Identification of 4F-MDMB-BICA using a molecular network strategy in a case of severe poisoning with coma

Synthetic cannabinoids remain one of the most important groups of new psychoactive substances and are responsible for many cases of poisoning in Europe. Deaths from acute 4F-MDMB-BICA poisoning have recently been reported. Severe poisonings may be underreported because 4F-MDMB-BICA is not routinely screened for in most forensic and toxicology laboratories. We report the case of a young man in France who presented with poisoning after orally consuming a powdered substance sold online as an opioid. The coma required intensive care unit management with emergent chest tube insertion and mechanical ventilation. The outcome was favorable with no sequelae due to early medical care. In the absence of remaining product and preserved urine samples, qualitative toxicological screening was performed on plasma, cerebrospinal fluid, and a hair strand. Using ultra-high-performance liquid chromatography-high-resolution tandem mass spectrometry and a molecular network data processing strategy, 4F-MDMB-BICA and two of its metabolites were identified only in plasma and cerebrospinal samples. These results were consistent with a single exposure. The identification of the substance consumed was crucial because of discrepancy between the symptoms observed and those expected after presumed exposure. Identification of 4F-MDMB-BICA and two of its metabolites was achieved in early plasma and cerebrospinal fluid samples. This documented case is helping to improve knowledge of 4F-MDMB-BICA poisoning, which could be an emerging public health issue.

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Publication Details

Journal
Journal of Analytical Toxicology
Published
2025-02-27
DOI
https://doi.org/10.1093/jat/bkaf019
Citations
3
Primary Topic
Computational Drug Discovery Methods
Type
article
Field-Weighted Citation Impact
4.51
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article

Identification of 4F-MDMB-BICA using a molecular network strategy in a case of severe poisoning with coma

Hervé Laborde‐Castérot, Laurence Labat, Dominique Vodovar, Jérôme Langrand et al.
3 citations
Journal of Analytical Toxicology
Computational Drug Discovery Methods
4.51
article

Identification of 4F-MDMB-BICA using a molecular network strategy in a case of severe poisoning with coma

Hervé Laborde‐Castérot, Laurence Labat, Dominique Vodovar, Jérôme Langrand, Romain Magny, Pascal Houzé, Weniko Caré, Frédérik Bélot-de Saint-Léger
article en
3 citations

Abstract

Synthetic cannabinoids remain one of the most important groups of new psychoactive substances and are responsible for many cases of poisoning in Europe. Deaths from acute 4F-MDMB-BICA poisoning have recently been reported. Severe poisonings may be underreported because 4F-MDMB-BICA is not routinely screened for in most forensic and toxicology laboratories. We report the case of a young man in France who presented with poisoning after orally consuming a powdered substance sold online as an opioid. The coma required intensive care unit management with emergent chest tube insertion and mechanical ventilation. The outcome was favorable with no sequelae due to early medical care. In the absence of remaining product and preserved urine samples, qualitative toxicological screening was performed on plasma, cerebrospinal fluid, and a hair strand. Using ultra-high-performance liquid chromatography-high-resolution tandem mass spectrometry and a molecular network data processing strategy, 4F-MDMB-BICA and two of its metabolites were identified only in plasma and cerebrospinal samples. These results were consistent with a single exposure. The identification of the substance consumed was crucial because of discrepancy between the symptoms observed and those expected after presumed exposure. Identification of 4F-MDMB-BICA and two of its metabolites was achieved in early plasma and cerebrospinal fluid samples. This documented case is helping to improve knowledge of 4F-MDMB-BICA poisoning, which could be an emerging public health issue.

Journal of Analytical ToxicologyVol. 49(5)
Délégation Paris 5 (FR), Inserm (FR), Université Paris Cité (FR), Sorbonne Paris Cité (FR), Assistance Publique – Hôpitaux de Paris (FR), Hôpital Fernand-Widal (FR), Hôpital Lariboisière (FR), Hôpital d'Instruction des Armées Bégin (FR), Paris Cardiovascular Research Center (FR)
Good health and well-being
Openalex Percentile: Top 6%
Computational Drug Discovery Methods
4.51
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