Advances in spray drying technology for anti-tubercular formulation development: A comprehensive review
Bibliographic record
Abstract
Global health is still seriously threatened by tuberculosis (TB), especially considering the increasing incidence of strains of the disease that are extensively drug-resistant (XDR) and multidrug-resistant (MDR). These TB superbugs necessitate innovative therapeutic strategies to improve treatment efficacy and patient compliance. Spray-drying technology has emerged as a promising method for the formulation of anti-TB therapies, offering advantages, such as enhanced drug stability, targeted delivery, and controlled release profiles. Key aspects include the principles of spray drying, the physicochemical characteristics of the resulting powders, and their impact on drug delivery to the lungs. Research focuses on optimizing these formulations to improve drug delivery directly to the lungs, enhance bioavailability, and abate side effects. This review underscores the transformative potential of these innovative spray-drying techniques, offering an effective and patient-friendly treatment regimen for TB. Additionally, the review highlights the potential of spray-dried formulations in developing host-directed therapies, antimicrobial peptides, vaccines, phages, and monoclonal antibodies. By integrating multidisciplinary approaches and cutting-edge technologies, spray-dried formulations promise to revolutionize TB treatment, ultimately contributing to better patient outcomes and the global effort to eradicate this persistent disease.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".