TxtViz is a powerful and flexible analytics tool that allows the rapid, unbiased assessment of text documents. Here we describe a workflow for the analysis of drug target literature, which could support drug development by revealing new therapeutic applications and possible safety liabilities. Conventional literature searches have the potential to reveal new insights around a drug or target, such as potential novel indications or side effects. However, one drug’s side effect can be another’s indication, so a standard keyword-based search would return a mixture of both, which can only be resolved through manual review. TxtViz, however, allows the detection and automatic categorisation of these two scenarios, allowing the user to focus their literature searches more accurately.
Ipilimumab, a CTLA-4 antagonist developed by Bristol-Myers Squibb, was approved for the treatment of melanoma in 2011 in the US and 2012 in the UK. A PubMed search of the Ipilimumab target ‘CTLA-4’ was imported and analysed in TxtViz, and articles were categorised according to the potential indications and side effects of drugs that target this receptor.
TxtViz is inclusive and adaptable; by providing a visual overview of the literature around a biological target you can learn about a topic, refine the dataset and then use vocabularies to tease out specific information, such as disease associations. The ThemeMap view allows you to quickly get to grips with the major topics in a dataset (Figure 1). Navigating around the diagram, prevalent ideas such as treatment of melanoma and the association between CTLA-4 and graft survival can be immediately identified. This is a good example of how a biological target with several indications can be used as a drug target for a number of different diseases.
Figure 1. ThemeMap of PubMed ‘CTLA-4’ search results
Peaks represent clusters of similar papers. The larger the peak, the richer in papers the corresponding cluster is, i.e the more prevalent the theme is in the literature. The terms shown in the red boxes were obtained by placing probes around the diagram, these are the main terms associating the papers in the peak on which the probe was placed.
Views such as ThemeMap provide an instant overview of a broad thematic area. To investigate more detailed associations, such as indications and side effects, the TxtViz heat map, or CoMet view, is used (Figure 2). At this stage it can beneficial to select a subset of literature that addresses your area of interest. For example, to investigate the effects associated with antagonism of a particular target, the subset of articles referring to antagonism, blockade, etc. can be rapidly identified with a simple synonym-enhanced text query. This tends to be more important with established biological targets which already have therapeutic use, less so for new targets.
Figure 2. CoMet View of a CTLA-4 ‘Blockade’ subset, which has been indexed using thesauri of disease terminology and indication/side effect language. Red colouration indicates statistical overassociation between disease terms and indications/side effects, with blue colouration denoting underassociation. Double clicking a cell in this view gives the underlying evidence in the record viewer. This view identified indications such as Non-Hodgkin lymphoma, experimental autoimmune myocarditis, hepatitis and leishmanias. Side effects such as exacerbation of malaria and Myasthenia Gravis can be seen.
The CoMet view can be used alongside thesauri developed on any subject; adding more flexibility, groups can be created and used as rows or columns in CoMet. For example, to analyse the trends in research associated with diffferent diseases over time, the publications can be grouped by publication date and ran against a disease termlist. In this way, new or emerging ideas or disease areas can be differentiated from well established ones, therefore novel potential indications can be identified.
After analysis, reading lists can be exported to excel to produce a searchable database, based on the CoMet associations, for personal use or to share with colleages.
A key strength of TxtViz is that it is inclusive; an unbiased overview can be gained allowing you to learn broadly about a topic. From this understanding, the dataset and views can be adapted to address specific questions, using thesauri, subsets and groups.
Find out more at http://www.txtviz.com/