Nutritional markers and proteome in patients undergoing treatment for pulmonary tuberculosis differ by geographic region.

INTRODUCTION: Contemporary phase 2 TB disease treatment clinical trials have found that microbiologic treatment responses differ between African versus non-African regions, the reasons for which remain unclear. Understanding host and disease phenotypes that may vary by region is important for optimizing curative treatments. METHODS: We characterized clinical features and the serum proteome of phase 2 TB clinical trial participants undergoing treatment for smear positive, culture-confirmed TB, comparing host serum protein expression in clinical trial participants enrolled in African and Non-African regions. Serum samples were collected from 289 participants enrolled in the Centers for Disease Control and Prevention TBTC Study 29 (NCT00694629) at time of enrollment and at the end of the intensive phase (after 40 doses of TB treatment). RESULTS: After a peptide level proteome analysis utilizing a unique liquid chromatography IM-MS platform (LC-IM-MS) and subsequent statistical analysis, a total of 183 core proteins demonstrated significant differences at both baseline and at week 8 timepoints between participants enrolled from African and non-African regions. The majority of the differentially expressed proteins were upregulated in participants from the African region, and included acute phase proteins, mediators of inflammation, as well as coagulation and complement pathways. Downregulated proteins in the African population were primarily linked to nutritional status and lipid metabolism pathways. CONCLUSIONS: We have identified differentially expressed nutrition and lipid pathway proteins by geographic region in TB patients undergoing treatment for pulmonary tuberculosis, which appear to be associated with differential treatment responses. Future TB clinical trials should collect expanded measures of nutritional status and further evaluate the relationship between nutrition and microbiologic treatment response.

Application of multiplexed ion mobility spectrometry towards the identification of host protein signatures of treatment effect in pulmonary tuberculosis.

RATIONALE: The monitoring of TB treatments in clinical practice and clinical trials relies on traditional sputum-based culture status indicators at specific time points. Accurate, predictive, blood-based protein markers would provide a simpler and more informative view of patient health and response to treatment. OBJECTIVE: We utilized sensitive, high throughput multiplexed ion mobility-mass spectrometry (IM-MS) to characterize the serum proteome of TB patients at the start of and at 8 weeks of rifamycin-based treatment. We sought to identify treatment specific signatures within patients as well as correlate the proteome signatures to various clinical markers of treatment efficacy. METHODS: Serum samples were collected from 289 subjects enrolled in CDC TB Trials Consortium Study 29 at time of enrollment and at the end of the intensive phase (after 40 doses of TB treatment). Serum proteins were immunoaffinity-depleted of high abundant components, digested to peptides and analyzed for data acquisition utilizing a unique liquid chromatography IM-MS platform (LC-IM-MS). Linear mixed models were utilized to identify serum protein changes in the host response to antibiotic treatment as well as correlations with culture status end points. RESULTS: A total of 10,137 peptides corresponding to 872 proteins were identified, quantified, and used for statistical analysis across the longitudinal patient cohort. In response to TB treatment, 244 proteins were significantly altered. Pathway/network comparisons helped visualize the interconnected proteins, identifying up regulated (lipid transport, coagulation cascade, endopeptidase activity) and down regulated (acute phase) processes and pathways in addition to other cross regulated networks (inflammation, cell adhesion, extracellular matrix). Detection of possible lung injury serum proteins such as HPSE, significantly downregulated upon treatment. Analyses of microbiologic data over time identified a core set of serum proteins (TTHY, AFAM, CRP, RET4, SAA1, PGRP2) which change in response to treatment and also strongly correlate with culture status. A similar set of proteins at baseline were found to be predictive of week 6 and 8 culture status. CONCLUSION: A comprehensive host serum protein dataset reflective of TB treatment effect is defined. A repeating set of serum proteins (TTHY, AFAM, CRP, RET4, SAA1, PGRP2, among others) were found to change significantly in response to treatment, to strongly correlate with culture status, and at baseline to be predictive of future culture conversion. If validated in cohorts with long term follow-up to capture failure and relapse of TB, these protein markers could be developed for monitoring of treatment in clinical trials and in patient care.

Population pharmacokinetic drug-drug interaction pooled analysis of existing data for rifabutin and HIV PIs.

OBJECTIVES: Extensive but fragmented data from existing studies were used to describe the drug-drug interaction between rifabutin and HIV PIs and predict doses achieving recommended therapeutic exposure for rifabutin in patients with HIV-associated TB, with concurrently administered PIs. METHODS: Individual-level data from 13 published studies were pooled and a population analysis approach was used to develop a pharmacokinetic model for rifabutin, its main active metabolite 25-O-desacetyl rifabutin (des-rifabutin) and drug-drug interaction with PIs in healthy volunteers and patients who had HIV and TB (TB/HIV). RESULTS: Key parameters of rifabutin affected by drug-drug interaction in TB/HIV were clearance to routes other than des-rifabutin (reduced by 76%-100%), formation of the metabolite (increased by 224% in patients), volume of distribution (increased by 606%) and distribution to the peripheral compartment (reduced by 47%). For des-rifabutin, clearance was reduced by 35%-76% and volume of distribution increased by 67%-240% in TB/HIV. These changes resulted in overall increased exposure to rifabutin in TB/HIV patients by 210% because of the effects of PIs and 280% with ritonavir-boosted PIs. CONCLUSIONS: Given together with non-boosted or ritonavir-boosted PIs, rifabutin at 150 mg once daily results in similar or higher exposure compared with rifabutin at 300 mg once daily without concomitant PIs and may achieve peak concentrations within an acceptable therapeutic range. Although 300 mg of rifabutin every 3 days with boosted PI achieves an average equivalent exposure, intermittent doses of rifamycins are not supported by current guidelines.

Clinical evaluation of the nelfinavir-rifabutin interaction in patients with tuberculosis and human immunodeficiency virus infection.

STUDY OBJECTIVE: To characterize the bidirectional interaction between twice-daily nelfinavir and twice-weekly rifabutin and isoniazid in patients with tuberculosis and human immunodeficiency virus (HIV) infection. DESIGN: Prospective cohort study. SETTING: Three clinical research centers. PATIENTS: Seven patients with HIV-related tuberculosis. INTERVENTION: Rifabutin 300 mg and isoniazid 15 mg/kg (maximum dose 900 mg) twice/week were administered for at least 2 weeks during the continuation phase of tuberculosis treatment. Antiretroviral therapy with nelfinavir 1250 mg twice/day and two nucleoside reverse transcriptase inhibitors was then added. MEASUREMENTS AND MAIN RESULTS: Patients underwent blood sampling for pharmacokinetic analysis during the continuation phase of tuberculosis therapy and after a median of 21 days after the addition of antiretroviral treatment. When rifabutin was coadministered with nelfinavir, its area under the concentration-time curve from 0-21 hours (AUC(0-21)) increased 22% (geometric mean 5.01 microg.hr/ml [90% confidence interval (CI) 3.25-7.71] with nelfinavir vs 4.10 microg.hr/ml [90% CI 3.18-5.27] without nelfinavir; geometric mean ratio 1.22 [90% CI 0.78-1.92]). Also, the AUC(0-21) for the active metabolite, desacetylrifabutin, increased significantly (geometric mean ratio 3.46, 90% CI 1.84-6.47, p=0.009). In the presence of rifabutin, the pharmacokinetic parameters of nelfinavir and its principal metabolite M8 were similar to those of patients not taking rifabutin. No drug interaction between nelfinavir and isoniazid was detected. CONCLUSIONS: Coadministration of rifabutin and isoniazid without dosage adjustment during twice-weekly tuberculosis therapy with nelfinavir-based antiretroviral therapy resulted in rifabutin exposures within the acceptable ranges for safety and efficacy. Therefore, this combination is an appropriate option for the simultaneous treatment of tuberculosis and HIV infection when tuberculosis therapy is given twice weekly.