of diet in renal disease (MDRD), or modified MDRD (MDRDm; adjusted to individual body surface area) equation as a covariate influencing gentamicin serum clearance. Goodness of fit and predictive performance of the four models were compared using standard criteria in both the learning set and in a validation set of 28 patients. A final analysis was performed to estimate the population pharmacokinetic parameter values of the entire 92-patient group. In the learning set, the CG-based model best fit the data, followed by JEL-, MDRD-, and MDRDm-based models, with relative reductions of the Akaike information criterion of 29.4, 20.2, 14.2, and 4.2, respectively. Bias and precision of population predictions were significantly different among the four models. In the validation set, individual predictions from the four models showed marginally different biases. The final estimation confirmed the previous results. Specifically, the CG-based model showed predictive performance that was comparable to or better than that of the MDRD-based model at each stage of the analysis. This study shows that methods used to estimate renal function should not be considered interchangeable for the model-based estimation of gentamicin concentrations.