Personalised Medicine
Breaking the cycle: methods for determining patient stratification/ disease segmentation early in drug discovery and development
In order to address this problem, knowledge regarding important molecular drivers in a disease needs to be acquired early, preferably before target selection for the investigational drug occurs. It can be argued that this work is already ongoing – it is the nature of biomedical research sponsored by a variety of esteemed organisations such as the NIH. Although these research endeavours are key to future success, what is also needed for more effec- tive application of their rendered knowledge are methods to integrate these observations from indi- vidual experiments into a more holistic picture of the dynamic biology of the system. As discussed above, most research focuses on one variable at a time in order to more decisively conclude the rela- tionship of that specific variable to experimental outcome. It needs to be recognised, however, that multiple elements are changing within that biolog- ical system. In order to understand them better, these elements need to be integrated. With the advent of the molecular biology revo- lution, a wide array of technologies has been devel- oped to systematically and comprehensively char- acterise disease at a variety of biological levels. Over the past 20 years, the human genome has been sequenced; gene expression arrays have start- ed to provide a comprehensive description of mRNA expression levels; proteomics and metabolomics have yielded broad assessments of protein and metabolite levels; and, most recently, Next Generation Sequencing provides access to whole genome information, not to mention microRNA focused arrays and SNP arrays. Although these methodologies have produced a lot of information, the ability to interpret the data at a mechanistic level in a similarly systematic way has
Figure 3
Illustrating the problem of the ‘all-comers’ approach. In panel A, if trastazumab was studied in breast cancer patients
without Her2 overexpression, the likely clinical outcome
would have been insufficient efficacy. By preselecting subjects who would likely respond, an important
treatment was identified for a subpopulation of breast cancer patients (panel B)
been lagging. In addition, the transformation of collections of data into insight (across these methodologies) has not been well developed. In the quest to understand patient subsets in a specific disease, most analyses have systematically focused on genetic data. The most effective of these analytical methods have focused on using the data as rich fingerprints, rather than indications of dis- ease pathophysiology. Numerous examples exist of the generation of post hoc classifiers able to distin- guish responders and non-responders to given interventions using genetics. For example, seminal work by Golub et al resulted in a much deeper understanding of AML subsets27. Similar work using genetics led to the identification of numerous variables driving common diseases, albeit weak- ly28. Although there are isolated examples of how broad analysis impacted research portfolios (cf PCSK9)29-30, overall this type of work did not result in the revolution of research portfolios or more widespread implementation of early patient stratification in clinical studies. Briefly, there are three reasons for the limited application or utility of these abundant and interesting results. First, most rare variants only identify small segments of a disease population and the methodology does not take into account non-genetic drivers of disease. This means that extremely small populations can be identified as likely responders, making DDD commercially non-viable. Second, there is a large gap between the identification of a disease-driving genetic variant and the development of a specific thera- py to address that variant. This gap is only becoming wider as we discover more non-coding change variants as disease drivers. Finally, many classifiers based on the post hoc signature approach lack statistical robustness in the fol- low-on trials. This ‘replication attrition’ has resulted in disappointing performance for strong
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Drug Discovery World Summer 2011
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