WO2023230702A1 - Nouveaux composés antimicrobiens isolés à l'aide d'un modèle d'apprentissage automatique entraîné sur un écran antibactérien à haut débit - Google Patents

Nouveaux composés antimicrobiens isolés à l'aide d'un modèle d'apprentissage automatique entraîné sur un écran antibactérien à haut débit Download PDF

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WO2023230702A1
WO2023230702A1 PCT/CA2023/050684 CA2023050684W WO2023230702A1 WO 2023230702 A1 WO2023230702 A1 WO 2023230702A1 CA 2023050684 W CA2023050684 W CA 2023050684W WO 2023230702 A1 WO2023230702 A1 WO 2023230702A1
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compounds
testing
compound
aureus
training
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Silvia CARDONA
Pingzhao HU
Rebecca Davis
Chengyou Liu
Zisanur RAHMAN
Andrew Hogan
Hunter STURM
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University Of Manitoba
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    • A61K—PREPARATIONS FOR MEDICAL, DENTAL OR TOILETRY PURPOSES
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    • A61K31/16—Amides, e.g. hydroxamic acids
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    • A61K31/19—Carboxylic acids, e.g. valproic acid
    • A61K31/195—Carboxylic acids, e.g. valproic acid having an amino group
    • A61K31/197—Carboxylic acids, e.g. valproic acid having an amino group the amino and the carboxyl groups being attached to the same acyclic carbon chain, e.g. gamma-aminobutyric acid [GABA], beta-alanine, epsilon-aminocaproic acid or pantothenic acid
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    • A61K31/403—Heterocyclic compounds having nitrogen as a ring hetero atom, e.g. guanethidine or rifamycins having five-membered rings with one nitrogen as the only ring hetero atom, e.g. sulpiride, succinimide, tolmetin, buflomedil condensed with carbocyclic rings, e.g. carbazole
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    • A61K31/4353—Heterocyclic compounds having nitrogen as a ring hetero atom, e.g. guanethidine or rifamycins having six-membered rings with one nitrogen as the only ring hetero atom ortho- or peri-condensed with heterocyclic ring systems
    • A61K31/437—Heterocyclic compounds having nitrogen as a ring hetero atom, e.g. guanethidine or rifamycins having six-membered rings with one nitrogen as the only ring hetero atom ortho- or peri-condensed with heterocyclic ring systems the heterocyclic ring system containing a five-membered ring having nitrogen as a ring hetero atom, e.g. indolizine, beta-carboline
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    • A61K31/395—Heterocyclic compounds having nitrogen as a ring hetero atom, e.g. guanethidine or rifamycins
    • A61K31/435—Heterocyclic compounds having nitrogen as a ring hetero atom, e.g. guanethidine or rifamycins having six-membered rings with one nitrogen as the only ring hetero atom
    • A61K31/44—Non condensed pyridines; Hydrogenated derivatives thereof
    • A61K31/445—Non condensed piperidines, e.g. piperocaine
    • A61K31/4523—Non condensed piperidines, e.g. piperocaine containing further heterocyclic ring systems
    • A61K31/4525—Non condensed piperidines, e.g. piperocaine containing further heterocyclic ring systems containing a five-membered ring with oxygen as a ring hetero atom
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    • A61K31/395—Heterocyclic compounds having nitrogen as a ring hetero atom, e.g. guanethidine or rifamycins
    • A61K31/435—Heterocyclic compounds having nitrogen as a ring hetero atom, e.g. guanethidine or rifamycins having six-membered rings with one nitrogen as the only ring hetero atom
    • A61K31/47—Quinolines; Isoquinolines
    • A61K31/4709—Non-condensed quinolines and containing further heterocyclic rings
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    • A61K31/395—Heterocyclic compounds having nitrogen as a ring hetero atom, e.g. guanethidine or rifamycins
    • A61K31/435—Heterocyclic compounds having nitrogen as a ring hetero atom, e.g. guanethidine or rifamycins having six-membered rings with one nitrogen as the only ring hetero atom
    • A61K31/47—Quinolines; Isoquinolines
    • A61K31/472—Non-condensed isoquinolines, e.g. papaverine
    • A61K31/4725—Non-condensed isoquinolines, e.g. papaverine containing further heterocyclic rings
    • A—HUMAN NECESSITIES
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    • A61K—PREPARATIONS FOR MEDICAL, DENTAL OR TOILETRY PURPOSES
    • A61K31/00—Medicinal preparations containing organic active ingredients
    • A61K31/33—Heterocyclic compounds
    • A61K31/395—Heterocyclic compounds having nitrogen as a ring hetero atom, e.g. guanethidine or rifamycins
    • A61K31/435—Heterocyclic compounds having nitrogen as a ring hetero atom, e.g. guanethidine or rifamycins having six-membered rings with one nitrogen as the only ring hetero atom
    • A61K31/47—Quinolines; Isoquinolines
    • A61K31/473—Quinolines; Isoquinolines ortho- or peri-condensed with carbocyclic ring systems, e.g. acridines, phenanthridines
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61P—SPECIFIC THERAPEUTIC ACTIVITY OF CHEMICAL COMPOUNDS OR MEDICINAL PREPARATIONS
    • A61P31/00—Antiinfectives, i.e. antibiotics, antiseptics, chemotherapeutics
    • A61P31/04—Antibacterial agents
    • C—CHEMISTRY; METALLURGY
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    • C07C—ACYCLIC OR CARBOCYCLIC COMPOUNDS
    • C07C35/00—Compounds having at least one hydroxy or O-metal group bound to a carbon atom of a ring other than a six-membered aromatic ring
    • C07C35/21—Compounds having at least one hydroxy or O-metal group bound to a carbon atom of a ring other than a six-membered aromatic ring polycyclic, at least one hydroxy group bound to a non-condensed ring
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    • C40B30/06—Methods of screening libraries by measuring effects on living organisms, tissues or cells
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Definitions

  • Multidrug-resistant (MDR) bacterial infections present a serious threat to public health.
  • MDR Multidrug-resistant
  • CDC Centers for Disease Control and Prevention
  • One of many interdisciplinary actions to address the crisis of antibiotic resistance is the acceleration of early antibiotic discovery (2,3).
  • HTS high throughput screens
  • These target-based approaches often fail to find active molecules against Gram-negative bacteria because most of the identified compounds do not penetrate the Gram-negative cell envelope (5).
  • Whole cell-based screens, where small molecule libraries are examined for inhibition of bacterial growth can overcome this problem.
  • the high costs and low success rate (1 -2%) of HTS approaches have discouraged these efforts.
  • D-MPNN directed-message passing neural network
  • D-MPNN works by propagating atom and bond information in a directed manner during the message passing phase, resulting in a high-level feature (hidden state) for each atom in a molecule.
  • hidden state high-level feature
  • all hidden states of atoms are aggregated together and form a molecule-level feature vector, which can be fed into a feed-forward neural network (FFN) for the task-specific predictions.
  • FNN feed-forward neural network
  • a method of screening a library of test compounds for candidate compounds having a specific biological activity comprising: providing a training library comprising a plurality of training compounds wherein:
  • the relative biological activity of each respective one training compound of the training library is known and is stored in a corresponding biological activity data entry in the training library; subjecting the structural data entry and the corresponding biological activity data entry of respective one training compounds of the training library to direct- message passing neural network (D-MPNN) analysis to detect at least one feature of training compounds of the training library corresponding to biological activity above a threshold level, thereby training a machine learning model for identifying candidate compounds; providing a testing library comprising a plurality of testing compounds wherein:
  • a typical HT screening, not using Al will have a hit rate of 0.1 - 1 % depending on the threshold set. But, as demonstrated herein, if you run the Al algorithm with virtual libraries and then use the compounds that the Al predicted as active for a real screen in the lab, then you will get an increase in the hit rate. In our case the increase was ten-fold as between 10 to 20 % of the compounds we tested in the lab were active, as discussed herein.
  • this method can be used with any type of high-throughput screen, for example, screening for antibacterial agents, antineoplastics, antifungals, enzymatic inhibitors and the like, essentially anything for which the screen is done in whole cells and has a phenotypic response: for example but by no means limited to growth live-dead and/or fluorescence due to a reporter system.
  • an antibacterial compound selected from the group consisting of: PHAR261659; STL529920; STL558147; 0167-0032; 6414-0936; STK760075; NAT5-396799; STL552768; STK802026; STOCK1 N-11202; STL513145; STL525490; AG-205/33771006; 8019- 5018; Z1205495496; Z1982493964; Z1029465088; and AS-69879.
  • the bacteria is selected from the group consisting of: B. cenocepacia; A. baumannii; E. cloacae; K. pneumoniae; S. aureus; methicillin- resistant S. aureus and P. aeruginosa.
  • a method for inhibiting growth of bacteria comprising: administering an effective amount of an antibacterial compound selected from the group consisting of: PHAR261659; STL529920; STL558147; 0167-0032; 6414- 0936; STK760075; NAT5-396799; STL552768; STK802026; STOCK1 N-11202; STL513145; STL525490; AG-205/33771006; 8019-5018; Z1205495496;
  • the bacteria is selected from the group consisting of: B. cenocepacia; A. baumannii; E. cloacae; K. pneumoniae; S. aureus; methicillin- resistant S. aureus; and P. aeruginosa.
  • growth inhibition is determined by comparing numbers of colony forming units from a specific volume, as discussed herein.
  • a method of treating a bacterial infection comprising: administering an effective amount of a compound selected from the group consisting of: PHAR261659; STL529920; STL558147; 0167-0032; 6414-0936; STK760075; NAT5-396799; STL552768; STK802026; STOCK1 N-11202; STL513145; STL525490; AG- 205/33771006; 8019-5018; Z1205495496; Z1982493964; Z1029465088; and AS- 69879 to an individual who has or who is suspected of having a bacterial infection.
  • a compound selected from the group consisting of: PHAR261659; STL529920; STL558147; 0167-0032; 6414-0936; STK760075; NAT5-396799; STL552768; STK802026; STOCK1 N-11202; STL513145; STL525490; AG-
  • the bacteria are selected from the group consisting of: B. cenocepacia; A. baumannii; E. cloacae; K. pneumoniae; S. aureus; methicillin- resistant S. aureus; and P. aeruginosa.
  • an anti- methicillin-resistant Staphylococcus aureus compound selected from the group consisting of: STL546315; STL547239; NP-019210; NAT2-252601 ; STL007987; and STL512984.
  • a method for inhibiting growth of methicillin-resistant S. aureus bacteria comprising: administering an effective amount of an antibacterial compound selected from the group consisting of: STL546315; STL547239; NP-019210; NAT2-252601 ; STL007987; and STL512984, to a region comprising the methicillin-resistant S. aureus bacteria, said methicillin-resistant S. aureus bacteria growth being inhibited compared to methicillin-resistant S. aureus bacteria growing under identical growth conditions except for the presence of the antibacterial compound.
  • an antibacterial compound selected from the group consisting of: STL546315; STL547239; NP-019210; NAT2-252601 ; STL007987; and STL512984
  • growth inhibition is determined by comparing the optical density or the colony forming units from a specific volume, as discussed herein.
  • a method of treating a methicillin-resistant S. aureus bacterial infection comprising: administering an effective amount of a compound selected from the group consisting of: STL546315; STL547239; NP-019210; NAT2-252601 ; STL007987; and STL512984, to an individual who has or who is suspected of having a methicillin-resistant S. aureus bacterial infection.
  • a method of developing an improved antibacterial compound comprising: providing an antibacterial compound selected from the group consisting of: PHAR261659; STL529920; STL558147; 0167-0032; 6414-0936; STK760075; NAT5- 396799; STL552768; STK802026; STOCK1 N-11202; STL513145; STL525490; AG- 205/33771006; 8019-5018; Z1205495496; Z1982493964; Z1029465088; and AS- 69879; and modifying said antibacterial compound such that one or more properties of antibacterial compound is improved.
  • the improved property may be for example but by no means limited to improved anti-bacterial activity.
  • improved anti-bacterial activity include but are by no means limited to: greater antibacterial activity against one or more bacterial strains, increased residency time, increased stability, increased solubility, increased organism range and the like.
  • Other suitable improvements will be well known to those of skill in the art, as will methods for making such modifications.
  • Figure 1 Initial training and performance evaluation of the machine learning model.
  • A High-throughput screening data generated by screening a compound library of 29,537 compounds against B. cenocepacia K56-2 wild-type. Using B-score ⁇ -17.5 as threshold, the screening yielded 256 active compounds. Darkblue are inactive and red are active compounds.
  • B A graph-based machine learning model, namely D-MPNN, was trained on atom and bond features of molecules. To further increase the model’s performance, additional global features were also incorporated into the model and the results were compared. Dataset was split into 80:10:10 ratio to train, validate and test the model.
  • C ROC-AUC plot evaluating model performance after training. The model (binary classification model trained with RDKit descriptors) attained a ROC-AUC of 0.823.
  • Figure 2 In vitro testing of top ranked predicted compounds from an FDA approved compound library.
  • A Schematic of the screening protocol. 81 commercially available compounds (from the top 100) were screened.
  • B The screening identified 21 bioactive compounds with positive predictive value (PPV) of 25.9%. Darkblue are inactive and red are active compounds.
  • C Top 100 ranked compounds selected for empirical testing belong to different drug families. As expected, most of the compounds exhibiting bioactivity were antibiotics or antimicrobial compounds.
  • D The ratio of OD 600nm and prediction scores were plotted against the predicted rank of the corresponding compounds. The results show a linear correlation between the prediction score and bioactivity. Predicted score is the probability of a compound being active as predicted by the ML model.
  • Predicted rank is the order of the compounds based on the predicted score where compounds with the higher predicted scores ranked higher. Darkblue and red indicate compounds’ probability of being inactive and active, respectively. Results are average of at least three independent biological replicates.
  • Figure 3 Enhanced sensitivity of the CRISPRi knockdown mutants indicated RpoB as the in vivo target of STL558147.
  • A Chemical structures of STL558147 and Rifampicin.
  • B-D Comparison of hypersensitive CRISRPi knockdown mutants to novobiocin (B), rifampicin (C) and STL558147 (D).
  • CRISPRi knockdown mutants exhibited hypersusceptibility to their cognate antibiotics and suggested RpoB as the in vivo target of STL558147. Blue indicates more growth (less inhibition) and red indicates less growth (more inhibition). Results are average of at least three independent biological replicates.
  • Figure 4 Synergy maps of STL558147 and rifampicin in combination of other antibiotics against B. cenocepacia K56-2. Synergy plots of STL558147 (A) and rifampicin (B) with ceftazidime, colistin, and polymyxin B. The observed synergistic interactions of STL558147 were 2-3 times stronger than rifampicin and similar to the widely used synergistic antimicrobial combination of avibactam and ceftazidime (C). The synergy scores were calculated based on the widely used Bliss independence (52) and Loewe additivity (53) models.
  • FIG. 5 Screening of PHAR261659 analogs. PHAR261659 analogs with different side chains were selected based on lower predicted logP values. STL529920, a stereoisomer of PHAR261659 exhibited growth inhibitory activity against all six pathogens tested. The activity of compounds identified as growth inhibitory and non-growth inhibitory are shown in red and blue, respectively. Results are the average of three independent biological replicates. Error bars indicate mean ⁇ SD.
  • Figure 6 Determination of PHAR261659 mechanism of action with CRISPRi-Seq. A) Overview of the CRISPRi-Seq workflow.
  • (B) Exposure of a Burkholderia cenocepacia K56-2 pooled essential gene knockdown mutant library to PHAR261659 indicated pth as the most significantly depleted mutants (from the pool). Blue and red dots represent enriched and depleted mutants, respectively. Dashed line indicates the significant threshold (a 0.05).
  • the developed ML approach is used for compound prioritization prior to screening, thereby providing a method by which a library of compounds can be screened for any detectable biological activity to identify a subset of candidate compounds for testing, with a greatly increased hit rate compared to typical prior art high throughput screens.
  • cenocepacia is part of the Burkholderia cepacia complex (Bcc), naturally antibiotic-resistant bacteria that cause infections in immunocompromised individuals (15).
  • Bcc Burkholderia cepacia complex
  • Our goals were 1 ) to find new antibacterial compounds against B. cenocepacia and, 2) to test the predictive power of the deep learning model for broad range activity when trained on HTS datasets performed in antibiotic-resistant bacteria.
  • we applied the trained model to a natural product library and identified a panel of growth inhibitory compounds active against both Gram-positive and Gram-negative pathogens, demonstrating that predictions of antibacterial activity against B.
  • Compound STL558147 was active against all six species tested.
  • Compound PHAR261659 was active against 4 species: A. baumannii 1225, E. cloacae ENT001_EB001 , P, aeruginosa PAO1 , K. pneumoniae ESBL_120310 but not against S. aureus ATCC33592 and B. cenocepacia K56-2.
  • Other compounds had a more narrow range of activity and inhibited only the Gram-positive S. aureus AT CC33592 (STL546315, STL547239, NP-0192110, STK760075, STL552768).
  • combinatorial antibiotic treatments can be an effective therapeutic strategy to prevent antimicrobial resistance.
  • Combinatorial antibiotic strategies achieve the therapeutic effect at relatively lower concentration, decreasing the adverse and toxic effects of high antibiotic concentration and severely restrict the acquisition of drug resistance.
  • Rifampicin is known to enhance the activity of other antibiotics when used in combination (29-32) which is known as synergy.
  • B. cenocepacia K56-2 we could observe the synergistic growth inhibitory effect of rifampicin with ceftazidime and colistin but not with polymyxin B ( Figure 4B).
  • STL558147 has a relatively high minimum inhibitory concentration (MIC) (256 ⁇ g/mL) compared to rifampicin (64 ⁇ g/mL) against B. cenocepacia K56-2.
  • MIC minimum inhibitory concentration
  • STL558147 exerted 2-3 times stronger synergistic growth inhibitory activity with ceftazidime, colistin and polymyxin B at the same drug concentrations ( Figure 4), warranting the further development of STL558147 to generate a more potent derivative.
  • Our method utilizes a HTS dataset of an antibiotic-resistant bacterium to train a machine learning model in the discovery of antibacterial molecules of broad-range spectrum. We believe that our methodology can be applied to any previously obtained HTS datasets. Bioactivity predictions can be used to prioritize compounds to be tested, which will reduce the library size and increase the hit rate of subsequent screens.
  • a method of screening a library of test compounds for candidate compounds having a specific biological activity comprising: providing a training library comprising a plurality of training compounds wherein:
  • the relative biological activity of each respective one training compound of the training library is known and is stored in a corresponding biological activity data entry in the training library; subjecting the structural data entry and the corresponding biological activity data entry of respective one training compounds of the training library to direct- message passing neural network (D-MPNN) analysis to detect at least one feature of training compounds of the training library corresponding to biological activity above a threshold level, thereby training a machine learning model for identifying candidate compounds; providing a testing library comprising a plurality of testing compounds wherein:
  • an antibacterial compound selected from the group consisting of: PHAR261659; STL529920; STL558147; 0167-0032; 6414-0936; STK760075; NAT5-396799; STL552768; STK802026; STOCK1 N-1 1202; STL513145; STL525490; AG-205/33771006; 8019- 5018; Z1205495496; Z1982493964; Z1029465088; and AS-69879.
  • the bacteria is selected from the group consisting of: B. cenocepacia; A. baumannii; E. cloacae; K. pneumoniae; S. aureus; methicillin- resistant S. aureus; and P. aeruginosa.
  • a method for inhibiting growth of bacteria comprising: administering an effective amount of an antibacterial compound selected from the group consisting of: PHAR261659; STL529920; STL558147; 0167-0032; 6414- 0936; STK760075; NAT5-396799; STL552768; STK802026; STOCK1 N-1 1202; STL513145; STL525490; AG-205/33771006; 8019-5018; Z1205495496;
  • the bacteria is selected from the group consisting of: B. cenocepacia; A. baumannii; E. cloacae; K. pneumoniae; S. aureus; methicillin- resistant S. aureus; and P. aeruginosa.
  • growth inhibition is determined by comparing the optical density or the colony forming units from a specific volume, as discussed herein.
  • wavelengths for determining optical density of bacterial cultures are well-known in the art.
  • region refers to a location capable of and/or known to support bacterial growth.
  • a method of treating a bacterial infection comprising: administering an effective amount of a compound selected from the group consisting of: PHAR261659; STL529920; STL558147; 0167-0032; 6414-0936; STK760075; NAT5-396799; STL552768;
  • the bacteria is selected from the group consisting of: B. cenocepacia; A. baumannii; E. cloacae; K. pneumoniae; S. aureus; methicillin- resistant S. aureus; and P. aeruginosa.
  • an anti- methicillin-resistant Staphylococcus aureus compound selected from the group consisting of: STL546315; STL547239; NP-019210; NAT2-252601 ; STL007987; and STL512984.
  • a method for inhibiting growth of methicillin-resistant S. aureus bacteria comprising: administering an effective amount of an antibacterial compound selected from the group consisting of: STL546315; STL547239; NP-019210; NAT2-252601 ; STL007987; and STL512984, to a region comprising the methicillin resistant S. aureus bacteria, said methicillin-resistant S. aureus bacteria growth being inhibited compared to a methicillin-resistant S. aureus bacteria growing under identical growth conditions except for the presence of the antibacterial compound.
  • an antibacterial compound selected from the group consisting of: STL546315; STL547239; NP-019210; NAT2-252601 ; STL007987; and STL512984
  • growth inhibition is determined by comparing the optical density or the colony forming units from a specific volume, as discussed herein.
  • a method of treating a methicillin-resistant S. aureus bacterial infection comprising: administering an effective amount of a compound selected from the group consisting of: STL546315; STL547239; NP-019210; NAT2-252601 ; STL007987; and STL512984, to an individual who has or who is suspected of having a methicillin- resistant S. aureus bacterial infection.
  • a method of developing an improved antibacterial compound comprising: providing an antibacterial compound selected from the group consisting of: PHAR261659; STL529920; STL558147; 0167-0032; 6414-0936; STK760075; NAT5- 396799; STL552768; STK802026; ST0CK1 N-11202; STL513145; STL525490; AG- 205/33771006; 8019-5018; Z1205495496; Z1982493964; Z1029465088; and AS- 69879; and modifying said antibacterial compound such that one or more properties of antibacterial compound is improved.
  • the improved property may be for example but by no means limited to improved anti-bacterial activity.
  • improved anti-bacterial activity include but are by no means limited to: greater antibacterial activity against one or more bacterial strains, increased residency time, increased stability, increased solubility, increased organism range and the like.
  • Other suitable improvements will be well known to those of skill in the art, as will methods for making such modifications.
  • Bioisosteres are compounds where one or more functional groups of a molecule are changed to other functional groups that are of similar size or electronic properties.
  • BROOD program from Openeye scientific, 34 bioisosteres of PHAR261659 have been generated. None of the generated compounds are commercially available, however Yan-Ni Sun and coworkers (57) have found a method to reliably synthesize tetrahydro-p-carbolines. This is important as tetrahydro-p-carbolines, as pictured below are the class of compounds that PHAR261659 falls into.
  • Thescaffold known as tetrahydro-p-carbolines.
  • this compound is stereoselective, meaning that stereochemistry would not have to be introduced later on in the synthesis.
  • the dataset used in the ML approach consisted of 29,537 compounds with residual growth (RG) values and average B-scores (17).
  • the RG measures the ratio of bacterial growth in the presence and absence of the compounds.
  • the B-score is a measure of relative potency that adjusts the RG for any screening artifacts resulting from well position (row and column) in the assay plate during the HTS.
  • the B-score is inversely proportional to compound potency where negative B-scores indicate greater growth inhibitory activity of the compounds.
  • D-MPNN directed-message passing neural network
  • the D- MPNN was previously used to train a binary classification deep learning model with 2,335 compounds for predicting antibiotic activity (13).
  • a larger library approximately 30,000 compounds
  • the trained model generated scores between 0 and 1 for each molecule, with 1 indicating the highest probability for growth inhibitory activity.
  • regression tasks on both the average B-score and RG where the two potency measurements of activity against B. cenocepacia were used simultaneously to train multi-task models.
  • the scaffold split separates samples into subsets based on molecular scaffolds.
  • Model 6 To validate the model’s ability to make predictions on compounds outside of the training dataset, we employed Model 6 on an FDA-approved compound library containing 1 ,614 compounds that were not present in the training set and predicted their growth inhibitory activity. The model generated a single value between 0 and 1 for each molecule, indicating the probability of the molecule being active. The 100 top- ranked compounds contained a large fraction (-48.75%) of antibiotics. After removal of duplicated compounds, we tested 81 commercially available compounds for growth inhibitory activity against B. cenocepacia K56-2 ( Figure 2A). These 81 compounds were also enriched in known antibiotics, antimicrobials, and antineoplastic agents. For experimental validation, we defined as inhibitory those compounds that inhibited at least 20% of normal growth.
  • Rifampicin targets the [3 subunit of the bacterial DNA-dependent RNA polymerase (21 , 22), encoded by rpoB.
  • STL558147 targets RpoB
  • the CRISPRi system developed for Burkholderia comprises of a chromosomally integrated dCas9 from Streptococcus pyogenes placed under the control of a rhamnose-inducible promoter and plasmid- borne target-specific single guide RNA (sgRNA) driven by a constitutively active synthetic promoter PJ23119 (25). Addition of rhamnose induces dCas9 expression which binds to sgRNA and sterically blocks the transcription of the target specified by the sgRNA.
  • sgRNA single guide RNA
  • PJ23119 constitutively active synthetic promoter PJ23119
  • Antibiotic combinatorial therapy has been a common strategy to enhance the efficacy of the antibiotics (27, 28).
  • Rifampicin is known to have synergistic interactions with colistin and meropenem in vitro against Pseudomonas spp., Acinetobacter spp., and carbapenemase-producing Enterobacteriaceae (29-32).
  • STL558147 exerts synergistic interactions with clinically relevant antibiotics against B. cenocepacia K56-2
  • we performed a microdilution checkerboard assay Using the Bliss interaction score and Loewe additivity score, we considered scores >15 as synergistic and ⁇ -15 as antagonistic (33).
  • PHAR261659 a compound with no previously reported antibiotic activity, has antibacterial activity against a broad range of pathogens (Tables 1 and 2). Specifically, PHAR261659 exhibited growth inhibitory activity against A. baumannii 1225, E. cloacae ENT001_EB001 and K. pneumoniae ESBL_120310 (Tables 1 and 2). However, we did not observe growth inhibitory activity against B. cenocepacia K56-2 and S. aureus AT CC33592 (MRSA). Moreover, while PHAR261659 displayed bioactivity at the screening concentration (50 ⁇ M), the compound was not soluble beyond 128 ⁇ M.
  • mutants that were partially sensitive to STL529920 were aminopeptidase P, mnmA, rpsA, and waaA. Products of the pth, aminopeptidase P, mnmA, rpsA genes involved in protein translation suggesting that STL529920 inhibits bacterial growth by inhibiting translation, specifically by inhibiting peptidyl-tRNA hydrolase.
  • D-MPNN The code of D-MPNN used in this study is implemented in the package Chemprop (46), which was built based on the architecture proposed by Gilmer etal., named message passing neural network (MPNN) (47).
  • MPNNs take atom and bond features as inputs and aggregate the features through a message passing phase and a readout phase (47).
  • message passing neural network MPNNs
  • the readout phase of D-MPNN follows the same paradigm of typical MPNNs, in which hidden states for atoms in a molecule are aggregated together and form a molecule-level representation.
  • RDKit descriptors are comprehensive cheminformatics descriptors which include a broad spectrum of chemical properties at the molecular level, thus providing a rich source of chemical information on multiple aspects.
  • normalization was applied to the additional features to scale the values to a fixed range before entering into networks.
  • min-max scaling was applied to normalize count-based Morgan fingerprints and 200 RDKit descriptors were normalized by fitting to the cumulative density functions.
  • Bayesian hyperparameter optimization using Hyperopt package (48).
  • the Bayesian hyperparameter optimization uses the results of prior trials to make informed decisions for what parameter values to try in the next trial. Compared to the grid search method, this method requires fewer iterations to find the optimal hyperparameters.
  • Ensembling a commonly used technique in machine learning for improving model's performance, was also applied in our training process.
  • the ensemble method combines the predicted results from several identically structured models with different initial weights. That is, models are trained independently and separately, then the prediction values were averaged with equal weights, resulting the final prediction (19).
  • the deep learning model was trained with a HTS dataset that used the Canadian Compound Collection (CCC) against B. cenocepacia K56-2 (14).
  • the training dataset consisted of 29,537 compounds.
  • the textual molecular representation from the SMILES strings were transformed into numeric representations to generate molecular features during training.
  • the SMILES strings are converted to numerical features by using algorithms integrated in cheminformatics software RDKit. Similar to Selin et al., (14), we used Residual Growth (RG) ( ⁇ 0.7 RG) and average B-Score ( ⁇ - 17.5) to call growth inhibitory activity of the compounds against B. cenocepacia K56- 2.
  • the average B-Score -17.5 was used as the bioactivity threshold.
  • Compounds with average B-Score of less than -17.5 were considered as growth inhibitory, which resulted in 256 active compounds.
  • the data is severely imbalanced, to maximize the utilization of data, we decided against enforcing class balance on data during training.
  • the active compounds are labeled as 1 while the rest are labeled as 0, then used as targets for classification training.
  • the D-MPNN output was a single value between 0 and 1 for each molecule, indicating the probability of the compound to have growth inhibitory activity.
  • For the regression task of D-MPNN both the average B-Score and RG were used in the training together but evaluated separately in the results.
  • scaffold split Different from the random split that is commonly used in ML, the scaffold split is specifically designed for the QSPR/QSAR tasks, where the Murcko scaffold for each molecule is calculated and used during the splitting process (49). Assigning compounds to data bins based on Scaffold scores enforces validation and testing sets to have more molecular diversities and reduces similarities between data sets. Therefore, the scaffold split strategy has a more realistic and challenging evaluation comparing to the random split (11). Besides the scaffold split, we also trained models on data bins that were randomly partitioned as comparison.
  • F1 Scores were calculated using the following formula:
  • CRISPRi knockdown mutants targeting the rpoBC were created as previously mentioned (25).
  • the conditional growth phenotype was determined by performing growth kinetics. Overnight cultures of the mutants were back diluted to OD 600nm 0.01 and arrayed onto 96-well plate containing LB broth with trimethoprim 100 ⁇ g/mL and with or without 1 % rhamnose at 37°C with shaking (230 rpm). OD 600nm readings were taken using BioTek Synergy 2 plate reader at 1 -hour interval for 22-24h.
  • Rhamnose concentration that inhibits 50% growth of the mutants (Rha IC 50 ) compared to the wild-type was determined by growing the mutants in a rhamnose gradient at 37°C with shaking (230 rpm) in 96-well format. OD 600nm reading was taken after 20 hours of growth and dose-response curve was created with Graphpad PRISM version 6.0.0. Rha IC 50 values were calculated from the rhamnose dose-response curve using the Hill coefficient of the equation.
  • synergy scores represent the mean response deviated from the reference model due to interactions between the combined drugs.
  • Table 1 Compounds from the natural product library that exhibited broad-spectrum growth inhibitory activity.
  • RG residual growth
  • Table 2 Compounds from the natural product library that exhibited growth inhibitory activity against Gram-positive Methicillin- resistant Staphylococcus aureus (MRSA) only. Note: The numbers show the residual growth (RG) of the organisms after 5h of exposure to the compounds in LB media. RG is the ratio of growth (measured as optical density) in the presence and absence (DMSO) of compound. Results are average of at
  • Table 3 Deep learning models trained with different combinations of molecule- level features.

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Abstract

Le criblage de nouveaux composés antibactériens dans des bibliothèques de petites molécules a un faible taux de réussite. Ici, nous avons appliqué un criblage virtuel basé sur l'apprentissage automatique (ML) pour une activité antibactérienne et évalué sa puissance prédictive par validation expérimentale. Nous avons d'abord binarisé trente-mille composés en fonction de leur activité inhibitrice de croissance (taux de succès de 0,67 %) contre la bactérie résistante aux antibiotiques Burkholderia cenocepacia et décrit leurs caractéristiques moléculaires à l'aide d'un réseau neuronal à propagation de messages dirigés (D-MPNN). Ensuite, nous avons utilisé les données pour entraîner un modèle ML qui a atteint un score de caractéristiques de fonctionnement de récepteur (ROC) de 0,823 sur l'ensemble de test. Enfin, nous avons prédit une activité antibactérienne dans des bibliothèques virtuelles correspondant à 1614 composés à partir de la liste approuvée par administration d'aliments et de médicaments (FDA) et de 224205 produits naturels. Un taux de réussite de 26 % et 12 %, respectivement, a été obtenu lorsque nous avons testé les composés prédits de rang supérieur pour une activité inhibitrice de croissance contre B. cenocepacia, qui représente au moins une augmentation de 12 fois à partir du taux de réussite précédent. De plus, plus de 51 % des composés naturels antibactériens prédits inhibés des pathogènes ESKAPE montrant que des prédictions se dilatent au-delà de l'ensemble de données spécifique à un organisme dans une large plage de bactéries. Globalement, l'approche ML développée peut être utilisée pour la priorisation de composés avant le criblage, augmentant le taux de réussite typique de criblages à haut débit. L'invention concerne également des composés antibactériens isolés par l'écran.
PCT/CA2023/050684 2022-06-03 2023-05-17 Nouveaux composés antimicrobiens isolés à l'aide d'un modèle d'apprentissage automatique entraîné sur un écran antibactérien à haut débit WO2023230702A1 (fr)

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CN118072869A (zh) * 2024-02-27 2024-05-24 华南农业大学 一种基于深度生成模型的离子液体设计方法

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