Schizophrenia Clinical Trials Research Articles

Effects of Goal Management Training on self-efficacy, self-esteem, and quality of life for persons with schizophrenia spectrum disorders.

Persons with schizophrenia often show executive dysfunction assessed with both subjective (self-report) and objective (neuropsychological tests) measures. In a recent randomized controlled trial (RCT), subjective executive functioning in everyday life was improved following Goal Management Training (GMT). The aim of the current study is to investigate the potential of GMT to improve secondary well-being outcomes from that RCT, including self-esteem, self-efficacy, and quality of life in persons with schizophrenia spectrum disorders. Since well-being is frequently lower in persons with schizophrenia compared to healthy individuals, further knowledge about well-being as an outcome after cognitive remediation may have implications for clinical treatment. Sixty-five participants were randomly assigned to GMT (n = 31) or a waiting list control condition (n = 34). Assessments were conducted at baseline (T1), immediately after the intervention (T2-5 weeks), and at six-month follow-up (T3). Measures included the Rosenberg Self-Esteem Scale, the Perceived Quality of Life Scale, and the General Self-Efficacy Scale. Results were analyzed using a linear mixed model analysis for repeated measures. There were no significant effects of GMT on self-esteem or quality of life. Only the GMT group showed a significant increase in self-efficacy that was most evident at six months follow-up, F(1, 34) = 10.71, p = 0.002, d = 0.71. Improved self-efficacy was found to correlate significantly with a reduction in perceived executive dysfunction in an exploratory post hoc analysis. Our findings demonstrate the potential of GMT in improving self-efficacy in schizophrenia Clinical trial registration:https://clinicaltrials.gov, NCT03048695.

Predictors of treatment discontinuation during an 18-month multi-site randomized trial of Cognitive Enhancement Therapy for early course schizophrenia

Treatment discontinuation during clinical trials in schizophrenia is a critical challenge, especially for longer-term interventions in the early course. This research explored predictors of treatment discontinuation in an outpatient early course schizophrenia sample (N = 102) during an 18-month multi-site trial of Cognitive Enhancement Therapy (n = 58) and Enriched Supportive Therapy (n = 44). Fifty-three (52%) participants discontinued, with no significant difference between the treatment groups in discontinuation rate. Univariate and multivariate binary logistic regression models explored differences in key demographic and cognitive and behavioral outcomes between participants who completed and discontinued treatment. Significant multivariate predictors of discontinuation included IQ (linear) and problem solving (curvilinear). The concave shape of the problem solving prediction demonstrated that initially as scores were increasing the probability of non-completion was increasing. However, after a score of 41 (below average problem solving), the probability of being a non-completer decreased as performance increased. Non-completers had significantly lower IQ scores compared to completers. Post-hoc analyses indicated that participants who discontinued prior to mid-treatment exhibited the greatest intellectual challenges, with comparisons moderate-to-large in strength. IQ and problem solving are likely important factors to assess at pre-treatment in early course schizophrenia trials to identify those most vulnerable to discontinuation.

An objective Bayesian approach to estimation in multistage experiments.

This article presents a Bayesian approach to estimation in multistage experiments based on the reference prior theory. The idea of deriving design-dependent priors was first introduced using Jeffreys' criterion. A theoretical framework was then established by showing that explicit reference to the design is fully Bayesian justified and Bayesian objectivity cannot ignore such information. Extending the work to multi-parameter problems, a general form of priors was derived from the reference prior theory. In this article, I evidence the good frequentist properties of the reference posterior estimators with normally distributed data. As a notable advance, I address the issue of the point and the interval estimations upon experiment termination. The approach is applied to a data set collected in a clinical trial in schizophrenia with the possibility to stop the trial early if interim results provide sufficient evidence of efficacy or futility. Finally, I discuss the idea of using the reference posterior estimators as a default choice for objective estimation in multistage experiment.

Identification of a Potent Human Trace Amine-Associated Receptor 1 Antagonist.

Human trace amine-associated receptor subtype 1 (hTAAR1) is a G protein-coupled receptor that has therapeutic potential for multiple diseases, including schizophrenia, drug addiction, and Parkinson's disease (PD). Although several potent agonists have been identified and have shown positive results in various clinical trials for schizophrenia, the discovery of potent hTAAR1 antagonists remains elusive. Herein, we report the results of structure-activity relationship studies that have led to the discovery of a potent hTAAR1 antagonist (RTI-7470-44, 34). RTI-7470-44 exhibited an IC50 of 8.4 nM in an in vitro cAMP functional assay, a Ki of 0.3 nM in a radioligand binding assay, and showed species selectivity for hTAAR1 over the rat and mouse orthologues. RTI-7470-44 displayed good blood-brain barrier permeability, moderate metabolic stability, and a favorable preliminary off-target profile. Finally, RTI-7470-44 increased the spontaneous firing rate of mouse VTA dopaminergic neurons and blocked the effects of the known TAAR1 agonist RO5166017. Collectively, this work provides a promising hTAAR1 antagonist probe that can be used to study TAAR1 pharmacology and the potential therapeutic role in hypodopaminergic diseases such as PD.

Endogenous oxytocin levels are associated with facial emotion recognition accuracy but not gaze behavior in individuals with schizophrenia.

Difficulties in social cognition are common in individuals with schizophrenia (SZ) and are not ameliorated by antipsychotic treatment. Intranasal oxytocin (OT) administration has been explored as a potential intervention to improve social cognition; however, results are inconsistent, suggesting potential individual difference variables that may influence treatment response. Less is known about the relationship between endogenous OT and social cognition in SZ, knowledge of which may improve the development of OT-focused therapies. We examined plasma OT in relationship to facial emotion recognition and visual attention to salient facial features in SZ and controls. Forty-two individuals with SZ and 23 healthy controls viewed photographs of facial expressions of varying emotional intensity and identified the emotional expression displayed. Participants' gaze behavior during the task was recorded via eye tracking. Plasma oxytocin concentrations were determined by radioimmunoassay. SZ were less accurate than controls at identifying high-intensity fearful facial expressions and low-intensity sad expressions. Lower overall and high-intensity facial emotion recognition accuracy was associated with lower plasma OT levels in SZ but not controls. OT was not associated with visual attention to salient facial features; however, SZ had reduced visual attention to the nose region compared to controls. Individual differences in endogenous OT predict facial emotion recognition ability in SZ but are not associated with visual attention to salient facial features. Increased understanding of the association between endogenous OT and social cognitive abilities in SZ may help improve the design and interpretation of OT-focused clinical trials in SZ.

Risk of Unmasking on Schizophrenia Double Blind Clinical Trials Using Risperidone versus Comparator As A Result of Laboratory Analysis

Background: Randomized double blind clinical trial is the most used and accepted method of clinical research involving human subjects. Clinical researches are performed under strict regulatory regulations including Helsinki Declaration and International Conference for Harmonization Good Clinical Practice (ICH – GCP) recommendations. As per ICH GCP principles safety and well being of clinical trials patients should prevail over research objectives and their health status should be monitored throughout the trial using objective and concise indicators such as but not limited to laboratory analysis. This study aims to identify factors that might unblind risperidone treatment arms for patients enrolled in randomized double blind schizophrenia clinical trials using risperidone versus comparator, as a result of reviewing safety laboratory analysis. Methods: This study included 134 controlled schizophrenia patients who received treatment with risperidone versus antipsychotic agents with lower dopamine antagonist potency. Prolactine values were determined using imunoenzimatic and colorimetric method (ELISA) for cantitative determination of prolactine from serum or plasma. Results: 84% percent of the patients treated with risperidone showed elevated levels for prolactine above laboratory reference ranges defined as: Men ( 1.8 – 17.0 ng/ml) ; Women ( fertile 1.2 – 19.5 ng/ml, menopause 1.5 – 18.5 ng/ml) Conclusions: High elevated values of prolactin were determined for risperidone treated patients which might disclose the patient treatment arm to blinded investigator and jeopardize the research. As lab results are an essential safety factor of clinical trial those cannot be overruled and establishing Independent Data Monitoring Committee to oversee patients’ data and warn the investigator is recommended.

Depicting Safety Profile of TAAR1 Agonist Ulotaront Relative to Reactions Anticipated for a Dopamine D2-Based Pharmacological Class in FAERS.

Background and ObjectivesIn clinical trials, the safety of drugs is summarized by the incidence of adverse events, while post-marketing reporting systems use disproportionate reporting of adverse drug reactions. Here, we propose a method to evaluate the novelty of a safety profile of a drug in a new class (in clinical trials), against that of those already on the market (using pharmacovigilance data).MethodsThrough Bayesian disproportionality analyses of the US Food and Drug Administration Adverse Event Reporting System (FAERS) data, we identified and ranked Preferred Terms for a pool of 30 antipsychotics. Adverse event rates in randomized, double-blind, placebo-controlled schizophrenia clinical trials were summarized by their class specificity. One study (N = 245) of the trace amine-associated receptor 1 (TAAR1) agonist ulotaront (SEP-363856) was compared with five studies of dopamine D2 receptor-based antipsychotics lurasidone (N = 1041), quetiapine (N = 119), olanzapine (N = 122), and placebo (N = 504).ResultsIn clinical trials of antipsychotics, cumulative rates for adverse events at and above a threshold of disproportional reporting (Empirical Bayes Geometric Mean 50 > 3 in FAERS) were 52%, 42%, and 60% for lurasidone, quetiapine, and olanzapine, respectively, indicating that over half of the adverse events reported in clinical trials of an atypical antipsychotic are class-specific risks. In contrast, in the clinical trial of ulotaront, the cumulative rate was 23%, indicating a lower rate of antipsychotic class-specific risk.ConclusionsThese results demonstrate a novel approach to summarize adverse events in clinical trials, where the cumulative burden of class-specific risks describes the emerging safety profile of a new drug in clinical development, relative to reactions anticipated for drugs in an established pharmacological class.ClinicalTrials.gov IdentifiersNCT0296938, NCT00088634, NCT00549718, NCT00615433, NCT00790192.Supplementary InformationThe online version contains supplementary material available at 10.1007/s40261-021-01094-7.

Audio-digital recordings to assess ratings reliability in clinical trials of schizophrenia

We examined ratings reliability in 5 clinical trials of subjects with schizophrenia experiencing an acute exacerbation of psychosis. Audio-digital recordings of site-based interviews of the Positive and Negative Syndrome Scale (PANSS) or Brief Psychiatric Rating Scale (BPRS) were used to obtain blinded, site-independent scores to evaluate paired scoring concordance.High intraclass correlations were noted between 1810 paired site-based and site-independent PANSS scores (r = 0.801) and 1837 paired BPRS scores (r = 0.897) with high limits of agreement such that 93.9% of paired scores were within the calculated 95% confidence intervals. In 2 studies where sufficient PANSS data was available at baseline and endpoint, blinded site-independent ratings yielded a predictive value of 84.2% for replicating site-based response/nonresponse treatment outcomes.There was a significant positive correlation between site-based scores and paired scoring deviations (PANSS: r = 0.246; p < 0.0001; BPRS: r = 0.176; p < 0.0001). The magnitude (symptom severity) of PANSS or BPRS scores affected the directionality of paired scoring deviations in each study. Site-based raters scored the most symptomatic subjects higher and less symptomatic subjects lower than the paired site-independent raters on either instrument.This analysis affirms the utility of paired audio-digital scoring of site-based interviews as a surveillance strategy for schizophrenia studies. We noted a high predictive value of blinded site-independent raters to replicate site-based treatment outcomes. The bi-directionality of paired scoring deviations observed for both the PANSS and BPRS is consistent with findings found for depression rating instruments.

Application of machine learning to predict reduction in total PANSS score and enrich enrollment in schizophrenia clinical trials.

Clinical trial efficiency, defined as facilitating patient enrollment, and reducing the time to reach safety and efficacy decision points, is a critical driving factor for making improvements in therapeutic development. The present work evaluated a machine learning (ML) approach to improve phase II or proof‐of‐concept trials designed to address unmet medical needs in treating schizophrenia. Diagnostic data from the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) trial were used to develop a binary classification ML model predicting individual patient response as either “improvement,” defined as greater than 20% reduction in total Positive and Negative Syndrome Scale (PANSS) score, or “no improvement,” defined as an inadequate treatment response (<20% reduction in total PANSS). A random forest algorithm performed best relative to other tree‐based approaches in model ability to classify patients after 6 months of treatment. Although model ability to identify true positives, a measure of model sensitivity, was poor (<0.2), its specificity, true negative rate, was high (0.948). A second model, adapted from the first, was subsequently applied as a proof‐of‐concept for the ML approach to supplement trial enrollment by identifying patients not expected to improve based on their baseline diagnostic scores. In three virtual trials applying this screening approach, the percentage of patients predicted to improve ranged from 46% to 48%, consistently approximately double the CATIE response rate of 22%. These results show the promising application of ML to improve clinical trial efficiency and, as such, ML models merit further consideration and development.

Digital phenotyping adherence, feasibility, and tolerability in outpatients with schizophrenia

Digital phenotyping has potential for use as an objective and ecologically valid form of symptom assessment in clinical trials for schizophrenia. However, there are critical methodological factors that must be addressed before digital phenotyping can be used for this purpose. The current study evaluated levels of adherence, feasibility, and tolerability for active (i.e., signal and event contingent ecological momentary assessment surveys) and passive (i.e., geolocation, accelerometry, and ambulatory psychophysiology) digital phenotyping methods recorded from smartphone and smartband devices. Participants included outpatients diagnosed with schizophrenia (SZ: n = 54) and demographically matched healthy controls (CN: n = 55), who completed 6 days of digital phenotyping. Adherence was significantly lower in SZ than CN for active recordings, but not markedly different for passive recordings. Some forms of passive recordings had lower adherence (ambulatory psychophysiology) than others (accelerometry and geolocation). Active digital phenotyping adherence was predicted by higher psychosocial functioning, whereas passive digital phenotyping adherence was predicted by education, positive symptoms, negative symptoms, and psychosocial functioning in people with SZ. Both groups found digital phenotyping methods tolerable and feasibility was supported by low frequency of invalid responding, brief survey completion times, and similar impediments to study completion. Digital phenotyping methods can be completed by individuals with SZ with good adherence, feasibility, and tolerability. Recommendations are provided for using digital phenotyping methods in clinical trials for SZ.

The Impact of Aberrant Data Variability on Drug-Placebo Separation and Drug/Placebo Response in an Acute Schizophrenia Clinical Trial.

ObjectiveIn the current posthoc analyses, we evaluated the impact of markers of aberrant data variability on drug placebo separation and placebo and drug response in an acute schizophrenia clinical trial.MethodsPositive and negative syndrome scale data were obtained from a phase 2, randomized, double-blind, placebo controlled trial in hospitalized adults with schizophrenia experiencing an acute exacerbation. We assessed the impact of a total of six markers of aberrant data variability: erratic ratings, unusually large postbaseline improvement, high and low mean square successive difference (MSSD), identical and nearly identical ratings and compared the drug placebo difference, drug and treatment response at last visit in affected subjects vs those not affected. All analyses were conducted using generalized linear models.ResultsIn this posthoc analysis, drug placebo separation decreased with the presence of most markers of aberrant data variability. The only exception was high MSSD was associated with significant increase in the signal. In the affected subjects, the presence of indicators of increased data variability augmented the response to placebo, in the case of large postbaseline change and high MSSD, significantly. The presence of indicators of decreased variability numerically but not statistically decreased the response to placebo. Similar findings were observed in the drug treatment group with the exception of erratic ratings that numerically but not statistically decreased the response to the drug.DiscussionThe presence of most indicators of aberrant data variability had a detrimental effect on drug-placebo separation and showed different effects on placebo and treatment response.

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

Understanding the specificity of symptom change in schizophrenia can facilitate the evaluation antipsychotic efficacy for different symptom domains. Previous work identified a transform of PANSS using an uncorrelated PANSS score matrix (UPSM) to reduce pseudospecificity among symptom domains during clinical trials of schizophrenia. Here we used UPSM-transformed factor scores to identify 5 distinct patient types, each having elevated and specific severity among each of 5 symptom domains. Subjects from placebo-controlled clinical trials of acute schizophrenia were clustered (baseline) and classified (post-baseline) by a machine-learning algorithm. At baseline, all 5 patient types were similar in PANSS total score. Post-baseline, subjects’ memberships among the 5 UPSM patient types were relatively stable over treatment duration and were relatively insensitive to overall improvements in symptoms, in contrast to other methods based on untransformed PANSS items. Using UPSM-transformed PANSS, drug treatment effect sizes versus placebo were doubly-dissociated for specificity across symptom domains and within specific patient types. This approach illustrates how broader clinical trial populations can nevertheless be utilized to characterize the specificity of new mechanisms across the dimensions of schizophrenia psychopathology.

P.508 COVID-19 impact on entry symptom severity in schizophrenia clinical trials – preliminary data

P.508 COVID-19 impact on entry symptom severity in schizophrenia clinical trials – preliminary data

S246. THE EFFECT OF INCORRECT SCALE ADMINISTRATION ON DATA QUALITY IN SCHIZOPHRENIA CLINICAL TRIALS

BackgroundThe availability of date and time stamps of interview start times on eCOA systems permits monitoring and documentation of the order of administration of scales and instruments at each visit. The Clinical Global Impression Scale (CGI) is a holistic instrument that synthesizes all information available from the subject, caregivers, medical notes, etc. and is therefore typically required to be rated last at a particular visit. In the current retrospective analysis of double blind eCOA collected schizophrenia data pooled from multiple clinical trials we assessed the percent of visits where CGI was not administered last among other efficacy assessments. Additionally we assessed whether the inappropriate administration order of Clinical Global Impression Scale was associated with discrepancies in the data and change from baseline between the CGI and the primary efficacy outcome.MethodsAvailable eCOA data were pooled from schizophrenia double blind, placebo controlled clinical trials. Within the data, we identified those visits where the CGI was not administered last among other efficacy assessments (inappropriate administration order). Within each trial we identified as discordant those data where the actual primary efficacy score or its change from baseline differed by at least two standard deviations from the expected score after linear regression. For this procedure the CGI- S score used as a predictor and the primary efficacy outcome as dependent variable. Logistic regression was then used on pooled data to explore whether the incorrect order of CGI administration increased the odds of discordant ratings between the CGI and primary outcome measure.ResultsThe dataset consisted of 5,784 paired ratings of the CGI and the primary efficacy outcome. Among these, a total of 4,628 visits allowed to calculate change from baseline. Inappropriate order of CGI administration was identified in a total of 443 visits (7.7% of all visits). Discrepancies between CGI-S and the primary efficacy outcome were identified in 292 visits (5.1% of data) and discrepancies between change from baseline in CGI-S and in the primary efficacy outcome were observed in 249 cases (5.3% of data). The presence of incorrectly administered CGI increased the odds of raw score discrepancy 1.6x (95%CI 1.1–2.4), and the odds of discrepancy in change from baseline 1.8x (95%CI 1.2–2.7), both significant with p <0.01.DiscussionOur data indicate a relatively large percent of visits suffer from an incorrect scale administration order. We have previously explored a number of sources of possible noise in schizophrenia clinical trials. Current analyses identified a significant effect of incorrect scale administration on the presence of between scale discordances. Such findings indicate that order of CGI administration should be mandated in schizophrenia clinical trials and enforced by eCOA platforms. Additionally, violations to correct scale administration should be monitored through analytic programs and acted upon in case of repeated occurrences at the rater or site level.

S35. THE PLACEBO CONUNDRUM: MITIGATING THE RESPONSE AT THE SITE LEVEL

BackgroundThe placebo effect within Schizophrenia clinical trials has been noted as alarmingly high (Kinon et al., 2011). While numerous methodological approaches have been implemented to reduce this phenomenon (e.g., centralized ratings, remote rater monitoring, and data surveillance before subject randomization), research indicates the placebo effect is increasing over time (Chen et al., 2010). This may be because there has been no intervention aimed to minimize the placebo effect specifically targeting both the study subject and site staff. This is surprising given that the primary ‘culprits’ of the effect are from both individuals. The Placebo-Control Reminder Script (PCRS) was explicitly developed to help rectify this problem as it simultaneously educates subjects and reminds site raters (who are crucial in assessing symptoms which ultimately determine drug-placebo separation) of the key factors most cited as inducing these effects or Placebo Response Factors (PRFs – participant expectations of benefit, lack of placebo understanding, misconception of expected interactions with research site staff, and subject role uncertainty; Alphs et al., 2012). The current study explored the efficacy of the systemically designed PCRS in helping to reduce the placebo effect among subjects with a primary psychotic diagnosis.MethodsAcross two US sites, Schizophrenia / Schizoaffective adult patients experiencing at least moderate depression, per the self-reported Beck Depression Inventory-II (BDI-II; Beck et al., 1996), were randomized into two groups (70% of enrolled subjects per site were required to have a Schizophrenia diagnosis). The Intervention Group (IG) was read at each of the three study visits the one-page, brief (2 minute) PCRS before the BDI-II was administered, whereas the Control Group (CG) was not read the PCRS. Subjects’ perceptions of improvement regarding their depressive symptoms were also collected. Depression, and not psychosis, was the dependent variable due to a combination of scale psychometric limitations (e.g., the lack of targeted self-report psychosis measures) and psychosis assessment length and duration. All subjects were informed via the Informed Consent Form there was a 50% chance of receiving placebo or active drug developed to improve their depressive symptoms, but all subjects received placebo. Given this deception, subjects received a Debriefing Form at the end of the study revealing the investigation’s true intent and procedures.ResultsAs expected, IG (n=23) and CG (n=23) subjects did not differ in baseline characteristics, including depression (BDI-II: IG M=24.09, SD=2.39 vs. CG M=24.48, SD=3.37; p=.651) and diagnosis of either Schizophrenia or Schizoaffective Disorder (all p>.05). As hypothesized, time-by-group interaction showed that compared to the CG, IG subjects reported significantly (p=0.002) higher BDI-II scores at the endpoint visit (IG M=22.00, SD= 5.17 vs. CG M=17.17, SD=5.63), and perceived themselves as less improved in their sadness/depression (p=.013).DiscussionThe findings indicate that providing education at every study visit focusing on PRFs seems to significantly manage the placebo effect among participants with a primary psychotic diagnosis. Additionally, given that the PCRS helped subjects report continued depressive symptoms while in fact on a placebo (i.e., they described their depression more accurately) than subjects who were not read the PCRS, the current results have implications for the valid use of patient-reported outcome measures in clinical trials when the PCRS, or similar script, is administered. Other methodological ramifications as well as study limitations will be discussed in the poster.

M42. INDEPENDENT REVIEW &amp; MONITORING IMPROVES QUALITY OF PANSS DATA IN GLOBAL CLINICAL TRIALS

BackgroundThe Positive and Negative Syndrome Scale (PANSS) (1) is the most widely used endpoint for measuring change in schizophrenia clinical trials. A set of flags have been developed by ISCTM expert working group to identify potential scoring errors in PANSS assessments (2). Measures have been taken by sponsors (pharmaceutical industry) with the goal of increasing scoring reliability and data quality, such as the use of Independent Review (IRev). We evaluated changes in data quality when site raters stop being recorded and monitored via IRev by comparing two studies with the same cohort of raters, one with independent review and one without.MethodsData from PANSS assessments in two global multisite schizophrenia clinical trials were analyzed. We selected data from raters participating in both studies (which run concurrently for a significant period of time). Raters were rigorously trained on administration and scoring conventions and certified prior to the study through demonstration of adequate interrater reliability. In addition to these steps, raters in study A were required to audio record all PANSS assessments with a selected subset of visits being subject to IRev. PANSS assessments in study B were neither recorded nor monitored via IRev. Data quality after study completion was examined by calculating the frequency of anomalous data patterns identified as “high” (very probable or definite error) by the ISCTM Working Group in both studies. Additionally, we examined the percentage of assessments with lower than expected PANSS interview duration as captured via an eCOA platform.ResultsThere were 9441 eCOA PANSS assessments in study A and 6178 in study B included in this analysis. The proportions of flags that represented highly probable/definite error differed significantly between the studies (9% vs 18% for Study A and B, respectively, p<.01). The most significant differences in ISCTM flags were related to overly consistent scoring patterns (27 or more items scored identically to the prior visit) occurring with higher frequency in study B. Additionally, study B also had a significantly higher frequency of assessments flagged for low interview duration (< 15 minutes) (1% vs 4% for Study A and B, respectively, p<.01).DiscussionInitial rater training is necessary but not sufficient to ensure adequate data quality in schizophrenia trials. Implementation of additional in-study oversight through Independent Review or similar methods reduces the probability of data error in PANSS assessments, including the appearance of improbable rating patterns and decreased time spent interviewing study subjects. One potential limitation is that study A is a double-blind study whereas study B is an open label extension of study A.

P.385 Exploring the differences in relationship between the CGI-S and the PANSS between acute and negative symptom schizophrenia clinical trials

P.385 Exploring the differences in relationship between the CGI-S and the PANSS between acute and negative symptom schizophrenia clinical trials

&lt;p&gt;Assessing effectiveness of aripiprazole lauroxil vs placebo for the treatment of schizophrenia using number needed to treat and number needed to harm&lt;/p&gt;

ObjectiveSchizophrenia clinical trials commonly measure observed changes in Positive and Negative Syndrome Scale (PANSS) total score. However, it is more intuitive to think of response vs nonresponse, a binary outcome. Assessing binary outcomes enables calculation of number needed to treat (NNT) for therapeutic outcomes, number needed to harm (NNH) for adverse outcomes, and likelihood to be helped or harmed (LHH) to demonstrate benefit/risk tradeoffs. Here, NNT, NNH, and LHH were used to evaluate the clinical usefulness of aripiprazole lauroxil in patients with an acute schizophrenia exacerbation.MethodsCategorical efficacy and tolerability data were taken from the pivotal Phase 3 trial evaluating aripiprazole lauroxil for treatment of an acute exacerbation of schizophrenia. NNT and NNH values, with 95% CIs, were calculated in this post hoc analysis.ResultsUsing the intent-to-treat population for the pooled doses of aripiprazole lauroxil (441 mg [n=196] and 882 mg [n=204] q4w), responder rates (≥30% improvement from baseline PANSS total score) were 35.3% for aripiprazole lauroxil arms vs 18.4% for placebo (n=196), yielding a NNT of 6 (95% CI: 5–11). Discontinuation rates due to adverse events (AEs) were higher among patients randomized to placebo than to either aripiprazole lauroxil dose. Akathisia was the only AE with an incidence ≥5% in each aripiprazole lauroxil group and at least twice that of placebo (11.6%, 11.5%, and 4.3% of the patients receiving aripiprazole lauroxil 441 mg, 882 mg, and placebo, respectively), producing a NNH of 14 (95% CI: 9–33) for pooled aripiprazole lauroxil doses vs placebo. Calculating LHH for therapeutic response vs akathisia, aripiprazole lauroxil was 2.3 times more likely to result in a therapeutic response than an incident of akathisia.ConclusionUsing metrics of NNT, NNH, and LHH, aripiprazole lauroxil was an efficacious and well-tolerated intervention in a pivotal study in patients with an acute schizophrenia exacerbation.

Evaluating visual neuroplasticity with EEG in schizophrenia outpatients

Deficient neuroplasticity has been implicated in schizophrenia and can be examined with non-invasive methods in humans. High frequency visual stimulation (HFS) induces neuroplastic changes in visual evoked potential (VEP) components, similar to the tetanizing electrical stimulation that induces synaptic long-term potentiation (LTP). While visual HFS paradigms have been used in schizophrenia, the use of a single visual stimulus has precluded demonstration of whether the plasticity effects are specific to the stimulus presented during HFS (i.e., input specific). Additionally, test-retest reliability of VEP plasticity effects, an important consideration for applications of HFS paradigms in schizophrenia clinical trials, remains unknown. Accordingly, we administered a visual HFS paradigm to 38 schizophrenia patients and 27 healthy controls at baseline and two-weeks later. VEPs were elicited by horizontal and vertical line gratings before and after HFS; only one orientation was tetanized with HFS. Using a mass univariate permutation approach, we identified an input-specific cluster across groups that was broadly distributed over parietal-occipital areas between 108 and 183 ms. However, the groups did not differ in terms of the strength of plasticity effect. The test-retest reliability of the input-specific plasticity effect was modest over two weeks, suggesting that this HFS paradigm requires further development before it could be used to track plasticity change in clinical trials. Moreover, while the current HFS paradigm induced significant input-specific neuroplasticity, it did not replicate prior studies showing deficient neuroplasticity in schizophrenia. Accordingly, demonstration of deficient visual LTP-like neuroplasticity in schizophrenia may depend on paradigm parameters that remain to be fully elucidated.

A Quantitative Justification of Similarity in Placebo Response Between Adults and Adolescents With Acute Exacerbation of Schizophrenia in Clinical Trials.

Early-onset schizophrenia, or "adolescent schizophrenia," has a global incidence ranging up to 4% of all schizophrenia cases. Clinical data from antipsychotic programs were collected from new drug applications submitted to the US Food and Drug administration from 1993 to 2015. A placebo response-dropout model was developed to describe the time course of total positive and negative syndrome scale (PANSS) scores in adults and adolescents. The final model in both populations suggested that patients with higher baseline scores exhibited a greater absolute reduction from baseline. Higher baseline total PANSS, enrollment in US trials, and increases or small improvements in total PANSS were found to be predictors of dropout in both populations. Simulated adolescent data using the final adult placebo response model resembled the observed adolescent data. By confirming similar changes in disease symptomology during an acute exacerbation, efficient regulatory pathways for adolescents can be facilitated by using the extrapolation paradigm.