search.noResults

search.searching

dataCollection.invalidEmail
note.createNoteMessage

search.noResults

search.searching

orderForm.title

orderForm.productCode
orderForm.description
orderForm.quantity
orderForm.itemPrice
orderForm.price
orderForm.totalPrice
orderForm.deliveryDetails.billingAddress
orderForm.deliveryDetails.deliveryAddress
orderForm.noItems
Genomics


CRISPR


One approach is to use CRISPR to generate


polygenic cellular or animal models of disease, then carry out genome-wide screens on cells or organoids derived from them. Even against that background, manipulating a single additional gene may not be sufficient to generate a detectable phe- notype, so it might be necessary to alter two or more genes at the same time. However, the numbers quickly become unman-


ageable – even something as simple as pairwise combinations of less than a hundred genes in a handful of cell types quickly adds up to more than 140,000 interactions6. It is impossible to test all possible permutations, so it is vital that we develop ways to identify combinations that are most likely to have an effect. Our Functional Genomics team will employ cut-


ting-edge AI and machine learning algorithms to analyse large-scale genomic information and other biological datasets from model organisms, experi- mental results and patients. By drilling down into the biology of cancer cells, the algorithm can reveal rational combinations of genes or drugs that are likely to act within the same network or synergisti- cally (for example, to induce synthetic lethality).


Applications in oncology Fundamentally, we see this integration of genomics, functional genomics and AI technology becoming a routine part of drug discovery, putting science front and centre in strategic decision-mak- ing about target identification and validation to increase the chances of a novel drug candidate suc- cessfully making it to patients. The most important application of this research programme is therefore


26


the identification and validation of biologically rel- evant targets that impact disease progression. Many potential therapeutics have biological


plausibility – they have been designed to hit a par- ticular molecular component in a pathway that has been implicated in disease and ‘should’ work. Yet numerous potential medicines have failed because they turn out not to act upon their expected target, with some failing as late as Phase III clinical trials. Others have unacceptable toxicity or pharmacoki- netic properties, which are also related to selecting an inappropriate target. Our functional genomics approach provides a


new way of quickly and efficiently generating bio- logically-relevant targets for further investigation, hand in hand with more conventional methods of target hunting such as genomic analysis. It also provides a means of rapid phenotypic validation, enabling us to screen out misleading targets at an early stage that do not validate in ‘real life’, despite having strong biological plausibility. For example, if altering the activity of a partic-


ular gene in a cancer cell leads to cell death, then that reveals a potential novel drug target for fur- ther investigation and validation. Alternatively, if a CRISPR screen reveals a gene that appears to be essential for a cancer cell to become resistant to a particular therapy, we can then carry out further experiments in cells or animal models – also gen- erated using CRISPR – to investigate that mecha- nism of resistance and develop ways of targeting it. As proof of principle, this approach has already been used by the Wellcome Sanger Institute team to reveal genetic vulnerabilities and potential ther- apeutic targets in leukaemia7. We already have


Drug Discovery World Spring 2019


Page 1  |  Page 2  |  Page 3  |  Page 4  |  Page 5  |  Page 6  |  Page 7  |  Page 8  |  Page 9  |  Page 10  |  Page 11  |  Page 12  |  Page 13  |  Page 14  |  Page 15  |  Page 16  |  Page 17  |  Page 18  |  Page 19  |  Page 20  |  Page 21  |  Page 22  |  Page 23  |  Page 24  |  Page 25  |  Page 26  |  Page 27  |  Page 28  |  Page 29  |  Page 30  |  Page 31  |  Page 32  |  Page 33  |  Page 34  |  Page 35  |  Page 36  |  Page 37  |  Page 38  |  Page 39  |  Page 40  |  Page 41  |  Page 42  |  Page 43  |  Page 44  |  Page 45  |  Page 46  |  Page 47  |  Page 48  |  Page 49  |  Page 50  |  Page 51  |  Page 52  |  Page 53  |  Page 54  |  Page 55  |  Page 56  |  Page 57  |  Page 58  |  Page 59  |  Page 60  |  Page 61  |  Page 62  |  Page 63  |  Page 64