Development of Antibiofilm Therapeutics Strategies to Overcome Antimicrobial Drug Resistance
Bibliographic record
Abstract
A biofilm is a community of stable microorganisms encapsulated in an extracellular matrix produced by themselves. Many types of microorganisms that are found on living hosts or in the environment can form biofilms. These include pathogenic bacteria that can serve as a reservoir for persistent infections, and are culpable for leading to a broad spectrum of chronic illnesses and emergence of antibiotic resistance making them difficult to be treated. The absence of biofilm-targeting antibiotics in the drug discovery pipeline indicates an unmet opportunity for designing new biofilm inhibitors as antimicrobial agents using various strategies and targeting distinct stages of biofilm formation. The strategies available to control biofilm formation include targeting the enzymes and proteins specific to the microorganism and those involved in the adhesion pathways leading to formation of resistant biofilms. This review primarily focuses on the recent strategies and advances responsible for identifying a myriad of antibiofilm agents and their mechanism of biofilm inhibition, including extracellular polymeric substance synthesis inhibitors, adhesion inhibitors, quorum sensing inhibitors, efflux pump inhibitors, and cyclic diguanylate inhibitors. Furthermore, we present the structure-activity relationships (SAR) of these agents, including recently discovered biofilm inhibitors, nature-derived bioactive scaffolds, synthetic small molecules, antimicrobial peptides, bioactive compounds isolated from fungi, non-proteinogenic amino acids and antibiotics. We hope to fuel interest and focus research efforts on the development of agents targeting the uniquely complex, physical and chemical heterogeneous biofilms through a multipronged approach and combinatorial therapeutics for a more effective control and management of biofilms across diseases.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 0.001 |
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".