The efficacy of anticancer drugs usually varies from individual to individual, mainly due to genetic heterogeneity. Therefore, it is feasible to design personalized therapy strategies based on the genetic background of patients. iGMDR database collects predictive models of response for anticancer drugs. The predictive model of drug response is a combination of genetic characteristics analyzed from pharmacogenetic studies using big data of the patient population. In recent years, with the development of pharmacogenetic studies and the application of clinical sequencing of the individual genome, the prediction models were more and more used in clinical practices, but only a few drugs have had the matching drug genetic information, and some drug genetic information even was not very clear.

iGMDR database aims to build an open community collecting the results of the current phase of pharmacogenetic studies, and extracting the prediction models of response for anticancer drugs. According to the differences of technique background, the prediction models involve different genetic characteristics mainly including single nucleotide variation (SNV), copy number variation (CNV), gene expression variation (GEX) and gene structure variation (SV). This database collects the models not only that have already been used in clinical practices, but also that have investigated in vivo or in vitro experiments. Although most of these models might be not clinically significant, the integration of these models will help us to understand the mechanism of cancer therapies from a macro-perspective and the response mechanism of individual drugs from a micro-perspective, and further release the value of those pharmacogenetic studies to promote clinical practices.

As shown in below, the iGMDR database collected 154,146 prediction models formed by 1040 drugs and 4420 genes on 144 cancer types of 30 tissue types. The models we have integrated were extracted from 1812 references including FDA, NCCN, ASCO, large-scale pharmacogenetic studies on cell lines, and so on. To meet the requirements of big data analysis, the information such as cancers, drugs and genetic characteristics was standardized and stored using structured manner.

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