A Systematic Review of Metabolite-to-Drug Ratios of Pharmaceuticals in Hair for Forensic Investigations

After ingestion, consumed drugs and their metabolites are incorporated into hair, which has a long detection window, ranging up to months. Therefore, in addition to conventional blood and urine analyses, hair analysis can provide useful information on long-term drug exposure. Meta-bolite-to-drug (MD) ratios are helpful in interpreting hair results, as they provide useful information on drug metabolism and can be used to distinguish drug use from external contamination, which is otherwise a limitation in hair analysis. Despite this, the MD ratios of a wide range of pharmaceuticals have scarcely been explored. This review aims to provide an overview of MD ratios in hair in a range of pharmaceuticals of interest to forensic toxicology, such as antipsychotic drugs, antidepressant drugs, benzodiazepines, common opiates/opioids, etc. The factors influencing the ratio were evaluated. MD ratios of 41 pharmaceuticals were reported from almost 100 studies. MD ratios below 1 were frequently reported, indicating higher concentrations of the parent pharmaceutical than of its metabolite in hair, but wide-ranging MD ratios of the majority of pharmaceuticals were found. Intra- and interindividual differences and compound properties were variables possibly contributing to this. This overview presents guidance for future comparison and evaluation of MD ratios of pharmaceuticals.

Comparing CLE-AdCSV applications using SA and TAC to determine the Fe-binding characteristics of model ligands in seawater

Competitive ligand exchange–adsorptive cathodic stripping voltammetry (CLE-AdCSV) is used to determine the conditional concentration ([L]) and the conditional binding strength (logKcond) of dissolved organic Fe-binding ligands, which together influence the solubility of Fe in seawater. Electrochemical applications of Fe speciation measurements vary predominantly in the choice of the added competing ligand. Although different applications show the same trends, [L] and logKcond differ between the applications. In this study, binding of two added ligands in three different common applications to three known types of natural binding ligands is compared. The applications are (1) salicylaldoxime (SA) at 25 µM (SA25) and short waiting time, (2) SA at 5 µM (SA5), and (3) 2-(2-thiazolylazo)-ρ-cresol (TAC) at 10 µM, the latter two with overnight equilibration. The three applications were calibrated under the same conditions, although having different pH values, resulting in the detection window centers (D) DTAC > DSA25 ≥ SA5 (as logD values with respect to Fe3+: 12.3 > 11.2 ≥ 11). For the model ligands, there is no common trend in the results of logKcond. The values have a considerable spread, which indicates that the error in logKcond is large. The ligand concentrations of the nonhumic model ligands are overestimated by SA25, which we attribute to the lack of equilibrium between Fe-SA species in the SA25 application. The application TAC more often underestimated the ligand concentrations and the application SA5 over- and underestimated the ligand concentration. The extent of overestimation and underestimation differed per model ligand, and the three applications showed the same trend between the nonhumic model ligands, especially for SA5 and SA25. The estimated ligand concentrations for the humic and fulvic acids differed approximately 2-fold between TAC and SA5 and another factor of 2 between SA5 and SA25. The use of SA above 5 µM suffers from the formation of the species Fe(SA)x (x>1) that is not electro-active as already suggested by Abualhaija and van den Berg (2014). Moreover, we found that the reaction between the electro-active and non-electro-active species is probably irreversible. This undermines the assumption of the CLE principle, causes overestimation of [L] and could result in a false distinction into more than one ligand group. For future electrochemical work it is recommended to take the above limitations of the applications into account. Overall, the uncertainties arising from the CLE-AdCSV approach mean we need to search for new ways to determine the organic complexation of Fe in seawater.

A data multiverse analysis investigating non-model based SCR quantification approaches

Data inevitably need to be processed, typically involving multiple decision nodes with decisions often being equally justifiable. Electrodermal signals are the most common outcome measure in fear conditioning research, but response quantification approaches vary strongly. It remains an open question whether different approaches result in convergent results. Using fear conditioning research as a case example, we identified that baseline-correction (BLC) and trough-to-peak (TTP) quantification are used most frequently in the literature. Furthermore, heterogeneity of specifications in BLC formulas was observed, i.e., within the pre-CS baseline window and the post-CS peak detection or mean detection window. Here we systematically scrutinize the robustness of results when applying different processing methods to one pre-existing dataset (N= 118). The study was pre-registered. We report high agreement between different BLC approaches for US and CS+ trials, but moderate to poor agreement for CS- trials. Furthermore, a specification curve of the main effect of CS discrimination during fear acquisition training from all potential and reasonable combinations of specifications (N=150) and a prototypical TTP approach indicates that resulting effect sizes are largely comparable. Crucially, however, we show that BLC approaches often misclassify the peak SCR - particularly for CS- trials, which leads to a stimulus-specific bias and challenges for post-processing and replicability. Lastly, we investigate how physiologically implausible (negative) skin conductance values in BLC appearing most frequently for CS- (CS- > CS+ > US), correspond to in TTP quantification. We discuss the results in terms of robustness and replicability and provide insights into challenges, opportunities, and implications.

Comparing CLE-AdCSV applications using SA and TAC to determine the Fe binding characteristics of model ligands in seawater

Competitive ligand exchange–adsorptive cathodic stripping voltammetry (CLE-AdCSV) is used to determine the conditional concentration ([L]) and the conditional binding strength (logKcond) of dissolved organic Fe-binding ligands, which together influence the solubility of Fe in seawater. Electrochemical applications of Fe speciation measurements vary predominantly in the choice of the added competing ligand. Although different applications show the same trends, [L] and logKcond differ between the applications. In this study, binding of two added ligands in three different common applications to three known types of natural binding ligands are compared. The applications are: 1) Salicylaldoxime (SA) at 25µM (SA25) and short waiting time, 2) SA at 5µM (SA5) and 3)2-(2-thiazolylazo)-ρ-cresol (TAC) at 10 µM, the latter two with overnight equilibration. The three applications were calibrated under the same conditions, although having different pH values, resulting in the detection window centers (D) DTAC > DSA25 ≥ SA5 (as log D values with respect to Fe3+: 12.3 > 11.2 ≥ 11). For the model ligands, there is no common trend in the results of logKcond. The values have a considerable spread, which indicates that the error in logKcond is large. The ligand concentrations of the non humic model ligands are overestimated by SA25 which we attribute to the lack of equilibrium between Fe-SA species in the SA25 application. The application TAC more often underestimated the ligand concentrations and the application SA5 over and under estimated the ligand concentration. The extent of overestimation and underestimation differed per model ligand, and the three applications showed the same trend between the non humic model ligands especially for SA5 and SA25. The estimated ligand concentrations for the humic and fulvic acids differed approximately 2 fold between TAC and SA5 and another factor of 2 between SA5 and SA25. The use of SA above 5 µM suffers from the formation of the species Fe(SA)x (x > 1) that is not electro-active as already suggested by Abualhaija and Van den Berg (2014). Moreover, we found that the reaction between the electro-active and non-electro-active species is probably irreversible. This undermines the assumption of the CLE principle, causes overestimation of [L] and could result in a false distinction into more than one ligand group. For future electrochemical work it is recommended to take the above limitations of the applications into account. Overall, the uncertainties arising from the CLE-AdCSV approach mean we need to search for new ways to determine the organic complexation of Fe in seawater.

Towards Extending the Detection Window of Gamma-Hydroxybutyric Acid—An Untargeted Metabolomics Study in Serum and Urine Following Controlled Administration in Healthy Men

In forensic toxicology, gamma-hydroxybutyrate (GHB) still represents one of the most challenging drugs of abuse in terms of analytical detection and interpretation. Given its rapid elimination, the detection window of GHB in common matrices is short (maximum 12 h in urine). Additionally, the differentiation from naturally occurring endogenous GHB, is challenging. Thus, novel biomarkers to extend the detection window of GHB are urgently needed. The present study aimed at searching new potential biomarkers of GHB use by means of mass spectrometry (MS) metabolomic profiling in serum (up to 16.5 h) and urine samples (up to 8 h after intake) collected during a placebo-controlled crossover study in healthy men. MS data acquired by different analytical methods (reversed phase and hydrophilic interaction liquid chromatography; positive and negative electrospray ionization each) were filtered for significantly changed features applying univariate and mixed-effect model statistics. Complementary to a former study, conjugates of GHB with glycine, glutamate, taurine, carnitine and pentose (ribose) were identified in urine, with particularly GHB-pentose being promising for longer detection. None of the conjugates were detectable in serum. Therein, mainly energy metabolic substrates were identified, which may be useful for more detailed interpretation of underlying pathways but are too unspecific as biomarkers.

The small silver nanoparticle-assisted homogeneous sensing of thiocyanate ions with an ultra-wide window based on surface-enhanced Raman-extinction spectroscopy

For the first time, we present an original sensing strategy with an ultra-wide detection window from 17 nM to 20 mM to detect SCN− ions.

Real-World IgG Testing Frequency and Characteristics Associated with Hypogammaglobulinemia after Anti-CD20 Exposure in Patients with Chronic Lymphocytic Leukemia (CLL)

Introduction: Hypogammaglobulinemia is a known potential adverse event in patients with CLL receiving anti-CD20 monoclonal antibody (mAb) therapy. However, real-world immunoglobulin G (IgG) testing rates have not been extensively studied. We used a real-world database to investigate IgG testing rates in patients with CLL before and after anti-CD20 exposure. We further examined factors that increased the likelihood of new onset hypogammaglobulinemia following anti-CD20 therapy. Methods: The Flatiron Health EHR-derived de-identified database was used to select patients with abstraction-confirmed CLL diagnosed before 6/1/2020 with: (1) Exposure to an anti-CD20 mAb, (2) At least one IgG lab within 180 days prior to first anti-CD20 line start, and (3) No evidence of hypogammaglobulinemia (IgG < 7g/L) within 6 months prior to first anti-CD20 line start. A "hypogammaglobulinemia detection window" was defined for each patient as the time between first anti-CD20 administration through to 365 days thereafter. Patients were grouped into 3 categories: (A) "Unknown status": those with no IgG testing during the window, (B) "Confirmed hypogam": those with hypogammaglobulinemia detected on any test during the window, and (C) "No detected hypogam": those tested for IgG during the window with 0 tests meeting the hypogammaglobulinemia threshold. Demographic and clinical baseline characteristics were compared across the groups. A logistic regression was performed to compare factors associated with higher likelihood of detecting hypogammaglobulinemia among patients with an IgG test in the detection window. Factors included were: patient age at start of detection window, exposure to chemotherapy before detection window (Y/N), concurrent chemotherapy to anti-CD20 during the detection window (Y/N), and whether anti-CD20 was given in the first line (1L) setting (Y/N). We also performed a separate scenario analysis with outcome of severe hypogammaglobulinemia (IgG < 5 g/L, compared to < 7 g/L in primary analysis). Results: We found 5,838 anti-CD20 mAb-exposed patients with CLL, of which 1,927 (33%) had at least 1 IgG lab within 180 days of anti-CD20 start. Of those, 922 (48%) had hypogammaglobulinemia within 6 months prior to starting anti-CD20 therapy, leaving 1,005 patients in the cohort. 526 patients (52%) fell into the 'unknown status' category. 182 (18%) patients fell into the 'confirmed hypogam' category, and 297 (30%) fell into the 'no hypogam detected' category. Patients across these 3 categories had similar baseline characteristics (Table 1). Among the patients with an IgG test in the detection window (N=479), patients with 'confirmed hypogam' had more IgG tests during the window than the patients with 'no detected hypogam' (mean 3.9 vs. 2.8 tests, p<0.001). Results of the logistic regression for IgG < 7 g/L did not yield any factors significantly associated with higher likelihood of hypogammaglobulinemia (Table 2). However, the regression at IgG < 5 g/L (incl. changing cohort inclusion criteria 3, N=703) found that concurrent exposure to chemotherapy was associated with higher likelihood of severe documented hypogammaglobulinemia (odds ratio 1.89; 95% CI 1.23-2.96; p = 0.004). Conclusions: In this real-world cohort of patients with CLL with at least one IgG test within 6 months prior to anti-CD20 mAb initiation, hypogammaglobulinemia was common. A majority of the cohort was not tested for IgG post anti-CD20 mAb initiation, which likely reflects clinical practice of infrequently obtaining IgG levels in patients with normal pre-treatment IgG levels. As clinically expected, patients with hypogammaglobulinemia were tested for IgG more frequently than those who were not. This hypothesis-generating study suggests that administering chemotherapy concurrently to anti-CD20 mAbs increases the likelihood of documented severe hypogammaglobulinemia (IgG < 5g/L) in patients with CLL. Further research should confirm this. Our analyses did not account for patients who may have had transient hypogammaglobulinemia during anti-CD20 treatment, and did not stratify by intravenous immunoglobulin (IVIG) usage in this population. Further research should examine the effect of anti-CD20-associated hypogammaglobulinemia on outcomes, and determine if IgG surveillance should be standardized as part of clinical guidelines. Disclosures Hooley: Flatiron Health Inc: Current Employment, Research Funding; Roche Group: Current equity holder in publicly-traded company. Maignan:Flatiron Health Inc: Current Employment, Research Funding; Roche Group: Current equity holder in publicly-traded company. Jacob:Flatiron Health Inc: Current Employment, Research Funding; Roche Group: Current equity holder in publicly-traded company. Medina:Flatiron Health Inc: Current Employment, Research Funding; Roche Group: Current equity holder in publicly-traded company. Benderoff:Roche Group: Current equity holder in publicly-traded company; Flatiron Health Inc: Current Employment, Research Funding. Chen:Flatiron Health Inc: Current Employment, Research Funding; Roche Group: Current equity holder in publicly-traded company. Huntington:AbbVie: Consultancy; Astrazeneca: Honoraria; Bayer: Consultancy, Honoraria; Celgene: Consultancy, Research Funding; DTRM: Research Funding; Genentech: Consultancy; Novartis: Consultancy; Pharmacyclics: Honoraria; TG Therapeutics: Research Funding.