This article examines the development of antipsychotic medications in the period following the discovery of chlorpromazine in the mid-1950s up to the early 21st century. This era is defined by the identification of dopamine modulation as the central mechanism for treating psychosis, and by the development of a diversity of treatments around this mechanism. This strategy yielded a broad range of effective treatments and transformed clinical care; however, with the notable exception of clozapine, it failed to produce substantial gains in overall efficacy. Newer agents primarily differed in tolerability rather than therapeutic impact. Partial dopamine agonists refined, rather than replaced, this approach. As a result, the dopaminergic era offers a cautionary lesson for psychiatric drug development and underscores the need to move beyond dopamine-based models per se.

The recent introduction of the first non-dopaminergic antipsychotic marks a pivotal moment in psychopharmacology. This article revisits the development of chlorpromazine, the world’s first licensed antipsychotic, and considers what its history reveals about innovation in psychiatric drug discovery. Chlorpromazine emerged in the early 1950s as a treatment for surgical shock. Its subsequent recognition as an antipsychotic transformed psychiatric care, ushering in the modern era of psychopharmacology. This article argues that chlorpromazine’s success was not serendipitous: it depended on specific enabling conditions, including rigorous behavioural neuroscience, astute clinical observation across disciplines, close collaboration between clinicians and industry, and relatively agile regulatory frameworks. Understanding these factors is essential as psychiatry enters a new phase of drug development for the treatment of psychosis.

After more than seven decades in which antipsychotic drug development has been dominated by dopaminergic mechanisms, the recent emergence of a muscarinic receptor-based treatment represents a conceptual shift in the pharmacotherapy of psychosis. This article reviews the history and development of the first clinically effective non-dopaminergic antipsychotic, tracing its origins to unexpected behavioural effects observed during trials of muscarinic agonists developed for Alzheimer’s disease. Early studies of the muscarinic agonist xanomeline demonstrated antipsychotic efficacy, but were limited by poor tolerability due to peripheral cholinergic adverse effects. Subsequent progress depended on the dissociation of central therapeutic effects from peripheral side-effects, through the combination of xanomeline with the peripherally restricted muscarinic antagonist trospium. The development of this treatment highlights several broader lessons for psychiatric drug discovery, including the value of revisiting previously abandoned compounds, the benefits of leveraging existing human safety data on the importance of early-phase translational research and the role of innovative companies.

Alzheimer’s disease is a progressive and incurable condition and the leading cause of dementia worldwide. Lim et al’s (2024) Cochrane Review on the acetylcholinesterase inhibitor galantamine and its effects in Alzheimer’s disease and mild cognitive impairment (MCI) concluded that the drug slows cognitive and functional decline in Alzheimer’s disease, but not in MCI. However, in several studies the handling of data after drop-out was a potential source of bias. In this month’s Round the Corner, we also propose that the characteristics of the patient sample limit the external validity of the findings, and that the research question, although valid, is of limited practical importance.

Social media offers research teams new opportunities for outreach and study recruitment; however, research teams often need guidance and tools on how to use social media to effectively recruit for their clinical trials. To address this gap, the Recruitment Innovation Center (RIC) developed a new, comprehensive toolkit informed by community feedback and extensive RIC experience. The toolkit contains guidance and considerations for using social media to recruit clinical trial participants, including best practices, platform-specific strategies, and customizable templates. This paper details the creation and contents of this new, free resource.

Although randomised controlled trials (RCTs) have many merits and are suitable for interventions such as pharmaceuticals, there is a mismatch between RCTs and therapy. RCTs presuppose interventions that are standardised, specific, time-limited and measurable, characteristics that are less straightforward for therapy. Instead, a pluralistic evidence base is required for therapy.

Recruitment and retention challenges continue to hinder the success of clinical trials. Artificial intelligence (AI) has emerged as a promising means to optimize various clinical trial processes; however, its impact specifically on recruitment and retention has not been comprehensively evaluated. This scoping review utilized the Joanna Briggs Institute framework and adhered to PRISMA-ScR guidelines, systematically searching literature published between January 2018 and June 2024 across multiple databases. Of the 21,573 records screened, 121 studies were included. A meta-analysis was conducted to quantitatively assess the performance of AI-driven tools. AI applications for patient screening demonstrated strong performance, achieving a pooled sensitivity of 0.91 (95% CI: 0.84–0.95) and an area under the curve (AUC) of 0.79 (95% CI: 0.72–0.85). AI tools employed for eligibility identification and classification also exhibited strong outcomes, with pooled sensitivities of 0.80 (95% CI: 0.76–0.84) and 0.92 (95% CI: 0.84–0.96), respectively, and precisions of 0.84 (95% CI: 0.80–0.88) and 0.91 (95% CI: 0.85–0.95). AI tools aimed at identifying patient cohorts showed moderate effectiveness (pooled sensitivity: 0.70 [95% CI: 0.52–0.84]; AUC: 0.74 [95% CI: 0.61–0.84]). Overall, AI presents significant potential for enhancing clinical trial recruitment and retention, with effectiveness varying across specific applications. These findings underscore AI’s valuable role in improving trial efficiency and data quality.

This case report presents the case of a 25-year-old woman who developed ketamine addiction following a single sub-anaesthetic dose of intranasal ketamine in a pilot study investigating intranasal racemic ketamine for acute suicidality. She had a history of depression, obsessive–compulsive disorder, autism spectrum disorder and anorexia nervosa, and she had sporadically used alcohol and cannabis. Following the intervention, she reported a transient reduction in suicidal ideation but later sought illicit ketamine to recreate its calming effects on intrusive thoughts. Subsequently she also started abusing cocaine and 3-methylmethcathinone (3-MMC). Within weeks she had escalated to daily use, which led to financial distress, housing instability and a suicide attempt when access was cut off. Although she initially ceased use, she later relapsed into ketamine and cocaine addiction. This case highlights the addictive risk of ketamine, even in controlled settings. Given ketamine’s rising use in psychiatric treatment, careful screening, monitoring and awareness of addiction potential are essential. Future research should evaluate patient-specific risk factors and dosing strategies to minimise abuse liability.