Systems Toxicology Approach to Identifying Paracetamol Overdose

Concurrent administration of acetaminophen and ethanol: impact on mouse liver and testis

OBJECTIVES

Acetaminophen (paracetamol) and alcohol are widely consumed as analgesic/antipyretic and recreational agent, respectively. High doses of both agents induce liver and male reproductive toxicities. This study investigated the toxicological outcome of concurrent administration of paracetamol and ethanol in the liver and testis in mice.

METHODS

Animals were gavaged paracetamol (250 mg/kg), ethanol (3 g/kg) or paracetamol + ethanol for 2 d. Some groups were sacrificed 12 h after the last dose, while others were sacrificed 21 d posttreatment for reversibility study. Control group received carboxymethylcellulose sodium (0.2%). Serum levels of liver biochemical indices and epididymal sperm were analysed. Histopathological analysis of the liver and testis were also performed.

RESULTS

Alkaline phosphatase (ALP), aspartate aminotransferase (AST), alanine aminotransferase (ALT), and bilirubin in serum were elevated (p<0.001); whereas albumin and total protein were reduced (p<0.001) in paracetamol or ethanol groups compared to control. In the combination group, only mild elevation of ALT (p<0.05) was observed. Additionally, hepatocyte necrosis occurred in the livers of paracetamol and ethanol groups, while only mild inflammatory changes were seen in the combination group. All liver indices were normal in reversibility study animals. Furthermore, sperm count, motility, viability and morphology did not change in all treated animals, except that sperm count was decreased (p<0.05) in paracetamol group. Testis histology of all animal groups were normal.

CONCLUSIONS

The results demonstrated that simultaneous treatment with acute paracetamol and ethanol doses will possibly minimize hepatotoxicity and reduction of epididymal sperm reserve by the individual agents, and the toxicities are reversible.

In silico models in drug development: where we are

The use and utility of computational models in drug development has significantly grown in the last decades, fostered by the availability of high throughput datasets and new data analysis strategies. These in silico approaches are demonstrating their ability to generate reliable predictions as well as new knowledge on the mode of action of drugs and the mechanisms underlying their side effects, altogether helping to reduce the costs of drug development. The aim of this review is to provide a panorama of developments in the field in the last two years.