Rifampicin nanocrystals: Towards an innovative approach to treat tuberculosis
Bibliographic record
Abstract
Tuberculosis (TB) is one of the top ten causes of death worldwide and a leading cause of death in HIV patients. Rifampicin (Rif), a low water-soluble drug, is a critical first-line treatment and the most effective drug substance for therapy of drug-susceptible TB. However, Rif has high interindividual pharmacokinetic variability, mainly due to its highly variable absorption caused by its poor solubility. Drug nanocrystals are a promising technology to overcome this variability by increasing the surface area. This strategy allows for increasing the dissolution rate and improving the bioavailability of this BCS class II drug. In this study, Rif nanocrystals were prepared by a wet-bead milling method. A 3-factor, 3-level Box-Behnken design was used to investigate the independent variables: the concentration of rifampicin, the concentration of the stabilizing agent (Povacoat® type F), and the mass of zirconia beads. Two optimized formulations, F1-Rif and F2-Rif, were characterized by determining their particle size and size distribution, morphology, crystal properties, and antimicrobial activity. Differential scanning calorimetry (DSC) and powder X-ray diffraction (PXRD) revealed that rifampicin's polymorph II crystal structure was unchanged. The reduced particle size of <500 nm (100-fold decrease) increased the saturation solubility and dissolution rate up to 1.74-fold. The novel polymer, Povacoat®, demonstrated to be a suitable stabilizer to maintain the physical stability of nanosuspensions over two years. The Rif nanocrystals showed antimicrobial activity (0.25 μg/mL) not significantly different from standard rifampicin powder. However, the low cytotoxicity of the nanosuspensions in HepG2 cells was determined. When compared to the commercial product, the nanosuspension increased the rifampicin concentration 2-fold. In conclusion, the Rif nanosuspension allows half the needed volume of administration, which might increase compliance among children and elderly patients throughout the long-term treatment of TB.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".