CN113571202B

CN113571202B - Method and device for predicting drug resistance of combined drug resistance control bacteria and electronic equipment

Info

Publication number: CN113571202B
Application number: CN202110790881.1A
Authority: CN
Inventors: 李耘; 赵海晴; 刘哲; 钱永忠; 邱静; 梁严内
Original assignee: Institute of Agricultural Quality Standards and Testing Technology for Agro Products of CAAS
Current assignee: Institute of Agricultural Quality Standards and Testing Technology for Agro Products of CAAS
Priority date: 2021-07-13
Filing date: 2021-07-13
Publication date: 2023-07-14
Anticipated expiration: 2041-07-13
Also published as: CN113571202A

Abstract

The application provides a prediction method and device for resistance control of bacteria by combined drug and electronic equipment. The method comprises the following steps: cumulative probability distribution of exposure EC according to the combination _i Judging when the second medicine is y _i Whether the corresponding experiment at the concentration meets the possibility of bacterial drug resistance mutation or not; if so, then MIC is determined based on the minimum inhibitory concentration _i Concentration of MPC for mutation prevention _i The cumulative probability distribution of exposure EC _i And said inhibition rate dose response cumulative probability distribution IC _i Obtaining probability curve PC of drug resistance mutation _i The method comprises the steps of carrying out a first treatment on the surface of the Acquiring Q probability curves PC _i Is a curve area a under the line _i And said second drug is y _i Probability of occurrence p corresponding to concentration _i According to the area a of the curve under the line _i And the occurrence probability p _i And obtaining the total probability of bacterial drug resistance mutation. By means of the technology, simulation and prediction can be carried out, and the problems that the possibility of predicting bacterial drug resistance mutation based on limited experiments is not accurate enough and the practicability is lacking can be solved.

Description

Method and device for predicting drug resistance of combined drug resistance control bacteria and electronic equipment

Technical Field

The application relates to the field of biotechnology, in particular to a method and a device for predicting resistance of bacteria by combined medication and electronic equipment.

Background

Combination therapy is widely attempted for the control of drug resistance with the aim of achieving inhibition and killing of the flora at low dosage levels while preventing as much as possible the development of bacterial resistance, in particular multiple resistance. Under the condition of strict control in a laboratory, the proportion and the action mode of the combined drug can constantly meet the requirements of the narrow spectrum region of the optimal mutation selection window period, and meanwhile, the optimal resistance control on the drug resistance of the flora in a specific environment can be realized.

However, in the practical application scene, the final drug resistance induction and resistance control are affected to different degrees due to a plurality of factors such as different dosage combinations and ratios, and the final drug resistance induction and resistance control are uncertain. Moreover, only a few representative combinations of combinations can be considered in the experiments, and all combinations cannot be exhausted, so that the possibility of predicting the bacterial drug resistance mutation based on limited experiments is not accurate enough and lacks practicality.

Disclosure of Invention

The embodiment of the application aims to provide a method and a device for predicting drug resistance of bacteria by combined drug resistance control and electronic equipment, so as to solve the problems of inaccurate and lack of practicality of predicting drug resistance mutation possibility of bacteria based on limited experiments.

The invention is realized in the following way:

in a first aspect, embodiments of the present application provide a method for predicting resistance of bacteria by combination therapy, the method comprising: taking y according to the second medicament _i The inhibition rate dose response cumulative probability distribution IC of the combined drug is obtained by n drug concentrations corresponding to the combined drug in concentration and the antibacterial effect generated in each experimental scene _i The experimental scenes are that the first medicine takes x respectively ₁ 、…、x _n The concentration and the second medicine are respectively y ₁ 、…、y _m When the concentration is high, the inhibition rate dose response cumulative probability distribution IC is combined with each other to perform combined medication _i Characterizing the probability of the corresponding bacteriostatic effect under the condition that the combined medication takes different medication concentrations, wherein i takes 1 to m; taking y according to the second medicine _i The concentration of the n drugs corresponding to the combined drug at the concentration, the cumulative probability distribution of the first drug exposure in a single case and the cumulative probability distribution of the second drug exposure in a single case are obtainedCumulative probability distribution of exposure EC for combination drugs _i The cumulative probability distribution of exposure EC _i Characterizing the probability of residual concentration of the drug under different drug concentrations of the combined drug; cumulative probability distribution of exposure EC according to the combination _i Judging when the second medicine is y _i Whether the corresponding experiment at the concentration meets the possibility of bacterial drug resistance mutation or not; if yes, taking y according to the second medicine _i Minimum inhibitory concentration MIC of the n drug concentrations at concentration _i The second medicine is taken y _i Mutation preventing concentration MPC in the n drug concentrations at concentration _i The cumulative probability distribution of exposure EC _i And said inhibition rate dose response cumulative probability distribution IC _i Obtaining probability curve PC of drug resistance mutation _i The minimum inhibitory concentration is the lowest concentration of the first drug and the second drug which can inhibit the growth and the reproduction of bacteria, and the mutation prevention concentration is the lowest concentration of the antibacterial drug required for preventing the selective reproduction of the first-step drug-resistant mutant strain; acquiring Q probability curves PC _i Is a curve area a under the line _i And said second drug is y _i Probability of occurrence p corresponding to concentration _i According to the probability curve PC _i Is a curve area a under the line _i And the occurrence probability p _i Obtaining the total probability of bacterial drug resistance mutation, wherein the total probability of bacterial drug resistance mutation represents the probability of occurrence of mutation of bacterial drug resistance under actual conditions, and Q is less than or equal to m.

In the embodiment of the application, the inhibition rate dose response cumulative probability distribution IC is obtained through a combined drug experiment _i And an exposure cumulative probability distribution EC _i By exposing cumulative probability distribution EC _i Removing experimental data which do not meet the possibility of breaking through bacterial drug resistance, and taking y with the rest second drug _i Probability of occurrence p corresponding to concentration _i And accumulating probability distribution EC according to the exposure _i The inhibition rate dose response cumulative probability distribution IC _i Said mutation probability Y _i Probability of effect X _i And the occurrence probability p _i Acquisition ofThe total probability of bacterial drug resistance mutation is estimated from limited combined drug experiment data, namely, under the condition that other experiments are not performed, the influence of combined drugs with different concentrations and different proportions on drug resistance is estimated, and further, the prediction of the ability of binary combined drug to induce bacterial drug resistance mutation in a real scene is realized. And, according to the exposure cumulative probability distribution EC corresponding to each experiment _i Judging when the second medicine is y _i The probability that whether the corresponding experiment meets the bacterial drug resistance mutation or not in concentration ensures that the experimental data participated in calculation are all effective experimental data, thereby ensuring the accuracy of the total probability of the bacterial drug resistance mutation finally obtained.

With reference to the foregoing technical solution provided in the first aspect, in some possible implementation manners, the method includes taking the second medicine _i Acquiring the cumulative probability distribution EC of the combined drug according to the n drug concentrations corresponding to the combined drug at the concentration, the cumulative probability distribution of the first drug exposure in the single case and the cumulative probability distribution of the second drug exposure in the single case _i Comprising: acquiring x of the first medicine according to the cumulative probability distribution of the exposure of the first medicine under single condition ₁ 、…、x _n Probability value F corresponding to concentration _j Wherein j is 1 to n; acquiring y of the second medicine according to the cumulative probability distribution of the second medicine exposure in a single case ₁ 、…、y _m Probability value G corresponding to concentration _i Wherein i is 1 to m; taking y according to the second medicine _i The concentration of the n drugs corresponding to the combined drug in the concentration is x for the first drug respectively ₁ 、…、x _n The probability value M corresponding to the concentration and the second medicine respectively take y ₁ 、…、y _m Probability value G corresponding to concentration _i Acquiring an exposure cumulative probability distribution EC of the combination _i Wherein M= { F ₁ ,...,F _j ,...,F _n }。

In the embodiment of the application, the cumulative probability distribution of the exposure of the first drug under a single condition is the cumulative probability distribution of the exposure obtained by only carrying out experiments on the first drugIn the combined medication experiment, the first medicine respectively takes x ₁ 、…、x _n The concentration value of (2) is brought into the above-mentioned exposure cumulative probability distribution, so that the first medicine can obtain x respectively under single condition ₁ 、…、x _n Probability value at concentration; similarly, the second drugs in the combined drug experiments are respectively taken as y ₁ 、…、y _m The concentration value of the second medicine is brought into the cumulative probability distribution of the exposure of the second medicine under the single condition, and the second medicine under the single condition can be obtained ₁ 、…、y _m Probability value at concentration. Taking y the second medicine _i Taking the concentrations of the n medicaments corresponding to the combined medicament as the abscissa, and taking y of the second medicament _i The probability value at concentration is respectively taken as x with the first medicine ₁ 、…、x _n Multiplying probability values in concentration as ordinate to fit the cumulative probability distribution EC of the combined drug exposure _i . In the above way, the exposure cumulative probability distribution EC containing other unexperienced scenes can be constructed from limited experimental data _i 。

With reference to the foregoing technical solution provided in the first aspect, in some possible implementation manners, the method includes taking the second medicine _i The concentration of the n drugs corresponding to the combined drug in the concentration is x for the first drug respectively ₁ 、…、x _n The probability value M corresponding to the concentration and the second medicine respectively take y ₁ 、…、y _m Probability value G corresponding to concentration _i Acquiring an exposure cumulative probability distribution EC of the combination _i Comprising: taking x from the first medicine respectively ₁ 、…、x _n The probability value M corresponding to the concentration is respectively y with the second medicine ₁ 、…、y _m Probability value G corresponding to concentration _i Multiplying to obtain y of the second medicine _i Probability of co-administration concentration at the concentration; y taking the second medicine _i Taking the probability of the combined drug concentration as an ordinate, and taking y of the second drug _i The n drug concentrations corresponding to the combined drug are taken as abscissa when the concentration is reached, and the exposure cumulative probability distribution EC of the combined drug is obtained _i 。

In the embodiment of the application, the first medicine is taken as x respectively ₁ 、…、x _n The probability value M corresponding to the concentration is respectively y with the second medicine ₁ 、…、y _m Probability value G corresponding to concentration _i Multiplying the two medicines to obtain y _i The probability of the combined drug concentration in concentration is that the occurrence probability corresponding to different concentrations of the first drug under a single condition and the occurrence probability corresponding to different concentrations of the second drug under a single condition are multiplied by each other, so that the probability of the simultaneous occurrence of the first drug and the second drug can be obtained; and then taking y from the second medicine _i Taking the probability of the combined drug concentration as an ordinate, and taking y of the second drug _i And when the concentration is the concentration, the n drug concentrations corresponding to the combined drug are taken as the abscissa, so that the cumulative probability distribution of the combined drug exposure can be obtained. By the method, the exposure cumulative probability distribution corresponding to the combined administration of the first medicine and the second medicine can be effectively obtained, and the accuracy of the exposure cumulative probability distribution can be ensured.

With reference to the foregoing aspect, in some possible implementation manners, the cumulative probability distribution EC of exposure according to the combination _i Judging when the second medicine is y _i The corresponding experiments at concentration, including whether or not the possibility of bacterial drug resistance mutation is met, include: MIC according to the minimum inhibitory concentration _i Acquiring the cumulative probability distribution of exposure EC _i Corresponding mutation probability Y _i The method comprises the steps of carrying out a first treatment on the surface of the Judging the mutation probability Y _i If it is less than 1, if the mutation probability Y _i Less than 1, then means when the second drug is y _i The corresponding experiments at concentration met the possibility of bacterial drug resistance mutation.

In the examples of the present application, the minimum inhibitory concentration MIC _i Bringing into the cumulative probability distribution of exposure EC _i The mutation probability Y can be obtained _i Since the minimum inhibitory concentration is the lowest concentration of the first drug and the second drug which can inhibit the growth and the reproduction of bacteria, Y is _i Probability of not developing drug resistance for bacteria; therefore, if mutation probability Y _i When the number of the bacterial resistance is less than 1, the bacterial resistance in the experiment is shownThe possibility of mutation is 1-Y _i I.e. when the second medicament is taken y _i The corresponding experiments at concentration met the possibility of bacterial drug resistance mutation. By the method, experimental data which do not meet the possibility of bacterial drug resistance mutation can be screened out, and only the experimental data meeting the conditions are subjected to subsequent steps, so that the accuracy of subsequent calculation is ensured.

With reference to the foregoing technical solution provided in the first aspect, in some possible implementation manners, the method includes taking the second medicine _i Minimum inhibitory concentration MIC of the n drug concentrations at concentration _i The second medicine is taken y _i Mutation preventing concentration MPC in the n drug concentrations at concentration _i The cumulative probability distribution of exposure EC _i And said inhibition rate dose response cumulative probability distribution IC _i Obtaining probability curve PC of drug resistance mutation _i Comprising: MIC according to the minimum inhibitory concentration _i Obtaining the inhibition rate dose response cumulative probability distribution IC _i And the cumulative probability distribution of exposure EC _i Respectively corresponding effect probability X _i And mutation probability Y _i The method comprises the steps of carrying out a first treatment on the surface of the MPC at a concentration preventing against said mutation _i Acquiring a plurality of points (X ', Y '), wherein Y '<1-Y _i 、X'>X _i The method comprises the steps of carrying out a first treatment on the surface of the Acquiring the probability curve PC according to the plurality of points _i 。

In the examples of the present application, the minimum inhibitory concentration MIC _i Respectively carrying out inhibition rate dose response cumulative probability distribution IC _i And an exposure cumulative probability distribution EC _i The effect probability X corresponding to the two can be obtained _i And mutation probability Y _i Due to X _i The concentrations above the corresponding concentrations are at risk of bacterial drug resistance mutation, so the value of the X' on the abscissa is required to be larger than X _i And due to mutation probability Y _i 1-Y is a threshold point at which no drug resistance mutation occurs _i Is a region where drug resistance mutation can occur. Thus, MPC was prevented according to mutation prevention concentration _i After a plurality of points are acquired, a probability curve PC can be fitted according to the acquired points _i . By the above method, the second medicine y can be obtained ₁ 、…、y _m Probability curve PC of drug resistance mutation of binary combination drug at concentration _i 。

With reference to the foregoing first aspect, in some possible implementations, the method further includes determining the probability curve PC _i Is a curve area a under the line _i And the occurrence probability p _i Obtaining the total probability of bacterial drug resistance mutation, comprising: according to the area a of the curve under the line _i And the occurrence probability p _i Acquiring a total probability curve; and calculating the off-line curve area of the total probability curve, wherein the off-line curve area of the total probability curve is the total probability of bacterial drug resistance mutation.

The probability curve PC _i Is a curve area a under the line _i As the ordinate, the probability of occurrence p will occur _i As an abscissa, fitting the abscissa and the ordinate in one-to-one correspondence to obtain a total probability curve; and obtaining the off-line curve area of the total probability curve to obtain the total probability of bacterial drug resistance mutation. Area of curve under line a _i In order to get y for the second medicine _i The corresponding bacterial drug resistance mutation probability at concentration, namely the area a of the curve under the line _i The probability of occurrence depends on the second drug's y-axis _i Probability at concentration, therefore, the area of the curve a under the line _i And a second drug y _i Probability of occurrence p corresponding to concentration _i The total probability curve is constructed, so that the probability condition of the integral bacterial drug resistance mutation can be obtained. In addition, the accuracy of the obtained total probability of the bacterial drug resistance mutation can be ensured through the mode.

With reference to the foregoing aspect, in some possible implementations, the suppression-rate dose response cumulative probability distribution IC of the combination is obtained _i And an exposure cumulative probability distribution EC _i Previously, the method further comprises: obtaining residual concentrations of the first medicine and the second medicine in each experimental scene when the c% bacteriostasis rate is realized, wherein c is more than or equal to 0 and less than or equal to 100; according to the concentration of the first medicine in the single condition of c% of the antibacterial rate, the concentration of the second medicine in the single condition of c% of the antibacterial rate, and the second medicine y _i Residue of the first drug at concentrationAnd obtaining n drug concentrations corresponding to the combined drug by the reserved concentration and the residual concentration of the second drug.

In the embodiment of the application, after the residual concentration of the first medicine and the second medicine in each experimental scene when the c% antibacterial rate is obtained, the concentration of the first medicine in the single case when the c% antibacterial rate, the concentration of the second medicine in the single case when the c% antibacterial rate, and the second medicine taking y are used according to the concentration of the first medicine in the single case when the c% antibacterial rate _i The method comprises the steps of obtaining n drug concentrations corresponding to combined drug by using a residual concentration of a first drug and a residual concentration of a second drug in concentration, and obtaining n drug concentrations corresponding to combined drug of the first drug and the second drug by using an effect superposition model, wherein the concentration of the first drug under a single condition, the concentration of the second drug under a single condition and the second drug taking y are obtained _i The residual concentration of the first medicine and the residual concentration of the second medicine in the concentration are both values under the same bacteriostasis rate.

With reference to the foregoing first aspect, in some possible implementations, the suppression rate dose response cumulative probability distribution IC is obtained _i And the cumulative probability distribution of exposure EC _i Thereafter, the method further comprises: cumulative probability distribution of the inhibition dose response IC _i And the cumulative probability distribution of exposure EC _i Presented in the same coordinate system.

In the present embodiment, the probability distribution IC is accumulated by dose-response of the inhibition rate _i Corresponding inhibition rate dose response cumulative probability distribution curve and exposure cumulative probability distribution EC _i The corresponding exposure cumulative probability distribution curves are presented in the same coordinate system, so that users can conveniently and intuitively observe the conditions corresponding to different experiments, such as: when a certain experiment does not meet the possibility of bacterial drug resistance mutation, a user can directly see why the experiment does not meet the conditions through the exposure cumulative probability distribution curve, whether the actual exposure water average is lower or far lower than the minimum antibacterial concentration or the experiment directly exceeds the mutation prevention concentration, so that the drug resistance mutation probability does not exist, and the user can judge the experimental condition conveniently.

In combination with the technique provided in the first aspectIn some possible implementations, the probability curve PC _i Is a curve area a under the line _i Are all larger than the preset area value.

In the embodiment of the present application, a preset area value is set in advance, and a probability curve PC is obtained _i Is a curve area a under the line _i After that, the area of the curve a under the line is _i Comparing the curve area with the preset area value, and eliminating the curve area a under the line _i Experimental data less than a preset area value. Through the mode, experimental data with very small influence on the result can be removed, so that the operation amount of the data is reduced under the condition of ensuring the accuracy of the result.

In a second aspect, embodiments of the present application provide a device for predicting resistance to bacteria by combination therapy, the device comprising: a processing module for taking y according to the second medicine _i The inhibition rate dose response cumulative probability distribution IC of the combined drug is obtained by n drug concentrations corresponding to the combined drug in concentration and the antibacterial effect generated in each experimental scene _i The experimental scenes are that the first medicine takes x respectively ₁ 、…、x _n The concentration and the second medicine are respectively y ₁ 、…、y _m When the concentration is high, the inhibition rate dose response cumulative probability distribution IC is combined with each other to perform combined medication _i Characterizing the probability of the corresponding bacteriostatic effect under the condition that the combined medication takes different medication concentrations, wherein i takes 1 to m; taking y according to the second medicine _i Acquiring the cumulative probability distribution EC of the combined drug according to the n drug concentrations corresponding to the combined drug at the concentration, the cumulative probability distribution of the first drug exposure in the single case and the cumulative probability distribution of the second drug exposure in the single case _i The cumulative probability distribution of exposure EC _i Characterizing the probability of residual concentration of the drug under different drug concentrations of the combined drug; a judging module for judging the cumulative probability distribution EC of the exposure of the combined drug _i Judging when the second medicine is y _i Whether the corresponding experiment at the concentration meets the possibility of bacterial drug resistance mutation or not; if yes, taking y according to the second medicine _i Minimum inhibitory concentration MIC of the n drug concentrations at concentration _i The second medicine is taken y _i Mutation preventing concentration MPC in the n drug concentrations at concentration _i The cumulative probability distribution of exposure EC _i And said inhibition rate dose response cumulative probability distribution IC _i Obtaining probability curve PC of drug resistance mutation _i The minimum inhibitory concentration is the lowest concentration of the first drug and the second drug which can inhibit the growth and the reproduction of bacteria, and the mutation prevention concentration is the lowest concentration of the antibacterial drug required for preventing the selective reproduction of the first-step drug-resistant mutant strain; a prediction module for obtaining Q probability curves PC _i Is a curve area a under the line _i And said second drug is y _i Probability of occurrence p corresponding to concentration _i According to the probability curve PC _i Is a curve area a under the line _i And the occurrence probability p _i Obtaining the total probability of bacterial drug resistance mutation, wherein the total probability of bacterial drug resistance mutation represents the probability of occurrence of mutation of bacterial drug resistance under actual conditions, and Q is less than or equal to m.

In a third aspect, an embodiment of the present application provides an electronic device, including: the device comprises a processor and a memory, wherein the processor is connected with the memory; the memory is used for storing programs; the processor is configured to invoke a program stored in the memory to perform a method as provided by the embodiments of the first aspect described above and/or in combination with some possible implementations of the embodiments of the first aspect described above.

In a fourth aspect, embodiments of the present application provide a computer readable storage medium having stored thereon a computer program which, when executed by a processor, performs a method as provided by the embodiments of the first aspect described above and/or in connection with some possible implementations of the embodiments of the first aspect described above.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments of the present application will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and should not be considered as limiting the scope, and other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

Fig. 1 is a dose design example of a binary combination according to an embodiment of the present application.

FIG. 2 is a graph of resistance to antagonism, synergy, and additive effects of a binary combination provided in the examples of the present application.

Fig. 3 is a drug resistance spectrum showing antagonistic effect of the combination drug according to the embodiment of the present application.

Fig. 4 is a flowchart of steps of a method for predicting drug resistance of bacteria by combined drug resistance according to an embodiment of the present application.

FIG. 5 shows a second drug delivery system according to an embodiment of the present application ₁ Concentration, inhibition rate dose response cumulative probability distribution IC ₁ And an exposure cumulative probability distribution EC ₁ All types of situations occur.

FIG. 6 shows a second drug delivery system according to an embodiment of the present application ₁ At concentration, probability curves occur for all types of situations.

FIG. 7 is a graph showing cumulative probability distribution of bacterial inhibition dose response for enrofloxacin and florfenicol, respectively, in a single instance, as provided in the examples herein.

FIG. 8 is data for MIC and MPC of enrofloxacin and florfenicol binary combination experiments provided in the examples of the present application.

FIG. 9 is a graph showing cumulative probability distributions of exposure of enrofloxacin and florfenicol, respectively, in a single instance thereof, as provided in the examples herein.

FIG. 10 is a graph showing cumulative probability distribution of exposure and cumulative probability distribution of bacterial inhibition dose response for enrofloxacin and florfenicol combinations at a concentration of 0.085 μg/mL for florfenicol as provided in the examples herein.

FIG. 11 is a graph of probability of enrofloxacin and florfenicol combined at a concentration of 0.085 μg/mL for florfenicol provided in the examples herein.

FIG. 12 is a graph of probability of enrofloxacin and florfenicol combined administration at concentrations of 0.085 μg/mL and 0.17 μg/mL, respectively, for florfenicol as provided in the examples herein.

Fig. 13 is a block diagram of a device for predicting resistance of bacteria by combined medication and control according to an embodiment of the present application.

Fig. 14 is a schematic structural diagram of an electronic device according to an embodiment of the present application.

Detailed Description

The technical solutions in the embodiments of the present application will be described below with reference to the drawings in the embodiments of the present application.

In view of the lack of accuracy and practicality of predicting the possibility of drug-resistant mutation of bacteria based on limited experiments, the present inventors have studied and studied the following examples to solve the above problems.

First, prior to a binary combination experiment using a first drug and a second drug, experimental dosages of the first drug and the second drug may be designed according to the following method.

Determining the minimum inhibitory concentration MIC of the first medicament and the second medicament on target bacteria under a single condition respectively according to a standard method for determining the minimum inhibitory concentration MIC, and determining the inhibition rate dose response cumulative probability distribution of the mutation prevention concentration MPC and the inhibition rate dose response cumulative probability distribution of the first medicament and the second medicament under the single condition respectively, wherein the single condition refers to the condition that the first medicament and the second medicament are respectively used independently; re-comparing the MIC of the first drug _x And MIC of the second drug _y Selecting a drug with a relatively high MIC (minimum inhibitory concentration), and adjusting the experimental concentration gradient ratio according to the MIC weight of the drug, for example: MIC (MIC) _x MIC at 10. Mu.g/mL _y At 0.10. Mu.g/mL, i.e., MIC _x Greater than MIC _y The dosage ratio of the first drug and the second drug should be based on MIC _x To adjust. Thus, the dilution ratio span is selected to be 0.10 times, 0.25 times, 0.50 times, 1 times, 1.5 times, and more preferably 0.1 times, 1 times, 5 times, and 10 times. In addition, the proportioning concentration preferentially reaches the medicine of the MPC under the single condition, and a higher dosage concentration does not need to be designed, so that the concentration numbers of the first medicine and the second medicine can be different in the experiment.

As shown in FIG. 1, the concentration value of the first drug is x ₁ 、…、x _n The concentration value of the second drug is y ₁ 、…、y _m Each grid in fig. 1 represents a different combination experiment, wherein, it is noted that the concentration values of the first drug and the second drug in fig. 1 are n, that is, m is equal to n at this time, but in the experiment, the concentration numbers of the first drug and the second drug may be different, that is, n and m may be the same or different, which is not limited in the application; the left-hand value of each cell represents the concentration of the first agent and the right-hand value represents the concentration of the second agent, such as x for the first agent in the bottom left-most cell of FIG. 1 ₁ Concentration and second drug y ₁ Concentration-time combination experiments in which 0.1IC _50M Take the value of the concentration of the first drug, 0.1IC _50N IC for the concentration of the second drug _50M Is the semi-bacteriostatic concentration of the first drug, IC _50N Is the semi-bacteriostatic concentration of the second drug. The experimental design under the equal ratio should cover the whole range of MIC and MPC as much as possible, and consider the respective half inhibitory concentration IC of different drugs ₅₀ Wherein the IC of the first and second drugs ₅₀ Are derived from their own cumulative probability distribution of inhibition dose response in a single instance. In addition, because the actual drug residue will have a high probability of being lower than the MIC of the drug, and only a few cases will be higher than the MIC, more different gradient concentration ratios should be designed in the MIC area as much as possible under the limited concentration design combination.

After the experimental doses of the first medicament and the second medicament are designed according to the principle, the two medicaments are combined pairwise and binary for experiment. Wherein the medicine is dissolved in dimethyl sulfoxide (DMSO) solvent for use, and the final concentration of DMSO is less than or equal to 0.03%, and the bacterial liquid concentration is adjusted to OD=0.1 (about 1×10) according to different culture plate addition amounts ¹⁰ CFU/mL) and after incubation at 28 ℃ for 24h, the MIC and MPC growth of the bacteria were evaluated automatically on OD600 using a microplate reader according to broth microdilution technique, i.e. the MIC and MPC of the combination at different concentrations of the second drug were obtained by experimental analysis.

As shown in FIG. 2 and FIG. 3, because the binary combination can utilize Drug interaction to generate antagonism, synergy or additive equivalent, a graph of the binary combination affecting Drug resistance can be obtained according to experimental analysis, wherein Drug M in FIG. 2 and FIG. 3 represents a first Drug, drug N represents a second Drug, and x ₁ 、…、x _n Representing the first drug taking different concentration values, y ₁ 、…、y _m Representing the second drug at different concentration values, fig. 3 shows the case where m is equal to n. And the small square in the left square in FIG. 3 is the combination of the concentration value of the first drug and the concentration value of the second drug, for example, the square at the bottom left corner of the graph corresponds to M ₁ Concentration and second drug N ₁ Experiments performed on concentration; circles in the right graph in fig. 3 correspond to squares in the left graph one by one, MSW is a concentration range between MIC and MPC, i.e., a mutation selection window period, and in this range, a drug-resistant strain resistant selective enrichment dangerous region is formed, and the wider the MSW is, the more likely drug-resistant strain is to appear. After the spectrum of the binary drug combination affecting the drug resistance is obtained through the experimental analysis, a user can intuitively obtain the relation of the drug combination.

The following describes the specific flow and steps of the method for predicting resistance of bacteria by combination drug resistance with reference to fig. 4. It should be noted that, the method for predicting drug resistance of bacteria by combined drug resistance provided in the embodiments of the present application is not limited by the sequence shown in fig. 4 and the following.

Step S101: taking y according to the second medicament _i The inhibition rate dose response cumulative probability distribution IC of the combined drug is obtained by the n drug concentrations corresponding to the combined drug in concentration and the antibacterial effect generated in each experimental scene _i Wherein i is 1 to m.

Alternatively, the cumulative probability distribution IC of inhibition rate dose response in the combination is obtained _i And an exposure cumulative probability distribution EC _i Previously, the method further comprises: obtaining the residual concentration of the first medicine and the second medicine in each experimental scene when the c% bacteriostasis rate is realized, wherein c is more than or equal to 0 and less than or equal to 100; according to the concentration of the first medicine in single case at c% inhibition rate, the first Concentration of the two medicines under single condition at c% antibacterial rate, and taking y from the second medicine _i And obtaining n drug concentrations corresponding to the combined drug by the residual concentration of the first drug and the residual concentration of the second drug in concentration.

It is noted that the value of c is any one of values ranging from 0 to 100, i.e. after the value of c is set, the second drug is taken according to the concentration of the first drug in the single case at the c% antibacterial rate, the concentration of the second drug in the single case at the c% antibacterial rate, and the y of the second drug _i The residual concentration of the first medicament and the residual concentration of the second medicament in the concentration process can obtain n medicament concentrations corresponding to the combined medicament.

The n drug concentrations corresponding to the combined drug are obtained through an effect superposition model, and the formula is as follows:

in the case of the formula (1),

concentration of combination at c% inhibition>

And->

When the c% antibacterial rate is achieved by the combined administration, the concentrations of the first drug and the second drug in the mixture are->

And->

Is the concentration of the first drug and the second drug in achieving c% inhibition in a single case, respectively. Wherein c% can take IC ₁₀ 、IC ₂₀ 、IC ₃₀ 、IC ₄₀ 、IC ₅₀ 、IC ₇₀ 、IC ₆₀ 、IC ₈₀ 、IC ₉₀ IC and method for manufacturing the same ₁₀₀ I is 1 to m, and j is 1 to n.In addition, because the first medicine takes x respectively ₁ 、…、x _n The concentration and the second medicine are respectively y ₁ 、…、y _m Concentration, therefore, in the second medicine y _i When the concentration is the same, n drug concentrations corresponding to the combined drug can be obtained according to the formula (1), such as when the second drug is y ₁ At the concentration, the above formula (1) becomes:

taking y the second medicine _i Taking the concentrations of n drugs corresponding to the combined drug at the concentration as the abscissa, and taking y of the second drug obtained through experiments _i The antibacterial effect generated in each experimental scene during concentration is taken as an ordinate, and the inhibition rate dose response cumulative probability distribution IC of the combined drug can be fitted _i . Wherein, each experimental scene is that the first medicine takes x respectively ₁ 、…、x _n The concentration and the second medicine are respectively y ₁ 、…、y _m When the concentration is high, the combination of the two is combined for the combined use, and the inhibition rate dose response cumulative probability distribution IC _i And (3) characterizing the probability of the corresponding bacteriostatic effect under the condition that different drug concentrations are taken by combined drug. Wherein n and m are positive integers, which may be the same or different, and the application is not limited.

Step S102: taking y according to the second medicament _i Acquiring the exposure cumulative probability distribution EC of the combined drug according to the concentration of n drugs corresponding to the combined drug at the concentration, the exposure cumulative probability distribution of the first drug under the single condition and the exposure cumulative probability distribution of the second drug under the single condition _i 。

Specifically, x is taken from the first medicine according to the cumulative probability distribution of the exposure of the first medicine under a single condition ₁ 、…、x _n Probability value F corresponding to concentration _j Wherein j is 1 to n; obtaining the second drug according to the cumulative probability distribution of the exposure of the second drug in a single case ₁ 、…、y _m Probability value G corresponding to concentration _i Wherein i is 1 to m; according to the second medicamentTaking y _i The concentration of n medicines and the concentration of the first medicine are respectively x ₁ 、…、x _n The probability value M corresponding to the concentration and the second medicine respectively take y ₁ 、…、y _m Probability value G corresponding to concentration _i Acquiring cumulative probability distribution of exposure of combination drug EC _i Wherein M= { F ₁ ,...,F _j ,...,F _n }. The corresponding exposure cumulative probability distribution of the first medicine and the second medicine under a single condition is respectively constructed after the residual concentration levels of the first medicine and the second medicine in an actual scene are monitored; cumulative probability distribution of exposure EC _i The probability of residual concentration of the drug at different drug concentrations taken in combination is characterized.

The above-mentioned second medicine is taken according to y _i The concentration of n medicines and the concentration of the first medicine are respectively x ₁ 、…、x _n The probability value M corresponding to the concentration and the second medicine respectively take y ₁ 、…、y _m Probability value G corresponding to concentration _i Acquiring cumulative probability distribution of exposure of combination drug EC _i The method specifically comprises the following steps: taking x from the first medicine respectively ₁ 、…、x _n The probability value M corresponding to the concentration is respectively y with the second medicine ₁ 、…、y _m Probability value G corresponding to concentration _i Multiplying to obtain y _i Probability of co-administration concentration at the concentration; taking y the second medicine _i Taking the probability of the combined drug concentration as the ordinate, taking y of the second drug _i The n drug concentrations corresponding to the combined drug are taken as abscissa when the concentration is reached, and the exposure cumulative probability distribution EC of the combined drug is obtained _i 。

In the above way, the exposure cumulative probability distribution EC containing other unexperienced scenes can be constructed from limited experimental data _i 。

Step S103: cumulative probability distribution of exposure based on combination _i Judging when the second medicine is y _i The corresponding experiments at concentration met the possibility of bacterial resistance mutation.

Specifically, according to the minimumMIC of inhibitory concentration _i Acquiring an exposure cumulative probability distribution EC _i Corresponding mutation probability Y _i The method comprises the steps of carrying out a first treatment on the surface of the Judging mutation probability Y _i If it is less than 1, if the mutation probability Y _i Less than 1, then means when the second drug is taken y _i The corresponding experiments at concentration met the possibility of bacterial drug resistance mutation.

In the embodiment of the application, the minimum inhibitory concentration is the concentration of the combination of the first drug and the second drug which can inhibit the growth and the reproduction of bacteria. MIC of minimum inhibitory concentration _i Bringing into the cumulative probability distribution of exposure EC _i The mutation probability Y can be obtained _i Since the minimum inhibitory concentration is the concentration of the combination of the first drug and the second drug which can inhibit the growth and the reproduction of bacteria, Y _i Probability of not developing drug resistance for bacteria; therefore, if mutation probability Y _i Below 1, this indicates a probability of 1-Y for bacterial resistance mutation in the experiment _i I.e. when the second drug is taken y _i The corresponding experiments at concentration met the possibility of bacterial drug resistance mutation. Through the mode, experimental data which does not meet the possibility of bacterial drug resistance mutation can be removed, and only the experimental data which meets the conditions are subjected to subsequent calculation, so that the accuracy of the subsequent calculation is ensured.

Alternatively, the inhibition rate dose response cumulative probability distribution IC _i And an exposure cumulative probability distribution EC _i Presented in the same coordinate system.

As shown in FIG. 5, when the second drug is taken y ₁ Concentration by cumulative probability distribution of inhibition dose response IC ₁ And an exposure cumulative probability distribution EC ₁ A total of 6 relationships may appear in the same coordinate system, respectively, fig. 5 (a) to (f). Wherein the horizontal axis represents the drug concentration corresponding to the combined drug administration, the vertical axis represents the probability, and MIC represents y of the second drug ₁ Minimum inhibitory concentration MIC at concentration ₁ MPC takes y for the second drug ₁ Mutation preventing concentration MPC at concentration ₁ G (x) is the second medicine y ₁ Concentration-dependent inhibition rate dose response cumulative probability distribution IC ₁ F (x) is the second drug y ₁ At a concentration ofCumulative probability distribution of exposure EC ₁ 。

The mutation probability Y in 4 cases of graphs (a) to (d) in FIG. 5 ₁ All are smaller than 1, the actual exposure level is in the MSW range, and the possibility of drug-resistant mutation is provided; mutation probability Y in the graph (e) of FIG. 5 ₁ Equal to 1, which means that the actual exposure level is lower or far lower than MIC, and the probability of drug resistance mutation does not exist, and the situation represented by the relation is common in a good culture environment and a natural ecological environment; the case in graph (f) in fig. 5 is an extreme case directly exceeding the MPC value, i.e. not in the MSW region, there is no probability of drug resistance mutation, and the drug concentration levels in the system are extremely high, possibly with a large or extremely large co-toxicity effect on the host. Therefore, the experiments represented by the graphs (e) and (f) in fig. 5 are such that there is no possibility of drug resistance mutation, so that it is unnecessary to proceed to the next calculation. In addition, when the second medicine is taken y _i Concentration, inhibition rate dose response cumulative probability distribution IC _i And an exposure cumulative probability distribution EC _i The above 6 relationships may also appear in the same coordinate system, so that repetition is avoided, and no further description is given here.

By accumulating the probability distribution IC of inhibition rate dose response _i And an exposure cumulative probability distribution EC _i Is presented in the same coordinate system, so that the user can intuitively see that when the second medicine is taken _i At the concentration, the experiment was what was the case. And when the second medicine is y _i When the probability of the bacterial resistance mutation is not satisfied by the corresponding experiment at the concentration, the user can intuitively understand which of the two cases the probability of the bacterial resistance mutation is not satisfied by the experiment.

Step S104: if yes, taking y according to the second medicine _i Minimum inhibitory concentration MIC of n drug concentrations at concentration _i Taking y from the second medicine _i Mutation preventing concentration MPC in n drug concentrations at concentration _i Cumulative probability distribution of exposure EC _i And inhibition rate dose response cumulative probability distribution IC _i Obtaining probability curve PC of drug resistance mutation _i 。

Specifically, MIC based on minimum inhibitory concentration _i Obtaining inhibition rateDose response cumulative probability distribution IC _i And an exposure cumulative probability distribution EC _i Respectively corresponding effect probability X _i And mutation probability Y _i The method comprises the steps of carrying out a first treatment on the surface of the MPC based on mutation prevention concentration _i Acquiring a plurality of points (X ', Y '), wherein Y ' <1-Y _i 、X'>X _i The method comprises the steps of carrying out a first treatment on the surface of the Acquiring probability curves PC from multiple points _i 。

In the examples of the present application, the minimum inhibitory concentration MIC _i Respectively carrying out inhibition rate dose response cumulative probability distribution IC _i And an exposure cumulative probability distribution EC _i The effect probability X corresponding to the two can be obtained _i And mutation probability Y _i Due to X _i The concentrations above the corresponding concentrations are at risk of bacterial drug resistance mutation, so the value of the X' on the abscissa is required to be larger than X _i And due to mutation probability Y _i 1-Y is a threshold point at which no drug resistance mutation occurs _i Is a region where drug resistance mutation can occur. Thus, MPC was prevented according to mutation prevention concentration _i After a plurality of points (X ', Y') are acquired, a probability curve PC can be fitted according to the acquired plurality of points _i 。

As shown in FIG. 6, FIG.1 is a probability curve PC corresponding to FIG. 5, FIG. (a) ₁ Fig.2 is the probability curve PC corresponding to the graph (b) in fig. 5 ₁ FIG.3 is the probability curve PC corresponding to the graph (c) in FIG. 5 ₁ FIG.4 is the probability curve PC corresponding to the graph (d) in FIG. 5 ₁ The method comprises the steps of carrying out a first treatment on the surface of the Probability curves PC corresponding to the 4 cases of fig. 5 (a) to (d) ₁ Approximately as shown in fig. 6, fig.1 to fig.4, when the second medicine is taken as y _i At concentration, the probability curve PC is obtained _i One of the 4 curve cases described above will also occur, avoiding repetition and will not be repeated here.

Step S105: acquiring Q probability curves PC _i Is a curve area a under the line _i And a second drug y _i Probability of occurrence p corresponding to concentration _i According to the probability curve PC _i Is a curve area a under the line _i And occurrence probability p _i And obtaining the total probability of bacterial drug resistance mutation, wherein Q is less than or equal to m.

In the embodiment of the application, the Q bar is firstly obtainedRate curve PC _i Is a curve area a under the line _i And a second drug y _i Probability of occurrence p corresponding to concentration _i . Wherein, Q probability curves PC _i In step S103, a probability curve PC corresponding to the experiment for judging the possibility of satisfying the bacterial drug resistance mutation is obtained _i Q is less than or equal to m because of the possibility that the experiment does not meet the drug resistance mutation of bacteria; probability of occurrence p _i Taking y for the second drugs in step S102 respectively ₁ 、…、y _m Probability value G corresponding to concentration _i . Based on probability curve PC _i Is a curve area a under the line _i And occurrence probability p _i And obtaining the total probability of bacterial drug resistance mutation. Wherein, the total probability of bacterial drug resistance mutation characterizes the probability of bacterial drug resistance mutation in actual conditions.

Specifically, the probability curve PC is used as the basis _i Is a curve area a under the line _i And occurrence probability p _i Obtaining the total probability of bacterial drug resistance mutation, comprising: according to the area a of the curve under the line _i And occurrence probability p _i Acquiring a total probability curve; and calculating the area of the curve under the line of the total probability curve, wherein the area of the curve under the line of the total probability curve is the total probability of bacterial drug resistance mutation. Wherein, according to the curve area a under the line _i And occurrence probability p _i The total probability curve is obtained specifically as follows: area a of curve under line _i As the ordinate, the probability of occurrence p will occur _i The overall probability curve is fitted as an abscissa.

Therefore, the total probability TP of bacterial drug resistance mutation is:

in equation (3), TPC is the total probability curve.

Alternatively, probability curve PC _i Is a curve area a under the line _i Are all larger than the preset area value. By combining the area of the curve a _i And eliminating experimental data smaller than a preset area value, so that the operation amount of the data is reduced under the condition of ensuring accurate results.

As shown in FIGS. 7 to 12, a method for predicting resistance of bacteria by combination therapy will be described below.

Taking enrofloxacin (Enr) and florfenicol (Flo) in highly contaminated wastewater as an example, drug resistance control to aeromonas hydrophila (a.hydrophila) is combined.

First, the MIC and MPC of Enr and Flo for aeromonas hydrophila were measured, respectively, and fitting was performed using residual data monitored in a certain region on the assumption, based on a basic model of probability distribution, to obtain Enr and Flo bacterial inhibition dose response cumulative probability distribution in a single case. As shown in fig. 7, graphs (a) and (b) in fig. 7 are cumulative probability distributions of bacterial inhibition dose response in a single case of Enr and Flo, respectively. From the cumulative probability distribution of bacterial inhibition dose response in a single case of Enr and Flo obtained, ICs of Enr and Flo, respectively, can be obtained ₅₀ . The results are shown in Table 1.

TABLE 1

Due to MIC _Enr <<MIC _Flo Thus, the MIC concentration levels of the reference Flo are simultaneously measured as the respective ICs ₅₀ Different gradient concentration ratios were set for the baseline, with dilution factors of 0.1, 0.2, 0.4, 0.6, 0.8, 1.0, 4.0, 8.0, 12.0, and 16.0 times. The final experimental dose design is shown in table 2.

TABLE 2

As shown in fig. 8, fig. 8 is data of MIC and MPC of the above experiment. Wherein the concentration of the drug Enr is Enr ₁ ～Enr ₁₄ Therein, enr ₁ ～Enr ₁₄ 0.0016. Mu.g/mL, 0.0032. Mu.g/mL, 0.0064. Mu.g/mL, 0.0096. Mu.g/mL, 0.0128. Mu.g/mL, 0.016. Mu.g/mL, 0.064. Mu.g/mL, 0.128. Mu.g/mL, 0.192. Mu.g/mL, 0.256. Mu.g/mL, 0.5. Mu.g/mL, 0.8. Mu.g/mL, 1. Mu.g/mL and 1.45. Mu.g/mL, respectively; medicineThe medicine concentration of Flo is Flo ₁ ～Flo ₁₀ Wherein Flo is ₁ ～Flo ₁₀ 0.085 μg/mL, 0.17 μg/mL, 0.34 μg/mL, 0.51 μg/mL, 0.68 μg/mL, 0.85 μg/mL, 3.4 μg/mL, 6.8 μg/mL, 10.2 μg/mL and 13.6 μg/mL, respectively.

As shown in fig. 9, fitting is performed according to a basic model of probability distribution using residual data monitored in a certain region under assumption, to obtain cumulative probability distributions of exposure in a single case of Enr and Flo, respectively. Wherein, the graph (a) and the graph (b) in fig. 9 are cumulative probability distributions of exposure in a single case of Enr and Flo, respectively.

The cumulative probability distribution of bacterial inhibition dose response and the cumulative probability distribution of exposure in the combination scenario can be obtained based on the cumulative probability distribution of exposure of Enr and Flo in a single case. Wherein, the medicine Flo is taken during the process of taking Flo ₁ When the concentration is reached, the corresponding n drug concentrations of the combined drug are obtained through an effect superposition model, and the formula is as follows:

in formula (4), C _com1,mix To take Flo in the process of medicine Flo ₁ When the concentration is the concentration, the concentration of the combined drug at the c% bacteriostasis rate is realized; c (C) _Enr,i,mix And C _Flo1,mix Respectively taking Flo from the medicine Flo ₁ When the concentration is the same, the concentration of the medicine Flo and the medicine Enr in the mixture is the same as the concentration of the medicine Enr when the medicine Flo and the medicine Enr are combined to realize the c% bacteriostasis rate; c (C) _Enr,i And C _Flo1 The concentration of the first medicine and the second medicine in the single condition for realizing the c% bacteriostasis rate; i is 1 to 14.

Similarly, the medicine Flo can be obtained ₂ ～Flo ₁₀ At concentration, the concentration of the combination drug at the c% inhibition rate is achieved.

The concentration of the medicine Enr is respectively brought into the exposure cumulative probability distribution under the single condition, and the probability value P corresponding to each concentration is obtained _Enr,i The method comprises the steps of carrying out a first treatment on the surface of the Then the medicine Flo is Flo ₁ Is brought into its single case exposure cumulative probability distribution to obtain probability value P _Flo1 The method comprises the steps of carrying out a first treatment on the surface of the Finally P is arranged _Enr,i And P _Flo1 Multiplying to obtain the ordinate of the cumulative probability distribution of the exposure of the combined drug, wherein the formula is as follows:

S(C _comi,mix )＝P _Enr,i ×P _Flo1 (5)

Wherein i is 1 to 14.

As shown in fig. 10, the medicine Flo is taken in the process of Flo taking ₁ The concentration of the combination drug at the c percent of inhibition rate obtained is taken as the abscissa, and P is taken as the following _Enr,i And P _Flo1 The probability value obtained by multiplication is taken as an ordinate to be fitted with the Flo of the medicine ₁ Cumulative probability distribution of exposure EC for combination at concentration ₁ . The medicine Flo is taken from the container ₁ In the case of concentration, the concentration of the combined drug at the time of the obtained c percent bacteriostasis rate is taken as an abscissa, and the drug Flo is Flo ₁ The effect obtained by experiments corresponding to the concentration is taken as an ordinate, and the Flo is obtained by fitting the Flo in the medicine ₁ Concentration, inhibition rate dose response cumulative probability distribution IC for combination ₁ . Wherein S (C) in FIG. 10 _comi,mix ) Is the medicine Flo is Flo ₁ Cumulative probability distribution of exposure EC for concentration-dependent combination ₁ W (comi, mix) is the medicine Flo to Flo ₁ Concentration-dependent inhibition rate dose response cumulative probability distribution IC for combination ₁ 。

Similarly, the medicine Flo can be obtained ₂ ～Flo ₁₀ At concentration, the corresponding combination exposure cumulative probability distribution and inhibition dose response cumulative probability distribution.

As shown in FIG. 11, according to the exposure cumulative probability distribution EC ₁ And inhibition rate dose response cumulative probability distribution IC ₁ Acquiring a corresponding probability curve PC ₁ . And the probability curve PC is obtained by the software Origin ₁ Area a enclosed by transverse axis and longitudinal axis ₁ 0.0035, i.e. the drug Flo is Flo ₁ The concentration-corresponding combination corresponds to the case of graph (a) in fig. 5, but is close to the case of graph (e) in fig. 5, the overall performance may be mutated, but with very low or very low probability.

Similarly, a can be found ₂ To a ₁₀ Is a numerical value of (2). As shown in FIG. 12, the two curves in FIG. 12 are probability curves PC, respectively ₁ And probability curve PC ₂ A can be intuitively seen from FIG. 12 ₁ And a ₂ The relation of (a) remaining a ₃ To a ₁₀ And a ₁ The positional relationship presented may be as shown in fig. 12.

Suppose a ₁ To a ₁₀ All are 0.0035, and the total probability of bacterial drug resistance mutation is calculated by adopting the following formula:

TP＝12.25×0.0035＝0.0429 (6)

of these, 12.25 is the highest concentration of the combination. The total probability TP of the bacterial drug resistance mutation is a unitless dimensional parameter, and can comprehensively reflect the probability of drug resistance variation in actual conditions, and the larger the value is, the higher the probability of drug resistance variation is.

Referring to fig. 13, based on the same inventive concept, an embodiment of the present application further provides a device 100 for predicting drug resistance of bacteria by combined drug resistance control, where the device 100 includes: a processing module 101, a judging module 102 and a predicting module 103.

A processing module 101 for taking y according to the second medicine _i The inhibition rate dose response cumulative probability distribution IC of the combined drug is obtained by the n drug concentrations corresponding to the combined drug in concentration and the antibacterial effect generated in each experimental scene _i Each experimental scene is that the first medicine takes x respectively ₁ 、…、x _n The concentration and the second medicine are respectively y ₁ 、…、y _m When the concentration is high, the combination of the two is combined for the combined use, and the inhibition rate dose response cumulative probability distribution IC _i Characterizing the probability of the corresponding bacteriostatic effect under the condition that the combined medication takes different medication concentrations, wherein i takes 1 to m; taking y according to the second medicament _i Acquiring the exposure cumulative probability distribution EC of the combined drug according to the concentration of n drugs corresponding to the combined drug at the concentration, the exposure cumulative probability distribution of the first drug under the single condition and the exposure cumulative probability distribution of the second drug under the single condition _i Exposure cumulative probability distribution EC _i The probability of residual concentration of the drug at different drug concentrations taken in combination is characterized.

A judging module 102 for accumulating the probability distribution EC according to the exposure of the combination _i Judging when the second medicine is y _i Whether the corresponding experiment at the concentration meets the possibility of bacterial drug resistance mutation or not; if yes, taking y according to the second medicine _i Minimum inhibitory concentration MIC of n drug concentrations at concentration _i Taking y from the second medicine _i Mutation preventing concentration MPC in n drug concentrations at concentration _i Cumulative probability distribution of exposure EC _i And inhibition rate dose response cumulative probability distribution IC _i Obtaining probability curve PC of drug resistance mutation _i The minimum inhibitory concentration is the concentration of the combination of the first drug and the second drug which can inhibit the growth and the reproduction of bacteria, and the mutation prevention concentration is the minimum antibacterial drug concentration required for preventing the selective reproduction of the drug-resistant mutant strain in the first step.

A prediction module 103 for obtaining Q probability curves PC _i Is a curve area a under the line _i And a second drug y _i Probability of occurrence p corresponding to concentration _i According to the probability curve PC _i Is a curve area a under the line _i And occurrence probability p _i And obtaining the total probability of bacterial drug resistance mutation, wherein the total probability of bacterial drug resistance mutation represents the probability of occurrence of mutation of bacterial drug resistance under actual conditions, and Q is less than or equal to m.

Optionally, the processing module 101 is specifically configured to obtain x from the first drug according to the cumulative probability distribution of exposure of the first drug in a single case ₁ 、…、x _n Probability value F corresponding to concentration _j Wherein j is 1 to n; obtaining the second drug according to the cumulative probability distribution of the exposure of the second drug in a single case ₁ 、…、y _m Probability value G corresponding to concentration _i Wherein i is 1 to m; taking y according to the second medicament _i The concentration of n medicines and the concentration of the first medicine are respectively x ₁ 、…、x _n The probability value M corresponding to the concentration and the second medicine respectively take y ₁ 、…、y _m Probability value G corresponding to concentration _i Acquiring cumulative probability distribution of exposure of combination drug EC _i Wherein M= { F ₁ ,...,F _j ,...,F _n }。

Optionally, the processing module 101 is specifically configured to take x from the first medicines respectively ₁ 、…、x _n The probability value M corresponding to the concentration is respectively y with the second medicine ₁ 、…、y _m Probability value G corresponding to concentration _i Multiplying to obtain y _i Probability of co-administration concentration at the concentration; taking y the second medicine _i Taking the probability of the combined drug concentration as the ordinate, taking y of the second drug _i The n drug concentrations corresponding to the combined drug are taken as abscissa when the concentration is reached, and the exposure cumulative probability distribution EC of the combined drug is obtained _i 。

Optionally, the judging module 102 is specifically configured to determine the MIC according to the minimum inhibitory concentration _i Acquiring an exposure cumulative probability distribution EC _i Corresponding mutation probability Y _i The method comprises the steps of carrying out a first treatment on the surface of the Judging mutation probability Y _i If it is less than 1, if the mutation probability Y _i Less than 1, then means when the second drug is taken y _i The corresponding experiments at concentration met the possibility of bacterial drug resistance mutation.

Optionally, the judging module 102 is specifically configured to determine the MIC according to the minimum inhibitory concentration _i Acquiring a suppression rate dose response cumulative probability distribution IC _i And an exposure cumulative probability distribution EC _i Respectively corresponding effect probability X _i And mutation probability Y _i The method comprises the steps of carrying out a first treatment on the surface of the MPC based on mutation prevention concentration _i Acquiring a plurality of points (X ', Y '), wherein Y '<1-Y _i 、X'>X _i The method comprises the steps of carrying out a first treatment on the surface of the Acquiring probability curves PC from multiple points _i 。

Optionally, the prediction module 103 is specifically configured to determine the curve area a according to the line _i And occurrence probability p _i Acquiring a total probability curve; and calculating the area of the curve under the line of the total probability curve, wherein the area of the curve under the line of the total probability curve is the total probability of bacterial drug resistance mutation.

Optionally, the processing module 101 is further configured to obtain residual concentrations of the first drug and the second drug in each experimental scenario when the c% antibacterial rate is achieved, where c is greater than or equal to 0 and less than or equal to 100; according to the concentration of the first medicine in the single case of c% bacteriostasis rate and the single case of the second medicine in the single case of c% bacteriostasis rateIs the concentration of (2) and the second medicine y _i And obtaining n drug concentrations corresponding to the combined drug by the residual concentration of the first drug and the residual concentration of the second drug in concentration.

Optionally, the processing module 101 is further configured to accumulate the suppression rate dose response cumulative probability distribution IC _i And an exposure cumulative probability distribution EC _i Presented in the same coordinate system.

Referring to fig. 14, a schematic block diagram of an electronic device 200 for predicting a drug resistance of bacteria by combined drug resistance control is provided in an embodiment of the present application. In the present embodiment, the electronic device 200 may be, but is not limited to, a personal computer (Personal Computer, PC), a smart phone, a tablet computer, a personal digital assistant (Personal Digital Assistant, PDA), a mobile internet device (Mobile Internet Device, MID), and the like. Structurally, the electronic device 200 may include a processor 210 and a memory 220.

The processor 210 is electrically connected to the memory 220, either directly or indirectly, to enable data transmission or interaction, for example, the elements may be electrically connected to each other via one or more communication buses or signal lines. The processor 210 may be an integrated circuit chip with signal processing capability. The processor 210 may also be a general purpose processor, for example, a central processing unit (Central Processing Unit, CPU), digital signal processor (Digital Signal Processor, DSP), application specific integrated circuit (Application Specific Integrated Circuit, ASIC), discrete gate or transistor logic, discrete hardware components, and may implement or perform the methods, steps, and logic blocks disclosed in embodiments of the present application. Further, the general purpose processor may be a microprocessor or any conventional processor or the like.

The Memory 220 may be, but is not limited to, random access Memory (Random Access Memory, RAM), read Only Memory (ROM), programmable Read Only Memory (Programmable Read-Only Memory, PROM), erasable programmable Read Only Memory (Erasable Programmable Read-Only Memory, EPROM), and electrically erasable programmable Read Only Memory (Electric Erasable Programmable Read-Only Memory, EEPROM). The memory 220 is used for storing a program, and the processor 210 executes the program after receiving an execution instruction.

It should be noted that, since it will be clearly understood by those skilled in the art, for convenience and brevity of description, the specific working processes of the systems, apparatuses and units described above may refer to the corresponding processes in the foregoing method embodiments, which are not repeated herein.

Based on the same inventive concept, the present embodiments also provide a computer-readable storage medium having stored thereon a computer program which, when executed, performs the method provided in the above embodiments.

The storage media may be any available media that can be accessed by a computer or a data storage device such as a server, data center, or the like that contains an integration of one or more available media. The usable medium may be a magnetic medium (e.g., floppy Disk, hard Disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid State Disk (SSD)), etc.

In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other manners. The above-described apparatus embodiments are merely illustrative, for example, the division of units is merely a logical function division, and there may be other manners of division in actual implementation, and for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some communication interface, device or unit indirect coupling or communication connection, which may be in electrical, mechanical or other form.

Further, the units described as separate units may or may not be physically separate, and units displayed as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

Furthermore, functional modules in various embodiments of the present application may be integrated together to form a single portion, or each module may exist alone, or two or more modules may be integrated to form a single portion.

In this document, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions.

The above is only an example of the present application, and is not intended to limit the scope of the present application, and various modifications and variations will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

Claims

1. A method for predicting resistance of a combination drug-resistant bacterium, the method comprising:

taking y according to the second medicament _i The inhibition rate dose response cumulative probability distribution IC of the combined drug is obtained by n drug concentrations corresponding to the combined drug in concentration and the antibacterial effect generated in each experimental scene _i The experimental scenes are that the first medicine takes x respectively ₁ 、…、x _n The concentration and the second medicine are respectively y ₁ 、…、y _m When the concentration is high, the inhibition rate dose response cumulative probability distribution IC is combined with each other to perform combined medication _i Characterizing the probability of the corresponding bacteriostatic effect under the condition that the combined medication takes different medication concentrations, wherein i takes 1 to m;

taking y according to the second medicine _i Acquiring the cumulative probability distribution EC of the combined drug according to the n drug concentrations corresponding to the combined drug at the concentration, the cumulative probability distribution of the first drug exposure in the single case and the cumulative probability distribution of the second drug exposure in the single case _i The cumulative probability distribution of exposure EC _i Characterizing the probability of residual concentration of the drug under different drug concentrations of the combined drug;

cumulative probability distribution of exposure EC according to the combination _i Judging when the second medicine is y _i Whether the corresponding experiment at the concentration meets the possibility of bacterial drug resistance mutation or not;

if yes, taking y according to the second medicine _i Minimum inhibitory concentration MIC of the n drug concentrations at concentration _i The second medicine is taken y _i Mutation preventing concentration MPC in the n drug concentrations at concentration _i The cumulative probability distribution of exposure EC _i And said inhibition rate dose response cumulative probability distribution IC _i Obtaining probability curve PC of drug resistance mutation _i The minimum inhibitory concentration is the lowest concentration of the first drug and the second drug which can inhibit the growth and the reproduction of bacteria, and the mutation prevention concentration is the lowest concentration of the antibacterial drug required for preventing the selective reproduction of the first-step drug-resistant mutant strain;

acquiring Q probability curves PC _i Is a curve area a under the line _i And said second drug is y _i Probability of occurrence p corresponding to concentration _i According to the probability curve PC _i Is a curve area a under the line _i And the occurrence probability p _i Obtaining the total probability of bacterial drug resistance mutation, wherein the total probability of bacterial drug resistance mutation represents the probability of occurrence of mutation of bacterial drug resistance under actual conditions, and Q is less than or equal to m.

2. The method of claim 1, wherein said taking y is in accordance with said second medicament _i Acquiring the cumulative probability distribution EC of the combined drug according to the n drug concentrations corresponding to the combined drug at the concentration, the cumulative probability distribution of the first drug exposure in the single case and the cumulative probability distribution of the second drug exposure in the single case _i Comprising:

acquiring x of the first medicine according to the cumulative probability distribution of the exposure of the first medicine under single condition ₁ 、…、x _n Probability value F corresponding to concentration _j Wherein j is 1 to n;

acquiring y of the second medicine according to the cumulative probability distribution of the second medicine exposure in a single case ₁ 、…、y _m Probability value G corresponding to concentration _i Wherein i is 1 to m;

taking y according to the second medicine _i The concentration of the n drugs corresponding to the combined drug in the concentration is x for the first drug respectively ₁ 、…、x _n The probability value M corresponding to the concentration and the second medicine respectively take y ₁ 、…、y _m Probability value G corresponding to concentration _i Acquiring an exposure cumulative probability distribution EC of the combination _i Wherein M= { F ₁ ,...,F _j ,...,F _n }。

3. The method of claim 2, wherein said taking y is in accordance with said second medicament _i The concentration of the n drugs corresponding to the combined drug in the concentration is x for the first drug respectively ₁ 、…、x _n The probability value M corresponding to the concentration and the second medicine respectively take y ₁ 、…、y _m Probability value G corresponding to concentration _i Acquiring an exposure cumulative probability distribution EC of the combination _i Comprising:

taking x from the first medicine respectively ₁ 、…、x _n The probability value M corresponding to the concentration is respectively y with the second medicine ₁ 、…、y _m Probability value G corresponding to concentration _i Multiplying to obtain y of the second medicine _i Probability of co-administration concentration at the concentration;

y taking the second medicine _i Taking the probability of the combined drug concentration as an ordinate, and taking y of the second drug _i The n drug concentrations corresponding to the combined drug are taken as abscissa when the concentration is reached, and the exposure cumulative probability distribution EC of the combined drug is obtained _i 。

4. The method according to claim 1, characterized in thatThe cumulative probability distribution EC of exposure according to the combination _i Judging when the second medicine is y _i The corresponding experiments at concentration, including whether or not the possibility of bacterial drug resistance mutation is met, include:

MIC according to the minimum inhibitory concentration _i Acquiring the cumulative probability distribution of exposure EC _i Corresponding mutation probability Y _i ；

Judging the mutation probability Y _i If it is less than 1, if the mutation probability Y _i Less than 1, then means when the second drug is y _i The corresponding experiments at concentration met the possibility of bacterial drug resistance mutation.

5. The method of claim 1, wherein said taking y is in accordance with said second medicament _i Minimum inhibitory concentration MIC of the n drug concentrations at concentration _i The second medicine is taken y _i Mutation preventing concentration MPC in the n drug concentrations at concentration _i The cumulative probability distribution of exposure EC _i And said inhibition rate dose response cumulative probability distribution IC _i Obtaining probability curve PC of drug resistance mutation _i Comprising:

MIC according to the minimum inhibitory concentration _i Obtaining the inhibition rate dose response cumulative probability distribution IC _i And the cumulative probability distribution of exposure EC _i Respectively corresponding effect probability X _i And mutation probability Y _i ；

MPC at a concentration preventing against said mutation _i Acquiring a plurality of points (X ', Y '), wherein Y '<1-Y _i 、X'>X _i ；

Acquiring the probability curve PC according to the plurality of points _i 。

6. The method according to claim 1, characterized in that the probability curve PC is based on _i Is a curve area a under the line _i And the occurrence probability p _i Obtaining the total probability of bacterial drug resistance mutation, comprising:

according to the area a of the curve under the line _i And institute(s)The occurrence probability p _i Acquiring a total probability curve;

and calculating the off-line curve area of the total probability curve, wherein the off-line curve area of the total probability curve is the total probability of bacterial drug resistance mutation.

7. The method of claim 1, wherein a cumulative probability distribution IC of inhibition dose response of the combination is obtained _i And an exposure cumulative probability distribution EC _i Previously, the method further comprises:

obtaining residual concentrations of the first medicine and the second medicine in each experimental scene when the c% bacteriostasis rate is realized, wherein c is more than or equal to 0 and less than or equal to 100;

according to the concentration of the first medicine in the single condition of c% of the antibacterial rate, the concentration of the second medicine in the single condition of c% of the antibacterial rate, and the second medicine y _i And obtaining n drug concentrations corresponding to the combined drug according to the residual concentration of the first drug and the residual concentration of the second drug in concentration.

8. The method of claim 1 wherein, in obtaining the suppression rate dose response cumulative probability distribution IC _i And the cumulative probability distribution of exposure EC _i Thereafter, the method further comprises:

cumulative probability distribution of the inhibition dose response IC _i And the cumulative probability distribution of exposure EC _i Presented in the same coordinate system.

9. The method according to claim 1, characterized in that the probability curve PC _i Is a curve area a under the line _i Are all larger than the preset area value.

10. A device for predicting resistance to a combination drug-resistant bacterium, the device comprising:

a processing module for taking y according to the second medicine _i Concentration of n drugs corresponding to combined drug administration at concentration and each experimentThe inhibition effect generated in the scene obtains the inhibition rate dose response cumulative probability distribution IC of the combined drug _i The experimental scenes are that the first medicine takes x respectively ₁ 、…、x _n The concentration and the second medicine are respectively y ₁ 、…、y _m When the concentration is high, the inhibition rate dose response cumulative probability distribution IC is combined with each other to perform combined medication _i Characterizing the probability of the corresponding bacteriostatic effect under the condition that the combined medication takes different medication concentrations, wherein i takes 1 to m; taking y according to the second medicine _i Acquiring the cumulative probability distribution EC of the combined drug according to the n drug concentrations corresponding to the combined drug at the concentration, the cumulative probability distribution of the first drug exposure in the single case and the cumulative probability distribution of the second drug exposure in the single case _i The cumulative probability distribution of exposure EC _i Characterizing the probability of residual concentration of the drug under different drug concentrations of the combined drug;

a judging module for judging the cumulative probability distribution EC of the exposure of the combined drug _i Judging when the second medicine is y _i Whether the corresponding experiment at the concentration meets the possibility of bacterial drug resistance mutation or not; if yes, taking y according to the second medicine _i Minimum inhibitory concentration MIC of the n drug concentrations at concentration _i The second medicine is taken y _i Mutation preventing concentration MPC in the n drug concentrations at concentration _i The cumulative probability distribution of exposure EC _i And said inhibition rate dose response cumulative probability distribution IC _i Obtaining probability curve PC of drug resistance mutation _i The minimum inhibitory concentration is the lowest concentration of the first drug and the second drug which can inhibit the growth and the reproduction of bacteria, and the mutation prevention concentration is the lowest concentration of the antibacterial drug required for preventing the selective reproduction of the first-step drug-resistant mutant strain;

a prediction module for obtaining Q probability curves PC _i Is a curve area a under the line _i And said second drug is y _i Probability of occurrence p corresponding to concentration _i According to the probability curve PC _i Is a curve area a under the line _i And the occurrence probability p _i Obtaining the total probability of bacterial drug resistance mutation, wherein the total probability of bacterial drug resistance mutation represents the probability of occurrence of mutation of bacterial drug resistance under actual conditions, and Q is less than or equal to m.

11. An electronic device, comprising: the device comprises a processor and a memory, wherein the processor is connected with the memory;

the memory is used for storing programs;

the processor being configured to execute a program stored in the memory for performing the method of any one of claims 1-9.

12. A computer-readable storage medium, characterized in that a computer program is stored thereon, which, when being run by a computer, performs the method according to any of claims 1-9.