2023-02-02·International journal of molecular sciences
Physical and Functional Characterization of PLGA Nanoparticles Containing the Antimicrobial Peptide SAAP-148.
作者: Muhanad Ali ; Miriam E van Gent ; Amy M de Waal ; Bjorn R van Doodewaerd ; Erik Bos ; Roman I Koning ; Robert A Cordfunke ; Jan Wouter Drijfhout ; Peter H Nibbering
Synthetic antimicrobial and antibiofilm peptide (SAAP-148) commits significant antimicrobial activities against antimicrobial resistant (AMR) planktonic bacteria and biofilms. However, SAAP-148 is limited by its low selectivity index, i.e., ratio between cytotoxicity and antimicrobial activity, as well as its bioavailability at infection sites. We hypothesized that formulation of SAAP-148 in PLGA nanoparticles (SAAP-148 NPs) improves the selectivity index due to the sustained local release of the peptide. The aim of this study was to investigate the physical and functional characteristics of SAAP-148 NPs and to compare the selectivity index of the formulated peptide with that of the peptide in solution. SAAP-148 NPs displayed favorable physiochemical properties [size = 94.1 ± 23 nm, polydispersity index (PDI) = 0.08 ± 0.1, surface charge = 1.65 ± 0.1 mV, and encapsulation efficiency (EE) = 86.7 ± 0.3%] and sustained release of peptide for up to 21 days in PBS at 37 °C. The antibacterial and cytotoxicity studies showed that the selectivity index for SAAP-148 NPs was drastically increased, by 10-fold, regarding AMR Staphylococcus aureus and 20-fold regarding AMR Acinetobacter baumannii after 4 h. Interestingly, the antibiofilm activity of SAAP-148 NPs against AMR S. aureus and A. baumannii gradually increased overtime, suggesting a dose-effect relationship based on the peptide's in vitro release profile. Using 3D human skin equivalents (HSEs), dual drug SAAP-148 NPs and the novel antibiotic halicin NPs provided a stronger antibacterial response against planktonic and cell-associated bacteria than SAAP-148 NPs but not halicin NPs after 24 h. Confocal laser scanning microscopy revealed the presence of SAAP-148 NPs on the top layers of the skin models in close proximity to AMR S. aureus at 24 h. Overall, SAAP-148 NPs present a promising yet challenging approach for further development as treatment against bacterial infections.
2022-12-27·Journal of molecular modeling
On the ability of machine learning methods to discover novel scaffolds.
作者: Rishi Jagdev ; Thomas Bruun Madsen ; Paul W Finn
The recent advances in the application of machine learning to drug discovery have made it a 'hot topic' for research, with hundreds of academic groups and companies integrating machine learning into their drug discovery projects. Nevertheless, there remains great uncertainty regarding the most appropriate ways to evaluate the relative performance of these powerful methods against more traditional cheminformatics approaches, and many pitfalls remain for the unwary. In 2020, researchers at MIT (Stokes et al., Cell 180(4), 688-702, 2020) reported the discovery of a new compound with antibacterial activity, halicin, through the use of a neural network machine learning method. A robust ability to identify new active chemotypes through computational methods would be very useful. In this study, we have used the Stokes et al. dataset to compare the performance of this method to two other approaches, Mapping of Activity Through Dichotomic Scores (MADS) by Todeschini et al. (J Chemom 32(4):e2994, 2018) and Random Matrix Theory (RMT) by Lee et al. (Proc Natl Acad Sci 116(9):3373-3378, 2019). Our results demonstrate that all three methods are capable of predicting halicin as an active antibacterial compound, but that this result is dependent on the dataset composition, pre-processing and the molecular fingerprint used. We have further assessed overall performance as determined by several performance metrics. We also investigated the scaffold hopping potential of the methods by modifying the dataset by removal of the β-lactam and fluoroquinolone chemotypes. MADS and RMT are able to identify actives in the test set that contained these substructures. This ability arises because of high scoring fragments of the withheld chemotypes that are in common with other active antibiotic classes. Interestingly, MADS is relatively better compared to the other two methods based on general predictive performance.
2022-09-24·Pathogens and disease
Study on antibacterial effect of halicin (SU3327) against Enterococcus faecalis and Enterococcus faecium.
作者: Zubair Hussain ; She Pengfei ; Li Yimin ; Liu Shasha ; Li Zehao ; Yang Yifan ; Li Linhui ; Zhou Linying ; Wu Yong
Enterococci are important pathogens of nosocomial infections and are increasingly difficult to treat due to their intrinsic and acquired resistance to a range of antibiotics. Therefore, there is an urgent need to develop novel antibacterial agents, while drug repurposing is a promising approach to address this issue. Our study aimed to determine the antimicrobial efficacy of halicin against enterococci, and it has been found that the minimum inhibitory concentrations (MIC) of halicin against different strains of E. faecalis and E. faecium ranged from 4-8 μg/mL. In addition, the synergistic antibacterial effect between halicin and doxycycline (DOX) against enterococcus was observed through the checkerboard method, and it was found that halicin and DOX could significantly synergistically inhibit biofilm formation and eradicate preformed biofilms at sub-MICs. Moreover, the electron microscope results revealed that halicin could also disrupt the bacterial cell membrane at high concentrations. Furthermore, the cytotoxicity study confirmed that the combination of halicin and DOX has no significant cytotoxic effect on erythrocytes and other human-derived cells. Moreover, the mouse subcutaneous model and H&E staining showed that the combination of halicin and DOX could effectively reduce the bacterial load and inflammatory infiltration without obvious side effects. In nutshell, these results demonstrate the potential of halicin in combination with DOX as a novel therapy against infections by enterococcus.
Not only was the drug able to inhibit all A. baumanni strains with known resistance, but it was specific to that bacteria alone.
Developing a new antibiotic can take as long as 15 years. But in as few as two, the target bug can develop resistance to it. Jon Stokes, Ph.D., a professor from Canada’s McMaster University, sees a world where antibiotics get to patients more quickly—with a little help from artificial intelligence.
“I think that [AI models], when appropriately trained and leveraged for various prediction tasks, can help increase the rate at which we discover new candidates for drug development,” Stokes told Fierce Biotech Research.
Alongside Massachusetts Institute of Technology Professor James Collins, Ph.D., Stokes has developed a machine learning algorithm that can screen thousands of compounds at once to see whether any of them have activity against a particular bacteria. The tech has yielded one discovery already, and, now, it’s made another: an agent that, at least in mice, appears to fend off antibiotic-resistant strains of the deadly bacteria Acinetobacter baumanni. The findings were published May 25 in Nature Chemical Biology.
“We believe that this is a tremendously powerful, efficient and accurate way to move drug development into a new era,” said Akhila Kosaraju, M.D., CEO at Phare Bio, a nonprofit biotech linked to Stokes’ and Collins’ labs. “It’s particularly well-suited for antibiotics, where you can quickly test what’s generated out of the AI in a petri dish and determine how efficacious those predictions are.”
In silico to in vivo
Public health officials have been sounding the alarm about antibiotic resistance for more than a decade, but the problem is growing faster than scientists can come up with solutions. Annual deaths from antibiotic-resistant infections in the U.S. have grown from 23,000 to 35,000 in the past six years, data from the Centers for Disease Control and Prevention show. Globally, the figure is closer to 5 million a year, according to the World Health Organization (WHO). The organization expects that to double to 10 million by 2050.
But developing novel antibiotics is an arduous process. Bacteria evolve quickly, and it’s tough to find new ways to target them that haven’t already been tried. Twenty novel cancer drugs were approved by the U.S. FDA in 2022 alone, while roughly the same number of new antibiotics have been approved in the past 13 years combined.
Given the onerous task at hand, AI’s integration into antibiotic R&D feels inevitable. Stokes has been working on that since he was a postdoc in Collins’ lab at MIT, where he helped train and employ the lab’s machine learning algorithm for antibiotic discovery. The AI's first breakthrough in 2020 was the identification of an old diabetes drug called halicin that was able to kill several different strains of multidrug-resistant bacteria in mice.
“That really launched our entire effort,” Kosaraju said. The finding led to an investment in Collins’ lab and the launch of Phare with funding from the TED Audacious Project. Now, researchers in Stokes’ and Collins’ labs perform the computational work using their AI platform then hand their hits off to a team at Phare to translate them into therapies. The translational arm helps refine the AI, too, by providing feedback Stokes can incorporate into the model to improve its predictive power.
“I can say to Jon, ‘Hey, I notice we’re getting a lot of this particular kind of compound coming out of the AI, and for industry standards, it would be more helpful to have X, Y or Z,’” Kosaraju said. “Then he can iterate on the platform, incorporating more drug-like characteristics into the training dataset.”
Big screen, narrow spectrum
In their latest study, the MIT and McMaster teams chose to focus specifically on A. baumanni, enemy No. 1 on the WHO’s list of priority pathogens and a deadly foe in hospital-acquired infections. They screened around 7,500 molecules against the bacteria in a dish to see which ones could inhibit its growth. They then used those data to train their machine learning model to identify chemical structures with anti-A. baumanni effects.
When the model was ready, they used it to analyze the structures of 6,680 compounds from the Drug Repurposing Hub, an open-access library of molecules with potential therapeutic benefits. The model turned up several hundred candidates. The team moved forward with 240 of them, selecting chemicals with very different structures from existing antibiotics—an important consideration, as bacteria might have cross-resistance to medicines that look like ones they’ve encountered before.
Additional screening rounds revealed a winner, which the researchers dubbed “abaucin” for its ability to kill the bacteria. Further in vitro studies revealed something surprising: Not only was abaucin able to inhibit all A. baumanni strains with known resistance, but it was more potent relative to other bacteria species.
“What’s interesting is that the model wasn’t trained to predict narrow-spectrum compounds—it was only trained to predict compounds with activity against Acinetobacter,” Stokes said. “The fact that abaucin displayed enhanced activity against Acinetobacter was simply a unique laboratory observation.”
In vivo studies confirmed their findings. Using mice with wounds that had been infected with A. baumanni, the researchers repeatedly applied either an unmedicated cream or one containing abaucin over the course of 24 hours. When they analyzed the tissue, they found that the wounds that had been treated with the cream looked virtually the same as they did pre-infection, while the untreated wounds were inflamed and colonized by the bacteria.
The researchers noted that conventional antibiotics might outperform abaucin against A. baumanni in a clinical setting. Though the antibiotic killed the bacteria in a petri dish, it didn’t sterilize cultures completely, unlike fluoroquinolones, beta-lactams and other older antibiotics. Still, in the face of multi-drug resistance, less-than-perfect performance is a reasonable trade-off.
“So many isolates of A. baumanni are resistant to conventional antibiotics that we need something structurally and functionally novel,” Stokes said.
Traversing the 'valley of death'
Next comes the task of taking abaucin from the bench to clinical trials—the phase between exciting preclinical data and human testing that’s been called the “valley of death” in drug development.
“That’s where most drugs fail—right during the handoff from academia to industry, where you’re trying to do some of the highest risk, low probability of success preclinical work,” Kosaraju explained. “This is kind of the less glorious part of drug development and incredibly important.”
That valley is so steep in the world of antibiotics that it’s practically a chasm, she added. The financial reward for developing a new antibiotic is much smaller than for, say, a novel oncology drug or a blockbuster weight loss medication, and it takes much longer to recoup an investment—30 years, on average. Antibiotics are prescribed for shorter durations than other medicines, and, while treatments for deadly, hospital-acquired infections are badly needed by susceptible populations, those populations are relatively small. They’re even smaller for any one pathogen.
Few major pharmas are still in the game, GSK being the most active, while others like Johnson & Johnson or Pfizer commit to initiatives like the AMR Action Fund to aid the effort instead.
All that adds up to make developing narrow-spectrum antibiotics with potentially better resistance profiles, like abaucin, an even riskier bet than usual for investors, despite a growing need for them.
“It’s harder to make that choice, instead of going after something broad-spectrum where you can treat many different kinds of infections,” Kosaraju said.
Phare’s nonprofit status acts as a bridge over the chasm of death. Funding for the next phase of development—finding analogues of abaucin with better medicinal profiles and conducting studies to support future human testing on them—will come from philanthropy and grants in addition to what’s provided by the Audacious Project. Then, once Phare emerges with a viable product, it plans to partner with a company in Big Pharma for further development or spin out its own venture-funded company, according to Kosaraju.
“This helps us bridge to the point that our drugs are de-risked, where the chance of success is much higher,” she said. On top of that, she added, “the nonprofit model enables us to do truly innovative work—to go after the most difficult-to-treat pathogens on the WHO list and stay laser-focused without the pressures of commercial funding.”
Let the robots in (but keep the humans, too)
Like other AI tools in drug discovery, Stokes’ and Collins’ machine learning platform is already mitigating expenses by streamlining the process of mining for candidates. While the model is ultimately a “suggestion generation machine,” Stokes said, it cuts down the number of experiments humans have to do—and the price. On average, it takes drug developers 4.5 years and $2.5 million to get from discovery through the pre-investigational new drug application stage, Kosaraju said. Thanks to the AI platform, Phare estimates that time can be cut down to 2.5 years at a third of the cost.
Those figures may have room to fall further as the technology’s predictive power improves with the addition of more data sets. It will get better at being able to tell what compounds are most likely to work not only against microbes, but in animals and humans, too.
“I think that in the future we will be able to more robustly apply machine learning models to help us not only predict which compounds might be antibacterial in a dish, but also those that perhaps are most likely to successfully traverse preclinical and clinical development,” Stokes said.
The platform can’t do all the work alone, of course. Just as important as training the models on new data is optimizing the way researchers interact with them, Stokes explained. Like any other laboratory tool, scientists must learn to use AI effectively.
“Maybe this is a misconception on my part, but I feel that oftentimes, people think of AI models as these static entities,” Stokes said. “I don’t see them as that. I see these models as entities that work with us, and that we have to learn to work with them appropriately in order to maximize the net benefit to drug discovery.”
Using AI, researchers identified a new antibiotic that can kill Acinetobacter baumannii, a type of bacteria that is responsible for many drug-resistant infections.
Using an artificial intelligence algorithm, researchers at MIT and McMaster University have identified a new antibiotic that can kill a type of bacteria that is responsible for many drug-resistant infections.
If developed for use in patients, the drug could help to combat Acinetobacter baumannii, a species of bacteria that is often found in hospitals and can lead to pneumonia, meningitis, and other serious infections. The microbe is also a leading cause of infections in wounded soldiers in Iraq and Afghanistan.
"Acinetobacter can survive on hospital doorknobs and equipment for long periods of time, and it can take up antibiotic resistance genes from its environment. It's really common now to find A. baumannii isolates that are resistant to nearly every antibiotic," says Jonathan Stokes, a former MIT postdoc who is now an assistant professor of biochemistry and biomedical sciences at McMaster University.
The researchers identified the new drug from a library of nearly 7,000 potential drug compounds using a machine-learning model that they trained to evaluate whether a chemical compound will inhibit the growth of A. baumannii.
"This finding further supports the premise that AI can significantly accelerate and expand our search for novel antibiotics," says James Collins, the Termeer Professor of Medical Engineering and Science in MIT's Institute for Medical Engineering and Science (IMES) and Department of Biological Engineering. "I'm excited that this work shows that we can use AI to help combat problematic pathogens such as A. baumannii."
Collins and Stokes are the senior authors of the new study, which appears today in Nature Chemical Biology. The paper's lead authors are McMaster University graduate students Gary Liu and Denise Catacutan and recent McMaster graduate Khushi Rathod.
Over the past several decades, many pathogenic bacteria have become increasingly resistant to existing antibiotics, while very few new antibiotics have been developed.
Several years ago, Collins, Stokes, and MIT Professor Regina Barzilay (who is also an author on the new study), set out to combat this growing problem by using machine learning, a type of artificial intelligence that can learn to recognize patterns in vast amounts of data. Collins and Barzilay, who co-direct MIT's Abdul Latif Jameel Clinic for Machine Learning in Health, hoped this approach could be used to identify new antibiotics whose chemical structures are different from any existing drugs.
In their initial demonstration, the researchers trained a machine-learning algorithm to identify chemical structures that could inhibit growth of E. coli. In a screen of more than 100 million compounds, that algorithm yielded a molecule that the researchers called halicin, after the fictional artificial intelligence system from "2001: A Space Odyssey." This molecule, they showed, could kill not only E. coli but several other bacterial species that are resistant to treatment.
"After that paper, when we showed that these machine-learning approaches can work well for complex antibiotic discovery tasks, we turned our attention to what I perceive to be public enemy No. 1 for multidrug-resistant bacterial infections, which is Acinetobacter," Stokes says.
To obtain training data for their computational model, the researchers first exposed A. baumannii grown in a lab dish to about 7,500 different chemical compounds to see which ones could inhibit growth of the microbe. Then they fed the structure of each molecule into the model. They also told the model whether each structure could inhibit bacterial growth or not. This allowed the algorithm to learn chemical features associated with growth inhibition.
Once the model was trained, the researchers used it to analyze a set of 6,680 compounds it had not seen before, which came from the Drug Repurposing Hub at the Broad Institute. This analysis, which took less than two hours, yielded a few hundred top hits. Of these, the researchers chose 240 to test experimentally in the lab, focusing on compounds with structures that were different from those of existing antibiotics or molecules from the training data.
Those tests yielded nine antibiotics, including one that was very potent. This compound, which was originally explored as a potential diabetes drug, turned out to be extremely effective at killing A. baumannii but had no effect on other species of bacteria including Pseudomonas aeruginosa, Staphylococcus aureus, and carbapenem-resistant Enterobacteriaceae.
This "narrow spectrum" killing ability is a desirable feature for antibiotics because it minimizes the risk of bacteria rapidly spreading resistance against the drug. Another advantage is that the drug would likely spare the beneficial bacteria that live in the human gut and help to suppress opportunistic infections such as Clostridium difficile.
"Antibiotics often have to be administered systemically, and the last thing you want to do is cause significant dysbiosis and open up these already sick patients to secondary infections," Stokes says.
A novel mechanism
In studies in mice, the researchers showed that the drug, which they named abaucin, could treat wound infections caused by A. baumannii. They also showed, in lab tests, that it works against a variety of drug-resistant A. baumannii strains isolated from human patients.
Further experiments revealed that the drug kills cells by interfering with a process known as lipoprotein trafficking, which cells use to transport proteins from the interior of the cell to the cell envelope. Specifically, the drug appears to inhibit LolE, a protein involved in this process.
All Gram-negative bacteria express this enzyme, so the researchers were surprised to find that abaucin is so selective in targeting A. baumannii. They hypothesize that slight differences in how A. baumannii performs this task might account for the drug's selectivity.
"We haven't finalized the experimental data acquisition yet, but we think it's because A. baumannii does lipoprotein trafficking a little bit differently than other Gram-negative species. We believe that's why we're getting this narrow spectrum activity," Stokes says.
Stokes' lab is now working with other researchers at McMaster to optimize the medicinal properties of the compound, in hopes of developing it for eventual use in patients.
The researchers also plan to use their modeling approach to identify potential antibiotics for other types of drug-resistant infections, including those caused by Staphylococcus aureus and Pseudomonas aeruginosa.