Use of functional imaging across clinical phases in CNS drug development

Neuroimaging to Facilitate Clinical Trials in Huntington's Disease: Current Opinion from the EHDN Imaging Working Group

 Neuroimaging is increasingly being included in clinical trials of Huntington's disease (HD) for a wide range of purposes from participant selection and safety monitoring, through to demonstration of disease modification. Selection of the appropriate modality and associated analysis tools requires careful consideration. On behalf of the EHDN Imaging Working Group, we present current opinion on the utility and future prospects for inclusion of neuroimaging in HD trials. Covering the key imaging modalities of structural-, functional- and diffusion- MRI, perfusion imaging, positron emission tomography, magnetic resonance spectroscopy, and magnetoencephalography, we address how neuroimaging can be used in HD trials to: 1) Aid patient selection, enrichment, stratification, and safety monitoring; 2) Demonstrate biodistribution, target engagement, and pharmacodynamics; 3) Provide evidence for disease modification; and 4) Understand brain re-organization following therapy. We also present the challenges of translating research methodology into clinical trial settings, including equipment requirements and cost, standardization of acquisition and analysis, patient burden and invasiveness, and interpretation of results. We conclude, that with appropriate consideration of modality, study design and analysis, imaging has huge potential to facilitate effective clinical trials in HD.

Experimental Medicine Approaches in Early-Phase CNS Drug Development

Traditionally, Phase 1 clinical trials were largely conducted in healthy normal volunteers and focused on collection of safety, tolerability, and pharmacokinetic data. However, in the CNS therapeutic area, with more drugs failing in later phase development, Phase 1 trials have undergone an evolution that includes incorporation of novel approaches involving novel study designs, inclusion of biomarkers, and early inclusion of patients to improve the pharmacologic understanding of novel CNS-active compounds early in clinical development with the hope of improving success in later phase pivotal trials. In this chapter, the authors will discuss the changing landscape of Phase 1 clinical trials in CNS, including novel trial methodology, inclusion of pharmacodynamic biomarkers, and experimental medicine approaches to inform early decision-making in clinical development.

The Role of fMRI in Drug Development: An Update

Functional magnetic resonance imaging (fMRI) of the brain is a technology that holds great potential for increasing the efficiency of drug development for the central nervous system (CNS). In preclinical studies and both early- and late-phase human trials, fMRI has the potential to improve cross-species translation of drug effects, help to de-risk compounds early in development, and contribute to the portfolio of evidence for a compound's efficacy and mechanism of action. However, to date, the utilization of fMRI in the CNS drug development process has been limited. The purpose of this chapter is to explore this mismatch between potential and utilization. This chapter provides introductory material related to fMRI and drug development, describes what is required of fMRI measurements for them to be useful in a drug development setting, lists current capabilities of fMRI in this setting and challenges faced in its utilization, and ends with directions for future development of capabilities in this arena. This chapter is the 5-year update of material from a previously published workshop summary (Carmichael et al., Drug DiscovToday 23(2):333-348, 2018).

The Difficult Path to the Discovery of Novel Treatments in Psychiatric Disorders

CNS diseases, including psychiatric disorders, represent a significant opportunity for the discovery and development of new drugs and therapeutic treatments with the potential to have a significant impact on human health. CNS diseases, however, present particular challenges to therapeutic discovery efforts, and psychiatric diseases/disorders may be among the most difficult. With specific exceptions such as psychostimulants for ADHD, a large number of psychiatric patients are resistant to existing treatments. In addition, clinicians have no way of knowing which psychiatric patients will respond to which drugs. By definition, psychiatric diagnoses are syndromal in nature; determinations of efficacy are often self-reported, and drug discovery is largely model-based. While such models of psychiatric disease are amenable to screening for new drugs, whether cellular or whole-animal based, they have only modest face validity and, more importantly, predictive validity. Multiple academic, pharmaceutical industry, and government agencies are dedicated to the translation of new findings about the neurobiology of major psychiatric disorders into the discovery and advancement of novel therapies. The collaboration of these agencies provide a pathway for developing new therapeutics. These efforts will be greatly helped by recent advances in understanding the genetic bases of psychiatric disorders, the ongoing search for diagnostic and therapy-responsive biomarkers, and the validation of new animal models.

Biomarkers in Psychiatric Drug Development: From Precision Medicine to Novel Therapeutics

Burden of psychiatric disorders is compounded by their wide prevalence as well as the limited efficacy of currently available treatments and the current approaches for prescribing these treatments. The selection of treatments continues to be subjective and often results in a trial-and-error approach. Emerging research suggests that biological markers (or biomarkers) can be used to develop precision medicine approaches for psychiatric disorders. Furthermore, the biomarkers also promise to elucidate the underlying pathophysiological mechanisms which in turn can be used to develop novel therapeutic treatments. In this chapter we have focused on mood disorders and reviewed studies on electroencephalography (EEG), magnetic resonance imaging (MRI), and blood-based biomarkers that can guide selection of one treatment versus another (treatment-selection biomarker) as well as biomarkers that can guide the development of novel therapeutics. These studies suggest that the use of objective physiological data is poised to alter the landscape of psychiatric diagnosis and treatment. However, practical and economic barriers remain as major hurdles. The key to finding such translational diagnostic and therapeutic biomarkers is a better understanding of the underlying pathophysiology, and despite the tremendous advances in neuroscience, it is clear there remains much left to be elucidated.

Can magnetic resonance imaging enhance the assessment of potential new treatments for cognitive impairment in mood disorders? A systematic review and position paper by the International Society for Bipolar Disorders Targeting Cognition Task Force

BACKGROUND

Developing treatments for cognitive impairment is key to improving the functioning of people with mood disorders. Neuroimaging may assist in identifying brain-based efficacy markers. This systematic review and position paper by the International Society for Bipolar Disorders Targeting Cognition Task Force examines the evidence from neuroimaging studies of pro-cognitive interventions.

METHODS

We included magnetic resonance imaging (MRI) studies of candidate interventions in people with mood disorders or healthy individuals, following the procedures of the Preferred Reporting Items for Systematic reviews and Meta-Analysis 2020 statement. Searches were conducted on PubMed/MEDLINE, PsycInfo, EMBASE, Cochrane Library, and Clinicaltrials.gov from inception to 30th April 2021. Two independent authors reviewed the studies using the National Heart, Lung, Blood Institutes of Health Quality Assessment Tool for Controlled Intervention Studies and the quality of neuroimaging methodology assessment checklist.

RESULTS

We identified 26 studies (N = 702). Six investigated cognitive remediation or pharmacological treatments in mood disorders (N = 190). In healthy individuals, 14 studies investigated pharmacological interventions (N = 319), 2 cognitive training (N = 73) and 4 neuromodulatory treatments (N = 120). Methodologies were mostly rated as 'fair'. 77% of studies investigated effects with task-based fMRI. Findings varied but most consistently involved treatment-associated cognitive control network (CCN) activity increases with cognitive improvements, or CCN activity decreases with no cognitive change, and increased functional connectivity. In mood disorders, treatment-related default mode network suppression occurred.

CONCLUSIONS

Modulation of CCN and DMN activity is a putative efficacy biomarker. Methodological recommendations are to pre-declare intended analyses and use task-based fMRI, paradigms probing the CCN, longitudinal assessments, mock scanning, and out-of-scanner tests.

Role of Brain Imaging in Drug Development for Psychiatry

Background:

Over the last decades, many brain imaging studies have contributed to new insights in the pathogenesis of psychiatric disease. However, in spite of these developments, progress in the development of novel therapeutic drugs for prevalent psychiatric health conditions has been limited.

Objective:

In this review, we discuss translational, diagnostic and methodological issues that have hampered drug development in CNS disorders with a particular focus on psychiatry. The role of preclinical models is critically reviewed and opportunities for brain imaging in early stages of drug development using PET and fMRI are discussed. The role of PET and fMRI in drug development is reviewed emphasizing the need to engage in collaborations between industry, academia and phase I units.

Conclusion:

Brain imaging technology has revolutionized the study of psychiatric illnesses, and during the last decade, neuroimaging has provided valuable insights at different levels of analysis and brain organization, such as effective connectivity (anatomical), functional connectivity patterns and neurochemical information that may support both preclinical and clinical drug development. Since there is no unifying pathophysiological theory of individual psychiatric syndromes and since many symptoms cut across diagnostic boundaries, a new theoretical framework has been proposed that may help in defining new targets for treatment and thus enhance drug development in CNS diseases. In addition, it is argued that new proposals for data-mining and mathematical modelling as well as freely available databanks for neural network and neurochemical models of rodents combined with revised psychiatric classification will lead to new validated targets for drug development.

Impact of Donepezil on Brain Glucose Metabolism Assessed Using [18F]2-Fluoro-2-deoxy-D-Glucose Positron Emission Tomography Imaging in a Mouse Model of Alzheimer's Disease Induced by Intracerebroventricular Injection of Amyloid-Beta Peptide

Translational methods are needed to monitor the impact of the Alzheimer's disease (AD) and therapies on brain function in animal models and patients. The formation of amyloid plaques was investigated using [¹⁸F]florbetapir autoradiography in a mouse model of AD consisting in unilateral intracerebroventricular (i.c.v) injection of amyloid peptide Aβ_25-35. Then, an optimized positron emission tomography (PET) imaging protocol using [¹⁸F]2-fluoro-2-deoxy-D-glucose ([¹⁸F]FDG) was performed to estimate brain glucose metabolism: [¹⁸F]FDG was injected in awake animals to allow for 40 min brain uptake in freely moving mice. Anesthesia was then induced for 30 min PET acquisition to capture the slow and poorly reversible brain uptake of [¹⁸F]FDG. Impact of donepezil (0.25 mg/kg daily, 7 days, orally) on brain function was investigated in AD mice (n = 6 mice/group). Formation of amyloid plaques could not be detected using autoradiography. Compared with sham controls (injection of scramble peptide), significant decrease in [¹⁸F]FDG uptake was observed in the AD group in the subcortical volume of the ipsilateral hemisphere. Donepezil restored normal glucose metabolism by selectively increasing glucose metabolism in the affected subcortical volume but not in other brain regions. In mice, [¹⁸F]FDG PET imaging can be optimized to monitor impaired brain function associated with i.c.v injection of Aβ_25-35, even in the absence of detectable amyloid plaque. This model recapitulates the regional decrease in [¹⁸F]FDG uptake observed in AD patients. [¹⁸F]FDG PET imaging can be straightforwardly transferred to AD patients and may aid the development of certain therapies designed to restore the altered brain function in AD.

The neurologic pain signature responds to nonsteroidal anti-inflammatory treatment vs placebo in knee osteoarthritis

Supplemental Digital Content is Available in the Text.

fMRI-based measures, validated for nociceptive pain, respond to acute osteoarthritis pain, are not sensitive to placebo, and are mild-to-moderately sensitive to naproxen.

Introduction:

Many drug trials for chronic pain fail because of high placebo response rates in primary endpoints. Neurophysiological measures can help identify pain-linked pathophysiology and treatment mechanisms. They can also help guide early stop/go decisions, particularly if they respond to verum treatment but not placebo. The neurologic pain signature (NPS), an fMRI-based measure that tracks evoked pain in 40 published samples and is insensitive to placebo in healthy adults, provides a potentially useful neurophysiological measure linked to nociceptive pain.

Objectives:

This study aims to validate the NPS in knee osteoarthritis (OA) patients and test the effects of naproxen on this signature.

Methods:

In 2 studies (50 patients, 64.6 years, 75% females), we (1) test the NPS and other control signatures related to negative emotion in knee OA pain patients; (2) test the effect of placebo treatments; and (3) test the effect of naproxen, a routinely prescribed nonsteroidal anti-inflammatory drug in OA.

Results:

The NPS was activated during knee pain in OA (d = 1.51, P < 0.001) and did not respond to placebo (d = 0.12, P = 0.23). A single dose of naproxen reduced NPS responses (vs placebo, NPS d = 0.34, P = 0.03 and pronociceptive NPS component d = 0.38, P = 0.02). Naproxen effects were specific for the NPS and did not appear in other control signatures.

Conclusion:

This study provides preliminary evidence that fMRI-based measures, validated for nociceptive pain, respond to acute OA pain, do not appear sensitive to placebo, and are mild-to-moderately sensitive to naproxen.

Brain Imaging Biomarkers for Chronic Pain

The prevalence of chronic pain has reached epidemic levels. In addition to personal suffering chronic pain is associated with psychiatric and medical co-morbidities, notably substance misuse, and a huge a societal cost amounting to hundreds of billions of dollars annually in medical cost, lost wages, and productivity. Chronic pain does not have a cure or quantitative diagnostic or prognostic tools. In this manuscript we provide evidence that this situation is about to change. We first start by summarizing our current understanding of the role of the brain in the pathogenesis of chronic pain. We particularly focus on the concept of learning in the emergence of chronic pain, and the implication of the limbic brain circuitry and dopaminergic signaling, which underly emotional learning and decision making, in this process. Next, we summarize data from our labs and from other groups on the latest brain imaging findings in different chronic pain conditions focusing on results with significant potential for translation into clinical applications. The gaps in the study of chronic pain and brain imaging are highlighted in throughout the overview. Finally, we conclude by discussing the costs and benefits of using brain biomarkers of chronic pain and compare to other potential markers.

Effects of Ketamine and Midazolam on Simultaneous EEG/fMRI Data During Working Memory Processes

Reliable measures of cognitive brain activity from functional neuroimaging techniques may provide early indications of efficacy in clinical trials. Functional magnetic resonance imaging and electroencephalography provide complementary spatiotemporal information and simultaneous recording of these two modalities can remove inter-session drug response and environment variability. We sought to assess the effects of ketamine and midazolam on simultaneous electrophysiological and hemodynamic recordings during working memory (WM) processes. Thirty participants were included in a placebo-controlled, three-way crossover design with ketamine and midazolam. Compared to placebo, ketamine administration attenuated theta power increases and alpha power decreases and midazolam attenuated low beta band decreases to increasing WM load. Additionally, ketamine caused larger blood-oxygen-dependent (BOLD) signal increases in the supplementary motor area and angular gyrus, and weaker deactivations of the default mode network (DMN), whereas no difference was found between midazolam and placebo. Ketamine administration caused positive temporal correlations between frontal-midline theta (fm-theta) power and the BOLD signal to disappear and attenuated negative correlations. However, the relationship between fm-theta and the BOLD signal from DMN areas was maintained in some participants during ketamine administration, as increasing theta strength was associated with stronger BOLD signal reductions in these areas. The presence of, and ability to manipulate, both positive and negative associations between the BOLD signal and fm-theta suggest the presence of multiple fm-theta components involved in WM processes, with ketamine administration disrupting one or more of these theta-linked WM strategies.

Positron emission tomography evaluation of oxime countermeasures in live rats using the tracer O-(2-[18 F]fluoroethyl)-O-(p-nitrophenyl)methylphosphonate [18 F]-VXS

Oxime antidotes regenerate organophosphate-inhibited acetylcholinesterase (AChE). Although they share a common mechanism of AChE reactivation, the rate and amount of oxime that enters the brain are critical to the efficacy, a process linked to the oxime structure and charge. Using a platform based on the organophosphate [¹⁸ F]-VXS as a positron emission tomography tracer for active AChE, the in vivo distribution of [¹⁸ F]-VXS was evaluated after an LD₅₀ dose (250 μg/kg) of the organophosphate paraoxon (POX) and following oximes as antidotes. Rats given [¹⁸ F]-VXS tracer alone had significantly higher radioactivity (two- to threefold) in the heart and lung than rats given LD₅₀ POX at 20 or 60 min prior to [¹⁸ F]-VXS. When rats were given LD₅₀ POX followed by 2-PAM (cationic), RS194b (ionizable), or monoisonitrosoacetone (MINA) (neutral), central nervous system (CNS) radioactivity returned to levels at or above untreated naive rats (no POX), whereas CNS radioactivity did not increase in rats given the dication oximes HI-6 or MMB-4. MINA showed a significant, pairwise increase in CNS brain radioactivity compared with POX-treated rats. This new in vivo dynamic platform using [¹⁸ F]-VXS tracer measures and quantifies peripheral and CNS relative changes in AChE availability after POX exposure and is suitable for comparing oxime delivery and AChE reactivation in rats.

Whole-Brain Exploratory Analysis of Functional Task Response Following Erythropoietin Treatment in Mood Disorders: A Supervised Machine Learning Approach

A core symptom of mood disorders is cognitive impairment in attention, memory and executive functions. Erythropoietin (EPO) is a candidate treatment for cognitive impairment in unipolar and bipolar disorders (UD and BD) and modulates cognition-related neural activity across a fronto-temporo-parietal network. This report investigates predicting the pharmacological treatment from functional magnetic resonance imaging (fMRI) data using a supervised machine learning approach. A total of 84 patients with UD or BD were included in a randomized double-blind parallel-group study in which they received eight weekly infusions of either EPO (40 000 IU) or saline. Task fMRI data were collected before EPO/saline infusions started (baseline) and 6 weeks after last infusion (follow-up). During the scanning sessions, participants were given an n-back working memory and a picture encoding task. Linear classification models with different regularization techniques were used to predict treatment status from both cross-sectional data (at follow-up) and longitudinal data (difference between baseline and follow-up). For the n-back and picture encoding tasks, data were available and analyzed for 52 (EPO; n = 28, Saline; n = 24) and 59 patients (EPO; n = 31, Saline; n = 28), respectively. We found limited evidence that the classifiers used could predict treatment status at a reliable level of performance (≤60% accuracy) when tested using repeated cross-validation. There was no difference in using cross-sectional versus longitudinal data. Whole-brain multivariate decoding applied to pharmaco-fMRI in small to moderate samples seems to be suboptimal for exploring data driven neuronal treatment mechanisms.

Pharmaco-fMRI: A Tool to Predict the Response to Antiepileptic Drugs in Epilepsy

Pharmacological treatment with antiepileptic medications (AEDs) in epilepsy is associated with a variety of neurocognitive side effects. However, the mechanisms underlying these side effects, and why certain brain anatomies are more affected still remain poorly understood. Advanced functional magnetic resonance imaging (fMRI) methods, such as pharmaco-fMRI, can investigate medication-related effects on brain activities using task and resting state fMRI and showing reproducible activation and deactivation patterns. This methodological approach has been used successfully to complement neuropsychological studies of AEDs. Here we review pharmaco-fMRI studies in people with epilepsy targeting the most-widely prescribed AEDs. Pharmco-fMRI has advanced our understanding of the impact of AEDs on specific brain networks and thus may provide potential biomarkers to move beyond the current “trial and error” approach when commencing anti-epileptic medication.

The Opioid Crisis and the Future of Addiction and Pain Therapeutics

Opioid misuse and addiction are a public health crisis resulting in debilitation, deaths, and significant social and economic impact. Curbing this crisis requires collaboration among academic, government, and industrial partners toward the development of effective nonaddictive pain medications, interventions for opioid overdose, and addiction treatments. A 2-day meeting, The Opioid Crisis and the Future of Addiction and Pain Therapeutics: Opportunities, Tools, and Technologies Symposium, was held at the National Institutes of Health (NIH) to address these concerns and to chart a collaborative path forward. The meeting was supported by the NIH Helping to End Addiction Long-Term^SM (HEAL) Initiative, an aggressive, trans-agency effort to speed scientific solutions to stem the national opioid crisis. The event was unique in bringing together two research disciplines, addiction and pain, in order to create a forum for crosscommunication and collaboration. The output from the symposium will be considered by the HEAL Initiative; this article summarizes the scientific presentations and key takeaways. Improved understanding of the etiology of acute and chronic pain will enable the discovery of novel targets and regulatable pain circuits for safe and effective therapeutics, as well as relevant biomarkers to ensure adequate testing in clinical trials. Applications of improved technologies including reagents, assays, model systems, and validated probe compounds will likely increase the delivery of testable hypotheses and therapeutics to enable better health outcomes for patients. The symposium goals were achieved by increasing interdisciplinary collaboration to accelerate solutions for this pressing public health challenge and provide a framework for focused efforts within the research community. SIGNIFICANCE STATEMENT: This article summarizes key messages and discussions resulting from a 2-day symposium focused on challenges and opportunities in developing addiction- and pain-related medications. Speakers and attendees came from 40 states in the United States and 15 countries, bringing perspectives from academia, industry, government, and healthcare by researchers, clinicians, regulatory experts, and patient advocates.

A 24-week double-blind placebo-controlled study of the efficacy and safety of the AMPA modulator S47445 in patients with mild to moderate Alzheimer's disease and depressive symptoms

Introduction

S47445 is a novel positive allosteric modulator of alpha-amino-3-hydroxy-5-methyl-4-isoxazole-propionic acid receptors that may emerge as a favorable candidate for the symptomatic treatment of cognitive and depressive disorders in patients suffering from Alzheimer's disease (AD) of mild to moderate severity and with depressive symptoms.

Methods

For this double-blind, placebo-controlled 24-week phase II trial, 520 outpatients aged between 55 and 85 years, with probable AD at mild to moderate stages (a Mini-Mental State Examination score of 24-15 inclusive) and exhibiting depressive symptoms (Cornell Scale for Depression in Dementia [CSDD] ≥ 8) were recruited in twelve countries and randomized to 3 doses of S47445 (5-15-50 mg) or placebo. The primary end point was the change from baseline in the 11-item Alzheimer's Disease Assessment Scale-Cognitive subscale (ADAS-Cog) total score at week 24. Secondary measures included the Disability Assessment for Dementia, Mini-Mental State Examination, ADAS-Cog 13-item, CSDD, Clinical Global Impression of Change (Alzheimer's Disease Cooperative Study-CGIC), Neuropsychiatric Inventory (NPI), and safety criteria.

Results

Baseline characteristics were comparable between the 4 groups. After 24 weeks, no statistically significant treatment difference was demonstrated between S47445 (5, 15 or 50 mg/d) and placebo on cognition (ADAS-Cog), function (Disability Assessment for Dementia), or depressive symptoms (CSDD). An improvement on neuropsychiatric symptoms assessed by NPI was evidenced at the lower dose 5 mg/d (Δ -2.55, P = .023, post hoc analysis) compared to placebo. CSDD and total NPI scores improved in all groups including placebo. There were no specific and/or unexpected safety signals observed with any of the S47445 doses.

Discussion

S47445 administered for 24 weeks was safe and well tolerated by patients with mild to moderate AD; the compound did not show significant benefits over placebo on cognition, function, or depressive symptoms.

Positron emission tomography studies of organophosphate chemical threats and oxime countermeasures

There is a unique in vivo interplay involving the mechanism of inactivation of acetylcholinesterase (AChE) by toxic organophosphorus (OP) compounds and the restoration of AChE activity by oxime antidotes. OP compounds form covalent adducts to this critical enzyme target and oximes are introduced to directly displace the OP from AChE. For the most part, the in vivo inactivation of AChE leading to neurotoxicity and antidote-based therapeutic reversal of this mechanism are well understood, however, these molecular-level events have not been evaluated by dynamic imaging in living systems at millimeter resolution. A deeper understanding of these critically, time-dependent mechanisms is needed to develop new countermeasures. To address this void and to help accelerate the development of new countermeasures, positron-emission tomography (PET) has been investigated as a unique opportunity to create platform technologies to directly examine the interdependent toxicokinetic/pharmacokinetic and toxicodynamic/pharmacodynamic features of OPs and oximes in real time within live animals. This review will cover two first-in-class PET tracers representing an OP and an oxime antidote, including their preparation, requisite pharmacologic investigations, mechanistic interpretations, biodistribution and imaging.

Development and validation of a prediction model for the probability of responding to placebo in antidepressant trials: a pooled analysis of individual patient data

BACKGROUND

Identifying potential placebo responders among apparent drug responders is critical to dissect drug-specific and nonspecific effects in depression.

OBJECTIVE

This project aimed to develop and test a prediction model for the probability of responding to placebo in antidepressant trials. Such a model will allow us to estimate the probability of placebo response among drug responders in antidepressants trials.

METHODS

We identified all placebo-controlled, double-blind randomised controlled trials (RCTs) of second generation antidepressants for major depressive disorder conducted in Japan and requested their individual patient data (IPD) to pharmaceutical companies. We obtained IPD (n=1493) from four phase II/III RCTs comparing mirtazapine, escitalopram, duloxetine, paroxetine and placebo. Out of 1493 participants in the four clinical trials, 440 participants allocated to placebo were included in the analyses. Our primary outcome was response, defined as 50% or greater reduction on Hamilton Rating Scale for Depression at study endpoint. We used multivariable logistic regression to develop a prediction model. All available candidate of predictor variables were tested through a backward variable selection and covariates were selected for the prediction model. The performance of the model was assessed by using Hosmer-Lemeshow test for calibration and the area under the ROC curve for discrimination.

FINDINGS

Placebo response rates differed between 31% and 59% (grand average: 43%) among four trials. Four variables were selected from all candidate variables and included in the final model: age at onset, age at baseline, bodily symptoms, and study-level difference. The final model performed satisfactorily in terms of calibration (Hosmer-Lemeshow p=0.92) and discrimination (the area under the ROC curve (AUC): 0.70).

CONCLUSIONS

Our model is expected to help researchers discriminate individuals who are more likely to respond to placebo from those who are less likely so.

CLINICAL IMPLICATIONS

A larger sample and more precise individual participant information should be collected for better performance. Examination of external validity in independent datasets is warranted.

TRIAL REGISTRATION NUMBER

CRD42017055912.

[Translational behavioral research using common marmosets in the psychiatric field]

The drug discovery activities for novel compounds with the superior efficacies to current drugs have been largely unsuccessful in the psychiatric field. One of the main reasons is the lack of appropriate behavioral assays and animal models for psychiatric disorders. Since the prefrontal cortex has great roles in their pathophysiology, non-human primate common marmosets with the well-developed prefrontal cortex would be useful as experimental animals in the future translational research. To measure objectively and quantitatively the psychiatric symptoms like motivational deficits, negative affective bias and cognitive impairments in patients with schizophrenia or major depressive disorder, the clinical laboratory tasks have been developed. The development of marmoset behavioral paradigms, which may correspond to the clinical laboratory tasks, have been progressed for the translational research. On the other hand, there are still limitations to develop the marmoset models resembling the pathophysiology of psychiatric disorders. We review the current state and future perspective of translational behavioral research using marmosets.

Comparison of local spectral modulation, and temporal correlation, of simultaneously recorded EEG/fMRI signals during ketamine and midazolam sedation

RATIONALE AND OBJECTIVES

The identification of biomarkers of drug action can be supported by non-invasive brain imaging techniques, such as electroencephalography (EEG) and functional magnetic resonance imaging (fMRI), with simultaneous collection plausibly overcoming the limitations of either modality alone. Despite this, few studies have assessed the feasibility and utility of recording simultaneous EEG/fMRI in a drug study.

METHODS

We used simultaneous EEG/fMRI to assess the modulation of neural activity by ketamine and midazolam, in a placebo-controlled, single-blind, three-way cross-over design. Specifically, we analysed the sensitivity and direction of the spectral effects of each modality and the temporal correlations between the modulations of power of the common EEG bands and the blood-oxygen-level-dependent (BOLD) signal.

RESULTS AND CONCLUSIONS

Demonstrating feasibility, local spectral effects were similar to those found in previous non-simultaneous EEG and fMRI studies. Ketamine administration resulted in a widespread reduction of BOLD fractional amplitude of low frequency fluctuations (fALFF) and a diverse pattern of effects in the different EEG bands. Midazolam increased fALFF in occipital, parietal, and temporal areas, and frontal delta and beta EEG power. While EEG spectra were more sensitive to pharmacological modulations than the fALFF bands, there was no clear spatial relationship between the two modalities. Additionally, ketamine modulated the temporal correlation strengths between the theta EEG band and the BOLD signal, whereas midazolam altered temporal correlations with the alpha and beta bands. Taken together, these results demonstrate the utility of simultaneous recording: each modality provides unique insights, and combinatorial analyses elicit more information than separate recordings.

The role of fMRI in drug development

Functional magnetic resonance imaging (fMRI) has been known for over a decade to have the potential to greatly enhance the process of developing novel therapeutic drugs for prevalent health conditions. However, the use of fMRI in drug development continues to be relatively limited because of a variety of technical, biological, and strategic barriers that continue to limit progress. Here, we briefly review the roles that fMRI can have in the drug development process and the requirements it must meet to be useful in this setting. We then provide an update on our current understanding of the strengths and limitations of fMRI as a tool for drug developers and recommend activities to enhance its utility.

Brain substrates of early (4h) cigarette abstinence: Identification of treatment targets

INTRODUCTION

Research indicates that overnight nicotine abstinence disrupts neural activity in the mesocorticolimbic reward network; however, less is known about the time course of abstinence-induced brain changes. To examine the potential neural effects of early abstinence, we used arterial spin labeling perfusion fMRI, to measure regional cerebral blood flow (rCBF) changes in the resting brain induced by 4h of nicotine abstinence.

METHODS

In a repeated measures design, 5min of resting perfusion fMRI data were acquired in awake nicotine-dependent individuals (eyes open) during 'smoking as usual' (SMK) and following 4h of monitored nicotine abstinence (ABS) conditions (N=20). Conditions were compared using a paired t test in SPM8. Craving was assessed prior to each condition.

RESULTS

Compared to SMK, ABS significantly increased craving and reduced rCBF in select regions, including the hippocampus and ventral striatum (cluster corr, α=0.01, 943 contiguous voxels). The magnitude of the abstinence-induced change in rCBF correlated with the magnitude of the change in craving across conditions in select regions, including the medial and lateral orbitofrontal cortices and the anterior ventral insula (r values ranging from 0.59-0.74).

CONCLUSIONS

Results show that as few as 4h of abstinence can reduce resting rCBF in multiple nodes of the brain's mesocorticolimbic network, disrupting neural processing. Identifying early withdrawal treatment targets has far-reaching implications, which include thwarting relapse proclivities. Results parallel those of the extant human literature and are in agreement with an extensive preclinical literature showing compromised mesolimbic dopaminergic function and impairments in reward function during nicotine withdrawal.

Novel CNS drug discovery and development approach: model-based integration to predict neuro-pharmacokinetics and pharmacodynamics

INTRODUCTION

CNS drug development has been hampered by inadequate consideration of CNS pharmacokinetic (PK), pharmacodynamics (PD) and disease complexity (reductionist approach). Improvement is required via integrative model-based approaches. Areas covered: The authors summarize factors that have played a role in the high attrition rate of CNS compounds. Recent advances in CNS research and drug discovery are presented, especially with regard to assessment of relevant neuro-PK parameters. Suggestions for further improvements are also discussed. Expert opinion: Understanding time- and condition dependent interrelationships between neuro-PK and neuro-PD processes is key to predictions in different conditions. As a first screen, it is suggested to use in silico/in vitro derived molecular properties of candidate compounds and predict concentration-time profiles of compounds in multiple compartments of the human CNS, using time-course based physiology-based (PB) PK models. Then, for selected compounds, one can include in vitro drug-target binding kinetics to predict target occupancy (TO)-time profiles in humans. This will improve neuro-PD prediction. Furthermore, a pharmaco-omics approach is suggested, providing multilevel and paralleled data on systems processes from individuals in a systems-wide manner. Thus, clinical trials will be better informed, using fewer animals, while also, needing fewer individuals and samples per individual for proof of concept in humans.

Clinical Concepts Emerging from fMRI Functional Connectomics

Recent advances in connectomics have led to a synthesis of perspectives regarding the brain's functional organization that reconciles classical concepts of localized specialization with an appreciation for properties that emerge from interactions across distributed functional networks. This provides a more comprehensive framework for understanding neural mechanisms of normal cognition and disease. Although fMRI has not become a routine clinical tool, research has already had important influences on clinical concepts guiding diagnosis and patient management. Here we review illustrative examples. Studies demonstrating the network plasticity possible in adults and the global consequences of even focal brain injuries or disease both have had substantial impact on modern concepts of disease evolution and expression. Applications of functional connectomics in studies of clinical populations are challenging traditional disease classifications and helping to clarify biological relationships between clinical syndromes (and thus also ways of extending indications for, or "re-purposing," current treatments). Large datasets from prospective, longitudinal studies promise to enable the discovery and validation of functional connectomic biomarkers with the potential to identify people at high risk of disease before clinical onset, at a time when treatments may be most effective. Studies of pain and consciousness have catalyzed reconsiderations of approaches to clinical management, but also have stimulated debate about the clinical meaningfulness of differences in internal perceptual or cognitive states inferred from functional connectomics or other physiological correlates. By way of a closing summary, we offer a personal view of immediate challenges and potential opportunities for clinically relevant applications of fMRI-based functional connectomics.

The Potential Role of Sensory Testing, Skin Biopsy, and Functional Brain Imaging as Biomarkers in Chronic Pain Clinical Trials: IMMPACT Considerations

Valid and reliable biomarkers can play an important role in clinical trials as indicators of biological or pathogenic processes or as a signal of treatment response. Currently, there are no biomarkers for pain qualified by the U.S. Food and Drug Administration or the European Medicines Agency for use in clinical trials. This article summarizes an Initiative on Methods, Measurement, and Pain Assessment in Clinical Trials meeting in which 3 potential biomarkers were discussed for use in the development of analgesic treatments: 1) sensory testing, 2) skin punch biopsy, and 3) brain imaging. The empirical evidence supporting the use of these tests is described within the context of the 4 categories of biomarkers: 1) diagnostic, 2) prognostic, 3) predictive, and 4) pharmacodynamic. Although sensory testing, skin punch biopsy, and brain imaging are promising tools for pain in clinical trials, additional evidence is needed to further support and standardize these tests for use as biomarkers in pain clinical trials.

PERSPECTIVE

The applicability of sensory testing, skin biopsy, and brain imaging as diagnostic, prognostic, predictive, and pharmacodynamic biomarkers for use in analgesic treatment trials is considered. Evidence in support of their use and outlining problems is presented, as well as a call for further standardization and demonstrations of validity and reliability.

Pharmaco fMRI: Determining the functional anatomy of the effects of medication

Functional MRI studies have helped to elucidate underlying mechanisms in complex neurological and neuropsychiatric disorders. Disease processes often involve complex large-scale network interactions, extending beyond the presumed main disease focus. Given both the complexity of the clinical phenotype and the underlying dysfunctional brain circuits, so called pharmaco-fMRI (ph-MRI) studies probe pharmacological effects on functional neuro-anatomy, and can help to determine early treatment response, mechanisms of drug efficacy and side effects, and potentially advance CNS drug development. In this review, we discuss recent ph-MRI research in three major neuropsychiatric and neurological disorders and associated network alterations, namely selective serotonin and noradrenergic reuptake inhibitors in affective disorders and emotional processing circuits; antiepileptic drugs in epilepsy and cognitive networks; and stimulants in attention-deficit/hyperactivity disorder and networks of attention control. We conclude that ph-MRI studies show consistent and reproducible changes on disease relevant networks, and prove sensitive to early pharmacological effects on functional anatomy associated with disease. Further CNS drug research and development would benefit greatly from improved disease phenotyping, or biomarkers, using advanced imaging techniques.

Multimodal imaging biomarkers in premanifest and early Huntington's disease: 30-month IMAGE-HD data

BACKGROUND

The discovery of potential disease-modifying therapies in a neurodegenerative condition like Huntington's disease depends on the availability of sensitive biomarkers that reflect decline across disease stages and that are functionally and clinically relevant.

AIMS

To quantify macrostructural and microstructural changes in participants with premanifest and symptomatic Huntington's disease over 30 months, and to establish their functional and clinical relevance.

METHOD

Multimodal magnetic resonance imaging study measuring changes in macrostructural (volume) and microstructural (diffusivity) measures in 40 patients with premanifest Huntington's disease, 36 patients with symptomatic Huntington's disease and 36 healthy control participants over three testing sessions spanning 30 months.

RESULTS

Relative to controls, there was greater longitudinal atrophy in participants with symptomatic Huntington's disease in whole brain, grey matter, caudate and putamen, as well as increased caudate fractional anisotropy; caudate volume loss was the only measure to differ between premanifest Huntington's disease and control groups. Changes in caudate volume and fractional anisotropy correlated with each other and neurocognitive decline; caudate volume loss also correlated with clinical and disease severity.

CONCLUSIONS

Caudate neurodegeneration, especially atrophy, may be the most suitable candidate surrogate biomarker for consideration in the development of upcoming clinical trials.

The role of machine learning in neuroimaging for drug discovery and development

Neuroimaging has been identified as a potentially powerful probe for the in vivo study of drug effects on the brain with utility across several phases of drug development spanning preclinical and clinical investigations. Specifically, neuroimaging can provide insight into drug penetration and distribution, target engagement, pharmacodynamics, mechanistic action and potential indicators of clinical efficacy. In this review, we focus on machine learning approaches for neuroimaging which enable us to make predictions at the individual level based on the distributed effects across the whole brain. Crucially, these approaches can be trained on data from one study and applied to an independent study and, unlike group-level statistics, can be readily use to assess the generalisability to unseen data. In this review, we present examples and suggestions for how machine learning could help answer fundamental questions spanning the drug discovery pipeline: (1) Who should I recruit for this study? (2) What should I measure and when should I measure it? (3) How does the pharmacological agent behave using an experimental medicine model?, and (4) How does a compound differ from and/or resemble existing compounds? Specifically, we present studies from the literature and we suggest areas for the focus of future development. Further refinement and tailoring of machine learning techniques may help realise their tremendous potential for drug discovery and drug validation.

Antidepressant drug development: Focus on triple monoamine reuptake inhibition

Many patients with major depressive disorder (MDD) only partially respond, and some have no clinically meaningful response, to current widely used antidepressant drugs. Due to the purported role of dopamine in the pathophysiology of depression, triple-reuptake inhibitors (TRIs) that simultaneously inhibit serotonin (5-HT), norepinephrine (NE) and dopamine reuptake could be a useful addition to the armamentarium of treatments for MDD. A TRI should more effectively activate mesolimbic dopamine-related reward-networks, restore positive mood and reduce potent 5-HT reuptake blockade associated "hypodopaminergic" adverse effects of decreased libido, weight gain and "blunting" of emotions. On the other hand, dopaminergic effects raise concern over abuse liability and TRIs may have many of the cardiovascular effects associated with NET inhibition. Several clinical development programs for potential TRI antidepressants have failed to demonstrate significantly greater efficacy than placebo or standard of care. Successful late-stage clinical development of a TRI is more likely if experimental research studies in the target population of depressed patients have demonstrated target engagement that differentially and dose-dependently improves assessments of reward-network dysfunction relative to existing antidepressants. TRI treatment could be individualized on the basis of predictive markers such as the burden of decreased positive mood symptoms and/or neuroimaging evidence of reward network dysfunction. This review focuses on how the next generation of monoamine-based treatments could be efficiently developed to address unmet medical need in MDD.

Antiglucocorticoid therapy for older adults with anxiety and co-occurring cognitive dysfunction: results from a pilot study with mifepristone

OBJECTIVES

In older adults with anxiety disorders, chronically elevated cortisol may contribute to cognitive impairment and elevated anxiety. We conducted a pilot study with mifepristone, a glucocorticoid receptor antagonist, as a potential treatment for late-life anxiety disorders and co-occurring cognitive dysfunction.

METHODS

Fifteen individuals 60 years and older with an anxiety disorder plus cognitive dysfunction participated in the 12-week study. In the first week, participants were randomly assigned to mifepristone 300 mg daily or placebo. In the subsequent 3 weeks, all participants received mifepristone 300 mg. Mifepristone was then discontinued, and the participants were reassessed 8 weeks later. We examined the following: (1) cognitive changes; (2) worry symptom severity; (3) safety and tolerability; and (4) salivary cortisol before, during, and after mifepristone exposure.

RESULTS

Overall safety, tolerability, and high retention supported the feasibility of this research. Participants with higher baseline cortisol levels (peak cortisol >6.0 ng/ml, n = 5) showed improvements in memory, executive function, and worry severity after 3-4 weeks of mifepristone with persistent memory and worry improvements 8 weeks after mifepristone discontinuation. Individuals with low-to-normal baseline cortisol (n = 8) showed little to no improvement. As expected, cortisol levels rose during mifepristone exposure and returned to pretreatment levels 8 weeks after mifepristone discontinuation. In the first week of treatment, there were no differences between placebo-treated and mifepristone-treated participants.

CONCLUSION

The results of this pilot study warrant further testing of antiglucocorticoid agents in late-life anxiety disorders with co-occurring cognitive dysfunction. Mifepristone is hypothesized to have benefits in patients with evidence of glucocorticoid excess. Directions for further study are discussed.

Brain network informed subject community detection in early-onset schizophrenia

Early-onset schizophrenia (EOS) offers a unique opportunity to study pathophysiological mechanisms and development of schizophrenia. Using 26 drug-naïve, first-episode EOS patients and 25 age- and gender-matched control subjects, we examined intrinsic connectivity network (ICN) deficits underlying EOS. Due to the emerging inconsistency between behavior-based psychiatric disease classification system and the underlying brain dysfunctions, we applied a fully data-driven approach to investigate whether the subjects can be grouped into highly homogeneous communities according to the characteristics of their ICNs. The resultant subject communities and the representative characteristics of ICNs were then associated with the clinical diagnosis and multivariate symptom patterns. A default mode ICN was statistically absent in EOS patients. Another frontotemporal ICN further distinguished EOS patients with predominantly negative symptoms. Connectivity patterns of this second network for the EOS patients with predominantly positive symptom were highly similar to typically developing controls. Our post-hoc functional connectivity modeling confirmed that connectivity strength in this frontotemporal circuit was significantly modulated by relative severity of positive and negative syndromes in EOS. This study presents a novel subtype discovery approach based on brain networks and proposes complex links between brain networks and symptom patterns in EOS.

Capturing brain metrics of neuropathic pain using nuclear magnetic resonance

SUMMARY Neuropathic pain is typically caused by a lesion or dysfunction in the nervous system that results in both negative (i.e., reduced sensitivity) and positive symptoms (i.e., paraesthesia, spontaneous ongoing pain with shooting, electric shock-like sensations and abnormal responses to evoked pain). Intriguingly, chronic pain disorders manifest profound alterations in brain structure and function, and thus, modern nuclear magnetic resonance (NMR) techniques have allowed us to begin to dissect the complexities of how neuropathic pain affects the brain. NMR approaches can be used as an independent measure to improve our understanding of key changes in brain structure, function and chemistry in chronic neuropathic pain. Accordingly, NMR techniques provide neurobiological metrics that allow us to understand the neurobiological basis of chronic neuropathic pain. Additionally, although at an early stage, NMR methods can also be useful to define clinical metrics to predict chronification of neuropathic pain and responses to drugs. This article provides a review of NMR techniques and their capacity to study spontaneous pain and evoked pain, as well as structural, functional and neurochemical alterations that have repeatedly been associated with chronic neuropathic pain. Finally, the importance for quantifying disease state and treatment efficacy in neuropathic pain using NMR techniques is discussed.