The Role of Novel Approaches and New Findings in the Pharmacology of Tuberculosis Medicines in Improving Treatment Outcomes

Therapeutic drug monitoring in tuberculosis

PURPOSE

Therapeutic drug monitoring (TDM) is a standard clinical procedure that uses the pharmacokinetic and pharmacodynamic parameters of the drug in the body to determine the optimal dose. The pharmacokinetic variability of the drug(s) is a significant contributor to poor treatment outcomes, including the development of acquired drug resistance. TDM aids in dose optimization and improves outcomes while lessening drug toxicity. TDM is used to manage patients with tuberculosis (TB) who exhibit a slow response to therapy, despite good compliance and drug-susceptible organisms. Additional indications include patients at risk of malabsorption or delayed absorption of TB drugs and patients with drug-drug interaction and drug toxicity, which confirm compliance with therapy. TDM usually requires two blood samples: the 2 h and the 6 h post-dose. This narrative review will discuss the pharmacokinetics and pharmacodynamics of TB drugs, determinants of poor response to therapy, indications of TDM, methods of performing TDM, and its interpretations.

METHODS

This is a narrative review. We searched PubMed, Embase, and the CINAHL from inception to April 2024. We used the following search terms: tuberculosis, therapeutic drug monitoring, anti-TB drugs, pharmacokinetics, pharmacodynamics, limited sample strategies, diabetes and TB, HIV and TB, and multidrug-resistant TB. All types of articles were selected.

RESULTS

TDM is beneficial in managing TB, especially in patients with slow responses, drug-resistance TB, recurrent TB, and comorbidities such as diabetes mellitus and human immunodeficiency virus infection.

CONCLUSION

TDM is beneficial for improving outcomes, reducing the risk of acquired drug resistance, and avoiding side effects.

Integrative analysis of multimodal patient data identifies personalized predictors of tuberculosis treatment prognosis

Tuberculosis (TB) afflicted 10.6 million people in 2021, and its global burden is increasing due to multidrug-resistant TB (MDR-TB) and extensively resistant TB (XDR-TB). Here, we analyze multi-domain information from 5,060 TB patients spanning 10 countries with high burden of MDR-TB from the NIAID TB Portals database to determine predictors of TB treatment outcome. Our analysis revealed significant associations between radiological, microbiological, therapeutic, and demographic data modalities. Our machine learning model, built with 203 features across modalities outperforms models built using each modality alone in predicting treatment outcomes, with an accuracy of 83% and area under the curve of 0.84. Notably, our analysis revealed that the drug regimens Bedaquiline-Clofazimine-Cycloserine-Levofloxacin-Linezolid and Bedaquiline-Clofazimine-Linezolid-Moxifloxacin were associated with treatment success and failure, respectively, for MDR non-XDR-TB. Drug combinations predicted to be synergistic by the INDIGO algorithm performed better than antagonistic combinations. Our prioritized set of features predictive of treatment outcomes can ultimately guide the personalized clinical management of TB.

Integrative analysis of clinical health records, imaging and pathogen genomics identifies personalized predictors of disease prognosis in tuberculosis

Tuberculosis (TB) afflicts over 10 million people every year and its global burden is projected to increase dramatically due to multidrug-resistant TB (MDR-TB). The Covid-19 pandemic has resulted in reduced access to TB diagnosis and treatment, reversing decades of progress in disease management globally. It is thus crucial to analyze real-world multi-domain information from patient health records to determine personalized predictors of TB treatment outcome and drug resistance. We conduct a retrospective analysis on electronic health records of 5060 TB patients spanning 10 countries with high burden of MDR-TB including Ukraine, Moldova, Belarus and India available on the NIAID-TB portals database. We analyze over 200 features across multiple host and pathogen modalities representing patient social demographics, disease presentations as seen in cChest X rays and CT scans, and genomic records with drug susceptibility features of the pathogen strain from each patient. Our machine learning model, built with diverse data modalities outperforms models built using each modality alone in predicting treatment outcomes, with an accuracy of 81% and AUC of 0.768. We determine robust predictors across countries that are associated with unsuccessful treatmentclinical outcomes, and validate our predictions on new patient data from TB Portals. Our analysis of drug regimens and drug interactions suggests that synergistic drug combinations and those containing the drugs Bedaquiline, Levofloxacin, Clofazimine and Amoxicillin see more success in treating MDR and XDR TB. Features identified via chest imaging such as percentage of abnormal volume, size of lung cavitation and bronchial obstruction are associated significantly with pathogen genomic attributes of drug resistance. Increased disease severity was also observed in patients with lower BMI and with comorbidities. Our integrated multi-modal analysis thus revealed significant associations between radiological, microbiological, therapeutic, and demographic data modalities, providing a deeper understanding of personalized responses to aid in the clinical management of TB.

Treatment of Drug-Resistant Tuberculosis. An Official ATS/CDC/ERS/IDSA Clinical Practice Guideline

Background: The American Thoracic Society, U.S. Centers for Disease Control and Prevention, European Respiratory Society, and Infectious Diseases Society of America jointly sponsored this new practice guideline on the treatment of drug-resistant tuberculosis (DR-TB). The document includes recommendations on the treatment of multidrug-resistant TB (MDR-TB) as well as isoniazid-resistant but rifampin-susceptible TB.Methods: Published systematic reviews, meta-analyses, and a new individual patient data meta-analysis from 12,030 patients, in 50 studies, across 25 countries with confirmed pulmonary rifampin-resistant TB were used for this guideline. Meta-analytic approaches included propensity score matching to reduce confounding. Each recommendation was discussed by an expert committee, screened for conflicts of interest, according to the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) methodology.Results: Twenty-one Population, Intervention, Comparator, and Outcomes questions were addressed, generating 25 GRADE-based recommendations. Certainty in the evidence was judged to be very low, because the data came from observational studies with significant loss to follow-up and imbalance in background regimens between comparator groups. Good practices in the management of MDR-TB are described. On the basis of the evidence review, a clinical strategy tool for building a treatment regimen for MDR-TB is also provided.Conclusions: New recommendations are made for the choice and number of drugs in a regimen, the duration of intensive and continuation phases, and the role of injectable drugs for MDR-TB. On the basis of these recommendations, an effective all-oral regimen for MDR-TB can be assembled. Recommendations are also provided on the role of surgery in treatment of MDR-TB and for treatment of contacts exposed to MDR-TB and treatment of isoniazid-resistant TB.

Pharmacokinetic/Pharmacodynamic Background and Methods and Scientific Evidence Base for Dosing of Second-line Tuberculosis Drugs

A World Health Organization workshop systematically examined the evidence base for dosing second-line tuberculosis drugs, identifying knowledge gaps. To fill these in, pharmacokinetics/pharmacodynamics, Monte Carlo experiments, and artificial intelligence algorithms were used in hollow-fiber model studies and clinical data analyses.