Growth in health care data causing an evolution in the pharmaceutical industry.

A health care ecosystem is evolving in which all stakeholders will need to work together, apply new technologies, and use disparate data sources to gain insights, increase efficiencies, and improve patient outcomes. The pharmaceutical industry is leveraging its experience and analytics capabilities to play an important role in this evolution.

A proposed modification to Hy's law and Edish criteria in oncology clinical trials using aggregated historical data.

PURPOSE: Identifying drug-induced liver injury is a critical task in drug development and postapproval real-world care. Severe liver injury is identified by the liver chemistry threshold of alanine aminotransferase (ALT) >3× upper limit of normal (ULN) and bilirubin >2× ULN, termed Hy's law by the Food and Drug Administration. These thresholds require discontinuation of the causative drug and are seldom exceeded in most patient populations. However, because maintenance of therapy is critical in the treatment of advanced cancer, customized thresholds may be useful in oncology patient populations, particularly for those with baseline liver chemistries elevations. METHODS: Liver chemistry data from 31 aggregated oncology clinical trials were modeled through a truncated robust multivariate outlier detection (TRMOD) method to develop the decision boundary or threshold for examining liver injury in oncology clinical trials. RESULTS: The boundary of TRMOD identified outliers with an ALT limit 5.0× ULN and total bilirubin limit 2.7× ULN. In addition, TRMOD was applied to the aggregated oncology data to examine fold-baseline ALT and total bilirubin, revealing limits of ALT 6.9× baseline and bilirubin 6.5× baseline. Similar ALT and bilirubin threshold limits were observed for oncology patients both with and without liver metastases. CONCLUSIONS: These higher liver chemistry thresholds examining fold-ULN and fold-baseline data may be valuable in identifying potential severe liver injury and detecting liver safety signals of clinical concern in oncology clinical trials and postapproval settings while helping to avoid premature discontinuation of curative therapy.

Domain-enhanced analysis of microarray data using GO annotations.

MOTIVATION: New biological systems technologies give scientists the ability to measure thousands of bio-molecules including genes, proteins, lipids and metabolites. We use domain knowledge, e.g. the Gene Ontology, to guide analysis of such data. By focusing on domain-aggregated results at, say the molecular function level, increased interpretability is available to biological scientists beyond what is possible if results are presented at the gene level. RESULTS: We use a 'top-down' approach to perform domain aggregation by first combining gene expressions before testing for differentially expressed patterns. This is in contrast to the more standard 'bottom-up' approach, where genes are first tested individually then aggregated by domain knowledge. The benefits are greater sensitivity for detecting signals. Our method, domain-enhanced analysis (DEA) is assessed and compared to other methods using simulation studies and analysis of two publicly available leukemia data sets. AVAILABILITY: Our DEA method uses functions available in R (http://www.r-project.org/) and SAS (http://www.sas.com/). The two experimental data sets used in our analysis are available in R as Bioconductor packages, 'ALL' and 'golubEsets' (http://www.bioconductor.org/). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Social Media Listening for Routine Post-Marketing Safety Surveillance.

INTRODUCTION: Post-marketing safety surveillance primarily relies on data from spontaneous adverse event reports, medical literature, and observational databases. Limitations of these data sources include potential under-reporting, lack of geographic diversity, and time lag between event occurrence and discovery. There is growing interest in exploring the use of social media ('social listening') to supplement established approaches for pharmacovigilance. Although social listening is commonly used for commercial purposes, there are only anecdotal reports of its use in pharmacovigilance. Health information posted online by patients is often publicly available, representing an untapped source of post-marketing safety data that could supplement data from existing sources. OBJECTIVES: The objective of this paper is to describe one methodology that could help unlock the potential of social media for safety surveillance. METHODS: A third-party vendor acquired 24 months of publicly available Facebook and Twitter data, then processed the data by standardizing drug names and vernacular symptoms, removing duplicates and noise, masking personally identifiable information, and adding supplemental data to facilitate the review process. The resulting dataset was analyzed for safety and benefit information. RESULTS: In Twitter, a total of 6,441,679 Medical Dictionary for Regulatory Activities (MedDRA(®)) Preferred Terms (PTs) representing 702 individual PTs were discussed in the same post as a drug compared with 15,650,108 total PTs representing 946 individual PTs in Facebook. Further analysis revealed that 26 % of posts also contained benefit information. CONCLUSION: Social media listening is an important tool to augment post-marketing safety surveillance. Much work remains to determine best practices for using this rapidly evolving data source.