The scale of the global problem provides the backdrop. According to the World Health Organization, approximately 10.8 million people developed tuberculosis and 1.25 million died from the disease in 2023, and drug-resistant strains continue to undermine progress toward the End TB Strategy. Rifampicin-resistant tuberculosis (RR-TB), multidrug-resistant tuberculosis (MDR-TB), pre-extensively drug-resistant tuberculosis (pre-XDR-TB), and extensively drug-resistant tuberculosis (XDR-TB) all demand complex, prolonged treatment and are associated with higher costs, worse clinical outcomes, and ongoing transmission. Rapid and accurate identification of resistance is therefore central to controlling the epidemic, yet in rural settings access to molecular testing can be constrained by laboratory capacity, shortages of trained personnel, specimen transport logistics, and infrastructure limitations.

The Eastern Cape illustrates these constraints vividly. Its communities are geographically dispersed, and specialized diagnostic services may be far away. Molecular technologies such as Xpert MTB/RIF Ultra and line probe assays have improved TB detection nationally, but the researchers argue that decentralized, point-of-care approaches could complement existing laboratory-based services where centralized testing falls short. Their study, led by Ncomeka Sineke and colleagues including senior author Lindiwe Modest Faye, set out to answer a deceptively simple question: if you could build only a small, affordable diagnostic panel for this region, which resistance genes should it target first?

To answer it, the team conducted a retrospective secondary analysis of routinely collected programme data drawn from the Electronic Drug-Resistant Tuberculosis Register (EDRWeb), facility treatment registers, district reports, and laboratory records. The dataset covered patients with bacteriologically confirmed DR-TB who initiated treatment between January 2020 and December 2024. Resistance categories followed established WHO definitions: RR-TB for resistance to rifampicin with or without other drugs, MDR-TB for resistance to at least rifampicin and isoniazid, pre-XDR-TB for MDR/RR-TB with additional fluoroquinolone resistance, and XDR-TB for MDR/RR-TB that is also resistant to bedaquiline or linezolid.

The headline finding was the overwhelming dominance of first-line drug resistance. RR-TB accounted for 191 of the 402 cases (47.5 percent) and MDR-TB for 178 cases (44.3 percent), meaning these two categories together made up 91.8 percent of the DR-TB burden. Pre-XDR-TB represented just 23 cases (5.7 percent) and XDR-TB only 10 cases (2.5 percent). This distribution mattered enormously for the prioritization exercise, because it suggested that a diagnostic capable of detecting rifampicin and isoniazid resistance would address the vast majority of locally relevant resistance, while fluoroquinolone and second-line resistance detection could follow in later iterations.

The researchers formalized this reasoning in a conceptual tool they call the Diagnostic Priority Index (DPI), which weighs five domains: disease burden, frequency of resistance categories, treatment outcome vulnerability, programme impact, and potential suitability for decentralized implementation. Resistance categories accounting for at least 40 percent of the caseload were deemed high epidemiological priority, those between 5 and 39 percent medium priority, and those below 5 percent candidates for future expansion. The authors are careful to note that these thresholds were pragmatic, study-specific decision boundaries rather than statistically validated cutoffs. Applying the framework, the genes rpoB, katG, and inhA emerged as high-priority targets, because mutations in rpoB drive most rifampicin resistance while katG and the inhA promoter region are the principal determinants of isoniazid resistance. The medium-priority tier comprised gyrA, gyrB, rrs, and eis, which are associated with fluoroquinolone and injectable drug resistance, while embB, pncA, Rv0678, atpE, and pepQ were flagged for future expansion.

The surveillance data also revealed a troubling trend in treatment outcomes. Among patients with MDR-TB, treatment success declined from 80.9 percent in 2021 to 38.5 percent in 2024, while the proportion of outcomes classified as not evaluated rose from zero to 30.8 percent over the same period. Treatment success among RR-TB patients fell from 79.5 percent to 51.6 percent, with unevaluated outcomes climbing to 25.8 percent. The researchers interpret these patterns as signals of programme vulnerability, but they are emphatic that the data do not permit causal attribution to diagnostic limitations. DR-TB outcomes are shaped by many factors, including HIV status, treatment regimens, adherence, loss to follow-up, comorbidities, socioeconomic circumstances, and healthcare access, none of which could be assessed from the aggregated records.

The second half of the framework is where the study earns its molecular-biophotonic label, though the authors stress that this component is entirely conceptual. They mapped the prioritized genes onto candidate optical biosensing technologies drawn from the published literature: surface plasmon resonance (SPR), localized surface plasmon resonance (LSPR), fluorescence-based biosensing, surface-enhanced Raman spectroscopy (SERS), photonic crystal biosensors, and integrated optical waveguide biosensors. These approaches exploit optical phenomena to detect nucleic acids or biomolecules, and published studies suggest some offer advantages in analytical sensitivity, multiplexing capability, miniaturization, and potential portability. In the conceptual compatibility assessment, SPR, LSPR, and fluorescence-based platforms appeared compatible with all three high-priority targets, while SERS and photonic crystal sensors could accommodate high- and medium-priority targets, and waveguide biosensors could theoretically span the full panel.

The limitations of the study are as important as its contributions, and the authors enumerate them with unusual candor. No isolate-level mutation analysis or sequencing was performed, so the study cannot report the actual frequency of specific mutations in the population; the gene priorities reflect the distribution of resistance categories combined with established resistance mechanisms from the literature. No biosensor was fabricated or tested, and no analytical sensitivity, specificity, limit of detection, reproducibility, diagnostic accuracy, or clinical feasibility was evaluated. The DPI thresholds were exploratory rather than externally validated, and the two-facility setting may limit generalizability to regions with different resistance patterns. The authors also acknowledge that the dataset lacked bacterial load, diagnostic turnaround times, detailed phenotypic susceptibility results, and patient-level clinical characteristics.

What the study offers, then, is a hypothesis-generating translational pathway rather than a finished technology. The researchers recommend that future work begin with mutation-level characterization of the local resistance spectrum using targeted or whole-genome sequencing, followed by laboratory development and analytical validation of candidate assays starting with rpoB, katG, and inhA, and then prospective clinical validation against reference methods such as Xpert MTB/RIF Ultra, line probe assays, and phenotypic drug-susceptibility testing. Implementation studies would ultimately need to assess cost, workflow integration, quality assurance, acceptability, and compatibility with national TB programmes before any claims about faster diagnosis or better outcomes could be made. For now, the value lies in the demonstration that routine surveillance data, often collected merely for programme monitoring, can be repurposed to steer early-stage diagnostic research toward the targets that matter most in the communities where the burden is heaviest.

Subject of Research: Epidemiology-guided prioritization of molecular targets for drug-resistant tuberculosis diagnostics in rural Eastern Cape, South Africa

Article Title: Translating drug-resistant tuberculosis surveillance into molecular–biophotonic diagnostic innovation in rural Eastern Cape, South Africa

Article References: Sineke, N., Sabisa, K., Dlatu, N., Ombinda-Lemboumba, S., Apalata, T., & Faye, L. M. (2026). Translating drug-resistant tuberculosis surveillance into molecular–biophotonic diagnostic innovation in rural Eastern Cape, South Africa. Frontiers in Tuberculosis, 4, Article 1937755. https://doi.org/10.3389/ftubr.2026.1937755

Image Credits: AI Generated

DOI: 10.3389/ftubr.2026.1937755

Keywords: drug-resistant tuberculosis, molecular diagnostics, biophotonics, point-of-care testing, South Africa, Eastern Cape, surveillance, rpoB, katG, inhA, biosensors, MDR-TB

The logic behind the study begins with the pharmacology of the starting material. Isoniazid is a prodrug that must be activated by the bacterial KatG enzyme before it can inhibit enoyl-acyl carrier protein reductase, or InhA, the enzyme responsible for building the mycolic acids that form the waxy protective coat of the tuberculosis bacillus. Mutations in KatG are a principal route by which the bacterium escapes the drug. Schiff bases, compounds defined by an azomethine linkage of the form C=N, have long attracted medicinal chemists because they are easy to synthesize, interact strongly with biological targets, and display a wide range of pharmacological activities. By attaching substituted aromatic aldehydes to the hydrazide portion of isoniazid, the researchers hoped to create molecules that could engage InhA directly and retain activity even where the classical activation pathway fails.

The team designed a focused virtual library of 30 novel isoniazid-based Schiff base derivatives by condensing isoniazid with structurally diverse substituted aromatic aldehydes. The aldehydes were chosen to span both electron-withdrawing and electron-donating substituents in different positional orientations, allowing a preliminary structure-activity relationship assessment of how these electronic changes influence lipophilicity and predicted interactions with the InhA active site. Structures were drawn in ChemDraw, converted into three-dimensional conformations, and geometry-optimized with the MMFF94 force field. Before any synthesis was attempted, the entire library was subjected to a sequential computational funnel designed to eliminate weak candidates early and concentrate laboratory effort on the most promising molecules.

That funnel is the methodological heart of the study. First, drug-likeness and pharmacokinetic properties were estimated with the SwissADME and pkCSM web servers, evaluating molecular weight, lipophilicity as LogP, hydrogen bond donors and acceptors, and topological polar surface area against Lipinski’s rule of five. Of the 30 designed compounds, 13 passed this filter and advanced. Next, toxicity prediction using the ProTox web server trimmed the field to 9 compounds with acceptable safety profiles. The Prediction of Activity Spectra for Substances, or PASS, analysis then estimated the probability of antitubercular activity, and 5 compounds emerged with favorable predicted activity. This progressive reduction, from 30 to 13 to 9 to 5, illustrates how an integrated in silico workflow can spare chemists the expense of synthesizing and testing compounds that are unlikely to survive downstream development.

The five survivors, designated SLB-1, SLB-3, SLB-5, SLB-7, and SLB-10, were then docked into the active site of InhA using AutoDock Vina. The crystal structure of the enzyme from M. tuberculosis, retrieved from the Protein Data Bank under accession 6EP8, was prepared by removing water molecules and co-crystallized ligands, adding polar hydrogens, and assigning Kollman charges. A 20 by 20 by 20 angstrom grid box centered on the active site defined the search space, and identical docking parameters were applied to all ligands, the reference drug isoniazid, and the co-crystallized ligand to ensure a fair comparison. The five Schiff base derivatives achieved binding affinities ranging from minus 8.7 to minus 9.5 kcal per mole, substantially more favorable than the minus 5.8 kcal per mole score recorded for isoniazid under the same conditions.

SLB-5, subsequently identified as N’,N”’-((5-nitro-1,3-phenylene)bis(methaneylylidene))di(isonicotinohydrazide), topped the ranking with a binding affinity of minus 9.5 kcal per mole. Interaction analysis revealed hydrogen bonds, hydrophobic contacts, and pi-pi stacking with key active-site residues including SER94, LYS165, and PHE149. The docking score also compared favorably with several known InhA inhibitors reported in the literature, including luteolin and piperine at minus 9.1 kcal per mole and tiliacorinine at minus 8.3 kcal per mole. A database and literature search across PubChem, Reaxys, and related resources found no previously reported identical structure, supporting the novelty of the compound and justifying its selection for synthesis. The authors are careful to note, however, that molecular dynamics simulations and RMSD-based redocking validation were not performed, leaving formal computational validation of the predicted binding modes for future work.

Synthesis of SLB-5 followed a classic Schiff base condensation. A substituted nitrobenzene-1,3-dicarbaldehyde was dissolved in ethanol, and a warm ethanolic solution of isoniazid was added dropwise with a few drops of glacial acetic acid as catalyst. The mixture was refluxed at 70 to 80 degrees Celsius for three to four hours, with reaction progress tracked by thin-layer chromatography. Cooling the reaction mixture produced a precipitate that was filtered, washed with cold ethanol, and dried. Structural characterization then confirmed the transformation: the FT-IR spectrum displayed a prominent azomethine C=N stretching band in the 1600 to 1650 per centimeter region alongside nitro group absorptions near 1550 and 1350 per centimeter, while the disappearance of the aldehydic C=O band confirmed consumption of the starting aldehyde.

Proton nuclear magnetic resonance spectroscopy provided further confirmation. A singlet at 8.80 parts per million corresponded to the azomethine proton, the signature of successful Schiff base formation, and a downfield singlet at 12.17 parts per million was assigned to the amide NH of the hydrazide moiety. Aromatic and pyridyl protons appeared between 7.59 and 8.68 parts per million, their deshielding consistent with the electron-withdrawing nitro and azomethine groups. Crucially, the absence of aldehydic proton signals around 9.5 to 10.0 parts per million verified complete conversion. High-resolution mass spectrometry sealed the case, showing a molecular ion at m/z 417.1722 and a characteristic fragment at m/z 371.1828 corresponding to loss of the nitro group, along with a stable base peak at m/z 285.1381 arising from cleavage of one pyridine carbohydrazide unit and the azomethine linkage.

The decisive test came in the biology. Using the Alamar Blue assay against M. tuberculosis H37Rv, SLB-5 demonstrated concentration-dependent growth inhibition, reaching 17.68, 35.48, 49.47, 60.16, and 75.14 percent inhibition at concentrations of 20, 40, 60, 80, and 100 micrograms per milliliter respectively. Isoniazid tested under identical conditions produced 16.27, 25.85, 43.95, 54.94, and 65.63 percent inhibition across the same range. At every concentration, the Schiff base derivative outperformed the standard drug, and at the highest dose the margin was nearly ten percentage points. Although complete inhibition was not achieved, the consistent superiority over a first-line agent suggests that Schiff base modification can genuinely enhance the antitubercular potential of the isoniazid scaffold rather than merely decorate it computationally.

The authors are appropriately measured about what these results do and do not prove. Enzyme inhibition assays and mechanistic studies are still needed to experimentally confirm InhA as the molecular target of SLB-5, since the docking evidence, however encouraging, remains predictive. Expanded testing against drug-sensitive and drug-resistant clinical strains, additional structural characterization by carbon-13 NMR, synthesis and evaluation of other high-ranking library members, and eventually in vivo studies of pharmacokinetics, bioavailability, and safety all lie ahead. Yet the study stands as a tidy demonstration of a workflow that many laboratories are now embracing: design broadly, filter computationally through drug-likeness, ADMET, activity prediction, and docking, and synthesize only the compound that survives every gate. In an era when multidrug-resistant and extensively drug-resistant tuberculosis continue to spread, that kind of efficiency is not just elegant chemistry; it is a practical strategy for getting new antitubercular candidates to the bench, and eventually perhaps to the clinic, faster than the bacterium can adapt.

Subject of Research: Computer-aided design and biological evaluation of isoniazid-based Schiff base derivatives as antitubercular agents targeting InhA

Article Title: Design and in silico screening of novel isoniazid-based Schiff base derivatives followed by synthesis and biological evaluation of the lead compound against Mycobacterium tuberculosis H37Rv

Article References: Bhuse, S. L., Patil, P. M., & Durbule, R. R. (2026). Design and in silico screening of novel isoniazid-based Schiff base derivatives followed by synthesis and biological evaluation of the lead compound against Mycobacterium tuberculosis H37Rv. Discover Chemistry, 3(1), Article 516. https://doi.org/10.1007/s44371-026-00909-w

Image Credits: AI Generated

DOI: 10.1007/s44371-026-00909-w

Keywords: tuberculosis, isoniazid, Schiff bases, molecular docking, InhA, drug resistance, ADMET, virtual screening, medicinal chemistry, H37Rv, Alamar Blue assay, lead compound