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.

Evidence of Biological Mechanisms and Health Predictions: An Insight into Clinical Reasoning.

Traditionally, the understanding of biological mechanisms has played a central role in clinical reasoning. With the rise of the evidence-based paradigm, however, this role has come under scrutiny. On the one hand, clinical guidelines now place less emphasis on the evidence of pathophysiological mechanisms, a shift motivated by the unreliability of our understanding of complex biological mechanisms. On the other hand, some scholars defend evidence of mechanisms as crucial for clinical practice. This article assesses the relevance of evidence of biological mechanisms in two types of clinical predictions: predictions about the efficacy and about the safety of a certain intervention for a particular patient. For each type of prediction, the article analyzes the two roles that evidence of mechanisms might have-confirming and disconfirming-depending on whether the evidence supports that certain epidemiological results apply to the single patient. The analysis shows that the "unreliability because of incompleteness" argument against the emphasis on mechanistic clinical thinking only applies to some of the considered cases. The article concludes by offering a model for a more granular view of the role that evidence of mechanisms should play in clinical practice.

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.

Underdetermination and evidence-based policy.

Safety assessment of technologies and interventions is often underdetermined by evidence. For example, scientists have collected evidence concerning genetically modified plants for decades. This evidence was used to ground opposing safety protocols for "stacked genetically modified" plants, in which two or more genetically modified plants are combined. Evidence based policy would thus be rendered more effective by an approach that accounts for underdetermination. Douglas (2012) proposes an explanatory approach, based on the criteria of transparency, empirical competence, internal consistency of explanations, and predictive potency. However, sometimes multiple explanations can satisfy these criteria. We propose an additional criterion based on converse abduction, where explanations are selected on the basis of ontological background assumptions as well as by evidence. We then apply our proposed scheme to the case of the regulation of stacked genetically modified plants. We discuss the implications and suggest follow-up work concerning the generalizability of the approach.

Erice Call for Change: Utilising Patient Experiences to Enhance the Quality and Safety of Healthcare.

This 'Erice Call for Change' is a report from a group of experts, patients and patient representatives who met in Erice in September 2019 following previous similar meetings after the original Erice Declaration (1996). The aim of the meeting was to discuss the challenge of causal complexity and individual variation in modern healthcare. The group's concern was the impact that new clinical decision-making tools, based on statistical correlations in large databases, could have on individual patient care if they replace other types of clinical investigation and knowledge. The group calls for a change in the approach to the care of the individual patient, and indicates some specific challenges to overcome for such changes to happen.

Causal Evidence and Dispositions in Medicine and Public Health.

Since the introduction of evidence-based medicine, there have been discussions about the epistemic primacy of randomised controlled trials (RCTs) for establishing causality in medicine and public health. A growing movement within philosophy of science calls instead for evidential pluralism: that we need more than one single method to investigate health outcomes. How should such evidential pluralism look in practice? How useful are the various methods available for causal inquiry? Further, how should different types of causal evidence be evaluated? This paper proposes a constructive answer and introduces a framework aimed at supporting scientists in developing appropriate methodological approaches for exploring causality. We start from the philosophical tradition that highlights intrinsic properties (dispositions, causal powers or capacities) as essential features of causality. This abstract idea has wide methodological implications. The paper explains how different methods, such as lab experiments, case studies, N-of-1 trials, case control studies, cohort studies, RCTs and patient narratives, all have some strengths and some limitations for picking out intrinsic causal properties. We explain why considering philosophy of causality is crucial for evaluating causality in the health sciences. In our proposal, we combine the various methods in a temporal process, which could then take us from an observed phenomenon (e.g., a correlation) to a causal hypothesis and, finally, to improved theoretical knowledge.

Pharmacovigilance as Scientific Discovery: An Argument for Trans-Disciplinarity.

Pharmacovigilance currently faces several unsolved challenges. Of particular importance are issues concerning how to ascertain, collect, confirm, and communicate the best evidence to assist the clinical choice for individual patients. Here, we propose that these practical challenges partially stem from deeper fundamental issues concerning the epistemology of pharmacovigilance. After reviewing some of the persistent challenges, recent measures, and suggestions in the current pharmacovigilance literature, we support the argument that the detection of potential adverse drug reactions ought to be seen as a serendipitous scientific discovery. We further take up recent innovations from the multidisciplinary field of serendipity research about the importance of networks, diversity of expertise, and plurality of methodological perspectives for cultivating serendipitous discovery. Following this discussion, we explore how pharmacovigilance could be systematized in a way that optimizes serendipitous discoveries of untargeted drug effects, emerging from the clinical application. Specifically, we argue for the promotion of a trans-disciplinary responsive network of scientists and stakeholders. Trans-disciplinarity includes extending the involvement of stakeholders beyond the regulatory community, integrating diverse methods and sources of evidence, and enhancing the ability of diverse groups to raise signals of harms that ought to be followed up by the network. Consequently, promoting a trans-disciplinary approach to pharmacovigilance is a long-term effort that requires structural changes in medical education, research, and enterprise. We suggest a number of such changes, discuss to what extent they are already in process, and indicate the advantages from both epistemological and ethical perspectives.

Philosophical bias is the one bias that science cannot avoid.

Scientists seek to eliminate all forms of bias from their research. However, all scientists also make assumptions of a non-empirical nature about topics such as causality, determinism and reductionism when conducting research. Here, we argue that since these 'philosophical biases' cannot be avoided, they need to be debated critically by scientists and philosophers of science.

From Ideal to Real Risk: Philosophy of Causation Meets Risk Analysis.

A question has been raised in recent years as to whether the risk field, including analysis, assessment, and management, ought to be considered a discipline on its own. As suggested by Terje Aven, unification of the risk field would require a common understanding of basic concepts, such as risk and probability; hence, more discussion is needed of what he calls "foundational issues." In this article, we show that causation is a foundational issue of risk, and that a proper understanding of it is crucial. We propose that some old ideas about the nature of causation must be abandoned in order to overcome certain persisting challenges facing risk experts over the last decade. In particular, we discuss the challenge of including causally relevant knowledge from the local context when studying risk. Although it is uncontroversial that the receptor plays an important role for risk evaluations, we show how the implementation of receptor-based frameworks is hindered by methodological shortcomings that can be traced back to Humean orthodoxies about causation. We argue that the first step toward the development of frameworks better suited to make realistic risk predictions is to reconceptualize causation, by examining a philosophical alternative to the Humean understanding. In this article, we show how our preferred account, causal dispositionalism, offers a different perspective in how risk is evaluated and understood.

The judgements that evidence-based medicine adopts.

In "The evidence that evidence-based medicine omits", Brendan Clarke and colleagues argue that when establishing causal facts in medicine, evidence of mechanisms ought to be included alongside evidence of correlations. One of the reasons they provide is that correlations can be spurious and generated by unknown confounding variables. A causal mechanism can provide a plausible explanation for the correlation, and the absence of such an explanation is an indication that the correlation is not causal. Evidence-based medicine (EBM) proponents remain sceptical about this argument, one problem being that the formulation of a mechanism requires judgements that are external to the evaluation of data and experimental designs-for instance judgements of plausibility against, or derivability from, background knowledge. Because background knowledge is always incomplete and therefore unreliable, EBM proponents maintain that the plausibility of a hypothesis should be evaluated mainly by the quality of population data that yielded it. Here, I use the example of oestrogen replacement therapy's effect on coronary heart disease, an example that is often quoted in defence of the epistemic advantage of randomized controlled trials, to show that the evaluation of the most reliable study design necessarily implies the adoption of judgements that are external to the specific evidence of correlation. The exclusion of evidence of mechanism, therefore, is not effective in bypassing paradigm-dependent judgements, which are external to specific evidence. Because such judgements cannot be excluded by evidence evaluation, they can only be kept under scrutiny, or adopted uncritically. I propose that the latter option can hinder the maintenance of an active critical inquiry, as well as the analysis of experts' disagreement.

How biological background assumptions influence scientific risk evaluation of stacked genetically modified plants: an analysis of research hypotheses and argumentations.

Scientific risk evaluations are constructed by specific evidence, value judgements and biological background assumptions. The latter are the framework-setting suppositions we apply in order to understand some new phenomenon. That background assumptions co-determine choice of methodology, data interpretation, and choice of relevant evidence is an uncontroversial claim in modern basic science. Furthermore, it is commonly accepted that, unless explicated, disagreements in background assumptions can lead to misunderstanding as well as miscommunication. Here, we extend the discussion on background assumptions from basic science to the debate over genetically modified (GM) plants risk assessment. In this realm, while the different political, social and economic values are often mentioned, the identity and role of background assumptions at play are rarely examined. We use an example from the debate over risk assessment of stacked genetically modified plants (GM stacks), obtained by applying conventional breeding techniques to GM plants. There are two main regulatory practices of GM stacks: (i) regulate as conventional hybrids and (ii) regulate as new GM plants. We analyzed eight papers representative of these positions and found that, in all cases, additional premises are needed to reach the stated conclusions. We suggest that these premises play the role of biological background assumptions and argue that the most effective way toward a unified framework for risk analysis and regulation of GM stacks is by explicating and examining the biological background assumptions of each position. Once explicated, it is possible to either evaluate which background assumptions best reflect contemporary biological knowledge, or to apply Douglas' 'inductive risk' argument.

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.

TSHZ1-dependent gene regulation is essential for olfactory bulb development and olfaction.

The olfactory bulb (OB) receives odor information from the olfactory epithelium and relays this to the olfactory cortex. Using a mouse model, we found that development and maturation of OB interneurons depends on the zinc finger homeodomain factor teashirt zinc finger family member 1 (TSHZ1). In mice lacking TSHZ1, neuroblasts exhibited a normal tangential migration to the OB; however, upon arrival to the OB, the neuroblasts were distributed aberrantly within the radial dimension, and many immature neuroblasts failed to exit the rostral migratory stream. Conditional deletion of Tshz1 in mice resulted in OB hypoplasia and severe olfactory deficits. We therefore investigated olfaction in human subjects from families with congenital aural atresia that were heterozygous for TSHZ1 loss-of-function mutations. These individuals displayed hyposmia, which is characterized by impaired odor discrimination and reduced olfactory sensitivity. Microarray analysis, in situ hybridization, and ChIP revealed that TSHZ1 bound to and regulated expression of the gene encoding prokineticin receptor 2 (PROKR2), a G protein­coupled receptor essential for OB development. Mutations in PROKR2 lead to Kallmann syndrome, characterized by anosmia and hypogonadotrophic hypogonadism. Our data indicate that TSHZ1 is a key regulator of mammalian OB development and function and controls the expression of molecules involved in human Kallmann syndrome.