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Microbes in the Media: In Silico Screening and Halicin
- 1. IN SILICO SCREENING AND HALICIN Roma Kankaria BIOZ 303-L53
- 2. Past and Present • In the past few decades • Few new antibiotics have been developed • The antibiotics that have been developed are not novel • Rising issue of antibiotic resistance • Current methods for screening for new antibiotics are inadequate
- 3. In Silico Screening • Machine-learning algorithms • Screens more than one hundred million chemical compounds to identify antibiotics that kill bacteria using different mechanisms than those of current drugs • Goal:To use artificial intelligence to design new drugs
- 4. MIT’s Computer Model • Computer model: Correlated molecular structures of compounds with characteristics like ability to kill bacteria • MIT’s Model • Designed to look for chemical features that make molecules effective at killing Escherichia coli • Tested on a large library of compounds • Identified halicin
- 5. Halicin • Chemical structure different from any existing antibiotics • Strong antibacterial activity • Low toxicity to human cells • Killed antibiotic-resistant bacteria: • Clostridium difficile • Acinetobacter baumannii • Mycobacterium tuberculosis • Disrupts bacteria’s ability to maintain electrochemical gradient across membranes • ATP cannot be produced
- 6. Halicin • Mechanism of chemical makes it difficult for bacteria to develop halicin resistance Halicin prevented development of antibiotic resistance in E. coli Ciproflaxin did not prevent development of antibiotic resistance in E. coli
- 7. Bactericidal Mechanism • Many antibiotics are bacteriostatic • Slows down microbial reproduction so that our own immune systems have a chance to kill them • Halicin: Bactericidal • Halicin killed bacteria with initial cell densities of 106 CFU/ml • Potency of halicin decreases as initial cell density increases Additional Information
- 8. Halicin’s MIC and Efficacy • Minimum inhibitory concentration (MIC): Smallest amount of an agent needed to inhibit growth of a microbe • MIC of halicin did not change for bacteria displaying resistance to commonly used antibiotics • Classification: Broad-spectrum bactericidal antibiotic • Pseudomonas aeruginosa • Lung pathogen • Encapsulated • Resistant against halicin Additional Information
- 9. Bias • Not extremely skewed • Facts were presented straightforwardly • Neutral presentation of information • But MIT News was presenting research conducted at MIT itself • Slight bias could have been present
- 10. References 1. Trafton, Ann. (2020, February 20). Artificial intelligence yields new antibiotic. MIT News. http://news.mit.edu/2020/artificial-intelligence-identifies-new-antibiotic- 0220 2. Stokes, J. M.,Yang, K., Swanson, K., Jin,W., Cubillos-Ruiz, A., Donghia, N. M., MacNair, C. R., French, S., Carfrae, L. A., Bloom-Ackerman, Z.,Tran,V. M., Chiappino-Pepe, A., Badran,A. H.,Andrews, I.W., Chory, E. J., Church, G. M., Brown, E. D., Jaakkola,T. S., Barzilay, R., & Collins, J. J. (2020). A deep learning approach to antibiotic discovery. Cell. 180(4), 688-702. https://doi.org/10.1016/j.cell.2020.01.021