key: cord-0780732-j369fy55
authors: Sturkenboom, Marieke G. G.; Märtson, Anne-Grete; Svensson, Elin M.; Sloan, Derek J.; Dooley, Kelly E.; van den Elsen, Simone H. J.; Denti, Paolo; Peloquin, Charles A.; Aarnoutse, Rob E.; Alffenaar, Jan-Willem C.
title: Population Pharmacokinetics and Bayesian Dose Adjustment to Advance TDM of Anti-TB Drugs
date: 2021-03-06
journal: Clin Pharmacokinet
DOI: 10.1007/s40262-021-00997-0
sha: 2084d47739be66b9f6c6f3de2a11f99b90ae9373
doc_id: 780732
cord_uid: j369fy55

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.

Tuberculosis (TB) is still the number one cause of death due to an infectious disease. The World Health Organization estimated that about 1.6 million individuals died from TB in 2018. Of the 10 million people that developed TB in 2018, 3.4% of new cases and 18% of previously treated TB cases were estimated to carry a drug-resistant TB strain [1] . Most of the new cases with drug-resistant TB were diagnosed with multi-drug-resistant (MDR)-TB, defined as TB that is resistant to at least isoniazid and rifampicin. Despite all current efforts, the global burden of disease is not falling fast enough to reach the milestones of the 'WHO End TB Strategy' [1] . Next to that, the COVID-19 pandemic will further jeopardize TB control [2] . Providing patients with more active and less toxic treatment may help to achieve goals. In addition to the development of new drugs, optimization of current treatment is important to ensure that drugs for drugsusceptible (DS) and drug-resistant TB are given at doses (and achieve exposures) most likely to be effective without causing significant toxicity. The aim of this narrative review is to summarize the knowledge on the pharmacokinetics and pharmacodynamics of the currently available anti-TB drugs and show how population pharmacokinetics coupled with therapeutic drug monitoring (TDM) can be used to optimize treatment for individual patients.

Pharmacokinetics describes the behavior of a drug in the patient's body, including absorption, distribution, metabolism, and excretion, whereas pharmacodynamics describes the biochemical or pharmacological effect of a drug at the site of action in the patient's body. Over the past decade, pharmacokinetic (PK) and pharmacodynamic (PD) science has greatly contributed to the understanding of TB treatment response and outcome [3] . In infectious diseases, it is common to link exposure of the drug to the sensitivity of the bacteria, the minimum inhibitory concentration (MIC). In TB, the PK/PD indexes are similar to those for other infectious diseases; maximal plasma concentration (C max )/MIC, area under the concentration-time curve of a dosing interval (AUC 0-τ )/MIC, and time of the concentration over MIC (T > MIC). Often, the total concentration, i.e., the sum of the unbound and plasma protein bound fraction of the drug, is determined. Only the free (f) drug can diffuse through biological membranes to the site of infection and exert its pharmacological effect [4] . Therefore, especially for highly bound drugs, the above indexes should be converted to fC max /MIC and fAUC 0-τ /MIC. For dose optimization, most studies aim at achieving drug exposure associated with efficacy in 80% of the patients. In clinical practice, MIC determinations and free drug concentrations are not measured because of logistical or financial limitations. In such cases, low exposure of a certain drug is often reported as a risk factor for suboptimal treatment. As there is no formal definition of 'low concentration', we have defined low exposure to be < 50% inhibitory concentration, which is the concentration that causes 50% of the maximum kill. This is, in our opinion, more meaningful than expressing it as a percentage of exposure achieved in healthy volunteers because that does not include a measure of response. For most drugs, specific PK/PD indexes have been linked to their efficacy, reflecting their time-or concentrationdependent activity. However, it is important to note that C max and AUC 0-τ are closely linked and therefore their PK/ PD indexes as well.

Hollow fiber infection models have been very useful to explore PK/PD relationships as it is possible to simulate human concentration-time profiles, and test different dosing strategies [5] . Although hollow fiber infection models have limitations, i.e., a lack of human immune response and a maximum number of drugs that can be tested simultaneously, the results correlate well with treatment response [6] , which resulted in endorsement by the European Medicines Agency to make use of these models to guide dose selection for clinical trials [7] .

The typical PK profile of a drug and the variability among individuals can be characterized with population PK models. Also known as nonlinear mixed-effect models, this is a type of compartmental model consisting of a structural part describing the typical behavior, and a stochastic part accounting for random variability [8] . The structural part includes the primary PK parameters such as the rate of absorption, volume of distribution, and clearance, as well as a quantitative estimate of the influence of patient characteristics (covariates, e.g., body size, age, genetic polymorphisms) on those parameters. The stochastic part describes the magnitude and distribution of random variability between individuals and within an individual between different dosing occasions. It also contains the unexplained variability, i.e., the residual error. Population PK models are developed using drug concentration measurements gathered after controlled dosing. The development and evaluation of the population PK models are done according to widely accepted standards in the field [9] , generally employing software packages such as NONMEM (ICON plc, Gaithersburg, MD, USA) and Monolix (Lixoft, Antony, France). An overview of the set-up and development using population PK models is presented in Fig. 1 .

Therapeutic drug monitoring is a tool used to integrate PK and PD knowledge to optimize and personalize drug therapy. Therapeutic drug monitoring uses drug plasma concentrations to personalize drug therapy to bring and keep the concentration within the targeted therapeutic range. Below this range, the treatment is less effective, whereas high concentrations may result in toxicity [4] . When selecting a population PK model for TDM purposes, it is important to consider if the population in which the model was developed is representative for the population where TDM is to be conducted.

Therapeutic drug monitoring is mostly performed using blood samples, i.e., serum, plasma, or dried blood spot, while the TB infection is located elsewhere, in the lungs or extra-pulmonary, for instance, in the central nervous system in the case of TB meningitis. Drug penetration at the site of mycobacterial infection may be hampered, if the disease progresses and caseous lesions are formed [10] . Strydom et al. investigated the penetration of nine anti-TB drugs in resected lung tissue and showed that, irrespective of the drug choice, patients with cavitary disease were at risk for subtherapeutic concentrations [10] .

Derived or secondary PK parameters commonly used in TDM such as AUC 0-τ , C max , and trough concentration (C min ) can be predicted for specific individuals based on a population PK model, relevant dosing, and covariate information plus any observed drug concentrations. Correct handling of inter-occasion variability is important to maximize the precision of the prediction [11] .

For most anti-TB drugs, the AUC 0-τ (often AUC 0-24 h )/ MIC is the most predictive PK/PD index. However, a specific AUC 0-24 h /MIC target value has not yet been established for all anti-TB drugs. Here, it must be addressed that individual MIC values might not always be reliable and often are not available early in treatment. Moreover, MIC values might be significantly different when using different methods (e.g., solid or liquid media) [5, 12, 13] . This has important implications for the interpretation of AUC 0-24 h / MIC target values when taking into account how the MIC was determined in that study.

Obtaining a full PK curve to calculate the AUC 0-24 h using non-compartmental analysis techniques is a laborious and expensive procedure and is thus not feasible in clinical practice. A limited sampling strategy (LSS) either based on a population PK model or on multiple linear regression may help to overcome these problems. This method implies that a limited number, usually one to three, of appropriately timed blood samples is needed to adequately predict the AUC 0-24 h as a measure for drug exposure [14] . Population PK models are often used in clinical dosing software that aids in estimating PK parameters such as AUC and that uses Bayesian simulations to estimate appropriate drug exposure [15, 16] .

Bayesian-informed dosing takes into account the prior information (including patient-specific values of covariates such as body size, age, and genetics) from a population PK model and uses individual measurements, usually drug concentrations, to provide a patient-tailored posterior dosing recommendation. This allows individual patients plasma drug exposures to meet the PK/PD target [17] . The advantages of the Bayesian approach are the flexible timing of samples, as the population PK model can correct for deviations, and that it takes a number of covariates into account leading to a more accurate estimation of AUC 0-24 h [18] . The advantage of multiple linear regression-based LSSs is that these do not require modeling software and AUC 0-24 h can be easily estimated using only an equation and the measurement of drug concentrations. The disadvantage is that samples should be taken as close as possible to predefined schedules, and the population of interest should be comparable to the population in which the multiple linear regression has been performed because patient characteristics are not included in the equations to estimate drug exposure [18] .

In the section below, we summarize the mechanism of action, PK/PD drivers from preclinical models, human pharmacokinetics (with sources of variability, where known), and clinical PK/PD targets for each drug that is used in the treatment of drug-susceptible or MDR-TB. An overview on relevant PK models is included and recommendations on sampling strategies and dosing strategies are summarized. Table 1 gives an overview of the published population PK models per anti-TB drug. Table 2 indicates PK/PD targets and normal ranges of exposures that have been identified for the different drugs, this is not repeated in the text.

We used a non-systematic literature search in PubMed using a combination of the following keywords: tuberculosis, [drug name], pharmacokinetics, pharmacodynamics, sampling, and concentration. Case reports, abstracts, and posters were excluded. Included articles were limited to the English language. References of included articles were scanned for possible relevant articles. The search was last performed on 4 August, 2020.

Isoniazid is active against Mycobacterium tuberculosis (MTB), inhibiting the mycolic acid synthesis, which affects cell wall synthesis, resulting in a bactericidal effect [19] . Furthermore, isoniazid has an effect on DNA biosynthesis and has it has been shown that early bacterial activity (EBA) on the first 2 days of therapy is related to the acetylator type and 600 mg achieves the highest EBA [19, 20] . Hollow fiber infection models showed that isoniazid has bactericidal activity that is driven by AUC 0-24 h /MIC [21, 22] . Studies in the mouse confirm this bactericidal activity, but isoniazid appears to be a weak sterilizing drug [23] . In the mouse, the activity of isoniazid appeared to be more concentration than time dependent, with C max /MIC and AUC 0-24 h /MIC having a strong association with activity [24] .

Isoniazid is fairly hydrophilic, with moderate clinical plasma concentrations 2 h after ingestion (C 2 ) of 3-6 mg/L after a 300-mg dose, and 9-15 mg/L after a 900-mg dose [25, 26] . Isoniazid is unable to penetrate and accumulate in caseous granulomas [10] . In healthy volunteers and patients with TB starting on therapy, the administration of isoniazid with a high-fat meal modestly reduced C max and bioavailability [27, 28] . Both orange juice and aluminium-magnesium antacid produced little effect on the bioavailability of isoniazid [27] . Isoniazid metabolism is genetically determined based on N-acetyl transferase 2 activity [25, 27] . It has been proposed that patients be split between having none, one or two rapid N-acetyl transferase 2 alleles and that dosing could be then adapted to 2.5 (slow acetylators), 5.0, and 7.5 mg/kg (fast acetylators), respectively [29] . Pharmacokinetic data for isoniazid in adults and children with TB suggested that low and variable concentrations are common [30, 31] . In 142 drug-susceptible patients with TB, Pasipanodya et al. showed that to achieve a favorable outcome, in order of importance, AUC 0-24 h of pyrazinamide, rifampicin, and isoniazid should be over 363, 13, and 52 mg h/L respectively. Low C max of rifampicin (< 6.6 mg/L) and isoniazid (< 8.8 mg/L) preceded all (three) cases of acquired drug resistance [31] .

Standardized doses may not be adequate for isoniazid, especially given the differences between fast and slow acetylators. Therapeutic drug monitoring could be used to rapidly find the correct dose for each patient [32] .

Twelve population PK models were found from the time period of 1997-2019 ( Table 1 ). Most of the models were built with NONMEM and included the acetylation status/Nacetyl transferase 2 genotype as a covariate on clearance.

Limited sampling strategies were developed for all four first-line TB drugs and moxifloxacin in an ethnically heterogenous population, using intensive PK sampling after the intake of drugs on an empty stomach and after more than 2 weeks of therapy [33] . For isoniazid, LSSs of 1, 2.5, and 6 h post-dose performed best [33] . Recently, several LSSs were proposed for isoniazid alone or in combination with other first-line drugs. Limited sampling strategies were derived from a study with intense sampling of 20 patients conducted in Indonesia, who started on TB medication [34] . Both studies used the best subset selection, multiple linear regression to calculate LSS and the chosen LSS performed best at estimating the AUC 0-24 h .

Rifampicin binds to and inhibits the action of the DNAdependent RNA polymerase of mycobacteria. It has both bactericidal and sterilizing activity. In the past decades, it was assumed that the efficacy of rifampicin is associated with C max /MIC, but recent preclinical studies have established that the microbial killing can also be explained by AUC/MIC [35, 36] . Given the short half-life of rifampin of around 2 h at steady state, there is a high correlation between C max and AUC. Moreover, in one of these studies, it was well presented that in a mouse model with dose fractionation and ranging, the PK/PD parameter that correlated best with a decrease of bacterial counts was AUC/MIC [36] .

Rifampicin is preferably taken on an empty stomach, as intake with food reduces C max and causes a modest decrease [28, 37] . However, in the case of gastrointestinal adverse effects, concomitant intake with a light meal is recommended to prevent or alleviate these effects. The distribution of rifampicin is fast and includes all fluids and organs, but only about 5% of rifampicin reaches the cerebrospinal fluid [38] . Repeated daily administration of rifampicin results in a decrease in exposure and half-life of the drug, owing to an increased rate of drug clearance caused by auto-induction [39] . This is separate from induction of the cytochrome P450 system by rifampicin (see below). Increasing the dose of rifampicin results in a more than proportional increase of exposure in plasma (non-linear pharmacokinetics). Rifampicin is excreted mainly through the bile. Urinary excretion is about 10-15% of biliary excretion but increases with the dose.

Rifampicin is a potent inducer of several phase I and II metabolic enzymes and drug transporter proteins, often resulting in a decrease in exposure of concomitantly used drugs [40] . This is especially true for cytochrome P450 3A4, a major drug-metabolizing pathway. Significant induction after starting rifampicin is reached after a few doses and is complete after a week [40] . Baseline enzyme activity after discontinuing rifampicin is attained in about 2 weeks.

The drug is currently used in a dose of 10 mg/kg daily, but this is at the lower end of the dose-response curve. A dose of 35 mg/kg daily, resulting in a ten-fold higher AUC 0-24 h value in plasma, is safe and tolerable for 3 months [41, 42] . Moreover, higher dosing has shown a significant relationship between exposure to rifampicin and EBA [43] . Higher rifampicin exposures also reduce the time to culture conversion in pulmonary TB [44] . A meta-analysis on the exposure of rifampicin reported that at least 25-mg/kg dosing is required to achieve PK/PD targets [45] .

Nineteen population PK models for rifampicin in adult humans with fully re-implementable parameter values could be identified in the time period from 1997 up to 2018 (Table 1) . These models differed with respect to the number of compartments, the inclusion of components for nonlinearity and for auto-induction, which are both typical to rifampicin. Only one model was based on higher doses of rifampicin up to 40 mg/kg [46] . Most of the models were built in NONMEM.

Limited sampling strategies were developed using the best subset selection, multiple linear regression for all four first-line TB drugs and moxifloxacin in an ethnically heterogenous population, using intensive PK sampling after the intake of drugs on an empty stomach and after more than 2 weeks of therapy [33] . Limited sampling at various fixed sampling points up to 6 h enabled an accurate and precise [14] . They extended the population used by Magis-Escurra et al. [33] , which took rifampicin on an empty stomach, to patients who ingested the drug with a light meal and derived a population PK model in MWPharm (Mediware, Zuidhorn, the Netherlands). This study showed that rifampin AUC 0-24 h could be predicted with acceptable bias and precision with optimal sampling at time points 1, 3, and 8 h post-dose. Furthermore, a sampling strategy using blood collection at convenient 2-and 4-h sampling times has been shown in another study to be the most suitable [47] . This NONMEM model was successfully implemented in the InsightRX (San Francisco, CA, USA) precision dosing platform.

Pyrazinamide is a prodrug, which is activated to pyrazinoic acid inside the MTB by the bacterial pyrazinamidase [48] . It is mainly active against non-replicating bacilli, especially at low pH [49] , which allows for accumulation of pyrazinoic acid inside the MTB [50] . In pre-clinical hollow fiber infection models, the sterilizing effect of pyrazinamide was most closely related to the AUC 0-24 h /MIC ratio [51] . The MIC determination is challenging, owing to the difficulty of reproducibly growing MTB at low pH across laboratories. [54] , it reaches C max approximately 2 h post-dose and with a terminal half-life of approximately 9 h it only moderately accumulates, reaching steady state in 2-3 days. It does not significantly bind to plasma proteins [55] and it is metabolized by liver deaminase [56] . A recent review summarizing exposures in patients (human immunodeficiency virus positive/negative) reported median AUC 0-24 h in the range of 250-450 mg h/L and C max in the range of 25-55 mg/L [57] . Several reports indicated that the current weight-banded dosing approach achieves lower concentrations in patients with lower weight [58] [59] [60] [61] .

In clinical studies in patients with drug-susceptible TB, worse outcomes were observed in patients with C max < 35 mg/L, while a positive association between AUC 0-24h > 363 mg h/L and long-term TB treatment outcome has been reported [31, 62] . The main adverse reactions to pyrazinamide are arthralgia and hepatotoxicity [63] . Doses higher than the current doses have previously been used, and there is a debate regarding increasing toxicity with increasing doses [64] .

Nine population PK models were found, from which three were made with NONMEM, three with Monolix, one with ADAPT5, and one with NPEM2 (Table 1 ). Only one model included a covariate sex on clearance [65] . Although a robust target for efficacy has not been established and safety is not of concern at the current levels, a TDM strategy could be useful to identify patients with low exposure using, for example, LSSs of 0, 2, and 6 h or 2, 4, and 8 h post-dose [33, 34] .

The precise mode of action of ethambutol is not fully understood, but it appears to inhibit mycobacterial cell wall arabino-transferases, leading to depletion of arabinogalactan and lipoarabinomannan. In an early study by Dickinson et al. in guinea pigs, the effects of ethambutol were concentration dependent (C max /MIC) [66] . More recent experiments in the hollow fiber infection model showed that the microbial kill to ethambutol was linked to AUC 0-24 h /MIC, although in some experiments C max /MIC could also have explained the kill, whereas T > MIC was relevant for suppressing resistance [67] .

It often is used in a dose of 15-20 mg/kg daily, with a maximum of 25 mg/kg daily. Higher doses and exposures, especially in patients with decreased renal function, are associated with a higher incidence of retrobulbar (optic) neuritis [68] . The EBA of the 15-20 mg/kg dose is negligible and the drug has very little sterilizing activity. However, ethambutol is added to other first-line TB drugs during the first weeks of treatment to prevent rifampin resistance in cases where there may be unrecognized isoniazid resistance. A clinical trial in Indian patients with TB showed that microbiological response was AUC/MIC driven [69, 70] .

Ethambutol can be taken with or without food, as food modestly reduces C max but does not affect the AUC 0-24 h of the drug [28, 37] . It has rapid good distribution throughout the body (including in large lung lesions), but it only penetrates the cerebrospinal fluid in the presence of inflammation, probably having a minor contribution to the treatment of TB meningitis [71] . A recent evaluation using ultrafiltration showed an average protein binding of 12% [55] . Ethambutol is largely excreted unchanged by the kidneys. Patients with a creatinine clearance less than 30 mL/min are at risk for accumulation of the drug and resulting retrobulbar neuritis. They should receive a longer interval between doses with three times a week administration of the same 15-20 mg/kg dose. In persons with good renal function, ethambutol shows a biphasic decline in plasma concentration with a terminal half-life of about 10-12 h. The drug shows no relevant drug interactions, apart from a reduction in its C max caused by aluminum-containing antacids [72] .

Traditionally, limited sampling of ethambutol aimed at 'catching' and estimating the C max of the drug in an individual by sampling at 2 and 6 h after dosing. Recently developed LSSs aim to predict the AUC 0-24 h for ethambutol as well. As described earlier, Magis-Escurra et al. developed an LSS using best subset selection, multiple linear regression for all first-line TB drugs (including ethambutol) separately and simultaneously [33] . Saktiawati et al. also applied a linear regression approach, but focused on prediction of the AUC 0-24 h of first-line TB drugs during the first 3 days of treatment, based on data from Indonesian patients with TB [34] . Horita et al. were the only investigators to use a Bayesian approach to estimate AUC 0-24 h of ethambutol in children [30] .

Ten population PK models for ethambutol could be identified (Table 1) [30, [72] [73] [74] [75] [76] [77] [78] [79] . These models differ with respect to the number of compartments, the inclusion of a lag time, or transit compartment absorption. Most of the models were built in NONMEM. It is important to note that a decreased renal function may be the most relevant indication for TDM of ethambutol, but relatively few patients with renal dysfunction were included in the various cohorts. This means that the effect of this covariate on clearance has not been adequately evaluated in population PK models. Similarly, only one model was derived in obese patients taking ethambutol [75] .

Fluoroquinolones are amongst the most potent drugs against MTB. They produce their rapid killing effect by inhibiting the bacterial DNA replication and transcription through affecting the type II topoisomerases [80] .

The efficacy of fluoroquinolones is correlated with the ratio of the fAUC 0-24 h /MIC [81] [82] [83] . In addition, an 800-mg daily dose for moxifloxacin is suggested to achieve maximal kill and suppress resistance [84, 85] .

Therapeutic drug monitoring of moxifloxacin and levofloxacin is proposed because of high PK variability and a substantial proportion of patients showing low drug exposure [86] [87] [88] . Fluoroquinolones are well tolerated and adverse effects do not require TDM (e.g., QT interval prolongation by moxifloxacin). Therapeutic drug monitoring is helpful to ensure adequate exposure after dose adjustments of levofloxacin due to kidney failure or in the case of drug-drug interactions. For instance, moxifloxacin exposure is approximately 25% decreased if concomitantly used with rifampicin owing to an increase of moxifloxacin clearance [18] . Four population PK models were identified for levofloxacin and six for moxifloxacin ( Table 1 ). The models were made with NONMEM, MWPharm, NPEM2, and Monolix and the majority were one-compartment models. To reduce the burden and costs of TDM, LSSs can be applied. The under the concentration-time curve from time 0 to 24 h can be adequately estimated using a population PK model and two optimally timed samples for levofloxacin (0 and 5 h) and for moxifloxacin (0 and 6 h, either with or without rifampicin) [18, 89] .

Bedaquiline is a diarylquinoline that targets mycobacterial ATP synthase, disrupting the bacteria's energy metabolism [90] . Dose-fractionation experiments in mice identified AUC/MIC as the main driver of bactericidal effect [91] .

The standard dosing regimen in the treatment of MDR-TB consists of a 2-week loading phase with 400 mg daily followed by 22 weeks with 200 mg dosed three times per week. An alternative dosing regimen with 200 mg daily during the first 8 weeks, thereafter 100 mg daily, has been utilized in recent clinical trials [92] . Uptake of bedaquiline is increased when given together with food and maximal concentrations are expected 4-6 h after dosing. The binding to plasma proteins is very high, more than 99.9%, and the distribution to tissues is extensive, resulting in an extremely long terminal half-life [93] . One case report showed undetectably low bedaquiline concentrations in cerebrospinal fluid [94] .

Bedaquiline is mainly hepatically cleared. Cytochrome P450 3A4 (primarily) transforms bedaquiline into the N-monodesmethyl metabolite M2, which in turn is metabolized in the same way to M3 [93, 95] .

A model-based PK/PD analysis of phase II data found a weekly average concentration (analogous to weekly AUC) to be the best driver and estimated that a concentration of 1.42 mg/L would give half of the maximal possible effect [96] . The relationship has also been confirmed in a separate study [97] . With the standard regimen typical (mean ± one standard deviation) maximal, average, and trough concentrations at day 14 are 1.6-3.2 mg/L, 0.96-2.1 mg/L, and 0.44-1.4 mg/L, respectively [98] . At the end of the continuation phase, the corresponding values are 0.9-2.1 mg/L, 0.41-1.2 mg/L, and 0.26-0.91 mg/L [98] .

There are two published population PK models of bedaquiline in patients with drug-resistant pulmonary TB, partly based on the same data and both developed in NONMEM ( Table 1 ). The first only describes bedaquiline [99] , while the second also incorporates the main metabolite M2 [98] . There are no published evaluations of TDM sampling strategies for bedaquiline. The time on treatment needs to be considered, accounting for the loading phase and the accumulation. Assessing the C min at the end of the loading phase is a practically feasible option. A retrospective study described potential risk factors of low and high bedaquiline concentrations, where administration of food, drug-drug interactions, gastrointestinal complaints, lower body weight, and age > 70 years were among the named factors [100] . In addition, black race has been connected to approximately 50% clearance [99] . Therapeutic drug monitoring could be of benefit for specific patients using bedaquiline; however, standardized drug susceptibility testing must first be developed [101] . The M2 metabolite is the main driver of side effects such as QT prolongation and phospholipidosis [102] .

Linezolid exerts its antibacterial effect by binding 70S ribosomal components and disrupting the initiation of protein synthesis. A hollow fiber infection model study has indicated optimal mycobacterial kill to be associated with fAUC 0-24 h / MIC [103] . In murine models, its sterilizing activity is dose related and can occur within 2 months of effective combination therapy, leading some investigators to consider short high-dose therapy followed by discontinuation or intermittent dosing to retain efficacy whilst reducing toxicity [104, 105] .

The bioavailability after oral administration of linezolid is nearly 100%, and it shows good tissue penetration into tuberculous lung cavities caseating granulomas and cerebrospinal fluid [10, 106] . The standard adult dose (600 mg twice daily) is associated with myelosuppression, lactic acidosis, and neurotoxicity during long-term use. Consequently, a dose of 600 mg, or occasionally 300 mg, once daily is often recommended for patients with TB [104, 107] .

The PK/PD target for myelotoxicity is suggested as a C min of 2 mg/L [108] . While meta-analyses and simulations from some of these data indicate high attainment of putative efficacy targets at 600 mg once or twice daily, C min regularly exceeds the toxicity threshold with twice-daily dosing [109, 110] . One study reported that higher plasma linezolid exposure and greater toxicity risk are associated with advancing age and lower weight [111] . It has been reported that 300 mg twice daily might be effective [104] . Studies looking into 300-mg daily dosing have shown promising results in some patients [112, 113] ; however, until larger trials show results, 600 mg has been suggested to keep the balance between efficacy and toxicity [105] . No association between human immunodeficiency virus or concurrent antiretroviral therapy and linezolid exposure has been described [111] . An approach of short-duration higher dose linezolid to maximize efficacy whilst avoiding toxicities associated with cumulative dose requires further evaluation; however, an interim analysis of the Nix-TB trial showed a significant amount of patients experiencing linezolid toxicity [114] .

The narrow therapeutic window has prompted suggestion that TDM might be beneficial. An LSS consisting of 0 and 2 h has been described to predict the plasma AUC 0-24 h with acceptable bias and precision [115] . Three population PK linezolid models were identified (Table 1) , which were all one-compartment models and two were developed with MWPharm and one with NONMEM.

Studies in the mouse model show that clofazimine did not have EBA, but subsequently demonstrated potent, doseand concentration-independent bactericidal activity [116] . The authors suggested that much lower doses could be used effectively for TB and that clofazimine exhibits slow elimination [116] . In another mouse model, the activity of clofazimine was confirmed to be best when administered with other first-line anti-TB drugs [117] .

Most clinical studies have used 100-mg daily doses, thus the literature lacks human dose-ranging data for TB. Very limited PK/PD data are available for clofazimine, with most only available as meeting abstracts.

Clofazimine is highly lipophilic, leading to high accumulation in fat tissues and relatively low serum concentrations (0.7-1.0 mg/L) [118] . Clofazimine is unable to penetrate and accumulate in caseous granulomas, but it does accumulate in the highly cellular peripheral zone of the granuloma, consisting of macrophages, epithelioid macrophages, and lymphocytes [118] . In healthy volunteers, the administration of clofazimine with a high-fat meal provided the greatest bioavailability. However, bioavailability was associated with high inter-and intra-subject variability. Both orange juice and an aluminum-magnesium antacid reduced a mean bioavailability of clofazimine [119] .

Overall, three NONMEM population PK models were identified (Table 1) . A recent population PK model on NONMEM described clofazimine disposition being strongly affected by body fat, thus clofazimine showed lower plasma concentrations in women [120] . Additionally, it was suggested that patients with extreme body composition might need to have individualized dosing and a high-fat content might require longer loading periods [120] . As mentioned, specific PK/PD targets for clofazimine are scarce. Clofazimine is suggested to be sampled 2-3 h after the dose and 6 h to assess delayed absorption, whereas C max is expected at 0.5-2.0 mg/L [26, 121, 122] .

Cycloserine is an amino-acid derivative, which suppresses the growth of MTB by inhibiting the enzymes that produce peptidoglycan causing changes in the cell wall [123] . Terizodone contains two cycloserine molecules. Hollow fiber infection models have proposed that T > MIC predicts efficacy best [124] [125] [126] .

Cycloserine has been suggested to be used in patients with rifampicin-resistant TB as a Group B drug. It may also be of value in the therapy of TB meningitis owing to its penetration into cerebrospinal fluid [126] .

As cycloserine has a long half-life, different C max ranges have been reported. To determine exposure, the first measurement should be done after 3-4 days of therapy and C max is expected to be around 20-35 mg/L [26, 127, 128] .

Two population PK models have used the preclinical established PK/PK index T > MIC over 30% model [125, 129] . Five cycloserine/terizidone population PK models were identified and all of these were one-compartment models made with different software, Monolix, NONMEM, and MWPharm (Table 1) . Recently, a sampling strategy using blood collection at a convenient 4 h has been proposed in combination with a population PK model to estimate AUC 0-24h obtaining a low relative error [125] .

Delamanid is a nitroimidazole antibiotic with mechanisms of action including inhibition of mycolic acid synthesis and generation of nitrous oxide in anaerobic conditions [130] . In animal models, the activity of delamanid activity appears to be time dependent [130] .

The licensed dose is 100 mg twice daily, for 24 weeks, but an alternative dosing regimen of 100 mg twice daily for 8 weeks followed by 200 mg once daily for 16 weeks was used in the recent phase III trial [131] . The main route of metabolism of delamanid is purported to be via albumin, with cytochrome P450 3A playing a minor role [132] . It has several metabolites, but DM-6705 circulates at the highest concentrations. Delamanid and DM-6705 have half-lives of 30-38 h and 121-425 h, respectively, and the latter is linked to the key, albeit modest, adverse effect of delamanid-prolongation of the cardiac QT interval [133] . The bioavailability of delamanid is enhanced two-to four-fold by food, exposures are less than dose proportional, the drug is highly protein bound (> 99.5%), and the T max is at 4 h [134, 135] . This drug appears to reach high concentrations in the brain; its utility in central nervous system TB is being explored [136] . Delamanid is not an inducer or inhibitor of cytochrome P450 enzymes, and drug-drug interaction liability is low. In human EBA studies, doses of 200-300 mg total daily dose produced higher microbiologic activity than 100 mg per day [134] ; in a phase II trial, there was a trend towards better activity of delamanid 200 mg twice daily compared with 100 mg twice daily on solid medium [137] .

Median delamanid AUC 0-24 h after 56 days of dosing is 7.9 mg h/L (coefficient of variation 37.5%). Maximum plasma concentration values are 0.4 mg/L (coefficient of variation 40.5%) at 56 days [132, 136] . There are no clear target concentrations owing to a lack of clinical PK/PD data. It has been reported that delamanid acquires resistance quickly, thus optimization of therapy using TDM may help in reducing the acquired resistance [138] .

Pretomanid was introduced with the BPal regimen (bedaquiline and pretomanid with linezolid). An in vivo model presented activity comparable to isoniazid, where the suggested reasoning was lower bioavailability in necrotic lung granulomas [139] . In another mouse model, the addition of pretomanid showed bactericidal activity increase while preventing emergence of resistance to bedaquiline and duration of therapy [140] . Later, a synergistic three-drug combination, including pretomanid, was suggested in a time kill assay combined with population modeling [141] .

Pretomanid has shown to have higher bioavailability when taken with food [142] . In healthy adults it has been shown that AUC and C max could be almost doubled [142] . Moreover, drug interactions have been described with rifampicin, lopinavir/ritonavir, and efavirenz-all of these reduce the AUC of pretomanid [143] .

Two population PK models were identified, one in NON-MEM and the other developed on GNU MCSim modeling and simulation suite (Table 1 ).

Meropenem, a carbapenem, is a member of the β-lactam class of antibiotics. It inhibits cell wall synthesis through inhibition of classical D, D-transpeptidases, and non-classical L, D transpeptidases [144] . Clavulanate (not available except co-formulated with amoxicillin) irreversibly inhibits MTB β-lactamase, potentiating the activity of meropenem. Ertapenem has shown to a have sterilizing effect against M. tuberculosis in a hollow fiber infection model; however, falsely high MIC values might have been reported because of ertapenem degradation in acidic conditions, which could have led to underuse of ertapenem in TB therapy [145, 146] .

In an EBA study, meropenem given at a dose of 2 g three times daily intravenously (supplemented with twice-daily amoxicillin/clavulanate) produced measurable activity in patients with pulmonary TB [147] . Recently presented EBA dose-finding trial results suggest that there is a significant drop in microbiologic activity when the meropenem dose is reduced from 6 g per day (given as 2 g three times daily) to 3 g per day (with 1 g three times daily performing modestly better than 3 g once daily). Meropenem, together with amoxicillin-clavulanate, is used off-label for TB. Doses for other bacterial infections range from 1.5 to 6 g per day, given in divided doses to maximize the activity of this time-dependent drug. Most (around 70%) of an intravenous meropenem dose is excreted unchanged in the urine [148] . Protein binding is very low (2%). Ertapenem in a dose of 1 g once daily intramuscularly in combination with amoxicillin-clavulanic acid showed no EBA [149] . The lack of activity of this dose is in line with an earlier published study using the hollow fiber infection model indicating that a dose of at least 2 g once daily was required [146] .

Mean meropenem AUC 0-24 h given at 2 g three times daily is 573 mg h/L, with a C max of 133 mg/L; at that dose, with an MIC of 1 mg/L, T > MIC is appropriately 75% [134] . In the absence of a PK target that maximizes efficacy against slow-growing MTB, it seems reasonable to strive for these average PK values. Collecting samples for the PK pre-dose and mid-dosing interval is most informative. While carbapenems are generally well tolerated, there is an increased risk of neurotoxicity with C min > 16 mg/L (and the doselimiting toxicity is chiefly diarrhea from the amoxicillin-clavulanate). Therapeutic drug monitoring of meropenem in TB could thus serve to help with individualized dosing of meropenem, achieving exposures similar to those seen with maximal EBA activity and avoiding toxic C min .

Ertapenem in a 2000-mg once-daily pharmacokinetic exploratory study showed that f40% of T > MIC was reached for most patients [150] . A PK model based on data from patients with MDR-TB proposed a limited sampling strategy of 1 h and 5 h [16] .

In a hollow fiber infection model for TB, the local amikacin C max /MIC ratio of 10 was determined to be the effective PK/ PD index, closely followed by AUC/MIC [151, 152] .

Aminoglycoside use is limited by both the parenteral administration and significant toxicity that is observed after prolonged use [153] and should only be used if drug susceptibility testing results confirm susceptibility and audiometry monitoring can be ensured [53] . A 10% probability of ototoxicity occurred with a threshold cumulative AUC > 87 g h/L [154] . It has been revealed that the probability of ototoxicity increased sharply starting after 6 months of amikacin therapy to near maximum at 9 months. Considering the poor penetration of aminoglycosides in lung tissue, this translates into a serum C max /MIC ratio of 70-90 or an AUC 0-24 h /MIC ratio of 103 [151] .

Therapeutic drug monitoring of aminoglycosides is indicated to ensure efficacy and should be combined with determination of MIC as the risk of ototoxicity can be reduced if the MIC is low enough to lower the (cumulative) dose and exposure [155] . A one-compartment population PK model has been reported for amikacin. This included a LSS of 1 h and 4 h suggested for predicting AUC 0-24 h [156] .

The thiomides ethionamide and its propyl-analog prothionamide are drugs with bactericidal activity against MTB [157] . They are considered interchangeable within anti-tubercular regimens [158] . In hollow fiber systems, the target exposure was identified as AUC 0-24 h /MIC [159] .

It is currently used for MDR-TB at 15-20 mg/kg daily (maximum 1000 mg) [53] and for drug-susceptible TB in children or TB meningitis. The plasma protein binding of ethionamide is approximately 30% [160] and it is metabolized by hepatic monooxygenases [161] .

Three population PK ethionamide models were identified, from which all were one-compartment models using NON-MEM and Pmetrics software (Table 1) . Clinically, after administration of 250-500 mg of ethionamide, a C max in the range of 1-5 mg/L has been suggested [26] . A TDM strategy could identify patients with low concentrations by evaluating two samples at 2 h and 6 h with a C max of 1-5 mg/L [26, 162, 163] .

The mechanism of action of P-amino salicylic acid (PAS) remains unknown but it is assumed that it interferes with bacterial folate synthesis [164] .

Although one study described a bactericidal effect of PAS at high (20 g) once-daily dosing [165] , it is generally regarded as a bacteriostatic agent. It is associated with considerable gastrointestinal intolerance and was quickly replaced by isoniazid, rifampicin, and pyrazinamide in first-line anti-TB therapy. It has recently been downgraded in the World Health Organization MDR-TB guidelines but retains a role as component of treatment for patients with extensively drug resistant TB (XDR-TB) and limited other options [53] . Current PAS dosing for adults is 150 mg/kg/day in two to four divided oral doses (usually 8-12 g/day), and 200-300 mg/ kg/day in two to four divided doses for children. Absorption is improved by consumption with acidic food or yoghurt [166] . It attains its maximum serum concentration within about 2 h, and has a fairly short half-life [165] . Protein binding is between 50 and 60% [167] . Current granular formulations are designed to provide slow release, assuming a fC min > 1 mg/L will be maintained [167] . Some authors have reported that a high C max may be more important for prevention of resistance to partner drugs [168] .

Three NONMEM population PK models were identified, which were all one-compartment models (Table 1) . A role for TDM in PAS dosing has not yet been explored.

In this review, over 80 different population PK models for 14 anti-TB drugs are presented.

As different models are difficult to compare and because the true model cannot be identified, there is no such thing as the 'best' or most recommended model. This was already stated in 1976, in one of the most famous adages in pharmacokinetics: 'all models are wrong, some are useful' [169] . Our personal preference would be using a validated model and performing TDM with Bayesian estimation to most accurately estimate AUC for the individual patient and situation. This can be done using different modeling and Bayesian simulation software. A good overview of different available methods has recently been presented [170] .

By including TDM as an opportunity for optimizing TB treatment in TB guidelines [53, 171, 172] , the first step towards implementation of precision medicine has been made. The guidelines include indications and clinical conditions for which TDM may be useful. To be of practical use, guidance is required on how drug exposure should be evaluated and how dosages could be adjusted [173] . We therefore performed this review evaluating the available literature on pharmacokinetics and PK modeling of anti-TB drugs. For the first-line anti-TB drugs, it is clear that studies focussed mainly on rifampicin and isoniazid [25, [174] [175] [176] . Pharmacokinetic variability is substantial and can be explained by pharmacogenetic differences and other factors such as co-morbidities [45, 177, 178] . With respect to dose optimization, there is a clear rationale based on in vitro PK/PD and clinical studies. Unfortunately, a well-designed study comparing TDM with standard of care is lacking. Although higher dosages are currently being trailed for rifampicin, TDM will likely still have a role as PK variability is still high, which leads to the conclusion that a high dose does not automatically result in a high exposure in every patient.

For anti-TB drugs used for the treatment of M/XDR-TB, there is a significant knowledge gap for many of the drugs. Only for the fluoroquinolones, linezolid, and aminoglycosides does a substantial body of evidence support TDM. The new drugs such as bedaquiline, delamanid, and pretomanid lack this abundance of data but publications on acquired resistance, drug-drug interactions, and significant variability of bioavailability based on concomitant food intake already provide a rationale that TDM may be of use for the newer drugs as well. However, it needs to be mentioned again that a well-designed prospective study comparing TDM in M/ XDR-TB is still lacking. As researchers, we ourselves often struggle with conducting prospective studies to show the value of TDM. Recently, a review by Märtson et al. identified knowledge gaps and provided guidance with an appropriate checklist and data elements that are suggested to take into account when designing and conducting high-quality TDM studies in the field of infectious diseases [179] . Briefly, the focus was on estimating drug exposure and assessing the susceptibility of the pathogen, but also suggestions on how to select study endpoints and clinical trial design were provided [179] .

Logistical issues regarding TDM samples seem to be a hurdle that will be overcome in the next couple of years owing to the introduction of dried blood spot monitoring, point of care saliva tests, and urine testing [173, 180] . To support a physician in making informed dosing decisions in individual patients, easy-to-use model-informed precision dosing software has to be made freely accessible via mobile platforms such as smartphones and tablets. On-site patient covariates and drug concentrations can be used to simulate dosing regimens to maximize the likelihood to attain the therapeutic target. Such software is currently under development for drugs such as vancomycin but there is a clear need to expand the panel with anti-TB drugs.

As TB treatment duration is long, there is an urgent need to find biomarkers that predict clinical outcome. A combination of dose optimization based on measured drug concentration and pathogen susceptibility with a sensitive and responsive biomarker would be helpful to optimize treatment.

Since the introduction of TDM for TB over 3 decades ago, further development of TDM in TB next steps will again depend on academic and clinical initiatives supported by funding institutions such as EDCTP or the Bill & Melinda Gates Foundation, as the pharmaceutical industry is not interested in personalized prescribing for the novel compounds and the generic pharmaceutical industry has no role in drug development or treatment optimization. We recommend that researchers work closely together with guideline issuing bodies like the World Health Organization to ensure the evidence generated will be reviewed during guideline updates. With such an approach, TDM will become available within the next 5 years and can help to contribute to END TB.

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Limited sampling strategies using linear regression and the Bayesian approach for therapeutic drug monitoring of moxifloxacin in tuberculosis patients

The isoniazid paradigm of killing, resistance, and persistence in Mycobacterium tuberculosis

The early bactericidal activity of isoniazid related to its dose size in pulmonary tuberculosis

Isoniazid bactericidal activity and resistance emergence: integrating pharmacodynamics and pharmacogenomics to predict efficacy in different ethnic populations

New susceptibility breakpoints for first-line antituberculosis drugs based on antimicrobial pharmacokinetic/pharmacodynamic science and population pharmacokinetic variability

Biphasic kill curve of isoniazid reveals the presence of drug-tolerant, not drug-resistant, Mycobacterium tuberculosis in the guinea pig

Isoniazid pharmacokinetics-pharmacodynamics in an aerosol infection model of tuberculosis

Population pharmacokinetic modeling of isoniazid, rifampin, and pyrazinamide

Therapeutic drug monitoring in the treatment of tuberculosis: an update

Pharmacokinetics of isoniazid under fasting conditions, with food, and with antacids

Impact of food on the pharmacokinetics of first-line anti-TB drugs in treatment-naive TB patients: a randomized cross-over trial

Should we use N-acetyltransferase type 2 genotyping to personalize isoniazid doses?

Evaluation of the adequacy of WHO revised dosages of the first-line anti-tuberculosis drugs in children with tuberculosis using population pharmacokinetic modeling and simulations

Serum drug concentrations predictive of pulmonary tuberculosis outcomes

The role of therapeutic drug monitoring in mycobacterial infections

Population pharmacokinetics and limited sampling strategy for first-line tuberculosis drugs and moxifloxacin

Optimal sampling strategies for therapeutic drug monitoring of first-line tuberculosis drugs in patients with tuberculosis

Concentration-dependent Mycobacterium tuberculosis killing and prevention of resistance by rifampin

Pharmacokinetics-pharmacodynamics of rifampin in an aerosol infection model of tuberculosis

Impact of food and antacids on the pharmacokinetics of anti-tuberculosis drugs: systematic review and meta-analysis

Model-based metaanalysis of rifampicin exposure and mortality in Indonesian tuberculosis meningitis trials

Model-based evaluation of higher doses of rifampin using a semimechanistic model incorporating autoinduction and saturation of hepatic extraction

Pharmacokinetic interactions with rifampicin: clinical relevance

A dose ranging trial to optimize the dose of rifampin in the treatment of tuberculosis

High-dose rifampicin, moxifloxacin, and SQ109 for treating tuberculosis: a multi-arm, multi-stage randomised controlled trial

Greater early bactericidal activity at higher rifampicin doses revealed by modeling and clinical trial simulations

The potential for treatment shortening with higher rifampicin doses: relating drug exposure to treatment response in patients with pulmonary tuberculosis

Pharmacokinetics of rifampicin in adult TB patients and healthy volunteers: a systematic review and meta-analysis

A population pharmacokinetic model incorporating saturable pharmacokinetics and autoinduction for high rifampicin doses

Personalized tuberculosis treatment through model-informed dosing of rifampicin

Mutations in pncA, a gene encoding pyrazinamidase/nicotinamidase, cause resistance to the antituberculous drug pyrazinamide in tubercle bacillus

The action of antituberculosis drugs in shortcourse chemotherapy

A global perspective on pyrazinamide resistance: systematic review and metaanalysis

Pharmacokinetics-pharmacodynamics of pyrazinamide in a novel in vitro model of tuberculosis for sterilizing effect: a paradigm for faster assessment of new antituberculosis drugs

Treatment of tuberculosis guidelines

WHO consolidated guidelines on drug-resistant tuberculosis treatment

Variability in the population pharmacokinetics of pyrazinamide in South African tuberculosis patients

Protein binding of first-line antituberculosis drugs

Pyrazinamide susceptibility and amidase activity of Tubercle bacilli

A systematic review on the effect of HIV infection on the pharmacokinetics of first-line tuberculosis drugs

Pharmacokinetics of pyrazinamide and optimal dosing regimens for drug-sensitive and -resistant tuberculosis

Low antituberculosis drug concentrations in HIV-tuberculosis-coinfected adults with low body weight: is it time to update dosing guidelines?

How long will treatment guidelines for TB continue to overlook variability in drug exposure?

Reduced antituberculosis drug concentrations in HIV-infected patients who are men or have low weight: implications for international dosing guidelines

Isoniazid, rifampin, ethambutol, and pyrazinamide pharmacokinetics and treatment outcomes among a predominantly HIV-infected cohort of adults with tuberculosis from Botswana

The hepatic toxicity of antituberculosis regimens containing isoniazid, rifampicin and pyrazinamide

Clinical and toxicodynamic evidence that high-dose pyrazinamide is not more hepatotoxic than the low doses currently used

Pyrazinamide clearance is impaired among HIV/tuberculosis patients with high levels of systemic immune activation

Suitability of isoniazid and ethambutol for intermittent administration in the treatment of tuberculosis

Efflux-pumpderived multiple drug resistance to ethambutol monotherapy in Mycobacterium tuberculosis and the pharmacokinetics and pharmacodynamics of ethambutol

Minimum daily efficient dose of ethambutol: general review

Ethambutol plus isoniazid for the treatment of pulmonary tuberculosis: a controlled trial of our regimens

An oracle: antituberculosis pharmacokinetics-pharmacodynamics, clinical correlation, and clinical trial simulations to predict the future

Cerebrospinal fluid concentrations of antituberculosis agents in adults and children

Pharmacokinetics of ethambutol under fasting conditions, with food, and with antacids

Pharmacokinetics of ethambutol in children and adults with tuberculosis

Population pharmacokinetics of ethambutol in South African tuberculosis patients. Antimicrob Agents Chemother

Ethambutol pharmacokinetic variability is linked to body mass in overweight, obese, and extremely obese people

Pharmacokinetics of isoniazid, pyrazinamide, and ethambutol in newly diagnosed pulmonary TB patients in Tanzania

Optimizing ethambutol dosing among HIV/tuberculosis co-infected patients: a population pharmacokinetic modelling and simulation study

Population pharmacokinetics and pharmacogenetics of ethambutol in adult patients coinfected with tuberculosis and HIV

Population pharmacokinetics of isoniazid, pyrazinamide, and ethambutol in pregnant South African women with tuberculosis and HIV

Structural basis of quinolone inhibition of type IIA topoisomerases and target-mediated resistance

Moxifloxacin, ofloxacin, sparfloxacin, and ciprofloxacin against Mycobacterium tuberculosis: evaluation of in vitro and pharmacodynamic indices that best predict in vivo efficacy

Selection of a moxifloxacin dose that suppresses drug resistance in Mycobacterium tuberculosis, by use of an in vitro pharmacodynamic infection model and mathematical modeling

Levofloxacin pharmacokinetics/pharmacodynamics, dosing, susceptibility breakpoints, and artificial intelligence in the treatment of multidrug-resistant tuberculosis

Dose optimization of moxifloxacin and linezolid against tuberculosis using mathematical modeling and simulation

Activity of Moxifloxacin against Mycobacterium tuberculosis in acid phase and nonreplicative-persister phenotype phase in a hollowfiber infection model

Evaluation of moxifloxacin for the treatment of tuberculosis: 3 years of experience

Levofloxacin pharmacokinetics, pharmacodynamics and outcome in multidrugresistant tuberculosis patients

Pharmacokinetics of levofloxacin in multidrug-and extensively drug-resistant tuberculosis patients

Population pharmacokinetic model and limited sampling strategies for personalized dosing of levofloxacin in tuberculosis patients

Diarylquinolines target subunit c of mycobacterial ATP synthase

Pharmacokinetics and pharmacodynamics of TMC207 and its N-desmethyl metabolite in a murine model of tuberculosis

Daily dosing for bedaquiline in patients with tuberculosis

Bedaquiline: a review of human pharmacokinetics and drug-drug interactions

Pharmacokinetics of bedaquiline in cerebrospinal fluid and serum in multidrugresistant tuberculous meningitis

Model-based estimates of the effects of efavirenz on bedaquiline pharmacokinetics and suggested dose adjustments for patients coinfected with HIV and tuberculosis

Modelling of mycobacterial load reveals bedaquiline's exposure-response relationship in patients with drug-resistant TB

Understanding the drug exposure-response relationship of bedaquiline to predict efficacy for novel dosing regimens in the treatment of multidrug-resistant tuberculosis

Population pharmacokinetics of bedaquiline and metabolite M2 in patients with drug-resistant tuberculosis: the effect of time-varying weight and albumin

Population pharmacokinetics of bedaquiline (TMC207), a novel antituberculosis drug

Should we worry about bedaquiline exposure in the treatment of multidrug-resistant and extensively drug-resistant tuberculosis?

Bedaquiline resistance: its emergence, mechanism, and prevention

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Linezolid dose that maximizes sterilizing effect while minimizing toxicity and resistance emergence for tuberculosis

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Linezolid manifests a rapid and dramatic therapeutic effect for patients with life-threatening tuberculous meningitis

Linezolid for drug-resistant pulmonary tuberculosis

Linezolid trough concentrations correlate with mitochondrial toxicity-related adverse events in the treatment of chronic extensively drug-resistant tuberculosis

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Contribution of oxazolidinones to the efficacy of novel regimens containing bedaquiline and pretomanid in a mouse model of tuberculosis

Linezolid pharmacokinetics in South African patients with drug-resistant tuberculosis and a high prevalence of HIV coinfection

Is there still room for therapeutic drug monitoring of linezolid in patients with tuberculosis?

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Treatment of highly drug-resistant pulmonary tuberculosis

Simple strategy to assess linezolid exposure in patients with multi-drug-resistant and extensively-drug-resistant tuberculosis

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Mechanisms of action and therapeutic efficacies of the lipophilic antimycobacterial agents clofazimine and bedaquiline

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Clofazimine pharmacokinetics in patients with TB: dosing implications

Clofazimine: a review of its use in leprosy and Mycobacterium avium complex infection

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Pharmacokinetic modeling, simulation, and development of a limited sampling strategy of cycloserine in patients with multidrug-/extensively drug-resistant tuberculosis

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Susceptibility testing of antibiotics that degrade faster than the doubling time of slow-growing Mycobacteria: ertapenem sterilizing effect versus Mycobacterium tuberculosis

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Meropenem clinical pharmacokinetics

Optimizing β-lactams against tuberculosis

Pharmacokinetics of 2,000 milligram ertapenem in tuberculosis patients

Amikacin optimal exposure targets in the hollow-fiber system model of tuberculosis

Amikacin dosing for MDR tuberculosis: a systematic review to establish or revise the current recommended dose for tuberculosis treatment

Aminoglycoside toxicity: daily versus thrice-weekly dosing for treatment of mycobacterial diseases

Amikacin concentrations predictive of ototoxicity in multidrug-resistant tuberculosis patients

Reduced chance of hearing loss associated with therapeutic drug monitoring of aminoglycosides in the treatment of multidrug-resistant tuberculosis

Limited sampling strategies for therapeutic drug monitoring of amikacin and kanamycin in patients with multidrug-resistant tuberculosis

Comparison of bacteriostatic and bactericidal activity of isoniazid and ethionamide against Mycobacterium avium and Mycobacterium tuberculosis

A review of the use of ethionamide and prothionamide in childhood tuberculosis

Ethionamide pharmacokinetics/pharmacodynamics-derived dose, the role of MICs in clinical outcome, and the resistance arrow of time in multidrug-resistant tuberculosis

The binding of antituberculous drugs to normal and kwashiorkor serum

Metabolism of the anti-tuberculosis drug ethionamide by mouse and human FMO1, FMO2 and FMO3 and mouse and human lung microsomes

Population pharmacokinetics of ethionamide in patients with tuberculosis. Tuberculosis (Edinb)

Low serum concentrations of moxifloxacin, prothionamide, and cycloserine on sputum conversion in multi-drug resistant TB

para-Aminosalicylic acid is a prodrug targeting dihydrofolate reductase in Mycobacterium tuberculosis

The early bactericidal activity of drugs in patients with pulmonary tuberculosis

Pharmacokinetics of para-aminosalicylic acid granules under four dosing conditions

Pharmacokinetics of para-aminosalicylic acid in HIV-uninfected and HIVcoinfected tuberculosis patients receiving antiretroviral therapy, managed for multidrug-resistant and extensively drug-resistant tuberculosis

Bactericidal activity of streptomycin and isoniazid in combination with p-aminosalicylic acid against Mycobacterium tuberculosis

Pharmacokinetic-pharmacodynamic modeling and simulation

Software tools for model-informed precision dosing: how well do they satisfy the needs?

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Variability in the population pharmacokinetics of isoniazid in South African tuberculosis patients

Population pharmacokinetics of rifampicin, pyrazinamide and isoniazid in children with tuberculosis: in silico evaluation of currently recommended doses

Population pharmacokinetics of rifampin in pulmonary tuberculosis patients, including a semimechanistic model to describe variable absorption

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Population pharmacokinetic analysis of isoniazid, acetyl-isoniazid and isonicotinic acid in healthy volunteers

Population modeling and simulation study of the pharmacokinetics and antituberculosis pharmacodynamics of isoniazid in lungs

HIV-1 coinfection does not reduce exposure to rifampin, isoniazid, and pyrazinamide in South African tuberculosis outpatients

Isoniazid clearance is impaired among human immunodeficiency virus/tuberculosis patients with high levels of immune activation

Effect of efavirenzbased antiretroviral therapy and high-dose rifampicin on the pharmacokinetics of isoniazid and acetyl-isoniazid

Optimization of dosing regimens of isoniazid and rifampicin in children with tuberculosis in India

Population modeling and Monte Carlo simulation study of the pharmacokinetics and antituberculosis pharmacodynamics of rifampin in lungs. Antimicrob Agents Chemother

A semimechanistic pharmacokinetic-enzyme turnover model for rifampin autoinduction in adult tuberculosis patients

Population pharmacokinetics of rifampicin in Mexican patients with tuberculosis

Nutritional supplementation increases rifampin exposure among tuberculosis patients coinfected with HIV

Population pharmacokinetics of rifampicin and 25-deacetyl-rifampicin in healthy Asian adults

Population pharmacokinetics of rifampicin in Chinese patients with pulmonary tuberculosis

Population pharmacokinetics of rifampin in pregnant women with tuberculosis and HIV coinfection in Soweto, South Africa

A simultaneous population pharmacokinetic analysis of rifampicin in Malawian adults and children

Population pharmacokinetic modeling of pyrazinamide in children and adults with tuberculosis

Population pharmacokinetic modelling of pyrazinamide and pyrazinoic acid in patients with multidrug resistant tuberculosis

Population pharmacokinetics of levofloxacin, gatifloxacin, and moxifloxacin in adults with pulmonary tuberculosis

Levofloxacin population pharmacokinetics in South African children treated for multidrug-resistant tuberculosis

Fluoroquinolones in drug-resistant tuberculosis: culture conversion and pharmacokinetic/pharmacodynamic target attainment to guide dose selection

Limited-sampling strategies for therapeutic drug monitoring of moxifloxacin in patients with tuberculosis

Moxifloxacin population pharmacokinetics and model-based comparison of efficacy between moxifloxacin and ofloxacin in African patients

Population pharmacokinetics of moxifloxacin, cycloserine, p-aminosalicylic acid and kanamycin for the treatment of multi-drug-resistant tuberculosis

Limited sampling strategies for therapeutic drug monitoring of linezolid in patients with multidrug-resistant tuberculosis

Pharmacokinetics, optimal dosing, and safety of linezolid in children with multidrug-resistant tuberculosis: combined data from two prospective observational studies

Population pharmacokinetics of linezolid in tuberculosis patients: dosing regimen simulation and target attainment analysis

Drug effect of clofazimine on persisters explains an unexpected increase in bacterial load in patients

Steady-state population pharmacokinetics of terizidone and its metabolite cycloserine in patients with drug-resistant tuberculosis

Population pharmacokinetics of cycloserine, and pharmacokinetic/pharmacodynamic target attainment, in MDR-tuberculosis patients dosed with terizidone

Modeling and simulation of pretomanid pharmacokinetics in pulmonary tuberculosis patients

Population pharmacokinetics of the antituberculosis agent pretomanid

Population pharmacokinetics and dosing of ethionamide in children with tuberculosis

Ethionamide population pharmacokinetic model and target attainment in multidrug-resistant tuberculosis

Probability of mycobactericidal activity of para-aminosalicylic acid with novel dosing regimens

Drug exposure and minimum inhibitory concentration predict pulmonary tuberculosis treatment response

The diarylquinoline TMC207 for multidrug-resistant tuberculosis

Linezolid: the first oxazolidinone antimicrobial

Zyvox 600 mg film-coated tablets, Summary of Product Characteristics

Pharmacokinetics of cycloserine and terizidone: a comparative study

Pharmacokinetics of ertapenem in patients with multidrug-resistant tuberculosis

Artificial intelligence and amikacin exposures predictive of outcome in multidrug-resistant tuberculosis patients

Pharmacokinetics of second-line antituberculosis drugs after multiple administrations in healthy volunteers

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