What is NDD?


Embracing the complexity of biology

We take our health for granted.  The cells and tissues in our bodies remain stable and reliable in the face of constant challenge.  Our immune systems deal with invading viruses and remove pre-cancerous cells and our kidneys maintain levels of salts and other vital chemicals whatever we eat or drink. These systems are reliable because of their organization into networks. The building blocks of these networks include proteins, nucleic acids, fats and sugars. On their own, these are fragile, unstable and highly vulnerable to attack; but connected together in the right way the whole becomes strong, stable and resilient and gives rise to reliable and predictable behaviour (a phenotype).

Recent advances in systems and network biology confirm our view of phenotypic robustness and the importance of network structure.

Unfortunately for us, when we develop a disease, the underlying networks remain very robust and difficult to change. Imagine a normal cell that has become cancerous: all the processes that ensure that the cell grows, multiplies, takes in nutrients and performs its role continue to operate but the cell has become deaf to the signals telling it when to stop dividing. Traditionally, the pharmaceutical industry has concentrated on identifying single receptors and enzymes whose selective targeting leads to a desirable therapeutic effect such as stopping the rogue cell in the previous example from dividing. This ‘top-down’ targeting yields many useful drugs but is very focussed on finding a ‘single switch that works’.  Such targeting obviously affects the networks we have just described but not necessarily in an optimal way and this can translate into efficacy failures and lower productivity later in the development process.

Network-driven drug discovery ("NDD")

There is a growing recognition that combinations of drugs, and more recently ‘bi- (or even multi) specific’ drugs that individually bind to more than one target, can achieve benefits that are not possible with single target drugs. This is particularly true in complex diseases such as cancer where multi-target drugs (e.g. kinase inhibitors) and combination treatments are routine; but where the best way to find and combine them is still an unsolved challenge.

At e-therapeutics we have adopted a complementary ‘bottom-up’ approach to address some of the difficulties associated with top down single target discovery and to help solve the challenge of identifying rational multi-target and combination strategies. We create and analyse complex models of the networks that make up our phenotype. This allows us to prioritise compounds to test in the lab based on their network impact and ensure that they are having the best possible effect in a realistic model of the biological system. Even more critically we can identify desirable patterns of intervention that may not be achievable through single target binding: something that would be impractical by trial and error in the laboratory.

Our approach is different not just because it is ‘bottom-up’ helping to ensure optimal target choice; but also in its potential to yield drugs that could not be found by conventional screening. It can also be viewed as a rational way to select compounds for phenotypic screening (another ‘bottom-up strategy).

We do not need to know the binding target in advance – even receptors that are currently unknown can be taken account of and drugs with novel modes of action ("MoA") can be found. And we can identify synergistic combinations of targets that can be engaged by combinations of drugs or by new multivalent drugs without knowing in advance what those targets are. We can do this quickly and efficiently because our network models allow us to be more focussed than a more conventional phenotypic screen. Our drug Discovery Engine is an in-silico laboratory in which we can carry out millions of thought experiments in a very short amount of time and translate these into the new generation of effective medicines.

The ultimate benefit is producing more effective drugs in a quicker and more cost-effective way for our commercial partners, ultimately leading to better health outcomes for the public.