Application of large-scale quantum mechanical simulation to the development of future drug therapies

Rational computational design plays an increasingly important role in today's society, and is widely used in, for example, the construction and automotive industries to reduce costs associated with conventional experiments. If we are to apply the same principles to the design of pharmaceutical molecules, then it is necessary to be able to predict with high accuracy which of the multitude of molecules that we can potentially synthesise in the lab actually have therapeutic benefits. Ideally, the computer program would be able to perform this function using only established laws of physics, rather than relying on data input from experimental measurements. The modelling of atoms at this fundamental level is known as first principles simulation.

First principles simulations are used today by researchers in many industries, including microelectronics and renewable energy, to rapidly scan multitudes of hypothetical material compositions. Only once a set of materials matching the desired properties is discovered, does the costly process of manufacturing those materials in the lab begin. So why are the same first principles techniques not used to design new pharmaceutical molecules? The equations of quantum mechanics were written down and shown to describe the atomic-scale behaviour of materials with remarkable accuracy as early as the beginning of the twentieth century. Therefore, the answer is not a lack of physical understanding. Instead, it is largely a problem of the computational effort required to model the large numbers of atoms that are involved in interactions between a pharmaceutical molecule and its therapeutic target.

There are an unimaginable number of silicon atoms in typical modern electronic devices, but importantly the homogeneity of the structures means that the bulk material can be represented by just two atoms periodically repeated in 3D, and it is a relatively straightforward problem to computationally model the properties of this simple system. In contrast, biological systems are much more complex and often we need to simulate many thousands of atoms in order to accurately predict the relationships between the molecule's structure and its function. However, due to increases in computer power and, more importantly, fundamental advances in software design, first principles approaches can now access these biological systems with precisely the same accuracy that is used to study silicon.

Traditional approaches to computational drug discovery rely heavily on hundreds of model parameters that have been collected over many decades from experiments or computational analysis of small molecules. My idea is to dispense with these parameters and instead compute them directly from first principles quantum mechanical simulations of the biological therapeutic target, such as a protein that is implicated in disease. These new model parameters, rather than being generic, will be specific to the system under study and will thereby transform the accuracy of computational biomolecular modelling. The improved computational models will be used to scan hundreds of potential pharmaceutical molecules for therapeutic benefit, thus allowing us to rationally and rapidly design new therapeutic candidates. Medical researchers will be able to focus their design efforts on synthesising only the most promising molecules, thereby improving the likelihood of success in the early stages of pharmaceutical development and decreasing the cost of medicines to the patient. This concept will be put into practice in collaboration with the Northern Institute for Cancer Research at Newcastle University for the design of novel cancer therapies.

Globally, between 1986 and 2000, human life expectancy increased by two years, and 40% of that increase is attributed to the use of new pharmaceuticals. The research and development of new medicines is an important sector of the UK economy. Medicines originating from UK companies accounted for 14% of the total sales of the world's top 100 selling drugs in 2014, and sales from the UK pharmaceutical industry are estimated to contribute a net £1 billion to the UK economy (source: www.abpi.org.uk).

This project will create a computational pre-screening method that will allow medicinal chemistry researchers to rapidly test hundreds of potential drug candidates on the computer and only synthesise in the lab a handful of molecules that are predicted to be the most successful, thus decreasing the experimental workload and costs. As a consequence, it is expected to have a significant economic impact by improving the efficiency and increasing the success rate of pharmaceutical research, and a societal impact by potentially bringing new medicines to market on a shorter time scale. To deliver this impact, the research team will design workflows that are accessible to medicinal chemists, and work with the Northern Institute for Cancer Research at Newcastle University and industrial partners at Astex Pharmaceuticals to demonstrate that this theoretical research is accurate enough to be translated into a protocol that can impact live drug discovery programmes. In turn, these project partners will obtain early access to the developed methods, thus enhancing the efficiency and competitiveness of UK pharmaceutical research and development.

The UK has a strong track record in quantum mechanical simulation and a particular competitive advantage in applying these methods to large, complex systems such as proteins. This is due in a large part to EPSRC funding of large-scale density functional theory codes, including ONETEP. This project will translate these strengths into the field of drug discovery by developing computational models of drug-target interactions based on large-scale quantum mechanical simulations using the ONETEP code, thus widening the scope and potential user base of this software. The open-source distribution of scripts and input/output files for force field design will facilitate the uptake of these methods by users in industry and academia.

The proposed project will provide a source of highly-trained Ph.D. and post-doctoral researchers with combined skills in scientific computing and medicinal chemistry. These skills will be highly sought after as the UK pharmaceutical industry continues to enhance the role of molecular modelling in its drug discovery processes.

This research will continue to have an impact beyond the timeframe of the current project. After 5 years, the designed workflows will be ready to be made available to the wider academic and industrial research communities for use in computer-aided drug design. After 10 years, lead optimisation efforts that incorporate the designed methods should demonstrate a measurable decrease in costs. In the longer term, new medicines will be designed with a substantial computational input from pre-screening methods such as the ones proposed here. Cost savings in pharmaceutical research will be passed onto the NHS, ultimately resulting in greater treatment options and improving the health and wellbeing of UK citizens.