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Minimizing bias in target selection by exploiting multidisciplinary Big Data and the protein interactome.

B. Al-Lazikani,P. Workman

Published 2016 in Future Medicinal Chemistry

ABSTRACT

Despite insights from large-scale genomic studies, there has been limited success in reversing the declining approval rate for new drugs, across all types of human diseases [1]. Even in oncology, where a significant number of personalized medicines that target the genetic drivers of cancers have been approved, the number of new drugs is dwarfed by the volume of driver genes identified [2]. Attrition rates in the clinic remain unacceptably high, with only 10% of drugs transitioning from Phase I to approval [3]. Some studies show that oncology has the lowest success rate at around 7% [3] and the highest rate of late-stage failures in the clinic [4]. The major cause of failure is lack of efficacy [4] which can often be attributed to inadequate patient stratification for the pivotal trial or, significantly earlier, poor preclinical target validation. Apocryphal tales and increasingly more detailed reports of a lack of reproducibility of biological data fill the scientific airways [5,6]. With the cost of developing a single drug estimated in excess of US$2.5 billion, it is not surprising that the Pharma industry has focused heavily on well-established targets and pathways – commonly sacrificing innovation for the sake of risk mitigation [7]. Indeed, our analysis shows that most approved cancer drugs target only a small part of cancer’s intricate cellular networks [2,8]. Yet, our need for mechanistically innovative cancer drugs has never been greater. The genetic and epigenetic heterogeneity of cancer means that oncology’s previous one-sizefits-all approach to treatment is no longer valid. Moreover, we are fighting a constant battle against adaptive biochemical and transcriptional responses, clonal evolution and the incessant emergence of drug resistance to previously successful targeted therapies – making new approaches essential [9–13]. Therefore as a community, we face clear and urgent challenges: How do we expand our repertoire of innovative, mechanistically distinct drugs against robustly validated targets for cancer? How do we overcome the inexorable march of drug resistance? A key underlying solution is making the best possible choice of new, innovative drug targets. Here, we argue the case for minimizing bias in target selection by exploiting multidisciplinary Big Data and assessing targets based on their biological, chemical and physical properties, as well as their role in the cellular protein interactome.

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