Systems and methods for in vitro evaluation of the interaction between bacteriophages and antibiotics

Abstract

The invention relates to methods and systems for in vitro evaluation of the interaction between a bacteriophage and an antibiotic in the treatment of a collection of bacterial isolates. The methods and systems described in the present application are capable of predicting the type of interaction between bacteriophages or cocktails of bacteriophages and bacteria, wherein the type of interaction is selected from synergism, antagonism, additivism or neutralism. The methods and systems are also based on the surprising fact identified by the inventors that sequentially adding bacteriophages then antibiotic, and not at the same time, enables the prediction of the type of interaction between bacteriophages and antibiotics in relation to the growth of a given collection of bacterial isolates.

Description

[0001] SYSTEMS AND METHODS FOR IN VITRO EVALUATION OF THE INTERACTION BETWEEN BACTERIOPHAGES AND ANTIBIOTICS

[0002] TECHNICAL FIELD

[0003] The invention consists of methods and systems for the in vitro evaluation of the interaction between a bacteriophage and an antibiotic in the treatment of a collection of bacterial isolates. The methods and systems described herein are capable of predicting the type of interaction that exists between bacteriophages or bacteriophage cocktails and bacteria, where the type of interaction is selected from among synergism, antagonism, additives, or neutralism. The methods and systems are also based on the inventors' surprising discovery that the sequential addition of bacteriophages first, and then the antibiotic, rather than simultaneously, allows for the prediction of the type of interaction between bacteriophages and antibiotics on the growth of a given collection of bacterial isolates.

[0004] BACKGROUND

[0005] In recent years, the proliferation of antibiotic-resistant bacteria has become one of the most serious public health problems (Prada-Peñaranda C, Holguín Moreno AV, González-Barrios AF, Vives-Flórez MJ. (2015). Phage therapy, an alternative for the control of bacterial infections. Perspectives in Colombia. Universitas Scientiarum 20(1): 43-60 doi: 10.11144 / Javeriana.SC20-1.faci). Modern medicine has frequently resorted to the use of antibiotics to improve or cure life-threatening bacterial infections. However, the ability of bacteria to mutate rapidly can allow the development of antibiotic-resistant bacterial strains (O'Neill, J. (2014). Antimicrobial resistance. Tackling a crisis for the health and wealth of nations).The increase in treatments with these drugs and their inappropriate use to treat diseases can cause significant problems stemming from the proliferation of antibiotic-resistant bacteria (The next pandemic has already begun: Covid has accelerated the emergence of superbugs. El País. November, 2021. URL: elpais.com / ciencia / 2021-ll-18 / la-siguiente-pandemia-ya-ha-empezado-la-covid-ha-acelerado-la-aparicion-de-superbacterias.html).

[0006] Faced with the potential threat of the rapid emergence and spread of multidrug-resistant microorganisms, alternative treatments for bacterial infections are being developed. Bacteriophages, viruses that infect and kill bacteria, can be used as a natural alternative to combat various bacterial infections when conventional antibiotic treatment is ineffective, especially in the food industry.

[0007] Antibiotics are still used in various animal production systems, such as chicken, pig, and cattle farming. For example, the main classes of antibiotics used in major poultry-producing countries (USA, Brazil, China, Poland, UK, Germany, France, and Spain) include aminoglycosides, beta-lactams (penicillins, first- and third-generation cephalosporins), phenicols, fluoroquinolones, ionophores, lincosamides, macrolides, polypeptides, polymyxins, sulfonamides, and tetracyclines. The type and extent of antibiotic use differ between countries, depending on the country's economy, level of development, and animal husbandry (Roth, N., A. Käsbohrer, S. Mayrhofer, U. Zitz, C. Hofacre, and KJ Domig. 2019. The application of antibiotics in broiler production and the resulting antibiotic resistance in Escherichia coli: A global overview. Poult. Sci. 98:1791–1804). In Europe and the USA.In this country, antibiotics used as growth promoters are banned, unlike in countries such as Brazil and China. Therefore, antibiotics continue to be used in animal production systems, and bacteriophage formulations used in these systems will interact in some way with the antibiotics administered to the animals.

[0008] Therefore, understanding the interaction between bacteriophages, and particularly bacteriophage formulations, with different antibiotics is vital to determining whether a bacteriophage formulation could combat the growth of a specific bacterial population in a particular production system. However, determining the type of interaction that specific bacteriophages have with various antibiotics can be a laborious and time-consuming task, carried out through one-by-one experiments and manual recording of interactions between samples. Furthermore, there are some examples that demonstrate that in certain situations where an antibiotic or a bacteriophage formulation has good antibacterial activity against certain bacterial isolates independently, when combined, they do not maintain their efficacy. In some cases, bacteriophages and antibiotics have antagonistic activities, and this interaction can be unpredictable a priori.

[0009] Although some developments have shown promising results, these methods cannot be incorporated and tested on an industrial scale under real production conditions. While it is possible to predict the type of interaction between a bacteriophage formulation and a limited number of antibiotics, the analysis becomes complicated when dealing with a large collection of bacterial isolates. This application addresses this gap in the state of the art by describing methods and systems that utilize data obtained through in vitro experimentation using growth inhibition assays between bacterial isolates belonging to a specific bacterial genus that were challenged with combinations of bacteriophages and antibiotics.This allows for the identification of the type of interaction for a given bacterial population, where the interaction can be selected as additive, synergistic, neutral, or antagonistic. Once the system is trained, if a new population of bacterial isolates belonging to the same bacterial species or serotype as the one used for training is introduced, the system can automatically predict, without experimentation, the best combination of bacteriophage and antibiotic formulations to combat the growth of those bacterial isolates.

[0010] BRIEF DESCRIPTION

[0011] This disclosure relates to the field of methods and systems for predicting the interaction between bacteriophage formulations in combination with antibiotics on a set of bacterial isolates.

[0012] In a first aspect, the present invention provides a method that allows predicting the type of interaction that exists between an antibiotic and a phage or a cocktail of phages, comprising the following steps:

[0013] a) Inoculate a bacterial culture solution into a culture medium, and grow them under temperature and agitation conditions that are ideal for the bacteria, until reaching a culture in the exponential phase;

[0014] b) Inoculate a volume of the exponential phase culture from point a) into a well of a 96-well plate containing a volume of the culture medium and also inoculate into the same well an appropriate amount of the bacteriophage or bacteriophage cocktail to a determined MOI;

[0015] c) Incubate the mixture obtained in b) for between 1 and 5 hours at a temperature where the bacteria reach exponential growth, constantly monitoring the OD600 nm using appropriate spectrophotometry equipment;

[0016] d) After the incubation period, add an appropriate volume of the antibiotic at a 10X concentration so that it is at a concentration that can be selected from 0.25, 0.5, 1, 2, 4, 8, 16, 32, 64 and 128 g / mL; e) Continue the incubation under the same conditions as in step c) continuously monitoring the OD600 nm, until the total 24 hours of incubation are completed;

[0017] f) Obtain the OD600 nm data from the spectrophotometry equipment, and use a system that is based on determining the type of interaction that exists between the antibiotic and the phage or phage cocktail, where the interaction can be identified as synergism, addivism, neutralism and antagonism.

[0018] In some respects, this disclosure provides a computer-implemented system for predicting the type of interaction between bacteriophage formulations in combination with antibiotics on the growth of bacterial isolates.

[0019] In some respects a bacteriophage formulation may comprise a single bacteriophage (or phage) and in others the formulations of the invention may comprise at least two, at least three, at least four, at least five, etc.

[0020] In some respects, this disclosure provides a computer-implemented system for predicting the type of interaction between bacteriophage formulations in combination with antibiotics on the growth of bacterial isolates for use in phage therapy, comprising the following modules:

[0021] A) a susceptibility prediction module of one or more bacterial isolates of a given genus to a combination of a bacteriophage formulation and an antibiotic, which was trained with real-world interaction data between bacterial isolates of the same species or serovar as the target bacterial isolates, using growth curve assays in which the bacterial isolates were incubated with bacteriophage formulations, with antibiotics, or with a combination of bacteriophage formulations with antibiotics;

[0022] B) a module for generating the best combinations of bacteriophage and antibiotic formulations with the objective of inhibiting the growth of one or more bacterial isolates of a given species or serovar, wherein said bacterial isolates of interest were not used to train the module described in point A); and C) a module for producing bacteriophage formulations, wherein the bacteriophage formulations are produced based on the results delivered by the module described in point B). This disclosure also relates to methods that utilize the system with the modules described above, and to methods for producing bacteriophage formulations.

[0023] INCORPORATION BY REFERENCE

[0024] All publications, patents, and patent applications mentioned in this description are incorporated herein by reference to the same extent as if each publication, patent, or patent application were specifically and individually stated to be incorporated by reference. To the extent that publications, patents, or patent applications incorporated by reference contradict the disclosure contained in the descriptive memorandum, the descriptive memorandum supersedes and / or takes precedence over any conflicting material.

[0025] FIGURES

[0026] Figure 1 illustrates a multi-stage diagram and elements of an example system for determining the best combinations of bacteriophages and antibiotics to combat the growth of a bacterial isolate of interest.

[0027] Figure 2. Lytic efficiency of the APE-009 cocktail alone (Control), the antibiotics Trimethoprim-sulfadimidine (TMS), Florfenicol (FLOR), Doxycycline (DOXY), and the sequential combined treatments of the cocktail first and antibiotic later (DOXY APE-009, FLOR APE-009, TMS APE-009) (Kruskal-Wallis p-value <0.05). The p-values ​​in the graph correspond to the value obtained in the multiple comparisons with Bonferroni correction.

[0028] Figure 3. Comparison of the error (A1C) between the regression models evaluated under different conditions. The Y-axis shows the AIC values, and the X-axis shows the four evaluated models. The linear model without interaction has the fewest parameters, and the mixed (slope) model has the most. These models were evaluated using three different antibiotics (Doxy, Flor, and Tms) and the FORMIDA-A cocktail in a collection of 154 E. coli isolates.

[0029] DETAILED DESCRIPTION OF THE INVENTION

[0030] Although several embodiments of the disclosure have been shown and described herein, it will be obvious to those skilled in the art that such embodiments are provided by way of example only. Those skilled in the art may make numerous variations, changes, and substitutions without departing from the disclosure. It should be understood that several alternatives to the embodiments of the disclosure described herein may be employed.

[0031] This disclosure provides systems and methods for determining the optimal combination of a bacteriophage formulation and an antibiotic to inhibit or eliminate the growth of one or more isolates of a specific bacterial species or serovar. The bacteriophage and antibiotic combinations recommended by this system may exhibit high bactericidal activity against a group of host bacteria, based on knowledge of the bacteriophage-antibiotic-host bacteria interaction.

[0032] In some respects, this disclosure provides an in vitro method for predicting the type of interaction between bacteriophage formulations in combination with antibiotics on the growth of bacterial isolates for use in phage therapy, comprising the following steps:

[0033] a) Inoculate 4 ml of a bacterial culture solution into 196 ml of a culture medium where the bacteria grow well under ideal temperature and agitation conditions for the bacteria, until reaching a culture in the exponential phase;

[0034] b) Inoculate 9 ml of the exponential phase culture from point a) into a well of a 96-well plate containing 151 µl of culture medium and also inoculate into the same well 20 µL of the bacteriophage or bacteriophage cocktail to be evaluated at a determined MOI; c) Incubate the mixture obtained in b) for 3 hours at a temperature where the bacteria grow well, constantly monitoring the OD600 nm using appropriate spectrophotometry equipment;

[0035] d) After 3 hours of incubation, add 20 μL of the antibiotic at a 10X concentration so that it is at a concentration that can be selected from 0.25, 0.5, 1, 2, 4, 8, 16, 32, 64 and 128 pg / mL;

[0036] e) Continue incubation under the same conditions as in step c), continuously monitoring the OD600 nm until the total 24 hours of incubation are completed;

[0037] f) Obtain the OD600 nm data from the spectrophotometry equipment, and use a trained mathematical model to determine the type of interaction that exists between the antibiotic and the phage or phage cocktail, where the interaction can be selected between synergism, addivism, neutralism and antagonism. In a more specific preferred embodiment, the culture medium can be selected from TSB, LB, Brain Heart Infusion (BHI), Peptone Water, MacConkey Medium, Nutrient Medium, Mueller Hinton Medium (MHB), CAYE Medium, M Medium, Urea Medium, Selenite Cystine Medium, Lauryl Sulfate Medium, among other media appropriate for bacterial growth.

[0038] This system aims to accelerate the study of bacteriophage-antibiotic-host interactions and the optimization of the bacteriophages used in each formulation, leading to optimized formulations (for example, those with the minimum number of phages while maximizing the host range and functioning well in combination with an antibiotic). The system can learn from data retrieved from open-source internal and external databases, but it primarily relies on predetermined information about bacteriophage-antibiotic-host interactions, which serve as the starting point for training the system.

[0039] Figure 1 illustrates a diagram of an example of the system described in this disclosure. In some embodiments, the system comprises the following modules:

[0040] 1. a susceptibility prediction module of one or more bacterial isolates of a given species or serovar, by a combination between a bacteriophage formulation and a given antibiotic (105), which allows the in silico prediction of those combinations of bacteriophage formulations and antibiotics with a higher probability of producing lysis in a group of bacterial isolates of interest;

[0041] 2. a module for generating the best combinations of bacteriophage and antibiotic formulations with the aim of inhibiting the growth of one or more bacterial isolates of a certain species or bacterial serovar (106), which allows the selection of the best combinations between bacteriophage and antibiotic formulations based on the bacterial isolates of interest.

[0042] 3. a bacteriophage formulation production module (107) that allows the production of such formulations on an industrial scale.

[0043] In some embodiments, the system comprises the following stages:

[0044] a) receive data from the databases of interactions of bacteriophage formulations with the host bacteria of a certain species or serovar with which the system was trained (101), receive data from the databases of interactions of antibiotics with the host bacteria of a certain species or serovar with which the system was trained (102); and receive data from the databases of interactions of combinations of bacteriophage formulations with antibiotics evaluated against the host bacteria of a certain species or serovar with which the system was trained (103).

[0045] b) When one or more bacterial isolates of interest of the same species or serovar (104) with which the module (105) was trained are obtained, this information is received by the module (106) and an optimization and selection process is initiated to determine the best combinations of bacteriophage and antibiotic formulations capable of inhibiting or eliminating the growth of said one or more bacterial isolates of interest. c) Send the best combination suggested by the module (106) to the bacteriophage formulation production module (107).

[0046] The system uses three types of information to function: (1) it receives information from in vitro experiments of interactions of bacteriophage formulations with the host bacteria of a certain species or serovar with which the system was trained (101); (2) it receives data from databases of interactions of antibiotics with the host bacteria of a certain species or serovar with which the system was trained (102); and (3) it receives data from databases of interactions of combinations of bacteriophage formulations with antibiotics evaluated against the host bacteria of a certain species or serovar with which the system was trained (103).

[0047] The information about the one or more bacterial isolates that the system needs to know can be selected from at least the bacterial genus, the bacterial species, and the bacterial serotype or serovar. In a more preferred embodiment, the required information about the bacteria is the serotype.

[0048] The database can be implemented using memory, for example, random access memory (RAM), electrically erasable programmable read-only memory (EEPROM), flash memory, hard disk drives, optical disk drives, solid-state drives, or any type of memory suitable for storing the database.

[0049] Methods for in vitro interaction between bacteriophage formulations, antibiotics, or combinations of bacteriophage and antibiotic formulations, with one or more bacterial isolates of a given genus are well-established. Furthermore, the prior art indicates that traditionally, the interaction between one or more bacteriophages and a specific bacterium can be determined by inoculating a known quantity of a bacterial isolate onto a semi-solid agar plate, followed by the addition of a known quantity of a bacteriophage or phage whose antibacterial activity against that bacterial isolate is to be evaluated. The plate is grown at a specific growth temperature (generally the temperature at which the bacteria grows best), and after a predetermined incubation period, which varies from bacterium to bacterium, the presence or absence of a translucent halo at the site where the bacteriophage inoculum was deposited is observed.The degree of transparency of the halo provides a qualitative indication of whether the bacteriophage inoculum exhibited strong antibacterial activity; that is, more translucent halos indicate greater antibacterial activity. With the development of other optical methodologies, it is now possible to monitor bacterial growth by tracking the OD600 nm of a specific bacterial isolate. In this methodology, known quantities of a particular bacterial isolate are inoculated into a liquid culture medium where the bacteria grow well, and known quantities of the bacteriophage or bacteriophage cocktail to be analyzed are also inoculated. The liquid culture is then allowed to grow under appropriate temperature and agitation conditions, and the OD600 nm is monitored at specific time intervals.Under suitable growing conditions, if a bacterium is not inoculated with any other growth-inhibiting agent, exponential growth should be observed, evidenced by OD600 nm readings. Conversely, if one or more bacteriophages are inoculated along with the bacterium in a treatment, a decrease in OD600 nm will be observed, mediated by the bacterial lysis effect of the bacteriophage(s). The degree of decrease in OD600 nm allows for the quantitative determination of the antibacterial effect. These methods are well-established in the field of bacteriophages, and for further details, see the following reference (Konopacki, M. (2020). PhageScore: A simple method for comparative evaluation of bacteriophages lytic activity. Biochemical Engineering Journal: Volume 161).

[0050] The MOI, or multiplicity of infection, refers to the ratio of viral particles to host cells in a given assay. For example, an MOI of 1.0 means there is one viral particle for every bacterial cell, and an MOI of 20 means there are twenty viral particles for every bacterial cell.

[0051] Regarding the determination of the antibacterial effect of a given antibiotic on a bacterium, the state of the art includes numerous types of tests, one of the most used being the determination of the MIC (Wiegand I., et al. 2008. Agar and broth dilution methods to determine the minimum inhibitory concentration (MIC) of antimicrobial substances. NATURE PROTOCOLS; 3(2): 163-175).

[0052] As someone versed in the subject will understand, a bacterial genus is a taxonomic group into which a family of bacteria is divided and which contains one or more species. The systems of the invention could be used with the following bacterial genera, but are not limited to these: Acinetobacter, Actinomyces, Aeromonas, Bacillus, Bartonella, Bordetella, Borrelia, Brucella, Burkholderia, Campylobacter, Chlamydia, Chlamydophila, Clostridium, Corynebacterium, Escherichia, Enterobacter, Ehrlichia, Enterococcus, Erysipelothrix, Francisella, Fusobacterium, Gardnerella, Haemophilus, Helicobacter, Klebsiella, Lactobacillus, Legionella, Leptospira, Listeria, Moraxella, Mycobacterium, Mycoplasma, Neisseria, Nocardia, Pasteurella, Pseudomonas, Proteus, Propionibacterium (e.g., Cutibacterium acnes), Rickettsia, Salmonella, Shigella, Staphylococcus, Streptococcus, Treponema, Ureaplasma, Vibrio, Yersinia, Xanthomonas, and Serratia.

[0053] In a preferred embodiment, the system of the invention can be trained and used to predict interactions between bacteriophage and antibiotic formulations on bacterial isolates of the following bacterial species: Escherichia coli, Staphylococcus aureus, Streptococcus pneumoniae, Mycobacterium tuberculosis, Salmonella enterica, Helicobacter pylori, Neisseria meningitidis, Clostridium difficile, Pseudomonas aeruginosa, and Vibrio cholerae. In a further preferred embodiment, the bacterial species are Escherichia coli and Salmonella enterica.

[0054] Antibiotics that may be used in combination with bacteriophage formulations may include, but should not be limited to, the following: Penicillin G, Ampicillin, Amoxicillin, Cloxacillin, Cephalexin, Ceftriaxone, Cefotaxime, Ceftiofur, Cefquinome, Tetracycline, Oxytetracycline, Doxycycline, Chlortetracycline, Erythromycin, Tylosin, Tulathromycin, Tilmicosin, Spiramycin, Azithromycin, Streptomycin, Gentamicin, Neomycin, Amikacin, Apramycin, Enrofloxacin, Ciprofloxacin, Norfloxacin, Marbofloxacin, Danofloxacin, Sulfadiazine, Sulfamethoxazole, Sulfadimethoxine, Sulfathiazole, Trimethoprim, Ormethoxazole, Chloramphenicol, Florfenicol, Lincomycin, Clindamycin, Polymyxin B Colistin, Metronidazole, Ronidazole, Monensin, Salinomycin, Narasin, Rifampicin, Vancomycin, Bacitracin, and Fosfomycin.

[0055] In a more particular preferred embodiment, the antibiotics that can be used in combination with bacteriophage formulations are Florfenicol, Amoxicillin, Tetracycline, Trimethoprim, Sulfamidine, and Enrofloxacin.

[0056] The methods and systems of the invention can use learning models to predict the interaction between combinations of phage formulations with antibiotics with a given bacterial isolate at the individual level and discard early on those combinations of phage and antibiotic formulations that will not be useful to inhibit or reduce the growth of said bacterial isolate, whether in vitro, in vivo, or in real intensive animal production systems.

[0057] In some embodiments, a module is provided for generating the best combinations of bacteriophage and antibiotic formulations with the aim of inhibiting the growth of one or more bacterial isolates of a given bacterial genus.

[0058] The module for generating optimal bacteriophage and antibiotic formulation combinations recommends a set of the most suitable combinations. This implementation can include in silico determination of the susceptibility of each isolate or host to a specific bacteriophage and antibiotic formulation combination, and can alert the user if there are insufficient or no combinations in the collection that possess lytic activity against the bacterial isolates of interest.

[0059] Although preferred embodiments of the present invention have been shown and described herein, it will be obvious to those skilled in the art that such embodiments are provided only by way of example. The invention is not intended to be limited by the specific examples provided in the specification. Although the invention has been described with reference to the aforementioned specification, the descriptions and illustrations of the embodiments herein should not be construed in a limiting manner. Numerous variations, changes, and substitutions will now occur to those skilled in the art without departing from the invention. Furthermore, it should be understood that all aspects of the invention are not limited to the specific representations, configurations, or relative proportions set forth herein, which depend on a variety of conditions and variables.It should be understood that several alternatives to the embodiments of the invention described herein may be used to implement the invention. Therefore, it is envisaged that the invention will also encompass such alternatives, modifications, variations, or equivalents. The following claims are intended to define the scope of the invention, and the methods and structures within the scope of these claims and their equivalents are to be covered by them.

[0060] EXAMPLES EXAMPLE 1. Evaluation of the interaction between bacteriophage(s) and antibiotic(s) regarding their bacterial activity on bacterial isolates of interest when evaluated in combination or sequentially. To develop a method that allows determining the type of interaction that exists between a bacteriophage or cocktail of bacteriophages in combination with antibiotics, it is first necessary to establish the steps required to discriminate this type of interaction. One of the variables that could influence this is the order in which the phages and antibiotics are incubated with the bacteria; one of them could be added before the other, or both together.To determine if this is a variable that affects the identification method, experiments were conducted in which the joint activity of a bacteriophage cocktail called APE-009 was evaluated in combination with the antibiotics Trimethoprim-sulfadimidine, Florfenicol or Doxycycline, against selected isolates of Escherichia Coli.

[0061] A total of 11 bacterial isolates were evaluated, selected based on their resistance or susceptibility profile to the antibiotic and the APE-009 cocktail. The aim was to cover all phenotypic cases of interest: resistant to the cocktail and susceptible to the antibiotic, susceptible to the cocktail and resistant to the antibiotic, resistant to both, and susceptible to both. The isolates and their characteristics are detailed in Table 1. The isolates were tested against the APE-009 phage cocktail and with increasing concentrations of the respective antibiotic (0, 0.25, 0.5, 1, 2, 4, 8, 16, 32, 64, and 128 μg / mL). Exponential culture was performed by loading 4 μL of ON culture into 196 μL of TSB. The challenge plates were loaded with 151 μL of TSB, 9 μL of diluted bacteria, 20 μL of APE-009 cocktail (MOI 20) and 20 μL of antibiotic at a concentration of 10X (0, 0.25, 0.5, 1, 2, 4, 8, 16, 32, 64 and 128 μg / mL).Each challenge plate was tested with four bacterial isolates and one antibiotic; each test was performed in triplicate. The OD was monitored. 600 every 10 min for a duration of 18 h in a LogPhase 600 and EPOCH 2 instrument. The plates with Trimethoprim-sulfadimidine were tested for 18 and 48 h.

[0062] Table 1. Characteristics of the isolates in the study.

[0063] i'redíceín B KioÍBfsí- íisáíka

[0064] inhibition

[0065] IhsxicitiiíiB Horttnieoi Süifííraetis'ojizoi? rimeu'prtaus ODT017-042 H10:O101 APEC 0.00% R | SRR ODT017- 132 H30:O102 APEC 60.08% R 1 SRR R053 O78:H9 High Risk 99.25% ss S s APEC ODT019-024 O78:H9 High Risk 99.83% RS ss APEC ODT017-175 H49:O8 APEC 24.12% RRRR ODT017-156 H51:O8 non-APEC 47.50% RRRR ODT017-189 H32: non-APEC 99.96% RRRS

[0066]

[0067] ODT017-020 H45:O106 APEC 99.72% R s RR ODT017-136 H25:O11 APEC 29.56% RRRS ODT017-019 H45:O17 / O77 APEC 99.85% RSRR ODT017-094 H21:O101 APEC 0.00% S ss

[0068]

[0069] R means that the bacteria would be resistant to the antibiotic; and S would mean sensitive.

[0070] The OD data were used to calculate the MIC (Minimum Inhibitory Concentration). 600 at the endpoint at 18 h of the four conditions studied (control bacteria, cocktail, antibiotic, and cocktail-antibiotic). Since a quantitative liquid culture assay was performed, the growth cutoff point was determined to be less than 1.5%; at the endpoint of growth at 18 h.

[0071] However, the developed method proved cumbersome to perform and was not effective in detecting synergistic interactions between phages and antibiotics. Furthermore, this method requires determining the MIC for each condition, which is expensive and laborious. In addition, the industry uses only one concentration for each antibiotic.

[0072] Therefore, a different approach was evaluated, involving a combined treatment performed sequentially. A similar experiment was conducted, with the sole difference being that each bacterium was co-incubated for 3 hours with the bacteriophage cocktail alone. After this period, 10 pL of one of the evaluated antibiotics (Trimethoprim-sulfadimidine, Horfenicol, and Doxycycline) were added at a 10X concentration. Experiments were also performed incubating the bacteria with the phage cocktail alone (control APE-009) and with the antibiotic alone. The OD (Opposition Density) was monitored. 600For each treatment, every 10 ml for a total duration of 24 h on the LogPhase 000 and EPOCH2 instruments. The efficiency and effectiveness of the APE-009 cocktail and the combined treatments were calculated by evaluating the reduction in the area under the curve of the challenged bacteria compared to the area of ​​the untreated bacteria between 3 and 24 h of the assay. Efficiency was calculated as the median inhibition among isolates for a group. The effect level was established as Null (inhibition < 15%), Partial (15% <inhibición<85%) y Total (inhibición> 85%). Effectiveness was defined as the number of isolates (Partial + Total) / (Null + Partial + Total). The results obtained are shown in Figure 2.

[0073] The lytic efficiency of each of the combination treatments was significantly higher than that of the control treatment (APE-009) and the respective antibiotic-only treatments. Furthermore, the lytic efficiencies of the combination antibiotic treatments did not differ significantly from one another. The median bacterial growth inhibition for the control treatment was 26%, while the median inhibition for the combination treatments with doxycycline, florfenicol, and trimethoprim-sulfadimidine was 85%, 77%, and 86%, respectively. Finally, the median inhibition for the antibiotic-only treatments was 31%, 26%, and 69% for doxycycline, florfenicol, and trimethoprim-sulfadimidine, respectively. Only the lytic efficiency of the trimethoprim-sulfadimidine treatment was significantly higher than that of the control (APE-009).

[0074] It is important to highlight that this sequential combined treatment allowed us to detect the synergy between each of the antibiotics and the bacteriophage cocktail, unlike the combined treatment of both antibacterial agents. This finding is unexpected and surprising given the current state of the art. Therefore, this sequential combined treatment was the one used in the following examples.

[0075] EXAMPLE 2. Development of a method and system that allows the detection of the type of interaction between antibiotics and phages or phage cocktail.

[0076] With the aim of developing a method and system that allows predicting the type of interaction that exists between an antibiotic and phages or phage cocktails, a way to detect this type of interaction had to be developed.

[0077] For this purpose, initial tests were performed with different models to evaluate the fit, measured using the Akaike Information Criterion (Akaike, Hirotugu (1974), "A new look at the statistical model identification", IEEE Transactions on Automatic Control 19 (6): 716-723). Given a set of candidate models for the data, the preferred model is the one with the lowest AIC value. An evaluation was performed using a dataset obtained with the FORMIDA cocktail and the antibiotics doxycycline, florfenicol, and trimethoprim-sulfadimidine in a collection of 154 E. coli isolates.Among the models evaluated, the first was a linear regression model without interaction between the terms "cocktail" and "antibiotic." This model assumes that the observed inhibition is solely due to the presence of either the cocktail or the antibiotic, with these variables being independent and not interacting with each other. Second, a linear regression model considering the interaction between the terms "cocktail" and "antibiotic" was evaluated. This model does consider that the fixed variables can interact, but that the effect is the same for all bacterial isolates, without taking into account that some isolates may exhibit greater resistance or susceptibility to one of the antimicrobials used. Third, a mixed linear regression model with a random intercept and interaction between the fixed variables "cocktail" and "antibiotic" was evaluated. This model also considers that the effect (intercept) of each antimicrobial can vary among bacterial isolates.Finally, a mixed linear regression model with random intercept and slope was evaluated, along with interaction between the fixed variables cocktail and antibiotic. This model also allows for flexibility, as the difference in inhibition (slope) can vary between bacterial isolates. When comparing the AIC of each model, a decrease in AIC was observed as a function of model complexity. This suggests that, despite using more parameters in the model, the gain in goodness of fit is greater, and therefore, a better model was obtained.

[0078] As can be seen in Figure 3, the sloped mixed model performed best, consistently exhibiting the lowest AIC value, and its predictive value was reproducible across all three evaluated conditions (Doxycycline + FORMIDA-A, Florfenicol + FORMIDA-A, and TMS + FORMIDA-A). For these reasons, this model was selected for subsequent validations. This model follows the equation β0 + β1*Cocktail + β2*Ab + β3*(Cocktail*Ab) + (1 + Cocktail*Ab |Bacteria) + (1 + Cocktail*Ab |Replicate).

[0079] EXAMPLE 3. Training of the lytic susceptibility prediction module using the mixed model with random slope.

[0080] To train the model developed in the previous example, a large number of bacteria were incubated with combinations of antibiotics and phage cocktails, with the aim of obtaining a large amount of data to train it.

[0081] In this example, the growth of 39 Escherichia coli APEC bacterial isolates was evaluated. Each bacterium was cultured in TSB medium at 37°C with constant shaking overnight. Subculture was then performed in 96-well plates with fresh TSB medium until an optimal oxidation density (OD) was reached. 600 of ~0.1. The antibiotic used in the combination treatment was florfenicol, and the phage cocktail used was called FORMIDA-A. Two multiplicities of infection (MOI) of FORMIDA-A were used to better resolve the interaction effect. For this purpose, 2 x 10 dilutions of the experimental batch of FORMIDA-A were prepared. 8 PFU / mL and 2x10 6 PFU / mL in TSB medium. Then, in a 96-well plate, a mixture of bacteria, FORMIDA-A, and TSB was prepared to reach a final concentration of 1x10 6 CFU / mL and 2x10 7 PFU / mL or 2x10 5PFU / mL, for an entry MOI of 20 and 0.2, respectively. For each bacterium, a condition without FORMIDA-A was included as a control. The mixture was cultured for 3 h at 37°C in a plate reader (LogPhase 600) with 10-minute reading intervals. After this, following a sequential application design, florfenicol (final concentration of 6 jig / mL) was added to the mixture, including the conditions of FORMIDA-A alone, FORMIDA-A + florfenicol, florfenicol alone, and the control. The plate was then incubated for 21 h in the plate reader (LogPhase 600) for a total incubation time of 24 h. Each bacterium constitutes an experimental unit, and each condition was performed in n=4, with each replicate carried out on a different plate.

[0082] The R software version 3.5.1 was used for data analysis. The OD 600Bacterial growth was measured between 3 and 24 h using the Area Under the Curve (AUC). This time interval was selected for AUC comparison because both substances are active. Bacterial growth inhibition was calculated as the percentage reduction in AUC compared to the control bacteria. The interaction between fiorphenicol and both concentrations of FORMID-A was analyzed for each bacterium (n=39) using a generalized linear model. Most interactions were classified as synergistic, additive, or neutral (Table 2), and antagonism was found in only one bacterial isolate.However, in order to predict the interaction between fiorfenicol and both concentrations of FORMIDA-A, the data from the entire set of bacteria (n=39) were used to train the mixed model with random slope, predicting that the interaction between FORMIDA-A and fiorfenicol in a new isolate of Escherichia coli would be synergistic, with an increase in inhibition of 8.98% at MOI 0.2 and 16.19% at MOI 20.

[0083] Table 2. Percentage of growth inhibition for each isolate under each individual condition of FORMI-A MOI 0.2, FORMI-A MOI 20 and florfenicol between 3 and 24 h of incubation, n=4.

[0084] FORMJDA-A FORM-A Fh>rt'enii-<>! 6 FORM-A MOI 9.2 FORMID-A MOI 20 and M<>- <>.?. MOI 2” f Hnri'feífiw»! 6 ug / rifl.. if Elnrifeífiw»! 6 íia / siU, E. cotí 001 0.3% ± 0.0% 1.3% ± 0.9% 45.2% ± 2.4% 46.0% ± 1.9% 51.3% ± 1.80% E. coli. 0.7% ± 0.0% 0.8% ± 0.7% 45.4% ± 1.4% 34.9% ± 23.0% 48.3% ± 2.4% E. col i 003 0.8% ± 0.0% 0.8% ± 0.7% 44.1% ± 0.4% ± 1.6%. 47.5% ± 2.9% E. coli 004 0.4% ± 0.0% 0.8% ± 0.5% 74.9% ± 0.6% 75.4% ± 1.3% 75.0% ± 1.5% E. coli 005 2.5% ± 1.7% ± 1.3%. 0.7% ± 5.3% E. coli 006 -0.4% ± 0.0% 0.0% ± 0.9% 49.0% ± 10.4% 50.4% ± 11.0% 53.8% ± 1.0.0% E. coli 006. 36.0% ± 4.6% 38.6%' ± 1.3% 99.3% ± 0.2% 99.8% ± 0.1% E. coli 008 1.5% ± 0.0% 0.3% ± 3.4% 46.2% ± 3.8% E. coli ± 49.4%. 009 0.7% ± 0.0% 1.2% ± 0.5% 50.9%> ± 3.7% 49.6% ± 6.5% 52.1% ± 3.3% E. coli 010 -0.3% ± 0.0% 1.1% ± 1.1% 25.6% ± 1.2% ± 0.6% ± 6.6% 27.7% ± 1.2% E. coli 011 28.6% ± 0.0% 30.0% ± 1.6% 38.6% ± 3.3% 99.8% ± 0.1% 99.9% ± 0.1% E .coli 012 28.5% ± 0.0% 29.1% ± 1.1% 37.9% ± 1.8% 99.8% ± 0.1% 99.9% ± 0.1% E. coli 013 24.1% ± 0.0% 22.2% ± 1.5% 42.9% ± 4.8%. 99.7% ± 0.1%. 99.8% ± 0.1%. E. coli 014 16.2% ± 0.0% 17.9% ± 2.1% 22.2% ± 1.4% 86.6% ± 12.8% 94.2% ± 5.7% E. coli 015 1.2% ± 0.0% 1.1% ± 0.4% 41.3% ± 1.8% 42.2% ± 1.3% 43.7% ± 1.0% E. coli 016 -0.1% ± 0.0% 0.1% ± 1.0% 52.7% ± 5.1%. 54.3% ± 3.9% 56.3% ±4.6% E. coli 017 -2.9% ± 0.0%< -0.2% ± 3.3% 62.2%> ± 2.8% 62.0%. ± 2.5% 63.8%. ± 2.0%.

[0085]

[0086] E. coli 018 16.0% ± 0.0% 28.7% ± 7.4% 48.0% ± 8.0% 92.8% ± 13.9% 99.9% ± 0.2% E. coli 019 0.8% ± 0.0% 0.8% ± 0.4% 57.3% ± 8.5% 56.3% ± 10.4% 69.4% ± 10.3% E. coli 020 4.5% ± 0.0% 9.1% ± 2.8% 17.8% ± 2.0% 21.2% ± 2.7% 32.0% ± 3.8% E. coli 021 17.4% ± 0.0% 49.2% ± 5.1% 43.5% ± 8.2%' 99.2% ± 0.6%' 100.0% ± 0.2% E. coli 022 11.3% ± 0.0% 51.7% ± 11.0% 20.6% ± 1.0% 21.8% ± 2.6% 97.1% ± 5.0% E. coli 023 0.9% ± 0.0% 1.5% ± 1.5% 46.9% ± 3.1% 45.2% ± 2.5% 50.0% ± 5.8% E. coli 024 -0.2%' ± 0.0% 1.6% ± 0.7% 57.6% ± 27.4%' 44.2% ± 4.6%: 51.2% ± 11.6%' E. coli 025 -1.9% ± 0.0%' 47.1% ± 2.6% 36.3%> ± 5.7% 35.5%' ± 4.3% 78.9%' ± 25.8%: E. coli 026 0.7% ± 0.0% 10.0% ± 1.2% 52.0% ± 4.4% 52.9% ± 4.9% 72.2% ± 19.4% E. coli 027 0.1% ± 0.0% -0.1% ± 0.3% 28.0% ± 0.9% 29.8% ± 1.8% 29.2% ± 2.3% E. coli 028 37.2% ± 0.0% 35.1% ± 13.1%' 38.7%' ± 7.5% 98.2%: ± 1.4% 99.4%: ± 0.4% E. coli 029 0.6%: ± 0.0%! -3.3% ± 0.9%' 70.1% ± 4.0%' 69.3% ± 3.9%' 39.8% ± 1.7%' E.coli 030 0.5% ± 0.0% 12.5% ​​± 0.4% 65.2% ± 3.7% 65.4% ± 3.2% 98.7% ± 1.9% E. coli 031 0.2% ± 0.0% 0.9% ± 1.2% 32.0% ± 0.4% 32.8% ± 0.4% 33.9% ± 0.3% E. coli 032 -0.4% ± 0.0% 17.0% ± 0.1% 46.7% ± 2.9% 48.2% ± 1.7% 93.9% ± 11.8% E. coli 033 0.5%! ± 0.0%: 14.5% ± 0.6% 44.5%> ± 0.6% 45.1%' ± 0.9% 92.4%' ± 14.5% E. coli 034 99.8% ± 0.0%¡ 99.9% ± 0.1% 42.4% ± 1.8% 99.9% ± 0.0% 100.0% ± 0.0% E. coli 035 0.7% ± 0.0% 17.3% ± 0.5% 47.4% ± 1.1% 47.3% ± 2.3% 99.7% ± 0.2% E. coli 036 99.8% ± 0.0% 100.0% ± 0.1%' 40.6%' ± 1.0% 99.8%: ± 0.0% 100.0%' ± 0.0% E. coli 037 0.2% ± 0.0% 20.3% ± 0.4% 45.2% ± 1.4%' 45.1% ± 2.1%' 99.8% ± 0.2%: E. coli 038 0.2%: ± 0.0%: 19.3% ± 0.6% 46.0%: ± 1.2% 46.3%' ± 1.3% 99.9%' ± 0.0% E. coli 039 95.6% ± 0.0%¡ 99.9% ± 8.2% 44.5% ± 3.0% 99.9% ± 0.0% 100.0% ± 0.1%.

[0087]

[0088] This module was then trained using interaction data from different bacterial species incubated with bacteriophage cocktails alone or in combination with antibiotics such as TMP / SMX, doxycycline, fosfomycin, and enrofloxacin. This allowed the system to be trained with a large amount of data.

[0089] EXAMPLE 4: Construction and validation of the module that generates the best formulations of bacteriophages and antibiotics.

[0090] Using the systems and methods described in this disclosure, a module was constructed to generate the best combinations of bacteriophage and antibiotic formulations. This module takes as input all the interaction data used to train the system in the previous example, as well as the species or serovar of the one or more bacterial isolates of interest. This initiates a process of optimization and selection of the best combinations of bacteriophage and antibiotic formulations capable of inhibiting or eliminating the growth of these one or more bacterial isolates of interest.

[0091] EXAMPLE 5: Construction and Validation of the Bacteriophage Formulation Production Module. This module takes the bacteriophage formulation suggestions provided by the module for generating the best combinations of bacteriophage and antibiotic formulations and creates a production order for this cocktail so that the manufacturing area can produce the bacteriophage cocktails using good manufacturing practices. The bacteriophage formulations can be produced using well-established methods.

Claims

CLAIMS 1. An in vitro method for predicting the type of interaction between bacteriophage formulations in combination with antibiotics on the growth of bacterial isolates for use in phage therapy, CHARACTERIZED in that it comprises the following steps: a) Inoculate 4 ml of a bacterial culture solution into 196 ml of a culture medium where the bacteria grow well under ideal temperature and agitation conditions for the bacteria, until reaching a culture in the exponential phase; b) Inoculate 9 ml of the exponential phase culture from point a) into a well of a 96-well plate containing 151 ml of culture medium and also inoculate into the same well 20 iL of the bacteriophage or bacteriophage cocktail to be evaluated at a specific MOI; c) Incubate the mixture obtained in b) for 3 hours at a temperature where the bacteria grow well, constantly monitoring the OD600 nm using appropriate spectrophotometry equipment; d) After 3 hours of incubation, add 20. L of the antibiotic at a concentration of 10X so that it is at a concentration that can be selected from 0.25, 0.5, 1, 2, 4, 8, 16, 32, 64 and 128 wg / mL; e) Continue incubation under the same conditions as in stage c) continuously monitoring the OD600 nm, until completing the total 24 hours of incubation; f) Obtain the OD600 nm data from the spectrophotometry equipment, and use a trained mathematical model to determine the type of interaction that exists between the antibiotic and the phage or phage cocktail, where the interaction can be selected between synergism, addivism, neutralism and antagonism.

2. The in vitro method of claim 1, CHARACTERIZED in that the culture medium of step a) can be selected from TSB, LB, Brain Heart Infusion (BHI), Peptone Water, MacConkey Medium, Nutrient Medium, Mueller-Hinton Medium (MHB), CAYE Medium, M Medium, Urea Medium, Selenite Cystine Medium, and Laurii Sulfate Medium.

3. The in vitro method of any of claims 1 or 2, CHARACTERIZED in that the bacteriophage cocktail comprises at least two bacteriophages.

4. The in vitro method of any of claims 1 to a mixed linear regression model with random intercept and slope.

5. A computer-implemented system trained to predict the type of interaction between bacteriophage formulations in combination with antibiotics on the growth of bacterial isolates using the methods of any of claims 1 to 4, CHARACTERIZED in that the system comprises: a) a susceptibility prediction module of one or more bacterial isolates of a given genus by a combination of a bacteriophage formulation and an antibiotic, which was trained with data on actual interactions between bacterial isolates of the same species or serovar as the target bacterial isolates, by means of growth curve assays in which said bacterial isolates were incubated with bacteriophage formulations, with antibiotics, or with a combination of bacteriophage formulations with antibiotics; b) a module for generating the best combinations of bacteriophage and antibiotic formulations with the aim of inhibiting the growth of one or more bacterial isolates of a given species or serovar, wherein said bacterial isolates of interest were not used to train the module described in point a); and c) a bacteriophage formulation production module, wherein the bacteriophage formulations are produced based on the results delivered by the module described in point b).

6. The computer-implemented system of claim 5, CHARACTERIZED in that the lytic susceptibility prediction module is further configured to perform at least: 38receive data from the databases of interactions of bacteriophage formulations with the host bacteria of a certain species or serovar with which the system was trained (101), receive data from the databases of interactions of antibiotics with the host bacteria of a certain species or serovar with which the system was trained (102); and receive data from the databases of interactions of combinations of bacteriophage formulations with antibiotics evaluated against the host bacteria of a certain species or serovar with which the system was trained (103).

7. The system of claim 6, CHARACTERIZED in that the module for generating the best combinations of bacteriophage and antibiotic formulations is further trained to at least: When one or more bacterial isolates of interest of the same species or serovar (104) with which the module (105) was trained are obtained, this information is received by the module (106) and an optimization and selection process is initiated to determine the best combinations of bacteriophage and antibiotic formulations that are capable of inhibiting or eliminating the growth of said one or more bacterial isolates of interest.

8. The system of claim 7, further CHARACTERIZED by the bacteriophage formulation production module, produces the bacteriophage formulations based on the results delivered by the module for generating the best combinations of bacteriophage and antibiotic formulations.

9. The system of any of claims 5 to 8, CHARACTERIZED in that the isolates can be selected from the following bacterial genera: Acinetobacter, Actinomyces, Aeromonas, Bacillus, Bartonella, Bordetella, Borrelia, Brucella, Burkholderia, Campylobacter, Chlamydia, Chlamydophila, Clostridium, Corynebacterium, Escherichia, Enterobacter, Ehrlichia, Enterococcus, Erysipelothrix, Francisella, Fusobacterium, Gardnerella, Haemophilus, Helicobacter, Klebsiella, Lactobacillus, Legionella, Leptospira, Listeria, Moraxella, Mycobacterium, Mycoplasma, Neisseria, Nocardia, Pasteurella, Pseudomonas, Proteus, Propionibacterium (e.g., Cutibacterium acnes), Rickettsia, Salmonella, Shigella, Staphylococcus, Streptococcus, Treponema, Ureaplasma, Vibrio, Yersinia, Xanthomonas, and Serratia.

10. The system of any of claims 5 to 9, CHARACTERIZED in that the bacterial isolates can be selected from the following bacterial species: Escherichia coli, Staphylococcus aureus, Streptococcus pneumoniae, Mycobacterium tuberculosis, Salmonella enterica, Helicobacter pylori, Neisseria meningitidis, Clostridium difficile, Pseudomonas aeruginosa, and Vibrio cholerae.

11. The method of any one of claims 1 to 4, CHARACTERIZED in that the antibiotics can be selected from Penicillin G, Ampicillin, Amoxicillin, Cloxacillin, Cephalexin, Ceftriaxone, Cefotaxime, Ceftiofur, Cefquinome, Tetracycline, Oxytetracycline, Doxycycline, Chlortetracycline, Erythromycin, Tylosin, Tulathromycin, Tilmicosin, Spiramycin, Azithromycin, Streptomycin, Gentamicin, Neomycin, Amikacin, Apramycin, Enrofloxacin, Ciprofloxacin, Norfloxacin, Marbofloxacin, Danofloxacin, Sulfadiazine, Sulfamethoxazole, Sulfadimethoxine, Sulfathiazole, Trimethoprim, Ormethoprim, Chloramphenicol, Florfenicol, Lincomycin, Clindamycin, Polymyxin B, Colistin, Metronidazole, Ronidazole, Monensin, Salinomycin, Narasin, Rifampicin, Vancomycin, Bacitracin, and Fosfomycin.

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