Discovery and Development of Antibacterial Agents: Fortuitous and Designed
Bibliographic record
Abstract
Today, antibacterial drug resistance has turned into a significant public health issue. Repeated intake, suboptimal and/or unnecessary use of antibiotics, and, additionally, the transfer of resistance genes are the critical elements that make microorganisms resistant to conventional antibiotics. A substantial number of antibacterials that were successfully utilized earlier for prophylaxis and therapeutic purposes have been rendered inadequate due to this phenomenon. Therefore, the exploration of new molecules has become a continuous endeavour. Many such molecules are at various stages of the investigation. A surprisingly high number of new molecules are currently in the stage of phase 3 clinical trials. A few new agents have been commercialized in the last decade. These include solithromycin, plazomicin, lefamulin, omadacycline, eravacycline, delafloxacin, zabofloxacin, finafloxacin, nemonoxacin, gepotidacin, zoliflodacin, cefiderocol, BAL30072, avycaz, zerbaxa, vabomere, relebactam, tedizolid, cadazolid, sutezolid, triclosan, and afabiacin. This article aims to review the investigational and recently approved antibacterials with a focus on their structure, mechanisms of action/resistance, and spectrum of activity. Delving deep, their success or otherwise in various phases of clinical trials is also discussed while attributing the same to various causal factors.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".