ORPHAN GPCRS – A NEW FRONTIER IN SCHIZOPHRENIA DRUG DISCOVERY
Bibliographic record
Abstract
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
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".