Patient-specific Pharmacokinetic Parameters Research Articles

Maximum a posteriori Bayesian methods out-perform non-compartmental analysis for busulfan precision dosing

Dose personalization improves patient outcomes for many drugs with a narrow therapeutic index and high inter-individuality variability, including busulfan. Non-compartmental analysis (NCA) and model-based methods like maximum a posteriori Bayesian (MAP) approaches are two methods routinely used for dose optimization. These approaches vary in how they estimate patient-specific pharmacokinetic parameters to inform a dose and the impact of these differences is not well-understood. Using busulfan as an example application and area under the concentration–time curve (AUC) as a target exposure metric, these estimation methods were compared using retrospective patient data (N = 246) and simulated precision dosing treatment courses. NCA was performed with or without peak extension, and MAP Bayesian estimation was performed using either the one-compartment Shukla model or the two-compartment McCune model. All methods showed good agreement on real-world data (correlation coefficients of 0.945–0.998) as assessed by Bland–Altman plots, although agreement between NCA and MAP methods was higher during the first dosing interval (0.982–0.994) compared to subsequent dosing intervals (0.918–0.938). In dose adjustment simulations, both NCA and MAP estimated high target attainment (> 98%) although true simulated target attainment was lower for NCA (63–66%) versus MAP (91–93%). The largest differences in AUC estimation were due to different assumptions for the shape of the concentration curve during the infusion phase, followed by how the methods considered time-dependent clearance and concentration–time points collected in earlier intervals. In conclusion, although AUC estimates between the two methods showed good correlation, in a simulated study, MAP lead to higher target attainment. When changing from one method to another, or changing infusion duration and other factors, optimum estimated exposure targets may require adjusting to maintain a consistent exposure.

Concordance of Vancomycin Population-Predicted Pharmacokinetics with Patient-Specific Pharmacokinetics in Adult Hospitalized Patients: A Case Series.

BackgroundVancomycin empiric therapy is commonly dosed using clinical algorithms adapted from population-predicted pharmacokinetic parameters. However, precise dosing of vancomycin can be designed using patient-specific pharmacokinetic calculations.ObjectiveThe objective of this study is to assess the correlational fit between vancomycin population-predicted and patient-specific pharmacokinetic parameters [elimination rate constant (Ke) and half-life (t1/2)] in a case series of adult hospitalized patients.MethodsThis is a single-center case series of hospitalized adult patients who received vancomycin, had creatinine clearance calculation for derivation of population-predicted pharmacokinetic parameters, and had two vancomycin concentrations for calculation of patient-specific pharmacokinetic parameters. The primary objective of this case series is to evaluate the correlation between population-predicted and patient-specific pharmacokinetic parameters. The secondary objectives of this study are to evaluate the mean bias and precision between the population-predicted and patient-specific pharmacokinetic parameters and to assess the correlation between population-predicted and patient-specific pharmacokinetic parameters in special population subgroups (obese patients with body mass index ≥ 30 kg/m2 and patients with renal dysfunction). All correlation analyses were performed on the population-predicted pharmacokinetics using diverse methods of estimating renal function (Salazar–Corcoran and Cockcroft–Gault methods using either ideal, actual, or adjusted body weights). All significance testing was set at an α of < 0.05. IBM SPSS Statistics version 25 and SAS version 9.4 were used to conduct all statistical analyses.ResultsA total of 30 patients were included in the study; 33.3% (10/30) of the patients were obese and 56.7% (17/30) had renal dysfunction. In all patients in the study, the calculated population-predicted Ke and t1/2 using all four creatinine clearance estimation methods were each significantly correlated with patient-specific Ke and t1/2 (all Pearson correlation coefficients [r]: > + 0.7, p < 0.001). The population-predicted Ke and t1/2 calculated using Cockcroft–Gault creatinine clearance using adjusted body weight showed the strongest association with patient-specific Ke and t1/2. In the subgroup analyses, all the population-predicted Ke and t1/2 using four creatinine clearance estimation methods were each significantly correlated with patient-specific Ke and t1/2. The exception was the population-predicted t1/2 derived from Cockcroft–Gault creatinine clearance using actual body weight that did not show a significant correlation with patient-specific t1/2 in obese patients.ConclusionsIn this case series, population-predicted pharmacokinetic parameters were strongly correlated with patient-specific pharmacokinetic parameters. The vancomycin population-predicted pharmacokinetic formula can be used safely to predict a patient’s vancomycin pharmacokinetic disposition and can be maintained as an empiric dosing strategy in various hospitalized adult patients.

An AUC Target Simulation for Vancomycin in Patients With Class III Obesity

Objective: The objective of this simulation is to compare 24-hour vancomycin (Vanc24) dosage requirements between a target area under the curve (AUC) versus a target trough approach in patients with class III obesity. Methods: Adult patients were included if they received vancomycin in accordance with the University of Vermont Medical Center’s class III obesity dosage protocol from June 2016 through December 2018. Patient-specific pharmacokinetic parameters were calculated for each patient using the Sawchuck-Zaske method. For this simulation, Vanc24 dosages were calculated to achieve an AUC of 400 mg/L h and a trough concentration of 15 mg/L. Results: Sixty-three patients had Vanc24 dosage requirements calculated. The median age was 59 years (interquartile range [IQR]: 51.5-68), body mass index (BMI): 45.7 kg/m2 (IQR: 42.4-51.5), and 50.7% were male. The mean Vanc24 dosage requirements were 3995 mg (standard deviation [SD] ±1673) in the target trough approach versus 2783 mg (SD ±1149) in the target AUC approach (P < .0001). Conclusion: A target AUC approach required less vancomycin over a 24-hour time period relative to a target trough approach. Vancomycin therapeutic drug monitoring that explicitly targets AUC may reduce vancomycin exposure and potentially decrease the risk of nephrotoxicity in patients with class III obesity.

Vancomycin volume of distribution estimation in adults with class III obesity

To compare methods of estimating vancomycin volume of distribution (V) in adults with class III obesity. A retrospective, multicenter pharmacokinetic analysis of adults treated with vancomycin and monitored through measurement of peak and trough concentrations was performed. Individual pharmacokinetic parameter estimates were obtained via maximum a posteriori Bayesian analysis. The relationship between V and body weight was assessed using linear regression. Mean bias and root-mean-square error (RMSE) were calculated to assess the precision of multiple methods of estimating V. Of 241 patients included in the study sample, 159 (66.0%) had a body mass index (BMI) of 40.0-49.9 kg/m2, and 82 (34.0%) had a BMI of ≥50.0 kg/m2. The median (5th, 95th percentile) weight of patients was 136 (103, 204) kg, and baseline characteristics were similar between BMI groups. The mean ± S.D. V was lower in patients with a BMI of 40.0-49.9 kg/m2 than in those with a BMI of ≥50.0 kg/m2 (72.4 ± 19.6 L versus 79.3 ± 20.6 L, p = 0.009); however, body size poorly predicted V in regression analyses (R2 < 0.20). A fixed estimate of V (75 L) and use of a weight-based value (0.52 L/kg by total body weight [TBW]) yielded similar bias and error in this population. Results of the largest analysis of vancomycin V in class III obesity to date indicated that use of a fixed V value (75 L) and use of a TBW-based estimate (0.52 L/kg) for estimation of vancomycin V in patients with a BMI of ≥40.0 kg/m2 have similar bias. Two postdistribution vancomycin concentrations are needed to accurately determine patient-specific pharmacokinetic parameters, estimate area under the curve, and improve the precision of vancomycin dosing in this patient population.

A More Accurate Method to Estimate Aminoglycoside Clearance From Serum Creatinine in Patients With Spinal Cord Injury.

Background: Current literature reports that the traditional methods overestimate renal function in spinal cord injury (SCI); however, there is no accepted standard method. Objective: This study evaluated 6 published methods against measured aminoglycoside (AG) drug clearance and determined the frequency with which each method would achieve target peak and trough AG concentrations within a specified range. Methods: A chart-based investigation was conducted at a hospital with a large SCI population, and a total of 35 patients met the inclusion criteria: a diagnosis of long-standing SCI, administration of AG via intravenous infusion, and at least one set of steady-state AG peak and trough concentrations. Pharmacokinetic analysis was performed to compare the measured AG clearance values against the values resulting from 6 methods of estimating the glomerular filtration rate (GFR). Patient-specific pharmacokinetic parameters were used to simulate steady-state peak and trough AG concentrations from doses derived from each method. Results: Compared with the other methods, the Lee-Dang method was found to be more accurate, with the smallest magnitude of variance from the measured AG clearance values. Five alternative methods significantly overestimated AG clearance, by approximately 70% to 160% (P < .05). The Lee-Dang method underestimated AG clearance (by 10%), however not to a significant degree (P = .079). Compared with the alternative methods, the Lee-Dang method resulted in a higher frequency of steady-state peak and trough AG concentrations within the target range specified. Conclusion: The Lee-Dang equation for predicting GFR was more accurate relative to the other methods in the study population of patients with long-term SCI.

The importance of active learning and practice on the students' mastery of pharmacokinetic calculations for the intermittent intravenous infusion dosing of antibiotics

BackgroundEstimation of pharmacokinetic parameters after intermittent intravenous infusion (III) of antibiotics, such as aminoglycosides or vancomycin, has traditionally been a difficult subject for students in clinical pharmacology or pharmacokinetic courses. Additionally, samples taken at different intervals during repeated dose therapy require manipulation of sampling times before accurate calculation of the patient-specific pharmacokinetic parameters. The main goal of this study was to evaluate the effectiveness of active learning tools and practice opportunities on the ability of students to estimate pharmacokinetic parameters from the plasma samples obtained at different intervals following intermittent intravenous infusion.MethodsAn extensive reading note, with examples, and a problem case, based on a patient’s chart data, were created and made available to students before the class session. Students were required to work through the case before attending the class. The class session was devoted to the discussion of the case requiring active participation of the students using a random participation program. After the class, students were given additional opportunities to practice the calculations, using online modules developed by the instructor, before submitting an online assignment.ResultsThe performance of students significantly (P < 0.001) improved from a baseline of 11.3% (pretest) to 60.3% (posttest) after the class discussion. The grades of students further improved (P < 0.001) to 89.3% on the take-home assignment after they had a chance to study on their own and work on the online practices. Finally, students scored 82.6% in a formal mid-term examination, suggesting significant retention of the materials.ConclusionsDespite being a difficult subject, students achieve mastery of pharmacokinetic calculations for the topic of intermittent intravenous infusion when appropriate active learning strategies and practice opportunities are employed.

Confounding Issues in Estimation of Patient-Specific Pharmacokinetic Parameters and Dosage Individualization of Aminoglycosides

Aminoglycoside antibiotics are usually administered by multiple short intravenous infusions at fixed intervals. Today, equations reported 35 years ago by Sawchuk and Zaske are still the cornerstone of methods used for determination of patient-specific pharmacokinetic parameters of aminoglycosides and individualization of drug dosage regimens in many clinical settings. Additionally, these methods are included in many clinical pharmacology curricula in pharmacy and other related fields. However, there are a few issues with regard to the application and/or modification of this method in clinical settings, which may result in some confusion among novice clinicians. For example, serum samples collected from different intervals at steady state, instead of samples obtained during the same interval, require special manipulation of sampling time before they can be used for estimation of pharmacokinetic parameters. Furthermore, there are various ways that the original equations are modified or simplified, which can result in some degree of error in the estimates of pharmacokinetic parameters and ensuing dosage regimen calculations. Simulation data presented here indicate that in some cases, these errors may be substantial, depending on the length of short infusion, half life of the drug, and the dosage interval. For instance, using equations developed for intravenous bolus mode of administration, ignoring the short infusion, may result in ≥ 25% error for a typical patient and dosing scenario. Although experts may use modified equations, understanding their error ramifications, these modifications may be confusing to the novice clinician. Therefore, it is recommended that exact equations developed specifically for multiple intravenous infusions be used without any modification, particularly in settings where clinicians are being trained.

Optimization of Aminoglycoside Therapy

Aminoglycosides are experiencing a resurgence in use because of the spread of multiresistant Gram-negative pathogens. Use of these agents is attended by the occurrence of nephrotoxicity. Aminoglycoside optimization of dose can be defined as the dose having the highest likelihood of a good outcome and the lowest likelihood of toxicity. We have defined the metric Δ as the difference between the likelihoods of good outcome and toxicity, with higher values being better. We developed a method for explicitly evaluating Δ for different daily doses of drug and different schedules of administration. In the empirical therapy setting, when aminoglycosides are administered every 12 h, treatment of infections caused by microbes with MIC values greater than 1 mg/liter cannot attain a high enough likelihood of a good outcome without engendering an unacceptable toxicity likelihood. Daily administration, by decrementing the likelihood of toxicity, allows higher doses to be employed with more acceptable probabilities of toxicity. Obtaining patient-specific information (concentration-time data) allows better identification of the patient's specific pharmacokinetic parameters and dispersion. As these become better identified, optimal doses become rapidly identified so that optimal outcomes are attained. Optimization of therapy for aminoglycosides requires understanding the relationship between exposure and response as well as that between exposure and toxicity. Furthermore, daily administration is much preferred, and stopping therapy as quickly as possible (a week or less may be optimal) will contribute to the ability to optimize therapy.

ORIGINAL ARTICLE: Investigation of the prediction accuracy of vancomycin concentrations determined by patient-specific parameters as estimated by Bayesian analysis

There have been many studies of therapeutic drug monitoring (TDM) of vancomycin (VCM) based on Bayesian analysis, but there have been no reports of the accuracy of prediction based on Bayesian-estimated patient-specific parameters. This study was conducted to compare the accuracy of prediction based on the population pharmacokinetic (PPK) method and Bayesian-estimated parameters. The subjects were 22 patients who were treated with VCM for MRSA infection and whose blood was sampled twice or more during the administration period. The concentrations between the blood samples were predicted based on the concentrations in the first blood samples based on the PPK method using mean parameters for the Japanese population and Bayesian-estimated patient-specific pharmacokinetic parameters. The mean prediction error (ME), mean absolute error (MAE) and root mean squared error (RMSE) were compared to examine the accuracy of prediction based on Bayesian-estimated patient-specific parameters. The mean measured VCM concentration was 10·43±5·19 μg/mL, whereas the mean concentration predicted based on the PPK method was 8·52±4·34 μg/mL, with an ME of -1·91, MAE of 2·93 and RMSE of 3·21. The mean concentration predicted based on patient-specific parameters was 9·62±4·95 μg/mL with ME of -0·81, MAE of 1·38 and RMSE of 1·74. The ME and MAE for the concentrations predicted using patient-specific parameters were smaller compared with those predicted using the PPK method (P=0·0471 and 0·0003, respectively), indicating superior prediction with a significant difference between approaches. Prediction using Bayesian estimates of patient-specific parameters was better than by the PPK method. However, when using patient-specific parameters it is still necessary to fully understand the clinical status of the patient and frequently determine VCM concentrations.

Once‐Daily Gentamicin Dosing in Pediatric Patients Without Cystic Fibrosis

To estimate an appropriate once-daily gentamicin dose and dosing interval for non-critical care pediatric patients older than 3 months of age without cystic fibrosis. Pharmacokinetic analysis of data from a retrospective medical record review. Large academic children's hospital. One hundred fourteen non-critical care pediatric patients older than 3 months of age without cystic fibrosis who received multiple-daily dosing regimens of gentamicin between September 2007 and April 2008. Patient-specific pharmacokinetic parameters were calculated using drug concentrations obtained at steady state. Once-daily doses were extrapolated for each patient to achieve goal peak and trough concentrations. Using the average of these doses and the patient-specific pharmacokinetic parameters, theoretical once-daily peak and trough concentrations were calculated for each patient. Patient characteristics were analyzed to determine differences between patients who did and those who did not achieve adequate peak concentrations. Mean +/- SD pharmacokinetic parameters were as follows: elimination rate constant 0.32 +/- 0.06 hour(-1), half-life 2.28 +/- 0.54 hours, and volume of distribution 0.24 +/- 0.08 L/kg. The only patient demographic characteristic found to have a significant effect on the extrapolated peak concentration was age. The following age-specific once-daily doses were calculated: 3 months to less than 2 years, 9.5 mg/kg; 2 years to less than 8 years, 8.5 mg/kg; and 8-18 years, 7 mg/kg. Age was the primary factor in determining the once-daily dose of gentamicin in our pediatric population. Further prospective research is necessary to determine the safety and efficacy of these age-based, once-daily doses for gentamicin.

Antiretroviral Therapeutic Drug Monitoring in Canada: Current Status and Recommendations for Clinical Practice

Therapeutic drug monitoring (TDM) is a strategy whereby the plasma concentration of one or more drugs is measured and drug doses are adjusted accordingly to achieve concentrations within an acceptable therapeutic range. The goal of TDM is to maximize pharmacologic efficacy and minimize the drug’s toxic effects, and hence to contribute to optimizing the patient’s outcomes. In general, TDM is indicated if the following conditions are present: there is a direct relationship between drug exposure and pharmacologic response, the drug has a narrow therapeutic index and is used to treat a condition for which drug failure or toxicity is associated with substantial morbidity or mortality, and there is limited intrapatient but wide interpatient pharmaco kinetic variation. Differences in pharmacokinetic factors among patients are multifactorial and may include age, sex, ethnic background, pregnancy, and body weight, as well as comorbid conditions causing variations in hepatic function, renal function, drug absorption, and drug disposition. In some cases, genetic polymorphisms in the cytochrome P450 enzymes may explain the large interpatient variability for certain antiretroviral agents. For example, certain CYP2B6 and CYP2A6 polymorphisms are independent predictors of plasma concentrations of efavirenz. Moreover, the potential for complex or unpredictable drug–drug or drug–food interactions can significantly affect plasma drug concentrations. Finally, TDM depends on the availability of an accurate and feasible analytical method for a given drug. Among antiretroviral agents, the protease inhibitors and the non-nucleoside reverse transcriptase inhibitors (NNRTIs) are considered suitable candidates for TDM. Evidence exists of a relation between exposure to antiretrovirals and virologic response and, in more limited instances, between exposure to the drug and toxic effects. Recent data have suggested that intraindividual variation is limited (19.0% for NNRTIs and 38.1% for protease inhibitors). Preliminary data have also suggested that trough levels of maraviroc, a chemokine (C–C motif) receptor 5 (CCR5) antagonist, are predictive of virologic outcome, although TDM experience with this agent is extremely limited. In retrospective and prospective studies, observed interindividual variability in drug concentrations has been 45% to 112% for protease inhibitors and 75% for NNRTIs. Thus, a given dose of a particular antiretroviral agent may result in unacceptable toxic effects in some patients while yielding subtherapeutic concentrations in others. TDM represents a tool by which the effectiveness of these agents in clinical practice can be optimized through dosage adjustments tailored to patient-specific pharmacokinetic and pharmaco dynamic parameters. The current article reviews the literature on antiretroviral TDM, describes the development of an antiretroviral TDM program in Canada, and illustrates the role of the pharmacist in the clinical application of TDM. Given current evidence and personal experience, the authors support the use of antiretro viral TDM as a clinical tool in special patient populations and for specific clinical situations.

Evaluation of population pharmacokinetic models for amikacin dosage individualization in critically ill patients

Abstract Objectives The aim of this study was to evaluate the reliability for dosage individualization and Bayesian adaptive control of several literature-retrieved amikacin population pharmacokinetic models in patients who were critically ill. Methods Four population pharmacokinetic models, three of them customized for critically-ill patients, were applied using pharmacokinetic software to fifty-one adult patients on conventional amikacin therapy admitted to the intensive care unit. An estimation of patient-specific pharmacokinetic parameters for each model was obtained by retrospective analysis of the amikacin serum concentrations measured (n = 162) and different clinical covariates. The model performance for a priori estimation of the area under the serum concentration-time curve (AUC) and maximum serum drug concentration (Cmax) targets was obtained. Key findings Our results provided valuable confirmation of the clinical importance of the choice of population pharmacokinetic models when selecting amikacin dosages for patients who are critically ill. Significant differences in model performance were especially evident when only information concerning clinical covariates was used for dosage individualization and over the two most critical determinants of clinical efficacy of amikacin i.e. the AUC and Cmax values. Conclusions Only a single amikacin serum level seemed necessary to diminish the influence of population model on dosage individualization.

Evaluation of population pharmacokinetic models for amikacin dosage individualization in critically ill patients

The aim of this study was to evaluate the reliability for dosage individualization and Bayesian adaptive control of several literature-retrieved amikacin population pharmacokinetic models in patients who were critically ill. Four population pharmacokinetic models, three of them customized for critically-ill patients, were applied using pharmacokinetic software to fifty-one adult patients on conventional amikacin therapy admitted to the intensive care unit. An estimation of patient-specific pharmacokinetic parameters for each model was obtained by retrospective analysis of the amikacin serum concentrations measured (n = 162) and different clinical covariates. The model performance for a priori estimation of the area under the serum concentration-time curve (AUC) and maximum serum drug concentration (C(max)) targets was obtained. Our results provided valuable confirmation of the clinical importance of the choice of population pharmacokinetic models when selecting amikacin dosages for patients who are critically ill. Significant differences in model performance were especially evident when only information concerning clinical covariates was used for dosage individualization and over the two most critical determinants of clinical efficacy of amikacin i.e. the AUC and C(max) values. Only a single amikacin serum level seemed necessary to diminish the influence of population model on dosage individualization.

Azacitidine Pharmacokinetics in an Adolescent Patient With Renal Compromise

Azacitidine pharmacokinetic parameters in adolescent patients with renal compromise are not available from the medical literature. We describe a 14-year-old with myelodysplastic syndrome treated with subcutaneous 5-azacitidine for disease relapse 2 years after hematopoietic stem cell transplant. Because of renal compromise, malnutrition, and poor functional status, pharmacokinetic parameters were projected from existing literature data to select the patient's first azacitidine treatment course (1.5 mg/kg/d for 7 d). Posttreatment azacitidine plasma concentrations used to calculate patient-specific pharmacokinetic parameters corroborated initial estimates and systemic exposure associated with therapeutic benefit in adults and permitted individualization of treatment.

Evaluation of potentially excessive plasma gemcitabine (dFdC) concentrations produced by 30-minute dFdC infusions

2019 Background: The phosphorylation of dFdC to active metabolites is saturable, whereas its deamination to the inactive metabolite, 2'2'-difluorodeoxyuridine (dFdU) is not. 30-min dFdC infusions produce plasma concentrations greater than the 10–15 μM that saturate intracellular phosphorylation. We utilized data from patients given 30-min infusions of dFdC to evaluate the percentage of plasma AUC associated with dFdC concentrations ≥ 10–15 μM. Methods: 31 patients with varying degrees of renal and hepatic function were enrolled in CALGB 9565 and underwent intensive pharmacokinetic sampling after being treated with 30-min dFdC infusions at doses of 650–950 mg/m2. Plasma concentrations of dFdC and dFdU were determined by HPLC. Plasma dFdC and dFdU concentration vs. time data were modeled using a 5-compartment model in ADAPT II and additional mathematical methods were developed using MATLAB. AUC was calculated using Simpson's rule and patient-specific pharmacokinetic parameters so as to calculate the total AUC (TAUC) and excessive AUC (EAUC). The fraction of each patient's dFdC AUC spent above 10 and 15 μM was then calculated from the ratio EAUC / TAUC. Results: 4 patients received 650 mg/m2, 17 patients received 800 mg/m2, and 10 patients received 950 mg/m2 of dFdC. The percentages of total dFdC AUC spent above the 10 μM threshold were 68 ± 16, 49 ± 15 and 57 ± 11% for doses of 650, 800, and 950 mg/m2, respectively. The percentages of total dFdC AUC spent above the 15 μM threshold were 58 ± 21, 35 ± 16, and 43 ± 13% for doses of 650, 800, and 950 mg/m2, respectively. Conclusions: Administering dFdC by 30-min infusion resulted in approximately half of the plasma drug exposure (as measured by AUC) occurring above dFdC concentrations that saturate its phosphorylation to active metabolites. This excessive drug may be clinically inconsequential because of its inactivation through deamination to dFdU. The data argue for studying additional patients with normal organ function and other schedules of dFdC delivery. Support: U10 CA44691, National Cancer Institute and a Whitaker Foundation Graduate Student Fellowship (JAF). Author Disclosure Employment or Leadership Consultant or Advisory Stock Ownership Honoraria Research Funding Expert Testimony Other Remuneration Eil Lilly Corp. Eli Lilly Corporation

Evaluation of potentially excessive plasma gemcitabine (dFdC) concentrations produced by 30-minute dFdC infusions

2019 Background: The phosphorylation of dFdC to active metabolites is saturable, whereas its deamination to the inactive metabolite, 2'2'-difluorodeoxyuridine (dFdU) is not. 30-min dFdC infusions produce plasma concentrations greater than the 10–15 μM that saturate intracellular phosphorylation. We utilized data from patients given 30-min infusions of dFdC to evaluate the percentage of plasma AUC associated with dFdC concentrations ≥ 10–15 μM. Methods: 31 patients with varying degrees of renal and hepatic function were enrolled in CALGB 9565 and underwent intensive pharmacokinetic sampling after being treated with 30-min dFdC infusions at doses of 650–950 mg/m2. Plasma concentrations of dFdC and dFdU were determined by HPLC. Plasma dFdC and dFdU concentration vs. time data were modeled using a 5-compartment model in ADAPT II and additional mathematical methods were developed using MATLAB. AUC was calculated using Simpson's rule and patient-specific pharmacokinetic parameters so as to calculate the total AUC (TAUC) and excessive AUC (EAUC). The fraction of each patient's dFdC AUC spent above 10 and 15 μM was then calculated from the ratio EAUC / TAUC. Results: 4 patients received 650 mg/m2, 17 patients received 800 mg/m2, and 10 patients received 950 mg/m2 of dFdC. The percentages of total dFdC AUC spent above the 10 μM threshold were 68 ± 16, 49 ± 15 and 57 ± 11% for doses of 650, 800, and 950 mg/m2, respectively. The percentages of total dFdC AUC spent above the 15 μM threshold were 58 ± 21, 35 ± 16, and 43 ± 13% for doses of 650, 800, and 950 mg/m2, respectively. Conclusions: Administering dFdC by 30-min infusion resulted in approximately half of the plasma drug exposure (as measured by AUC) occurring above dFdC concentrations that saturate its phosphorylation to active metabolites. This excessive drug may be clinically inconsequential because of its inactivation through deamination to dFdU. The data argue for studying additional patients with normal organ function and other schedules of dFdC delivery. Support: U10 CA44691, National Cancer Institute and a Whitaker Foundation Graduate Student Fellowship (JAF). Author Disclosure Employment or Leadership Consultant or Advisory Stock Ownership Honoraria Research Funding Expert Testimony Other Remuneration Eil Lilly Corp. Eli Lilly Corporation

Clinical Application and Evaluation of Vancomycin Dosing in Adults

Objective: To evaluate the accuracy of a vancomycin dosing method (study method) in estimating volume of distribution (Vd) and clearance of vancomycin (Clvanco) and the frequency with which it would provide serum vancomycin concentrations (SVCs) within a specified range compared with other published methods. Methods: One hundred and seven patients with 108 pairs of SVCs were used to calculate patient-specific Vd and Clvanco. Based on these patient-specific Vd and Clvanco values, the predictive ability of the estimated parameters from the study dosing method (Vd [L] = 0.17 [age] + 0.22 [actual body weight] + 15; Clvanco = creatinine clearance) was evaluated against three previously published pharmacokinetic dosing methods, using predictive performance analysis (precision and bias). Furthermore, the patient-specific pharmacokinetic parameters were used to simulate steady-state peak and trough SVCs, using first-order pharmacokinetic equations from doses derived from the study method, the three different pharmacokinetic methods, and two other published dosing schemes. The frequency with which each method would have achieved target peak and trough SVCs was determined. Results: The study method was found to be more precise and less biased (p &lt; 0.05) than comparative methods in predicting vancomycin Vd and Clvanco. The study method also resulted in a higher frequency of steady-state peak and trough SVCs within the target range specified. Conclusions: The study method presented here provided a reliable estimation of vancomycin pharmacokinetic parameters that was easily applied to various patient populations to individualize vancomycin dosing, and frequently yielded SVCs within a predictable and desired range.

Removal of vancomycin during plasmapheresis.

To examine the removal of vancomycin during plasmapheresis, determine whether drug administration should be withheld prior to or a supplemental dose given after the procedure, and determine whether a redistribution phenomenon in vancomycin serum concentrations occurs after plasmapheresis. Prospective, cohort study. An 800-bed, tertiary-care, teaching hospital. Twelve patients receiving vancomycin as prescribed who were also undergoing therapeutic plasmapheresis. Blood samples for determination of vancomycin concentrations were obtained from each patient immediately before, during, immediately after, and 2 hours after plasmapheresis. Vancomycin concentration in plasma removed by plasmapheresis and volume of plasma removed were measured. Patient-specific pharmacokinetic parameters were determined for each patient using serum concentration data and a one-compartment model. Percent of drug removed by plasmapheresis and percent increase in vancomycin total clearance secondary to plasmapheresis were calculated. A mean of 6.3% of the total body store of vancomycin was removed by plasmapheresis. Vancomycin clearance during plasmapheresis averaged 1.6 L/h, which was an average increase of 285% in the total clearance of vancomycin from the body. Nine of 10 patients had a higher observed vancomycin concentration 2 hours after plasmapheresis than that predicted by degrading the concentration observed immediately after the procedure, suggesting that redistribution in serum concentrations occurs after the procedure. A single one-volume plasmapheresis does not remove a clinically important amount of vancomycin; therefore, supplemental dosing after the procedure is not necessary. A redistribution phenomenon in vancomycin concentrations appears to exist after plasmapheresis. Further study is needed to determine how long the redistribution phase lasts and when vancomycin concentrations should be measured after plasmapheresis.

Bayesian Forecasting of Aminoglycoside Dosing Requirements in Obese Patients

An aminoglycoside Bayesian forecaster was evaluated in obese patients. This study assessed the influence of replacing the program-supplied general population parameters (GPP) with obese population parameters (OPP) determined from the study population (n = 26). After entering the required patient information and the first peak and trough levels, patient-specific pharmacokinetic parameters were generated by the Bayesian program based on GPP. These parameters were used to predict peak and trough levels for a second dosage regimen. Next, average OPP determined from a study population were substituted for the GPP, and the peak and trough levels were predicted again. Finally, Bayesian predictions of peak and trough levels were made in a validation population (n = 10), first with GPP, then with OPP. The accuracy of the predictions were evaluated through a prediction error analysis in which mean error indicates bias and mean absolute error and root mean-squared error indicate precision. Means were statistically compared through a Student's t test, with the significance level set at p less than 0.05. For the study and validation populations, peak level predictions based on the OPP had less bias and greater precision than those predicted with GPP. Peaks predicted with GPP were statistically different from the observed peaks as well as the peaks predicted using the OPP. There was no statistical difference between the observed peaks and the predicted peaks using the OPP. The trough level predictions using GPP in the study population had less bias than those predicted using OPP; however, the OPP predictions had less bias in the validation population.(ABSTRACT TRUNCATED AT 250 WORDS)