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Personalised Medicine


potential biomarkers, likely resulting from a ‘hitchhike effect’: that is, many analytes with very low predictive ability hitchhiking along with a small number of real markers of disease in the initial trials (eg31).


There has been a growing movement to make these large individual datasets interpretable at the mechanistic level. In other words, rather than observing changes, can we conclude what drove those changes at the patient level and use these drivers as stratification? Broadly, two analytical frameworks have been developed. The first is one where an a priori model of the disease is developed and relationships between entities in the model developed based on a deep, but non-systematic understanding of the disease. These computational and dynamic models are then used to simulate known endpoints and are validated against observed measures. Most times, these endpoints are measured dynamically over time to increase the ability to identify agreements and disagreements of the model with the patient data. This approach was used successfully to understand important issues in DDD, such as response to EGFR inhibitors in the context of NSCLC32, as well as (lack of) response to p38 inhibitors in RA33. These examples validate the utility of such an approach, but also point to the fact that dynamic molecular data are hard to obtain from primary patients data. As a consequence, validation is often dependent upon cell-based model systems, leaving open the issue of translatability. Other approaches use no a priori knowledge to understand drivers of a specific disease context based on primary patient data and no assump- tions are made about the distributional character- istics of the data or the equations to represent them. These approaches result in the ability to identify driving mechanisms of specific disease outcomes. The pioneering work by Schadt and colleagues in diabetes using such an approach has resulted in the identification of mechanistic driv- ers of the disease34-36. These approaches still need a priori reduction in complexity of the data, as network construction requires the number of observations to be bigger than the number of variables. With an average of six million SNPs differentiating one human from another, one can see that without a reduction in complexity, these approaches are hard to use. Gene expression data have been used in combina- tion with genetic differences to constrain the potential space for such a biological network37. Other approaches have used prior biological knowledge to constrain the search space. Such


Drug Discovery World Summer 2011


approaches have advantages because they identify drivers with prior biological validation, but leave parts of the data unused. In the context of DDD however, prior biological validation is an impor- tant driver, since it implies the availability of tools for development and testing and in that context these constraints may well not be limiting. A pre- liminary report from Drubin et al25 suggests that diseases such as ulcerative colitis or specific malig- nancies can be sub-classified into their respective key molecular contributors using prior knowledge and a primary human dataset to constrain it. Taken together, integration of complex datasets can yield important discoveries. However, effec- tive use of these tools requires having the right tools in the analytic toolbox, good understanding of the limitations of each approach, and ability to acquire the appropriate datasets.


Implications for changing the current drug discovery and development paradigm


It is important to consider the possible outcomes of segmenting a disease population. The most desirable is that the selected population reflects a substantial proportion and absolute number of patients with the disease (eg >30%). As in the aforementioned example in type 2 diabetes melli- tus, this subset would likely have significantly greater efficacy. However, it is also possible that segmenting a disease population may only yield small cohorts (eg <10%). Low prevalence sub- populations such as this could be hard to justify. For example, consider a Phase II trial that requires 100 subjects. If the ratio of screened to enrolled subjects is 2:1 for example (need to screen two subjects for every one enrolled), then for a subtype prevalence of 10% the project team would need to screen 2,000 subjects to enrol the 100. And if the screening process is complex and/or expensive, then the screening process alone could be very burdensome. If the project advances to Phase III, then this issue is magnified further (the same could be said for clinical prac- tice). Finally, commercial considerations could make pursuit of this subpopulation impractical – because of the low prevalence, the cost of the medication would make it impractical in compar- ison to other therapies. Although potentially daunting, sponsors should/could make an informed decision depending on the prevalence of the general disease and its subpopulation, the availability of alternative effective therapies, and the magnitude of observed efficacy. It is the basis for a formulary approach to DDD.


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