Plasma metabolomic and lipidomic alterations associated with anti-tuberculosis drug-induced liver injury

Longitudinal metabolomics of human plasma reveal metabolic dynamics and predictive markers of antituberculosis drug-induced liver injury

Tuberculosis (TB) remains the second leading cause of death from a single infectious agent and long-term medication could lead to antituberculosis drug-induced liver injury (ATB-DILI). We established a prospective longitudinal cohort of ATB-DILI with multiple timepoint blood sampling and used untargeted metabolomics to analyze the metabolic profiles of 107 plasma samples from healthy controls and newly diagnosed TB patients who either developed ATB-DILI within 2 months of anti-TB treatment (ATB-DILI subjects) or completed their treatment without any adverse drug reaction (ATB-Ctrl subjects). The untargeted metabolome revealed that 77 metabolites (of 895 total) were significantly changed with ATB-DILI progression. Among them, levels of multiple fatty acids and bile acids significantly increased over time in ATB-DILI subjects. Meanwhile, metabolites of the same class were highly correlated with each other and pathway analysis indicated both fatty acids metabolism and bile acids metabolism were up-regulated with ATB-DILI progression. The targeted metabolome further validated that 5 fatty acids had prediction capability at the early stage of the disease and 6 bile acids had a better diagnostic performance when ATB-DILI occurred. These findings provide evidence indicating that fatty acids metabolism and bile acids metabolism play a vital role during ATB-DILI progression. Our report adds a dynamic perspective better to understand the pathological process of ATB-DILI in clinical settings.

Unraveling the treatment effects of huanglian jiedu decoction on drug-induced liver injury based on network pharmacology, molecular docking and experimental validation

Huanglian Jiedu Decoction (HJD) is a well-known Traditional Chinese Medicine formula that has been used for liver protection in thousands of years. However, the therapeutic effects and mechanisms of HJD in treating drug-induced liver injury (DILI) remain unknown. In this study, a total of 26 genes related to both HJD and DILI were identified, which are corresponding to a total of 41 potential active compounds in HJD. KEGG analysis revealed that Tryptophan metabolism pathway is particularly important. The overlapped genes from KEGG and GO analysis indicated the significance of CYP1A1, CYP1A2, and CYP1B1. Experimental results confirmed that HJD has a protective effect on DILI through Tryptophan metabolism pathway. In addition, the active ingredients Corymbosin, and Moslosooflavone were found to have relative strong intensity in UPLC-Q-TOF-MS/MS analysis, showing interactions with CYP1A1, CYP1A2, and CYP1B1 through molecule docking. These findings could provide insights into the treatment effects of HJD on DILI.

Push forward LC-MS-based therapeutic drug monitoring and pharmacometabolomics for anti-tuberculosis precision dosing and comprehensive clinical management

The spread of tuberculosis (TB), especially multidrug-resistant TB and extensively drug-resistant TB, has strongly motivated the research and development of new anti-TB drugs. New strategies to facilitate drug combinations, including pharmacokinetics-guided dose optimization and toxicology studies of first- and second-line anti-TB drugs have also been introduced and recommended. Liquid chromatography-mass spectrometry (LC-MS) has arguably become the gold standard in the analysis of both endo- and exo-genous compounds. This technique has been applied successfully not only for therapeutic drug monitoring (TDM) but also for pharmacometabolomics analysis. TDM improves the effectiveness of treatment, reduces adverse drug reactions, and the likelihood of drug resistance development in TB patients by determining dosage regimens that produce concentrations within the therapeutic target window. Based on TDM, the dose would be optimized individually to achieve favorable outcomes. Pharmacometabolomics is essential in generating and validating hypotheses regarding the metabolism of anti-TB drugs, aiding in the discovery of potential biomarkers for TB diagnostics, treatment monitoring, and outcome evaluation. This article highlighted the current progresses in TDM of anti-TB drugs based on LC-MS bioassay in the last two decades. Besides, we discussed the advantages and disadvantages of this technique in practical use. The pressing need for non-invasive sampling approaches and stability studies of anti-TB drugs was highlighted. Lastly, we provided perspectives on the prospects of combining LC-MS-based TDM and pharmacometabolomics with other advanced strategies (pharmacometrics, drug and vaccine developments, machine learning/artificial intelligence, among others) to encapsulate in an all-inclusive approach to improve treatment outcomes of TB patients.

Pharmacokinetic Assessment of Pyrazinamide and Pyrazinoic Acid in Carbon tetrachloride-induced Liver Injury Model in Wistar Rats

Background

We investigated the pharmacokinetic behavior of pyrazinamide (PZA) and pyrazinoic acid (PA) in the presence of carbon-tetrachloride (CCl4) plus antitubercular treatment (ATT) drug-induced liver injury (DILI) in rats.

Methods

Thirty rats utilized in the experiment were separated equally into five groups. Each rat was injected with 0.5 ml/kg CCl4 intra-peritoneal injection on day zero. Group, I rats did receive only CCl4 (single i.p. injection, 0.5 ml/Kg in olive oil in a 1:1 ratio). Groups II, III, IV, and V did receive daily oral PZA, PZA plus isoniazid (INH), rifampicin (RMP) plus pyrazinamide (PZA), and three drugs together, respectively, for 21-days. Pharmacokinetic sampling was performed at 0, 0.5,1,3,6,12 and 24 hours post-dosing on day-20. Liver function test (LFT) was assessed at days 0,1,7, and 21 days after CCl4 and ATT administration, and rats were sacrificed on the last experiment day.

Results

ATT treatment maintained the liver function changes initiated by CCl4 administration. An evidential LFT rise was observed in groups administered with pyrazinamide. Co-administration of Isoniazid caused a 2.02 and 1.78 times increase in Area-under-the-curve (AUC) values of PZA and PA, respectively (p < 0.05). Histological and oxidative-stress changes supported the biochemical and pharmacokinetic observations.

Conclusion

The enzyme inhibitory capacity of isoniazid is well-preservd in CCl4-induced liver injury.

A nomogram model to predict the risk of drug-induced liver injury in patients receiving anti-tuberculosis treatment

Objectives: To establish an individualized nomogram to predict the probability of drug-induced liver injury (DILI) in tuberculosis patients receiving anti-tuberculosis treatment. Methods: The clinical information of patients admitted to a tertiary hospital between January 2010 and December 2022 was retrospectively reviewed from the clinical records. Patients with baseline liver diseases (hepatis B or C infection and fatty liver) or taking liver protective drugs were excluded. The maximum values in liver function test within 180 days after anti-tuberculosis treatment were collected to determine the occurrence of DILI. The candidate variables used for establishing prediction model in this study were the last results within the 30 days before the treatment onset. The final variables were included after univariate and multivariate logistic regression analyses and applied to establish the nomogram model. The discrimination power and prediction accuracy of the prediction model were assessed using the area under the receiver operating characteristic (AUC) curve and a calibration chart. The clinical effectiveness was assessed via decision curve analysis (DCA). The established model was validated in two validation groups. Results: A total of 1979 patients with 25 variables were enrolled in this study, and the incidence of DILI was 4.2% (n = 83). The patients with complete variables were divided into training group (n = 1,121), validation group I (n = 492) and validation group II (n = 264). Five variables were independent factors for DILI and included in the final prediction model presented as nomogram: age (odds ratio [OR] 1.022, p = 0.023), total bilirubin ≥17.1 μmol/L (OR 11.714, p < 0.001), uric acid (OR 0.977, p = 0.047), neutrophil count (OR 2.145, 0.013) and alcohol consumption (OR 3.209, p = 0.002). The AUCs of the prediction model in the training group, validation group I and validation group II were 0.833, 0.668, and 0.753, respectively. The p-values of calibration charts in the three groups were 0.800, 0.996, and 0.853. The DCA curves of the prediction model were above the two extreme curves. Conclusion: The nomogram model in this study could effectively predict the DILI risk among patients under anti-tuberculosis drug treatment.