The Indispensable Value of Small-Molecule Antivirals in Epidemic and Pandemic Preparedness
Bibliographic record
Abstract
Harbingers of infectious viral pandemics, such as the H1N1 influenza, SARS, Zika, Ebola, MERS, and SARS-CoV-2, caused major outbreaks in the first two decades of the 21st century. Despite warnings, therapeutic tools that could be rapidly and sustainably scaled at a global level when SARS-CoV-2 emerged were lacking. Small-molecule antivirals can play a crucial role in both individual patient care and broader public health strategies for controlling and mitigating the impact of viral diseases. Despite their utility, the lack of R&D investment in this class of intervention has prevented the world from reaping the benefits they can deliver. The INTREPID Alliance 2025 publication of the Antiviral Clinical and Preclinical Development Landscape-4th Edition, revealed significant gaps in the development pipeline. No antivirals are in clinical development for 4 of the 13 viral families designated by the World Health Organization as viral families of pandemic and endemic concern.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".