The Missing Link in India’s TB Elimination Plan

Tuberculosis remains one of India’s most persistent public-health challenges. The country accounts for an estimated 2.5–3 million TB cases annually, making it the single largest contributor to the global TB burden. India is also a hotspot for drug-resistant TB, including multi-drug-resistant (MDR-TB) and rifampicin-resistant TB (RR-TB), which together represented a significant share of global cases in 2024.

Yet, despite decades of research, progress in shortening TB treatment regimens and tackling resistance has been slow. One major reason lies in the way TB has traditionally been studied. Most laboratory models rely on:

• Planktonic bacterial cultures, where TB bacteria grow freely in liquid media, far removed from the human lung environment.
• Two-dimensional (2D) cell cultures, where mammalian cells are grown on flat plastic plates and then infected with bacteria.
• Animal models, such as mice, which offer more complexity but often fail to replicate human lung biology or drug responses accurately.

These systems have repeatedly produced misleading signals. Drugs that look promising in the lab often underperform in humans, while others—such as Pyrazinamide, a cornerstone of first-line TB therapy—show little or no activity in conventional lab models despite being clinically effective.

The 3D Lung Model: What’s New and Why It Matters

Researchers at IISc have addressed this disconnect by developing a 3D hydrogel-based lung model that more faithfully reproduces the mechanical and biochemical micro-environment of human lung tissue. The model uses a soft collagen gel, closely matching the elasticity and structure of lung tissue, rather than rigid plastic surfaces.

Human immune cells infected with TB bacteria are embedded within this gel. Almost immediately, the system begins to display features that are hallmarks of TB inside real patients:

• Foamy, fat-laden immune cells, which TB bacteria exploit as nutrient reservoirs.
• Clusters of TB bacteria, resembling early granuloma-like formations rather than dispersed microbes.
• Disease behaviour and growth patterns that diverge sharply from those seen in flat, 2D cultures.

According to Dr Rachit Agarwal, a DBT/Wellcome Trust Intermediate Fellow at IISc, both the host cells and the bacteria behave in ways that closely mirror human infection. Crucially, the model was able to demonstrate the activity of Pyrazinamide, a result that most existing laboratory systems fail to capture. This alone signals a major advance in predictive accuracy.

How This Changes Drug Discovery and Testing

The 3D model represents more than a scientific novelty—it offers a practical shift in the TB drug-development pipeline.

Unlike traditional systems, the hydrogel model:

• Provides realistic mechanical cues (such as tissue stiffness) and biochemical signals that influence how TB bacteria survive and respond to drugs.
• Is modular and scalable, allowing experiments to run over extended periods and be adapted for different research questions.
• Bridges the gap between simple cell cultures and expensive, ethically complex animal studies.

This is particularly significant in light of recent regulatory shifts. Under the FDA Modernization Act 3.0 in the United States and India’s New Drugs and Clinical Trials Rules, advanced in-vitro systems—such as 3D organoids and organ-on-a-chip platforms—are now explicitly encouraged as reliable pre-clinical tools for assessing drug safety and efficacy before human trials.

The Drug-Development Time Trap — How Poor Models Waste a Decade

Tuberculosis drug development is not slow because of a lack of ideas; it is slow because of systematic misprediction. Across infectious diseases, but especially TB, the largest losses occur when drugs that appear effective in early testing fail in humans. The root cause has repeatedly been traced to pre-clinical models that do not replicate human disease biology.

What the data shows

According to analyses collated by the World Health Organization and the TB Alliance, fewer than 5–7% of TB drug candidates entering pre-clinical pipelines ultimately reach regulatory approval. The average development timeline for a novel TB drug is 10–15 years, with cumulative costs estimated between USD 500 million and USD 1 billion, even after public and philanthropic subsidies.

Critically, Phase II clinical trials — where efficacy is first tested in humans — account for a disproportionate share of attrition. Studies published in Nature Reviews Drug Discovery and The Lancet Infectious Diseases have shown that many failures are not due to toxicity, but due to loss of efficacy once the drug encounters the complex lung micro-environment — hypoxia, acidic pH, immune pressure, and tissue barriers — that flat or animal models fail to reproduce.

Why TB is especially vulnerable

TB bacteria exist in multiple metabolic states within the lung, including slow-growing or dormant forms that are intrinsically drug-tolerant. Conventional planktonic cultures disproportionately select for drugs that kill rapidly dividing bacteria, creating a false positive bias early in development. This bias is a key reason why drugs with excellent laboratory profiles have repeatedly failed to shorten treatment duration in humans.

What the 3D lung model changes

By introducing tissue-level mechanical stiffness, biochemical gradients, and immune-cell architecture, the IISc hydrogel model offers a way to eliminate weak candidates before they enter costly animal studies or human trials. Even a modest improvement in predictive accuracy — reducing late-stage failure by 10–15 percentage points — could save years of development time and hundreds of millions of dollars per candidate.

India’s Drug-Resistant TB Map — Why DR and XDR Testing Is Urgent

The scale of the problem

India is not just the world’s largest TB burden country; it is also one of the largest reservoirs of drug-resistant TB. The WHO’s Global Tuberculosis Report 2024 estimates that India accounts for approximately 27–30% of global MDR/RR-TB cases, translating to over 130,000 new cases annually.

Rifampicin resistance — a proxy for MDR-TB — is particularly alarming because rifampicin is the backbone of first-line TB therapy. Once resistance emerges, patients are pushed into second-line regimens that are longer (18–24 months), more toxic, and 3–5 times more expensive than standard six-month therapy.

Outcomes and system strain

Treatment success rates tell the story starkly:

• Drug-sensitive TB: ~85% global success rate
• MDR-TB: ~60%, and often lower in high-burden settings
• XDR-TB: Frequently below 50%, even with newer drugs

Each MDR-TB patient can cost the public health system ₹3–5 lakh, compared to ₹10,000–15,000 for drug-sensitive TB, according to programme estimates under India’s TB control framework.

Why current testing pipelines fall short

Drug-resistant TB strains often reside deeper within lung lesions and granulomas, where drug penetration is poorest. Animal models frequently underestimate this barrier, while 2D cultures eliminate it altogether. As a result, drugs that appear potent against resistant strains in vitro often fail in real lungs.

What the new model enables

By allowing infection with clinical DR and XDR isolates inside lung-like tissue, the IISc platform could help identify:

• Drugs that retain activity under hypoxia and acidic stress
• Compounds that penetrate immune-cell clusters
• Combinations that suppress resistance emergence

For India, where resistant TB increasingly defines the epidemic, this capability is not incremental — it is foundational.

A Regulatory Inflection Point — Why 3D Models Matter Now

A global regulatory shift

For decades, drug regulation privileged animal testing as the gold standard for pre-clinical validation. That assumption is now being dismantled. In the United States, the U.S. Food and Drug Administration Modernization Acts (2.0 and 3.0) explicitly remove the statutory requirement for animal testing when human-relevant in-vitro systems can demonstrate safety and efficacy.

Parallel changes are underway in India. The New Drugs and Clinical Trials Rules, 2019, issued by the Central Drugs Standard Control Organisation, explicitly recognise organoids, organ-on-chip systems, and advanced in-vitro platforms as valid components of pre-clinical evidence.

Why this matters for Indian science

This regulatory opening changes the power equation. Countries that can build credible human-relevant disease models can now influence global drug pipelines rather than merely test imported compounds. India, with its disease burden, clinical diversity, and lower research costs, is uniquely positioned to lead — provided it has scientifically robust platforms.

Strategic timing

The IISc lung model arrives at a moment when:

• Global pharma is actively seeking animal-sparing, predictive systems
• Regulators are open to non-animal data
• TB drug pipelines are being rebuilt after decades of stagnation

In effect, the model is not just scientifically timely; it is regulator-ready.

Tackling Drug-Resistant TB: A Critical Next Step

India’s TB challenge is increasingly defined by resistance. Rifampicin, a first-line broad-spectrum antibiotic, has seen rising resistance, pushing more patients into longer, costlier, and more toxic treatment regimens.

While the IISc team has not yet tested drug-resistant (DR) or extensively drug-resistant (XDR) TB strains in the new model, the pathway is clear. By infecting immune cells within the hydrogel using drug-resistant clinical isolates, researchers expect to reliably evaluate which compounds can penetrate lung-like tissue, reach bacterial clusters, and remain effective under realistic conditions.

Such testing could dramatically improve early-stage screening, filtering out ineffective candidates before they reach costly animal studies or clinical trials—and accelerating promising therapies that genuinely work in lung-like environments.

 The Granuloma Frontier: Understanding TB’s Stronghold

The next phase of development focuses on replicating tuberculosis granulomas—tight aggregates of immune cells that surround TB bacteria in the lung. These structures are a defining pathological feature of TB and present one of the biggest challenges in treatment.

Granulomas can lead to vastly different outcomes: complete bacterial clearance, long-term containment (latent TB), or uncontrolled bacterial growth. They also act as physical and chemical barriers, limiting drug penetration into their cores.

By refining the 3D model to reproduce granuloma-like structures, IISc researchers aim to study:

• How TB bacteria survive within these niches.
• Why some drugs fail to reach effective concentrations inside granulomas.
• How new compounds might be designed or combined to overcome these barriers.

As Dr Agarwal notes, such models could be “of immense importance” in decoding host-pathogen interactions and accurately predicting drug efficacy.

From Lab Breakthrough to National TB Elimination Goals

India’s tuberculosis strategy, anchored in the National TB Elimination Programme (NTEP), set an ambitious target of eliminating TB by 2025—five years ahead of the global 2030 deadline. While diagnostics and treatment access have expanded rapidly, outcomes have lagged the ambition. India still records an estimated 2.5–3 million TB cases annually, accounting for roughly 27% of the global burden, and remains among the top contributors to MDR/RR-TB, with over 130,000 new cases each year. Treatment success rates drop sharply from ~85% for drug-sensitive TB to around 60% for MDR-TB, while treatment duration stretches from 6 months to 18–24 months, pushing per-patient costs from roughly ₹10,000–15,000 to ₹3–5 lakh. These dynamics mean that faster case detection alone cannot deliver elimination without more effective and shorter drug regimens.

In this context, the IISc three-dimensional lung model addresses a core bottleneck in India’s TB response: the slow and failure-prone drug development pipeline. Globally, fewer than 5–7% of TB drug candidates entering pre-clinical testing reach approval, with late-stage failures often linked to poor prediction of human lung biology. By enabling drug testing in lung-like micro-environments that replicate hypoxia, acidic stress, immune clustering, and tissue barriers, the IISc platform can help filter weak candidates earlier and refine promising combinations faster. Even a 10–15% improvementin predictive accuracy could shave years off development timelines and reduce costs that typically exceed USD 500 million per successful drug. Backed by India Alliance funding (DBT–Wellcome Trust), such platforms offer India a path to align frontier research with NTEP priorities—shifting TB elimination from an aspirational target to a problem of sustained execution.

 Why This Matters for India

For a country that bears the world’s largest TB burden, even incremental improvements in drug development can translate into millions of lives affected. A laboratory system that more accurately predicts human outcomes could:

• Reduce failures in late-stage clinical trials.
• Speed up the development of shorter, more effective TB regimens.
• Improve the fight against MDR-TB and XDR-TB, where options are currently limited and costly.

Backed by funding from India Alliance, a partnership between India’s Department of Biotechnology and the Wellcome Trust (UK), the IISc model exemplifies how targeted investment in biomedical research infrastructure can deliver high-impact returns.

In the long arc of India’s TB elimination goal, this 3D lung model does not promise a cure by itself—but it may finally give scientists a testing ground that looks and behaves like the disease they are trying to defeat.