Drug delivery
of a drug hitting its target. And they aren’t the only lab that has turned to AI to aid delivery. Researchers around the world are investigating its potential use cases, from designing better delivery systems to optimising excipient choices and more. While much of the hype around AI is focused on early discovery, notes assistant professor of Biomedical Engineering at Duke University, Daniel Reker, there’s plenty to be excited about in terms of improving delivery. “I think it’s a really exciting space.”
Crossing barriers If a drug can’t pass through the biological membranes it needs to in order to reach its target, it simply won’t work. Yet, estimating a drug’s permeability is a tricky business. In large part, that’s down to the challenges of collecting accurate and reproducible data. In the lab, you would attach a fluorescent probe to the drug molecule so it can be tracked, though this would also change the properties of the molecule, Jorgensen explains. “The permeability you’re now describing is not really the same compound.”
In vivo investigations, like the mouse models described above, can provide mathematical estimations but are complex and expensive to run, he adds. And computational models can only tell us so much. These simulations recreate real-world systems by following defined rules, such as the laws of physics. But essentially, they’re making an educated guess. Neural networks, which are capable of learning patterns from data, can directly predict whether the drug will cross the membrane. Though at the moment, applications can only provide a simple ‘yes’ or ‘no’ answer. “There have been a few attempts to use a neural network to obtain the actual permeability values,” says Jorgensen. “But we need more data to get this working.”
After all, you can only train your model using data you can access. Currently, one way to do this is to “obtain the experimental distributions for thousands of compounds, and from there, you make inferences to millions of compounds,” Jorgensen explains. Yet at present, this isn’t enough for models to extend beyond yes/no responses, he adds. To boot, there isn’t a standardised way for researchers to incorporate results from different types of experiments within the same data set.
In efforts to plug the data gap, Jorgensen and his colleagues created guidelines for how to compare results from lab investigations with those from AI models, which are published in the Journal of Chemical Information and Modeling. The aim is to create a framework where researchers can compare their findings and pool them into a larger database. And while AI could certainly be a game changer
here, it’s important not to overlook the importance of experimental data. Because ultimately, even if AI models can give us as accurate a prediction as possible,
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they’re still guessing. Rather, Jorgensen notes, the different types of data – from AI, simulations, in vitro and in vivo – are all important puzzle pieces within the bigger picture of what’s happening.
Better carriers
AI can also be used to design more effective delivery vehicles, such as nanoparticles. In a collaboration between Cardiff University and AstraZeneca, an AI model helped design a bespoke lipid nanoparticle to deliver messenger RNA (mRNA) to cancer cells. “The new nanoparticles were performing better in both cells and mice compared to the old nanoparticles,” says Arwyn Jones, professor at Cardiff University’s School of Pharmacy and Pharmaceutical Sciences. First, researchers from both Cardiff and AstraZeneca looked into how cancer cells derived from different tissues reacted to nanoparticles when they touched their surface, how the nanoparticles were taken inside the cell and processed, and how effective the delivery of the nanoparticles’ mRNA cargo was. Then, in a later investigation, they found that when a certain panel of proteins inside the cell were silenced, delivery was more efficient.
Meanwhile, informed by these results, AstraZeneca developed a machine learning model to identify other key endocytosis factors – cellular processes where substances are brought into a cell – that correlated with more effective delivery. This model was then used to suggest the design of a new nanoparticle for delivering mRNA. “From the raw data and the machine learning came the suggestion that we should maybe tailor the nanoparticle to go in a specific pathway,” Jones shares. “So, we changed the formulation to make them a little bit bigger, and it worked.” While the AI model doesn’t remove the need to test and iterate, you could imagine that the model was setting a new hypothesis, Jones says. “It gave us the insight and the confidence to make a different formulation, and in our case, there was a
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AI models are helping scientists predict if drugs can cross the blood-brain barrier and improve delivery systems, potentially revolutionising many treatments.
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