Advancing personalized medicine for tuberculosis through the application of immune profiling

Abstract

While early and precise diagnosis is the key to eliminating tuberculosis (TB), conventional methods using culture conversion or sputum smear microscopy have failed to meet demand. This is especially true in high-epidemic developing countries and during pandemic-associated social restrictions. Suboptimal biomarkers have restricted the improvement of TB management and eradication strategies. Therefore, the research and development of new affordable and accessible methods are required. Following the emergence of many high-throughput quantification TB studies, immunomics has the advantages of directly targeting responsive immune molecules and significantly simplifying workloads. In particular, immune profiling has been demonstrated to be a versatile tool that potentially unlocks many options for application in TB management. Herein, we review the current approaches for TB control with regard to the potentials and limitations of immunomics. Multiple directions are also proposed to hopefully unleash immunomics' potential in TB research, not least in revealing representative immune biomarkers to correctly diagnose TB. The immune profiles of patients can be valuable covariates for model-informed precision dosing-based treatment monitoring, prediction of outcome, and the optimal dose prediction of anti-TB drugs.

Keywords: biomarkers; immunomics; model-informed precision dosing; multi-omics; personalized medicine; tuberculosis.

Figure 1

The natural course of TB and potential points for biomarker-guided interventions. A better understanding of disease progression, accruable diagnosis, appropriate monitoring, and possible therapeutic interventions can be acquired by integrating demographic and conventional clinical data with high-throughput omics data. TB, tuberculosis.

Figure 2

Representative immune biomarkers corresponding to TB diagnosis and management. The approach utilized for TB investigation has been gradually converted from conventional ELISA to high-throughput technology with multiple advantages. More and more immune biomarkers have been reported for different disease stages and types. Robust and affordable high-throughput technologies and data analysis facilitate the discovery of clinically relevant biomarkers via well-established study designs. (ELISA, Enzyme-linked immunoassay; xMAP, Multi-Analyte Profiling; LTBI, Latent TB infection; MDR-TB, Multidrug-Resistant Tuberculosis).

Figure 3

An immune-centric approach for TB investigation in the omics era. Comparison of the high-throughput approach (left) and conventional assay (right) for immune profiling investigation in TB diagnosis and monitoring. Other matured omics technologies are capable of providing complementary information about the immune profiles of TB patients. LC-MS, liquid chromatography-mass spectrometry; TB, Tuberculosis.

Figure 4

Sample collection strategy of immunological biosignature discovery. The sample collection for immunological biosignature discovery should focus on three critical phases of tuberculosis (TB) treatment: 1) Baseline; 2) Evaluation; 3) Treatment completion. The samples taken in the baseline before TB treatment is started could be used as biomarker for diagnosis confirmation. Meanwhile, the samples collected in the evaluation phase could be used to identify biomarkers for treatment monitoring. Further, the biomarkers could predict the early bacterial conversion and/or adverse drug reaction potency. The samples taken during the treatment completion could be helpful to predict the successfulness of treatment and possibility of future recurrence. Contributions to each phase might be from either the same patients or different patients of a similar age and gender compared to the patients enrolled in other phases. DS, Drug Susceptibility; DR, Drug Resistance; TB, tuberculosis.

Figure 5

Integration of immunomics biosignature into semi-automated therapeutic drug monitoring application. Immune profile quantification in relation to a patient’s outcome and medical records are used to: (1) validate representative markers for TB diagnosis, outcome prediction, or susceptibility; (2) differentiate diagnosis of TB patients and outcome predictions; (3) provide initial optimal dose; (4) provide additional solid input for treatment monitoring; and (5) semi-automated TDM under population PK model evaluation. The quantified immune profile from each patient will be taken as part of comprehensive covariates evaluation during population PK model development. Our current semi-automated TDM algorithm takes PK results into account for each specific drug, immune markers (as both covariates and PD marker), ethnic differences, and clinical symptoms to provide optimal dose suggestions. Furthermore, the result of immune markers also can be provided in the TDM report to support the monitoring and evaluation phases. TDM, therapeutic drug monitoring; TB, tuberculosis; PK, pharmacokinetics; PD, pharmacodynamics; CL, clearance; Vd, volume of distribution; Ka, absorption rate; Tv, typical value.