Model-Informed Precision Dosing of Linezolid in Patients with Drug-Resistant Tuberculosis

Pharmacokinetic-pharmacodynamic modeling of tuberculosis time to positivity and colony-forming unit to assess the response to dose-ranging linezolid

According to the World Health Organization, the number of tuberculosis (TB) infections and the drug-resistant burden worldwide increased by 4.5% and 3.0%, respectively, between 2020 and 2021. Disease severity and complexity drive the interest for undertaking new clinical trials to provide efficient treatment to limit spread and drug resistance. TB Alliance conducted a phase 2 study in 106 patients to guide linezolid (LZD) dose selection using early bactericidal activity over 14 days of treatment. LZD is highly efficient for drug-resistant TB treatment, but treatment monitoring is required since serious adverse events can occur. The objective of this study was to develop a pharmacokinetic-pharmacodynamic (PKPD) model to analyze the dose-response relationship between linezolid exposure and efficacy biomarkers. Using time to positivity (TTP) and colony-forming unit (CFU) count data, we developed a PKPD model in six dosing regimens, differing on LZD dosing intensity. A one-compartment model with five transit absorption compartments and non-linear auto-inhibition elimination described best LZD pharmacokinetic characteristics. TTP and CFU logarithmic scaled [log(CFU)] showed a bactericidal activity of LZD against Mycobacterium tuberculosis. TTP was defined by a model with two significant covariates: the presence of uni- and bilateral cavities decreased baseline TTP value by 24%, and an increase on every 500 mg/L/h of cumulative area under the curve increased the rate at which TTP and CFU change from baseline by 20% and 11%, respectively.

CLINICAL TRIALS

This study is registered with ClinicalTrials.gov as NCT02279875.

Linezolid Population Pharmacokinetic Model in Plasma and Cerebrospinal Fluid Among Patients With Tuberculosis Meningitis

BACKGROUND

Linezolid is evaluated in novel treatment regimens for tuberculous meningitis (TBM). Linezolid pharmacokinetics have not been characterized in this population, particularly in cerebrospinal fluid (CSF), as well as, following its co-administration with high-dose rifampicin. We aimed to characterize linezolid plasma and CSF pharmacokinetics in adults with TBM.

METHODS

In the LASER-TBM pharmacokinetic substudy, the intervention groups received high-dose rifampicin (35 mg/kg) plus 1200 mg/day of linezolid for 28 days, which was then reduced to 600 mg/day. Plasma sampling was done on day 3 (intensive) and day 28 (sparse). A lumbar CSF sample was obtained on both visits.

RESULTS

Thirty participants contributed 247 plasma and 28 CSF observations. Their median age and weight were 40 years (range, 27-56) and 58 kg (range, 30-96). Plasma pharmacokinetics was described by a 1-compartment model with first-order absorption and saturable elimination. Maximal clearance was 7.25 L/h, and the Michaelis-Menten constant was 27.2 mg/L. Rifampicin cotreatment duration did not affect linezolid pharmacokinetics. CSF-plasma partitioning correlated with CSF total protein up to 1.2 g/L, where the partition coefficient reached a maximal value of 37%. The plasma-CSF equilibration half-life was ∼3.5 hours.

CONCLUSIONS

Linezolid was readily detected in CSF despite high-dose rifampicin coadministration. These findings support continued clinical evaluation of linezolid plus high-dose rifampicin for the treatment of TBM in adults. Clinical Trials Registration.  ClinicalTrials.gov (NCT03927313).

Model-based dose optimization framework for bedaquiline, pretomanid and linezolid for the treatment of drug-resistant tuberculosis

AIMS

Bedaquiline, pretomanid and linezolid (BPaL) combination treatment against Mycobacterium tuberculosis is promising, yet safety and adherence concerns exist that motivate exploration of alternative dosing regimens. We developed a mechanistic modelling framework to compare the efficacy of the current and alternative BPaL treatment strategies.

METHODS

Pharmacodynamic models for each drug in the BPaL combination treatment were developed using in vitro time-kill data. These models were combined with pharmacokinetic models, incorporating body weight, lesion volume, site-of-action distribution, bacterial susceptibility and pharmacodynamic interactions to assemble the framework. The model was qualified by comparing the simulations against the observed clinical data. Simulations were performed evaluating bedaquiline and linezolid approved (bedaquiline 400 mg once daily [QD] for 14 days followed by 200 mg three times a week, linezolid 1200 mg QD) and alternative dosing regimens (bedaquiline 200 mg QD, linezolid 600 mg QD).

RESULTS

The framework adequately described the observed antibacterial activity data in patients following monotherapy for each drug and approved BPaL dosing. The simulations suggested a minor difference in median time to colony forming unit (CFU)-clearance state with the bedaquiline alternative compared to the approved dosing and the linezolid alternative compared to the approved dosing. Median time to non-replicating-clearance state was predicted to be 15 days from the CFU-clearance state.

CONCLUSIONS

The model-based simulations suggested that comparable efficacy can be achieved using alternative bedaquiline and linezolid dosing, which may improve safety and adherence in drug-resistant tuberculosis patients. The framework can be utilized to evaluate treatment optimization approaches, including dosing regimen and duration of treatment predictions to eradicate both replicating- and non-replicating bacteria from lung and lesions.

Derivation and Clinical Utility of Safety Targets for Linezolid-Related Adverse Events in Drug-Resistant Tuberculosis Treatment

Long-term usage of linezolid can result in adverse events such as peripheral neuropathy, anemia and thrombocytopenia. Therapeutic drug monitoring data from 75 drug-resistant tuberculosis patients treated with linezolid were analyzed using a time-to-event (TTE) approach for peripheral neuropathy and anemia and indirect response modelling for thrombocytopenia. Different time-varying linezolid pharmacokinetic exposure indices (AUC0-24h,ss, Cav, Cmax and Cmin) and patient characteristics were investigated as risk factors. A treatment duration shorter than 3 months was considered dropout and was modelled using a TTE approach. An exposure-response relationship between linezolid Cmin and both peripheral neuropathy and anemia was found. The exposure index which best described the development of thrombocytopenia was AUC0-24h. The final TTE dropout model indicated an association between linezolid Cmin and dropout. New safety targets for each adverse event were proposed which can be used for individualized linezolid dosing. According to the model predictions at 6 months of treatment, a Cmin of 0.11 mg/L and 1.4 mg/L should not be exceeded to keep the cumulative probability to develop anemia and peripheral neuropathy below 20%. The AUC0-24h should be below 111 h·mg/L or 270 h·mg/L to prevent thrombocytopenia and severe thrombocytopenia, respectively. A clinical utility assessment showed that the currently recommended dose of 600 mg once daily is safer compared to a 300 mg BID dosing strategy considering all four safety endpoints.

Towards Model-Informed Precision Dosing of Voriconazole: Challenging Published Voriconazole Nonlinear Mixed-Effects Models with Real-World Clinical Data

BACKGROUND AND OBJECTIVES

Model-informed precision dosing (MIPD) frequently uses nonlinear mixed-effects (NLME) models to predict and optimize therapy outcomes based on patient characteristics and therapeutic drug monitoring data. MIPD is indicated for compounds with narrow therapeutic range and complex pharmacokinetics (PK), such as voriconazole, a broad-spectrum antifungal drug for prevention and treatment of invasive fungal infections. To provide guidance and recommendations for evidence-based application of MIPD for voriconazole, this work aimed to (i) externally evaluate and compare the predictive performance of a published so-called 'hybrid' model for MIPD (an aggregate model comprising features and prior information from six previously published NLME models) versus two 'standard' NLME models of voriconazole, and (ii) investigate strategies and illustrate the clinical impact of Bayesian forecasting for voriconazole.

METHODS

A workflow for external evaluation and application of MIPD for voriconazole was implemented. Published voriconazole NLME models were externally evaluated using a comprehensive in-house clinical database comprising nine voriconazole studies and prediction-/simulation-based diagnostics. The NLME models were applied using different Bayesian forecasting strategies to assess the influence of prior observations on model predictivity.

RESULTS

The overall best predictive performance was obtained using the aggregate model. However, all NLME models showed only modest predictive performance, suggesting that (i) important PK processes were not sufficiently implemented in the structural submodels, (ii) sources of interindividual variability were not entirely captured, and (iii) interoccasion variability was not adequately accounted for. Predictive performance substantially improved by including the most recent voriconazole observations in MIPD.

CONCLUSION

Our results highlight the potential clinical impact of MIPD for voriconazole and indicate the need for a comprehensive (pre-)clinical database as basis for model development and careful external model evaluation for compounds with complex PK before their successful use in MIPD.

Linezolid population pharmacokinetic model in plasma and cerebrospinal fluid among patients with tuberculosis meningitis

Background

Linezolid is being evaluated in novel treatment regimens for tuberculous meningitis (TBM). The pharmacokinetics of linezolid have not been characterized in this population, particularly in cerebrospinal fluid (CSF) where exposures may be affected by changes in protein concentration and rifampicin co-administration.

Methods

This was a sub-study of a phase 2 clinical trial of intensified antibiotic therapy for adults with HIV-associated TBM. Participants in the intervention groups received high-dose rifampicin (35 mg/kg) plus linezolid 1200 mg daily for 28 days followed by 600 mg daily until day 56. Plasma was intensively sampled, and lumbar CSF was collected at a single timepoint in a randomly allocated sampling window, within 3 days after enrolment. Sparse plasma and CSF samples were also obtained on day 28. Linezolid concentrations were analyzed using non-linear mixed effects modelling.

Results

30 participants contributed 247 plasma and 28 CSF linezolid observations. Plasma PK was best described by a one-compartment model with first-order absorption and saturable elimination. The typical value of maximal clearance was 7.25 L/h. Duration of rifampicin co-treatment (compared on day 3 versus day 28) did not affect linezolid pharmacokinetics. Partitioning between plasma and CSF correlated with CSF total protein concentration up to 1.2 g/L where the partition coefficient reached a maximal value of 37%. The equilibration half-life between plasma and CSF was estimated at ∼3.5 hours.

Conclusion

Linezolid was readily detected in CSF despite co-administration of the potent inducer rifampicin at high doses. These findings support continued clinical evaluation of linezolid plus high-dose rifampicin for the treatment of TBM in adults.

Pharmacokinetic analysis of linezolid for multidrug resistant tuberculosis at a tertiary care centre in Mumbai, India

Linezolid is an oxazolidinone used to treat multidrug-resistant tuberculosis (MDR-TB), including in the recently-endorsed shorter 6-month treatment regimens. Due to its narrow therapeutic index, linezolid is often either dose-adjusted or discontinued due to intolerance or toxicity during treatment, and the optimal balance between linezolid efficacy and toxicity remains unclear. India carries a significant burden of MDR-TB cases in the world, but limited information on the pharmacokinetics of linezolid and minimum inhibitory concentration (MIC) distribution is available from Indian MDR-TB patients. We enrolled participants from a tertiary care centre in Mumbai, India, treated for MDR-TB and receiving linezolid daily doses of 600 or 300 mg. Pharmacokinetic visits were scheduled between 1 and 15 months after treatment initiation to undergo intensive or sparse blood sampling. Linezolid concentration versus time data were analysed using non-linear mixed-effects modelling, with simulations to evaluate doses for different scenarios. We enrolled 183 participants (121 females), with a median age of 26 years (interquartile range [IQR] 21-35), weight 55.0 kg (IQR 45.6-65.8), and fat-free mass 38.7 kg (IQR 32.7-46.0). Linezolid pharmacokinetics was best described by a one-compartment model with first-order elimination allometrically scaled by fat-free mass and transit compartment absorption. The typical clearance value was 3.81 L/h. Simulations predicted that treatment with 300 mg daily achieves a high probability of target attainment (PTA) when linezolid MIC was ≤0.25 mg/L (61.5% of participant samples tested), while 600 mg daily would be required if MIC were 0.5 mg/L (29% of samples). While linezolid 300 mg daily is predicted to achieve effective targets for the majority of adults with MDR-TB, it failed to achieve the therapeutic target for 21% participants. A dose of 600 mg had a PTA >90% for all susceptible samples, but with a higher likelihood of exceeding toxicity thresholds (31% vs 9.6%). These data suggest potential benefit to individualized dosing taking host and microbial characteristics into account to improve the likelihood of treatment efficacy while minimizing risk of toxicity from linezolid for the treatment of MDR-TB. Further prospective evaluation in different clinical settings is urgently needed to inform safety and efficacy of these lower doses.

Model-Informed Precision Dosing (MIPD)

Model-informed precision dosing (MIPD) is an advanced quantitative approach focusing on individualized dosage optimization, integrating complex mathematical and statistical models of drugs and disease combined with individual demographic and clinical patient characteristics [...].

Individualized Patient Care Through Model-Informed Precision Dosing: Reflections on Training Future Practitioners

Prior to his passing, Dr. Roger Jelliffe, expressed the need for educating future physicians and clinical pharmacists on the availability of computer-based tools to support dose optimization in patients in stable or unstable physiological states. His perspectives were to be captured in a commentary for the AAPS J with a focus on incorporating population pharmacokinetic (PK)/pharmacodynamic (PD) models that are designed to hit the therapeutic target with maximal precision. Unfortunately, knowing that he would be unable to complete this project, Dr. Jelliffe requested that a manuscript conveying his concerns be completed upon his passing. With this in mind, this final installment of the AAPS J theme issue titled "Alternative Perspectives for Evaluating Drug Exposure Characteristics in a Population - Avoiding Analysis Pitfalls and Pigeonholes" is an effort to honor Dr. Jelliffe's request, conveying his concerns and the need to incorporate modeling and simulation into the training of physicians and clinical pharmacists. Accordingly, Dr. Jelliffe's perspectives have been integrated with those of the other three co-authors on the following topics: the clinical utility of population PK models; the role of multiple model (MM) dosage regimens to identify an optimal dose for an individual; tools for determining dosing regimens in renal dialysis patients (or undergoing other therapies that modulate renal clearance); methods to analyze and track drug PK in acutely ill patients presenting with high inter-occasion variability; implementation of a 2-cycle approach to minimize the duration between blood samples taken to estimate the changing PK in an acutely ill patient and for the generation of therapeutic decisions in advance for each dosing cycle based on an analysis of the previous cycle; and the importance of expressing therapeutic drug monitoring results as 1/variance rather than as the coefficient of variation. Examples showcase why, irrespective of the overall approach, the combination of therapeutic drug monitoring and computer-informed precision dosing is indispensable for maximizing the likelihood of achieving the target drug concentrations in the individual patient.