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. 2020 Oct 21;8(10):e20291.
doi: 10.2196/20291.

Building a Pharmacogenomics Knowledge Model Toward Precision Medicine: Case Study in Melanoma

Hongyu Kang  1   2 , Jiao Li  1 , Meng Wu  1 , Liu Shen  1 , Li Hou  1
Affiliations

Building a Pharmacogenomics Knowledge Model Toward Precision Medicine: Case Study in Melanoma

Hongyu Kang et al. JMIR Med Inform. .

Abstract

Background: Many drugs do not work the same way for everyone owing to distinctions in their genes. Pharmacogenomics (PGx) aims to understand how genetic variants influence drug efficacy and toxicity. It is often considered one of the most actionable areas of the personalized medicine paradigm. However, little prior work has included in-depth explorations and descriptions of drug usage, dosage adjustment, and so on.

Objective: We present a pharmacogenomics knowledge model to discover the hidden relationships between PGx entities such as drugs, genes, and diseases, especially details in precise medication.

Methods: PGx open data such as DrugBank and RxNorm were integrated in this study, as well as drug labels published by the US Food and Drug Administration. We annotated 190 drug labels manually for entities and relationships. Based on the annotation results, we trained 3 different natural language processing models to complete entity recognition. Finally, the pharmacogenomics knowledge model was described in detail.

Results: In entity recognition tasks, the Bidirectional Encoder Representations from Transformers-conditional random field model achieved better performance with micro-F1 score of 85.12%. The pharmacogenomics knowledge model in our study included 5 semantic types: drug, gene, disease, precise medication (population, daily dose, dose form, frequency, etc), and adverse reaction. Meanwhile, 26 semantic relationships were defined in detail. Taking melanoma caused by a BRAF gene mutation into consideration, the pharmacogenomics knowledge model covered 7 related drugs and 4846 triples were established in this case. All the corpora, relationship definitions, and triples were made publically available.

Conclusions: We highlighted the pharmacogenomics knowledge model as a scalable framework for clinicians and clinical pharmacists to adjust drug dosage according to patient-specific genetic variation, and for pharmaceutical researchers to develop new drugs. In the future, a series of other antitumor drugs and automatic relation extractions will be taken into consideration to further enhance our framework with more PGx linked data.

Keywords: BERT–CRF model; knowledge model; melanoma; named entity recognition; pharmacogenomics.

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Conflict of interest statement

Conflicts of Interest: None declared.

Figures

Figure 1
Figure 1
The framework of our study.
Figure 2
Figure 2
Annotation guidelines.
Figure 3
Figure 3
Annotation example of MEKINIST.
Figure 4
Figure 4
BERT–CRF architecture. BERT: Bidirectional Encoder Representations from Transformers; CRF: Conditional Random Field.
Figure 5
Figure 5
Overview of pharmacogenomics knowledge model.
Figure 6
Figure 6
An example of pharmacogenomics knowledge model data visualization.
Figure 7
Figure 7
User interface of pharmacogenomics knowledge model data set.
See this image and copyright information in PMC

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