High-Throughput Screening of Natural Product and Synthetic Molecule Libraries for Antibacterial Drug Discovery
Abstract
:1. Introduction
2. Approaches and Strategies for Antibacterial High-Throughput Screening (HTS) Assays
2.1. Cellular and Molecular Target-Based HTS
2.2. Mechanism Informed Phenotypic HTS Screening (Reporter-Based HTS)
2.3. Virulence and Quorum-Sensing Targeting HTS
2.4. Genome Science, Molecular Target Identification, and HTS
2.5. Combination of HTS Strategies and Multi-Target Hits
2.6. Externally Interceded HTS
3. Natural Product Library (NPL) Screening for Antibacterial Drug Discovery
3.1. Historical Perspectives and Major Bottlenecks
3.2. Why Are Natural Products Still Preferred for Drug Discovery despite the Challenges in Screening Them?
3.3. Challenges of Collecting Natural Sources for Screening
3.4. Challenges in Growing Natural Sources under Laboratory Conditions
3.5. Challenges in Extracting Natural Products
3.6. Challenges in Preparing a Natural Product Library for HTS
3.7. Available Resources for NPL for Drug Screening Research and Campaigns
3.8. Examples of Successful HTS of NPL for Antibacterial Drug Discovery
3.9. Antibacterial Biofilm Inhibitory Compounds from HTS of NPL
3.10. Antibacterial Agents from HTS of Unconventional Natural Sources
3.11. Metagenomics and Metabologenomics Aided NPL HTS for Antibacterial Drug Discovery
3.12. Microfabricated Chip-Based HTS of NPL for Antibacterial Drug Discovery from Uncultivable Organisms
3.13. Coculture-Based HTS of NPL for Antibacterial Drug Discovery
3.14. Integrated Platforms for HTS of NPL for Antibacterial Drug Discovery
4. Synthetic Molecule Library (SML) Screening for Antibacterial Drug Discovery
4.1. Historical Perspective and Available Resources
4.2. Cellular Target-Based HTS (CT-HTS) of the Synthetic Molecule Library (SML)
4.3. Molecular Target-Based HTS (MT-HTS) of the Synthetic Molecule Library (SML)
4.4. Other Miscellaneous HTS Assays Using the Synthetic Molecule Library (SML)
4.5. High-Throughput Synthetic Molecule Library Screening against Quorum-Sensing and Biofilm-Forming Bacteria
4.6. High-Throughput Synthetic Molecule Library Screening Using Biomimetic Conditions
4.7. High-Throughput Synthetic Molecule Library Screening, Drug Repurposing, and Synergy
4.8. The Library of Synthetic Peptides and Polymers and Antibacterial High-Throughput Screening
5. Technical Considerations for Designing High-Throughput Screening Assays for Antibacterial Drug Discovery
5.1. Library Selection
5.2. Logistics and Technology Platforms
5.3. Storage and Stability
5.4. Microorganisms and Culture Conditions
5.5. Orthogonal Assays for Hit Validation, Toxicity Screening, Dereplication, and Target Identification
5.6. Error Management, Quality Control, and HTS Triage
5.7. In Vivo Studies and Pharmacodynamic and Pharmacokinetic Characteristics
6. Technologies and Other Auxiliary Approaches for Antibacterial HTS Assays
6.1. Selective Screening
6.2. Genetic Engineering, Synthetic Biology, and Omics Technology
6.3. In Silico/Virtual Screening
6.4. Combinatorial Chemistry and the Focused Synthetic Approach
6.5. Microfluidic, Nanofluidic, and Imaging-Based Technologies
6.6. Phage Display and Antibody-Based Technologies
6.7. Metal Nanoparticles
6.8. Spectrometry, Cytometry, Spectroscopy, and Other Biophysical Approaches
7. Final Remarks and Future Perspectives
8. Conclusions
Funding
Conflicts of Interest
References
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|
Manufacturing and Pilot Scale Bioreactor | Lab-Scale Bioreactor | Microplate Culture Model | Microfluidic Culture Model | |
---|---|---|---|---|
Volume | 100–200,000 L | 0.25–30 L | 0.1–1.0 mL | <100 nL |
Throughput (genotypes/year) | <10 | ~103 | ~106 | ~109 |
Cost per fermentation | USD 10 K–USD 200 K | ~USD 1 K | <USD 0.5 | <USD 0.0001 |
Advantages | Scalable, cost-effective | High-confident strain Manufacturing with high control | High parallelization to rank genotypes for further testing | Highest throughput with the smallest cost |
Limitations | Hardware constraints hinder process control | Non-homogenous chemical environment | High false positives, irreproducibility, or unreliable readouts | Highly sophisticated instruments are required, very low sample size requires high sensitivity |
Predictive power for commercial scale | – | Reliable | Variable | Unexplored |
Successful Hit | Target Organism | Protein Target | MIC a/IC50 b | Reference |
---|---|---|---|---|
Bischloroanthrabenzoxocinone (BABX) (12) | E. coli (MB4902) S. aureus (MB2985) | Fatty acid synthesis Type II (FASII) | 0.2–0.4 μg/mLa | [120] |
Anziaic acid (13) | B. subtilis (ATCC 6633) E. coli (BAS3023) | Topoisomerase I | 6 μg/mL a 12 μg/mL a | [121] |
04E04 03F11 04E04 02F09, 02H08 | E. faecalis H. influenzae M. catarrhalis P. aeruginosa hypersensitive (ATCC 35151) | Phenylalanyl-tRNA Synthetases (PheRS) | 8 μg/mL a 4 μg/mL a 1 μg/mL a 4 μg/mL a | [122] |
Resveratrol Tetramer (-)-Hopeaphenol (14) | Y. pseudotuberculosis | Type III secretion | 6.6 μM b | [123] |
NAT13-338148 NAT18-355531 NAT18-355768 | Clostridioides difficile (ATCC BAA 1870) | ND | 0.5–2 μg/mL a | [124] |
3-methoxynordomesticine (15) | Mycobacterium tuberculosis H37Rv Mycobacterium bovis BCG | MurE (IC50 < 100 µM) | ≤5 μg/mL a | [125] |
HTS Strategy | Library Size and Name | Z/Z′-Score | Target | Hits (ID/name) | Activity * | Cytotoxicity Tested | Reference | ||
---|---|---|---|---|---|---|---|---|---|
MT | CT | C | |||||||
✔ | ✔ | 20,000 (ChemBridge) | 0.82 | N-acetylglucosamine-1-phosphate uridyltransferase (GlmU)(acetyltransferase domain) | 6624116 a 5655606 a 5810599 a 6012954 a | 65.15 ± 3.31 µM b 18.58 ± 0.81 µM b 9.01 ± 0.04 µM b 38.84 ± 0.29 µM b | Human liver hepatocellular carcinoma cells (HepG2 cells) | [190] | |
M. tuberculosis H37Rv | 6624116 a 5655606 a 5810599 a 6012954 a | 16 µg/mL c 2 µg/mL c >32 µg/mL c >32 µg/mL c | |||||||
✔ | 50,000 (Maybridge) | 0.71 | N-acetylglucosamine-1-phosphate uridyltransferase (GlmU) (acetyltransferase domain) | MAC0021939 MAC0008028 MAC0029665 | 189 ± 17.7 nM d 1.11 ± 0.08 ×103 nM d 28.2 ± 1.83 nM d | Not tested | [191] | ||
✔ | 20,502 (Broad Institute) | 0.7–0.8 | M. tuberculosis H37Rv, M. bovis BCG strain Pasteur, and M. smegmatis MC2155 | Benzimidazole derivative e Nitro-triazole derivative f | 37.5 µM c 0.488 µM g | Not tested | [192] | ||
Membrane protein large 3 (MmpL3) (target for ‘e’)Decaprenylphosphoryl-β-d-ribose 2′-epimerase (DprE1) (target for ‘f’) | |||||||||
✔ | 57,000 (Timtec, Cerep, and ChemBridge) | <0.5 | Mycobacterium tuberculosis H37Rv, H37Ra and BCG Pasteur | DNB1 h DNB2 i | 0.2 µM c 0.2 µM c | Mouse bone marrow-derived macrophages | [193] | ||
Decaprenylphosphoryl-β-D-ribose 2′-epimerase (DprE1/DprE2) | |||||||||
✔ | 125,000 | Not reported | ΔtolC Escherichia coli | Eight compounds containing 2-pyrazol-1-yl-thiazole scaffold | 0.037–8 µg/mL c | HEK293 cells A549 cells MCF7 cells | [194] | ||
✔ | 17,500 (Chemical Biology Consortium Sweden Primary Screening Set Collection) | Not reported | Streptococcus pneumonia T4 | THCz-1 (1-amino substituted Tetrahydrocarbazole) | 1.3 µg/mLc | Lung epithelial A549 cells | [195] | ||
Undecaprenyl pyrophosphate-containing lipid intermediates | |||||||||
✔ | 28,300
| 0.5–0.9 | Vibrio cholera N16961 and NM06-058 | vz0825 vz0500 1541-0004 | 1.6 µM c 3.1 µM c 6.3 µM c | Mouse fibroblast cell line L929 | [189] | ||
K+-channel sensor histidine kinase (KdpD) | |||||||||
✔ | 20,338
| 0.5–0.9 | 0139 V. cholerae MO10 (aphA transcript) | vz0761 vz0852 53760866 | 10 µM c 6 µM c 38 µM c | Mouse fibroblast cell line L929 j | [196] |
Target Bacteria | Compounds Showing Synergy | Reference |
---|---|---|
Acinetobacter baumannii strain AB5075 | 5-fluorouracil and azithromycin Colistin sulfate with fluspirilene and Bay 11-7082 | [218] |
MRSA (ATCC 43300) | Cefoxitin with floxuridine, gemcitabine, novobiocin, rifaximin, 4-quinazolinediamine, celastrol, and teniposide | [220,222,227] |
Ceftobiprole with cloxacillin, cefotaxime, oxacilline, flucloxacillin, dicloxacillin, nafcillin, imipenem, meropenem, cefoxitin, piperacillin, and tazobactam | [228] | |
Ceftaroline with cloxacillin | ||
MRSA USA300 | Cefuroxime and ticlopidine | [229] |
MRSA N315 | Meropenem, piperacillin, and tazobactam | [230] |
MRSA ATCC 29213 Pseudomonas aeruginosa | IITR00693 (2-Aminoperimidine) and polymyxin B | [231] |
Pseudomonas aeruginosa lasB-gfp (ASV) | Auranofin and colistin | [232] |
P. aeruginosa (Clinical isolates) | Tetracycline and polyamine scaffolds containing molecules | [233] |
P. aeruginosa ATCC 27853 | Amikacin and ceftriaxone | [234] |
P. aeruginosa P6540 | Rifampicin and imipenem Colistin and imipenem | [235] |
M. smegmatis mc2155 M. tuberculosis H37Rv (ATCC 27294) | Spectinomycin and bromperidol | [236] |
M. tuberculosis H37Rv (ATCC 27294) | Rifampicin with Compound 5655606 | [190] |
Legionella pneumophila serogroup 1 (Lp02::flaA::lux) | Rifampin, azithromycin, and minocycline in any combination | [237] |
Carbapenem-resistant enterobacteriaceae | Triclosan and meropenem | [238] |
Trimethoprim-resistant clinical E. coli | Azidothymidine with trimethoprim and sulfamethizole | [239] |
Pandoraea nosoerga P8103 | Rifampicin and minocycline | [235] |
Burkholderia multivorans P6539 | Fluoroquinolones and β-lactams | |
E. coli BW25113 P. aeruginosa PA01 | Novobiocin with pivmecillinam and echinomycin Novobiocin with niridazole | [240] |
Klebsiella pneumoniae N11-2218 | Meropenem with aspergillomarasmine A | [241] |
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Ayon, N.J. High-Throughput Screening of Natural Product and Synthetic Molecule Libraries for Antibacterial Drug Discovery. Metabolites 2023, 13, 625. https://doi.org/10.3390/metabo13050625
Ayon NJ. High-Throughput Screening of Natural Product and Synthetic Molecule Libraries for Antibacterial Drug Discovery. Metabolites. 2023; 13(5):625. https://doi.org/10.3390/metabo13050625
Chicago/Turabian StyleAyon, Navid J. 2023. "High-Throughput Screening of Natural Product and Synthetic Molecule Libraries for Antibacterial Drug Discovery" Metabolites 13, no. 5: 625. https://doi.org/10.3390/metabo13050625
APA StyleAyon, N. J. (2023). High-Throughput Screening of Natural Product and Synthetic Molecule Libraries for Antibacterial Drug Discovery. Metabolites, 13(5), 625. https://doi.org/10.3390/metabo13050625