Drug delivery
Researchers are using AI to design better drug carriers, like nanoparticles, and refine delivery processes, paving the way for faster and more effective treatments.
statistically significant improvement in the performance of these nanoparticles.”
Lipid nanoparticle carriers are widely used in cancer treatment, one of the most significant global health burdens. They’re also used in vaccines, including those for Covid-19. This approach to designing them could potentially shave years off development time. The alternative is trial and error: scientists might create and test hundreds of options before landing on the best one. If AI models can consolidate knowledge from work that is already done, they could make informed suggestions for nanoparticle designs that may have higher odds of success.
Untapped potential We’ve barely scratched the surface of how we could use AI to improve drug delivery. With a data set that’s robust enough, it could predict all sorts of values and eventually even estimate the chances of successful delivery. At Duke University, Reker’s lab takes a broad approach to machine learning and drug delivery. The team is currently investigating how various models – including random forest models, which make predictions based on decision trees, and language models like ChatGPT – can be used to enhance various aspects of delivery. “We’re optimising whichever model works best for every specific application,” he says.
Like Jones’ lab, the team is working on nanoparticle design, but are using small molecules rather than lipids or polymers as the stabilising excipients since they’re more established within the world of machine learning. Their model currently predicts how stable a nanoparticle will be, but they’re also generating data to build predictive systems that will eventually estimate whether the drug will reach its target.
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“We think machine learning can also help by predicting what kind of functional properties excipients might have,” Reker adds. Here, the team’s models predict an excipient’s compatibility within a particular formulation, as well as whether functional excipients could influence delivery. For instance, “Would this excipient inhibit this particular metabolic enzyme? Would it inhibit this particular transport protein?” he explains. Another potential avenue is optimising prodrugs, compounds that are metabolised inside the body and converted into an active drug, Reker shares. “We take a molecule that is already effective and then we auto-complete it by adding a few other chemical moieties, as designed by our generative models, and then let our predictive models decide which ones of these could lead to improved properties.” Yet one key problem remains: we need more data. Reker notes that data sets around delivery are relatively small compared to other aspects of the drug development pipeline. This is partly due to the limitations of animal models and incompatibility of results from different types of experiments. Plus, generating new results can take a long time. Jorgensen gives an example: a paper he consistently cites has maybe 50 or 100 data points. To generate each one, a researcher probably had to do a complex experiment. But how much we can get out of AI hinges not only on the depth of our data sets but whether we have enough computing power to process them. Cost also factors in: creating models that consider multiple aspects of the cell environment can be prohibitively expensive, Jorgensen says. And it’s these that would most closely mirror the biological environment. “We can’t make the model too big, and that’s the challenge. We would like to have the whole cell,” he says. “We need more complex models that are closer to reality.” ●
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