ReviewFrontiers in pharmacology2019
Treatment-Resistant Schizophrenia: Insights From Genetic Studies and Machine Learning Approaches.
Review in Frontiers in pharmacology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
14 citing papers in PubMed.
- Atypical antipsychotics alter microglial functions via astrocyte-derived extracellular vesicles.Brain, behavior, and immunity · 2026Article
- Pharmaco-Multiomics: A New Frontier in Precision Psychiatry.International journal of molecular sciences · 2025Review
- A hybrid model for predicting response to risperidone after first-episode psychosis.Revista brasileira de psiquiatria (Sao Paulo, Brazil : 1999) · 2025Article
- Multimodal prediction of the need of clozapine in treatment resistant schizophrenia; a pilot study in first-episode psychosis.Biomarkers in neuropsychiatry · 2024Article
- Identification of novel functional brain proteins for treatment-resistant schizophrenia: Based on a proteome-wide association study.European psychiatry : the journal of the Association of European Psychiatrists · 2023Article
- Article
- Machine Learning and Non-Affective Psychosis: Identification, Differential Diagnosis, and Treatment.Current psychiatry reports · 2022Review
- Cytokine Imbalance as a Biomarker of Treatment-Resistant Schizophrenia.International journal of molecular sciences · 2022Review
- Peripheral biomarkers of treatment-resistant schizophrenia: Genetic, inflammation and stress perspectives.Frontiers in pharmacology · 2022Review
- Cooperative Studies Program (CSP) #572: A Study of Serious Mental Illness in Veterans as a Pathway to personalized medicine in Schizophrenia and Bipolar Illness.Personalized medicine in psychiatry · 2021Article
- The promise of machine learning in predicting treatment outcomes in psychiatry.World psychiatry : official journal of the World Psychiatric Association (WPA) · 2021Article
- A unique missense variant in the E1A-binding protein P400 gene is implicated in schizophrenia by whole-exome sequencing and mutant mouse models.Translational psychiatry · 2021Article
- Precision Psychiatry Applications with Pharmacogenomics: Artificial Intelligence and Machine Learning Approaches.International journal of molecular sciences · 2020Review
- Challenges and Future Prospects of Precision Medicine in Psychiatry.Pharmacogenomics and personalized medicine · 2020Review
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Schizophrenia (SCZ) is a severe psychiatric disorder affecting approximately 23 million people worldwide. It is considered the eighth leading cause of disability according to the World Health Organization and is associated with a significant reduction in life expectancy. Antipsychotics represent the first-choice treatment in SCZ, but approximately 30% of patients fail to respond to acute treatment. These patients are generally defined as treatment-resistant and are eligible for clozapine treatment. Treatment-resistant patients show a more severe course of the disease, but it has been suggested that treatment-resistant schizophrenia (TRS) may constitute a distinct phenotype that is more than just a more severe form of SCZ. TRS is heritable, and genetics has been shown to play an important role in modulating response to antipsychotics. Important efforts have been put into place in order to better understand the genetic architecture of TRS, with the main goal of identifying reliable predictive markers that might improve the management and quality of life of TRS patients. However, the number of candidate gene and genome-wide association studies specifically focused on TRS is limited, and to date, findings do not allow the disentanglement of its polygenic nature. More recent studies implemented polygenic risk score, gene-based and machine learning methods to explore the genetics of TRS, reporting promising findings. In this review, we present an overview on the genetics of TRS, particularly focusing our discussion on studies implementing polygenic approaches.
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