Reviewing the Mechanistic Evidence Assessors E-Synthesis and EBM+: A Case Study of Amoxicillin and Drug Reaction with Eosinophilia and Systemic Symptoms (DRESS)

Current understanding of genetic associations with delayed hypersensitivity reactions induced by antibiotics and anti-osteoporotic drugs

Drug-induced delayed hypersensitivity reactions (DHRs) is still a clinical and healthcare burden in every country. Increasing reports of DHRs have caught our attention to explore the genetic relationship, especially life-threatening severe cutaneous adverse drug reactions (SCARs), including acute generalized exanthematous pustulosis (AGEP), drug reactions with eosinophilia and systemic symptoms (DRESS), Stevens-Johnson syndrome (SJS), and toxic epidermal necrolysis (TEN). In recent years, many studies have investigated the immune mechanism and genetic markers of DHRs. Besides, several studies have stated the associations between antibiotics-as well as anti-osteoporotic drugs (AOD)-induced SCARs and specific human leukocyte antigens (HLA) alleles. Strong associations between drugs and HLA alleles such as co-trimoxazole-induced DRESS and HLA-B*13:01 (Odds ratio (OR) = 45), dapsone-DRESS and HLA-B*13:01 (OR = 122.1), vancomycin-DRESS and HLA-A*32:01 (OR = 403), clindamycin-DHRs and HLA-B*15:27 (OR = 55.6), and strontium ranelate (SR)-SJS/TEN and HLA-A*33:03 (OR = 25.97) are listed. We summarized the immune mechanism of SCARs, update the latest knowledge of pharmacogenomics of antibiotics- and AOD-induced SCARs, and indicate the potential clinical use of these genetic markers for SCARs prevention in this mini review article.

Fast Methods for Drug Approval: Research Perspectives for Pandemic Preparedness

Public heath emergencies such as the outbreak of novel infectious diseases represent a major challenge for drug regulatory bodies, practitioners, and scientific communities. In such critical situations drug regulators and public health practitioners base their decisions on evidence generated and synthesised by scientists. The urgency and novelty of the situation create high levels of uncertainty concerning the safety and effectiveness of drugs. One key tool to mitigate such emergencies is pandemic preparedness. There seems to be, however, a lack of scholarly work on methodology for assessments of new or existing drugs during a pandemic. Issues related to risk attitudes, evidence production and evidence synthesis for drug approval require closer attention. This manuscript, therefore, engages in a conceptual analysis of relevant issues of drug assessment during a pandemic. To this end, we rely in our analysis on recent discussions in the philosophy of science and the philosophy of medicine. Important unanswered foundational questions are identified and possible ways to answer them are considered. Similar problems often have similar solutions, hence studying similar situations can provide important clues. We consider drug assessments of orphan drugs and drug assessments during endemics as similar to drug assessment during a pandemic. Furthermore, other scientific fields which cannot carry out controlled experiments may guide the methodology to draw defeasible causal inferences from imperfect data. Future contributions on methodologies for addressing the issues raised here will indeed have great potential to improve pandemic preparedness.

E-synthesis for carcinogenicity assessments: A case study of processed meat

RATIONALE, AIMS AND OBJECTIVES

Recent controversies about dietary advice concerning meat demonstrate that aggregating the available evidence to assess a putative causal link between food and cancer is a challenging enterprise.

METHODS

We show how a tool developed for assessing putative causal links between drugs and adverse drug reactions, E-Synthesis, can be applied for food carcinogenicity assessments. The application is demonstrated on the putative causal relationship between processed meat consumption and cancer.

RESULTS

The output of the assessment is a Bayesian probability that processed meat consumption causes cancer. This Bayesian probability is calculated from a Bayesian network model, which incorporates a representation of Bradford Hill's Guidelines as probabilistic indicators of causality. We show how to determine probabilities of indicators of causality for food carcinogenicity assessments based on assessments of the International Agency for Research on Cancer.

CONCLUSIONS

We find that E-Synthesis is a tool well-suited for food carcinogenicity assessments, as it enables a graphical representation of lines and weights of evidence, offers the possibility to make a great number of judgements explicit and transparent, outputs a probability of causality suitable for decision making and is flexible to aggregate different kinds of evidence.

EA3: A softmax algorithm for evidence appraisal aggregation

Real World Evidence (RWE) and its uses are playing a growing role in medical research and inference. Prominently, the 21st Century Cures Act—approved in 2016 by the US Congress—permits the introduction of RWE for the purpose of risk-benefit assessments of medical interventions. However, appraising the quality of RWE and determining its inferential strength are, more often than not, thorny problems, because evidence production methodologies may suffer from multiple imperfections. The problem arises to aggregate multiple appraised imperfections and perform inference with RWE. In this article, we thus develop an evidence appraisal aggregation algorithm called EA³. Our algorithm employs the softmax function—a generalisation of the logistic function to multiple dimensions—which is popular in several fields: statistics, mathematical physics and artificial intelligence. We prove that EA³ has a number of desirable properties for appraising RWE and we show how the aggregated evidence appraisals computed by EA³ can support causal inferences based on RWE within a Bayesian decision making framework. We also discuss features and limitations of our approach and how to overcome some shortcomings. We conclude with a look ahead at the use of RWE.

Artificial intelligence methods for a Bayesian epistemology-powered evidence evaluation

RATIONALE, AIMS AND OBJECTIVES

The diversity of types of evidence (eg, case reports, animal studies and observational studies) makes the assessment of a drug's safety profile into a formidable challenge. While frequentist uncertain inference struggles in aggregating these signals, the more flexible Bayesian approaches seem better suited for this quest. Artificial Intelligence (AI) offers great promise to these approaches for information retrieval, decision support, and learning probabilities from data.

METHODS

E-Synthesis is a Bayesian framework for drug safety assessments built on philosophical principles and considerations. It aims to aggregate all the available information, in order to provide a Bayesian probability of a drug causing an adverse reaction. AI systems are being developed for evidence aggregation in medicine, which increasingly are automated.

RESULTS

We find that AI can help E-Synthesis with information retrieval, usability (graphical decision-making aids), learning Bayes factors from historical data, assessing quality of information and determining conditional probabilities for the so-called 'indicators' of causation for E-Synthesis. Vice versa, E-Synthesis offers a solid methodological basis for (semi-)automated evidence aggregation with AI systems.

CONCLUSIONS

Properly applied, AI can help the transition of philosophical principles and considerations concerning evidence aggregation for drug safety to a tool that can be used in practice.

The use of mechanistic reasoning in assessing coronavirus interventions

RATIONALE

Evidence-based medicine (EBM), the dominant approach to assessing the effectiveness of clinical and public health interventions, focuses on the results of association studies. EBM+ is a development of EBM that systematically considers mechanistic studies alongside association studies.

AIMS AND OBJECTIVES

To explore examples of the importance of mechanistic evidence to coronavirus research.

METHODS

We have reviewed the mechanistic evidence in four major areas that are relevant to the management of COVID-19.

RESULTS AND CONCLUSIONS

(a) Assessment of combination therapy for MERS highlights the need for systematic assessment of mechanistic evidence. (b) That hypertension is a risk factor for severe disease in the case of SARS-CoV-2 suggests that altering hypertension treatment might alleviate disease, but the mechanisms are complex, and it is essential to consider and evaluate multiple mechanistic hypotheses. (c) Confidence that public health interventions will be effective requires a detailed assessment of social and psychological components of the mechanisms of their action, in addition to mechanisms of disease. (d) In particular, if vaccination programmes are to be effective, they must be carefully tailored to the social context; again, mechanistic evidence is crucial. We conclude that coronavirus research is best situated within the EBM+ evaluation framework.

Complicated Drug Reaction with Eosinophilia and Systemic Symptoms (DRESS) Syndrome History in a 14-Year-Old

BACKGROUND Drug reaction with eosinophilia and systemic symptoms (DRESS) syndrome is a drug-induced hypersensitivity reaction that can result in a severe cutaneous adverse drug reaction (SCAR). It is a rare and potentially life-threatening condition that occurs after exposure to sulfonamides, antibiotics, or antiepileptics. Its incidence in children is not established; however, the mortality rate is documented at approximately 10%. DRESS syndrome is believed to result from an interaction between multiple factors, including genetics, abnormalities of metabolism, and reactivation of certain herpes family viruses including EBV and HHV-6. The classic presentation includes fever, rash, and lymphadenopathy. Symptoms begin approximately 3 to 8 weeks after exposure to the offending agent. CASE REPORT We present a unique case of DRESS syndrome in a 14-year-old girl occurring after the ingestion of minocycline and amoxicillin-clavulanic acid (amoxicillin). Identification of the offending agent was complicated by the patient having been on multiple antibiotics within a short timeframe of the initial presentation of symptoms. In addition to swelling and pruritus, the patient experienced vision problems due to papilledema with bilateral hemorrhage. The treatment course was further complicated by a decrease in kidney function, requiring the patient's medication regimen to be adjusted accordingly. CONCLUSIONS This is a unique case of DRESS syndrome demonstrating the potential influence of certain viruses on the severity of its presentation. This case also highlights the need to adjust the steroid regimen to reduce the potentially harmful effects on various organ systems.

E-Synthesis: A Bayesian Framework for Causal Assessment in Pharmacosurveillance

Background: Evidence suggesting adverse drug reactions often emerges unsystematically and unpredictably in form of anecdotal reports, case series and survey data. Safety trials and observational studies also provide crucial information regarding the (un-)safety of drugs. Hence, integrating multiple types of pharmacovigilance evidence is key to minimising the risks of harm.

Methods: In previous work, we began the development of a Bayesian framework for aggregating multiple types of evidence to assess the probability of a putative causal link between drugs and side effects. This framework arose out of a philosophical analysis of the Bradford Hill Guidelines. In this article, we expand the Bayesian framework and add “evidential modulators,” which bear on the assessment of the reliability of incoming study results. The overall framework for evidence synthesis, “E-Synthesis”, is then applied to a case study.

Results: Theoretically and computationally, E-Synthesis exploits coherence of partly or fully independent evidence converging towards the hypothesis of interest (or of conflicting evidence with respect to it), in order to update its posterior probability. With respect to other frameworks for evidence synthesis, our Bayesian model has the unique feature of grounding its inferential machinery on a consolidated theory of hypothesis confirmation (Bayesian epistemology), and in allowing any data from heterogeneous sources (cell-data, clinical trials, epidemiological studies), and methods (e.g., frequentist hypothesis testing, Bayesian adaptive trials, etc.) to be quantitatively integrated into the same inferential framework.

Conclusions: E-Synthesis is highly flexible concerning the allowed input, while at the same time relying on a consistent computational system, that is philosophically and statistically grounded. Furthermore, by introducing evidential modulators, and thereby breaking up the different dimensions of evidence (strength, relevance, reliability), E-Synthesis allows them to be explicitly tracked in updating causal hypotheses.