Alternative Methods for Therapeutic Drug Monitoring and Dose Adjustment of Tuberculosis Treatment in Clinical Settings: A Systematic Review

Multidrug-resistant tuberculosis

Tuberculosis (TB) remains the foremost cause of death by an infectious disease globally. Multidrug-resistant or rifampicin-resistant TB (MDR/RR-TB; resistance to rifampicin and isoniazid, or rifampicin alone) is a burgeoning public health challenge in several parts of the world, and especially Eastern Europe, Russia, Asia and sub-Saharan Africa. Pre-extensively drug-resistant TB (pre-XDR-TB) refers to MDR/RR-TB that is also resistant to a fluoroquinolone, and extensively drug-resistant TB (XDR-TB) isolates are additionally resistant to other key drugs such as bedaquiline and/or linezolid. Collectively, these subgroups are referred to as drug-resistant TB (DR-TB). All forms of DR-TB can be as transmissible as rifampicin-susceptible TB; however, it is more difficult to diagnose, is associated with higher mortality and morbidity, and higher rates of post-TB lung damage. The various forms of DR-TB often consume >50% of national TB budgets despite comprising <5-10% of the total TB case-load. The past decade has seen a dramatic change in the DR-TB treatment landscape with the introduction of new diagnostics and therapeutic agents. However, there is limited guidance on understanding and managing various aspects of this complex entity, including the pathogenesis, transmission, diagnosis, management and prevention of MDR-TB and XDR-TB, especially at the primary care physician level.

Point-of-care urine tenofovir monitoring of adherence to drive interventions for HIV treatment and prevention

INTRODUCTION

Although effective antiretroviral and pre-exposure prophylaxis/PrEP regimens are available globally, adherence challenges persist. Objective measures of adherence can both measure adherence accurately and can be used to drive interventions. The first point-of-care pharmacologic adherence measure, urine tenofovir testing using a lateral flow assay, is now available.

AREAS COVERED

This review examines the ability of pharmacologic metrics of adherence to predict HIV and PrEP clinical outcomes and the past use of pharmacologic metrics of adherence as tools to drive adherence interventions. The success of preliminary studies using point-of-care adherence metrics to guide interventions is then discussed.

EXPERT OPINION

Large randomized clinical trials are now needed to test the impact of point-of-care adherence interventions on HIV and PrEP clinical outcomes, given promising results of the pilot studies summarized here. Hybrid implementation-effectiveness studies will be needed to examine optimal approaches to incorporating point-of-care testing into routine clinical care delivery, including in guiding resistance testing, adherence counseling, and delivery of other evidence-based adherence interventions. Given the ability of point-of-care tenofovir testing to be implemented in settings where viral load testing is not available, and at more frequent intervals due to its low cost, urine-based tenofovir assays have the potential to be highly scalable in diverse clinical settings.

Advancing Precision Medicine: A Review of Innovative In Silico Approaches for Drug Development, Clinical Pharmacology and Personalized Healthcare

The landscape of medical treatments is undergoing a transformative shift. Precision medicine has ushered in a revolutionary era in healthcare by individualizing diagnostics and treatments according to each patient's uniquely evolving health status. This groundbreaking method of tailoring disease prevention and treatment considers individual variations in genes, environments, and lifestyles. The goal of precision medicine is to target the "five rights": the right patient, the right drug, the right time, the right dose, and the right route. In this pursuit, in silico techniques have emerged as an anchor, driving precision medicine forward and making this a realistic and promising avenue for personalized therapies. With the advancements in high-throughput DNA sequencing technologies, genomic data, including genetic variants and their interactions with each other and the environment, can be incorporated into clinical decision-making. Pharmacometrics, gathering pharmacokinetic (PK) and pharmacodynamic (PD) data, and mathematical models further contribute to drug optimization, drug behavior prediction, and drug-drug interaction identification. Digital health, wearables, and computational tools offer continuous monitoring and real-time data collection, enabling treatment adjustments. Furthermore, the incorporation of extensive datasets in computational tools, such as electronic health records (EHRs) and omics data, is also another pathway to acquire meaningful information in this field. Although they are fairly new, machine learning (ML) algorithms and artificial intelligence (AI) techniques are also resources researchers use to analyze big data and develop predictive models. This review explores the interplay of these multiple in silico approaches in advancing precision medicine and fostering individual healthcare. Despite intrinsic challenges, such as ethical considerations, data protection, and the need for more comprehensive research, this marks a new era of patient-centered healthcare. Innovative in silico techniques hold the potential to reshape the future of medicine for generations to come.

Isoniazid urine spectrophotometry for prediction of serum pharmacokinetics in adults with TB

BACKGROUND

Isoniazid (INH) is an important drug in many TB regimens, and unfavorable treatment outcomes can be caused by suboptimal pharmacokinetics. Dose adjustment can be personalized by measuring peak serum concentrations; however, the process involves cold-chain preservation and laboratory techniques such as liquid chromatography (LC)/mass spectrometry (MS), which are unavailable in many high-burden settings. Urine spectrophotometry could provide a low-cost alternative with simple sampling and quantification methods.

METHODS

We enrolled 56 adult patients on treatment for active TB. Serum was collected at 0, 1, 2, 4, 6, and 8 h for measurement of INH concentrations using validated LC-MS/MS methods. Urine was collected at 0-4, 4-8, and 8-24 h intervals, with INH concentrations measured using colorimetric methods.

RESULTS

The median peak serum concentration and total serum exposure over 24 h were 4.8 mg/L and 16.4 mg*hour/L, respectively. Area under the receiver operator characteristic curves for urine values predicting a subtherapeutic serum concentration (peak <3.0 mg/L) were as follows: 0-4 h interval (AUC 0.85, 95% CI 0.7-0.96), 0-8 h interval (AUC 0.85, 95% CI 0.71-0.96), and 0-24 h urine collection interval (AUC 0.84, 95% CI 0.68-0.96).

CONCLUSION

Urine spectrophotometry may improve feasibility of personalized dosing in high TB burden regions but requires further study of target attainment following dose adjustment based on a urine threshold.

Development and Validation of a UPLC-MS/MS Method for Therapeutic Drug Monitoring, Pharmacokinetic and Stability Studies of First-Line Antituberculosis Drugs in Urine

Tuberculosis (TB) remains one of the leading global causes of mortality. Several methods have been established to detect anti-TB agents in human plasma and serum. However, there is a notable absence of studies analyzing TB drugs in urine. Thus, our objective was to validate a method for quantifying first-line anti-TB agents: isoniazid (INH), pyrazinamide (PZA), ethambutol (ETH), and rifampicin (RIF), along with its metabolite 25-desacetylrifampicin, and degradation products: rifampicin quinone and 3-formyl-rifampicin in 10 µL of urine. Chromatographic separation was achieved using a Kinetex Polar C18 analytical column with gradient elution (5 mM ammonium acetate and acetonitrile with 0.1% formic acid). Mass spectrometry detection was carried out using a triple-quadrupole tandem mass spectrometer operating in positive ion mode. The lower limit of quantification (LLOQ) was 0.5 µg/mL for INH, PZA, ETH, and RIF, and 0.1 µg/mL for RIF's metabolites and degradation products. The method was validated following FDA guidance criteria and successfully applied to the analysis of the studied compounds in urine of TB patients. Additionally, we conducted a stability study of the anti-TB agents under various pH and temperature conditions to mimic the urine collection process in different settings (peripheral clinics or central laboratories).

Push forward LC-MS-based therapeutic drug monitoring and pharmacometabolomics for anti-tuberculosis precision dosing and comprehensive clinical management

The spread of tuberculosis (TB), especially multidrug-resistant TB and extensively drug-resistant TB, has strongly motivated the research and development of new anti-TB drugs. New strategies to facilitate drug combinations, including pharmacokinetics-guided dose optimization and toxicology studies of first- and second-line anti-TB drugs have also been introduced and recommended. Liquid chromatography-mass spectrometry (LC-MS) has arguably become the gold standard in the analysis of both endo- and exo-genous compounds. This technique has been applied successfully not only for therapeutic drug monitoring (TDM) but also for pharmacometabolomics analysis. TDM improves the effectiveness of treatment, reduces adverse drug reactions, and the likelihood of drug resistance development in TB patients by determining dosage regimens that produce concentrations within the therapeutic target window. Based on TDM, the dose would be optimized individually to achieve favorable outcomes. Pharmacometabolomics is essential in generating and validating hypotheses regarding the metabolism of anti-TB drugs, aiding in the discovery of potential biomarkers for TB diagnostics, treatment monitoring, and outcome evaluation. This article highlighted the current progresses in TDM of anti-TB drugs based on LC-MS bioassay in the last two decades. Besides, we discussed the advantages and disadvantages of this technique in practical use. The pressing need for non-invasive sampling approaches and stability studies of anti-TB drugs was highlighted. Lastly, we provided perspectives on the prospects of combining LC-MS-based TDM and pharmacometabolomics with other advanced strategies (pharmacometrics, drug and vaccine developments, machine learning/artificial intelligence, among others) to encapsulate in an all-inclusive approach to improve treatment outcomes of TB patients.