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Beal, Stuart L.

doi:10.1023/a:1012299115260

Cited by 889 publications

(301 citation statements)

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“…The covariates evaluated included body weight, age, creatinine clearance, serum urea concentration, and temperature. Plasma concentrations below the limit of quantification were handled by the Beal M3 method (13). (The control stream for the final model can be found in the supplemental material.)…”

Section: Methodsmentioning

confidence: 99%

Population Pharmacokinetics of Colistin Methanesulfonate and Colistin in Critically Ill Patients with Acute Renal Failure Requiring Intermittent Hemodialysis

Jacobs

Grégoire

Mégarbane

et al. 2016

Antimicrob Agents Chemother

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Colistin is increasingly used as a last option for the treatment of severe infections due to Gram-negative bacteria in critically ill patients requiring intermittent hemodialysis (HD) for acute renal failure. Our objective was to characterize the pharmacokinetics (PK) of colistin and its prodrug colistin methanesulfonate (CMS) in this population and to suggest dosing regimen recommendations. Eight intensive care unit (ICU) patients who were under intermittent HD and who were treated by CMS (Colimycine) were included. Blood samples were collected between two consecutive HD sessions. CMS and colistin concentrations were measured by a specific chromatographic assay and were analyzed using a PK population approach (Monolix software). Monte Carlo simulations were conducted to predict the probability of target attainment (PTA). CMS nonrenal clearance was increased in ICU-HD patients. Compared with that of ICU patients included in the same clinical trial but with preserved renal function, colistin exposure was increased by 3-fold in ICU-HD patients. This is probably because a greater fraction of the CMS converted into colistin. To maintain colistin plasma concentrations high enough (>3 mg/liter) for high PTA values (area under the concentration-time curve for the free, unbound fraction of a drug [fAUC]/MIC of >10 and fAUC/MIC of >50 for systemic and lung infections, respectively), at least for MICs lower than 1.5 mg/liter (nonpulmonary infection) or 0.5 mg/liter (pulmonary infection), the dosing regimen of CMS should be 1.5 million international units (MIU) twice daily on non-HD days. HD should be conducted at the end of a dosing interval, and a supplemental dose of 1.5 MIU should be administered after the HD session (i.e., total of 4.5 MIU for HD days). This study has confirmed and complemented previously published data and suggests an a priori clear and easy to follow dosing strategy for CMS in ICU-HD patients. Over the last several years, colistin (polymyxin E) has been increasingly used as a last option for the treatment of infections caused by multidrug-resistant Gram-negative bacteria, such as Pseudomonas aeruginosa, Acinetobacter baumannii, and Klebsiella pneumoniae (1, 2), for which the mortality rate has increased (3).Colistin is administered as a prodrug, colistin methanesulfonate (CMS), which is mostly excreted unchanged in urine (70%) and is partly converted to colistin (30% at most), whereas renal excretion of colistin is negligible (4). As a result, in patients with renal failure, a greater fraction of the CMS dose may be converted into colistin (5); therefore, all other things being equal, colistin plasma concentrations at steady state should increase. The elimination of colistin is nonrenal-it undergoes extensive renal tubular reabsorption-and nonbiliary by unknown mechanism (1).It has recently been shown that in intensive care unit (ICU) patients, colistin plasma concentrations at steady state are mostly governed by renal function and that creatinine clearance can be used for dosing regimen adaptation ...

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Section: Methodsmentioning

confidence: 99%

Population Pharmacokinetics of Colistin Methanesulfonate and Colistin in Critically Ill Patients with Acute Renal Failure Requiring Intermittent Hemodialysis

Jacobs

Grégoire

Mégarbane

et al. 2016

Antimicrob Agents Chemother

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“…Concentrations in the data set that were below the limit of quantitation (BLQ) were flagged. The population analysis program then applied the Beal M3 method (8), such that the algorithm considered this BLQ value as a normally distributed, random value somewhere between negative infinity and the limit of quantification.…”

Section: Methodsmentioning

confidence: 99%

Population Pharmacokinetic Analyses for BC-3781 Using Phase 2 Data from Patients with Acute Bacterial Skin and Skin Structure Infections

Rubino

Bai

Bhavnani

et al. 2015

Antimicrob Agents Chemother

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BC-3781, a pleuromutilin antimicrobial agent, is being developed for the treatment of patients with acute bacterial skin and skin structure infections (ABSSSI) and community-acquired bacterial pneumonia. Data from a phase 2 study of patients with ABSSSI were used to refine a previous population pharmacokinetic (PK) model and explore potential predictors of PK variability. The previously derived population PK model based on data from three phase 1 studies was applied to sparse sampling data from a phase 2 ABSSSI study and modified as necessary. Covariate analyses were conducted to identify descriptors (e.g., body size, renal function, age) associated with interindividual variability in PK. All population PK analyses were conducted by using Monte Carlo parametric expectation maximization implemented in S-ADAPT 1.5.6. The population PK data set contained 1,167 concentrations from 129 patients; 95% of the patients had 5 or more PK samples (median, 11). The previous population PK model (three-compartment model with first-order elimination and nonlinear protein binding) provided an acceptable and unbiased fit to the data from the 129 patients. Population PK parameters were estimated with acceptable precision; individual clearance values were particularly well estimated (median individual precision of 9.15%). Graphical covariate evaluations showed no relationships between PK and age or renal function but modest relationships between body size and clearance and volume of distribution, which were not statistically significant when included in the population PK model. This population PK model will be useful for subsequent PK-pharmacodynamic analyses and simulations conducted to support phase 3 dose selection. (This study has been registered at ClinicalTrials.gov under registration no. NCT01119105.)

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“…The model was implemented in Monolix 4.12s using the SAEM algorithm (21). The M3 method described by Beal et al was employed to handle the data below the limit of quantification (BLQ) (22). This method is based on simultaneous modeling of continuous and categorical data where the BLQ observations are treated as categorical data.…”

Section: Tmdd Modelmentioning

confidence: 99%

Quantitative Prediction of Human Pharmacokinetics for mAbs Exhibiting Target-Mediated Disposition

Singh

Krzyzanski

Martin

et al. 2014

AAPS J

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Abstract. Prediction of human pharmacokinetics (PK) can be challenging for monoclonal antibodies (mAbs) exhibiting target-mediated drug disposition (TMDD). In this study, we performed a quantitative analysis of a diverse set of six mAbs exhibiting TMDD to explore translational rules that can be utilized to predict human PK. A TMDD model with rapid-binding approximation was utilized to fit PK and PD (i.e., free and/or total target levels) data, and average absolute fold error (AAFE) was calculated for each model parameter. Based on the comparative analysis, translational rules were developed and applied to a test antibody not included in the original analysis. AAFE of less than two-fold was observed between monkey and human for baseline target levels (R 0 ), body-weight (BW) normalized central elimination rate (K el /BW −0.25 ) and central volume (V c /BW 1.0 ). AAFE of less than three-fold was estimated for the binding affinity constant (K D ). The other four parameters, i.e., complex turnover rate (K int ), target turnover rate (K deg ), central to peripheral distribution rate constant (K pt ) and peripheral to central rate constant (K tp ) were poorly correlated between monkey and human. The projected human PK of test antibody based on the translation rules was in good agreement with the observed nonlinear PK. In conclusion, we recommend a TMDD model-based prediction approach that integrates in vitro human biomeasures and in vivo preclinical data using translation rules developed in this study.

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Cited by 889 publications

References 1 publication

Population Pharmacokinetics of Colistin Methanesulfonate and Colistin in Critically Ill Patients with Acute Renal Failure Requiring Intermittent Hemodialysis

Population Pharmacokinetics of Colistin Methanesulfonate and Colistin in Critically Ill Patients with Acute Renal Failure Requiring Intermittent Hemodialysis

Population Pharmacokinetic Analyses for BC-3781 Using Phase 2 Data from Patients with Acute Bacterial Skin and Skin Structure Infections

Quantitative Prediction of Human Pharmacokinetics for mAbs Exhibiting Target-Mediated Disposition

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