We propose using glomerular filtration rate (GFR) as the physiological basis for distinguishing components of renal clearance.
Gentamicin, amikacin and vancomycin are thought to be predominantly ...excreted by the kidneys. A mixed-effects joint model of the pharmacokinetics of these drugs was developed, with a wide dispersion of weight, age and serum creatinine. A dataset created from 18 sources resulted in 27,338 drug concentrations from 9,901 patients. Body size and composition, maturation and renal function were used to describe differences in drug clearance and volume of distribution.
This study demonstrates that GFR is a predictor of two distinct components of renal elimination clearance: (1) GFR clearance associated with normal GFR and (2) non-GFR clearance not associated with normal GFR. All three drugs had GFR clearance estimated as a drug-specific percentage of normal GFR (gentamicin 39%, amikacin 90% and vancomycin 57%). The total clearance (sum of GFR and non-GFR clearance), standardized to 70 kg total body mass, 176 cm, male, renal function 1, was 5.58 L/h (95% confidence interval CI 5.50-5.69) (gentamicin), 7.77 L/h (95% CI 7.26-8.19) (amikacin) and 4.70 L/h (95% CI 4.61-4.80) (vancomycin).
GFR provides a physiological basis for renal drug elimination. It has been used to distinguish two elimination components. This physiological approach has been applied to describe clearance and volume of distribution from premature neonates to elderly adults with a wide dispersion of size, body composition and renal function. Dose individualization has been implemented using target concentration intervention.
Background and Objectives
Uncertainty exists regarding the optimal dosing regimen for vancomycin in different patient populations, leading to a plethora of subgroup-specific pharmacokinetic models ...and derived dosing regimens. We aimed to investigate whether a single model for vancomycin could be developed based on a broad dataset covering the extremes of patient characteristics. Furthermore, as a benchmark for current dosing recommendations, we evaluated and optimised the expected vancomycin exposure throughout life and for specific patient subgroups.
Methods
A pooled population-pharmacokinetic model was built in NONMEM based on data from 14 different studies in different patient populations. Steady-state exposure was simulated and compared across patient subgroups for two US Food and Drug Administration/European Medicines Agency-approved drug labels and optimised doses were derived.
Results
The final model uses postmenstrual age, weight and serum creatinine as covariates. A 35-year-old, 70-kg patient with a serum creatinine level of 0.83 mg dL
−1
(73.4 µmol L
−1
) has a
V
1
,
V
2
, CL and
Q
2
of 42.9 L, 41.7 L, 4.10 L h
−1
and 3.22 L h
−1
. Clearance matures with age, reaching 50% of the maximal value (5.31 L h
−1
70 kg
−1
) at 46.4 weeks postmenstrual age then declines with age to 50% at 61.6 years. Current dosing guidelines failed to achieve satisfactory steady-state exposure across patient subgroups. After optimisation, increased doses for the Food and Drug Administration label achieve consistent target attainment with minimal (± 20%) risk of under- and over-dosing across patient subgroups.
Conclusions
A population model was developed that is useful for further development of age and kidney function-stratified dosing regimens of vancomycin and for individualisation of treatment through therapeutic drug monitoring and Bayesian forecasting.
A simple liquid chromatography tandem mass spectrometry method was developed and validated according to the guidelines of the US Food and Drug Administration and the European Medicines Agency for a ...simultaneous quantification of levetiracetam (LEV) and its metabolite, UCB L057 in the plasma of patients. A 0.050 mL plasma sample was prepared by a simple and direct protein precipitation with 0.450 mL acetonitrile (ACN) containing 1 µg/mL of internal standard (IS, diphenhydramine), then vortex mixed and centrifuged. A 0.100 mL of the clear supernatant was diluted with 0.400 mL water and well mixed. A 0.010 mL of the resultant solution was injected into an Agilent Zorbax SB-C18 (2.1 mm×100 mm, 3.5 µm) column with an isocratic elution at 0.5 mL/min using a mixture of 0.1% formic acid in water and ACN (40:60 v/v). Detection was performed using an AB Sciex API 3000 triple quadrupole mass spectrometer, equipped with a Turbo Ion Spray source, operating in a positive mode: LEV at transition 171.1>154.1, UCB L057 at 172.5>126.1, and IS at 256.3>167.3; with an assay run time of 2 minutes. The lower limit of quantification (LLOQ) for both LEV and UCB L057 was validated at 0.5 µg/mL, while their lower limit of detection (LOD) was 0.25 µg/mL. The calibration curves were linear between 0.5 and 100 µg/mL for both analytes. The inaccuracy and imprecision of both intra-assay and inter-assay were less than 10%. Matrix effects were consistent between sources of plasma and the recoveries of all compounds were between 100% and 110%. Stability was established under various storage and processing conditions. The carryovers from both LEV and UCB L057 were less than 6% of the LLOQ and 0.13% of the IS. This assay method has been successfully applied to a population pharmacokinetic study of LEV in patients with epilepsy.
In the absence of consensus, the present meta-analysis was performed to determine an optimal dosing regimen of vancomycin for neonates.
A 'meta-model' with 4894 concentrations from 1631 neonates was ...built using NONMEM, and Monte Carlo simulations were performed to design an optimal intermittent infusion, aiming to reach a target AUC0-24 of 400 mg·h/L at steady-state in at least 80% of neonates.
A two-compartment model best fitted the data. Current weight, postmenstrual age (PMA) and serum creatinine were the significant covariates for CL. After model validation, simulations showed that a loading dose (25 mg/kg) and a maintenance dose (15 mg/kg q12h if <35 weeks PMA and 15 mg/kg q8h if ≥35 weeks PMA) achieved the AUC0-24 target earlier than a standard 'Blue Book' dosage regimen in >89% of the treated patients.
The results of a population meta-analysis of vancomycin data have been used to develop a new dosing regimen for neonatal use and to assist in the design of the model-based, multinational European trial, NeoVanc.
Aims
Vancomycin is one of the most evaluated antibiotics in neonates using modeling and simulation approaches. However no clear consensus on optimal dosing has been achieved. The objective of the ...present study was to perform an external evaluation of published models, in order to test their predictive performances in an independent dataset and to identify the possible study‐related factors influencing the transferability of pharmacokinetic models to different clinical settings.
Method
Published neonatal vancomycin pharmacokinetic models were screened from the literature. The predictive performance of six models was evaluated using an independent dataset (112 concentrations from 78 neonates). The evaluation procedures used simulation‐based diagnostics visual predictive check (VPC) and normalized prediction distribution errors (NPDE).
Results
Differences in predictive performances of models for vancomycin pharmacokinetics in neonates were found. The mean of NPDE for six evaluated models were 1.35, −0.22, −0.36, 0.24, 0.66 and 0.48, respectively. These differences were explained, at least partly, by taking into account the method used to measure serum creatinine concentrations. The adult conversion factor of 1.3 (enzymatic to Jaffé) was tested with an improvement in the VPC and NPDE, but it still needs to be evaluated and validated in neonates. Differences were also identified between analytical methods for vancomycin.
Conclusion
The importance of analytical techniques for serum creatinine concentrations and vancomycin as predictors of vancomycin concentrations in neonates have been confirmed. Dosage individualization of vancomycin in neonates should consider not only patients' characteristics and clinical conditions, but also the methods used to measure serum creatinine and vancomycin.
...of such success, it turned out that WCoP2012 contributed to fueling the interest in pharmacometrics among Asian countries. TABLE 2 APN board members Name Affiliation a Country Kyungsoo Park b ...(Chair) Yonsei University Korea Atsunori Kaibara b Eli Lilly Japan Rujia Xie b,c Pfizer China Keyue Ma d Pfizer China Ao Peng e Pfizer China Chun-Jung Lin b National Taiwan University Taiwan Yunn-Fang Ho f National Taiwan University Taiwan Lai-San Tham b Eli Lilly Singapore Surulivelrajan Mallayasamy b,g Manipal College of Pharmaceutical Sciences (MCOPS), Manipal Academy of Higher Education India Korbtham Sathirakul Mahidol University Thailand Yoke-Lin Lo International Medical University Malaysia Van Toi Pham Oxford University Clinical Research Unit Vietnam Manh Hung Tran h University of Medicine and Pharmacy Vietnam Akhmad Kharis Nugroho Universitas Gadjah Mada Indonesia Khin Myo Oo University of Mandalay Myanmar Bimal Kunwar Nobel College of Health Science Nepal Uthpali Mannapperuma University of Colombo Sri Lanka Kimheang Ya University of Puthisastra Cambodia Muhammad Usman University of Veterinary and Animal Sciences Pakistan Long Chiau Ming Universiti Brunei Darussalam Brunei Abbreviations: APN, Asian Pharmacometrics Network; WCoP2012, World Conference on Pharmacometrics in 2012; AC, regional Advisory Committee of WCoP2012. aBoard members’ affiliation history with local pharmacometrics groups: Kyungsoo Park: president of Population Approach Group in Korea (2011~2014) and board member of Korean Society of Clinical Pharmacology and Therapeutics (2009~); Atsunori Kaibara: vice president of Population Approach Group in Japan (2003~2018) and leader of Model-Informed Drug Development (MIDD) Task Force in Japan–Pharmaceutical Research and Manufacturers of America (PhRMA) (2020~); Rujia Xie: committee member of Professional Committee of Pharmacometrics of China (2013–2017); Surulivelrajan Mallayasamy: secretary of Population Approach Group India (2010~2020); Yoke-Lin Lo: chairperson of the Population Approach Group of Malaysia (2019–2021 February) and committee member (February 2021–). bAC members of WCoP2012. cRujia Xie replaced Feng Guo, a founding AC member of China. dKeyue Ma replaced Rujia Xie as of November 2020. eAo Peng replaced Keyue Ma as of August 2021. fYunn-Fang Ho replaced Chun-Jung Lin as of August 2019. gSurulivelrajan Mallayasamy replaced Ram Sankaran, a founding AC member of India. hManh Hung Tran replaced Van Toi Pham as of August 2019. ...APN will be working toward having an annual continental meeting that is an educational opportunity open to all globally similar to Population Approach Group in Europe (PAGE), the flagship regional meeting on the globe.
High variability in vancomycin exposure in neonates requires advanced individualized dosing regimens. Achieving steady-state trough concentration (C
) and steady-state area-under-curve (AUC
) targets ...is important to optimize treatment. The objective was to evaluate whether machine learning (ML) can be used to predict these treatment targets to calculate optimal individual dosing regimens under intermittent administration conditions.
C
were retrieved from a large neonatal vancomycin dataset. Individual estimates of AUC
were obtained from Bayesian post hoc estimation. Various ML algorithms were used for model building to C
and AUC
. An external dataset was used for predictive performance evaluation.
Before starting treatment, C
can be predicted a priori using the Catboost-based C
-ML model combined with dosing regimen and nine covariates. External validation results showed a 42.5% improvement in prediction accuracy by using the ML model compared with the population pharmacokinetic model. The virtual trial showed that using the ML optimized dose; 80.3% of the virtual neonates achieved the pharmacodynamic target (C
in the range of 10-20 mg/L), much higher than the international standard dose (37.7-61.5%). Once therapeutic drug monitoring (TDM) measurements (C
) in patients have been obtained, AUC
can be further predicted using the Catboost-based AUC-ML model combined with C
and nine covariates. External validation results showed that the AUC-ML model can achieve an prediction accuracy of 80.3%.
C
-based and AUC
-based ML models were developed accurately and precisely. These can be used for individual dose recommendations of vancomycin in neonates before treatment and dose revision after the first TDM result is obtained, respectively.
s
Background
Transversus abdominis plane (TAP) block and intraperitoneal local anesthetics (IPLA) are widely investigated techniques that potentially improve analgesia after bariatric surgery. The ...analgesic efficacy of TAP block has been shown in previous studies, but the performance of TAP block can be difficult in patients with obesity. We performed a systematic review and meta-analysis to compare the analgesic efficacy of TAP block and IPLA. An alternative technique is useful in clinical setting when TAP block is not feasible.
Methods
We searched PubMed, Embase, and CENTRAL from inception until August 2020 for randomized controlled trials comparing both techniques. The primary outcome was cumulative morphine consumption at 24 h. Secondary pain-related outcomes included pain score at rest and on movement at 2, 6, 12, and 24 h; postoperative nausea and vomiting; and length of hospital stay.
Results
We included 23 studies with a total of 2,178 patients. TAP block is superior to control in reducing opioid consumption at 24 h, improving pain scores at all the time points and postoperative nausea and vomiting. The cumulative opioid consumption at 24 h for IPLA is less than control, while the indirect comparison between IPLA with PSI and control showed a significant reduction in pain scores at rest, at 2 h, and on movement at 12 h, and 24 h postoperatively.
Conclusions
Transversus abdominis plane block is effective for reducing pain intensity and has superior opioid-sparing effect compared to control. Current evidence is insufficient to show an equivalent analgesic benefit of IPLA to TAP block.
Graphical abstract
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