A population pharmacokinetic model for creatinine with and without ingestion of a cooked meat meal.
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ABSTRACT:
SUBMITTER: Chen Z
PROVIDER: S-EPMC9649480 | biostudies-literature | 2022 Dec
REPOSITORIES: biostudies-literature
Publications
A population pharmacokinetic model for creatinine with and without ingestion of a cooked meat meal.
Chen Zhendong Z Chen Chunli C Taubert Max M Mayersohn Michael M Fuhr Uwe U
European journal of clinical pharmacology 20221010 12
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Project description:While phosphates are key additives in sausage production, their use conflicts with consumer preferences for "natural" foods. In this study, we investigated the potential of using vegetables as "clean-label" phosphate substitutes and their effects on water holding capacity, consumer acceptance, color, softness, and tenderness. Six freeze-dried vegetables with a pH above 6.0 were added to sausage meat on a laboratory scale. Adding 1.6% freeze-dried Brussels sprouts or Red Kuri squash resulted in a similar weight gain (7.0%) as the positive control of 0.6% commercial phosphate additive. Higher vegetable concentrations (2.2-4.0%) caused a significant increase in weight (p ≤ 0.05, 10.4-18.4% weight gain). Similar stress was needed to compress sausages containing 1.6/4.0% Brussels sprouts (14.2/11.2 kPa) and the positive control (13.2 kPa). Indentation tests also led to similar softness results for the sausages prepared with 1.6/4.0% Brussels sprouts (15.5 kPa/16.6 kPa) and the positive control (16.5 kPa). A force of 1.25 N was needed to shear the positive control, while 1.60 N/1.30 N was needed for the samples (1.6/4% Brussels sprouts). In summary, the present study indicates that freeze-dried vegetables have the potential to effectively replace phosphate in meat products.
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Project description:A fit-for-purpose structural and statistical model is the first major requirement in population pharmacometric model development. In this manuscript we discuss how this complex and computationally intensive task could benefit from supervised machine learning algorithms. We compared the classical pharmacometric approach with two machine learning methods, genetic algorithm and neural networks, in different scenarios based on simulated pharmacokinetic data. Genetic algorithm performance was assessed using a fitness function based on log-likelihood, whilst neural networks were trained using mean square error or binary cross-entropy loss. Machine learning provided a selection based only on statistical rules and achieved accurate selection. The minimization process of genetic algorithm was successful at allowing the algorithm to select plausible models. Neural network classification tasks achieved the most accurate results. Neural network regression tasks were less precise than neural network classification and genetic algorithm methods. The computational gain obtained by using machine learning was substantial, especially in the case of neural networks. We demonstrated that machine learning methods can greatly increase the efficiency of pharmacokinetic population model selection in case of large datasets or complex models requiring long run-times. Our results suggest that machine learning approaches can achieve a first fast selection of models which can be followed by more conventional pharmacometric approaches.
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Project description:ObjectiveTo study the pharmacokinetics of micafungin in intensive care patients and assess pharmacokinetic (PK) target attainment for various dosing strategies.MethodsMicafungin PK data from 20 intensive care unit patients were available. A population-PK model was developed. Various dosing regimens were simulated: licensed regimens (I) 100 mg daily; (II) 100 mg daily with 200 mg from day 5; and adapted regimens 200 mg on day 1 followed by (III) 100 mg daily; (IV) 150 mg daily; and (V) 200 mg daily. Target attainment based on a clinical PK target for Candida as well as non-Candida parapsilosis infections was assessed for relevant minimum inhibitory concentrations [MICs] (Clinical and Laboratory Standards Institute). Parameter uncertainty was taken into account in simulations.ResultsA two-compartment model best fitted the data. Clearance was 1.10 (root square error 8%) L/h and V 1 and V 2 were 17.6 (root square error 14%) and 3.63 (root square error 8%) L, respectively. Median area under the concentration-time curve over 24 h (interquartile range) on day 14 for regimens I-V were 91 (67-122), 183 (135-244), 91 (67-122), 137 (101-183) and 183 (135-244) mg h/L, respectively, for a typical patient of 70 kg. For the MIC/area under the concentration-time curve >3000 target (all Candida spp.), PK target attainment was >91% on day 14 (MIC 0.016 mg/L epidemiological cut-off) for all of the dosing regimens but decreased to (I) 44%, (II) 91%, (III) 44%, (IV) 78% and (V) 91% for MIC 0.032 mg/L. For the MIC/area under the concentration-time curve >5000 target (non-C. parapsilosis spp.), PK target attainment varied between 62 and 96% on day 14 for MIC 0.016.ConclusionsThe licensed micafungin maintenance dose results in adequate exposure based on our simulations with a clinical PK target for Candida infections but only 62% of patients reach the target for non-C. parapsilosis. In the case of pathogens with an attenuated micafungin MIC, patients may benefit from dose escalation to 200 mg daily. This encourages future study.
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Project description:Heat cooking of meat gives it a specific taste and flavor which are favored by many consumers. While the characteristic taste components of chicken, duck, pig, and seafood have been studied, there is a lack of information about the molecular components that give donkey meat its unique taste. Here, the characterization profiles of raw donkey meat (RDM) and cooked donkey meat (CDM) meat by metabonomics and lipidomics. The results showed that a total of 186 metabolites belonging to 8 subclasses were identified in CDM and RDM, including carbohydrates (27.42%), amino acids (17.20%), lipids (13.44%), and nucleotides (9.14%). In total, 37 differential metabolites were identified between CDM and RDM. Among these, maltotriose, L-glutamate, and L-proline might predominantly contribute to the unique umami and sweet taste of donkey meat. Comprehensive biomarker screening detected 9 potential metabolite markers for the discrimination among RDM and CDM, including L-glutamate, gamma-aminobutyric acid, and butane-1, 2, 3, 4-tetrol. Moreover, a total of 992 and 1,022 lipids belonging to 12 subclasses were identified in RDM and CDM, respectively, mainly including triglycerides (TGs) and glycerophospholipids (GPs). Of these lipids, 116 were significantly different between CDM and RDM. The abundances of 61 TGs rich in saturated and monounsaturated fatty acids were retained in CDM, whereas the abundances of 37 GPs rich in polyunsaturated fatty acids were reduced, suggesting that TGs and GPs might be the predominant lipids for binding and generating aroma compounds, respectively. A total of 13 lipids were determined as potential markers for the discrimination among RDM and CDM, including PC(O-16:2/2:0), LPE(22:5/0:0), and PC(P-16:0/2:0). In conclusion, this study provided useful information about the metabolic and lipid profiles of donkey meat which may explain its unique taste and flavor, which could serve as a basis for the development and quality control of donkey meat and its products.
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Project description:Objective: Vancomycin is commonly used in postoperative neurosurgical patients for empirical anti-infective treatment due to the low success rate of bacterial culture in cerebrospinal fluid (about 20%) and the high mortality of intracranial infection. At conventional doses, the rate of target achievement for vancomycin trough concentration is low and the pharmacokinetics of vancomycin varies greatly in these patients, which often leads to treatment failure. The objective of this study was to establish a population pharmacokinetic (PPK) model of vancomycin in postoperative neurosurgical patients for precision medicine. Method: A total of 895 vancomycin plasma concentrations from 560 patients (497 postoperative neurosurgical patients) were retrospectively collected. The model was analyzed by nonlinear mixed effects modeling method. One-compartment model and mixed residual model was employed. The influence of covariates on model parameters was tested by forward addition and backward elimination. Goodness-of-fit, bootstrap and visual predictive check were used for model evaluation. Monte Carlo simulations were employed for dosing strategies with AUC24 targets 400-600. Result: Estimated glomerular filtration rate (eGFR), body weight (BW) and mannitol had significant influence on vancomycin clearance (CL). eGFR(mL/min)=144×(Scr/a)b×0.993age , for female, a = 0.7, Scr ≤ 0.7 mg/dl, b = -0.329, Scr > 0.7 mg/dl, b = -1.209; for male, a = 0.9, Scr ≤ 0.9 mg/dl, b = -0.411, Scr > 0.9 mg/dl, b = -1.210. Vancomycin clearance was accelerated when co-medicated with mannitol and increased with eGFR and BW. In the final model, the population typical value is 7.98 L/h for CL and 60.2 L for apparent distribution volume, CL (L/h)=7.98×(eGFR/115.2)0.8×(BW/70)0.3×eA , where A = 0.13 when co-medicated with mannitol, otherwise A = 0. The model is stable and effective, with good predictability. Conclusion: In postoperative neurosurgical patients, a higher dose of vancomycin may be required due to the augmented renal function and the commonly used mannitol, especially in those with high body weight. Our vancomycin PPK model could be used for individualized treatment in postoperative neurosurgical patients.
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Project description:Cefazolin is an antibiotic recommended for infection prevention in total hip arthroplasty (THA). However, the dosing regimen necessary to achieve therapeutic concentrations in obese patients remains unclear. The aim of this study was to conduct a population analysis of cefazolin pharmacokinetics (PK) and assess whether cefazolin administration should be weight adapted in THA. Adult patients undergoing THA surgery received an injection of 2000 mg of cefazolin, doubled in the case of BMI > 35 kg/m2 and total body weight > 100 kg. A population PK study was conducted to quantify cefazolin exposure over time compared to the therapeutic concentration threshold. A total of 484 cefazolin measurements were acquired in 100 patients, of whom 29% were obese. A 2-compartment model best fitted the data, and creatinine clearance determined interpatient variability in elimination clearance. Our PK simulations using a 2000 mg cefazolin bolus showed that cefazolin concentrations remained above the threshold throughout surgery, regardless of weight or renal function. A 2000 mg cefazolin single injection without adaptation to weight or renal function and without intraoperative reinjection was efficient in maintaining therapeutic concentrations throughout surgery. The optimal target concentration and necessary duration of its maintenance remain unclear.
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