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Accepted for/Published in: JMIR Medical Informatics

Date Submitted: May 15, 2020
Date Accepted: Sep 13, 2020

The final, peer-reviewed published version of this preprint can be found here:

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

Kang H, Li J, Wu M, Shen L, Hou L

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

JMIR Med Inform 2020;8(10):e20291

DOI: 10.2196/20291

PMID: 33084582

PMCID: 7641779

Building a Pharmacogenomics Knowledge Model towards precision medicine: A Case Study in Melanoma

  • Hongyu Kang; 
  • Jiao Li; 
  • Meng Wu; 
  • Liu Shen; 
  • Li Hou

ABSTRACT

Background:

Lots of drugs don't work the same way for everyone owing to distinctions in their genes. Pharmacogenomics 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 in-depth exploration and description of drug usage, dosage adjustment, and so on.

Objective:

We present a Pharmacogenomics Knowledge Model (PGxKM) to discover hidden relationships between pharmacogenomics entities such as drugs, genes, and diseases, especially details in precise medication.

Methods:

Pharmacogenomics open data such as DrugBank and RxNorm were integrated in this research, as well as drug labels published by the US Food and Drug Administration (FDA). We annotated 190 drug labels manually for entities and relationships. Based on the annotated result, we also trained three different natural language processing (NLP) models to complete entity recognition. Finally, Pharmacogenomics Knowledge Model was described in detail.

Results:

In entity recognition task, BERT-CRF model achieved better performance with F1=85.12%. PGxKM in our study included five 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 BRAF gene mutation into consideration, PGxKM covered seven related drugs and 4,846 triples were established in this case. All the corpora, relationship definitions and triples were made publically available.

Conclusions:

We highlighted the PGxKM 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 will be taken into consideration to fill up our framework with more linked data in pharmacogenomics.


 Citation

Please cite as:

Kang H, Li J, Wu M, Shen L, Hou L

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

JMIR Med Inform 2020;8(10):e20291

DOI: 10.2196/20291

PMID: 33084582

PMCID: 7641779

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