Predicting drug-target binding kinetics through multi-scale simulations

Drug-target binding kinetics has recently emerged as a critical parameter in determining the in vivo efficacy and toxicity of lead compounds. Unfortunately, the rational optimisation of this parameter to design more effective and less toxic drugs is extremely difficult as the features of small ligands and their protein targets that affect binding kinetics remain poorly understood.
The aim of this project is to systematically fill this knowledge gap by combining state of the art computational approaches. We propose to combine our state-of-the-art enhanced sampling and QM/MM methods to compute reliable association free energy profiles and rationalise the binding kinetics of receptor-ligand complexes in terms of the structures and energies of the transition state ensemble.
We will test and validate these methods, which have not been previously combined for this purpose. Together, they tackle the two central challenges of biomolecular simulation, i.e. conformational sampling and accuracy of potentials. We will apply the techniques we develop to well-characterised biomolecular systems of real biomedical importance, such as the influenza target neuraminidase, the anti-cancer targets p38a, Abl, Src and FGFr tyrosine-kinases, the chaperone HSP90, and other drug targets in collaboration with our pharmaceutical industrial partners (UCB, GSK and Sanofi). The results will shed new light on the kinetics of drug binding and their molecular origins. The methods we develop should find wide application, and we will make them available and accessible.

The proposed project will 1) lead to new computational methods that can predict the binding kinetics of small molecules to protein targets, and 2) apply these methods on key pharmaceutical test-cases, such as anti-cancer targets. Although drug-target binding kinetics have recently emerged as a critical parameter for the efficacy and toxicity of drug leads, there are currently no effective techniques that can assist in rationally optimizing binding kinetics. By combining state-of-the-art biomolecular simulation approaches, we will develop computational tools that can efficiently and accurately map the binding free energy pathway (and thereby the binding kinetics) of small molecules to biomolecular targets. These tools are likely to have significant impact on the drug-design process in pharmaceutical companies, and we are indeed collaborating with major pharmaceutical companies to apply and validate the methods and tools we develop. In addition to informing drug-design, the methods can also be used to increase our understanding of biochemical networks in organisms and their regulation more generally, as binding kinetics plays an important role. The methods we develop are therefore expected to be taken up in industry as well as academia; to facilitate this, we will make easy-to-use computational tools and workflows freely available (and advertise them).

In the short term, the project will deliver methods that can accurately predict binding kinetics, and, by application of these tools, offer detailed insight into the small-molecule binding pathways to key targets of biomedical importance, such as established influenza and anti-cancer targets. This insight can directly inform the ongoing research and drug development in industry, through our collaboration with several major pharmaceutical companies.

In the medium term, the methods that will be developed are likely to form a key new step in the tool-chain for drug development. By systematically predicting the binding kinetics of lead compounds to their targets and relevant off-target proteins, the methods have the potential to significantly reduce failure rates in clinical trials, e.g. due to in vivo toxicity or side-effects. This has obvious direct economic benefits; clinical trials are the most costly part of drug development, and failure of drugs at this stage represents a major loss of investment for pharmaceutical companies. As indicated earlier, the computational assessment of binding kinetics will also help increase the understanding of biochemical networks in organisms, which in turn may open new avenues for the prevention and treatment of disease, as well as other applications, such as improving the efficiency of production of valuable chemicals by microorganisms (industrial biotechnology).

In the longer term, once the developed methods are established in the drug-development pipeline, the general public (as well as pharmaceutical industry) will benefit as drugs will be developed more quickly (and thus more cost-effectively), are likely to be more effective at lower dosage, and should have less side-effects. Other long-term effects that will benefit society could be the increased efficiency of biotechnological production of valuable chemicals, with the potential to make this production significantly more sustainable (cleaner, more energy efficient and with efficient use of sustainable starting materials).