CN107092773B - Method for predicting ciprofloxacin residue in pig body by using physiological pharmacokinetic model - Google Patents

Method for predicting ciprofloxacin residue in pig body by using physiological pharmacokinetic model Download PDF

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CN107092773B
CN107092773B CN201710133312.3A CN201710133312A CN107092773B CN 107092773 B CN107092773 B CN 107092773B CN 201710133312 A CN201710133312 A CN 201710133312A CN 107092773 B CN107092773 B CN 107092773B
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张巧艳
杨波
杨华
林晶
商小金
毛江昌
朱凤香
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Zhejiang Academy of Agricultural Sciences
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Abstract

The invention relates to the field of food safety risk assessment, and discloses a method for predicting ciprofloxacin residue in a pig by using a physiological pharmacokinetic model, which comprises the following steps: (1) collecting physiological anatomical parameters of pigs; (2) obtaining the compound specificity parameters of ciprofloxacin in a pig body; (3) establishing a six-chamber model by using acslXtreme software; (4) and (5) evaluating the model. The invention establishes a six-chamber physiological pharmacokinetic model, successfully fits a residue elimination curve of ciprofloxacin in pig muscle, fat, liver and kidney, and a prediction result is consistent with actually measured data. The physiological pharmacokinetic model constructed by the invention can predict the dynamic change of ciprofloxacin in the edible tissues of pigs under different exposure conditions, is particularly suitable for residue monitoring of ciprofloxacin under the condition of label external application, and has the advantages of reduced monitoring cost, convenient operation and the like.

Description

Method for predicting ciprofloxacin residue in pig body by using physiological pharmacokinetic model
Technical Field
The invention relates to the field of food safety risk assessment, in particular to a method for predicting ciprofloxacin residue in a pig body by using a physiological pharmacokinetic model.
Background
Ciprofloxacin has excellent antibacterial effect and pharmacokinetic characteristic, and is widely used for preventing and treating gram-negative bacteria and mycoplasmal infection in the pig industry. Improper use of ciprofloxacin can result in its remaining edible tissue in pigs, compromising consumer health and sustainable development of the swine industry. Although the maximum using dosage, the maximum residue limit and the withdrawal period of the ciprofloxacin are regulated by the Ministry of agriculture in China, the residue of the ciprofloxacin under the condition of external application of various labels cannot be effectively avoided. The traditional residue monitoring method of 'sampling-slaughtering-detecting-judging' has certain false positive risk and higher monitoring cost. The physiological pharmacokinetic model is based on anatomical physiology, biochemistry and pharmacokinetic research, can simulate the dynamic change condition of the drug in various tissues, organs and body fluid, and has great application potential in the field of food safety risk assessment. The invention aims to establish a physiological pharmacokinetic model capable of predicting the elimination of residue of ciprofloxacin in pigs and provides a technical means for effectively monitoring the residue of the ciprofloxacin.
Disclosure of Invention
Aiming at the defects of false positive and high cost in the prior art, the invention provides a method for predicting ciprofloxacin residue in a pig body by using a physiological pharmacokinetic model, and the method is simple, convenient and feasible and has low cost.
In order to solve the technical problem, the invention is solved by the following technical scheme:
a method for predicting ciprofloxacin residues in pigs by using a physiological pharmacokinetic model comprises the following steps:
step one, the physiological pharmacokinetic model is divided into six chambers: injection site, muscle, liver, fat, kidney and other tissues;
step two, obtaining model parameters of ciprofloxacin in the pig body:
the initial values of the parameters come from existing books and published articles, or an initial value is randomly assigned firstly and then fitting is carried out according to the measured data by adopting a Nelder-Mead algorithm, the model fitting result is shown in figure 2, and the final value of the model parameters which enable the predicted value to be closest to the measured data is calculated; the model parameters include VM、VL、VA、VK、VB、Qtot、QM、QL、QA、QK、PM、PA、PL、PK、PC、Pb、Cl、kf、ksAnd kk; vM、VL、VA、VKAnd VBThe volume of muscle, liver, fat, kidney and blood, respectively; qtotThe heart output rate;QM、QL、QAand QKBlood flow rates of muscle, liver, fat and kidney, respectively; pM、PA、PL、PK、PCMuscle/plasma partition coefficient, liver/plasma partition coefficient, fat/plasma partition coefficient, kidney/plasma partition coefficient, and other tissue/plasma partition coefficient, respectively; pbIs the plasma protein binding rate; cl is renal clearance; k is a radical offIs a fast absorption rate constant; k is a radical ofsIs the slow absorption rate constant, kk is the slow absorption phase fraction;
the measured data used for the fitting was determined by the following residue elimination test and is shown in table 1:
25 healthy Du-growing three-way hybrid pigs, which are 18.23-20.54 kg in weight and randomly divided into a blank group and a test group, 5 blank groups and 20 test groups, wherein the feed is a complete daily ration without antibacterial drugs, the blank group is suitable for injecting ciprofloxacin lactate 20mg/kg bw at a single time in neck muscles after 7 days, after 12h, 24h, 72h, 120h and 240h of drug administration, 1 pig and 4 pigs are randomly selected from the blank group and the test groups to be slaughtered respectively, muscle, fat, liver and kidney are collected, and the ciprofloxacin concentrations in the muscle, liver, fat, kidney and blood are obtained by adopting high performance liquid chromatography detection; after the operation in the model fitting is operated by acslXtreme software, model parameters before and after optimization are obtained, as shown in table 2;
TABLE 1 actual values of ciprofloxacin residue in edible tissue of pig for model fitting
Figure BDA0001240633550000021
TABLE 2 model parameters before and after optimization
Figure BDA0001240633550000031
Establishing a six-chamber model by using acslXtreme software, wherein the six-chamber model is respectively an injection part, muscle, liver, fat, kidney and other tissues, bringing the obtained parameters into a chamber established based on the acslXtreme software, and outputting a prediction result; in the acslXtreme software, the mass balance equation of each chamber is as follows,
injection site:
Figure BDA0001240633550000032
muscle:
Figure BDA0001240633550000041
liver:
Figure BDA0001240633550000042
fat:
Figure BDA0001240633550000043
kidney:
Figure BDA0001240633550000044
other organizations:
Figure BDA0001240633550000045
blood:
Figure BDA0001240633550000046
QC＝Qtot-QIS-QM-QL-QK；
rate represents the rate of absorption of ciprofloxacin at the site of injection; t represents time, VIS、QISInjection site volume, injection site blood flow rate, respectively; vC、QCVolume of other tissues, blood flow rate of other tissues, CIS、CM、CL、CA、CKAnd CCConcentration of ciprofloxacin in injection site, muscle, liver, fat, kidney and other tissues, respectively, Ca is the concentration of ciprofloxacin in arterial blood, CBIs the concentration of ciprofloxacin in venous blood.
Preferably, V in step twoM、VL、VA、VK、VB0.4, 0.0294, 0.3, 0.004, 0.06, respectively; qtot4.944L/h/kg; qM、QL、QA、QK0.3583845, 0.0841901, 0.2217354, 0.3291174, respectively; pM、PA、PL、PK、PC0.9491230, 0.2582067, 1.234476, 6.195496, 38.80759, respectively; pb0.1121762; cl is 0.2067579L/h/kg; k is a radical off、ksAnd kk 0.3444798, 0.0004268, 0.9990295, respectively.
Preferably, after the third step, the model is evaluated by comparing the predicted value with the measured value.
Due to the adoption of the technical scheme, the invention has the remarkable technical effects that: (1) the physiological pharmacokinetic model constructed by the method can predict the dynamic change of ciprofloxacin in the edible tissues of the pigs under different exposure conditions, and is particularly suitable for residue monitoring under the condition of label external application; (2) the model only needs to sample and slaughter for predicting the residue, thereby effectively reducing the cost of residue monitoring; (3) according to the actual medication situation, the model can be used by breeding enterprises to predict the residue situation of ciprofloxacin in pigs, and the time to market is adjusted accordingly, so that the economic loss caused by the overproof residue can be effectively reduced; (4) the model can be extrapolated to other food animals provided sufficient model parameters are obtained.
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FIG. 1 is a schematic structural diagram of a six-chamber physiological pharmacokinetic model of ciprofloxacin in pigs; wherein CIP represents ciprofloxacin; vIS、VM、VL、VA、VK、VBAnd VCVolume of injection site, muscle, liver, fat, kidney, blood and other tissues, respectively; qIS、QM、QL、QA、QKAnd QCRespectively, blood flow at the injection site, muscle, liver, fat, kidney and other tissuesA rate; cIS、CM、CL、CA、CK、CBAnd CCConcentrations of ciprofloxacin in injection site, muscle, liver, fat, kidney, blood and other tissues, respectively; cl represents renal clearance;
FIG. 2 shows the predicted values and the measured values of the ciprofloxacin concentrations in the model fitting pigs, wherein the solid line is the predicted value, the circle is the measured value, and A, B, C, D are the predicted values and the measured values of the ciprofloxacin concentrations in muscles, fat, liver and kidney respectively;
FIG. 3 shows the predicted value and the measured value of the concentration of ciprofloxacin in the pig body in example 1, wherein the solid line is the predicted value, the circle is the measured value, and A, B, C, D are the predicted value and the measured value of the concentration of ciprofloxacin in muscle, fat, liver and kidney, respectively;
fig. 4 is a standard residual error graph of the predicted value and the actual value of the concentration of ciprofloxacin in pigs in example 1, wherein the abscissa is time, the ordinate is residual error, and A, B, C, D is residual error distribution in muscle, fat, liver and kidney.
Detailed Description
The following examples are intended to illustrate the invention in further detail, but are not intended to limit the scope of the invention.
Example 1
A method for predicting ciprofloxacin residues in pigs by using a physiological pharmacokinetic model comprises the following steps:
ciprofloxacin is a small molecular substance, and the drug residue is concerned with the drug concentration of edible tissues and important organs, so that the established model does not comprise organs containing special physiological barriers. Ciprofloxacin has high lipid solubility and strong permeability, distribution and transportation in a body mainly depend on blood flow perfusion rate, so that the absorption of ciprofloxacin after intramuscular injection is assumed to obey first-order rate, and the speed and degree of distribution in each tissue organ mainly depend on blood flow flowing through the tissue organ, namely obey blood flow rate-limiting distribution, is discharged through the kidney and obeys first-order rate. The atrioventricular of the pharmacokinetic model of the invention is six compartments including injection sites, muscles, liver, fat, kidney and other tissues, linked by blood circulation, as shown in fig. 1.
Step one, the physiological pharmacokinetic model is divided into six chambers: injection site, muscle, liver, fat, kidney and other tissues;
step two, obtaining model parameters of ciprofloxacin in the pig body:
the initial value of the parameter is from the existing books and published articles, or an initial value is randomly assigned and then fitting is carried out according to the measured data by adopting a Nelder-Mead algorithm, the final value of the model parameter which enables the predicted value to be closest to the measured data is calculated, and the model parameter comprises VM、VL、VA、VK、VB、Qtot、QM、QL、QA、QK、PM、PA、PL、PK、PC、Pb、Cl、kf、ksAnd kk; vM、VL、VA、VKAnd VBThe volume of muscle, liver, fat, kidney and blood, respectively; qtotThe heart output rate; qM、QL、QAAnd QKBlood flow rates of muscle, liver, fat and kidney, respectively; pM、PA、PL、PK、PCMuscle/plasma partition coefficient, liver/plasma partition coefficient, fat/plasma partition coefficient, kidney/plasma partition coefficient, and other tissue/plasma partition coefficient, respectively; pbIs the plasma protein binding rate; cl is renal clearance; k is a radical offIs a fast absorption rate constant; k is a radical ofsIs the slow absorption rate constant, kk is the slow absorption phase fraction; the final values of the model parameters are: vM、VL、VA、VK、VB0.4, 0.0294, 0.3, 0.004, 0.06, respectively; qtot4.944L/h/kg; qM、QL、QA、QK0.3583845, 0.0841901, 0.2217354, 0.3291174, respectively; pM、PA、PL、PK、PC0.9491230, 0.2582067, 1.234476, 6.195496, 38.80759, respectively; pb0.1121762; cl is 0.2067579L/h/kg;kf、ksand kk 0.3444798, 0.0004268, 0.9990295, respectively.
And step three, establishing a six-chamber model by using acslXtreme software, respectively representing an injection part, muscle, liver, fat, kidney and other tissues, bringing the obtained parameters into a chamber established based on the acslXtreme software, and outputting a prediction result.
In this example, the residual situation after injecting ciprofloxacin lactate injection into neck muscles at dose of 4mg/kgbw in continuous 3d of 18.08 + -1.64 kg/Dudu Daihong three-way hybrid pigs is predicted, and 4 pigs are randomly selected from test groups and slaughtered at 0.25d (6h), 0.5d (12h), 1d and 3d after administration, and are numbered as No. 1, No. 2, No. 3 and No. 4. The parameters obtained above were taken into the atrioventricular established by the acslXtreme version 2.5 software, the injection amount and the injection number of days were set in the injection module, and the output results are shown in fig. 3.
In the software, the mass balance equation for each compartment is as follows:
injection site:
Figure BDA0001240633550000071
muscle:
Figure BDA0001240633550000072
liver:
Figure BDA0001240633550000073
fat:
Figure BDA0001240633550000074
kidney:
Figure BDA0001240633550000075
other organizations:
Figure BDA0001240633550000081
blood:
Figure BDA0001240633550000082
QC＝Qtot-QIS-QM-QL-QK；
rate represents the rate of absorption of ciprofloxacin at the site of injection; t represents time; vIS、VM、VL、VA、VK、VBAnd VCThe volume of muscle, liver, fat, kidney, blood and other tissues at the injection site; qIS、QM、QL、QA、QKAnd QCBlood flow rates at the injection site, muscle, liver, fat, kidney and other tissues, respectively; cIS、CM、CL、CA、CKAnd CCRespectively the concentration of ciprofloxacin in muscle, liver, fat, kidney and other tissues of the injection site, Ca the concentration of ciprofloxacin in arterial blood, CBIs the concentration of ciprofloxacin in venous blood; pM、PA、PL、PK、PCMuscle/plasma partition coefficient, liver/plasma partition coefficient, fat/plasma partition coefficient, kidney/plasma partition coefficient, and other tissue/plasma partition coefficient, respectively; pbIs the plasma protein binding rate; cl is renal clearance.
Step four, evaluating the model by comparing the predicted value with the measured value
In this study, the residual amount of ciprofloxacin was measured by the detection method described in bulletin-14-2008 of Ministry of agriculture 1025, and the detection results are shown in Table 3. And comparing the actual measurement data with the model prediction result, calculating the residual error between the predicted value and the actual measurement value, and evaluating the model prediction capability by showing a standard residual error graph of the predicted value and the actual measurement value of the concentration of ciprofloxacin in the pig body as shown in figure 4. Residual analysis was performed on the predicted values and the measured values. If the residual values all bias towards the upper side of the x axis, the model underestimates the concentration of ciprofloxacin in the tissues; if the residual values all lean to the lower side of the x-axis, the model overestimates the concentration of ciprofloxacin in the tissues; if the residual values are evenly distributed on two sides of the x axis, the model shows that the concentration of ciprofloxacin in the tissues is well predicted by the model. Among the residues of edible animals, the residues in the muscles are concerned, and the accuracy of the residues in the muscles is high. Figure 4 shows that the muscle residual values are evenly distributed on both sides of the x-axis, indicating that the model predicts the concentration of ciprofloxacin in the tissues very well.
TABLE 3 practical values of ciprofloxacin residue in edible tissues of pigs after intramuscular injection
Figure BDA0001240633550000091
Remarking: ND means not detected.
As can be seen from the table 3, the figure 3 and the figure 4, the physiological pharmacokinetic model constructed by the method can accurately predict the residual concentration of the ciprofloxacin in the pig muscle, liver, kidney and fat under different dosages.
In summary, the above-mentioned embodiments are only preferred embodiments of the present invention, and all equivalent changes and modifications made in the claims of the present invention should be covered by the claims of the present invention.

Claims (3)

1. A method for predicting ciprofloxacin residues in pigs by using a physiological pharmacokinetic model comprises the following steps:
step one, the physiological pharmacokinetic model is divided into six chambers: injection site, muscle, liver, fat, kidney and other tissues; v and Q represent the volume and blood flow rate of each chamber, respectively;
step two, obtaining model parameters of ciprofloxacin in the pig body:
determining initial values of parameters according to existing books and published articles, or randomly assigning initial values and fitting according to measured data by adopting a Nelder-Mead algorithm to calculate a model parameter final value which enables a predicted value to be closest to the measured data; the model parameters include VM、VL、VA、VK、VB、Qtot、QM、QL、QA、QK、PM、PA、PL、PK、PC、Pb、Cl、kf、ksAnd kk; vM、VL、VA、VKAnd VBThe volume of muscle, liver, fat, kidney and blood, respectively; qtotThe heart output rate; qM、QL、QAAnd QKBlood flow rates of muscle, liver, fat and kidney, respectively; pM、PA、PL、PK、PCMuscle/plasma partition coefficients, liver/plasma partition coefficients, fat/plasma partition coefficients, kidney/plasma and other tissue/plasma partition coefficients, respectively; pbIs the plasma protein binding rate; cl is renal clearance; k is a radical offIs a fast absorption rate constant; k is a radical ofsIs the slow absorption rate constant, kk is the slow absorption phase fraction;
the measured data used for the fit was determined by the following residue elimination test:
selecting 25 healthy Du-growing three-way hybrid pigs, wherein the weight of the pigs is 18.23-20.54 kg, the pigs are randomly divided into a blank group and a test group, the blank group comprises 5 pigs, the test group comprises 20 pigs, food and drinking water are freely taken during the test, the feed is a complete daily ration without antibacterial drugs, the pigs are suitable for being injected with ciprofloxacin lactate 20mg/kg bw in a single time in neck muscles after 7 days, after the pigs are administered for 12h, 24h, 72h, 120h and 240h, 1 pig and 4 pigs are randomly selected from the blank group and the test group to be slaughtered respectively, muscle, fat, liver and kidney are collected, and the residual quantity of ciprofloxacin is measured;
establishing a six-chamber model by using acslXtreme software, wherein the six-chamber model is respectively an injection part, muscle, liver, fat, kidney and other tissues, bringing the obtained parameters into a chamber established based on the acslXtreme software, and outputting a prediction result; in the acslXtreme software, the mass balance equation of each chamber is as follows,
injection site:
Figure FDA0002950784240000021
muscle:
Figure FDA0002950784240000022
liver:
Figure FDA0002950784240000023
fat:
Figure FDA0002950784240000024
kidney:
Figure FDA0002950784240000025
other organizations:
Figure FDA0002950784240000026
blood:
Figure FDA0002950784240000027
QC＝Qtot-QIS-QM-QL-QK
rate represents the rate of absorption of ciprofloxacin at the site of injection; t represents time, VIS、QISInjection site volume, injection site blood flow rate, respectively; vC、QCVolume of other tissues, blood flow rate of other tissues, respectively.
2. The method for predicting ciprofloxacin residues in pigs according to the physiological pharmacokinetic model of claim 1, wherein the physiological pharmacokinetic model comprises the following steps: v in step twoM、VL、VA、VK、VB0.4, 0.0294, 0.3, 0.004, 0.06, respectively; qtot4.944L/h/kg; qM、QL、QA、QK0.3583845, 0.0841901, 0.2217354, 0.3291174, respectively; pM、PA、PL、PK、PC0.9491230, 0.2582067, 1.23 respectively4476、6.195496、38.80759；Pb0.1121762; cl is 0.2067579L/h/kg; k is a radical off、ksAnd kk 0.3444798, 0.0004268, 0.9990295, respectively.
3. The method for predicting ciprofloxacin residues in pigs according to the physiological pharmacokinetic model of claim 1, wherein the physiological pharmacokinetic model comprises the following steps: and step three, evaluating the model by comparing the predicted value with the measured value.
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