Safety of medicines and vaccines - building next generation capability

Unveiling the future: precision pharmacovigilance in the era of personalized medicine

In the era of personalized medicine, pharmacovigilance faces new challenges and opportunities, demanding a shift from traditional approaches. This article delves into the evolving landscape of drug safety monitoring in the context of personalized treatments. We aim to provide a succinct reflection on the intersection of tailored therapeutic strategies and vigilant pharmacovigilance practices. We discuss the integration of pharmacogenetics in enhancing drug safety, illustrating how genetic profiling aids in predicting drug responses and adverse reactions. Emphasizing the importance of phase IV-post-marketing surveillance, we explore the limitations of pre-marketing trials and the necessity for a comprehensive approach to drug safety. The article discusses the pivotal role of pharmacogenetics in pre-exposure risk management and the redefinition of pharmacoepidemiological methods for post-exposure surveillance. We highlight the significance of integrating patient-specific genetic profiles in creating personalized medication leaflets and the use of advanced computational methods in data analysis. Additionally, we examine the ethical, privacy, and data security challenges inherent in precision medicine, emphasizing their implications for patient consent and data management.

Finding Needles in the Haystack: Clinical Utility Score for Prioritisation (CUSP), an Automated Approach for Identifying Spontaneous Reports with the Highest Clinical Utility

INTRODUCTION

Spontaneous reporting of adverse events has increased steadily over the past decades, and although this trend has contributed to improving post-marketing surveillance pharmacovigilance activities, the consequent amount of data generated is challenging to manually review during assessment, with each individual report requiring review by pharmacovigilance experts. This highlights a clear need for alternative or complementary methodologies to help prioritise review.

OBJECTIVE

Here, we aimed to develop and test an automated methodology, the Clinical Utility Score for Prioritisation (CUSP), to assist pharmacovigilance experts in prioritising clinical assessment of safety data to improve the rapidity of case series review when case volumes are large.

METHODS

The CUSP method was tested on a reference dataset of individual case safety reports (ICSRs) associated to five drug-event pairs that led to labelling changes. The selected drug-event pairs were of varying characteristics across the portfolio of GSK's products.

RESULTS

The mean CUSP score for 'key cases' and 'cases of low utility' was 19.7 (median: 21; range: 7-27) and 17.3 (median: 19; range: 4-27), respectively. CUSP distribution for 'key cases' were skewed toward the higher range of scores compared with 'all cases'. The overall performance across each individual drug-event pair varied considerably, showing higher predictive power for 'key cases' for three of the drug-event pairs (average CUSP between these three: 22.8; range: 22.5-23.0) and lesser power for the remaining two (average CUSP between these two: 17.6; range: 14.5-20.7).

CONCLUSION

Although several tools have been developed to assess ICSR completeness and regulatory utility, this is the first attempt to successfully develop an automated clinical utility scoring system that can support the prioritisation of ICSRs for clinical review.

Developing an Artificial Intelligence-Guided Signal Detection in the Food and Drug Administration Adverse Event Reporting System (FAERS): A Proof-of-Concept Study Using Galcanezumab and Simulated Data

INTRODUCTION

Time- and resource-demanding activities related to processing individual case safety reports (ICSRs) include manual procedures to evaluate individual causality with the final goal of dismissing false-positive safety signals. Eminent experts and a representative from pharmaceutical industries and regulatory agencies have highlighted the need to automatize time- and resource-demanding procedures in signal detection and validation. However, to date there is a sparse availability of automatized tools for such purposes.

OBJECTIVES

ICSRs recorded in spontaneous reporting databases have been and continue to be the cornerstone and the most important data source in signal detection. Despite the richness of this data source, the incessantly increased amount of ICSRs recorded in spontaneous reporting databases has generated problems in signal detection and validation due to the increase in resources and time needed to process cases. This study aimed to develop a new artificial intelligence (AI)-based framework to automate resource- and time-consuming steps of signal detection and signal validation, such as (1) the selection of control groups in disproportionality analyses and (2) the identification of co-reported drugs serving as alternative causes, to look to dismiss false-positive disproportionality signals and therefore reduce the burden of case-by-case validation.

METHODS

The Summary of Product Characteristics (SmPC) and the Anatomical Therapeutic Chemical (ATC) classification system were used to automatically identify control groups within and outside the chemical subgroup of the proof-of-concept drug under investigation, galcanezumab. Machine learning, specifically conditional inference trees, has been used to identify alternative causes in disproportionality signals.

RESULTS

By using conditional inference trees, the framework was able to dismiss 20.00% of erenumab, 14.29% of topiramate, and 13.33% of amitriptyline disproportionality signals on the basis of purely alternative causes identified in cases. Furthermore, of the disproportionality signals that could not be dismissed purely on the basis of the alternative causes identified, we estimated a 15.32%, 25.39%, and 26.41% reduction in the number of galcanezumab cases to undergo manual validation in comparison with erenumab, topiramate, and amitriptyline, respectively.

CONCLUSION

AI could significantly ease some of the most time-consuming and labor-intensive steps of signal detection and validation. The AI-based approach showed promising results, however, future work is needed to validate the framework.

Current patient safety issues: the role of pharmacovigilance

In the current conditions of the pandemic, the burden on the healthcare system, including the pharmacovigilance system monitoring the safety of pharmacotherapy, has significantly increased in all countries. An integral component in ensuring the safety of pharmacotherapy is the identification and prevention of the development of adverse drug reactions (ADR), which are a serious health problem worldwide. One of the modern problems of healthcare, including pharmacovigilance, was the lack of vaccines and drugs for the treatment and prevention of COVID-19 in the first waves of the pandemic, which led to the use of off-label a large number of drugs (hydroxychloroquine, azithromycin, ivermectin) for the treatment of patients with COVID-19 despite the fact that scientific data their benefits were of poor quality and based on in vitro studies. The accelerated approval of drugs and vaccines to combat the COVID-19 pandemic also highlighted the need for rapid data on the safety of drugs in the post-marketing period. However, despite the fact that pharmacovigilance is developing, it still lags behind the impressive scientific and technological achievements achieved in other areas of medicine. Unfortunately, spontaneous reporting does not assess the true prevalence of ADR well, since reporting indicators can vary significantly depending on the motivation, availability of time, qualifications, fear of punishment and similar factors of the sender. Given these known limitations of the spontaneous messaging method, additional strategies for detecting ADR are often used, including trigger tools, manual viewing of medical records and automated monitoring.

Engaging Patients via Online Healthcare Fora: Three Pharmacovigilance Use Cases

Increasingly, patient-generated safety insights are shared online, via general social media platforms or dedicated healthcare fora which give patients the opportunity to discuss their disease and treatment options. We evaluated three areas of potential interest for the use of social media in pharmacovigilance. To evaluate how social media may complement existing safety signal detection capabilities, we identified two use cases (drug/adverse event [AE] pairs) and then evaluated the frequency of AE discussions across a range of social media channels. Changes in frequency over time were noted in social media, then compared to frequency changes in Food and Drug Administration Adverse Event Reporting System (FAERS) data over the same time period using a traditional disproportionality method. Although both data sources showed increasing frequencies of AE discussions over time, the increase in frequency was greater in the FAERS data as compared to social media. To demonstrate the robustness of medical/AE insights of linked posts we manually reviewed 2,817 threads containing 21,313 individual posts from 3,601 unique authors. Posts from the same authors were linked together. We used a quality scoring algorithm to determine the groups of linked posts with the highest quality and manually evaluated the top 16 groups of posts. Most linked posts (12/16; 75%) contained all seven relevant medical insights assessed compared to only one (of 1,672) individual post. To test the capability of actively engage patients via social media to obtain follow-up AE information we identified and sent consents for follow-up to 39 individuals (through a third party). We sent target follow-up questions (identified by pharmacovigilance experts as critical for causality assessment) to those who consented. The number of people consenting to follow-up was low (20%), but receipt of follow-up was high (75%). We observed completeness of responses (37 out of 37 questions answered) and short average time required to receive the follow-up (1.8 days). Our findings indicate a limited use of social media data for safety signal detection. However, our research highlights two areas of potential value to pharmacovigilance: obtaining more complete medical/AE insights via longitudinal post linking and actively obtaining rapid follow-up information on AEs.

A smart hospital-driven approach to precision pharmacovigilance

Researchers, regulatory agencies, and the pharmaceutical industry are moving towards precision pharmacovigilance as a comprehensive framework for drug safety assessment, at the service of the individual patient, by clustering specific risk groups in different databases. This article explores its implementation by focusing on: (i) designing a new data collection infrastructure, (ii) exploring new computational methods suitable for drug safety data, and (iii) providing a computer-aided framework for distributed clinical decisions with the aim of compiling a personalized information leaflet with specific reference to a drug's risks and adverse drug reactions. These goals can be achieved by using 'smart hospitals' as the principal data sources and by employing methods of precision medicine and medical statistics to supplement current public health decisions.

Artificial Intelligence and Machine Learning for Safe Medicines

Authors' views on the role of artificial intelligence and machine learning in pharmacovigilance. (MP4  139807 kb).

Black Swan Events and Intelligent Automation for Routine Safety Surveillance

Effective identification of previously implausible safety signals is a core component of successful pharmacovigilance. Timely, reliable, and efficient data ingestion and related processing are critical to this. The term 'black swan events' was coined by Taleb to describe events with three attributes: unpredictability, severe and widespread consequences, and retrospective bias. These rare events are not well understood at their emergence but are often rationalized in retrospect as predictable. Pharmacovigilance strives to rapidly respond to potential black swan events associated with medicine or vaccine use. Machine learning (ML) is increasingly being explored in data ingestion tasks. In contrast to rule-based automation approaches, ML can use historical data (i.e., 'training data') to effectively predict emerging data patterns and support effective data intake, processing, and organisation. At first sight, this reliance on previous data might be considered a limitation when building ML models for effective data ingestion in systems that look to focus on the identification of potential black swan events. We argue that, first, some apparent black swan events-although unexpected medically-will exhibit data attributes similar to those of other safety data and not prove algorithmically unpredictable, and, second, standard and emerging ML approaches can still be robust to such data outliers with proper awareness and consideration in ML system design and with the incorporation of specific mitigatory and support strategies. We argue that effective approaches to managing data on potential black swan events are essential for trust and outline several strategies to address data on potential black swan events during data ingestion.