molecular modelling Integrating for
Molecular modelling has become a staple tool of the pharmaceutical industry but it is at its most useful when combined with insight from the lab, writes
Siân Harris T
he process of drug discovery is long and expensive. It can take 12-15 years for a new drug to be approved for medicinal use and along the
way hundreds of thousands of potential compounds are considered. A couple of decades ago the idea of high-
throughput screening began to become popular. Te idea is to try out a vast array of chemical compounds and see how they react with a particular protein, for example. As Robert Scoffin, CEO of UK-based Cresset BioMolecular Discovery, explained: ‘When you have no information you don’t have anything to hang a design on, so you want a diverse range of options. Te argument from the mid 1990s until about four or five years ago was to just screen everything. Te more drug-like the better, but at the end of the day without any information you are just screening by catalogue.’ And this is a tall order, as he went on to
note: ‘You can probably buy 10-15 million compounds and could make many millions more, so even screening 100,000 is just scratching the surface. Te reality is that there is so much chemistry out there and so many possibilities that you are fishing in a very big pond.’ With such a scattergun approach the resulting hit rate is inevitably very low.
www.scientific-computing.com
FieldStere finds bioisosteric lead molecules that share the same biological activity, but which have a range of different core scaffolds
What’s more, it is a costly exercise for pharmaceutical companies that have to buy or make every compound they screen. More recently, however, this approach has become more effective thanks to the increased role of computational tools in the process. For Sander Nabuurs, head of
computational chemistry at the Netherlands- based pharmaceutical startup Lead Pharma, computational tools such as molecular modelling are essential for the drug discovery process. ‘Our company is relatively small – 32 people – but there is a substantial effort in computational chemistry because we believe it can make a big difference and that it needs to be as integrated as possible,’ he explained. Indeed, being only four years old and
small compared with the pharmaceutical giants, the notion of blanket screening hundreds of thousands of compounds in the hope that a small percentage might prove promising against aging-associated diseases,
which is the company’s focus, is impossible. ‘Using computational models to screen
can really help. We use in-house virtual screening to select promising compounds. We can screen a one-million-compound library of very diverse molecules and identify subsets of potentially bioactive molecules. Tese “in silico” hits will then be moved forward,’ he explained. ‘It allows us as a small company to screen a much larger library than we really could with wet screening.’ In addition, virtual screening enables
companies to screen compounds that have not yet been made and this can be used to guide the strategies for medicinal chemists. ‘In lead optimisation we typically use docking methods to prioritise what to synthesise from newly-designed libraries,’ Nabuurs commented. Elmar Krieger, founder of YASARA
Biosciences in Austria agreed on the benefits of computational techniques: ‘Being able to investigate and manipulate virtual
APRIL/MAY 2012 45
success
Cresset
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