CN106407676A - Monte Carlo simulation based drug effect calculating method for antibacterial drug - Google Patents
Monte Carlo simulation based drug effect calculating method for antibacterial drug Download PDFInfo
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Abstract
The invention discloses a Monte Carlo simulation based drug effect calculating method for an antibacterial drug. The method includes the following steps: 1) determining if the antibacterial drug is the concentration-dependent type drug or a time-dependent type drug, and selecting a PK/PD parameter and a target value thereof; 2) the PK/PD parameter is AUC/MIC if the antibacterial drug is a concentration-dependent type drug, wherein AUC=Dose/CL, and the AUC/MIC= Dose/CL/MIC; 3) the PK/PD parameter is %T>MIC if the antibacterial drug is the time-dependent type drug, wherein for different models, solution a t1 and a t2 of f(c, Theta, t)-MIC=0 are acquired through a dichotomy method, and then a value of %T>MIC is acquired through a formula (t2-t1)/24; 4) initializing a parameter, and setting a the number of times of Monte Carlo simulation N; 5) assuming distribution of pharmacokinetic parameters and bacterial MIC; 6) operating simulation and recording a result; 7) repeating simulation till circulation ends; and 8) calculating the target attainment rate TAR and making a chart according to the result. The method can predict the drug effect of the concentration -dependent type drug and the time-dependent type drug at the same time so as to provide a reasonable dosage regimen.
Description
Technical field
The invention belongs to external pharmacokinetics/pharmacodynamics (Pharmacokinetic/pharmacodynamic, PK/PD)
Research field, is related to one kind and is based on Monte Carlo simulation（Monte Carlo Simulations, MCS）Antibacterials drug effect
Computational methods.
Background technology
Monte Carlo simulation, or claim computer stochastic simulation method, it is a kind of side carrying out numerical simulation using random number
Method.The basic thought of Monte Carlo is just found by people a long time ago and utilizes.The 17th century, people are known that and are occurred with event
" frequency " " probability " to determine event.The mid-40 in 20th century sending out with scientific and technical development and electronic computer
Bright, in order to adapt to the development of atomic energy cause at that time it is thus proposed that Monte Carlo simulation, this is a kind of with probability statistics reason
By the very important numerical computation method of a class for instructing.MCS is using different statistical sampling techniques（As random digit, puppet
Random digit etc.）The Method of Stochastic of quantitative problem approximate solution to be provided.The basic thought of Monte Carlo simulation is：
When institute's Solve problems are the probability that certain chance event occurs, or during certain expectation of a random variable, " real by certain
Test " method, with this event occur this chance event of Frequency Estimation probability, or obtain this stochastic variable certain
A little numerical characteristics, and the solution as problem.In simple terms, MCS is based on one chance event of artificial creation or " experiment "
（Typically by computer）Analysis method, when running to the number of times specified, it is possible to obtain the probability of any specific objective.
Pharmacokinetics（pharmacokinetics, PK）And pharmacodynamics（pharmacodynamics, PD）It is clinical pharmacology
Two important component parts learned.PK is research body to the absorption of medicine, distribution, metabolism and total quantity control, mainly reflection medicine
The time dependent rule of thing blood concentration.PD is the relation between research blood concentration and drug effect.According to antibiotic P K
With PD correlation, concentration-dependant and time-dependent two class can be classified as.Area under the drug-time curve when medicine（AUC）Or
The Cmax of medicine（Cmax）When medicine sterilizing ability is had a significant impact, such antibiotic is referred to as concentration-dependant antimicrobial
Thing, such as aminoglycoside, FQNS, ketolide, amphotericin B and Daptomycin etc..The bactericidal action of such medicine
Ratio with rational AUC and M IC（AUC/M IC）Or the ratio of Cmax and M IC（Cmax/M IC）Related.Therefore, such
The administration target of medicine is substantially to improve the AUC of medicine or Cmax to improve clinical efficacy, but dosage can not exceed
Minimum toxic dose.Equations of The Second Kind refers to that its sterilized speed and drug concentration are more than MIC when drug concentration be MIC 2～4 times
Time（%T>M IC）Closely related, such medicine is also known as time-dependent antibiotic, such as beta-lactam and lincomycin
Deng.PK/PD parameter currently used for evaluation time dependent form medicine bactericidal action is mainly %T>MIC, the administration mesh of such medicine
Mark is to try to extend the time more than MIC for the drug concentration, and occurs without adverse drug reaction.
In the past, people mainly weigh medicine under the dosage of a fixation by the mean value and MIC90 of AUC
Drug effect；This assessment mode have ignored the MIC value of medicine individual difference in vivo and bacterium widely distributed so that simple
After the very difficult comprehensively assessment administration of the analysis method of single certain value by comparing AUC/MIC, medicine is true in animal body
Drug effect.
Content of the invention
The technical problem to be solved is to provide one kind and can prediction concentrations dependent form and time-dependent resist simultaneously
Bacterium medicine effect, and the computational methods of dosage regimen can be given.
The technical solution adopted in the present invention is：A kind of antibacterials drug effect computational methods based on Monte Carlo simulation,
Comprise the steps：
1）Determine that antibacterials belong to concentration-dependant or time-dependent, select to predict this medicine activity in vivo
PK/PD parameter（AUC/MIC or %T>MIC）；And determine and internal produce bactericidal activity and be not in the PK/PD ginseng of drug resistance
The desired value of number；
2）If medicine belongs to concentration-dependant medicine, PK/PD parameter is AUC/MIC, AUC=Dose/CL, AUC/MIC=Dose/
CL/MIC, wherein, Dose is 24h dosage（mg/kg b.w.）, CL is body clearance rate（L/h/kg）, MIC is minimum antibacterial
Concentration (μ g/mL)；
3）If this medicine belongs to time-dependent medicine, PK/PD parameter is %T>MIC, for intravenous injection one compartment model, one-level
Absorb one compartment model, intravenous injection two compartment model or first order absorption two compartment model, first using dichotomy obtain concentration equation f (c,
θ, t) solution t1 of-MIC=0 and t2, then %T is obtained by formula (t2-t1)/24>The value of MIC；
4）To parameter initialization, Monte Carlo simulation number of run is set（Usual 5 000 times or 10 000 times）；
5）Distribution to dynamic parameter and bacterium MIC is carried out it is assumed that usually assuming that pharmacokinetic parameters obey logarithm normal distribution
(Log Gaussian distribution), MIC obeys self-defined distribution (custom according to statistical result
distribution)；
6）Run simulation, to pharmacodynamics index AUC/MIC or %T>Whether MIC is up to standard to be judged, and records result；
7）Repeat to simulate, record the judged result of each cycle indicator, until loop termination；
8) according to each judged result, calculate compliance rate TAR, and export other parameters, and chart is made to result.
Described step 3）In, the concentration equation of intravenous injection one compartment model is：
；
The concentration equation of first order absorption one compartment model is：
；
The concentration equation of intravenous injection two compartment model is：
；
The concentration equation of first order absorption two compartment model is：
.
Concentration-dependant antibacterials drug effect computational methods based on Monte Carlo simulation provided by the present invention, its feature
It is, comprise the steps：
1）According to the bactericidal property of medicine A, determine AUC/MIC desired value T for Monte Carlo AnalysisA, according to the medicine of A medicine
Dynamic feature, determines that its body clearance rate CL value is, statistical to minimal inhibitory concentration MIC of clinically isolated bacterium
Analysis, obtains the probability distribution of bacterium MIC；
2）Determine initial parameter, determine dosage Dose, definition cycle-index i is N（As 10000）Secondary, initial value is 1, follows
Ring increases by 1 time, and i=i+1, k are AUC/MIC>= TANumber of times, initial value be 0, if i ＆ lt circulation when AUC/MIC [i]>=T, k=
K+1, j are AUC/MIC< TANumber of times, initial value be 0, if i ＆ lt circulation when AUC/MIC [i]< TA, j=j+1, according to bacterium
The distribution probability of the MIC to medicine A for the clinical separation strain, is defined on the corresponding bacterium MIC of [0,1] equally distributed interval
Value；
3）Matlab software is carried out to CL simulate for the first time, obtain the value of a CL [1], according to formula AUC=Dose/CL,
Obtain the value of an AUC [1]；
4）By being uniformly distributed rule in [0,1] interval, randomly draw value Random [1], judge Random [1] location
Between, give the value of a MIC [1]；
5）According to formula AUC/MIC [1]=AUC [1]/MIC [1], obtain the value of AUC/MIC [1], if AUC/MIC [1]>=
TA, k=k+1, conversely, j=j+1；
6）To step 3）~5）Carry out repeating simulating, record each parameter, such as CL [i], AUC [i], MIC [i], AUC/
The value of MIC [i], until i=N time, loop termination；
7）According to formula TAR=k/i, obtain the result of TAR, and export other parameters, and to CL, AUC, MIC, AUC/MIC
Result makees chart.
Time-dependent antibacterials drug effect computational methods based on Monte Carlo simulation provided by the present invention, its feature
It is, comprise the steps：
1）According to the bactericidal property of medicine B, determine the %T for Monte Carlo Analysis>The desired value of MIC is TB；For B medicine
Pharmacokinetic model, using first order absorption two compartment model, its formula is, A, B, α, β
For hybrid parameter, kaFor absorption rate constant, and determine each parameterValue, antibacterial dense to the minimum of clinically isolated bacterium
The statistical analysis of degree MIC, obtains the probability distribution of bacterium MIC；
2）Determine initial parameter, determine dosage Dose, definition cycle-index i is N（As 10000）Secondary, initial value is 1, follows
Ring increases by 1 time, and i=i+1, k are [(T>MIC)/24]>= TBNumber of times, initial value be 0, if i ＆ lt circulation when [(T>MIC)/
24]>= TB, k=k+1, j are [(T>MIC)/24]< TBNumber of times, initial value be 0, if i ＆ lt circulation when [(T>MIC)/24]<
TB, j=j+1, according to the distribution probability of the MIC to medicine A for the bacterium clinical separation strain, it is defined on [0,1] equally distributed value area
Between corresponding bacterium MIC value；
3）Matlab software is carried out to A, B, α, β, ka simulate for the first time, respectively obtain an A [1], B [1], α [1], β
[1], the value of ka [1], according to formula, can obtain
；
4）By being uniformly distributed rule in [0,1] interval, randomly draw value Random [1], judge Random [1] location
Between, give the value of a MIC [1]；
5）Nonlinear equation C [1]-MIC=0 is asked using dichotomy, that is,
Two
Solution t1 and t2；
6）Using formula %T>MIC=(t2-t1)/24, obtains %T>The value of MIC [1], if %T>MIC [1]>=0.35, k=k+1,
Conversely, j=j+1；
7）To step 3）~6）Carry out repeating to simulate, record each parameter, such as A [i], B [i], α [i], β [i], ka[i]、MIC
[i]、%T>The value of MIC [i], until i=N time, loop termination；
8）According to formula TAR=k/i, obtain the result of TAR and export other parameters, and to %T>The statistics parameter of MIC and
The correlated results such as probability statistics make chart.
Beneficial effects of the present invention are：Antibacterials drug effect computational methods provided by the present invention, are with Monte Carlo mould
Based on plan method, Monte Carlo simulation (Monte Carlo simulation, MCS) be antibacterials experiential therapy and
Individualized treatment provides the method finding efficient antibacterial therapy scheme, is typically expressed as the target of bacterial community MIC is obtained
Obtain probability（target attainment rate, TAR）, highest TAR or the dosage regimen possibility that 90%TAR can be reached can be reached
It is the reasonable selection improving antibacterials experiential therapy.The present invention can prediction concentrations dependent form and time-dependent antimicrobial simultaneously
Thing drug effect, to be provided that rational dosage regimen.
Brief description
The CL distribution map that Fig. 1 obtains for embodiment 1 Monte Carlo simulation；
The AUC distribution map that Fig. 2 obtains for embodiment 1 Monte Carlo Analysis；
Fig. 3 is the statistical value of embodiment 1 MIC（Left column）With Monte Carlo simulation value（Right row）；
The AUC/MIC distribution map that Fig. 4 obtains for embodiment 1 Monte Carlo simulation；
Fig. 5 is embodiment 1 dosage is 10mg/kg b.w.（Body weight, body weight）The bacterium to different MIC value for the medicine
Compliance rate（TAR）.
Specific embodiment
The present invention is further illustrated with reference to embodiments, but is not limited thereto, and those skilled in the art exist
Under the premise of the basic fundamental thought of the present invention, done modification, replacement, change each fall within the protection model of the present invention
Enclose.
Embodiment 1
To concentration-dependant medicine A, concretely comprise the following steps：
（1）According to the bactericidal property of medicine A, determine AUC/MIC desired value 120 h for Monte Carlo Analysis；According to A medicine
Characteristics of pharmacokinetics, determine that its body clearance rate CL value is 0.421 ± 0.058 L/h/kg（）；To clinically isolated bacterium
The statistical analysis of minimal inhibitory concentration MIC, obtains the probability distribution of bacterium MIC（It is shown in Table 1）.
The probability distribution of bacterium MIC is as shown in table 3-1.
The distribution probability of the MIC to medicine A for the table 1 bacterium clinical separation strain
（2）Determine initial parameter
Dosage Dose=10mg/kg.Defining cycle-index i is 10000 times, and initial value is 1, and circulation increases by 1 time, i=i+1.k
For AUC/MIC>The number of times of=120h, initial value is 0, if AUC/MIC [i] during i ＆ lt circulation>=120h, k=k+1, j are AUC/
MIC<The number of times of 120h, initial value is 0, if AUC/MIC [i] during i ＆ lt circulation<120h, j=j+1.It is clinically separated according to bacterium
The distribution probability of the MIC to medicine A for the strain, is defined on the corresponding bacterium MIC value of [0,1] equally distributed interval（Table 2）.
The corresponding MIC value of the equally distributed interval of table 2
（3）Matlab software is carried out to CL simulate for the first time, obtain the value of a CL [1], according to formula AUC=Dose/
CL, obtains the value of an AUC [1]
（4）By being uniformly distributed rule in [0,1] interval, randomly draw value Random [1], judge that Random [1] is located
Interval, gives the value of a MIC [1].
（5）According to formula AUC/MIC [1]=AUC [1]/MIC [1], obtain the value of AUC/MIC [1].If AUC/MIC [1]
>=120h, k=k+1, conversely, j=j+1.
（6）Step 3 ~ 5 are carried out repeat to simulate, record each parameter, such as CL [i], AUC [i], MIC [i], AUC/
The value of MIC [i], until i=10000 time, loop termination.
（7）According to formula TAR=k/i, the result obtaining TAR is 77.3%, i.e. 10 mg/kg b.w.（Body weight,
Body weight）Dosage have in clinical treatment 77.3% organism AUC/MIC value>=120h, reaches preferable treatment
Effect.After running simulation, the statistics parameter of the AUC/MIC obtaining is as shown in table 3.The bacterium different to MIC value, AUC/MIC
As shown in table 4 more than 30,40,50,60,70,80,90,100,110,120 probability statistics.
Table 3 describes the statistical parameter of AUC/MIC distribution
Table 4 bacterium reaches the probability results of specific AUC/MIC ratio in the range of each MIC
（8）After 10000 random samplings from respective probability density function, CL, AUC, MIC, AUC/MIC's of obtaining
Distribution map is respectively as shown in Fig. 1 ~ 4.Fig. 5 is the compliance rate distribution map of the AUC/MIC corresponding to different MIC value（Use box-shaped chart
Show）, AUC/MIC>=120h is up to standard.From the figure, it can be seen that when MIC is 0.125 μ g/mL or following, medicine is to bacterium
Bactericidal effect is very good, and 0.008 ~ 0.0625 compliance rate is all 100%, and with the increase of bacterium MIC, compliance rate is also corresponding
Diminish.
Embodiment 2
For time-dependent medicine, concretely comprise the following steps：
（1）According to the bactericidal property of medicine B, determine the %T for Monte Carlo Analysis>The desired value of MIC is 35%；For B medicine
The pharmacokinetic model of thing, the present embodiment adopts complex first order absorption two compartment model, and its formula is
, A, B, α, β are hybrid parameter, kaFor
Absorption rate constant.Wherein each parameterIt is respectively：A=10.40 ± 2.64, B=7.39 ± 2.22, α=1.98 ±
0.81, β=0.39 ± 0.03, ka=5.78±0.96.Statistical analysis to minimal inhibitory concentration MIC of clinically isolated bacterium, obtains
Obtain the probability distribution of bacterium MIC（It is shown in Table 5）.
The distribution probability of the MIC to medicine B for the table 5 bacterium clinical separation strain
（2）Determine initial parameter
Dosage Dose=5mg/kg.Defining cycle-index i is 10000 times, and initial value is 1, and circulation increases by 1 time, i=i+1.k
For [(T>MIC)/24]>=0.35 number of times, initial value is 0, if [(T during i ＆ lt circulation>MIC)/24]>=0.35, k=k+1, j
For [(T>MIC)/24]<0.35 number of times, initial value is 0, if [(T during i ＆ lt circulation>MIC)/24]<0.35, j=j+1.According to
The distribution probability of the MIC to medicine A for the bacterium clinical separation strain, is defined on the corresponding bacterium of [0,1] equally distributed interval
MIC value（Table 6）.
The corresponding MIC value of the equally distributed interval of table 6
（3）To A, B, α, β, k in Matlab softwareaCarry out simulating for the first time, respectively obtain an A [1], B [1], α [1], β
[1]、ka[1] value, according to formula, can obtain
.
（4）By being uniformly distributed rule in [0,1] interval, randomly draw value Random [1], judge Random [1]
It is located interval, give the value of a MIC [1].
（5）Nonlinear equation C [1]-MIC=0 is asked using dichotomy, that is,
Two solution t1 and t2.Concrete grammar includes：Take initially interval [a, b], as f (a) * f (b)<When 0, take midpoint x=(a+
B)/2 replacement a or b, when siding-to-siding block length is less than 1e-6, stops calculating, obtain two approximate solutions of t1 and t2.
（6）Using formula %T>MIC=(t2-t1)/24, obtains %T>The value of MIC [1], if %T>MIC [1]>=0.35, k=
K+1, conversely, j=j+1.
（7）Step 3 ~ 6 are carried out repeat to simulate, record each parameter, such as A [i], B [i], α [i], β [i], ka[i]、
MIC[i]、%T>The value of MIC [i], until i=10000 time, loop termination.
（8）According to formula TAR=k/i, the result obtaining TAR is 84.1%, i.e. 5 mg/kg b.w.（Body weight, body
Weight）Dosage have in clinical treatment 84.1% organism %T>MIC value>=0.35, reach and preferably control curative effect
Really.Run after simulating, the %T obtaining>The statistics parameter of MIC is as shown in table 7.The bacterium different to MIC value, %T>MIC is more than
0.3rd, 0.35,0.4,0.5,0.6,0.7,0.8,0.9 probability statistics are as shown in table 8.
Table 7 describes %T>The statistical parameter of MIC distribution
Table 8 bacterium reaches the probability results of specific AUC/MIC ratio in the range of each MIC
Merely illustrating the principles of the invention described in above-described embodiment and specification and most preferred embodiment, without departing from the present invention
On the premise of spirit and scope, the present invention also has various changes and modifications, and these changes and improvements both fall within claimed
In the scope of the invention.
Claims (4)
1. a kind of antibacterials drug effect computational methods based on Monte Carlo simulation are it is characterised in that comprise the steps：
1）Determine that antibacterials belong to concentration-dependant or time-dependent, select to predict this medicine activity in vivo
PK/PD parameter（AUC/MIC or %T>MIC）；And determine and internal produce bactericidal activity and be not in the PK/PD ginseng of drug resistance
The desired value of number；
2）If medicine belongs to concentration-dependant medicine, PK/PD parameter is AUC/MIC, AUC=Dose/CL, AUC/MIC=Dose/
CL/MIC, wherein, Dose is 24h dosage（mg/kg b.w.）, CL is body clearance rate（L/h/kg）, MIC is minimum antibacterial
Concentration (μ g/mL)；
3）If this medicine belongs to time-dependent medicine, PK/PD parameter is %T>MIC, for intravenous injection one compartment model, one-level
Absorb one compartment model, intravenous injection two compartment model or first order absorption two compartment model, first f (c, θ, t)-MIC=are obtained using dichotomy
0 solution t1 and t2, then %T is obtained by formula (t2-t1)/24>The value of MIC；
4）To parameter initialization, Monte Carlo simulation number of run N is set；
5）Distribution to dynamic parameter and bacterium MIC is carried out it is assumed that usually assuming that pharmacokinetic parameters obey logarithm normal distribution
(Log Gaussian distribution), MIC obeys self-defined distribution (custom according to statistical result
distribution)；
6）Run simulation, to pharmacodynamics index AUC/MIC or %T>Whether MIC is up to standard to be judged, and records result；
7）Repeat to simulate, record the judged result of each cycle indicator, until loop termination；
8) according to each judged result, calculate compliance rate TAR, and export other parameters, and chart is made to result.
2. the antibacterials drug effect computational methods based on Monte Carlo simulation according to claim 1 it is characterised in that：Institute
State step 3）In, the concentration equation of intravenous injection one compartment model is：
；
The concentration equation of first order absorption one compartment model is：
；
The concentration equation of intravenous injection two compartment model is：
；
The concentration equation of first order absorption two compartment model is：
.
3. a kind of concentration-dependant antibacterials drug effect computational methods based on Monte Carlo simulation it is characterised in that include as
Lower step：
1）According to the bactericidal property of medicine A, determine AUC/MIC desired value T for Monte Carlo AnalysisA, according to the medicine of A medicine
Dynamic feature, determines that its body clearance rate CL value is, statistical to minimal inhibitory concentration MIC of clinically isolated bacterium
Analysis, obtains the probability distribution of bacterium MIC；
2）Determine initial parameter, determine dosage Dose, definition cycle-index i is n times, initial value is 1, and circulation increases by 1 time,
I=i+1, k are AUC/MIC>= TANumber of times, initial value be 0, if i ＆ lt circulation when AUC/MIC [i]>=T, k=k+1, j are
AUC/MIC< TANumber of times, initial value be 0, if i ＆ lt circulation when AUC/MIC [i]< TA, j=j+1, according to bacterium clinic point
From the distribution probability of the MIC to medicine A for the strain, it is defined on the corresponding bacterium MIC value of [0,1] equally distributed interval；
3）Matlab software is carried out to CL simulate for the first time, obtain the value of a CL [1], according to formula AUC=Dose/CL,
Obtain the value of an AUC [1]；
4）By being uniformly distributed rule in [0,1] interval, randomly draw value Random [1], judge Random [1] location
Between, give the value of a MIC [1]；
5）According to formula AUC/MIC [1]=AUC [1]/MIC [1], obtain the value of AUC/MIC [1], if AUC/MIC [1]>=TA,
K=k+1, conversely, j=j+1；
6）To step 3）~5）Carry out repeating simulating, record each parameter, such as CL [i], AUC [i], MIC [i], AUC/
The value of MIC [i], until i=N time, loop termination；
7）According to formula TAR=k/i, obtain the result of TAR, and export other parameters, and to CL, AUC, MIC, AUC/MIC
Result makees chart.
4. a kind of time-dependent antibacterials drug effect computational methods based on Monte Carlo simulation it is characterised in that include as
Lower step：
1）According to the bactericidal property of medicine B, determine the %T for Monte Carlo Analysis>The desired value of MIC is TB；For B medicine
Pharmacokinetic model, using first order absorption two compartment model, its formula is
,
A, B, α, β are hybrid parameter, kaFor absorption rate constant, and determine each parameterValue, to clinically isolated bacterium
The statistical analysis of minimal inhibitory concentration MIC, obtains the probability distribution of bacterium MIC；
2）Determine initial parameter, determine dosage Dose, definition cycle-index i is n times, initial value is 1, and circulation increases by 1 time,
I=i+1, k are [(T>MIC)/24]>= TBNumber of times, initial value be 0, if i ＆ lt circulation when [(T>MIC)/24]>= TB, k=k+
1, j is [(T>MIC)/24]< TBNumber of times, initial value be 0, if i ＆ lt circulation when [(T>MIC)/24]< TB, j=j+1, according to
The distribution probability of the MIC to medicine A for the bacterium clinical separation strain, is defined on the corresponding bacterium of [0,1] equally distributed interval
MIC value；
3）To A, B, α, β, k in Matlab softwareaCarry out simulating for the first time, respectively obtain an A [1], B [1], α [1], β
[1]、ka[1] value, according to formula, can obtain
；
4）By being uniformly distributed rule in [0,1] interval, randomly draw value Random [1], judge Random [1] location
Between, give the value of a MIC [1]；
5）Nonlinear equation C [1]-MIC=0 is asked using dichotomy, that is,
Two solution t1
And t2；
6）Using formula %T>MIC=(t2-t1)/24, obtains %T>The value of MIC [1], if %T>MIC [1]>=0.35, k=k+1,
Conversely, j=j+1；
7）Step 3 ~ 6 are carried out repeat to simulate, record each parameter, such as A [i], B [i], α [i], β [i], ka[i]、MIC
[i]、%T>The value of MIC [i], until i=N time, loop termination；
8）According to formula TAR=k/i, obtain the result of TAR and export other parameters, and to %T>The statistics parameter of MIC and
The correlated results such as probability statistics make chart.
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| CN110223734A (en) * | 2019-07-22 | 2019-09-10 | 华中农业大学 | A kind of construction method of antibacterials Ceftiofur PK-PD model and its application |
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