Loprazolam Mesylate

Public laboratories must balance innovative and existing methods to keep up with designer drug trends. This article presents a strategy for handling designer benzodiazepines (DBZDs) in casework from screening to interpretation. The cross-reactivity of 22 DBZDs and metabolites was tested against the Immunalysis™ benzodiazepine (BZD) direct enzyme-linked immunosorbent assay kit. The kit had high intra-analyte precision (coefficients of variation <15%). Inter-analyte performance varied, triggering confirmation testing at concentrations ranging from 35 to 460 μg/L. The Cuyahoga County Regional Forensic Science Laboratory implemented a 40-analyte BZD and Z-drug confirmation method in 2019. Ten additional analytes were later validated for qualitative reporting, and the limits of detection for 13 analytes were lowered by 60%. The method of standard addition was also optimized for as-needed quantitation. Equal and 1/x weighting factors correlated well with target concentrations (coefficients of determination (r2) > 0.98), but 1/x weighting provided the most consistently accurate concentrations. Six computational models were developed to predict γ-aminobutyric acid-A receptor binding affinity to assist in case interpretation (r2 > 0.70 for cross-validation and test set prediction). These models were used to predict the binding affinity of analytes in the confirmation method. Other public laboratories can use this same practical strategy to adapt to any designer drug class (e.g., BZDs, opioids, cannabinoids and stimulants).

The local interpretable model-agnostic explanation (LIME) method was used to interpret two machine learning models of compounds penetrating the blood-brain barrier. The classification models, Random Forest, ExtraTrees, and Deep Residual Network, were trained and validated using the blood-brain barrier penetration dataset, which shows the penetrability of compounds in the blood-brain barrier. LIME was able to create explanations for such penetrability, highlighting the most important substructures of molecules that affect drug penetration in the barrier. The simple and intuitive outputs prove the applicability of this explainable model to interpreting the permeability of compounds across the blood-brain barrier in terms of molecular features. LIME explanations were filtered with a weight equal to or greater than 0.1 to obtain only the most relevant explanations. The results showed several structures that are important for blood-brain barrier penetration. In general, it was found that some compounds with nitrogenous substructures are more likely to permeate the blood-brain barrier. The application of these structural explanations may help the pharmaceutical industry and potential drug synthesis research groups to synthesize active molecules more rationally.