Informatics
study if the doses are too high, allowing for an early remediation if needed during the study conduct.
Example 4: Advanced analytics and predictive modelling A variety of opportunities to perform more advanced analytics and predictive modelling emerge if the challenges of study formatting and data cap- ture into large databases are overcome. These opportunities range from implementing traditional statistical methods in an automated manner, to more sophisticated data-led and machine learning tech- niques. From a single study perspective, novel imple- mentations could include automated methods to surface anomalous findings, generating dose response models for anomalous findings, and fitting simple pharmacokinetic and pharmacodynamic models to study data. When multiple studies are available, then emphasis can shift to either analysis of multiple compounds against any given study end point, or to a study end point specific focus, where the behaviour of endpoints can be investigated. Hierarchical dose response models, that fit end point specific data for multiple compounds in a sin- gle model and allow subsequent ordering of potency have been developed but are not yet available com- mercially. In addition, endpoint specific analysis that reveals, for instance, the background incidence rate of histopathology findings or putative maximum physiological responses for laboratory tests, have already been performed on large datasets of preclin- ical data, but not yet on data formatted in the SEND standard (see Figure 4). With the emergence of these approaches,
enabled by adoption of SEND as an exchange for- mat, the field will move towards increasing automation and more data-led toxicological pre- dictions. Since data will be stored consistently in databases, it will become easier to generate data representations for any given compound that link compound with target (primary and secondary) and with perturbed preclinical endpoints. This enables toxicology to engage with system biology approaches that offer another approach to the pre- diction of clinical adverse events.
Barriers to advanced analytics The tools are available to leverage cloud resources to share standard formatted data across research sites and partners. However, challenges remain in leveraging SEND datasets to their full capacity. Sponsors may lack the infrastructure (eg
resources, budget) to maintain a database and visu- alisation tools. Also, instituting that infrastructure often requires staff to change business processes to
Drug Discovery World Winter 2018/19
connect data science with operations, particularly when moving from fragmented, locally-owned data sources to centralised databases that offer richer data but more complexity. As a result, shared data that is available may be overanalysed or even misinterpreted. In addition, there are key data omissions from
the SEND standard that will limit its utility in terms of advanced analytics. For instance, there is no means to capture compound structure within the standard. In order to develop Quantitative Structure Activity Relationship (QSAR)-based models from the datasets, compound structure data must be linked with a SEND data warehouse. Furthermore, there remain issues with the homo-
geneity by which key information is captured in the standard by different partners. There is arguably too great a flexibility in how the representation of study design, dose group identification and study elements are captured in the format. However, these issues could be overcome with a focused effort by stakeholders.
Precompetitive SEND use cases for safety/tox Widespread use of the SEND standard will facilitate precompetitive collaborations by providing a stan- dard format for sharing preclinical data and reducing the amount of harmonisation needed in order to cre- ate integrated datasets. The Innovative Medicines Initiative (IMI) project, Enhancing TRANslational SAFEty Assessment through Integrative Knowledge Management (eTRANSAFE)5, aims to develop just such a dataset, utilising data from 12 pharmaceutical companies for translational safety analysis. In partic- ular, it plans to address the following questions:
lWhat is the relevance of preclinical safety studies for clinical study safety assessment? l Can we refine preclinical experiments to better predict clinical safety? l Can we build predictive models of safety out- comes from the data?
By supplementing the SEND data with informa-
tion on the structure and pharmacology of the drug, the dataset will support translational research analytics, such as determining the possible liabilities for new drug candidates or identifying mechanistic hypotheses for adverse effects. As the amount of data in SEND format evolves,
and by combining it with legacy data in the same format, it will be possible to identify the back- ground incidence of histopathological findings along with the most common drug-related
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