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Using computational models to aid drug discovery

Long term success for pharmaceutical businesses depends upon harnessing the best tools available to bring safe and effective medicines to market at a competitive price, and to be truly successful, for such drugs to be first in class.

Jon Timmis, Chief Executive Officer, SimOmics Ltd

On average, bringing medicines to market is now costing upwards of $2 billion. A company might succeed where others fail simply by virtue of a shorter development timeline and lower costs. Of course, it is often possible to increase the speed of development by simply throwing money at a problem, something which has become increasingly common due to first-in-class advantages. However, if this is done without giving due consideration to efficiency and risk, then in the long term it is not sustainable and may even lead to reputational damage if drugs produce health concerns or environmental impacts.

The use of vertebrate animals for research purposes is very tightly regulated by law, driving up the costs of the experiments, and severely limiting the types of experiments that can be undertaken and the range of conditions that can be applied. There is a very high level of variability between sites which makes experimental outcomes non-reproducible. And there is also increasing societal pressure to reduce the use of animals in research. These factors have combined to spur growing interest in the so called 3 “Rs” – reduction, refinement and replacement – in animal experimentation, across academia and industry.

In the UK alone, the chemical industry spends tens of millions of pounds each year conducting toxicology tests on fish. Standard toxicology tests, which include a fish experiment component, can take anything from a few days to one year to conduct, depending on complexity, scope and the mode of action of the drug or chemical being tested. Active pharmaceutical ingredients (APIs) are excreted and enter the environment, where they can stay in ecosystems for years. Due to the environmental impact, regulations require that before authorisa tion, new APIs must undergo an environmental risk assessment. Being able to accurately predict the risk of environmental harm far earlier in the drug development process would dramatically reduce the cost of regulatory compliance and permit better risk management and strategic decision-making in the drug industry.

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