Experimental versus theoretical log D7.4, pKa and plasma protein binding values for benzodiazepines appearing as new psychoactive substances. Issue 8 (7th May 2018)

• Record Type: Journal Article
• Authors: Manchester, Kieran R.; Maskell, Peter D.; Waters, Laura
• Is Part Of: Drug testing and analysis. Volume 10:Issue 8(2018)
• Page Start: 1258
• Page End: 1269
• Publication Date: 2018-05-07
• Subjects: Benzodiazepines -- logD -- NPS -- pKa -- plasma; Drugs -- Analysis -- Periodicals; Drug testing -- Periodicals; Chemistry, Forensic -- Periodicals; 615.1901
• DOI: 10.1002/dta.2387
• Languages: English
• ISSNs: 1942-7603

Abstract

Abstract: The misuse of benzodiazepines as new psychoactive substances is an increasing problem around the world. Basic physicochemical and pharmacokinetic data is required on these substances to interpret and predict their effects upon humans. Experimental log D7.4, pKa and plasma protein binding values were determined for 11 benzodiazepines that have recently appeared as new psychoactive substances (3‐hydroxyphenazepam, 4′‐chlorodiazepam, desalkylflurazepam, deschloroetizolam, diclazepam, etizolam, flubromazepam, flubromazolam, meclonazepam, phenazepam, and pyrazolam) and compared with values generated by various software packages (ACD/I‐lab, MarvinSketch, ADMET Predictor and PreADMET). ACD/I‐LAB returned the most accurate values for log D7.4 and plasma protein binding while ADMET Predictor returned the most accurate values for pKa . Large variations in predictive errors were observed between compounds. Experimental values are currently preferable and desirable as they may aid with the future 'training' of predictive models for these new psychoactive substances. Abstract : Experimental log D7.4, pKa and plasma protein binding values were determined for 8 well‐known benzodiazepines and a further 11 that have recently appeared as new psychoactive substances. The experimentally determined values were then compared with values generated by various software packages. It was found that experimental values are currently preferable; they may aid with the future 'training' of predictive models to improve their success.