Characterization of specific and distinct patient types in clinical trials of acute schizophrenia using an uncorrelated PANSS score matrix transform (UPSM)

Ulotaront: review of preliminary evidence for the efficacy and safety of a TAAR1 agonist in schizophrenia

Ulotaront is a trace amine-associated receptor 1 (TAAR1) agonist in Phase 3 clinical development for the treatment of schizophrenia. Ulotaront was discovered through a unique, target-agnostic approach optimized to identify drug candidates lacking D2 and 5-HT2A receptor antagonism, while demonstrating an antipsychotic-like phenotypic profile in vivo. The mechanism of action (MOA) of ulotaront is thought to be mediated by agonism at TAAR1 and serotonin 5-HT1A receptors. Ulotaront has completed two Phase 2 trials (4-week acute study and 26-week open-label extension) which led to Breakthrough Therapy Designation from the US Food and Drug Administration for the treatment of schizophrenia. In the double-blind, placebo-controlled, acute study, ulotaront was associated with significant (p < 0.001) improvement in Positive and Negative Syndrome Scale (PANSS) total score (effect size [ES]: 0.45), with improvements vs. placebo also observed across secondary endpoints. Post-hoc analyses of the acute trial revealed additional evidence to support the effect of ulotaront on negative symptoms. In the 4-week study, ulotaront was well-tolerated, with an incidence of adverse events (AEs) numerically lower compared to placebo (45.8% vs. 50.4%; with a number needed to harm [NNH] for individual ulotaront AEs all > 40). The open-label extension demonstrated further improvement across schizophrenia symptoms and confirmed the tolerability of ulotaront, with a 6-month completion rate of 67%. Based on current data, ulotaront shows potential to be a first-in-class TAAR1 agonist for the treatment of schizophrenia with a safety and efficacy profile distinct from current antipsychotics.

A Scoping Review of the Use of Machine Learning in Health Economics and Outcomes Research: Part 2-Data From Nonwearables

OBJECTIVES

Despite the increasing interest in applying machine learning (ML) methods in health economics and outcomes research (HEOR), stakeholders face uncertainties in when and how ML can be used. We reviewed the recent applications of ML in HEOR.

METHODS

We searched PubMed for studies published between January 2020 and March 2021 and randomly chose 20% of the identified studies for the sake of manageability. Studies that were in HEOR and applied an ML technique were included. Studies related to wearable devices were excluded. We abstracted information on the ML applications, data types, and ML methods and analyzed it using descriptive statistics.

RESULTS

We retrieved 805 articles, of which 161 (20%) were randomly chosen. Ninety-two of the random sample met the eligibility criteria. We found that ML was primarily used for predicting future events (86%) rather than current events (14%). The most common response variables were clinical events or disease incidence (42%) and treatment outcomes (22%). ML was less used to predict economic outcomes such as health resource utilization (16%) or costs (3%). Although electronic medical records (35%) were frequently used for model development, claims data were used less frequently (9%). Tree-based methods (eg, random forests and boosting) were the most commonly used ML methods (31%).

CONCLUSIONS

The use of ML techniques in HEOR is growing rapidly, but there remain opportunities to apply them to predict economic outcomes, especially using claims databases, which could inform the development of cost-effectiveness models.

Ulotaront: a TAAR1/5-HT1A agonist in clinical development for the treatment of schizophrenia

INTRODUCTION

Current antipsychotics are postsynaptic dopamine-2(D₂) receptor blockers, which often, but not always, effectively improve acute psychotic symptoms and prevent relapse in schizophrenia and other severe mental disorders, but are associated with various side effects, including parkinsonism, akathisia, sedation/somnolence, and cardiometabolic alterations. Furthermore, the efficacy of current antipsychotics for negative and cognitive symptoms in schizophrenia is limited. Ulotaront is a novel trace-amine-associated receptor-1(TAAR1) agonist with serotonin-1A receptor agonist activity, and without postsynaptic D2-receptor antagonism. Phase 2 clinical data for ulotaront in patients with acutely exacerbated schizophrenia are promising regarding the potential improvement in positive, negative, and depressive symptoms.

AREAS COVERED

An overview of the pharmacokinetic and pharmacodynamic properties of ulotaront is given. Summary of clinical efficacy and safety/tolerability from Phase 1/2-trials, and of ongoing Phase 3-trials, is also given.

EXPERT OPINION

Ulotaront is a promising agent for the treatment of schizophrenia with an apparent benign safety profile, which might provide a much-needed new and different treatment option for various domains of schizophrenia. Data from larger Phase 3-trials, including for relapse prevention, schizophrenia subdomains, and in adolescents, are awaited. If ongoing Phase 3-trials in adults are successful, further research on combination regimens with existing antipsychotics, and in treatment-resistant schizophrenia as well as in mood disorders would be desirable.