scholarly journals Use of Probabilistic Modeling within a Physiologically Based Pharmacokinetic Model To Predict Sulfamethazine Residue Withdrawal Times in Edible Tissues in Swine

2006 ◽  
Vol 50 (7) ◽  
pp. 2344-2351 ◽  
Author(s):  
Jennifer Buur ◽  
Ronald Baynes ◽  
Geof Smith ◽  
Jim Riviere
Keyword(s):  
Monte Carlo ◽  
Pbpk Model ◽  
Probabilistic Modeling ◽  
Monte Carlo Analysis ◽  
Withdrawal Time ◽  
Data Set ◽  
External Data ◽  
Physiologically Based Pharmacokinetic ◽  
Modeling Techniques ◽  
Edible Tissues

ABSTRACT The presence of antimicrobial agents in edible tissues of food-producing animals remains a major public health concern. Probabilistic modeling techniques incorporated into a physiologically based pharmacokinetic (PBPK) model were used to predict the amounts of sulfamethazine residues in edible tissues in swine. A PBPK model for sulfamethazine in swine was adapted to include an oral dosing route. The distributions for sensitive parameters were determined and were used in a Monte Carlo analysis to predict tissue residue times. Validation of the distributions was done by comparison of the results of a Monte Carlo analysis to those obtained with an external data set from the literature and an in vivo pilot study. The model was used to predict the upper limit of the 95% confidence interval of the 99th percentile of the population, as recommended by the U.S. Food and Drug Administration (FDA). The external data set was used to calculate the withdrawal time by using the tolerance limit algorithm designed by FDA. The withdrawal times obtained by both methods were compared to the labeled withdrawal time for the same dose. The Monte Carlo method predicted a withdrawal time of 21 days, based on the amounts of residues in the kidneys. The tolerance limit method applied to the time-limited data set predicted a withdrawal time of 12 days. The existing FDA label withdrawal time is 15 days. PBPK models can incorporate probabilistic modeling techniques that make them useful for prediction of tissue residue times. These models can be used to calculate the parameters required by FDA and explore those conditions where the established withdrawal time may not be sufficient.

scholarly journals Apply a Physiologically Based Pharmacokinetic Model to Promote the Development of Enrofloxacin Granules: Predict Withdrawal Interval and Toxicity Dose

Antibiotics ◽  
2021 ◽  
Vol 10 (8) ◽  
pp. 955
Author(s):  
Kaixiang Zhou ◽  
Aimei Liu ◽  
Wenjin Ma ◽  
Lei Sun ◽  
Kun Mi ◽  
...  
Keyword(s):  
Antimicrobial Agents ◽  
Pbpk Model ◽  
Monte Carlo Analysis ◽  
Withdrawal Time ◽  
Physiologically Based Pharmacokinetic ◽  
Physiologically Based ◽  
Intestinal Pathogens ◽  
And Control ◽  
Further Development ◽  
Potential Tool

Enrofloxacin (ENR) granules were developed to prevent and control the infections caused by foodborne zoonotic intestinal pathogens in our previous studies. To promote the further development of ENR granules and standardize their usage in pigs, a physiologically based pharmacokinetic (PBPK) model of the ENR granule in pigs was built to determine the withdrawal time (WT) and evaluate the toxicity to pigs. Meanwhile, the population WT was determined by a Monte Carlo analysis to guarantee pork safety. The fitting results of the model showed that the tissue residual concentrations of ENR, ciprofloxacin, and ENR plus ciprofloxacin were all well predicted by the built PBPK model (R2 > 0.82). When comparing with the EMA’s WT1.4 software method, the final WT (6 d) of the ENR granules in the population of pigs was well predicted. Moreover, by combining the cytotoxicity concentration (225.9 µg/mL) of ENR against pig hepatocytes, the orally safe dosage range (≤130 mg/kg b.w.) of the ENR granules to pigs was calculated based on the validated PBPK model. The well-predicted WTs and a few uses in animals proved that the PBPK model is a potential tool for promoting the judicious use of antimicrobial agents and evaluating the toxicity of the veterinary antimicrobial products.

scholarly journals Development and Application of a Water Temperature Related Physiologically Based Pharmacokinetic Model for Enrofloxacin and Its Metabolite Ciprofloxacin in Rainbow Trout

2021 ◽  
Vol 7 ◽  
Author(s):  
Fan Yang ◽  
Fang Yang ◽  
Dan Wang ◽  
Chao-Shuo Zhang ◽  
Han Wang ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Rainbow Trout ◽  
Water Temperature ◽  
Pharmacokinetic Model ◽  
Pbpk Model ◽  
Monte Carlo Analysis ◽  
Withdrawal Period ◽  
Degree Days ◽  
Physiologically Based Pharmacokinetic ◽  
Dosage Regimens

Enrofloxacin (ENR) has been approved for the treatment of infections in aquaculture, but it may cause tissue residue. This research aimed to develop and validate a water temperature related PBPK model, including both ENR and ciprofloxacin (CIP), in rainbow trout, and to predict further their residue concentrations and the withdrawal periods for ENR at different water temperatures. With the published concentrations data, a flow-limited PBPK model including both ENR and CIP sub-models was developed to predict ENR and CIP concentrations in tissues and plasma/serum after intravenous, oral, or immersion administration. A Monte Carlo simulation including 500 iterations was further incorporated into this model. Based on the model and Monte Carlo analysis, the withdrawal intervals were estimated for different dosage regimens and at different water temperatures, ranging from 80 to 272 degree-days. All of these values were shorter than the labeled withdrawal period (500 degree-days) in fish. This model provided a useful tool for predicting the tissue residues of ENR and CIP in rainbow trout under different dosage regimens and at different water temperatures.

scholarly journals 1,1-Difluoroethane Detection Time in Blood after Inhalation Abuse Estimated by Monte Carlo PBPK Modeling

Pharmaceutics ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 997 ◽  
Author(s):  
Raul Huet ◽  
Gunnar Johanson
Keyword(s):  
Body Mass Index ◽  
Monte Carlo ◽  
Body Mass ◽  
Pbpk Model ◽  
Sensitivity Analyses ◽  
Pbpk Modeling ◽  
Detection Time ◽  
Adverse Health Effects ◽  
Physiologically Based Pharmacokinetic ◽  
Physiologically Based

(1) Background: Inhalant abuse and misuse are still widespread problems. 1,1-Difluoroethane abuse is reported to be potentially fatal and to cause acute and chronic adverse health effects. Lab testing for difluoroethane is seldom done, partly because the maximum detection time (MDT) is unknown. We sought to reliably estimate the MDT of difluoroethane in blood after inhalation abuse; (2) Methods: MDT were estimated for the ad ult male American population using a physiologically based pharmacokinetic (PBPK) model and abuse patterns detailed by two individuals. Based on sensitivity analyses, variability in huffing pattern and body mass index was introduced in the model by Monte Carlo simulation; (3) Results: With a detection limit of 0.14 mg/L, the median MDT was estimated to be 10.5 h (5th–95th percentile 7.8–12.8 h) after the 2-h abuse scenario and 13.5 h (10.5–15.8 h) after the 6-h scenario. The ranges reflect variability in body mass index (and, hence, amount of body fat) and, more so, variable inhalation patterns; (4) Conclusions: Our simulations suggest that the MDT of difluoroethane in blood after abuse ranges from 7.8 to 15.8 h. Although shorter compared to many other drugs, these MDT are sufficient to allow for testing several hours after suspected intoxication in a patient.

scholarly journals The Odds of Profitable Market Timing

2021 ◽  
Vol 14 (6) ◽  
pp. 250
Author(s):  
Luigi Buzzacchi ◽  
Luca Ghezzi
Keyword(s):  
Monte Carlo ◽  
Statistical Study ◽  
Market Timing ◽  
Financial Analysts ◽  
Monte Carlo Analysis ◽  
Accurate Approximation ◽  
Data Set ◽  
Forecasting Accuracy ◽  
Common Stocks ◽  
Portfolio Managers

This statistical study refines and updates Sharpe’s empirical paper (1975, Financial Analysts Journal) on switching between US common stocks and cash equivalents. According to the original conclusion, profitable market timing relies on a representative portfolio manager who can correctly forecast the next year at least 7 times out of 10. Four changes are made to the original setting. The new data set begins and ends with similar price-earnings ratios; a more accurate approximation of commissions is given; the rationality of assumptions is examined; a prospective and basic Monte Carlo analysis is carried out so as to consider the heterogeneous performance of a number of portfolio managers with the same forecasting accuracy. Although the first three changes improve retrospectively the odds of profitable market timing, the original conclusion is corroborated once more.

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