Our aim is to enable people to live longer, healthier lives by translating innovative science into life-changing medicines. But we have a challenge: only around one in five of our drug candidates will be successful. While this is markedly higher than the industry average, we endeavour to do better.
Many potential drugs fail at some point during the drug discovery and development process – they either don’t work as expected or cause unacceptable side effects, often due to not properly understanding the underlying biology. Therefore, understanding exactly what’s going on at a fundamental biological level is essential if we are to boost the quality of our drug discovery pipeline and make a meaningful difference to people’s lives.
We are determined to advance our understanding of disease biology to uncover novel drivers for the diseases we aim to treat, prevent and even cure. Functional Genomics is one way we can do this.
To address this challenge, we focus on three areas – artificial intelligence (AI)/Machine Learning (ML), genomics and Functional Genomics.
AI has real potential to revolutionise all stages of our drug discovery and development, from target identification to clinical trials. AI enables us to explore huge data sets with a bias free approach to gain new biological insights into the causes of disease. To accompany this we can harness new technologies and further automate drug discovery processes, freeing up more time for discovering and delivering as many new drug candidate programmes as we can from our innovative pipeline. For example, we are applying ML methods to uncover novel biological insights from the images of drug action in cells and tissues, while saving scientist time and cost.
Through our Genomics Initiative, we now have access to an enormous amount of data about the sequence of the human genome. From the hundreds of thousands of patients, we can use these sequences to help transform drug discovery and development by delivering novel insights into the biology of diseases, identifying new drug targets, supporting patient selection for clinical trials and matching patients to the therapies most likely to benefit them.
Functional Genomics specifically investigates the link between the information contained in the genome and the functional effects of this information – i.e. the functional effects of genes, and proteins encoded by them. This is important because if we are to develop more effective therapies that are truly informed by the underlying biology, we need to open up the ‘black box’ between our genes (genotype) and what they do (phenotype), whether in health or disease.
To help us do this, we have launched the Functional Genomics Centre, teaming up with Cancer Research UK to understand more about how alterations in individual genes may work alone or together to drive cancer cells to grow, spread or become resistant to therapy. Our aim is to use this information to underpin the discovery and development of more effective treatments and identify new strategies to overcome resistance – the single biggest challenge in treating metastatic disease today.
The Centre represents our commitment to this science. Based at the Milner Therapeutics Institute in Cambridge, set within the Cambridge Biomedical Campus, the Functional Genomics Centre is a flagship initiative on an unprecedented scale, pushing the UK to the forefront of CRISPR applications and bringing vital new insights into cancer. By working together with Cancer Research UK, the Centre combines our expertise and enables us to collaborate to advance this science in the UK.
At the heart of this initiative lies CRISPR/Cas9 gene editing technology, usually just known as CRISPR. Since its discovery in 2012, CRISPR has been a transformational force in biology, and it is now used in labs across the world to alter specific sequences of DNA with unprecedented accuracy and speed.
We plan to use large-scale genome-wide CRISPR libraries from our collaborators at the Wellcome Sanger Institute in Cambridge, UK, to systematically alter every single gene in a wide range of cancer cell types and see how it affects growth and function or contributes to therapeutic resistance.
By using CRISPR in three different ways – to completely knock out the function of a gene (CRISPRn), to upregulate it (CRISPRa) or downregulate it (CRISPRi) – we hope to develop valuable new biological insights for cancer drug discovery. For example, if a CRISPR screen reveals a gene that appears to be essential for a cancer cell to become resistance to a particular therapy, we can then carry out further experiments in cells or animal models to validate that gene as a potential drug target.
However, most genes function within complex networks, rather than working alone. So we’ll be using cutting-edge AI and machine learning algorithms to analyse genomic information and other biological datasets, drilling down into the biology of cancer cells with the aim of revealing rational combinations of genes or drugs that are most likely to have an effect.
We also want to move beyond single cancer cells grown in plastic dishes in the lab, which aren’t representative of a real tumour growing in a patient, and hope to explore more complex systems like organoids, animal models, co-cultures or even patient samples to investigate the impact of the tumour microenvironment on cancer growth and drug resistance.
Right now, drug development research is focused on targets that are believed to be ‘druggable’, such as kinases or cell surface receptors that can be targeted with small molecule inhibitors. By targeting every single gene in the genome and understanding the networks in which they function, hand-in-hand with novel treatment approaches such as antisense oligonucleotides, we can expand the therapeutic world that is available to us. We also expect to make improvements to the CRISPR toolbox, improving its accuracy and efficiency, and reducing off-target effects.
We are hugely excited to be teaming up with other world-leading organisations to exploit the power of CRISPR – arguably the greatest advance in science in the 21st century. The ability to turn every single gene on or off and ask what happens may sound like a simple question but until recently we couldn’t do it. But now if we turn a gene off and the cell dies, there’s a potential drug target. Or if we turn a gene on or off and the cell no longer responds to a drug in the clinic, that’s the mechanism of resistance. This kind of approach on a genome-wide scale would have been unimaginable just a few years ago.