Understanding the Particle World – an Interview with Professor Jerry Heng, Imperial College London
July 11, 2023
We talked to Professor Jerry Heng from the Department of Chemical Engineering at Imperial College in London, about how the Cambridge Structural Database (CSD) and software play a fundamental role in his research as a particle technology specialist.
Professor Heng began by outlining his team’s research. “Our research focuses on solids, particularly surfaces and interfaces, including the isolation and purification of small pharmaceutical molecules, where particle technology plays an important (and interesting!) role. More recently we’ve been working on larger molecules, in particular monoclonal antibodies and peptides.”
What Is the Role of Crystallography and the CSD in Your Research?
“Our research investigates how to control nucleation crystallization. We are interested in polymorph, habit/shape control, and in scaling-up. We look at developing continuous manufacturing platforms through to control of particle size attributes, crystals attributes, shapes, forms, sizes, and size distributions.”
“We started using the CSD, in particular CSD-Particle, for our work around anisotropy. One of the key areas that we’ve been working on for a while was looking at specific properties of anisotropic crystal faces. Surface properties of facets of the same crystalline material in the same crystal structure are in fact different at different orientations. For example, 001 is different from 010 or 110. We used that as a means to design our approaches to control nucleation and understanding a little bit more about crystal growth.”
“Some other areas of our research in which crystallography is essential is the design of templates for polymorphic control of the nucleation of monoclonal antibodies, and very recently also the study of new regeneration phenomena, looking at how crystals grow after they are broken.”
We’ve also worked on plenty of systems that are not limited to pharmaceuticals. We looked at calcium carbonate and calcium sulphate from a fouling perspective, essential in traditional process engineering. In particular, we worked on heat exchangers, investigating inverse solubility phenomena, and we looked at scaling and how to prevent fouling.”
“More recently, we also applied this to so called ‘fatbergs’ which are masses of fats and other non-biodegradable solids that cause blockages in sewage systems. We are developing a ‘sacrificial nucleation approach’, that consists in adding particles that can adsorb the fats rather than having them depositing onto the walls of our old Victorian sewage systems in London.”
“My research is based on particles and systems in the solid-state. For this reason, it is essential to understand at a molecular level how hydrogen bonds, for example, are oriented across the molecule through intermolecular bonding, as this can define a whole range of particle properties such as wettability or dissolution rates. Crystallography provides an insight that helps to rationalize and explain our observations around crystal particles and crystal properties.”
“I started using Mercury during my Ph.D., mostly to look at the molecules along different axes and to identify the position of specific functional groups within the crystalline structure.”
“More recently, CSD-Particle has given insight into predicting crystal habits, shape changes, solvent influence, attachment energies, cleavage planes, and slip planes.”
“The greatest benefit of using CSD software is that the tools enabled us to start visualizing things. Being able to visualize the packing of molecules along a certain face was very beneficial and allowed us to validate those observations with our own experimental data for surface functionalities and surface analysis.”
What are the Advantages and Disadvantages of using the CSD and Software in Your Research?
“I would say that there aren’t any disadvantages because I believe in the complementarity of techniques. If we understand the capabilities and limitations of each technique, we can obtain a level of detail that we otherwise could not achieve.”
“The advantages of using the CSD and CSD software are certainly the ability to provide insights that are not routinely available from experiments, and the ability to accelerate our research work. Experiments can be expensive and time demanding: CSD software tools allowed us to have access to features that may not present themselves in an experimental setup.”
“Let’s look at the case of ibuprofen, for example. Ibuprofen is a simple molecule that is very hydrophobic. We know where the ring is placed, and where the carboxylate group is. However, its melting point is surprisingly low, almost half of paracetamol in degrees Celsius, a molecule with a similar molecular weight. This seems surprising, but looking at the crystalline structure we get an understanding of dimer formations, an aspect that can explain why that property was not a simple correlation to its molecular weight.”
What Do You Think About AI in Science? Do You Think It Could Change the Research Landscape?
“I think AI is exciting because advances are rapidly happening and things that might have taken a lifetime or several generations are progressing in a shorter time scale. It’s exciting, but of course, it can also be worrying as machine learning capabilities are very rapidly evolving. What I am very enthusiastic about is how accessible the technology is becoming, as this means that the barrier to entry for scientists and for researchers is much lower. Consequently, intersections between computer science, chemistry, engineering, and health would enable us to make breakthrough steps that might not have been possible if we had worked in silos, or maybe would have taken much longer.”
“My one caveat is that I believe in fundamental science. A model can only be as good as we train it to be, and AI can only be as powerful as we train it to be. We need to prompt it in the right way, we need to digest and understand what the outputs are, and this can only be possible if we’ve got the strong fundamentals grounded in science, engineering, and technology.”
“Let’s say a century, two, three ago, if we had a book and we could read we were considered very knowledgeable. Then books became a bit more accessible, and you only needed to know where to find them. If you knew where the library was, then you were very smart. In the last decades, with new search engines becoming available, if you had the ability to search for information, then you were considered knowledgeable. I think that now things have moved a bit more, and there’s almost an information overload when we search for answers. You can find responses to almost any of the questions you have, but the challenge is deciding what is real and what is not real. I think moving forward with AI means also to ask the right questions to then be able to prompt the models to get the best output.”
The Value of High-quality Data for Scientific AI
“One last point I would like to make is on big data. I think again that big data is important and valuable data, but I take it in two ways. Having a lot of data is good, but it’s also extremely important to focus on having high-quality data. This is a very critical point, and I think that the next phase would be to make good quality data accessible through data repositories, leading to a more open-access world.”
Visit the web page of Professor Jerry Heng.
Find out more about CSD.
Find out more about CSD-Particle.
Find out more about Mercury.