The aim of this article is to illustrate the importance of N-butane determination in postmortem samples through a case report and to propose actions and precautions to be taken into consideration when butane is suspected to be involved in cases of death. The case concerns a 15-year-old boy found dead after sniffing a cigarette lighter refill. Toxicological investigation revealed the presence of butane in the heart and femoral blood (1280 and 1170 μg/L, respectively), in the gastric contents (326 μg/L), and in the liver (1010 μg/kg) and lung tissues (210 μg/kg). Propane was present only in the blood samples at concentrations tenfolds lower.Butane can be involved in three kinds of fatalities: deliberate inhalations including volatile substance abuse (VSA), involuntary exposure, and homicides. A fatal outcome of butane inhalation can be caused by asphyxia and/or cardiac arrhythmia. In the context where butane exposure is evidenced by non-toxicological investigations, the usefulness of the determination of butane in postmortem samples is often questionable. However, it is admitted that butane-related deaths are generally underreported. Several difficulties including sample handling and storage, substantial variation in tissue concentrations, and lack of a lethal threshold make the interpretation of butane results challenging. In our opinion, systematic toxicological methods should be developed in order to analyze butane, at least when it concerns a typical VSA victim, even when butane is not actually suspected to be the cause of death.

The characterization of drug-drug interactions (DDIs) may require the use of several different tools, such as the thesaurus issued by our national health agency (i.e., ANSM), the metabolic pathways table from the Geneva University Hospital (GUH), and DDI-Predictor (DDI-P). We sought to (i) compare the three tools’ respective abilities to detect DDIs in routine clinical practice and (ii) measure the pharmacist intervention rate (PIR) and physician acceptance rate (PAR) associated with the use of DDI-P. The three tools’ respective DDI detection rates (in %) were measured. The PIRs and PARs were compared by using the area under the curve ratio given by DDI-P (RAUC) and applying a chi-squared test. The DDI detection rates differed significantly: 40.0%, 76.5%, and 85.2% for ANSM, GUH and DDI-P, respectively (p < 0.0001). The PIR differed significantly according to the DDI-P’s RAUC: 90.0%, 44.2% and 75.0% for RAUC ≤ 0.5; RAUC 0.5–2 and RAUC > 2, respectively (p < 0.001). The overall PAR was 85.1% and did not appear to depend on the RAUC category (p = 0.729). Our results showed that more pharmacist interventions were issued when details of the strength of the DDI were available. The three tools can be used in a complementary manner, with a view to refining medication adjustments.

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