Cedric Devos, PhD, Postdoctoral Associate at the Department of Chemical Engineering at MIT
Cedric Devos, PhD, Postdoctoral Associate at the Department of Chemical Engineering at MIT
Cedric Devos, PhD, Postdoctoral Associate at the Department of Chemical Engineering at MIT
Biography
Cedric Devos is a Postdoctoral Associate in the Department of Chemical Engineering at the Massachusetts Institute of Technology (MIT), where he has worked since 2023 in Professor Allan S. Myerson’s group, focusing on lipid nanoparticle technology. He received his BSc (2017) and MSc (2019) in Chemical Engineering from KU Leuven, where he later completed his PhD in 2023 as an FWO Fellow in Professor Simon Kuhn’s group. His research focuses on developing scalable, innovative solution for complex pharmaceutical processes. He has authored 20+ peer-reviewed publications, cited 500+ times, contributed to 50+ conference contributions, and is an inventor on multiple patent applications. He is particularly interested in translating scientific discoveries into technologies that improve the development, manufacturing, and effectiveness of pharmaceutical products, and he has received awards for both his scientific and translational contributions.
Interview
NanoSphere: Tell us a bit about yourself—your background, journey, and what led you to where you are today.
Cedric: I grew up in Belgium and studied chemical engineering because I was drawn to how the field connects math, chemistry, physics, engineering, and to a lesser extent biology, even if I would not have described it that way at the time.
I did not expect to enjoy research as much as I did, but it quickly pulled me in. I went on to pursue a PhD on the crystallization of small-molecule active pharmaceutical ingredients, where I learned how small process changes, often balancing thermodynamics and kinetics, can have a major impact on how pharmaceutical products are made, and ultimately on their efficacy and safety. Just as importantly, my PhD showed me how much good science depends on the environment around you. I was fortunate to work with mentors who gave me space to explore, and colleagues who challenged my thinking with questions.
After my PhD, I wanted to continue in that same spirit, and found it very quickly at MIT. There, I pivoted from crystallization research into studying drug delivery and lipid nanoparticle process development, working closely with brilliant people on a problem that felt like a natural extension of my background. LNP formation shares several similarities with anti-solvent crystallization, but the particles are more complex, and the process is still far less understood. That perspective helped my colleagues Aniket Uderpurkar, Peter Sagmeister, and I identify several process innovations that could change how LNPs are developed. Over the past year, my focus has increasingly been on translating those ideas into real industrial operations and development workflows.
NanoSphere: What is the biggest challenge in scaling LNP manufacturing today? What can process engineering teach us about better LNPs?
Cedric: I think there are several major challenges in LNP process development, and scale-up is one of the most important. But the root issue is deeper than scale itself: we still do not fully understand what determines critical LNP quality attributes such as size, morphology, internal structure, and payload distribution. That lack of understanding makes it very difficult to preserve these features when moving across scales. For example, during LNP formation, we know that mixing is important. But the effect of the exact mixing geometry on particle morphology is still difficult to predict without computationally expensive simulations (and even then…). That becomes a major problem when moving from lab-scale microfluidic mixers to manufacturing-scale systems such as impinging jet mixers that can handle the high volumetric throughput required for certain drug products.
A second challenge is that LNPs are dynamic particles. Their quality attributes do not simply lock in at the moment of formation. They can continue to evolve throughout the manufacturing process, across downstream unit operations, storage, freezing, thawing, and in some cases even up to the point of administration. Controlling that drift is already difficult at one scale. Bridging it across scales is even harder.
This is where process engineering becomes essential. Without a deeper process understanding, LNP development becomes largely empirical: trying different equipment, geometries, flow rates, and process parameters until the product looks similar across scales. That approach is expensive, slow, and reagent-intensive. More importantly, it carries scientific risk. If you do not understand why a certain particle qualities emerge, or how to stabilize them, you may unknowingly create particles that look similar by standard measurements but behave differently in the body. Process engineering can move the field beyond trial and error. It can help us understand how process conditions shape LNPs, how they evolve, and how they connect to biological performance. In my view, better LNPs will not come only from better chemistry. They will come from understanding and controlling the process that creates them.
NanoSphere: How can real-time monitoring improve LNP manufacturing? What does continuous manufacturing change for mRNA therapies?
Cedric: LNP manufacturing would benefit tremendously from real-time, inline monitoring. In an ideal situation, we would be able to follow (all) critical quality attributes as the particles are being formed and processed, and adjust the process before product quality drifts. The reality today is that we are still quite limited. Only a few important attributes can be measured in or near real time, with particle size distribution being the most obvious example. Many other critical attributes, such as encapsulation efficiency, still rely on slower offline assays. For some measurements, there is currently no realistic rapid analytical alternative at all. Cryo-TEM is probably the clearest example: it can provide extremely valuable morphological information, but it is far from a real-time manufacturing tool.
That said, I do think the field is moving in the right direction. We are seeing more advanced analytics emerge, especially single-particle techniques, that could provide much deeper insight into LNP heterogeneity, structure, and payload distribution. These may not become true inline manufacturing tools in the near term, but even as rapid at-line technologies they could be very powerful. This matters because better analytics do not only help with quality control. They help us understand how LNPs are actually made. If we can connect process conditions to particle attributes more quickly and more directly, we can make much faster progress in designing robust, scalable, and reproducible LNP manufacturing processes.
Continuous manufacturing adds another important layer. For mRNA therapies, it could help reduce batch-to-batch variability, improve process control, and make production more scalable and responsive. But continuous manufacturing only reaches its full potential if it is paired with the right analytics. Without real-time or rapid at-line measurements, you may have a continuous process, but you are still making decisions with delayed information.
NanoSphere: If there’s one key message or insight you’d like to share with readers about the future of nanomedicine, what would it be?
Cedric: “To get the most out of next-generation LNPs, we need to understand how process conditions shape the particle attributes that determine their stability and biological performance.”