Background/Objectives: Substance use disorder is a crisis impacting many people in the United States. This review aimed to identify the effect addictive substances have on the kynurenine pathway. Tryptophan is an essential amino acid metabolized by the serotonin and kynurenine pathways. The metabolites of these pathways play a role in the biological reward system. Addictive substances have been shown to cause imbalances in the ratios of these metabolites. With current treatment and therapeutic options being suboptimal, identifying biochemical mechanisms that are impacted during the use of addictive substances can provide alternative options for treatment or drug discovery. Methods: A systematic literature search was conducted to identify studies evaluating the relationship between substance use disorder and tryptophan metabolism through the kynurenine pathway. A total of 32 articles meeting eligibility criteria were used to review the relationship between the kynurenine pathway, tryptophan breakdown, and addictive substances. Results: The use of addictive substances dysregulates tryptophan metabolism and kynurenine metabolite concentrations. This imbalance directly affects the dopamine reward system and is thought to promote continued substance use. Conclusions: Further studies are needed to fully evaluate the metabolites of the kynurenine pathway, along with other options for treatment to repair the metabolite imbalance. Several possible therapeutics have been identified; drugs that restore homeostasis, such as Ro 61-8048 and natural products like Tinospora cordifolia or Decaisnea insignis, are promising options for the treatment of substance use disorder.

Introduction: Currently, there are few tools for monitoring recovery in substance use disorder. As substance use has increased in prevalence, tools for measuring recovery are needed to improve therapeutic outcomes. Measuring the kynurenine pathway for imbalances in metabolites could be a possible solution to monitor recovery. Methods: We developed a liquid chromatography–tandem mass spectrometry (LC-MS/MS) method to quantify tryptophan, kynurenine, kynurenic acid, and quinolinic acid in urine. Metabolites were separated using a stepwise gradient and detected with an Agilent 6460 triple quadrupole mass analyzer. The samples were extracted using a simple protein precipitation protocol. Method validation was performed using routine toxicology urine samples and laboratory contrived samples. The performance characteristics assessed included precision, linearity, stability, interference, and matrix effects. Additionally, urine samples from two cohorts (illicit drug-negative and drug-positive; n = 120 per cohort) were analyzed for significant concentration differences in the four metabolites using Mann–Whitney, PCA, and Area Under the Receiver Operating Characteristic Curve statistical analysis. Results: The LC-MS/MS assay was linear from 195 to 100,000 ng/mL for tryptophan, 6 to 3000 ng/mL for kynurenine, 14 to 7200 ng/mL for kynurenic acid, and 125 to 64,000 ng/mL for quinolinic acid using an 8-point calibration curve. Imprecision ranged from 1.17% to 12.46% CV using two controls that spanned the analytical measurement range. Matrix effects were observed; however, the use of labeled internal standards matching the metabolites of interest minimized the impact on quantification. The extraction recovery efficiency was acceptable for the analytical validation. Ambient stability extended to 10 days, resulting in individual sample biases of up to 22%. A statistically significant increase in TRP, KYN, and QA was observed in drug-positive urine compared to illicit drug-negative urine (p < 0.01). Conclusion: We developed a rapid and sensitive LC-MS/MS method for quantifying tryptophan, kynurenine, kynurenic acid, and quinolinic acid in urine that can aid in future research elucidating the relationship between substance use disorders and tryptophan metabolism.

Benzodiazepines are frequently prescribed and misused therefore urine drug screening (UDS) is performed in many patient populations. Most current benzodiazepine immunoassays have poor sensitivity, particularly for detecting the metabolites of newer benzodiazepines such as lorazepam in urine. We aimed to verify the clinical performance of the new qualitative Roche Benzodiazepines II (BNZ2) immunoassay, as well as compare its performance to the Roche Benzodiazepines Plus (BENZ) assay in two patient populations: UDS in the emergency department (ED) and compliance monitoring. An initial verification study was performed, selecting for samples containing clonazepam and lorazepam metabolites. Performance of the BNZ2 and BENZ assays was compared to liquid chromatography-tandem mass spectrometry (LC-MS/MS) as the reference method. Sensitivity, specificity, false positive rate (FPR) and false negative rate (FNR) were determined. We verified the performance claims in the initial verification and demonstrated similar precision, with coefficient of variations (CVs) of 12.8% and 7.7% for negative and positive controls, respectively. Furthermore, we observed higher clinical sensitivity and lower FNR with the BNZ2 assay in both the ED and compliance monitoring populations due to improved cross-reactivity for lorazepam and clonazepam metabolites. Despite these improvements, the BNZ2 assay was unable to detect 27% of specimens positive by LC-MS/MS, including specimens from patients using benzodiazepines without prescription. Due to its improved performance and rapid turnaround time, the BNZ2 assay should be implemented for UDS in the ED. However, the assay should not replace LC-MS/MS testing for compliance monitoring, as unsuspected benzodiazepine use may go undetected.

Background: Substance use disorder in the United States represents a complex and growing public health crisis, marked by increasing rates of overdose deaths and the misuse of prescription medications. There is a critical need for furthering the understanding of the molecular and genetic mechanisms that can lead to substance use disorder. Identifying significant variants in the kynurenine pathway could help identify therapeutic targets for intervention. Methods: The All of Us cohort builder evaluated the frequency of variants of four genes, TDO2, IDO1, IDO2, and KMO, encoding enzymes in the kynurenine pathway. The samples were broken into six cohorts: alcohol, cannabis, cocaine, opioid, other use disorder, and control. Using Chi-square analysis, the frequency of at least one copy of a variant allele was calculated. Results: Chi-square analysis showed a significant variation in genetic frequency (p-value < 0.005) in 14 of 18 polymorphisms analyzed. The cocaine cohort had the most significant variants (13), cannabis had 11, opioids had 3, other use disorders had 2, and alcohol had 1 significant variant. Conclusions: This study found associations of polymorphisms in the TDO2, IDO1, IDO2, and KMO genes of individuals with a substance use disorder. These results provide evidence of potential predictors of increased susceptibility to substance use disorder.