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Record W2963408872 · doi:10.1021/acs.accounts.9b00261

Strategies to Counteract Botulinum Neurotoxin A: Nature’s Deadliest Biomolecule

2019· article· en· W2963408872 on OpenAlexfundno aff
Lucy Lin, Margaret E. Olson, Lisa M. Eubanks, Kim D. Janda

Bibliographic record

VenueAccounts of Chemical Research · 2019
Typearticle
Languageen
FieldMedicine
TopicBotulinum Toxin and Related Neurological Disorders
Canadian institutionsnot available
FundersNational Institute of Allergy and Infectious DiseasesNational Institute on Drug AbuseNatural Sciences and Engineering Research Council of Canada
KeywordsBotulinum neurotoxinMedicineBotulismIntensive care medicineLethalityPharmacologyToxicologyBiologyToxin

Abstract

fetched live from OpenAlex

ConspectusBotulinum neurotoxin serotype A (BoNT/A), marketed commercially as Botox, is the most toxic substance known to man with an estimated intravenous lethal dose (LD50) of 1–2 ng/kg in humans. Despite its widespread use in cosmetic and medicinal applications, no postexposure therapeutics are available for the reversal of intoxication in the event of medical malpractice or bioterrorism. Accordingly, the Centers for Disease Control and Prevention categorizes BoNT/A as a Category A pathogen, posing the highest risk to national security and public health as a result of the ease with which BoNT/A can be weaponized and disseminated. BoNT/A-mediated lethality results from neurons impeded from releasing acetylcholine, which ultimately causes muscle paralysis and possible death by asphyxiation with the loss of diaphragm function. Currently, the only available respite for BoNT/A poisoning is antibody-based therapy; however, this intervention is only effective within 12–24 h postexposure. Small molecule therapeutics remain the only opportunity to reverse BoNT/A intoxication after neuronal poisoning and are urgently needed. Nevertheless, no small molecule BoNT/A inhibitors have reached the clinic or even advanced to clinical trials.This Account highlights the accomplishments and existing challenges facing BoNT/A drug discovery today. Using the comprehensive body of work from our laboratory, we illustrate our nearly two-decade endeavor to discover a clinically relevant BoNT/A inhibitor. Specifically, a discussion on the identification and characterization of new chemical leads, the development of in vitro and in vivo assays, and pertinent discoveries in BoNT/A structural biology related to small molecule inhibition is presented. Lead discovery efforts in our laboratory have leveraged both in vitro high-throughput screening and rational design, and an array of mechanistic strategies for inhibiting BoNT/A has been discovered, including noncovalent inhibition, metal-binding active site inhibition, covalent inhibition, and α- and β-exosite inhibition. We contrast the strengths and weaknesses of each of these mechanistic strategies and propose the most favorable approach for success. Finally, we discuss multiple serendipitous discoveries of antibotulism small molecules with alternative mechanisms of action. Remaining challenges facing clinically relevant BoNT/A inhibition are presented and analyzed, including the current inability to reconcile toxin half-life (months to greater than one year) in neurons with in vivo pharmaceutical lifetimes and reoccurring inconsistencies between in vitro, cellular, and in vivo translation.Our Account of BoNT/A chemical research emphasizes the present accomplishments and critically analyzes the remaining obstacles for drug discovery. Importantly, we call for an increased focus on the discovery of safe and effective covalent inhibitors of BoNT/A that compete with the inherent half-life of the toxin.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.083
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.029
GPT teacher head0.406
Teacher spread0.378 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations31
Published2019
Admission routes1
Has abstractyes

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