The missing link between clinical endpoints and drug targets in depression

Disease trajectory of SLE clinical endpoints and covariates affecting disease severity and probability of response: Analysis of pooled patient-level placebo (Standard-of-Care) data to enable model-informed drug development

Systemic lupus erythematosus (SLE) is an autoimmune disease affecting multiple organ systems. Many investigational agents have failed or shown only modest effects when added to standard of care (SoC) therapy in placebo-controlled trials, and only two therapies have been approved for SLE in the last 60 years. Clinical trial outcomes have shown discordance in drug effects between clinical endpoints. Herein, we characterized longitudinal disease activity in the SLE population and the sources of variability by developing a latent disease trajectory model for SLE component endpoints (Systemic Lupus Erythematosus Disease Activity Index [SLEDAI], Physician's Global Assessment [PGA], British Isles Lupus Assessment Group Index [BILAG]) and composite endpoints (Systemic Lupus Erythematosus Responder Index [SRI], BILAG-based Composite Lupus Assessment [BICLA], and Lupus Low Disease Activity State [LLDAS]) using patient-level historical SoC data from nine phase II and III studies. Across all endpoints, in predictions up to 52 weeks from the final disease trajectory model, the following baseline covariates were associated with a greater decrease in SLE disease activity and higher response to placebo + SoC: Hispanic ethnicity from Central/South America, absence of hypocomplementemia, recent SLE diagnosis, and high baseline disease activity score using SLEDAI and BILAG separately. No discernible differences were observed in the trajectory of response to placebo + SoC across different SoC medications (antimalarial and immunosuppressant such as mycophenolate, methotrexate, and azathioprine). Across all endpoints, disease trajectory showed no difference in Asian versus non-Asian patients, supporting Asia-inclusive global SLE drug development. These results describe the first population approach to support a model-informed drug development framework in SLE.

A Review of Biomarkers in Mood and Psychotic Disorders: A Dissection of Clinical vs. Preclinical Correlates

Despite significant research efforts aimed at understanding the neurobiological underpinnings of mood (depression, bipolar disorder) and psychotic disorders, the diagnosis and evaluation of treatment of these disorders are still based solely on relatively subjective assessment of symptoms as well as psychometric evaluations. Therefore, biological markers aimed at improving the current classification of psychotic and mood-related disorders, and that will enable patients to be stratified on a biological basis into more homogeneous clinically distinct subgroups, are urgently needed. The attainment of this goal can be facilitated by identifying biomarkers that accurately reflect pathophysiologic processes in these disorders. This review postulates that the field of psychotic and mood disorder research has advanced sufficiently to develop biochemical hypotheses of the etiopathology of the particular illness and to target the same for more effective disease modifying therapy. This implies that a "one-size fits all" paradigm in the treatment of psychotic and mood disorders is not a viable approach, but that a customized regime based on individual biological abnormalities would pave the way forward to more effective treatment. In reviewing the clinical and preclinical literature, this paper discusses the most highly regarded pathophysiologic processes in mood and psychotic disorders, thereby providing a scaffold for the selection of suitable biomarkers for future studies in this field, to develope biomarker panels, as well as to improve diagnosis and to customize treatment regimens for better therapeutic outcomes.

Antidepressants share the ability to increase catecholamine output in the bed nucleus of stria terminalis: a possible role in antidepressant therapy?

RATIONALE

Antidepressants include a relatively wide spectrum of drugs that increase the synaptic concentration of monoamines, mostly through neurotransmitter reuptake blockade. The bed nucleus of stria teminalis (BNST) is considered a relay station in mediating the activation of stress response but also in the acquisition and expression of emotions. BNST is richly innervated by monoamines and sends back projections to the nucleus of origin. We previously showed that the administration of selective blockers of norepinephrine transporter (NET) increases the extracellular concentration (output) of dopamine, suggesting that dopamine could be captured by NET in the BNST.

OBJECTIVES

The aim of this study, carried out by means of in vivo microdialysis, was to ascertain the acute effects that antidepressants with varying mechanisms of action have on dopamine and norepinephrine output in the BNST.

RESULTS

We observed that all the antidepressants tested (5-20 mg/kg i.p.) increased the output of catecholamines, dose dependently. In particular, the maximum increases (as a percent of basal) for norepinephrine and dopamine respectively, were as follows: desipramine, 239 and 137; reboxetine, 185 and 128; imipramine, 512 and 359; citalopram, 95 and 122; fluoxetine, 122 and 68; bupropion, 255 and 164.

CONCLUSIONS

These results suggest that catecholamine transmission in the BNST may be part of a common downstream pathway that is involved in the action mechanism of antidepressants. Consequently, it is hypothesized that a dysfunction of neuronal transmission in this brain area may have a role in the etiology of affective disorders.

Imaging genetics: implications for research on variable antidepressant drug response

Genetic variation of SLC6A4, HTR1A, MAOA, COMT and BDNF has been associated with depression, variable antidepressant drug responses as well as impacts on brain regions of emotion processing that are modulated by antidepressants. Pharmacogenetic studies are using psychometric outcome measures of drug response and are hampered by small effect sizes that might be overcome by the use of intermediate endophenotypes of drug response, which are suggested by imaging studies. Such an approach will not only tighten the relationship between genes and drug response, but also yield new insights into the neurobiology of depression and individual drug responses. This article provides a comprehensive overview of pharmacogenetic, imaging genetics and drug response studies, utilizing imaging techniques within the context of antidepressive drug therapy.

Trends in ambulatory self-report: the role of momentary experience in psychosomatic medicine

In this article, we review the differences between momentary, retrospective, and trait self-report techniques and discuss the unique role that ambulatory reports of momentary experience play in psychosomatic medicine. After a brief historical review of self-report techniques, we discuss the latest perspective that links ambulatory self-reports to a qualitatively different conscious self-the "experiencing self"--which is functionally and neuroanatomically different from the "remembering" and "believing" selves measured through retrospective and trait questionnaires. The experiencing self functions to navigate current environments and is relatively more tied to the salience network and corporeal information from the body that regulates autonomic processes. As evidence, we review research showing that experiences measured through ambulatory assessment have stronger associations with cardiovascular reactivity, cortisol response, immune system function, and threat/reward biomarkers compared with memories or beliefs. By contrast, memories and beliefs play important roles in decision making and long-term planning, but they are less tied to bodily processes and more tied to default/long-term memory networks, which minimizes their sensitivity for certain research questions. We conclude with specific recommendations for using self-report questionnaires in psychosomatic medicine and suggest that intensive ambulatory assessment of experiences may provide greater sensitivity for connecting psychological with biologic processes.

Structural models describing placebo treatment effects in schizophrenia and other neuropsychiatric disorders

Large variation in placebo response within and among clinical trials can substantially affect conclusions about the efficacy of new medications in psychiatry. Developing a robust placebo model to describe the placebo response is important to facilitate quantification of drug effects, and eventually to guide the design of clinical trials for psychiatric treatment via a model-based simulation approach. In addition, high dropout rates are very common in the placebo arm of psychiatric clinical trials. While developing models to evaluate the effect of placebo response, the data from patients who drop out of the trial should be considered for accurate interpretation of the results. The objective of this paper is to review the various empirical and semi-mechanistic models that have been used to quantify the placebo response in schizophrenia trials. Pros and cons of each placebo model are discussed. Additionally, placebo models used in other neuropsychiatric disorders like depression, Alzheimer's disease and Parkinson's disease are also reviewed with the objective of finding those placebo models that could be useful for clinical studies of both acute and chronic schizophrenic disease conditions. Better understanding of the patterns of dropout and the factors leading to dropouts are crucial in identifying the true placebo response. We therefore also review dropout models that are used in the development of models for treatment effects and in the optimization of clinical trials by simulation approaches. The use of an appropriate modelling strategy that is capable of identifying the potential sources of variable placebo responses and dropout rates is recommended for improving the sensitivity in discriminating between the effects of active treatment and placebo.

The genetic blueprint of major depressive disorder: contributions of imaging genetics studies

OBJECTIVES

To review neuroimaging intermediate phenotypes of MDD and their relation to genetic risk variants.

METHODS

A systematic literature search of peer-reviewed English language articels using PubMed ( www.pubmed.org ) was performed.

RESULTS

Comprehensive evidence on the influence of serotonergic genes (SLC6A4, HTR1A, MAOA, TPH2) and BDNF on the following neural intermediate phenotypes is displayed: amygdala reactivity, coupling of amygdala-anterior cingulate cortex (ACC) activity, ACC volume, hippocampal volume and serotonin receptor 1A (5-HT1A) binding potential (BP).

CONCLUSIONS

Intermediate phenotypes may bridge the gap between genotype and phenotype by reducing the impreciseness of psychiatric phenotypes and yield more insights into the underlying biology.

From trial and error to trial simulation III: a framework for interim analysis in efficacy trials with antidepressant drugs

Clinical trials with antidepressant drugs often fail to detect drug effect, even with drugs that are known to be efficacious. In a previous publication, we showed that a model-based approach is required to address some of the existing challenges in the design of clinical trial protocols. Here, we illustrate how the implementation of an interim analysis (IA) may help to identify studies that are headed for failure, early in the trial before completion of treatment. In contrast to traditional IA procedures, an adaptive Bayesian approach is proposed to optimize the timing of analysis and decision criteria for futility and efficacy, taking into account enrollment rate and treatment response at intermediate visits in the trial. Validation procedures involving re-enrollment of patients confirmed the performance of the method. Our findings reveal that optimization of the timing and decision criteria at the interim stage is critical for the accuracy of the conclusions about treatment efficacy or futility.

Is there a personalized medicine for mood disorders?

Major Depressive Disorder (MDD) and antidepressant therapy response are largely based on behavioral criteria, which are known to correlate at best modestly with biological measures. Therefore, it is not surprising that the search for peripheral biological markers (biomarkers) being assessed in distant biological systems such as body fluids has not yet resulted in clinically convincing measures for MDD diagnostics or treatment evaluation. Imaging genetics studies, however, have been successful in the search for intermediate imaging phenotypes of MDD and treatment response that are directly related to the neurobiological underpinnings of MDD, but are not suitable for a broad clinical use today. Hence, we argue that intermediate phenotypes derived from imaging genetics studies should be utilized as substitutes of behaviorally assessed psychiatric diagnoses or therapy response in the search for easily accessible peripheral biomarkers. This article will further cover the current state of peripheral and neural biomarker research.