gms | German Medical Science

Introduction: In the last few decades, precision medicine became a highly weighted subject as the risk of side effects grew with the increasing drug use. It is estimated that 20−95% of the response variability, depending on the drug, comes from the individual’s genetic profile [1]. At the Vanderbilt University Medical Center, 54% of the patients were prescribed pharmacogenetic relevant drugs [2]. Taking pharmacogenetic factors into account can decide about secondary illnesses, inefficiency or, more and more often, life or death. Genotyping and sequencing methods evolve rapidly so that, for example, an entire human genome can be sequenced for under $1,000 [3] and preemptive pharmacogenetic testings can be performed at costs similar to those of other medical examinations (e.g. like the determination of the drug plasma level) [4]. Because of that, the technical implementation of pharmacogenetic checks continues to become more and more interesting in personal medicine. Although reactive genotyping is already used in some areas of medicine, the mass of evolving pharmacogenetic data invites to establish a preemptive genotyping approach.

The present work addresses the integration of molecular data into the drug therapy workflow of an existing hospital information system to improve the safety of drug therapy.

Methods: The newly introduced software module GraphSAW2-DWHBuilder creates a graph-based, molecular data warehouse out of various data sources. Due to the graph structure, the highly connected nodes and their edges can both be queried in linear time. The modular concept of the program allows an easy extension of the data sources. Moreover, an update strategy was developed to ensure that only the latest version of the data is used. In order to eliminate redundancies and to guarantee efficient querying, a mapper strategy was also established.

Another new module named GraphSAW2-Check is embedded to the existing drug therapy safety workflow. It contains methods for the pharmacogenetic check which access the molecular data warehouse of the GraphSAW2-DWHBuilder and execute queries on the graph. These queries contain the drugs and the molecular data of interest and search for specific (e.g. toxic) connections between them. If such a connection is found, a warning will be issued to the user who can then reconsider their decision regarding their prescription.

Results: The results show that 35% of the 20 most prescribed drugs have a total of 51 significant pharmacogenetic annotations. It also turns out that the frequency of the gene variants are present in up to 37% of the European population [5].

Discussion: It can be seen clearly that these results speak in favor of regular, preemptive genotyping of patients and their verification with pharmacogenetic data. The update strategy is an important component regarding the pharmacogenetic data since the very frequently updated findings between drugs and gene variants improve the system and thus also the prescriptions.

Conclusion: With the presented methods an approach of integrating molecular data into a hospital information system is introduced. Furthermore, pharmacogenetic checks are implemented into an existing drug therapy workflow that can efficiently and reliably return pharmacogenetic recommendations.

The authors declare that they have no competing interests.

The authors declare that an ethics committee vote is not required.