Empirical assessment of case-based methods for identification of drugs associated with acute liver injury in the French National Healthcare System database (SNDS)

Ameliorative Effect of Macadamia Nut Protein Peptides on Acetaminophen-Induced Acute Liver Injury in Mice

This study aims to examine the ameliorative effect of macadamia nut protein peptides (MPP) on acetaminophen (APAP)-induced liver injury (AILI) in mice, and develop a new strategy for identifying hepatoprotective functional foods. The molecular weight distribution and amino acid composition of MPP were first studied. Forty mice were then randomized into four groups: control group (CON), APAP model group, APAP+MPP low-dose group (APAP+L-MPP), and APAP+MPP high-dose group (APAP+H-MPP). The APAP+L-MPP (320 mg/kg per day) and APAP+H-MPP (640 mg/kg per day) groups received continuous MPP gavage for 2 weeks. A 12 h of APAP (200 mg/kg) gavage resulted in liver damage. Pathological alterations, antioxidant index levels, expression of toll-like receptor 4 (TLR4)/nuclear factor-κB (NF-κB), and associated inflammatory factors were determined for each treatment group. The results revealed that the total amino acid content of MPP was 39.58 g/100 g, with Glu, Arg, Asp, Leu, Tyr, and Gly being the major amino acids. The molecular weight range of 0-1000 Da accounted for 73.54%, and 0-500 Da accounted for 62.84% of MPP. MPP ameliorated the pathological morphology and reduced the serum levels of alanine aminotransferase, aspartate aminotransferase, and alkaline phosphatase of AILI in mice. MPP significantly increased the activities of superoxide dismutase and glutathione peroxidase in the liver compared with the APAP group. MPP inhibited the expression of TLR4, NF-κB, interleukin-1β (IL-1β), and tumor necrosis factor-α (TNF-α) genes in AILI mice. MPP also inhibited the expression levels of inflammatory factors (TNF-α and IL-6). Our study concludes that MPP alleviates AILI in mice by enhancing antioxidant capacity and inhibiting TLR4/NF-κB pathway-related gene activation.

Implementation of a Taxonomy-Based Framework for the Selection of Appropriate Drugs and Outcomes for Real-World Data Signal Detection Studies

INTRODUCTION

For signal detection studies investigating either drug safety or method evaluation, the choice of drug-outcome pairs needs to be tailored to the planned study design and vice versa. While this is well understood in hypothesis-testing epidemiology, it should be as important in signal detection, but this has not widely been considered. There is a need for a taxonomy framework to provide guidance and a systematic reproducible approach to the selection of appropriate drugs and outcomes for signal detection studies either investigating drug safety or assessing method performance using real-world data.

OBJECTIVE

The aim was to design a general framework for the selection of appropriate drugs and outcomes for signal detection studies given a study design of interest. As a motivating example, we illustrate how the framework is applied to build a reference set for a study aiming to assess the performance of the self-controlled case series with active comparators.

METHODS

We reviewed criteria presented in two published studies which aimed to provide practical advice for choosing the appropriate signal evaluation methodology, and assessed their relevance for signal detection. Further characteristics specific to signal detection were added. The final framework is based on: the application of study design requirements, the database(s) of interest, and the clinical importance of the drug(s) and outcome(s) under consideration. This structure was applied by selecting drug-outcome pairs as a reference set (i.e. list of drug-outcome pairs classified as positive or negative controls) for which the method is expected to work well for a signal detection study aiming to assess the performance of self-controlled case series. Eight criteria were used, related to the application of self-controlled case series assumptions, choice of active comparators, coverage in the database of interest and clinical importance of the outcomes.

RESULTS

After application of the framework, two classes of antibiotics (seven drugs) were selected for the study, and 28 outcomes from all organ classes were chosen from the drug labels, out of the 273 investigated. In total, this corresponds to 104 positive controls (drug-outcome pairs) and 58 negative controls.

CONCLUSIONS

We proposed and applied a framework for the selection of drugs and outcomes for both drug safety signal detection and method assessment used in signal detection to optimise their performance given a study design. This framework will eliminate part of the bias relating to drugs and outcomes not being suited to the method or database. The main difficulty lies in the choice of the criteria and their application to ensure systematic selection, especially as some information remains unknown in signal detection, and clinical judgement was needed on occasions. The same framework could be adapted for other methods.