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Approaches to Targeting Adenylyl Cyclase 1 for Novel Pain Therapeutics

2020· article· en· W3016500498 on OpenAlexaff
Joseph B. O’Brien, Michael P. Hayes, Val J. Watts, David L. Roman

Bibliographic record

VenueThe FASEB Journal · 2020
Typearticle
Languageen
FieldNeuroscience
TopicNeuropeptides and Animal Physiology
Canadian institutionsPurdue Pharma (Canada)
Fundersnot available
KeywordsAdenylyl cyclaseForskolinDrug discoveryChronic painMedicineADCY9CalmodulinPharmacologyKnockout mouseSmall moleculeChemistryBioinformaticsBiologyBiochemistryReceptorInternal medicineEnzymePsychiatry

Abstract

fetched live from OpenAlex

Adenylyl cyclases (AC) catalyze the formation of cyclic AMP (cAMP) from ATP and are involved in a number of disease states, making them attractive potential drug targets. Recent preclinical studies have identified neuronal adenylyl cyclase type 1 (AC1) as a novel target for treating chronic pain. AC1 is highly expressed in neuronal tissues associated with pain processing and neuronal plasticity, and studies using AC1 knockout mice provide direct evidence linking AC1 to chronic inflammatory pain conditions. Furthermore, AC1 inhibitors would lack the side effects associated with other agents (e.g. opioids) used to treat chronic inflammatory pain. We have designed our studies to target NOT the conserved P‐site or forskolin‐binding site, but rather a novel approach, targeting the unique protein‐protein interaction of AC1 and calmodulin (CaM). AC1 and AC8 are both activated by CaM; however, the CaM binding domains are unique in structure and location providing an unprecedented opportunity to achieve AC1 selectivity. We hypothesize that developing a small molecule inhibitor of AC1 will allow us to mimic the AC1 knockout phenotype and provide a new avenue for the treatment of chronic inflammatory pain. Through the development and implementation of a novel biochemical high‐throughput screening paradigm we will interrogate a library of 100,000 compounds to identify inhibitors of the AC1/CaM protein‐protein interaction. Hit compounds will be validated and chemically optimized to lead molecules using cellular assays focused on selectivity and potency to guide medicinal chemistry efforts. To date, we have screened 14,080 compounds and identified 18 compounds capable of disrupting the AC1/CaM protein‐protein interaction, a hit rate of 0.13%. We anticipate the identification of selective AC1 inhibitors that will ultimately be improved and applied in models of chronic inflammatory pain. Support or Funding Information This work was supported by The National Institutes of Health (1R61NS111070 DLR & VJW), The University of Iowa Center for Biocatalysis and Bioprocessing and the NIH‐sponsored Predoctoral Training Program in Biotechnology (5T32GM008365 JBO).

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.297
GPT teacher head0.282
Teacher spread0.015 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations0
Published2020
Admission routes1
Has abstractyes

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