WHO target product profiles for TB preventive treatment
Bibliographic record
Abstract
BACKGROUND: The WHO has developed target product profiles (TPPs) describing the most appropriate qualities for future TPT regimens to assist developers in aligning the characteristics of new treatments with programmatic requirements.METHODS: A technical consultation group was convened by the WHO to determine regimen attributes with greatest potential impact for patients (i.e., improved risk/benefit profile) and populations (i.e., reduction in transmission and TB prevalence). The group categorised regimen attributes as ‘priority´ or ‘desirable´; and defined for each attribute the minimum requirements and optimal targets.RESULTS: Nine priority attributes were defined, including efficacy, treatment duration, safety, drug–drug interactions, barrier to emergence of drug resistance, target population, formulation, dosage, frequency and route of administration, stability and shelf life. Regimens meeting optimal targets were characterised, for example, as having superior efficacy, treatment duration of ≤2 weeks, and improved tolerability and safety profile compared with current regimens. The four desirable attributes included regimen cost, safety in special populations, treatment adherence and need for drug susceptibility testing in the index patient.DISCUSSION: It may be difficult for a single regimen to satisfy all characteristics so regimen developers may have to consider trade-offs. Additional operational aspects may be relevant to the feasibility and public health impact of new TPT regimens.
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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 | 0.014 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.007 |
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".