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Record W4404874621 · doi:10.1093/ijnp/pyae060

Combination of Haloperidol With UNC9994, β-arrestin-Biased Analog of Aripiprazole, Ameliorates Schizophrenia-Related Phenotypes Induced by NMDAR Deficit in Mice

2024· article· en· W4404874621 on OpenAlexafffund
Tatiana V. Lipina, Huy Giang, Jonathan S. Thacker, William C. Wetsel, Marc G. Caron, Jean‐Martin Beaulieu, Ali Salahpour, Amy J. Ramsey

Bibliographic record

VenueThe International Journal of Neuropsychopharmacology · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicReceptor Mechanisms and Signaling
Canadian institutionsLunenfeld-Tanenbaum Research InstituteMount Sinai HospitalUniversity of Toronto
FundersCanadian Institutes of Health Research
KeywordsAripiprazoleHaloperidolPartial agonistPharmacologyDopamine receptor D2Prefrontal cortexTypical antipsychoticSchizophrenia (object-oriented programming)AgonistNMDA receptorDopaminePsychologyAntipsychoticOpen fieldDopamine transporterAtypical antipsychoticNeuroscienceMedicineInternal medicineReceptorPsychiatryDopaminergicCognition

Abstract

fetched live from OpenAlex

BACKGROUND: Glutamatergic system dysfunction contributes to a full spectrum of schizophrenia-like symptoms, including the cognitive and negative symptoms that are resistant to treatment with antipsychotic drugs (APDs). Aripiprazole, an atypical APD, acts as a dopamine partial agonist, and its combination with haloperidol (a typical APD) has been suggested as a potential strategy to improve schizophrenia. Recently, an analog of aripiprazole, UNC9994, was developed. UNC9994 does not affect dopamine 2 receptor (D2R)-mediated Gi/o protein signaling but acts as a partial agonist for D2R/β-arrestin interactions. Hence, one of our objectives was to probe the behavioral effects of co-administrating haloperidol with UNC9994 in the N-methyl-D-aspartate receptor (NMDAR) mouse models of schizophrenia. The biochemical mechanisms underlying the neurobiological effects of dual haloperidol × UNC9994 action are currently missing. Hence, we aimed to explore D2R- and NMDAR-dependent signaling mechanisms that could underlie the effects of dual drug treatments. METHODS: NMDAR hypofunction was induced pharmacologically by acute injection of MK-801 (NMDAR pore blocker; 0.15 mg/kg) and genetically by knockdown of Grin1 gene expression in mice, which have a 90% reduction in NMDAR levels (Grin1 knockdown [Grin1-KD]). After intraperitoneal injections of vehicle, haloperidol (0.15 mg/kg), UNC9994 (0.25 mg/kg), or their combination, mice were tested in open field, prepulse inhibition (PPI), Y-maze, and Puzzle box. Biochemical effects on the phosphorylation of Akt, glycogen synthase kinase-3 (GSK-3), and CaMKII in the prefrontal cortex (PFC) and striatum of MK-801-treated mice were assessed by western blotting. RESULTS: Our findings indicate that low dose co-administration of UNC9994 and haloperidol reduces hyperactivity in MK-801-treated animals and in Grin1-KD mice. Furthermore, this dual administration effectively reverses PPI deficits, repetitive/rigid behavior in the Y-maze, and deficient executive function in the Puzzle box in both animal models. Pharmacological inhibition of NMDAR by MK-801 induced the opposite effects in the PFC and striatum on pAkt-S473 and pGSK3β-Ser9. Dual injection of haloperidol with UNC9994 reversed MK-801-induced effects on pAkt-S473 but not on pGSK3β-Ser9 in both brain structures. CONCLUSIONS: The dual administration of haloperidol with UNC9994 at low doses represents a promising approach to ameliorate symptoms of schizophrenia. The combined drug regimen elicits synergistic effects specifically on pAkt-S473, suggesting it as a potential biomarker for antipsychotic actions.

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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.009
GPT teacher head0.271
Teacher spread0.262 · 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 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

Citations1
Published2024
Admission routes2
Has abstractyes

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