Amount of Cycloserine Emanating from Terizidone Metabolism and Relationship with Hepatic Function in Patients with Drug-Resistant Tuberculosis

Role of therapeutic drug monitoring in the treatment of multi-drug resistant tuberculosis

Tuberculosis (TB) is a leading cause of mortality worldwide, and resistance to anti-tuberculosis drugs is a challenge to effective treatment. Multi-drug resistant TB (MDR-TB) can be difficult to treat, requiring long durations of therapy and the use of second line drugs, increasing a patient's risk for toxicities and treatment failure. Given the challenges treating MDR-TB, clinicians can improve the likelihood of successful outcomes by utilizing therapeutic drug monitoring (TDM). TDM is a clinical technique that utilizes measured drug concentrations from the patient to adjust therapy, increasing likelihood of therapeutic drug concentrations while minimizing the risk of toxic drug concentrations. This review paper provides an overview of the TDM process, pharmacokinetic parameters for MDR-TB drugs, and recommendations for dose adjustments following TDM.

Population Pharmacokinetics and Dose Evaluation of Cycloserine among Patients with Multidrug-Resistant Tuberculosis under Standardized Treatment Regimens

Although cycloserine is a recommended drug for the treatment of multidrug-resistant tuberculosis (MDR-TB) according to World Health Organization (WHO), few studies have reported on pharmacokinetics (PK) and/or pharmacodynamics (PD) data of cycloserine in patients with standardized MDR-TB treatment. This study aimed to estimate the population PK parameters for cycloserine and to identify clinically relevant PK/PD thresholds, as well as to evaluate the current recommended dosage. Data from a large cohort with full PK curves was used to develop a population PK model. This model was used to estimate drug exposure in patients with MDR-TB from a multicentre prospective study in China. The classification and regression tree was used to identify the clinically relevant PK/PD thresholds. Probability of target attainment was analyzed to evaluate the currently recommended dosing strategy. Cycloserine was best described by a two-compartment disposition model. A percentage of time concentration above MICs (T>MIC) of 30% and a ratio of area under drug concentration-time curve (AUC0-24h) over MIC of 36 were the valid predictors for 6-month sputum culture conversion and final treatment outcome. Simulations showed that with WHO-recommended doses (500 mg and 750 mg for patients weighing <45 kg and ≥45 kg), the probability of target attainment exceeded 90% at MIC ≤16 mg/L in MGIT for both T>MIC of 30% and AUC0-24h/MIC of 36. New clinically relevant PK/PD thresholds for cycloserine were identified in patients with standardized MDR-TB treatment. WHO-recommended doses were considered adequate for the MGIT MIC distribution in our cohort of Chinese patients with MDR-TB.

Interaction of Host Pattern Recognition Receptors (PRRs) with Mycobacterium Tuberculosis and Ayurvedic Management of Tuberculosis: A Systemic Approach

Tuberculosis (TB), an infectious disease caused by Mycobacterium tuberculosis (Mtb), infects the lungs' alveolar surfaces through aerosol droplets. At this stage, the disease progression may have many consequences, determined primarily by the reactions of the human immune system. However, one approach will be to more actively integrate the immune system, especially the pattern recognition receptor (PRR) systems of the host, which notices pathogen-associated molecular patterns (PAMPs) of Mtb. Several types of PRRs are involved in the detection of Mtb, including Toll-like receptors (TLRs), C-type lectin receptors (CLRs), Dendritic cell (DC) -specific intercellular adhesion molecule-3-grabbing non-integrin (DC-SIGN), Mannose receptor (MR), and NOD-like receptors (NLRs) related to inflammasome activation. In this study, we focus on reviewing the Mtb pathophysiology and interaction of host PPRs with Mtb as well as adverse drug effects of anti-tuberculosis drugs (ATDs) and systematic TB treatment via Ayurvedic medicine.

Influence of anti-tuberculosis drugs plus cycloserine on sputum negative conversion rate, adverse reactions and inflammatory factors in multi-drug resistant tuberculosis

OBJECTIVE

The aim of this investigation was to determine the influence of anti-tuberculosis (anti-TB) drugs plus cycloserine (CS) on the sputum negative conversion rate, adverse reactions and inflammatory factors in the treatment of multidrug-resistant tuberculosis (MDR-TB).

METHODS

Seventy patients with MDR-TB who were referred to Nanjing Hospital Affiliated with Nanjing University of Traditional Chinese Medicine from April 2017 to April 2020 were assigned into the research group (RG; 38 cases) for anti-TB drugs plus CS, and the control group (CG; 32 cases) for conventional anti-TB drugs. The two groups were compared in their sputum negative conversion rate, incidence of adverse reactions, and foci absorption rate after 6, 12 and 24 months of treatment. The levels of inflammatory factors; tumor necrosis factor (TNF-α), interleukin-6 (IL-6) and interferon-γ (IFN-γ), both pre- and post-treatment were detected. Also, pre- and post-treatment, pulmonary function (PF) indexes (forced expiratory volume in 1 s/forced vital capacity, FEV1/FVC; FEV1; peak expiratory flow, PEF), and the scores of anxiety and depression (self-rating anxiety/depression scale, SAS/SDS), as well as Pittsburgh Sleep Quality Index (PSQI) were compared.

RESULTS

After 6, 12 and 24 months of treatment, the sputum negative conversion rate and foci absorption rate were higher in the RG than in the CG (both P<0.05). The RG presented with fewer adverse reactions, lower TNF-α, IL-6 and IFN-γ levels, higher FEV1, FEV1/FVC and PEF, and lower SAS, SDS and PSQI scores than the CG, post treatment (all P<0.05).

CONCLUSIONS

While helping to raise the sputum negative conversion rate, improve prognosis, and reduce adverse reactions, anti-TB drugs plus CS can also inhibit the release of inflammatory factors, improve PF and alleviate negative emotion and sleep disorders.

Treatment Outcomes and Adverse Drug Effects of Ethambutol, Cycloserine, and Terizidone for the Treatment of Multidrug-Resistant Tuberculosis in South Africa

Treatment outcomes among multidrug-resistant tuberculosis (MDR-TB) patients receiving ethambutol, cycloserine, or terizidone as part of a standardized regimen were compared, determining occurrence of serious adverse drug events (SADEs). Newly diagnosed adult MDR-TB patients were enrolled between 2000 and 2004, receiving a standardized multidrug regimen for 18 to 24 months, including ethambutol, cycloserine, or terizidone. Cycloserine and terizidone were recorded individually. SADEs and factors associated with culture conversion and unfavorable treatment outcomes (default, death, treatment failure) were determined. Of 858 patients, 435 (51%) received ethambutol, 278 (32%) received cycloserine, and 145 (17%) received terizidone. Demographic and baseline clinical data were comparable. Successful treatment occurred in 56%, significantly more in patients receiving cycloserine (60%) and terizidone (62%) than in those receiving ethambutol (52% [P = 0.03]). Defaults rates were 30% in ethambutol patients versus 15% and 11% for cycloserine and terizidone patients, respectively. Terizidone was associated with fewer unfavorable outcomes (adjusted odds ratio [AOR], 0.4; P = 0.008; 95% confidence interval [CI], 0.2 to 0.8). Patients receiving cycloserine were more likely to achieve culture conversion than those receiving ethambutol or terizidone (AOR, 2.2; P = 0.02; 95% CI, 1.12 to 4.38). Failure to convert increased the odds of unfavorable outcomes (AOR, 23.7; P < 0.001; 95% CI, 13 to 44). SADEs were reported in two patients receiving ethambutol, seven patients receiving cycloserine, and three receiving terizidone (P = 0.05). Ethambutol was associated with high culture conversion and default rates. Cycloserine achieved higher culture conversion rates than terizidone. Fewer patients on terizidone experienced SADEs, with lower default rates. The differences that we observed between cycloserine and terizidone require further elucidation.

Pharmacokinetic Modeling, Simulation, and Development of a Limited Sampling Strategy of Cycloserine in Patients with Multidrug-/Extensively Drug-Resistant Tuberculosis

BACKGROUND AND OBJECTIVE

Multidrug-resistant tuberculosis has much poorer treatment outcomes compared with drug-susceptible tuberculosis because second-line drugs for treating multidrug resistant tuberculosis are less effective and are frequently associated with side effects. Optimization of drug treatment is urgently needed. Cycloserine is a second-line tuberculosis drug with variable pharmacokinetics and thus variable exposure when programmatic doses are used. The objective of this study was to develop a population pharmacokinetic model of cycloserine to assess drug exposure and to develop a limited sampling strategy for cycloserine exposure monitoring.

MATERIAL AND METHODS

Patients with multidrug-/extensively drug-resistant tuberculosis who were treated for > 7 days with cycloserine were eligible for inclusion. Patients received cycloserine 500 mg (body weight ≤ 50 kg) or 750 mg (body weight > 50 kg) once daily. MW/Pharm 3.83 (Mediware, Groningen, The Netherlands) was used to parameterize the population pharmacokinetic model. The model was compared with pharmacokinetic values from the literature and evaluated with a bootstrap analysis, Monte Carlo simulation, and an external dataset. Monte Carlo simulations were used to develop a limited sampling strategy.

RESULTS

Cycloserine plasma concentration vs time curves were obtained from 15 hospitalized patients (nine male, six female, median age 35 years). Mean dose/kg body weight was 11.5 mg/kg (standard deviation 2.04 mg/kg). Median area under the concentration-time curve over 24 h (AUC_0-24 h) of cycloserine was 888 h mg/L (interquartile range 728-1252 h mg/L) and median maximum concentration of cycloserine was 23.31 mg/L (interquartile range 20.14-33.30 mg/L). The final population pharmacokinetic model consisted of the following pharmacokinetic parameters [mean (standard deviation)]: absorption constant K_{a_po} of 0.39 (0.31) h^-1, distribution over the central compartment (V_d) of 0.54 (0.26) L/kg LBM, renal clearance as fraction of the estimated glomerular filtration rate of 0.092 (0.038), and metabolic clearance of 1.05 (0.75) L/h. The population pharmacokinetic model was successfully evaluated with a bootstrap analysis, Monte Carlo simulation, and an external dataset of Chinese patients (difference of 14.6% and 19.5% in measured and calculated concentrations and AUC_0-24 h, respectively). Root-mean-squared-errors found in predicting the AUC_0-24 h using a one- (4 h) and a two- (2 h and 7 h) limited sampling strategy were 1.60% and 0.14%, respectively.

CONCLUSIONS

This developed population pharmacokinetic model can be used to calculate cycloserine concentrations and exposure in patients with multidrug-/extensively drug-resistant tuberculosis. This model was successfully validated by internal and external validation methods. This study showed that the AUC_0-24 h of cycloserine can be estimated in patients with multidrug-/extensively drug-resistant tuberculosis using a 1- or 2-point limited sampling strategy in combination with the developed population pharmacokinetic model. This strategy can be used in studies to correlate drug exposure with clinical outcome. This study also showed that good target attainment rates, expressed by time above the minimal inhibitory concentration, were obtained for cycloserine with a minimal inhibitory concentration of 5 and 10 mg/L, but low rates with a minimal inhibitory concentration of 20 and 32.5 mg/L.