Depression Clinical Trials Research Articles

Interpreting clinical trial outcomes complicated by placebo response with an assessment of false-negative and true-negative clinical trials in depression using propensity-weighting

The objective of this study was to evaluate the performances of the propensity score weighted (PSW) methodology in a post-hoc re-analysis of a failed and a negative RCTs in depressive disorders. The conventional study designs, randomizations, and statistical approaches do not account for the baseline distribution of major non-specific prognostic and confounding factors such as the individual propensity to show a placebo effect (PE). Therefore, the conventional analysis approaches implicitly assume that the baseline PE is the same for all subjects in the trial even if this assumption is not supported by our knowledge on the impact of PE on the estimated treatment effect (TE). The consequence of this assumption is an inflation of false negative results (type II error) in presence of a high proportion of subjects with high PE and an inflation of false positive (type I error) in presence of a high proportion of subjects with low PE value. Differently from conventional approaches, the inverse of the PE probability was used as weight in the mixed-effects analysis to assess TE in the PSW analysis. The results of this analysis indicated an enhanced signal of drug response in a failed trial and confirmed the absence of drug effect in a negative trial. This approach can be considered as a reference prospective or post-hoc analysis approach that minimize the risk of inflating either type I or type II error in contrast to what happens in the analyses of RCT studies conducted with the conventional statistical methodology.

Impact of Independent Review on Potential MMSE Score Inflation at Screening in AD trials

AbstractBackgroundThe Mini‐Mental State Examination (MMSE) is widely used as a screening tool in Alzheimer’s Disease (AD) clinical trials. Screening score inflation is documented in clinical trials of depression and anxiety. We investigated whether score distributions on several multi‐site multinational AD trials showed truncated scores around MMSE inclusion criteria cut‐off, and whether Independent Review (IRev), where site assessments are reviewed by independent clinicians, is an effective methodology to mitigate inclusion bias.MethodMMSE screening score distributions from several clinical trials of Early Symptomatic and Mild to Moderate AD were examined. To assess the impact of IRev on screening score distribution, we compared two studies with similar inclusion criteria (Early AD, MMSE ≥ 22; CDR GS = 0.5 or 1) one with no IRev and one with IRev via audio recording.ResultMMSE screening frequency distributions showed a statistically significant difference between IRev and No‐IREV groups (p < 0.001). The distributions for the two groups were also compared using several score ranges around the threshold. Significant group differences were observed across the score ranges; 20‐24 (D = 0.099, p = 0.01), 19‐25 (D = 0.094, p = 0.002), and 18‐26 (D = 0.0987, p < 0001), suggesting a smaller inclusion bias around the threshold in the IRev group.ConclusionWe observed that screening score inflation occurs in AD trials and Independent Review may be an effective strategy to reduce the number of subjects inappropriately enrolled in the study. IRev should be applied to assessments that fall around the required scored for study participation.

A new method for analyzing clinical trials in depression based on individual propensity to respond to placebo estimated using artificial intelligence

One of the major reasons for trial failures in major depressive disorders (MDD) is the presence of unpredictable levels of placebo response as the individual baseline propensity to respond to placebo is not adequately controlled by the current randomization and statistical methodologies.The individual propensity to respond to any treatment or intervention assessed at baseline was considered as a major non-specific prognostic and confounding effect. The objective of this paper was to apply the propensity score methodology to control for potential imbalance at baseline in the propensity to respond to placebo in clinical trials in MDD.Individual propensity was estimated using artificial intelligence (AI) applied to observations collected in two pre-randomization occasions.Cases study are presented using data from two randomized, placebo-controlled trials to evaluate the efficacy of paroxetine in MDD. AI models were used to estimate the individual propensity probability to show a treatment non-specific placebo effect.The inverse of the estimated probability was used as weight in the mixed-effects analysis to assess treatment effect. The comparison of the results obtained with and without propensity weight indicated that the weighted analysis provided an estimate of treatment effect and effect size significantly larger than the conventional analysis.

Transcranial direct current stimulation (tDCS) in depression induces structural plasticity

Transcranial direct current stimulation (tDCS) is a non-invasive neuromodulation technique involving administration of well-tolerated electrical current to the brain through scalp electrodes. TDCS may improve symptoms in neuropsychiatric disorders, but mixed results from recent clinical trials underscore the need to demonstrate that tDCS can modulate clinically relevant brain systems over time in patients. Here, we analyzed longitudinal structural MRI data from a randomized, double-blind, parallel-design clinical trial in depression (NCT03556124, N = 59) to investigate whether serial tDCS individually targeted to the left dorso-lateral prefrontal cortex (DLPFC) can induce neurostructural changes. Significant (FWEc p < 0.05) treatment-related gray matter changes were observed with active high-definition (HD) tDCS relative to sham tDCS within the left DLPFC stimulation target. No changes were observed with active conventional tDCS. A follow-up analysis within individual treatment groups revealed significant gray matter increases with active HD-tDCS in brain regions functionally connected with the stimulation target, including the bilateral DLPFC, bilateral posterior cingulate cortex, subgenual anterior cingulate cortex, and the right hippocampus, thalamus and left caudate brain regions. Integrity of blinding was verified, no significant differences in stimulation-related discomfort were observed between treatment groups, and tDCS treatments were not augmented by any other adjunct treatments. Overall, these results demonstrate that serial HD-tDCS leads to neurostructural changes at a predetermined brain target in depression and suggest that such plasticity effects may propagate over brain networks.

Discovering Influential Core-Keywords, Researcher Networks and Research Trends of Acupuncture on Depression Using Bibliometric Analysis

Depression is a common illness worldwide. Acupuncture is used as an alternative to non-pharmacological therapy. This study aimed to identify the development and global trends in the study of acupuncture therapy for depression over the past two decades using a bibliometric analysis. Articles published between 2001 and 2020 on acupuncture for depression were retrieved from the Web of Science database. Extracted information included authors, organizations, countries, keywords, and journals. The VOSviewer program was used to visualize the impacts and network hubs of the keywords, authors, and affiliations. Analyses of 871 original and review articles revealed that the number of publications has continually increased over the past 20 years. China has published the maximum number of articles, followed by the United States and South Korea. As for research areas, integrative complementary medicine was most well represented in terms of the number of articles. Co-occurrence analysis of keywords indicated that each five sub-group cluster (including "pain related to depressive symptoms," "CAM therapies of depression," "comorbid disease or symptoms of depression," "clinical trials of depression," and "mechanism of acupuncture on depression") has its own impact keyword. The most recent keywords were "protocol" and "systematic review," whereas early keywords were "acupuncture analgesia" and "St john's wort." Co-authorship analysis of authors and organizations revealed 4 influential authors and 2 organizations in the field of acupuncture for depression. The present study provided influential keywords that show comorbid symptoms, treatments, and mechanism. Additionally, it revealed the influential persons or groups related to acupuncture therapy for depression.

Genomic-Analysis-Oriented Drug Repurposing in the Search for Novel Antidepressants.

From inadequate prior antidepressants that targeted monoamine neurotransmitter systems emerged the discovery of alternative drugs for depression. For instance, drugs targeted interleukin 6 receptor (IL6R) in inflammatory system. Genomic analysis-based drug repurposing using single nucleotide polymorphism (SNP) inclined a promising method for several diseases. However, none of the diseases was depression. Thus, we aimed to identify drug repurposing candidates for depression treatment by adopting a genomic-analysis-based approach. The 5885 SNPs obtained from the machine learning approach were annotated using HaploReg v4.1. Five sets of functional annotations were applied to determine the depression risk genes. The STRING database was used to expand the target genes and identify drug candidates from the DrugBank database. We validated the findings using the ClinicalTrial.gov and PubMed databases. Seven genes were observed to be strongly associated with depression (functional annotation score = 4). Interestingly, IL6R was auspicious as a target gene according to the validation outcome. We identified 20 drugs that were undergoing preclinical studies or clinical trials for depression. In addition, we identified sarilumab and satralizumab as drugs that exhibit strong potential for use in the treatment of depression. Our findings indicate that a genomic-analysis-based approach can facilitate the discovery of drugs that can be repurposed for treating depression.

Metformin attenuates depressive-like behaviour of methamphetamine withdrawal in mice: A mechanistic approach

Objectives Methamphetamine (METH) as a potent psychostimulant drug with a high potency of dependence rate that results in neurotoxicity has become a major drug of abuse in many parts of the world. Unfortunately, there is limited evidence regarding treatment of METH withdrawal syndrome. Therefore, we aimed to investigate whether metformin mitigate the methamphetamine (METH) withdrawal syndrome in male mice. Based on the literature, depression and anxiety are the major METH withdrawal symptoms. Methods Here, METH (2 mg/kg) was administered to mice twice a day for 14 constitutive days to induce animal model of METH-induced withdrawal syndrome. To do this, mice in control group and those with METH withdrawal syndrome were divided into treatment (receiving metformin in 3 doses of 50, 100 and 200 mg/kg for 10 days) and non-treatment sub-groups. Following the behavioural test, the animals were sacrificed; their hippocampus was dissected to measure oxidative stress parameters and expression of cellular energy homeostasis and immune-inflammatory genes. Results Our data revealed that metformin provoked antidepressant effects in behavioural tests through AMPK overexpression as an important mitochondrial energetic sensor and inhibition of Tlr4 overexpression in the immune system gene expression. In addition, metformin was able to improve oxidative stress biomarkers and neuronal damage in the hippocampus and restore cellular energy homeostasis and immune system gene expression. Conclusions The data suggested that metformin can influence the hippocampus through targeting mitochondria and their performance, and consequently, neuroinflammation responses and brain metabolic changes. It is supposed to be a new therapeutic option in clinical trials of depression and anxiety following METH withdrawal treatment.

A Study of Novel Exploratory Tools, Digital Technologies, and Central Nervous System Biomarkers to Characterize Unipolar Depression.

Background: Digital technologies have the potential to provide objective and precise tools to detect depression-related symptoms. Deployment of digital technologies in clinical research can enable collection of large volumes of clinically relevant data that may not be captured using conventional psychometric questionnaires and patient-reported outcomes. Rigorous methodology studies to develop novel digital endpoints in depression are warranted.Objective: We conducted an exploratory, cross-sectional study to evaluate several digital technologies in subjects with major depressive disorder (MDD) and persistent depressive disorder (PDD), and healthy controls. The study aimed at assessing utility and accuracy of the digital technologies as potential diagnostic tools for unipolar depression, as well as correlating digital biomarkers to clinically validated psychometric questionnaires in depression.Methods: A cross-sectional, non-interventional study of 20 participants with unipolar depression (MDD and PDD/dysthymia) and 20 healthy controls was conducted at the Centre for Human Drug Research (CHDR), the Netherlands. Eligible participants attended three in-clinic visits (days 1, 7, and 14), at which they underwent a series of assessments, including conventional clinical psychometric questionnaires and digital technologies. Between the visits, there was at-home collection of data through mobile applications. In all, seven digital technologies were evaluated in this study. Three technologies were administered via mobile applications: an interactive tool for the self-assessment of mood, and a cognitive test; a passive behavioral monitor to assess social interactions and global mobility; and a platform to perform voice recordings and obtain vocal biomarkers. Four technologies were evaluated in the clinic: a neuropsychological test battery; an eye motor tracking system; a standard high-density electroencephalogram (EEG)-based technology to analyze the brain network activity during cognitive testing; and a task quantifying bias in emotion perception.Results: Our data analysis was organized by technology – to better understand individual features of various technologies. In many cases, we obtained simple, parsimonious models that have reasonably high diagnostic accuracy and potential to predict standard clinical outcome in depression.Conclusion: This study generated many useful insights for future methodology studies of digital technologies and proof-of-concept clinical trials in depression and possibly other indications.

Medicinal chemistry of P2 and adenosine receptors: Common scaffolds adapted for multiple targets

Prof. Geoffrey Burnstock originated the concept of purinergic signaling. He demonstrated the interactions and biological roles of ionotropic P2X and metabotropic P2Y receptors. This review paper traces the historical origins of many currently used antagonists and agonists for P2 receptors, as well as adenosine receptors, in early attempts to identify ligands for these receptors – prior to the use of chemical libraries for screening. Rather than presenting a general review of current purinergic ligands, we focus on common chemical scaffolds (privileged scaffolds) that can be adapted for multiple receptor targets. By carefully analyzing the structure activity relationships, one can direct the selectivity of these scaffolds toward different receptor subtypes. For example, the weak and non-selective P2 antagonist reactive blue 2 (RB-2) was derivatized using combinatorial synthetic approaches, leading to the identification of selective P2Y2, P2Y4, P2Y12 or P2X2 receptor antagonists.A P2X4 antagonist NC-2600 is in a clinical trial, and A3 adenosine agonists show promise, for chronic pain. P2X7 antagonists have been in clinical trials for depression (JNJ-54175446), inflammatory bowel disease (IBD), Crohn’s disease, rheumatoid arthritis, inflammatory pain and chronic obstructive pulmonary disease (COPD). P2X3 antagonists are in clinical trials for chronic cough, and an antagonist named after Burnstock, gefapixant, is expected to be the first P2X3 antagonist filed for approval. We are seeing that the vision of Prof. Burnstock to use purinergic signaling modulators, most recently at P2XRs, for treating disease is coming to fruition.

A constrained single-index regression for estimating interactions between a treatment and covariates.

We consider a single-index regression model, uniquely constrained to estimate interactions between a set of pretreatment covariates and a treatment variable on their effects on a response variable, in the context of analyzing data from randomized clinical trials. We represent interaction effect terms of the model through a set of treatment-specific flexible link functions on a linear combination of the covariates (a single index), subject to the constraint that the expected value given the covariates equals 0, while leaving the main effects of the covariates unspecified. We show that the proposed semiparametric estimator is consistent for the interaction term of the model, and that the efficiency of the estimator can be improved with an augmentation procedure. The proposed single-index regression provides a flexible and interpretable modeling approach to optimizing individualized treatment rules based on patients' data measured at baseline, as illustrated by simulation examples and an application to data from a depression clinicaltrial.

Antidepressants for depression after concussion and traumatic brain injury are still best practice

BackgroundDepression is a common complication of traumatic brain injury (TBI). New evidence suggests that antidepressant medication may be no more effective than placebo in this population.Main bodySelective serotonin reuptake inhibitors are recommended as first-line treatment for depression in contemporary expert consensus clinical practice guidelines for management of TBI. This recommendation is based on multiple prior meta-analyses of clinical trials in depression after TBI as well as depression in the general population. The evidence is mixed. A recent clinical trial and new meta-analysis including that trial found no benefit of antidepressants for depression following TBI. We argue that this finding should not change practice, i.e., patients who present with depression after TBI should still be considered for antidepressant treatment, because they may (1) benefit from robust placebo effects, (2) benefit from an alternative or adjunctive medication if the agent prescribed first does not achieve a depression remission, and (3) make improvements that are not captured well by traditional depression outcome measures, which are confounded by TBI sequelae. Patients with mild TBI are especially appropriate for antidepressant therapy because they, on average, more closely resemble patients with no known TBI history enrolled in typical primary Major Depressive Disorder clinical trials than patients enrolled in TBI trials in placebo-controlled trials published to date.ConclusionTBI, and especially mild TBI, is not a contraindication for antidepressant therapy. Health providers should routinely screen and initiate treatment for depression after TBI.

A statistical method to convert published response rates into marginal distributions with an example application in psoriasis.

Assessment of severity is essential for the management of chronic diseases. Continuous variables like scores obtained from the Hamilton Rating Scale for Depression or the Psoriasis Area and Severity Index (PASI) are standard measures used in clinical trials of depression and psoriasis. In clinical trials of psoriasis, for example, the reduction of PASI from baseline in response to therapy, in particular the proportion of patients achieving at least 75%, 90%, or 100% improvement of disease (PASI 75, PASI 90, or PASI 100), is typically used to evaluate treatment efficacy. However, evaluation of the proportions of patients reaching absolute PASI values (eg, ≤1, ≤2, ≤3, or ≤5) has recently gained greater clinical interest and is increasingly being reported. When relative versus absolute scores are standard, as is the case with the PASI in psoriasis, it is difficult to compare absolute changes using existing published data. Thus, we developed a method to estimate absolute PASI levels from aggregated relative levels. This conversion method is based on a latent 2-dimensional normal distribution for the absolute score at baseline and at a specific endpoint with a truncation to allow for baseline inclusion criterion. The model was fitted to aggregated results from simulations and from 3 phase III studies that had known absolute PASI proportions. The predictions represented the actual results quite precisely. This model might be applied to other conditions, such as depression, to estimate proportions of patients achieving an absolute low level of disease activity, given absolute values at baseline and proportions of patients achieving relative improvements at a subsequent time point.

Experts Remain Enthusiastic About NMDA-Targeting Antidepressants

Experts Remain Enthusiastic About NMDA-Targeting Antidepressants

Decreased suicide rates in recent antidepressant clinical trials

RationaleThe last systematic analysis of suicidality in antidepressant clinical trials submitted for approval by the US Food and Drug Administration was in 2000. Given the attention to suicide and antidepressants in the early 2000s, the authors aimed to evaluate if there have been any changes in suicide rates in antidepressant clinical trials following 2000.Objective and MethodsThe Integrated Safety Summary data from approval packets for 14 investigational antidepressant programs (1991–2013, 40,857 patients, 10,890.5 exposure years) were used to calculate suicides and suicide attempts per 100,000 patient exposure years (standardized rates) for antidepressant and placebo treatment groups separately. Suicides/suicide attempt rates, mean age, and percent female were compared between 1991 and 1998 (pre-2000) and 2002–2013 (post-2000). Drug-placebo differences in suicide/suicide attempt rates were explored.ResultsAmong antidepressant-treated patients, the standardized suicide rate decreased significantly from pre- to post-2000 (643.6 to 25.8, p < 0.0001) as did the standardized suicide attempt rate (3975.7 to 645.4, p < 0.0001). For placebo-treated patients, the decrease was not significant for suicide rate (471.1 to 174.2, p = 0.66) but was significant for suicide attempt rate (from 3538.3 to 522.6, p < 0.001). Regression analysis showed a similar pattern with suicide/suicide attempt rates decreasing over time. None of the drug-placebo comparisons in suicide or suicide attempt rates were statistically significant. There was no change in percent female or mean age of patients in trials pre- and post-2000.ConclusionsDeaths by suicide and suicide attempts have decreased significantly in antidepressant clinical trials following 2000 compared to the decade before 2000. Basic demographic features of the patients have remained consistent and medication treatment effects on suicidality were not apparent. These findings may reflect enhanced screening procedures and effective exclusion of suicidal patients in clinical trials for depression.

Complementary and Integrative Therapies in Psychiatry

Complementary and Integrative Therapies in Psychiatry

Sequential advantage selection for optimal treatment regime.

Variable selection for optimal treatment regime in a clinical trial or an observational study is getting more attention. Most existing variable selection techniques focused on selecting variables that are important for prediction, therefore some variables that are poor in prediction but are critical for decision-making may be ignored. A qualitative interaction of a variable with treatment arises when treatment effect changes direction as the value of this variable varies. The qualitative interaction indicates the importance of this variable for decision-making. Gunter, Zhu and Murphy (2011) proposed S-score which characterizes the magnitude of qualitative interaction of each variable with treatment individually. In this article, we developed a sequential advantage selection method based on the modified S-score. Our method selects qualitatively interacted variables sequentially, and hence excludes marginally important but jointly unimportant variables or vice versa. The optimal treatment regime based on variables selected via joint model is more comprehensive and reliable. With the proposed stopping criteria, our method can handle a large amount of covariates even if sample size is small. Simulation results show our method performs well in practical settings. We further applied our method to data from a clinical trial for depression.

Time to abandon placebo control in pivotal phase III trials?

Time to abandon placebo control in pivotal phase III trials?

Fewer study participants needed to demonstrate superior antidepressant efficacy when using the Hamilton melancholia subscale (HAM-D6) as outcome measure

BackgroundIn the development of new antidepressant treatments, the failed study has unfortunately become a prevalent problem. The number of failed studies could probably be reduced significantly by applying more informative outcome measures. Previous studies have indicated that the 6-item melancholia subscale (HAM-D6) of the 17-item Hamilton Depression Rating Scale (HAM-D17) may be more informative than other scales, due to its superior psychometric properties. In the present study we investigated whether the HAM-D6 had higher informativeness than the HAM-D17 based on data from a randomized placebo-controlled trial (RCT) testing the effect of erythropoietin (EPO) as augmentation therapy in patients with treatment-resistant depression. MethodsWe assessed the scalability (Mokken analysis of unidimensionality), responsiveness (item responsiveness analysis) and ability to show drug-placebo separation (estimation of sample size needed to detect statistically significant difference between EPO and placebo) of the HAM-D6 and the HAM-D17. ResultsThe HAM-D6 demonstrated higher scalability, higher responsiveness, and better drug-placebo separation compared to the HAM-D17. As a consequence, only 39 participants per group would be required to detect a statistically significant difference between EPO and placebo when using the HAM-D6 as outcome measure, whereas the required group size for HAM-D17 would be 146 participants. LimitationsThe EPO RCT was not originally designed to investigate the research questions addressed in this study. ConclusionsBoth for ethical and financial reasons it is of interest to minimize the number of participants in clinical trials. Therefore, we suggest employing the HAM-D6 as outcome measure in clinical trials of depression.

The impact of anxious symptoms in the remission of depressive symptoms in a clinical trial for depression: Follow-up of six months

BackgroundStudies show high comorbidity between anxiety disorder and depression. Little is known regarding how anxiety symptoms affect prognosis in depression treatment, suggesting the importance of studying the impact of anxiety symptoms in the treatment of depression. We evaluated the impact of anxiety symptoms in the remission of depressive symptoms after brief psychotherapies for depression. MethodsThis randomized clinical trial of 18–29-year-old adults included individuals who met the diagnostic criteria for depression as assessed by the Structured Clinical Interview for DSM (SCID). Depressive symptoms were assessed using the Hamilton Rating Scale for Depression (HRSD); anxiety symptoms were assessed using the Hamilton Anxiety Rating Scale (HARS). The protocols of psychotherapy used were: Cognitive Narrative Psychotherapy (CNP) and Cognitive Behavioral Psychotherapy (CBP). Both treatments included seven sessions. At the end of the treatment and six-month follow-up, an evaluation was made with the HRSD and HARS. The sample included 97 patients divided between the protocols of psychotherapy. ResultsThere was a significant, positive, moderate correlation between the severity of anxiety symptoms at baseline and the remission of depressive symptoms at post-intervention (r=0.38 p<0.001) as well as a significant, positive, low correlation at follow up (r=0.20 p=0.049). We found remission of anxiety symptoms and depressive symptoms after brief psychotherapies, and the remission persisted at follow up. LimitationWe did not evaluate the diagnosis of anxiety disorders. ConclusionThe severity of anxiety symptoms did not compromise the treatment focused primarily on depressive symptoms.

Nocebo in clinical trials for depression: A meta-analysis

Nocebo refers to adverse events (AEs) related to negative expectations that medical treatment will likely harm instead of heal and can be assessed in placebo-controlled randomized clinical trials (RCTs). We sought to examine the AEs following placebo administration in RCTs for depression (D). After a systematic Medline search for RCTs in depression published in the last decade we assessed percentages of placebo-treated patients reporting at least one AE or discontinuing due to placebo intolerance and searched for factors influencing nocebo's extent. Data were extracted from 21 RCTs fulfilling search criteria. Of 3255 placebo-treated patients, 44.7% (95% CI: 22.3–68.3%) reported at least one AE, and 4.5% (95% CI: 3.4–5.8%) discontinued placebo treatment due to intolerance. AE rates in placebo and active drug treated patients were correlated quantitatively (r=0.915, p<0.001) and qualitatively, but not dropout rates (r=0.047). We conclude that almost one out of 20 placebo treated patients discontinued treatment due to AEs, indicating a significant nocebo in trials for depression treatment adversely affecting adherence and efficacy of current treatments in clinical practice, with additional implications for trial designing.