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applications Modelling for manufacture

Computational techniques are not just used at the drug discovery stage of the pharmaceutical industry, as Kristian Debus, life sciences sector manager of CD-adapco, observed. CD-adapco provides tools for computational fluid dynamics and discrete element modelling, which, he said, are used during various stages of drug development and production. ‘In the pharmaceutical industry, we see a lot of usage in process design and optimisation,’ he explained. ‘With the trend to develop new devices and tools to move towards continuous manufacturing, modelling becomes an essential tool to bring these technologies to market fast, yet with the required, extremely high design

quality. Other research areas would be crystallisation modelling, fermentation, or coupling of 1-D process with 3-D CFD or DEM models. In these areas we see a transition happening from academic research to commercially available tools.’

He continued: ‘The classic applications are scale up from lab, to prototype and production for batch processes, mostly mixing. Today, experts are looking at more complex mixing problems, but also into new application areas. Today’s engineers are required to look into new physics and processes, like particle-, power-, or liquid transport, filling processes, particle break up, spray analysis and so on.

the information in our solution is from real experiments,’ he said. ‘A reasonably common use case is the

predicted biological activity of an unknown compound,’ said van Arkel. ‘Customers also look at drug-to-drug interactions.’ Tis is interesting, for example, if a patient is taking a drug for a condition and a new drug is being developed that would be taken by people with the same condition you can model how the two drugs would interact. ‘Te ultimate outcome really helps the

life-sciences industry in general; where necessary a drug can fail quickly and fail early, which saves huge money on clinical trials,’ he explained. ‘Computational modelling is also becoming increasingly important in situations that in real life would be hard to replicate.’ Reaxys data is incorporated into

computational models in two ways, he said. Te company provides customers with either an API or a structure flat file. Customers can then integrate in their own data. ‘When we provide our data we provide a detailed guide to describe how our data is structured,’ he said, explaining that this is important because every dataset is differently designed. Van Arkel has noticed a related trend

happening too. ‘Because of our expertise in this area, we oſten see the life-sciences industry asking us to help them organise their proprietary data. We then also make sure this data is normalised to ours.’ Tere are some challenges in making data

available for modelling, he observed. ‘It is not that difficult to make data available but to make it truly useful for modelling is more of a


‘A key factor here is also the

improved understanding of the physics and processes as you look at a problem from a different angle. Analytical analysis, modelling, and experiment should always go hand-in- hand to produce the highest quality

challenge,’ he said. ‘You want a huge quantity of data but you also need it provided in a highly structured way. Tere are not many data providers who have both lots of data and are highly-structured.’ Tere are some industry efforts to help

standardise chemical data, which will help modellers and others (see box: Standardising data). Another issue for modellers handling big datasets is computing power. ‘I see still problems of modellers running into huge processing challenges,’ said van Arkel. In fact, Elsevier has a solution for

researchers in this situation. Te scientific publisher has its own supercomputer in



the USA that is configured to process huge datasets. Te company uses this internally for text and data mining but is increasingly approached by customers to offer this as a managed service to researchers. Te most common use case, according

to van Arkel, is that ‘we provide our data to researchers; they develop algorithms; and then they run into processing power problems, so we run their algorithms for them.’ He said that these are typically large projects, and that the company runs 10 to 20 per year of them in the life sciences. Despite such developments, however,

product. A world without modelling is hard to imagine today. Bench top experiments will always be essential, but with the growing use of modelling, and user expertise these experiments will become more directed, more effective and less costly.’

computer models on datasets do not give the whole picture. As van Arkel noted: ‘I’ve not seen a situation where modelling has taken over the whole approach to drug discovery. Ultimately the last component is still going back into the wet lab, to confirm that the model can be replicated in real life.’ Mackey of Cresset agreed: ‘Computational

chemistry went through a big hype cycle in the last decade or so where all sorts of wild claims were made about the ability of computers to revolutionise drug discovery. All of that has calmed down now, and there has been a lot of effort in the last five years or so to reassess computational chemistry techniques: when do they work, when don’t they, and under what circumstances? ‘Some of this has been disheartening. It

turns out that some techniques just aren’t as accurate or useful as we all thought they were, but I think that chemists are now much better served by their computational chemistry colleagues. Te computational chemist’s toolbox is now much better understood, and a good computational chemist should now have a better feel for which modelling techniques to use under what circumstances, and what to expect in the way of accuracy.’ And the importance of modelling as part of

the pharmaceutical story was reinforced with the award of the Nobel Prize in Chemistry 2013 to Martin Karplus, Michael Levitt and Arieh Warshel, ‘for the development of multiscale models for complex chemical systems’. As the press release for the award noted: ‘Today, the computer is just as important a tool for chemists as the test tube.’

@scwmagazine l


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