MétaCan
Menu
Back to cohort
Record W4407387182 · doi:10.1093/ijnp/pyae059.303

ORPHAN GPCRS – A NEW FRONTIER IN SCHIZOPHRENIA DRUG DISCOVERY

2025· article· en· W4407387182 on OpenAlexaff
G. A. Stewart, Yao Lü, Sheng Yu Ang, Daisy L. Spark, Juulke Castelijn, Trang Pham, Michelle A. Camerino, Monica Langiu, Natalie Diepenhorst, Clotilde Mannoury la Cour, Leonid Churilov, Jess Nithianantharajah, Christopher J. Langmead

Bibliographic record

VenueThe International Journal of Neuropsychopharmacology · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomics and Rare Diseases
Canadian institutionsDiscovery Centre
Fundersnot available
KeywordsSchizophrenia (object-oriented programming)Drug discoveryFrontierG protein-coupled receptorDrugData scienceMedicineComputational biologyPsychiatryBioinformaticsComputer scienceBiologyReceptorInternal medicineGeography

Abstract

fetched live from OpenAlex

Abstract Background Schizophrenia manifests as a broad and diverse symptomatology that creates a heterogenous patient population that responds to standard of care medicines to varying extents. The three characterised symptom domains; positive, negative and cognitive, represent, but are not limited to: hallucinations and delusions, negative affect, and impairments in learning and memory, respectively. Frontline drugs only effectively address the positive symptoms in ~70% of patients, yet it is the cognitive impairments associated with schizophrenia (CIAS) that pose the greatest hurdle to improve societal integration and quality of life. These issues culminate in a critical unmet medical need. There have been a number of clinical candidates and mechanisms that have sought to address CIAS, and all have failed. These failures point to two probable influences: 1. insufficient insight into the mechanisms capable of driving a change in disease symptoms; 2. the lack of stringent preclinical models and assays that derive endpoints similar to those tested in patients. Whilst schizophrenia standard of care medicines and investigational agents engage a number of G protein-coupled receptors (GPCRs), there remains an array of CNS-enriched orphan GPCRs that represent new opportunities as drug targets (Lu et al., 2023). Objective Our approach employs a holistic workflow to target validation and drug discovery through applying techniques that accelerates the process whilst increasing fidelity. We have applied structure- enabled drug design, disease-relevant pharmacology and advanced rodent models to multiple orphan GPCRs to create a global understanding of the target ranging from ligand-receptor interactions to in- depth behavioural insights. Method We used cryogenic electron microscopy to generate molecular models of our target orphan GPCRs in complex with their cognate G proteins to gain molecular insights into ligand binding and G protein coupling – facilitating structure-based drug design. We applied multi-endpoint pharmacology in recombinant and primary native cells to provide granularity to ligand-mediated signalling sequalae. Finally, we overlayed these findings with a comprehensive behavioural and cognitive battery (including rodent cognition touchscreens) to provide a correlation between our ligand-receptor mechanisms and psychosis- and cognition-relevant in vivo behavioural outcomes. Results Application of our technical workflow has provided molecular insights to accelerate ligand optimisation and deliver ligands with distinct, signal-biased pharmacological profiles. These ligands demonstrated antipsychotic activity (reversal of hyperlocomotion) and distinct cognition-enhancing profiles in mice, across working memory and attention tasks. Conclusions Development of this unique workflow has allowed the deconvolving of mechanisms of ligand-receptor interactions and signalling that accelerated development of ligands that target orphan GPCRs and display antipsychotic and pro-cognitive effects in mice. References Lu Y. 2023. Molecular insights into orphan G protein-coupled receptors relevant to schizophrenia. Br J Pharmacol. doi: 10.1111/bph.16221

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)0.002
metaresearch head score (Gemma)0.001
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: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.003
Open science0.0010.002
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0050.002

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.

Opus teacher head0.005
GPT teacher head0.282
Teacher spread0.277 · 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
GenreReview

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
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueThe International Journal of NeuropsychopharmacologySame topicGenomics and Rare DiseasesFrench-language works237,207