Database: A New Article Type in CPT: Pharmacometrics & Systems Pharmacology
Lang Li, PH van der Graaf · CPT Pharmacometrics & Systems Pharmacology · 2015
The immediate impact of big data to systems pharmacology research is highly significant and growing stronger.Current successful big data-driven pharmacology research comes from two major sources: population-based health record databases and molecular databases.The other driving force of big data research is the bioinformatics approach.Remarkably different from traditional hypothesis-driven research, bioinformatics resides in its natural power to inspire discovery.For example, using cutting-edge data mining methods and chemo-informatics data, Tatonetti et al. conducted association analysis between drugs, or drug interactions, and adverse events (ADEs) to assess all the US Food and Drug Administration's (FDA)-approved drugs and ADEs. 1 Their much-expanded pharmacovigilance research was based on the FDA's Adverse Event Reporting System (FAERS) and their local electronic medical record database (EMR).Another example is Duke et al.'s drug interaction data mining research using the EMR database.2 Their work further integrated a large-scale cytochrome P450 enzyme pathway-based pharmacokinetics interaction evidence to support epidemiological drug interaction signals identified from the EMR database.The eMERGE (Electronic Medical Records and Genomics) network is another salient example (http://www.genome.gov/27540473).It integrates both population data and genomics data with Biobank samples.Denny et al. 3 extended a Biobank-based genome-wide association study (GWAS) to 86 reported disease phenotypes in the National Human Genome Research Institute GWAS Catalog using eMERGE.They successfully replicated a majority of prior GWAS associations.A comprehensive description of the impact of big data and bioinformatics in pharmacological research is documented in a recent review article.4 Bioinformatics and big data have increasingly become a core component of publications in CPT:Pharmacometrics & Systems Pharmacology (PSP).Examples are recent articles on a new drug combinatory effect prediction based on gene expression data by Goswami et al. 5 ; and the medication-wide adverse event association analysis using medical record databases by Vilar et al. 6 However, there is a major barrier in delivering reproducible and transparent scientific findings if databases are not being made available.At the same time, scientists who pull together databases are not always recognized for their effort and contribution.It is obvious that the very first data