Application of the Hollow-Fiber Infection Model to Personalized Precision Dosing of Isoniazid in a Clinical Setting.

BACKGROUND: The hollow-fiber infection model (HFIM) is a valuable tool for evaluating pharmacokinetics/pharmacodynamics relationships and determining the optimal antibiotic dose in monotherapy or combination therapy, but the application for personalized precision medicine in tuberculosis treatment remains limited. This study aimed to evaluate the efficacy of adjusted antibiotic doses for a tuberculosis patient using HFIM. METHODS: Model-based Bayesian forecasting was utilized to assess the proposed reduction of the isoniazid dose from 300 mg daily to 150 mg daily in a patient with an ultra-slow-acetylation phenotype. The efficacy of the adjusted 150-mg dose was evaluated in a time-to-kill assay performed using the bacterial isolate Mycobacterium tuberculosis (Mtb) H37Ra in a HFIM that mimicked the individual pharmacokinetic profile of the patient. RESULTS: The isoniazid concentration observed in the HFIM adequately reflected the target drug exposures simulated by the model. After 7 days of repeated dose administration, isoniazid killed 4 log10 Mtb CFU/mL in the treatment arm, while the control arm without isoniazid increased 1.6 log10 CFU/mL. CONCLUSION: Our results provide an example of the utility of the HFIM for predicting the efficacy of specific recommended doses of anti-tuberculosis drugs in real clinical setting.

Population pharmacokinetic model of rifampicin for personalized tuberculosis pharmacotherapy: Effects of SLCO1B1 polymorphisms on drug exposure.

BACKGROUND: Rifampicin (RIF) exhibits high pharmacokinetic (PK) variability among individuals; a low plasma concentration might result in unfavorable treatment outcomes and drug resistance. This study evaluated the contributions of non- and genetic factors to the interindividual variability of RIF exposure, then suggested initial doses for patients with different weight bands. METHODS: This multicenter prospective cohort study in Korea analyzed demographic and clinical data, the solute carrier organic anion transporter family member 1B1 (SLCO1B1) genotypes, and RIF concentrations. Population PK modeling and simulations were conducted using nonlinear mixed-effect modeling. RESULTS: In total, 879 tuberculosis (TB) patients were divided into a training dataset (510 patients) and a test dataset (359 patients). A one-compartment model with allometric scaling for effect of body size best described the RIF PKs. The apparent clearance (CL/F) was 16.6% higher among patients in the SLCO1B1 rs4149056 wild-type group than among patients in variant group, significantly decreasing RIF exposure in the wild-type group. The developed model showed better predictive performance compared with previously reported models. We also suggested that patients with body weights of <40 kg, 40-55 kg, 55-70 kg, and >70 kg patients receive RIF doses of 450, 600, 750, and 1050 mg/day, respectively. CONCLUSIONS: Total body weight and SLCO1B1 rs4149056 genotypes were the most significant covariates that affected RIF CL/F variability in Korean TB patients. We suggest initial doses of RIF based on World Health Organization weight-band classifications. The model may be implemented in treatment monitoring for TB patients.

Risk adjustment model for tuberculosis compared to non-tuberculosis mycobacterium or latent tuberculosis infection: Center for Personalized Precision Medicine of Tuberculosis (cPMTb) cohort database.

BACKGROUND: The Center for Personalized Precision Medicine of Tuberculosis (cPMTb) was constructed to develop personalized pharmacotherapeutic systems for tuberculosis (TB). This study aimed to introduce the cPMTb cohort and compare the distinct characteristics of patients with TB, non-tuberculosis mycobacterium (NTM) infection, or latent TB infection (LTBI). We also determined the prevalence and specific traits of polymorphisms in N-acetyltransferase-2 (NAT2) and solute carrier organic anion transporter family member 1B1 (SLCO1B1) phenotypes using this prospective multinational cohort. METHODS: Until August 2021, 964, 167, and 95 patients with TB, NTM infection, and LTBI, respectively, were included. Clinical, laboratory, and radiographic data were collected. NAT2 and SLCO1B1 phenotypes were classified by genomic DNA analysis. RESULTS: Patients with TB were older, had lower body mass index (BMI), higher diabetes rate, and higher male proportion than patients with LTBI. Patients with NTM infection were older, had lower BMI, lower diabetes rate, higher previous TB history, and higher female proportion than patients with TB. Patients with TB had the lowest albumin levels, and the prevalence of the rapid, intermediate, and slow/ultra-slow acetylator phenotypes were 39.2%, 48.1%, and 12.7%, respectively. The prevalence of rapid, intermediate, and slow/ultra-slow acetylator phenotypes were 42.0%, 44.6%, and 13.3% for NTM infection, and 42.5%, 48.3%, and 9.1% for LTBI, respectively, which did not differ significantly from TB. The prevalence of the normal, intermediate, and lower transporter SLCO1B1 phenotypes in TB, NTM, and LTBI did not differ significantly; 74.9%, 22.7%, and 2.4% in TB; 72.0%, 26.1%, and 1.9% in NTM; and 80.7%, 19.3%, and 0% in LTBI, respectively. CONCLUSIONS: Understanding disease characteristics and identifying pharmacokinetic traits are fundamental steps in optimizing treatment. Further longitudinal data are required for personalized precision medicine. TRIAL REGISTRATION: This study registered ClinicalTrials.gov NO. NCT05280886.

Alterations of lipid-related genes during anti-tuberculosis treatment: insights into host immune responses and potential transcriptional biomarkers.

Background: The optimal diagnosis and treatment of tuberculosis (TB) are challenging due to underdiagnosis and inadequate treatment monitoring. Lipid-related genes are crucial components of the host immune response in TB. However, their dynamic expression and potential usefulness for monitoring response to anti-TB treatment are unclear. Methodology: In the present study, we used a targeted, knowledge-based approach to investigate the expression of lipid-related genes during anti-TB treatment and their potential use as biomarkers of treatment response. Results and discussion: The expression levels of 10 genes (ARPC5, ACSL4, PLD4, LIPA, CHMP2B, RAB5A, GABARAPL2, PLA2G4A, MBOAT2, and MBOAT1) were significantly altered during standard anti-TB treatment. We evaluated the potential usefulness of this 10-lipid-gene signature for TB diagnosis and treatment monitoring in various clinical scenarios across multiple populations. We also compared this signature with other transcriptomic signatures. The 10-lipid-gene signature could distinguish patients with TB from those with latent tuberculosis infection and non-TB controls (area under the receiver operating characteristic curve > 0.7 for most cases); it could also be useful for monitoring response to anti-TB treatment. Although the performance of the new signature was not better than that of previous signatures (i.e., RISK6, Sambarey10, Long10), our results suggest the usefulness of metabolism-centric biomarkers. Conclusions: Lipid-related genes play significant roles in TB pathophysiology and host immune responses. Furthermore, transcriptomic signatures related to the immune response and lipid-related gene may be useful for TB diagnosis and treatment monitoring.

Evaluation of pharmacokinetic drug-drug interaction between tegoprazan and clarithromycin in healthy subjects.

Tegoprazan is a novel potassium-competitive acid blocker that treats gastric acid-related diseases. Clarithromycin was widely used as one of various regimens for eradicating Helicobacter pylori. This study compared the pharmacokinetic and safety profile of tegoprazan and clarithromycin between combination therapy and monotherapy to evaluate the potential drug-drug interaction. An open-label, randomized, 6-sequence, 3-period crossover study was conducted in 24 healthy subjects. According to the assigned sequence, the subject was administered the assigned treatment during 5 days in each period. PK parameters of tegoprazan and clarithromycin administered in combination were compared with those of the respective monotherapies. The co-administration of tegoprazan with clarithromycin increased maximum steady-state plasma concentration (Css,max) and area under the plasma concentration-time curve in dosing interval at steady-state (AUCss,tau) of tegoprazan (1.6-fold in Css,max and 2.5-fold in AUCss,tau) and M1 (2.0-fold in Css,max, 2.5-fold in AUCss,tau) than tegoprazan alone. The Css,max and AUCss,tau of 14-hydroxyclarithromycin increased 1.8- and 2.0-fold in co-administration, respectively. The AUCss.tau of clarithromycin was slightly increased in co-administration, but Css,max was not changed. Combination of tegoprazan and clarithromycin and those of the respective monotherapies were tolerated in 24 healthy subjects. There may exist drug interaction that lead to reciprocal increase in plasma drug concentrations when tegoprazan and clarithromycin were administrated in combination and no safety concerns were raised. It is suggested that an in-depth analysis of the concentration-response relationship is necessary to determine whether these concentration changes warrant clinical action. Trial Registration: ClinicalTrials.gov Identifier: NCT02052336.

Pathway-level multi-omics analysis of the molecular mechanisms underlying the toxicity of long-term tacrolimus exposure.

Tacrolimus (TAC)-based treatment is associated with nephrotoxicity and hepatotoxicity; however, the underlying molecular mechanisms responsible for this toxicity have not been fully explored. This study elucidated the molecular processes underlying the toxic effects of TAC using an integrative omics approach. Rats were sacrificed after 4 weeks of daily oral TAC administration at a dose of 5 mg/kg. The liver and kidney underwent genome-wide gene expression profiling and untargeted metabolomics assays. Molecular alterations were identified using individual data profiling modalities and further characterized by pathway-level transcriptomics-metabolomics integration analysis. Metabolic disturbances were mainly related to an imbalance in oxidant-antioxidant status, as well as in lipid and amino acid metabolism in the liver and kidney. Gene expression profiles also indicated profound molecular alterations, including in genes associated with a dysregulated immune response, proinflammatory signals, and programmed cell death in the liver and kidney. Joint-pathway analysis indicated that the toxicity of TAC was associated with DNA synthesis disruption, oxidative stress, and cell membrane permeabilization, as well as lipid and glucose metabolism. In conclusion, our pathway-level integration of transcriptome and metabolome and conventional analyses of individual omics profiles, provided a more comprehensive picture of the molecular changes resulting from TAC toxicity. This study also serves as a valuable resource for subsequent investigations aiming to understand the mechanism underlying the molecular toxicology of TAC.

Development of a population pharmacokinetic model of pyrazinamide to guide personalized therapy: impacts of geriatric and diabetes mellitus on clearance.

Objectives: This study was performed to develop a population pharmacokinetic model of pyrazinamide for Korean tuberculosis (TB) patients and to explore and identify the influence of demographic and clinical factors, especially geriatric diabetes mellitus (DM), on the pharmacokinetics (PK) of pyrazinamide (PZA). Methods: PZA concentrations at random post-dose points, demographic characteristics, and clinical information were collected in a multicenter prospective TB cohort study from 18 hospitals in Korea. Data obtained from 610 TB patients were divided into training and test datasets at a 4:1 ratio. A population PK model was developed using a nonlinear mixed-effects method. Results: A one-compartment model with allometric scaling for body size effect adequately described the PK of PZA. Geriatric patients with DM (age >70 years) were identified as a significant covariate, increasing the apparent clearance of PZA by 30% (geriatric patients with DM: 5.73 L/h; others: 4.50 L/h), thereby decreasing the area under the concentration-time curve from 0 to 24 h by a similar degree compared with other patients (geriatric patients with DM: 99.87 µg h/mL; others: 132.3 µg h/mL). Our model was externally evaluated using the test set and provided better predictive performance compared with the previously published model. Conclusion: The established population PK model sufficiently described the PK of PZA in Korean TB patients. Our model will be useful in therapeutic drug monitoring to provide dose optimization of PZA, particularly for geriatric patients with DM and TB.

Recommendation of pharmacokinetics/pharmacodynamics target of ethambutol to suppress tuberculosis resistance: A population pharmacokinetics study on a large prospective cohort.

BACKGROUND: The ability of ethambutol (EMB) to suppress bacterial resistance has been demonstrated in a time-dependent manner. Through the development of a population pharmacokinetics (PK) model, this study aimed to suggest the PK/pharmacodynamics (PD) target and identify the significant covariates that influence interindividual variability (IIV) in the PK of EMB. METHODS: In total, 837 patients from 20 medical centres across Korea were enrolled in this study. The non-linear mixed-effect method was used to establish and validate the population PK model. RESULTS: A two-compartment model with transit compartment absorption was sufficient to describe the PK of EMB. Body weight and renal function were identified as significant covariates that affect IIV of the apparent clearance (CL/F) of EMB. Patients with moderate renal function showed 35% and 55% lower CL/F (CL/F 89.9 L/h) compared with those with mild and normal renal function, respectively. All the renal function groups with simulated doses ranging from 800 to 1200 mg achieved area under the curve over minimum inhibitory concentration (MIC) >119, and maintained T>MIC for >23 h for MIC of 0.5 µg/mL. Based on our simulation result, it is suggested that doses of 800, 1000, and 1200 mg should obtain the T>MIC target of 4, 6, and 8 h, respectively. This model was validated internally and externally. CONCLUSION: This study provides insight into the PK/PD indexes of EMB for three different renal function groups and T>MIC targets for different doses. The results could be used to provide optimal-dose suggestions for EMB.

Multimodal plasma metabolomics and lipidomics in elucidating metabolic perturbations in tuberculosis patients with concurrent type 2 diabetes.

Type 2 diabetes mellitus (DM) poses a major burden for the treatment and control of tuberculosis (TB). Characterization of the underlying metabolic perturbations in DM patients with TB infection would yield insights into the pathophysiology of TB-DM, thus potentially leading to improvements in TB treatment. In this study, a multimodal metabolomics and lipidomics workflow was applied to investigate plasma metabolic profiles of patients with TB and TB-DM. Significantly different biological processes and biomarkers in TB-DM vs. TB were identified using a data-driven, knowledge-based framework. Changes in metabolic and signaling pathways related to carbohydrate and amino acid metabolism were mainly captured by amide HILIC column metabolomics analysis, while perturbations in lipid metabolism were identified by the C18 metabolomics and lipidomics analysis. Compared to TB, TB-DM exhibited elevated levels of bile acids and molecules related to carbohydrate metabolism, as well as the depletion of glutamine, retinol, lysophosphatidylcholine, and phosphatidylcholine. Moreover, arachidonic acid metabolism was determined as a potentially important factor in the interaction between TB and DM pathophysiology. In a correlation network of the significantly altered molecules, among the central nodes, chenodeoxycholic acid was robustly associated with TB and DM. Fatty acid (22:4) was a component of all significant modules. In conclusion, the integration of multimodal metabolomics and lipidomics provides a thorough picture of the metabolic changes associated with TB-DM. The results obtained from this comprehensive profiling of TB patients with DM advance the current understanding of DM comorbidity in TB infection and contribute to the development of more effective treatment.

CYP2C19 Contributes to THP-1-Cell-Derived M2 Macrophage Polarization by Producing 11,12- and 14,15-Epoxyeicosatrienoic Acid, Agonists of the PPARγ Receptor.

Although the functional roles of M1 and M2 macrophages in the immune response and drug resistance are important, the expression and role of cytochrome P450s (CYPs) in these cells remain largely unknown. Differential expression of the 12 most common CYPs (CYP1A1, 1A2, 1B1, 2B6, 2C8, 2C9, 2C19, 2D6, 2E1, 2J2, 3A4, and 3A5) were screened in THP-1-cell-derived M1 and M2 macrophages using reverse transcription PCR. CYP2C19 was highly expressed in THP-1-cell-derived M2 macrophages, but it was negligibly expressed in THP-1-cell-derived M1 macrophages at the mRNA and protein levels as analyzed by reverse transcription quantitative PCR and Western blot, respectively. CYP2C19 enzyme activity was also very high in THP-1-cell-derived M2 compared to M1 macrophages (> 99%, p < 0.01), which was verified using inhibitors of CYP2C19 activity. Endogenous levels of the CYP2C19 metabolites 11,12-epoxyeicosatrienoic acid (11,12-EET) and 14,15-EET were reduced by 40% and 50% in cells treated with the CYP2C19 inhibitor and by 50% and 60% in the culture medium, respectively. Both 11,12-EET and 14,15-EET were identified as PPARγ agonists in an in vitro assay. When THP-1-cell-derived M2 cells were treated with CYP2C19 inhibitors, 11,12- and 14,15-EETs were significantly reduced, and in parallel with the reduction of these CYP2C19 metabolites, the expression of M2 cell marker genes was also significantly decreased (p < 0.01). Therefore, it was suggested that CYP2C19 may contribute to M2 cell polarization by producing PPARγ agonists. Further studies are needed to understand the endogenous role of CYP2C19 in M2 macrophages with respect to immunologic function and cell polarization.

Comprehensive lipid profiles investigation reveals host metabolic and immune alterations during anti-tuberculosis treatment: Implications for therapeutic monitoring.

In this study, we investigated the lipidome of tuberculosis patients during standard chemotherapy to discover biosignatures that could aid therapeutic monitoring. UPLC-QToF MS was used to analyze 82 baseline and treatment plasma samples of patients with pulmonary tuberculosis. Subsequently, a data-driven and knowledge-based workflow, including robust annotation, statistical analysis, and functional analysis, was applied to assess lipid profiles during treatment. Overall, the lipids species from 17 lipid subclasses were significantly altered by anti-tuberculosis chemotherapy. Cholesterol ester (CE), monoacylglycerols, and phosphatidylcholine (PC) were upregulated, whereas triacylglycerols, sphingomyelin, and ether-linked phosphatidylethanolamines (PE O-) were downregulated. Notably, PCs demonstrated a clear upward expression pattern during tuberculosis treatment. Several lipid species were identified as potential biomarkers for therapeutic monitoring, such as PC(42:6), PE(O-40:5), CE(24:6), and dihexosylceramide Hex2Cer(34:2;2 O). Functional and lipid gene enrichment analysis revealed alterations in pathways related to lipid metabolism and host immune responses. In conclusion, this study provides a foundation for the use of lipids as biomarkers for clinical management of tuberculosis.

Systems-level multi-omics characterization provides novel molecular insights into indomethacin toxicity.

The mechanism of indomethacin toxicity at the systemic level is largely unknown. In this study, multi-specimen molecular characterization was conducted in rats treated with three doses of indomethacin (2.5, 5, and 10 mg/kg) for 1 week. Kidney, liver, urine, and serum samples were collected and analyzed using untargeted metabolomics. The kidney and liver transcriptomics data (10 mg indomethacin/kg and control) were subjected to a comprehensive omics-based analysis. Indomethacin exposure at 2.5 and 5 mg/kg doses did not cause significant metabolome changes, whereas considerable alterations in the metabolic profile compared to the control were induced by a dose of 10 mg/kg. Decreased levels of metabolites and an increased creatine level in the urine metabolome indicated injury to the kidney. The integrated omics analysis in both liver and kidney revealed an oxidant-antioxidant imbalance due to an excess of reactive oxygen species, likely originating from dysfunctional mitochondria. Specifically, indomethacin exposure induced changes in metabolites related to the citrate cycle, cell membrane composition, and DNA synthesis in the kidney. The dysregulation of genes related to ferroptosis and suppression of amino acid and fatty acid metabolism were evidence of indomethacin-induced nephrotoxicity. In conclusion, a multi-specimen omics investigation provided important insights into the mechanism of indomethacin toxicity. The identification of targets that ameliorate indomethacin toxicity will enhance the therapeutic utility of this drug.

Development of population pharmacokinetics model and Bayesian estimation of rifampicin exposure in Indonesian patients with tuberculosis.

BACKGROUND: Interindividual variability in the pharmacokinetics (PK) of anti-tuberculosis (TB) drugs is the leading cause of treatment failure. Herein, we evaluated the influence of demographic, clinical, and genetic factors that cause variability in RIF PK parameters in Indonesian TB patients. METHODS: In total, 210 Indonesian patients with TB (300 plasma samples) were enrolled in this study. Clinical data, solute carrier organic anion transporter family member-1B1 (SLCO1B1) haplotypes *1a, *1b, and *15, and RIF concentrations were analyzed. The population PK model was developed using a non-linear mixed effect method. RESULTS: A one-compartment model with allometric scaling adequately described the PK of RIF. Age and SLCO1B1 haplotype *15 were significantly associated with variability in apparent clearance (CL/F). For patients in their 40s, each 10-year increase in age was associated with a 10% decrease in CL/F (7.85 L/h). Patients with the SLCO1B1 haplotype *15 had a 24% lower CL/F compared to those with the wild-type. Visual predictive checks and non-parametric bootstrap analysis indicated good model performance. CONCLUSION: Age and SLCO1B1 haplotype *15 were significant covariates of RIF CL/F. Geriatric patients with haplotype *15 had significantly greater exposure to RIF. The model could optimize TB pharmacotherapy through its application in therapeutic drug monitoring (clinical trial no. NCT05280886).

Advancing personalized medicine for tuberculosis through the application of immune profiling.

While early and precise diagnosis is the key to eliminating tuberculosis (TB), conventional methods using culture conversion or sputum smear microscopy have failed to meet demand. This is especially true in high-epidemic developing countries and during pandemic-associated social restrictions. Suboptimal biomarkers have restricted the improvement of TB management and eradication strategies. Therefore, the research and development of new affordable and accessible methods are required. Following the emergence of many high-throughput quantification TB studies, immunomics has the advantages of directly targeting responsive immune molecules and significantly simplifying workloads. In particular, immune profiling has been demonstrated to be a versatile tool that potentially unlocks many options for application in TB management. Herein, we review the current approaches for TB control with regard to the potentials and limitations of immunomics. Multiple directions are also proposed to hopefully unleash immunomics' potential in TB research, not least in revealing representative immune biomarkers to correctly diagnose TB. The immune profiles of patients can be valuable covariates for model-informed precision dosing-based treatment monitoring, prediction of outcome, and the optimal dose prediction of anti-TB drugs.

Population Pharmacokinetics of Levofloxacin and Moxifloxacin, and the Probability of Target Attainment in Ethiopian Patients with Multidrug-Resistant Tuberculosis.

Objective: This study aimed to explore the population pharmacokinetic modeling (PopPK) of levofloxacin (LFX) and moxifloxacin (MXF), as well as the percent probability of target attainment (PTA) as defined by the ratio of the area under the plasma concentration-time curve over 24 h and the in vitro minimum inhibitory concentration (AUC0-24/MIC) in Ethiopian multidrug resistant tuberculosis (MDR-TB) patients. Methods: Steady state-plasma concentration of the drugs in MDR-TB patients were determined using optimized liquid chromatography-tandem mass spectrometry. PopPK and simulations were run at various doses, and pharmacokinetic parameters were estimated. The effect of covariates on the PK parameters and PTA for maximum mycobacterial kill and resistance prevention was also investigated. Results: LFX and MXF both fit in a one-compartment model with adjustments. Serum-creatinine (Cr) influenced (p = 0.01) the clearance of LFX, whereas body mass index (BMI) influenced (p = 0.01) the apparent volume of distribution (V) of MXF. The PTA for LFX maximal mycobacterial kill at the critical MIC of 0.5 mg/L with the simulated 750 mg, 1000 mg, and 1500 mg doses were 29%, 62%, and 95%, respectively, whereas the PTA for resistance prevention at 1500 mg was only 4.8%, with none of the lower doses achieving this target. At the critical MIC of 0.25 mg/L, there was no change in the PTA for maximum bacterial kill when the MXF dose was increased (600 mg, 800 mg, and 1000 mg), but the PTA for resistance prevention was improved. Conclusion: The standard doses of LFX and MXF may not provide adequate drug exposure. PopPK of LFX is more predictable for maximum mycobacterial kill, whereas MXF's resistance prevention target increases with dose. Cr and BMI are likely important covariates for dose optimization in Ethiopian patients.

Quantitative Analysis of Isoniazid and Its Four Primary Metabolites in Plasma of Tuberculosis Patients Using LC-MS/MS.

Isoniazid and its metabolites are potentially associated with hepatotoxicity and treatment outcomes in patients who receive antituberculosis (TB) therapy. To further understand the pharmacokinetic profiles of these molecules, a method based on LC-MS/MS was developed to determine the concentration of these compounds in human plasma. Isoniazid, acetylisoniazid, and isonicotinic acid were directly analyzed, whereas hydrazine and acetylhydrazine were determined after derivatization using p-tolualdehyde. Chromatographic separation was conducted on reversed-phase C18 columns with gradient elution, and detection was carried out in multiple reaction monitoring mode. The calibration curves were linear with correlation coefficients (r) greater than 0.9947 for all analytes. The intra- and inter-day precision was less than 13.43%, and the accuracy ranged between 91.63 and 114.00%. The recovery and matrix effect of the analytes were also consistent (coefficient of variation was less than 9.36%). The developed method successfully quantified isoniazid and its metabolites in TB patients. The method has broad applications in clinical research, including isoniazid one-point-based therapeutic drug monitoring, genotype-phenotype association studies of isoniazid metabolic profile and isoniazid-induced hepatotoxicity, and the initial dose prediction of isoniazid using population pharmacokinetic modeling.

Characterization of Clofazimine Metabolism in Human Liver Microsomal Incubation In Vitro.

Clofazimine [N,5-bis(4-chlorophenyl)-3-[(propane-2-yl)rimino]-3,5-dihydrophenazin-2-amine] is an antimycobacterial agent used as a second-line antituberculosis (anti-TB) drug. Nonetheless, little information is known about the metabolic routes of clofazimine, and the enzymes involved in metabolism. This study aimed to characterize the metabolic pathways and enzymes responsible for the metabolism of clofazimine in human liver microsomes. Eight metabolites, including four oxidative metabolites, three glucuronide conjugates, and one sulfate conjugate were identified, and their structures were deduced based on tandem mass spectrometry (MS/MS) spectra. Hydroxylated clofazimine and hydrated clofazimine was generated even in the absence of the NADPH generating system presumably via a nonenzymatic pathway. Hydrolytic-dehalogenated clofazimine was catalyzed mainly by CYP1A2 whereas hydrolytic-deaminated clofazimine was formed by CYP3A4/A5. In case of glucuronide conjugates, UGT1A1, UGT1A3, and UGT1A9 showed catalytic activity toward hydroxylated and hydrated clofazimine glucuronide whereas hydrolytic-deaminated clofazimine glucuronide was catalyzed by UGT1A4, UGT1A9, UGT1A3, and UGT2B4. Our results suggested that CYP1A2 and CYP3A are involved in the formation of oxidative metabolites while UGT1A1, 1A3, 1A4, 1A9, and 2B4 are involved in the formation of glucuronide conjugates of oxidative metabolites of clofazimine.

Comprehensive lipid and lipid-related gene investigations of host immune responses to characterize metabolism-centric biomarkers for pulmonary tuberculosis.

Despite remarkable success in the prevention and treatment of tuberculosis (TB), it remains one of the most devastating infectious diseases worldwide. Management of TB requires an efficient and timely diagnostic strategy. In this study, we comprehensively characterized the plasma lipidome of TB patients, then selected candidate lipid and lipid-related gene biomarkers using a data-driven, knowledge-based framework. Among 93 lipids that were identified as potential biomarker candidates, ether-linked phosphatidylcholine (PC O-) and phosphatidylcholine (PC) were generally upregulated, while free fatty acids and triglycerides with longer fatty acyl chains were downregulated in the TB group. Lipid-related gene enrichment analysis revealed significantly altered metabolic pathways (e.g., ether lipid, linolenic acid, and cholesterol) and immune response signaling pathways. Based on these potential biomarkers, TB patients could be differentiated from controls in the internal validation (random forest model, area under the curve [AUC] 0.936, 95% confidence interval [CI] 0.865-0.992). PC(O-40:4), PC(O-42:5), PC(36:0), and PC(34:4) were robust biomarkers able to distinguish TB patients from individuals with latent infection and healthy controls, as shown in the external validation. Small changes in expression were identified for 162 significant lipid-related genes in the comparison of TB patients vs. controls; in the random forest model, their utilities were demonstrated by AUCs that ranged from 0.829 to 0.956 in three cohorts. In conclusion, this study introduced a potential framework that can be used to identify and validate metabolism-centric biomarkers.

Genome-wide gene expression analysis reveals molecular insights into the drug-induced toxicity of nephrotoxic agents.

Drug-induced nephrotoxicity is frequently reported. However, the mechanisms underlying nephrotoxic medications and their overlapping molecular events, which might have therapeutic value, are unclear. We performed a genome-wide analysis of gene expression and a gene set enrichment analysis to identify common and unique pathways associated with the toxicity of colistin, ifosfamide, indomethacin, and puromycin. Rats were randomly allocated into the treatment or control group. The treatment group received a toxic dose once daily of each investigated drug for 1 week. Differentially expressed genes were found in the drug-treated kidney and liver compared to the control, except for colistin in the liver. Upregulated pathways were mainly related to cell death, cell cycle, protein synthesis, and immune response modulation in the kidney. Cell cycle was upregulated by all drugs. Downregulated pathways were associated with carbon metabolism, amino acid metabolism, and fatty acid metabolism. Indomethacin, colistin, and puromycin shared the most altered pathways in the kidney. Ifosfamide and indomethacin affected molecular processes greatly in the liver. Our findings provide insight into the mechanisms underlying the renal and hepatic adverse effects of the four drugs. Further investigation should explore the combinatory drug therapies that attenuate the toxic effects and maximize the effectiveness of nephrotoxic drugs.