Diagnostics
This form of protein- based sensing also holds promise for broader applications in bioengineering.
setting. If it is successful, it could be the first time an artificially designed protein biosensor is suitable for a real-life diagnostic application.
Potential to scale But it’s not the specific application that Alexandrov finds most exciting. It’s the platform itself, which is modular, similar to building with Lego bricks, meaning that – in theory – you can replace parts easily to target something else, such as another drug or medical biomarker. Although the truth is that there are many scientific questions that need to be answered before the researchers can achieve anything close to the universal platform they are ultimately aiming to build. “The platform not only needs to be able to measure a wide range of entities – from small molecules to DNA ions – but it needs to be able to do that across a huge range of concentrations,” he says. “The answer also needs to come through in a timeframe that is acceptable for the healthcare provider, which is generally a few minutes.” Here, Alexandrov reminds me again that because this conversation is happening, we know it is possible for information processing to occur at this scale. But the big question he keeps coming back to is how do we build that real-time biological information processing system? What can we assemble it from? What can we compromise on? And what can we not compromise on? To begin to answer some of these questions, he plans to bring in experts with additional skill sets to the next phase of the research. “We need modellers, mathematicians, people who think about systems and information processing in biology,” he says. “We are also really at the beginning of building our expertise in building protein switches, so we need to get better at that too, especially building them to technical specifications.”
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www.practical-patient-care.com
Going out with a bang
Machine learning is making some aspects of this work easier. “While it hasn’t so far solved a problem we couldn’t have solved using traditional methods, it is reducing costs and speeding things up through sheer brute force,” Alexandrov says. “For example, it could be used to help us prototype a thousand switches, extract every useful parameter and feed them into a model, which would hopefully give us predictive guidance on where we should be taking the design.” Even AI can’t speed up the regulatory process, however. “Things move slowly in the medical device field, particularly on the translational end,” he notes. “So in the short term, given that it takes on average two years to approve a medical device, we can only hope to have one product on the market that uses a synthetic protein switch in the next five years,” Alexandrov notes. “That in itself, though, would be incredibly exciting.” Looking further ahead, this form of protein- based sensing has potential applications beyond the diagnostic testing industry, too. “It’s actually a preface to a much more sophisticated form of bioengineering,” Alexandrov grins. “I’m talking about smart drugs – protein nano machines that go into the patient, recognise a biomarker and turn on a therapeutic function. It would use the same switch; it would just have a different chemistry. The opportunities in this area are huge.” In the meantime, Alexandrov and his team are focusing on getting their first synthetic switch to patients, before pushing out other tests that can meet healthcare providers’ immediate needs. “It’s a long road and this concept will be the focus of my entire career,” he concludes. “But even if by the time I retire I launch three products, I’d be going out with a bang.”
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