Corrigendum to "Mass spectrometry for therapeutic drug monitoring of anti-tuberculosis drugs" [Clin. Mass Spectrom. 14(Part A) (2019) 34-45]

[This corrects the article DOI: 10.1016/j.clinms.2018.10.002.]

Population pharmacokinetics and Bayesian dose adjustment to advance TDM of anti-TB drugs

Funding: Anne-Grete Märtson was funded by Marie Skłodowska-Curie Actions [Grant agreement no. 713660—PRONKJEWAIL—H2020-MSCA-COFUND-2015].Tuberculosis (TB) is still the number one cause of death due to an infectious disease. Pharmacokinetics and pharmacodynamics of anti-TB drugs are key in the optimization of TB treatment and help to prevent slow response to treatment, acquired drug resistance, and adverse drug effects. The aim of this review was to provide an update on the pharmacokinetics and pharmacodynamics of anti-TB drugs and to show how population pharmacokinetics and Bayesian dose adjustment can be used to optimize treatment. We cover aspects on preclinical, clinical, and population pharmacokinetics of different drugs used for drug-susceptible TB and multidrug-resistant TB. Moreover, we include available data to support therapeutic drug monitoring of these drugs and known pharmacokinetic and pharmacodynamic targets that can be used for optimization of therapy. We have identified a wide range of population pharmacokinetic models for first- and second-line drugs used for TB, which included models built on NONMEM, Pmetrics, ADAPT, MWPharm, Monolix, Phoenix, and NPEM2 software. The first population models were built for isoniazid and rifampicin; however, in recent years, more data have emerged for both new anti-TB drugs, but also for defining targets of older anti-TB drugs. Since the introduction of therapeutic drug monitoring for TB over 3 decades ago, further development of therapeutic drug monitoring in TB next steps will again depend on academic and clinical initiatives. We recommend close collaboration between researchers and the World Health Organization to provide important guideline updates regarding therapeutic drug monitoring and pharmacokinetics/pharmacodynamics.Publisher PDFPeer reviewe

Lack of penetration of amikacin into saliva of tuberculosis patients

In the 2016 update of the World Health Organization treatment guideline for drug-resistant tuberculosis (TB), a shorter multidrug-resistant TB regimen was proposed because of its higher treatment outcomes [1]. However, therapeutic drug monitoring (TDM) is an excellent method to improve clinical outcomes as well and its practice is on the rise [2]. A well-known side-effect of group B injectable anti-TB drugs (e.g. amikacin) is ototoxicity [3]. TDM could also be a solution to minimise side-effects by lowering the drug exposure [4]. In the study by van Altena et al. [5], TDM was practised using the ratio of peak concentration (Cmax) to minimal inhibitory concentration (MIC) and this resulted in a reduction in patients with hearing loss. Saliva is considered as an alternative matrix for TDM because it is easy, noninvasive and more patient friendly to sample [6]. Studies found a limited penetration of gentamycin and tobramycin into saliva [7], while detectable levels of amikacin in saliva of neonates were reported [8]. Given the low penetration of aminoglycosides into saliva and interest in Cmax for TDM of amikacin, our objective was to study whether the salivary Cmax of amikacin is measurable and useful in salivary TDM.Salivary CmaxTDM of amikacin was not feasible in TB treatment due to the very low penetration into saliva http://ow.ly/d6v230h0bW

Systematic Review of Salivary Versus Blood Concentrations of Antituberculosis Drugs and Their Potential for Salivary Therapeutic Drug Monitoring

Background: Therapeutic drug monitoring is useful in the treatment of tuberculosis to assure adequate exposure, minimize antibiotic resistance, and reduce toxicity. Salivary therapeutic drug monitoring could reduce the risks, burden, and costs of blood-based therapeutic drug monitoring. This systematic review compared human pharmacokinetics of antituberculosis drugs in saliva and blood to determine if salivary therapeutic drug monitoring could be a promising alternative. Methods: On December 2, 2016, PubMed and the Institute for Scientific Information Web of Knowledge were searched for pharmacokinetic studies reporting human salivary and blood concentrations of antituberculosis drugs. Data on study population, study design, analytical method, salivary Cmax, salivary area under the time-concentration curve, plasma/serum Cmax, plasma/serum area under the time-concentration curve, and saliva-plasma or saliva-serum ratio were extracted. All included articles were assessed for risk of bias. Results: In total, 42 studies were included in this systematic review. For the majority of antituberculosis drugs, including the first-line drugs ethambutol and pyrazinamide, no pharmacokinetic studies in saliva were found. For amikacin, pharmacokinetic studies without saliva-plasma or saliva-serum ratios were found. Conclusions: For gatifloxacin and linezolid, salivary therapeutic drug monitoring is likely possible due to a narrow range of saliva-plasma and saliva-serum ratios. For isoniazid, rifampicin, moxifloxacin, ofloxacin, and clarithromycin, salivary therapeutic drug monitoring might be possible; however, a large variability in saliva-plasma and saliva-serum ratios was observed. Unfortunately, sali-vary therapeutic drug monitoring is probably not possible for doripenem and amoxicillin/clavulanate, as a result of very low salivary drug concentrations

Limited Sampling Strategies Using Linear Regression and the Bayesian Approach for Therapeutic Drug Monitoring of Moxifloxacin in Tuberculosis Patients

Therapeutic drug monitoring (TDM) of moxifloxacin is recommended to improve the response to tuberculosis treatment and reduce acquired drug resistance. Limited sampling strategies (LSSs) are able to reduce the burden of TDM by using a small number of appropriately timed samples to estimate the parameter of interest, the area under the concentration-time curve. This study aimed to develop LSSs for moxifloxacin alone (MFX) and together with rifampin (MFX+RIF) in tuberculosis (TB) patients. Population pharmacokinetic (popPK) models were developed for MFX (n = 77) and MFX+RIF (n = 24). In addition, LSSs using Bayesian approach and multiple linear regression were developed. Jackknife analysis was used for internal validation of the popPK models and multiple linear regression LSSs. Clinically feasible LSSs (one to three samples, 6-h timespan postdose, and 1-h interval) were tested. Moxifloxacin exposure was slightly underestimated in the one-compartment models of MFX (mean -5.1%, standard error [SE] 0.8%) and MFX+RIF (mean -10%, SE 2.5%). The Bayesian LSSs for MFX and MFX+RIF (both 0 and 6 h) slightly underestimated drug exposure (MFX mean -4.8%, SE 1.3%; MFX+RIF mean -5.5%, SE 3.1%). The multiple linear regression LSS for MFX (0 and 4 h) and MFX+RIF (1 and 6 h), showed mean overestimations of 0.2% (SE 1.3%) and 0.9% (SE 2.1%), respectively. LSSs were successfully developed using the Bayesian approach (MFX and MFX+RIF; 0 and 6 h) and multiple linear regression (MFX, 0 and 4h; MFX+RIF, 1 and 6h). These LSSs can be implemented in clinical practice to facilitate TDM of moxifloxacin in TB patients

Population Pharmacokinetic Model and Limited Sampling Strategies for Personalized Dosing of Levofloxacin in Tuberculosis Patients

Levofloxacin is an antituberculosis drug with substantial interindividual pharmacokinetic variability; therapeutic drug monitoring (TDM) could therefore be helpful to improve treatment results. TDM would be more feasible with limited sampling strategies (LSSs), a method to estimate the area under the concentration curve for the 24-h dosing interval (AUC(0-24)) by using a limited number of samples. This study aimed to develop a population pharmacokinetic (popPK) model of levofloxacin in tuberculosis patients, along with LSSs using a Bayesian and multiple linear regression approach. The popPK model and Bayesian LSS were developed using data from 30 patients and externally validated with 20 patients. The LSS based on multiple linear regression was internally validated using jackknife analysis. Only clinically suitable LSSs (maximum time span, 8 h; minimum interval, 1 h; 1 to 3 samples) were tested. Performance criteria were root-mean-square error (RMSE) of 0.95. A one-compartment model with lag time best described the data while only slightly underestimating the AUC(0-24) (mean, -7.9%; standard error [SE], 1.7%). The Bayesian LSS using 0- and 5-h postdose samples (RMSE, 8.8%; MPE, 0.42%; r(2=)0.957) adequately estimated the AUC(0-24), with a mean underestimation of -4.4% (SE, 2.7%). The multiple linear regression LSS using 0- and 4-h postdose samples (RMSE, 7.0%; MPE, 5.5%; r(2)=0.977) was internally validated, with a mean underestimation of -0.46% (SE, 2.0%). In this study, we successfully developed a popPK model and two LSSs that could be implemented in clinical practice to assist TDM of levofloxacin