A procedural framework for good imaging practice in pharmacological fMRI studies applied to drug development #1: processes and requirements

Magnetic resonance imaging and ultrasound elastography in the context of preclinical pharmacological research: significance for the 3R principles

The 3Rs principles-reduction, refinement, replacement-are at the core of preclinical research within drug discovery, which still relies to a great extent on the availability of models of disease in animals. Minimizing their distress, reducing their number as well as searching for means to replace them in experimental studies are constant objectives in this area. Due to its non-invasive character in vivo imaging supports these efforts by enabling repeated longitudinal assessments in each animal which serves as its own control, thereby enabling to reduce considerably the animal utilization in the experiments. The repetitive monitoring of pathology progression and the effects of therapy becomes feasible by assessment of quantitative biomarkers. Moreover, imaging has translational prospects by facilitating the comparison of studies performed in small rodents and humans. Also, learnings from the clinic may be potentially back-translated to preclinical settings and therefore contribute to refining animal investigations. By concentrating on activities around the application of magnetic resonance imaging (MRI) and ultrasound elastography to small rodent models of disease, we aim to illustrate how in vivo imaging contributes primarily to reduction and refinement in the context of pharmacological research.

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).

Comparison of test–retest reliability of BOLD and pCASL fMRI in a two-center study

Background

The establishment of test–retest reliability and reproducibility (TRR) is an important part of validating any research tool, including functional magnetic resonance imaging (fMRI). The primary objective of this study is to investigate the reliability of pseudo-Continuous Arterial Spin Labeling (pCASL) and Blood Oxygen Level Dependent (BOLD) fMRI data acquired across two different scanners in a sample of healthy adults. While single site/single scanner studies have shown acceptable repeatability, TRR of both in a practical multisite study occurring in two facilities spread out across the country with weeks to months between scans is critically needed.

Methods

Ten subjects were imaged with similar 3 T MRI scanners at the University of Pittsburgh and Massachusetts General Hospital. Finger-tapping and Resting-state data were acquired for both techniques. Analysis of the resting state data for functional connectivity was performed with the Functional Connectivity Toolbox, while analysis of the finger tapping data was accomplished with FSL. pCASL Blood flow data was generated using AST Toolbox. Activated areas and networks were identified via pre-defined atlases and dual-regression techniques. Analysis for TRR was conducted by comparing pCASL and BOLD images in terms of Intraclass correlation coefficients, Dice Similarity Coefficients, and repeated measures ANOVA.

Results

Both BOLD and pCASL scans showed strong activation and correlation between the two locations for the finger tapping tasks. Functional connectivity analyses identified elements of the default mode network in all resting scans at both locations. Multivariate repeated measures ANOVA showed significant variability between subjects, but no significant variability for location. Global CBF was very similar between the two scanning locations, and repeated measures ANOVA showed no significant differences between the two scanning locations.

Conclusions

The results of this study show that when similar scanner hardware and software is coupled with identical data analysis protocols, consistent and reproducible functional brain images can be acquired across sites. The variability seen in the activation maps is greater for pCASL versus BOLD images, as expected, however groups maps are remarkably similar despite the low number of subjects. This demonstrates that multi-site fMRI studies of task-based and resting state brain activity is feasible.

A methodological checklist for fMRI drug cue reactivity studies: development and expert consensus

Cue reactivity is one of the most frequently used paradigms in functional magnetic resonance imaging (fMRI) studies of substance use disorders (SUDs). Although there have been promising results elucidating the neurocognitive mechanisms of SUDs and SUD treatments, the interpretability and reproducibility of these studies is limited by incomplete reporting of participants' characteristics, task design, craving assessment, scanning preparation and analysis decisions in fMRI drug cue reactivity (FDCR) experiments. This hampers clinical translation, not least because systematic review and meta-analysis of published work are difficult. This consensus paper and Delphi study aims to outline the important methodological aspects of FDCR research, present structured recommendations for more comprehensive methods reporting and review the FDCR literature to assess the reporting of items that are deemed important. Forty-five FDCR scientists from around the world participated in this study. First, an initial checklist of items deemed important in FDCR studies was developed by several members of the Enhanced NeuroImaging Genetics through Meta-Analyses (ENIGMA) Addiction working group on the basis of a systematic review. Using a modified Delphi consensus method, all experts were asked to comment on, revise or add items to the initial checklist, and then to rate the importance of each item in subsequent rounds. The reporting status of the items in the final checklist was investigated in 108 recently published FDCR studies identified through a systematic review. By the final round, 38 items reached the consensus threshold and were classified under seven major categories: 'Participants' Characteristics', 'General fMRI Information', 'General Task Information', 'Cue Information', 'Craving Assessment Inside Scanner', 'Craving Assessment Outside Scanner' and 'Pre- and Post-Scanning Considerations'. The review of the 108 FDCR papers revealed significant gaps in the reporting of the items considered important by the experts. For instance, whereas items in the 'General fMRI Information' category were reported in 90.5% of the reviewed papers, items in the 'Pre- and Post-Scanning Considerations' category were reported by only 44.7% of reviewed FDCR studies. Considering the notable and sometimes unexpected gaps in the reporting of items deemed to be important by experts in any FDCR study, the protocols could benefit from the adoption of reporting standards. This checklist, a living document to be updated as the field and its methods advance, can help improve experimental design, reporting and the widespread understanding of the FDCR protocols. This checklist can also provide a sample for developing consensus statements for protocols in other areas of task-based fMRI.

fMRI as an outcome measure in clinical trials: A systematic review in clinicaltrials.gov

INTRODUCTION

More than one-thousand trials with functional magnetic resonance imaging (fMRI) as an outcome measure were registered in clinicaltrials.gov at the time of writing this article. However, 93% of these registered trials are still not completed with published results and there is no picture available about methodological dimensions of these ongoing trials with fMRI as an outcome measure.

METHODS

We collected trials that use fMRI as an outcome measure in the ClinicalTrials.gov registry on 13 October 2018 and reviewed each trial's record entry. Eligible trials' characteristics were extracted and summarized.

RESULTS

In total, 1,386 clinical trials were identified that reported fMRI in their outcome measures with fMRI as the only primary outcome in 33% of them. 82% of fMRI trials were started after 2011. The most frequent intervention was drug (pharmacological intervention) (29%). 57% of trials had parallel assignment design and 20% were designed for cross-over assignment. For task-based fMRI, cognitive systems (46%) based on Research Domain Criteria (RDoC) was the most frequent domain of tasks. Less than one-third of trials (28%) registered at least one region of interest for their analysis. Food cue reactivity task, pain perception task, n-back task, and monetary incentive delay task were recruited in more than 25 registered trials.

CONCLUSION

The number of fMRI trials (fMRI as an outcome measure) with both task and rest protocols is growing rapidly. Our study suggests a growing need for harmonization and standardized checklists on both methods and analysis for preregistration of fMRI-based outcomes in clinical trials.

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.

Targeting Functional Biomarkers in Schizophrenia with Neuroimaging

Many of the most debilitating symptoms for psychiatric disorders such as schizophrenia remain poorly treated. As such, the development of novel treatments is urgently needed. Unfortunately, the costs associated with high failure rates for investigational compounds as they enter clinical trials has led to pharmaceutical companies downsizing or eliminating research programs needed to develop these drugs. One way of increasing the probability of success for investigational compounds is to incorporate alternative methods of identifying biological targets in order to more effectively screen new drugs. A promising method of accomplishing this goal for psychiatric drugs is to use functional magnetic resonance imaging (fMRI). fMRI investigates neural circuits, shedding light on the biology that generates symptoms such as hallucinations. Once identified, relevant neural circuits can be targeted with pharmacologic interventions and the response to these drugs measured with fMRI. This review describes the early use of fMRI in this context, and discusses the alpha7 nicotinic receptor agonist 3-(2,4-dimethoxybenzylidene) anabaseine (DMXB-A), as an example of the potential value of fMRI for psychiatric drug development.

Biomarkers, designs, and interpretations of resting-state fMRI in translational pharmacological research: A review of state-of-the-Art, challenges, and opportunities for studying brain chemistry

A decade of research and development in resting-state functional MRI (RSfMRI) has opened new translational and clinical research frontiers. This review aims to bridge between technical and clinical researchers who seek reliable neuroimaging biomarkers for studying drug interactions with the brain. About 85 pharma-RSfMRI studies using BOLD signal (75% of all) or arterial spin labeling (ASL) were surveyed to investigate the acute effects of psychoactive drugs. Experimental designs and objectives include drug fingerprinting dose-response evaluation, biomarker validation and calibration, and translational studies. Common biomarkers in these studies include functional connectivity, graph metrics, cerebral blood flow and the amplitude and spectrum of BOLD fluctuations. Overall, RSfMRI-derived biomarkers seem to be sensitive to spatiotemporal dynamics of drug interactions with the brain. However, drugs cause both central and peripheral effects, thus exacerbate difficulties related to biological confounds, structured noise from motion and physiological confounds, as well as modeling and inference testing. Currently, these issues are not well explored, and heterogeneities in experimental design, data acquisition and preprocessing make comparative or meta-analysis of existing reports impossible. A unifying collaborative framework for data-sharing and data-mining is thus necessary for investigating the commonalities and differences in biomarker sensitivity and specificity, and establishing guidelines. Multimodal datasets including sham-placebo or active control sessions and repeated measurements of various psychometric, physiological, metabolic and neuroimaging phenotypes are essential for pharmacokinetic/pharmacodynamic modeling and interpretation of the findings. We provide a list of basic minimum and advanced options that can be considered in design and analyses of future pharma-RSfMRI studies. Hum Brain Mapp 38:2276-2325, 2017. © 2017 Wiley Periodicals, Inc.

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.

Optimizing Central Nervous System Drug Development Using Molecular Imaging

Advances in multimodality fusion imaging technologies promise to accelerate the understanding of the systems biology of disease and help in the development of new therapeutics. The use of molecular imaging biomarkers has been proven to shorten cycle times for central nervous system (CNS) drug development and thereby increase the efficiency and return on investment from research. Imaging biomarkers can be used to help select the molecules, doses, and patients most likely to test therapeutic hypotheses by stopping those that have little chance of success and accelerating those with potential to achieve beneficial clinical outcomes. CNS imaging biomarkers have the potential to drive new medical care practices for patients in the latent phases of progressive neurodegenerative disorders by enabling the detection, preventative treatment, and tracking of disease in a paradigm shift from today's approaches that have to see the overt symptoms of disease before treating it.

60 years of advances in neuropsychopharmacology for improving brain health, renewed hope for progress

Pharmacotherapy is effective in helping many patients suffering from psychiatric and neurological disorders, and both psychotherapeutic and stimulation-based techniques likewise have important roles to play in their treatment. However, therapeutic progress has recently been slow. Future success for improving the control and prevention of brain disorders will depend upon deeper insights into their causes and pathophysiological substrates. It will also necessitate new and more rigorous methods for identifying, validating, developing and clinically deploying new treatments. A field of Research and Development (R and D) that remains critical to this endeavour is Neuropsychopharmacology which transformed the lives of patients by introducing pharmacological treatments for psychiatric disorder some 60 years ago. For about half of this time, the European College of Neuropsychopharmacology (ECNP) has fostered efforts to enhance our understanding of the brain, and to improve the management of psychiatric disorders. Further, together with partners in academia and industry, and in discussions with regulators and patients, the ECNP is implicated in new initiatives to achieve this goal. This is then an opportune moment to survey the field, to analyse what we have learned from the achievements and failures of the past, and to identify major challenges for the future. It is also important to highlight strategies that are being put in place in the quest for more effective treatment of brain disorders: from experimental research and drug discovery to clinical development and collaborative ventures for reinforcing "R and D". The present article sets the scene, then introduces and interlinks the eight articles that comprise this Special Volume of European Neuropsychopharmacology. A broad-based suite of themes is covered embracing: the past, present and future of "R and D" for psychiatric disorders; complementary contributions of genetics and epigenetics; efforts to improve the treatment of depression, neurodevelopmental and neurodegenerative disorders; and advances in the analysis and neuroimaging of cellular and cerebral circuits.

Learning from the past and looking to the future: Emerging perspectives for improving the treatment of psychiatric disorders

Modern neuropsychopharmacology commenced in the 1950s with the serendipitous discovery of first-generation antipsychotics and antidepressants which were therapeutically effective yet had marked adverse effects. Today, a broader palette of safer and better-tolerated agents is available for helping people that suffer from schizophrenia, depression and other psychiatric disorders, while complementary approaches like psychotherapy also have important roles to play in their treatment, both alone and in association with medication. Nonetheless, despite considerable efforts, current management is still only partially effective, and highly-prevalent psychiatric disorders of the brain continue to represent a huge personal and socio-economic burden. The lack of success in discovering more effective pharmacotherapy has contributed, together with many other factors, to a relative disengagement by pharmaceutical firms from neuropsychiatry. Nonetheless, interest remains high, and partnerships are proliferating with academic centres which are increasingly integrating drug discovery and translational research into their traditional activities. This is, then, a time of transition and an opportune moment to thoroughly survey the field. Accordingly, the present paper, first, chronicles the discovery and development of psychotropic agents, focusing in particular on their mechanisms of action and therapeutic utility, and how problems faced were eventually overcome. Second, it discusses the lessons learned from past successes and failures, and how they are being applied to promote future progress. Third, it comprehensively surveys emerging strategies that are (1), improving our understanding of the diagnosis and classification of psychiatric disorders; (2), deepening knowledge of their underlying risk factors and pathophysiological substrates; (3), refining cellular and animal models for discovery and validation of novel therapeutic agents; (4), improving the design and outcome of clinical trials; (5), moving towards reliable biomarkers of patient subpopulations and medication efficacy and (6), promoting collaborative approaches to innovation by uniting key partners from the regulators, industry and academia to patients. Notwithstanding the challenges ahead, the many changes and ideas articulated herein provide new hope and something of a framework for progress towards the improved prevention and relief of psychiatric and other CNS disorders, an urgent mission for our Century.

Lost but making progress--Where will new analgesic drugs come from?

There is a critical need for effective new pharmacotherapies for pain. The paucity of new drugs successfully reaching the clinic calls for a reassessment of current analgesic drug discovery approaches. Many points early in the discovery process present significant hurdles, making it critical to exploit advances in pain neurobiology to increase the probability of success. In this review, we highlight approaches that are being pursued vigorously by the pain community for drug discovery, including innovative preclinical pain models, insights from genetics, mechanistic phenotyping of pain patients, development of biomarkers, and emerging insights into chronic pain as a disorder of both the periphery and the brain. Collaborative efforts between pharmaceutical, academic, and public entities to advance research in these areas promise to de-risk potential targets, stimulate investment, and speed evaluation and development of better pain therapies.

Considerations for the clinical use of contrast agents for cellular MRI in regenerative medicine

Advances in regenerative medicine are rapidly transforming healthcare. A cornerstone of regenerative medicine is the introduction of cells that were grown or manipulated in vitro. Key questions that arise after these cells are re-introduced are: whether these cells are localized in the appropriate site; whether cells survive; and whether these cells migrate. These questions predominantly relate to the safety of the therapeutic approach (i.e. tumorigenesis), but certain aspects can also influence the efficacy of the therapeutic approach (e.g. site of injection). The European Medicines Agency has indicated that suitable methods for stem cell tracking should be applied where these methods are available. We here discuss the European regulatory framework, as well as the scientific evidence, that should be considered to facilitate the potential clinical implementation of magnetic resonance imaging contrast media to track implanted/injected cells in human studies.

Use of translational pharmacodynamic biomarkers in early-phase clinical studies for schizophrenia

Schizophrenia is a severe mental disorder characterized by cognitive deficits, and positive and negative symptoms. The development of effective pharmacological compounds for the treatment of schizophrenia has proven challenging and costly, with many compounds failing during clinical trials. Many failures occur due to disease heterogeneity and lack of predictive preclinical models and biomarkers that readily translate to humans during early characterization of novel antipsychotic compounds. Traditional early-phase trials consist of single- or multiple-dose designs aimed at determining the safety and tolerability of an investigational compound in healthy volunteers. However, by incorporating a translational approach employing methodologies derived from preclinical studies, such as EEG measures and imaging, into the traditional Phase I program, critical information regarding a compound's dose-response effects on pharmacodynamic biomarkers can be acquired. Furthermore, combined with the use of patients with stable schizophrenia in early-phase clinical trials, significant 'de-risking' and more confident 'go/no-go' decisions are possible.

Use of functional imaging across clinical phases in CNS drug development

The use of novel brain biomarkers using nuclear magnetic resonance imaging holds potential of making central nervous system (CNS) drug development more efficient. By evaluating changes in brain function in the disease state or drug effects on brain function, the technology opens up the possibility of obtaining objective data on drug effects in the living awake brain. By providing objective data, imaging may improve the probability of success of identifying useful drugs to treat CNS diseases across all clinical phases (I-IV) of drug development. The evolution of functional imaging and the promise it holds to contribute to drug development will require the development of standards (including good imaging practice), but, if well integrated into drug development, functional imaging can define markers of CNS penetration, drug dosing and target engagement (even for drugs that are not amenable to positron emission tomography imaging) in phase I; differentiate objective measures of efficacy and side effects and responders vs non-responders in phase II, evaluate differences between placebo and drug in phase III trials and provide insights into disease modification in phase IV trials.

Test-retest reliability of evoked BOLD signals from a cognitive-emotive fMRI test battery

Even more than in cognitive research applications, moving fMRI to the clinic and the drug development process requires the generation of stable and reliable signal changes. The performance characteristics of the fMRI paradigm constrain experimental power and may require different study designs (e.g., crossover vs. parallel groups), yet fMRI reliability characteristics can be strongly dependent on the nature of the fMRI task. The present study investigated both within-subject and group-level reliability of a combined three-task fMRI battery targeting three systems of wide applicability in clinical and cognitive neuroscience: an emotional (face matching), a motivational (monetary reward anticipation) and a cognitive (n-back working memory) task. A group of 25 young, healthy volunteers were scanned twice on a 3T MRI scanner with a mean test-retest interval of 14.6 days. FMRI reliability was quantified using the intraclass correlation coefficient (ICC) applied at three different levels ranging from a global to a localized and fine spatial scale: (1) reliability of group-level activation maps over the whole brain and within targeted regions of interest (ROIs); (2) within-subject reliability of ROI-mean amplitudes and (3) within-subject reliability of individual voxels in the target ROIs. Results showed robust evoked activation of all three tasks in their respective target regions (emotional task=amygdala; motivational task=ventral striatum; cognitive task=right dorsolateral prefrontal cortex and parietal cortices) with high effect sizes (ES) of ROI-mean summary values (ES=1.11-1.44 for the faces task, 0.96-1.43 for the reward task, 0.83-2.58 for the n-back task). Reliability of group level activation was excellent for all three tasks with ICCs of 0.89-0.98 at the whole brain level and 0.66-0.97 within target ROIs. Within-subject reliability of ROI-mean amplitudes across sessions was fair to good for the reward task (ICCs=0.56-0.62) and, dependent on the particular ROI, also fair-to-good for the n-back task (ICCs=0.44-0.57) but lower for the faces task (ICC=-0.02-0.16). In conclusion, all three tasks are well suited to between-subject designs, including imaging genetics. When specific recommendations are followed, the n-back and reward task are also suited for within-subject designs, including pharmaco-fMRI. The present study provides task-specific fMRI reliability performance measures that will inform the optimal use, powering and design of fMRI studies using comparable tasks.