Simultaneous quantitative analysis of 39 common toxicological drugs for increased efficiency in an ante- and postmortem laboratory.

BACKGROUND: A novel forensic method was developed to quantitate 39 drugs of toxicological interest for ante-mortem and postmortem analysis. This method was created to combine and replace four existing quantitation methods as well as add three additional compounds of interest and serves to drastically increase the efficiency of the criminalists and reduce the case backlog. The method is currently applied to ante-mortem blood, postmortem blood, urine, liver, brain, and gastric contents. METHODS: The extraction was performed by using a protein precipitation and DPX WAX-S tips with analysis on a Waters® i-class Acquity ultra-performance liquid chromatography with a Phenomenex Kinetex® Column (1.7 µm Biphenyl Å, 2.1 ×100 mm) followed by a Waters® XeVo-TQS tandem mass spectrometer using positive electrospray ionization in multiple reaction monitor mode. The sample volume required for analysis was 0.5 mL, or 0.5 g, an improvement from 4 mL when performing previous methods utilized in the laboratory. RESULTS: The improved method incorporated the 2017 recommended cut-offs for toxicological investigation of driving under the influence of drugs and was validated following the SWGTOX and ANSI/ASB guidelines of method validation. The advantages of analyzing low volume cases and/or detecting drugs previously outside the laboratory's scope of analysis, (such as gabapentin, pregabalin and baclofen) will be presented in two case studies. CONCLUSION: The multi-drug quantitation method allowed for the analysis of 39 drugs including a hydrolysis step, if needed, with only 0.5 mL or 0.5 g of sample. The method condensed two previously un-validated quantitative methods and two additional qualitative methods, which detected many commonly seen drugs, all into a single method. Three additional analytes of interest, gabapentin, pregabalin and baclofen, which it had previously been unable to detect, were added to the new method. The added benefit of these new drugs added both the coroner's investigators in cause of death determination and driving under the influence of drugs investigation especially with the high prevalence of gabapentin.

A Library of Phosphoproteomic and Chromatin Signatures for Characterizing Cellular Responses to Drug Perturbations.

Although the value of proteomics has been demonstrated, cost and scale are typically prohibitive, and gene expression profiling remains dominant for characterizing cellular responses to perturbations. However, high-throughput sentinel assays provide an opportunity for proteomics to contribute at a meaningful scale. We present a systematic library resource (90 drugs × 6 cell lines) of proteomic signatures that measure changes in the reduced-representation phosphoproteome (P100) and changes in epigenetic marks on histones (GCP). A majority of these drugs elicited reproducible signatures, but notable cell line- and assay-specific differences were observed. Using the "connectivity" framework, we compared signatures across cell types and integrated data across assays, including a transcriptional assay (L1000). Consistent connectivity among cell types revealed cellular responses that transcended lineage, and consistent connectivity among assays revealed unexpected associations between drugs. We further leveraged the resource against public data to formulate hypotheses for treatment of multiple myeloma and acute lymphocytic leukemia. This resource is publicly available at https://clue.io/proteomics.

A Next Generation Connectivity Map: L1000 Platform and the First 1,000,000 Profiles.

We previously piloted the concept of a Connectivity Map (CMap), whereby genes, drugs, and disease states are connected by virtue of common gene-expression signatures. Here, we report more than a 1,000-fold scale-up of the CMap as part of the NIH LINCS Consortium, made possible by a new, low-cost, high-throughput reduced representation expression profiling method that we term L1000. We show that L1000 is highly reproducible, comparable to RNA sequencing, and suitable for computational inference of the expression levels of 81% of non-measured transcripts. We further show that the expanded CMap can be used to discover mechanism of action of small molecules, functionally annotate genetic variants of disease genes, and inform clinical trials. The 1.3 million L1000 profiles described here, as well as tools for their analysis, are available at https://clue.io.