Ontology-Based Vaccine Adverse Event Representation and Analysis

COVID-19 Vaccine Adverse Event Detection Based on Multi-Label Classification With Various Label Selection Strategies

Analyzing massive VAERS reports without medical context may lead to incorrect conclusions about vaccine adverse events (VAE). Facilitating VAE detection promotes continual safety improvement for new vaccines. This study proposes a multi-label classification method with various term-and topic-based label selection strategies to improve the accuracy and efficiency of VAE detection. Topic modeling methods are first used to generate rule-based label dependencies from Medical Dictionary for Regulatory Activities terms in VAE reports with two hyper-parameters. Multiple label selection strategies, namely one-vs-rest (OvsR), problem transformation (PT), algorithm adaption (AA), and deep learning (DL) methods, are used in multi-label classification to examine the model performance, respectively. Experimental results indicated that the topic-based PT methods improve the accuracy by up to 33.69% using a COVID-19 VAE reporting data set, which improves the robustness and interpretability of our models. In addition, the topic-based OvsR methods achieve an optimal accuracy of up to 98.88%. The accuracy of the AA methods with topic-based labels increased by up to 87.36%. By contrast, the state-of-art LSTM- and BERT-based DL methods have relatively poor performance with accuracy rates of 71.89% and 64.63%, respectively. Our findings reveal that the proposed method effectively improves the model accuracy and strengthens VAE interpretability by using different label selection strategies and domain knowledge in multi-label classification for VAE detection.

A Virtual Assistant in Vaccine Pharmacovigilance: Content and Usability Validation

This article describes the process of developing and validating a virtual assistant to perform vaccine pharmacovigilance. We performed a pilot study with a panel of 22 healthcare professionals who performed content validation of the virtual assistant prototype. Usability was tested with 126 users, using the System Usability Scale. The data analysis was performed by the agreement rate and content validity index, and the κ test was used to verify the agreement between the evaluators. The content domains of the virtual assistant achieved excellent suitability, relevance, and representativeness criteria, all greater than 86%; the content validity index ranged from 0.81 to 0.98, with an average of 0.90 and an interrater reliability index of 1.00. There was excellent interrater agreement (average κ value, 0.76). The total usability score among users was 80.1, ranging from 78.2 in group 1 (users without reactions to vaccines) to 82.1 in group 2 (users with reactions) (P = .002). The virtual assistant for vaccine pharmacovigilance obtained a satisfactory level of content validity and usability, giving greater credibility to the claim that this device provides greater surveillance and safety for patients.

Vaccine Efficacy Denial: A Growing Concern Affecting Modern Science, and Impacting Public Health

BACKGROUND

The discovery of the vaccination technique has been revealed by Edward Jenner in 1796: undoubtedly, it represents the first scientific attempt to control an infectious disease by vaccines, followed by other important studies carried out by Pasteur and Koch, and Sabin, who developed the first technique to attenuate the virus. In recent decades, numerous scholars have begun to create dangerous theories against the effectiveness of vaccines through scientifically invalid or fraudulent studies.

AIM

This critical review of the literature aims to analyse the main factors that have undermined the credibility of vaccines in the general population, to disprove false information and, on the other hand, emphasize the benefits of vaccines over the last 200 years.

DISCUSSIONS

Unfortunately, several studies have been carried out without the proper scientific rigour. The most impacting example is the study published by Andrew Wakefield in the Lancet journal that tried to correlate vaccines with the development of autism: this publication was withdrawn from the journal a few years after its publication, but the impact of incorrect scientific studies, fake news, and ambiguous healthcare policies has led to an adverse general opinion about the effectiveness of vaccines.

CONCLUSION

The excess of uncontrolled information is a serious concerning in the Coronavirus pandemic. The modern science must tackle this problem with a better willingness to communicate even the clinical studies to those people not able to understand the medical information autonomously. Nevertheless, a reliable science must also limit the dissemination of studies that do not meet the basic criteria of a methodological rigor and certainty of results, in order not to feed confusion in the scientific community.

COVID-19 Vaccines (Revisited) and Oral-Mucosal Vector System as a Potential Vaccine Platform

There are several emerging strategies for the vaccination of COVID-19 (SARS-CoV-2) however, only a few have yet shown promising effects. Thus, choosing the right pathway and the best prophylactic options in preventing COVID-19 is still challenging at best. Approximately, more than two-hundred vaccines are being tested in different countries, and more than fifty clinical trials are currently undergoing. In this review, we have summarized the immune-based strategies for the development of COVID-19 vaccines and the different vaccine candidate platforms that are in clinical stages of evaluation, and up to the recently licensed mRNA-based COVID-19 vaccines of Pfizer-BioNtech and Moderna's. Lastly, we have briefly included the potentials of using the 'RPS-CTP vector system' for the development of a safe and effective oral mucosal COVID-19 vaccine as another vaccine platform.

Causality assessment of adverse events following immunization: the problem of multifactorial pathology

The analysis of Adverse Events Following Immunization (AEFI) is important in a balanced epidemiological evaluation of vaccines and in the issues related to national vaccine injury compensation programs. If manufacturing defects or vaccine storage and delivering errors are excluded, the majority of adverse reactions to vaccines occur as excessive or biased inflammatory and immune responses. These unwanted phenomena, occasionally severe, are associated with many different endogenous and exogenous factors, which often interact in complex ways. The confirmation or denial of the causal link between an AEFI and vaccination is determined pursuant to WHO guidelines, which propose a four-step analysis and algorithmic diagramming. The evaluation process from the onset considers all possible "other causes" that can explain the AEFI and thus exclude the role of the vaccine. Subsequently, even if there was biological plausibility and temporal compatibility for a causal association between the vaccine and the AEFI, the guidelines ask to look for any possible evidence that the vaccine could not have caused that event. Such an algorithmic method presents some concerns that are discussed here, in the light of the multifactorial nature of the inflammatory and immune pathologies induced by vaccines, including emerging knowledge of genetic susceptibility to adverse effects. It is proposed that the causality assessment could exclude a consistent association of the adverse event with the vaccine only when the presumed "other cause" is independent of an interaction with the vaccine. Furthermore, the scientific literature should be viewed not as an exclusion criterion but as a comprehensive analysis of all the evidence for or against the role of the vaccine in causing an adverse reaction. These issues are discussed in relation to the laws that, in some countries, regulate the mandatory vaccinations and the compensation for those who have suffered serious adverse effects.

Causality assessment of adverse events following immunization: the problem of multifactorial pathology

The analysis of Adverse Events Following Immunization (AEFI) is important in a balanced epidemiological evaluation of vaccines and in the issues related to vaccine injury compensation programs. The majority of adverse reactions to vaccines occur as excessive or biased inflammatory and immune responses. These unwanted phenomena, occasionally severe, are associated with many different endogenous and exogenous factors, which often interact in complex ways. The confirmation or denial of the causal link between an AEFI and vaccination is determined pursuant to WHO guidelines, which propose a four-step analysis and algorithmic diagramming. The evaluation process from the onset considers all possible "other causes" that might explain the AEFI and thus exclude the role of the vaccine. Subsequently, even if there was biological plausibility and temporal compatibility for a causal association between the vaccine and the AEFI, the guidelines ask to look for any possible evidence that the vaccine could not have caused that event. Such an algorithmic method presents several concerns that are discussed here, in the light of the multifactorial nature of the inflammatory and immune pathologies induced by vaccines, including emerging knowledge of genetic susceptibility to adverse effects. It is proposed that the causality assessment could exclude a consistent association of the adverse event with the vaccine only when the presumed "other cause" is independent of an interaction with the vaccine. Furthermore, the scientific literature should be viewed not as an exclusion criterion but as a comprehensive analysis of all the evidence for or against the role of the vaccine in causing an adverse reaction. Given these inadequacies in the evaluation of multifactorial diseases, the WHO guidelines need to be reevaluated and revised. These issues are discussed in relation to the laws that, in some countries, regulate the mandatory vaccinations and the compensation for those who have suffered serious adverse effects.

Computational Advances in Drug Safety: Systematic and Mapping Review of Knowledge Engineering Based Approaches

Drug Safety (DS) is a domain with significant public health and social impact. Knowledge Engineering (KE) is the Computer Science discipline elaborating on methods and tools for developing “knowledge-intensive” systems, depending on a conceptual “knowledge” schema and some kind of “reasoning” process. The present systematic and mapping review aims to investigate KE-based approaches employed for DS and highlight the introduced added value as well as trends and possible gaps in the domain. Journal articles published between 2006 and 2017 were retrieved from PubMed/MEDLINE and Web of Science® (873 in total) and filtered based on a comprehensive set of inclusion/exclusion criteria. The 80 finally selected articles were reviewed on full-text, while the mapping process relied on a set of concrete criteria (concerning specific KE and DS core activities, special DS topics, employed data sources, reference ontologies/terminologies, and computational methods, etc.). The analysis results are publicly available as online interactive analytics graphs. The review clearly depicted increased use of KE approaches for DS. The collected data illustrate the use of KE for various DS aspects, such as Adverse Drug Event (ADE) information collection, detection, and assessment. Moreover, the quantified analysis of using KE for the respective DS core activities highlighted room for intensifying research on KE for ADE monitoring, prevention and reporting. Finally, the assessed use of the various data sources for DS special topics demonstrated extensive use of dominant data sources for DS surveillance, i.e., Spontaneous Reporting Systems, but also increasing interest in the use of emerging data sources, e.g., observational healthcare databases, biochemical/genetic databases, and social media. Various exemplar applications were identified with promising results, e.g., improvement in Adverse Drug Reaction (ADR) prediction, detection of drug interactions, and novel ADE profiles related with specific mechanisms of action, etc. Nevertheless, since the reviewed studies mostly concerned proof-of-concept implementations, more intense research is required to increase the maturity level that is necessary for KE approaches to reach routine DS practice. In conclusion, we argue that efficiently addressing DS data analytics and management challenges requires the introduction of high-throughput KE-based methods for effective knowledge discovery and management, resulting ultimately, in the establishment of a continuous learning DS system.

Vaccines Meet Big Data: State-of-the-Art and Future Prospects. From the Classical 3Is ("Isolate-Inactivate-Inject") Vaccinology 1.0 to Vaccinology 3.0, Vaccinomics, and Beyond: A Historical Overview

Vaccines are public health interventions aimed at preventing infections-related mortality, morbidity, and disability. While vaccines have been successfully designed for those infectious diseases preventable by preexisting neutralizing specific antibodies, for other communicable diseases, additional immunological mechanisms should be elicited to achieve a full protection. “New vaccines” are particularly urgent in the nowadays society, in which economic growth, globalization, and immigration are leading to the emergence/reemergence of old and new infectious agents at the animal–human interface. Conventional vaccinology (the so-called “vaccinology 1.0”) was officially born in 1796 thanks to the contribution of Edward Jenner. Entering the twenty-first century, vaccinology has shifted from a classical discipline in which serendipity and the Pasteurian principle of the three Is (isolate, inactivate, and inject) played a major role to a science, characterized by a rational design and plan (“vaccinology 3.0”). This shift has been possible thanks to Big Data, characterized by different dimensions, such as high volume, velocity, and variety of data. Big Data sources include new cutting-edge, high-throughput technologies, electronic registries, social media, and social networks, among others. The current mini-review aims at exploring the potential roles as well as pitfalls and challenges of Big Data in shaping the future vaccinology, moving toward a tailored and personalized vaccine design and administration.