Innovation in genomic technologies over the past two decades has ushered in an explosion of new drug targets for drug discovery. And while new opportunities abound in this larger target landscape, another important trend is unfolding. The human phenotype dimension is rapidly expanding to reveal a constantly evolving view of a more diverse phenotypic landscape. This landscape is made up of increasingly segmented diseases, with greater molecular association to clinical symptoms. This diverse landscape is more challenging to quantify due to lower incidence and prevalence rates and smaller sample sizes compared with the major disease classes of the past. A good example is blood cancers. According to the NCI’s Surveillance, Epidemiology and End Results Program, what was classified as a “disease of the blood” 60 years ago, is today 90x more specific, classified as over 40 different leukemia types and more than 50 unique lymphoma types.
Contributing to this growing complexity in the disease landscape are advances made in clinical diagnostics. Clinical science has advanced to exploit this greater genomic and molecular diagnostic capability, discovering and using biomarkers to stratify patients in clinical trials more effectively. In addition, as artificial intelligence and machine learning techniques continue to advance, patient stratification is being applied more often to better understand the molecular basis of the increasingly narrower patient segments. As evidence, disease coding has expanded, while scientists and clinicians have harnessed the molecular basis of that clinical know-how in several ways:
- The disease terms within meSH, used to index clinical study types from the biomedical literature, have expanded to capture more specific disease classification over the last three decades (Figure 1).
- In the last revision of The International Statistical Classification of Diseases and Related Health Problems (10th revision, 2nd), or ICD-10, published by the World Health Organization (WHO), codes for diagnosis of disease increased nearly five times from those published in ICD-9, implemented in 1979 (Figure 1).
- Investigational drugs entering Discovery Phase in Cortellis Competitive Intelligence with a new indication not previously recorded in Cortellis have increased recently (Figure 2).
- Biomarkers are increasingly used in clinical trials to select patients into studies from among those who are more likely to respond to the drug under investigation. Biomarkers are also used as surrogates for clinical endpoints or toxicity effects (and patient stratification). Use of biomarkers in clinical studies rose 15% between 2010 and 2015 alone.
The take-home message is clear: The complexity in both the target (molecular) and patient (phenotypic) dimensions of drug discovery and development has grown quickly in the last two decades, presenting enormous opportunity for pharmaceutical and biotech companies. And while new indications are making their way at an accelerated pace into discovery programs, still only 7% of investigational drugs tested in first human dose studies survive to market launch (according to the 2015 CMR Pharmaceutical R&D Factbook). While it is appealing to enter into this new expanding space of target and indication segmentation, it is important not to repeat the narrow view of increasing the “shots on goal” research strategy of the 1990s, which drove R&D spending higher and fed a declining return on drug R&D investment, down from 10.1% in 2010 to 4.8% in 2013, with no end to the decline in 2016.
More effective and efficient methods are required to evaluate this expanding indication space so more meaningful indications can be selected, resulting in more candidates likely to survive clinical development and generate meaningful financial returns.
At Clarivate Analytics, our staff of industry veterans and data science experts, including the author of this article, have worked extensively in all phases of drug development, from preclinical to commercial forecasting and market sizing domains with both large pharma and smaller biotech. Together we have developed an indication prioritization methodology that brings together computational and evidence-based approaches, employing efficient, multi-disciplinary metrics that are critical to making good indication prioritization decisions, and coupling them more cost-effectively with experimental models.
Read a full white paper on this topic by the author, along with Chief Scientific Officer Richard Harrison, also of Clarivate Analytics, “Advancing Evidence-Based Indication Prioritization to Navigate the Segmentation of the Disease Landscape.”