Global Text Mining and Development of Pharmacogenomic Knowledge Resource for Precision Medicine

Assessment of clinically actionable pharmacogenetic markers to stratify anti-seizure medications

Epilepsy treatment is challenging due to heterogeneous syndromes, different seizure types and higher inter-individual variability. Identification of genetic variants predicting drug efficacy, tolerability and risk of adverse-effects for anti-seizure medications (ASMs) is essential. Here, we assessed the clinical actionability of known genetic variants, based on their functional and clinical significance and estimated their diagnostic predictability. We performed a systematic PubMed search to identify articles with pharmacogenomic (PGx) information for forty known ASMs. Functional annotation of the identified genetic variants was performed using different in silico tools, and their clinical significance was assessed using the American College of Medical Genetics (ACMG) guidelines for variant pathogenicity, level of evidence (LOE) from PharmGKB and the United States-Food and drug administration (US- FDA) drug labelling with PGx information. Diagnostic predictability of the replicated genetic variants was evaluated by calculating their accuracy. A total of 270 articles were retrieved with PGx evidence associated with 19 ASMs including 178 variants across 93 genes, classifying 26 genetic variants as benign/ likely benign, fourteen as drug response markers and three as risk factors for drug response. Only seventeen of these were replicated, with accuracy (up to 95%) in predicting PGx outcomes specific to six ASMs. Eight out of seventeen variants have FDA-approved PGx drug labelling for clinical implementation. Therefore, the remaining nine variants promise for potential clinical actionability and can be improvised with additional experimental evidence for clinical utility.

Text Mining Protocol to Retrieve Significant Drug-Gene Interactions from PubMed Abstracts

Genes and proteins form the basis of all cellular processes and ensure a smooth functioning of the human system. The diseases caused in humans can be either genetic in nature or may be caused due to external factors. Genetic diseases are mainly the result of any anomaly in gene/protein structure or function. This disruption interferes with the normal expression of cellular components. Against external factors, even though the immunogenicity of every individual protects them to a certain extent from infections, they are still susceptible to other disease-causing agents. Understanding the biological pathway/entities that could be targeted by specific drugs is an essential component of drug discovery. The traditional drug target discovery process is time-consuming and practically not feasible. A computational approach could provide speed and efficiency to the method. With the presence of vast biomedical literature, text mining also seems to be an obvious choice which could efficiently aid with other computational methods in identifying drug-gene targets. These could aid in initial stages of reviewing the disease components or can even aid parallel in extracting drug-disease-gene/protein relationships from literature. The present chapter aims at finding drug-gene interactions and how the information could be explored for drug interaction.

PharmGKB, an Integrated Resource of Pharmacogenomic Knowledge

The Pharmacogenomics Knowledgebase (PharmGKB) is an integrated online knowledge resource for the understanding of how genetic variation contributes to variation in drug response. Our focus includes not only pharmacogenomic information useful for clinical implementation (e.g., drug dosing guidelines and annotated drug labels), but also information to catalyze scientific research and drug discovery (e.g., variant-drug annotations and drug-centered pathways). As of April 2021, the annotated content of PharmGKB spans 715 drugs, 1761 genes, 227 diseases, 165 clinical guidelines, and 784 drug labels. We have manually curated data from more than 9000 published papers to generate the content of PharmGKB. Recently, we have also implemented an automated natural language processing (NLP) tool to broaden our coverage of the pharmacogenomic literature. This article contains a basic protocol describing how to navigate the PharmGKB website to retrieve information on how genes and genetic variations affect drug efficacy and toxicity. It also includes a protocol on how to use PharmGKB to facilitate interpretation of findings for a pharmacogenomic variant genotype or metabolizer phenotype. PharmGKB is freely available at http://www.pharmgkb.org. © 2021 Wiley Periodicals LLC. Basic Protocol 1: Navigating the homepage of PharmGKB and searching by drug Basic Protocol 2: Using PharmGKB to facilitate interpretation of pharmacogenomic variant genotypes or metabolizer phenotypes.

Automated Extraction of Information From Texts of Scientific Publications: Insights Into HIV Treatment Strategies

Text analysis can help to identify named entities (NEs) of small molecules, proteins, and genes. Such data are very important for the analysis of molecular mechanisms of disease progression and development of new strategies for the treatment of various diseases and pathological conditions. The texts of publications represent a primary source of information, which is especially important to collect the data of the highest quality due to the immediate obtaining information, in comparison with databases. In our study, we aimed at the development and testing of an approach to the named entity recognition in the abstracts of publications. More specifically, we have developed and tested an algorithm based on the conditional random fields, which provides recognition of NEs of (i) genes and proteins and (ii) chemicals. Careful selection of abstracts strictly related to the subject of interest leads to the possibility of extracting the NEs strongly associated with the subject. To test the applicability of our approach, we have applied it for the extraction of (i) potential HIV inhibitors and (ii) a set of proteins and genes potentially responsible for viremic control in HIV-positive patients. The computational experiments performed provide the estimations of evaluating the accuracy of recognition of chemical NEs and proteins (genes). The precision of the chemical NEs recognition is over 0.91; recall is 0.86, and the F1-score (harmonic mean of precision and recall) is 0.89; the precision of recognition of proteins and genes names is over 0.86; recall is 0.83; while F1-score is above 0.85. Evaluation of the algorithm on two case studies related to HIV treatment confirms our suggestion about the possibility of extracting the NEs strongly relevant to (i) HIV inhibitors and (ii) a group of patients i.e., the group of HIV-positive individuals with an ability to maintain an undetectable HIV-1 viral load overtime in the absence of antiretroviral therapy. Analysis of the results obtained provides insights into the function of proteins that can be responsible for viremic control. Our study demonstrated the applicability of the developed approach for the extraction of useful data on HIV treatment.

A Novel Text-Mining Approach for Retrieving Pharmacogenomics Associations From the Literature

Text mining in biomedical literature is an emerging field which has already been shown to have a variety of implementations in many research areas, including genetics, personalized medicine, and pharmacogenomics. In this study, we describe a novel text-mining approach for the extraction of pharmacogenomics associations. The code that was used toward this end was implemented using R programming language, either through custom scripts, where needed, or through utilizing functions from existing libraries. Articles (abstracts or full texts) that correspond to a specified query were extracted from PubMed, while concept annotations were derived by PubTator Central. Terms that denote a Mutation or a Gene as well as Chemical compound terms corresponding to drug compounds were normalized and the sentences containing the aforementioned terms were filtered and preprocessed to create appropriate training sets. Finally, after training and adequate hyperparameter tuning, four text classifiers were created and evaluated (FastText, Linear kernel SVMs, XGBoost, Lasso, and Elastic-Net Regularized Generalized Linear Models) with regard to their performance in identifying pharmacogenomics associations. Although further improvements are essential toward proper implementation of this text-mining approach in the clinical practice, our study stands as a comprehensive, simplified, and up-to-date approach for the identification and assessment of research articles enriched in clinically relevant pharmacogenomics relationships. Furthermore, this work highlights a series of challenges concerning the effective application of text mining in biomedical literature, whose resolution could substantially contribute to the further development of this field.