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Current Pharmaceutical Design

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ISSN (Print): 1381-6128
ISSN (Online): 1873-4286

Review Article

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

Author(s): Ahmad Y. Abdin, Daniel Auker-Howlett, Jürgen Landes, Glorjen Mulla, Claus Jacob and Barbara Osimani*

Volume 25, Issue 16, 2019

Page: [1866 - 1880] Pages: 15

DOI: 10.2174/1381612825666190628160603

Price: $65

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Abstract

Background: Basic science has delivered unprecedented insights into intricate relationships on the smallest scales within well-controlled environments. Addressing pressing societal decision problems requires an understanding of systems on larger scales in real-world situations.

Objective: To assess how well the evidence assessors E-Synthesis and EBM+ assess basic science findings to support medical decision making.

Methods: We demonstrate the workings of E-Synthesis and EBM+ on a case study: the suspected causal connection between the widely-used drug amoxicillin (AMX) and the putative adverse drug reaction: Drug Reaction with Eosinophilia and Systemic Symptoms (DRESS).

Results: We determine an increase in the probability that AMX can cause DRESS within the E-Synthesis approach and using the EBM+ standards assess the basic science findings as supporting the existence of a mechanism linking AMX and DRESS.

Conclusions: While progress is made towards developing methodologies which allow the incorporation of basic science research in the decision making process for pressing societal questions, there is still considerable need for further developments. A continued dialogue between basic science researchers and methodologists, philosophers and statisticians seems to offer the best prospects for developing and evaluating continuously evolving methodologies.

Keywords: Evidence aggregation, E-synthesis, EBM+, amoxicillin, DRESS, drug safety, adverse drug reactions, pharmacovigilance, drug monitoring, pharmacosurveillance, bradford hill guidelines, causal inference.

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