A Data-Driven Medical Decision Framework for Associating Adverse Drug Events with Drug-Drug Interaction Mechanisms

Extracting adverse drug events from clinical Notes: A systematic review of approaches used

BACKGROUND

An adverse drug event (ADE) is any unfavorable effect that occurs due to the use of a drug. Extracting ADEs from unstructured clinical notes is essential to biomedical text extraction research because it helps with pharmacovigilance and patient medication studies.

OBJECTIVE

From the considerable amount of clinical narrative text, natural language processing (NLP) researchers have developed methods for extracting ADEs and their related attributes. This work presents a systematic review of current methods.

METHODOLOGY

Two biomedical databases have been searched from June 2022 until December 2023 for relevant publications regarding this review, namely the databases PubMed and Medline. Similarly, we searched the multi-disciplinary databases IEEE Xplore, Scopus, ScienceDirect, and the ACL Anthology. We adopted the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 statement guidelines and recommendations for reporting systematic reviews in conducting this review. Initially, we obtained 5,537 articles from the search results from the various databases between 2015 and 2023. Based on predefined inclusion and exclusion criteria for article selection, 100 publications have undergone full-text review, of which we consider 82 for our analysis.

RESULTS

We determined the general pattern for extracting ADEs from clinical notes, with named entity recognition (NER) and relation extraction (RE) being the dual tasks considered. Researchers that tackled both NER and RE simultaneously have approached ADE extraction as a "pipeline extraction" problem (n = 22), as a "joint task extraction" problem (n = 7), and as a "multi-task learning" problem (n = 6), while others have tackled only NER (n = 27) or RE (n = 20). We further grouped the reviews based on the approaches for data extraction, namely rule-based (n = 8), machine learning (n = 11), deep learning (n = 32), comparison of two or more approaches (n = 11), hybrid (n = 12) and large language models (n = 8). The most used datasets are MADE 1.0, TAC 2017 and n2c2 2018.

CONCLUSION

Extracting ADEs is crucial, especially for pharmacovigilance studies and patient medications. This survey showcases advances in ADE extraction research, approaches, datasets, and state-of-the-art performance in them. Challenges and future research directions are highlighted. We hope this review will guide researchers in gaining background knowledge and developing more innovative ways to address the challenges.

OPTIMIZATION AND CHARACTERIZATION OF ESSENTIAL OILS FORMULATION FOR ENHANCED STABILITY AND DRUG DELIVERY SYSTEM OF MEFLOQUINE

Objective: This work aims to choose suitable essential oil formulations to improve the bioavailability and long-term aqueous stability of mefloquine in drug delivery systems. Methods: Oil phases of pomegranate oil, black cumin seed oil, and garlic oil. To choose the proper oil and surfactant for creating pseudo-ternary phase diagrams, cremophore EL, tween®20 and tween®80 (surfactants), and brij 35 (co-surfactants) were used in a variety of concentrations and combinations (Smix). Mefloquine was estimated to be soluble in a variety of oils, surfactants, and co-surfactants. Drug solubility, drug release research, thermodynamic stability, mean hydrodynamic size and zeta potential. Results: Garlic with smix of cremophore EL and brij 35, Pomegranate with Tween 2.0, and Black cumin seed oil with Tween 80 showed the highest solubilization and emulsification capabilities and were further investigated using ternary phase diagrams. When combined with the co-surfactants under investigation, cremophore EL demonstrated a greater self-emulsification zone than tween® 80 and tween 20. Garlic oil, cremophore EL, and brij 35 nanoemulsion showed smaller size, greater zeta potential, less emulsification time, high transmittance, and better drug solubility than microemulsion formulations on especially those made with tween®20 and tween 80. Mefloquine loaded garlic oil nanoemulsion showed considerably low release in body fluid (32.48%) and a good release in intestinal fluid (82.78%) by 12 h in a drug release study. Conclusion: Garlic oil as the oil phase and a mixture of cremophore EL and brij 35 as the surfactant phase are ideal surfactants and co-surfactant for mefloquine loaded garlic oil nanoemulsion with greater drug release in release kinetics investigation.

Retracted: A Data-Driven Medical Decision Framework for Associating Adverse Drug Events with Drug-Drug Interaction Mechanisms

[This retracts the article DOI: 10.1155/2022/9132477.].

A Rule-Based Inference Framework to Explore and Explain the Biological Related Mechanisms of Potential Drug-Drug Interactions

As more drugs are developed and the incidence of polypharmacy increases, it is becoming critically important to anticipate potential DDIs before they occur in the clinic, along with those for which effects might go unobserved. However, traditional methods for DDI identification are unable to coalesce interaction mechanisms out of vast lists of potential or known DDIs, much less study them accurately. Computational methods have great promise but have realized only limited clinical utility. This work develops a rule-based inference framework to predict DDI mechanisms and support determination of their clinical relevance. Given a drug pair, our framework interrogates and describes DDI mechanisms based on a knowledge graph that integrates extensive available biomedical resources through semantic web technologies and backward chaining inference, effectively identifying facts within the graph that prove and explain the mechanisms of the drugs' interaction. The framework was evaluated through a case study combining a chemotherapy agent, irinotecan, and a widely used antibiotic, levofloxacin. The mutual interactions identified indicate that our framework can effectively explore and explain the mechanisms of potential DDIs. This approach has the potential to improve drug discovery and design and to support rapid and cost-effective identification of DDIs along with their putative mechanisms, a key step in determining clinical relevance and supporting clinical decision-making.

Recent Progress in Traditional Chinese Medicines and Their Mechanism in the Treatment of Allergic Rhinitis

Objective

To conduct a systematic review on the mechanism of action and use of traditional Chinese medicines (TCM) in allergic rhinitis treatment.

Background

Allergic rhinitis (AR) is a type I allergic disease of the immune system induced by immunoglobulin E mediated inflammation and is characterized by sneezing, nasal itching, paroxysmal nasal obstruction, mucosal edema, cough, and rhinorrhea. More than 500 million people have been affected by rhinitis worldwide in the past 20 years, leading to negative effects on health, quality of life, and social relationships. Currently, the trending medicines used in the case of AR include intranasal corticosteroids and oral H1 antihistamines, which are given as combinatorial medicines supplemented with immune therapy. These medications have been found to be very effective in either the short term or long term; however, they have been found to possess some serious side effects. Search Methodology. The information in this article on classical and traditional Chinese medications used to treat AR was derived from original papers and reviews published in Chinese and English language journals. Two Chinese databases (Wanfang and CNKI) and three English databases (Cochrane Library, PubMed, and Embase) were utilized for data gathering.

Results

Traditional Chinese remedies have been identified to influence the production of cytokines such as IL-5 and IL-6, which are key mediators of eosinophilic inflammation, TNF-α, which stimulates TH2 cells at the site of inflammation, and NF-кB, which is required for cytokine and IgE antibody production. TCM has also been shown to be successful in lowering histamine levels, preserving histological changes by decreasing the thickness of the lamina propria, and downregulating the expression of Orai1, STIM1, and TRYC1, showing low expression of Ca⁺² channel proteins.

Conclusion

In this review, we discussed a series of classical, traditional Chinese medications, including Centipeda minima, Scutellaria baicalensis, licorice root (Glycyrrhiza uralensis), and others, as potential antiallergic agents and investigate their in vivo effect upon the production of cytokines and release of histamines for allergic rhinitis treatment.

Anti-DDI Resource: A Dataset for Potential Negative Reported Interaction Combinations to Improve Medical Research and Decision-Making

Potential drug-drug interactions (DDIs) are a core concern across medical decision support systems. Among healthcare practitioners, the common practice for screening these interactions is via computer software. However, as real-world negative reporting is missing, counterexamples that serve as contradictory evidence may exist. In this study, we have developed an anti-DDI resource, a set of drug combinations having negative reported interactions. This resource was created from a set of the top 200 most-used drugs, resulting in 14365 prospective negative reported DDI pairs. During analysis and filtering, 2110 DDIs (14.69%) were found in publicly free DDI resources, another 11130 (77.48%) were filtered by a rule-based inference engine incorporating ten mechanisms of interaction, and 208 were identified through commercial resources. Additionally, 90 pairs were removed due to recent FDA approvals or being unapplicable in clinical use. The final set of 827 drug pairs represents combinations potentially having negative reported interactions. The anti-DDI resource is intended to provide a distinctly different direction from the state of the art and establish a ground focus more centered on the evaluation and utilization of existing knowledge for performing thorough assessments. Our negative reported DDIs resource shall provide healthcare practitioners with a level of certainty on DDIs that is worth investigating.