Genomics
by the typical cytostatic nature seen for single agent pathway-targeted drugs.
All this represents a major challenge for the next wave of targeted drug discovery. In the conven- tional arena, we need to find more functionally characterised targets, ie which of the many mutant genes are drivers vs passengers, and then determine which of these stack-up into more frequent mutat- ed pathways so that becomes viable for drug devel- opers. Here the ability to alter gene function posi- tively and negatively will enable the dissection of their normal vs disease biology. Moreover, the pointed search for key downstream effectors of undrugable genes will be significantly aided by simple ‘isogenic’ model systems, which will enable high-throughput expression profiling and siRNA screens to be performed. Such isogenic ‘X-MAN’ (gene-X; Mutant And Normal) cell-lines are being created by Horizon Discovery using rAAV, which now comprises more than 300 different disease models and will grow to thousands in the next two years based on internal production and the estab- lishment of 50 academic centres of excellence. As well as feeding the conventional drug discov- ery process, X-MAN disease models can also be used in ‘chemical genetic’ screens to identify new drugable targets that impact tumour-specific defects, especially those that are undrugable tumour suppressors. Moreover, if the compound libraries are chosen wisely, ie in vivo validated com- pounds, perhaps even isolate drug candidates directly. Many examples of such ‘synthetically lethality’ screens are now being described, the most notable and advanced of which is the toxic interac- tion of inhibiting PARP activity in BRCA2-null can- cers. This observation first gleaned from an isogenic BRCA-2 null mES cell system, is now being used to successfully treat BRCA-null breast cancer patients.
Disease models in later stages of drug discovery In conventional drug discovery, once sufficient ‘on- target’ chemistry has been obtained, sufficient on- target biology is next addressed. The question then arises, however, what is sufficient on-target biolo- gy? One could argue that this should be the selec- tive death of tumour cells carrying a specific cancer causing mutations given the direction we like to take as a field. At this stage, X-MAN disease mod- els can be assayed for target patient-specific activi- ty in vitro or in animal models, which often reveal phenotypes and drug effects that were unexpected (Figure 2). Moreover, if a target patient population is unknown, a wide range of patient-genotypes can be rapidly profiled prospectively in vitro for those
Drug Discovery World Spring 2011
Deciphering the mutant PI3K phenotype using X-MAN models
2D Growth-based assay GDC-0941
100 120
20 40 60 80
0 C -9 -8 -7 Log [M] GDC-0941 -6 -5 Parental+GDC-0941
Simple 2D growth-based assays show mild response of mutant PI3K cells to PI3K inhibitors Growth in 3D matrigel assays reveals a strong invasion phenotype Clear reversion of invasion phenotype with PI3K inhibitors
H1047R mutant+GDC-0941 3D Matrigel assay Parental DMSO control H1047R mutant DMSO control
Mutant Wild Type
that are likely to respond the best. Together, these profiling tools will allow the design of smaller clin- ical trials centred on the patients most likely to respond, and if a drug fails here, it fails quickly rather than continuing for many years in larger tri- als, probably to the same result. The massive amounts of money saved can then be used to bring a wider set of next-generation targets and drugs into the same efficient process, allowing the best chance for single agents to show anticancer activity, and thus build a diverse enough drug portfolio to be mixed and matched in the right ways. This is ultimately where we will need to be to significantly impact cancer. Moreover, this strategy will form a sustainable biotechnology model moving forward. Supporting this concept is AstraZeneca’s Iressa (gefitinib), the first clear example of how knowing ahead of time which patients would respond (in this case mutant EGFR lung cancer patients) could have saved many development years and dollars. We also know now that in addition to primary ‘sensitivity biomarkers’, one also needs to define other pre-existing, or treatment acquired alter- ations, that cause drug resistance. Here genome editing techniques can be used to create isogenic models that harbour defined combinations of dis- ease causing and/or candidate drug resistance genes and then used to prospectively profile for potential resistance mechanisms.
As a landmark example of this approach, Horizon’s co-founder Alberto Bardelli and research
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Figure 2
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