Dispositions and Causality Assessment in Pharmacovigilance: Proposing the Dx3 Approach for Assessing Causality with Small Data Sets.

A new approach is proposed for assessing causality in pharmacovigilance. The Dx3 approach is designed to qualitatively evaluate three types of dispositions when assessing whether a particular medicine has or could have caused a certain adverse event. These are: the drug disposition; the pre-disposition of the patient taking the drug (vulnerability) and; the disposition of the patient-drug interaction (mutuality). Each of these three types of dispositions will represent valuable causally relevant evidence for assessing a potential signal of harm. A checklist is provided to guide the assessment of causality for both single individual case safety reports (ICSRs) and case series. Different types of causal information are ranked according to how well suited they are for establishing a disposition. Two case examples are used to demonstrate how the approach can be used in practice for assessment purposes. One aim of the approach is to offer a qualitative way to assess causality and to make the reasoning of different assessors more transparent. A second aim is to encourage the collection of more qualitatively rich patient narratives in the ICSRs. Crucially, we believe this approach can support the inclusion of the single ICSR as a valid and valuable form of evidence.

Remdesivir in the COVID-19 Pandemic: An Analysis of Spontaneous Reports in VigiBase During 2020.

INTRODUCTION: The safety profile of remdesivir, conditionally approved for COVID-19, was limited at its 2020 introduction. Adverse drug reactions (ADRs) for medicines are collected in VigiBase, the WHO Global Database of Individual Case Safety Reports (ICSRs). OBJECTIVE: This study aimed to provide a descriptive analysis of COVID-19 ICSR data focusing on remdesivir, including a disproportionality analysis (DA) of ADRs. METHODS: A dedicated algorithm enabled retrieval of all COVID-19 treatment-specific ICSRs. A severity algorithm based on co-reported medicines and symptoms enabled selection of tocilizumab with its well established safety profile as comparator for remdesivir. Descriptive statistics were used for general ICSR demographics for all COVID-19-specific medicines, remdesivir and tocilizumab individually and furthermore to present treatment patterns of medicines co-reported with remdesivir. A COVID-19 indication-focused DA was deployed to minimize confounding from underlying polysymptomatic disease. RESULTS: 14,574 COVID-19-related ICSRs were entered into VigiBase during 2020. Remdesivir was the most common medicine reported. Of 4944 remdesivir ICSRs, where tocilizumab was not co-reported, 93% described remdesivir as the sole suspect medicine. Sixty percent of ICSRs concerned males, median age was 63 years and the majority originated from the Americas (72%). In 1089 (21%) of remdesivir ICSRs, data indicated severe/critical disease. Co-reported medicines peaked during the first 3 days of remdesivir treatment. The DA for the established tocilizumab and the new remdesivir were mainly in line with the safety profiles for both medicines but suggested new safety concerns. The most reported ADRs for remdesivir represented liver dysfunction, kidney injury, death and bradycardia. CONCLUSION: Global COVID-19-related ADR reporting proved useful in providing information on ADRs as well as on treatment patterns in this patient group. Indication-focused disproportionality analysis, together with the use of a comparator with a known safety profile, proved effective in identifying known safety information and suggested new safety concerns for remdesivir.

Bridging the boundaries between scientists and clinicians-mechanistic hypotheses and patient stories in risk assessment of drugs.

The cultural divide between scientists and clinicians has been described as undermining the advance of medical science, by hindering the production of practice-relevant research and of research-informed clinical decisions. Here, I consider the field of post-marketing risk assessment of drugs as an example of strict interdependence between basic biomedical research, clinical research, and clinical evaluation and show how it would benefit from a closer collaboration between scientists and clinicians. The risk assessment of drugs after their marketing relies on spontaneous adverse effect reports to drug agencies and on peer-reviewed case reports. I emphasize the importance of qualitative analysis of such reports for the improvement of mechanistic understanding of harmful effects of drugs. I argue that mechanistic explanations of drug effects are at least as important as determination of their frequency, in order to establish causation. An ideal risk assessment, then, verifies not only the frequency of undesired effects but also why and how the harm happens. For this purpose, the frequency or novelty of the unintended outcome, although contextually indicative, should not determine the epistemic value of a report. Details about the context that generated an unexpected outcome, instead, can offer the chance of improving causal understanding about how the intervention works. This is illustrated through examples from medical research. Mechanistic understanding is a domain of joint collaboration among (1) clinicians, in charge of detailed, qualitative reporting of patient stories about side effects, (2) qualitative clinical researchers, in charge of analyzing clinical contexts or harmful effects and formulating explanatory hypotheses, and (3) basic biomedical researchers, in charge of verifying such hypotheses. In addition, direct information flow can on one side focus clinicians' attention on knowledge gaps about drugs/effects where more research is needed, while on the other side create a more contextualized concept of mechanism among scientists.