Identifying candidate quality indicators of tools that support the practice of knowledge translation: a scoping review protocol.

OBJECTIVE: The objective of this scoping review is to identify and characterize relevant knowledge translation methods tools (those that provide guidance for optimized knowledge translation practice) to uncover candidate quality indicators to inform a future quality assessment tool for knowledge translation strategies. INTRODUCTION: Knowledge translation strategies (defined as including knowledge translation interventions, tools, and products) target various knowledge users, including patients, clinicians, researchers, and policy-makers. The development and use of strategies that support knowledge translation practice have been rapidly increasing, making it difficult for knowledge users to decide which to use. There is limited evidence-based guidance or measures to help assess the overall quality of knowledge translation strategies. INCLUSION CRITERIA: Empirical and non-empirical documents will be considered if they explicitly describe a knowledge translation methods tool and its development, evaluation or validation, methodological strengths or limitations, and/or use over time. The review will consider a knowledge translation methods tool if it falls within at least one knowledge translation domain (ie, implementation, dissemination, sustainability, scalability, integrated knowledge translation) in the health field. METHODS: We will conduct a systematic search of relevant electronic databases and gray literature. The search strategy will be developed iteratively by an experienced medical information specialist and peer-reviewed with the PRESS checklist. The search will be limited to English-only documents published from 2005 onward. Documents will be independently screened, selected, and extracted by 2 researchers. Data will be analyzed and summarized descriptively, including the characteristics of the included documents, knowledge translation methods tools, and candidate quality indicators. SCOPING REVIEW REGISTRATION: Open Science Framework ( https://osf.io/chxvq ).

Manifold medicine: A schema that expands treatment dimensionality.

Drug discovery currently focuses on identifying new druggable targets and drug repurposing. Here, we illustrate a third domain of drug discovery: the dimensionality of treatment regimens. We formulate a new schema called 'Manifold Medicine', in which disease states are described by vectorial positions on several body-wide axes. Thus, pathological states are represented by multidimensional 'vectors' that traverse the body-wide axes. We then delineate the manifold nature of drug action to provide a strategy for designing manifold drug cocktails by design using state-of-the-art biomedical and technological innovations. Manifold Medicine offers a roadmap for translating knowledge gained from next-generation technologies into individualized clinical practice.

Digital pathology and artificial intelligence in translational medicine and clinical practice.

Traditional pathology approaches have played an integral role in the delivery of diagnosis, semi-quantitative or qualitative assessment of protein expression, and classification of disease. Technological advances and the increased focus on precision medicine have recently paved the way for the development of digital pathology-based approaches for quantitative pathologic assessments, namely whole slide imaging and artificial intelligence (AI)-based solutions, allowing us to explore and extract information beyond human visual perception. Within the field of immuno-oncology, the application of such methodologies in drug development and translational research have created invaluable opportunities for deciphering complex pathophysiology and the discovery of novel biomarkers and drug targets. With an increasing number of treatment options available for any given disease, practitioners face the growing challenge of selecting the most appropriate treatment for each patient. The ever-increasing utilization of AI-based approaches substantially expands our understanding of the tumor microenvironment, with digital approaches to patient stratification and selection for diagnostic assays supporting the identification of the optimal treatment regimen based on patient profiles. This review provides an overview of the opportunities and limitations around implementing AI-based methods in biomarker discovery and patient selection and discusses how advances in digital pathology and AI should be considered in the current landscape of translational medicine, touching on challenges this technology may face if adopted in clinical settings. The traditional role of pathologists in delivering accurate diagnoses or assessing biomarkers for companion diagnostics may be enhanced in precision, reproducibility, and scale by AI-powered analysis tools.

Discovery in clinical and translational medicine.

With the rapid development of biotechnologies and deep improvement of knowledge, "Discovery" is the initial period and source of innovation of clinical and translational medicine. The international journal of Clinical and Translational Discovery serves to highlight unknown or unclear aspects of clinical and translational medicine-associated knowledge, technologies, mechanisms, and therapies (https://onlinelibrary.wiley.com/journal/27680622). The Discovery aims to define the interaction between genes, proteins, and cells, and explore molecular mechanisms of intercommunication and inter-regulation. More discoveries of technologies and equipment are expected to improve method sensitivity, specificity, stability, analysis, and clinical significance. The first priority of Clinical and Translational Discovery is to turn gene-, protein-, drug-, cell-, and interaction-based discoveries into health advancements. Clinical and Translational Discovery highly focuses on the discoveries of biological therapies and precision medicine-based therapy elicited from computational chemistry, DNA libraries, target-dependent small molecular drugs, high-throughput screening, vaccination, immune therapy, cell implantations, gene editing, and RNA- or protein-based inhibitors. Thus, Clinical and Translational Discovery sincerely welcome you to join and share the rapid development and future successes to come.

Effect of a multidisciplinary Severe Immunotherapy Complications Service on outcomes for patients receiving immune checkpoint inhibitor therapy for cancer.

BACKGROUND: In 2017, Massachusetts General Hospital implemented the Severe Immunotherapy Complications (SIC) Service, a multidisciplinary care team for patients hospitalized with immune-related adverse events (irAEs), a unique spectrum of toxicities associated with immune checkpoint inhibitors (ICIs). This study's objectives were to evaluate the intervention's (1) effect on patient outcomes and healthcare utilization, and (2) ability to collect biological samples via a central infrastructure, in order to study the mechanisms responsible for irAEs. METHODS: A hospital database was used to identify patients who received ICIs for a malignancy and were hospitalized with severe irAEs, before (April 2, 2016-October 3, 2017) and after (October 3, 2017-October 24, 2018) SIC Service initiation. The primary outcome was readmission rate after index hospitalization. Secondary outcomes included length of stay (LOS) for admissions, corticosteroid and non-steroidal second-line immunosuppression use, ICI discontinuation, and inpatient mortality. RESULTS: In the pre-SIC period, 127 of 1169 patients treated with ICIs were hospitalized for irAEs; in the post-SIC period, 122 of 1159. After SIC service initiation, reductions were observed in irAE readmission rate (14.8% post-SIC vs 25.9% pre-SIC; OR 0.46; 95% CI 0.22 to 0.95; p=0.036) and readmission LOS (median 6 days post-SIC vs 7 days pre-SIC; 95% CI -16.03 to -0.14; p=0.046). No significant pre-initiation and post-initiation differences were detected in corticosteroid use, second-line immunosuppression, ICI discontinuation, or inpatient mortality rates. The SIC Service collected 789 blood and tissue samples from 234 patients with suspected irAEs. CONCLUSIONS: This is the first study to report that establishing a highly subspecialized care team focused on irAEs is associated with improved patient outcomes and reduced healthcare utilization. Furthermore, the SIC Service successfully integrated blood and tissue collection safety into routine care.

Immigrant Neurologists in the Physician-Scientist Pipeline: An Intervenable Stenosis.

Immigrant neurologists on a visa make up one-fourth of our neurology resident workforce. In this article, we describe the challenges faced by them in pursuit of a career as physician-scientists. We highlight the key role that immigration status plays in various aspects of research advancement early along the neurology pipeline, pertaining to clinical career decisions and the associated delay in achieving these milestones. We conclude with a call to action to address these key roadblocks, which would have the additional potential benefit of improving inclusion and diversity in clinical and translational science. ANN NEUROL 2021;90:542-545.

Patient engagement in preclinical laboratory research: A scoping review.

BACKGROUND: 'Patient engagement' involves meaningful collaboration between researchers and 'patient partners' to co-create research. It helps ensure that research being conducted is relevant to its ultimate end-users. Although patient engagement within clinical research has been well documented, the prevalence and effects of patient engagement in translational preclinical laboratory research remain unclear. The aim of this scoping review is to present current patient engagement activities reported in preclinical laboratory research. METHODS: MEDLINE, Embase, and grey literature were systematically searched from inception to April 2021. Studies that described or investigated patient engagement in preclinical laboratory research were included. Patient engagement activities where patients (i.e. patients, family members, caregivers or community members) provided input, or consultation on at least one element of the research process were eligible for inclusion. Study characteristics and outcomes were extracted and organized thematically. FINDINGS: 32 reports were included (30 primary studies, 1 narrative review, and 1 researcher guide). Most studies engaged patients at the education or priority setting stages (n=26). The most frequently reported benefit of patient engagement was 'providing a mutual learning opportunity'. Reported barriers to patient engagement reflected concerns around 'differences in knowledge and research experience' and how this may challenge communication and limit meaningful collaboration. INTERPRETATION: Patient engagement is feasible and beneficial for preclinical laboratory research. Future work should focus on assessing the impacts of patient engagement in this area of research. FUNDING: None.

Addressing Compound Reactivity and Aggregation Assay Interferences: Case Studies of Biochemical High-Throughput Screening Campaigns Benefiting from the National Institutes of Health Assay Guidance Manual Guidelines.

Compound-dependent assay interferences represent a continued burden in drug and chemical probe discovery. The open-source National Institutes of Health/National Center for Advancing Translational Sciences (NIH/NCATS) Assay Guidance Manual (AGM) established an "Assay Artifacts and Interferences" section to address different sources of artifacts and interferences in biological assays. In addition to the frequent introduction of new chapters in this important topic area, older chapters are periodically updated by experts from academia, industry, and government to include new technologies and practices. Section chapters describe many best practices for mitigating and identifying compound-dependent assay interferences. Using two previously reported biochemical high-throughput screening campaigns for small-molecule inhibitors of the epigenetic targets Rtt109 and NSD2, the authors review best practices and direct readers to high-yield resources in the AGM and elsewhere for the mitigation and identification of compound-dependent reactivity and aggregation assay interferences.

Defective Autophagy and Mitophagy in Alzheimer's Disease: Mechanisms and Translational Implications.

The main histopathology of Alzheimer's disease (AD) is featured by the extracellular accumulation of amyloid-ß (Aß) plaques and intracellular tau neurofibrillary tangles (NFT) in the brain, which is likely to result from co-pathogenic interactions among multiple factors, e.g., aging or genes. The link between defective autophagy/mitophagy and AD pathologies is still under investigation and not fully established. In this review, we consider how AD is associated with impaired autophagy and mitophagy, and how these impact pathological hallmarks as well as the potential mechanisms. This complicated interplay between autophagy or mitophagy and histopathology in AD suggests that targeting autophagy or mitophagy probably is a promising anti-AD drug candidate. Finally, we review the implications of some new insights for induction of autophagy or mitophagy as the new therapeutic way that targets processes upstream of both NFT and Aß plaques, and hence stops the neurodegenerative course in AD.

Translation of a circulating miRNA signature of melanoma into a solid tissue assay to improve diagnostic accuracy and precision.

Aim: Successful treatment of cutaneous melanoma depends on early and accurate diagnosis of clinically suspicious melanocytic skin lesions. Multiple international studies have described the challenge of providing accurate and reproducible histopathological assessments of melanocytic lesions, highlighting the need for new diagnostic tools including disease-specific biomarkers. Previously, a 38-miRNA signature (MEL38) was identified in melanoma patient plasma and validated as a novel biomarker. In this study, MEL38 expression in solid tissue biopsies representing the benign nevi to metastatic melanoma spectrum is examined. Patients & methods: Nanostring digital gene expression assessment of the MEL38 signature was performed on 308 formalin-fixed paraffin-embedded biopsies of nevi, melanoma in situ and invasive melanoma. Genomic data were interrogated using hierarchical clustering, univariate and multivariate statistical approaches. Classification scores computed from the MEL38 signature were analyzed for their association with demographic data and histopathology results, including MPATH-DX class, AJCC disease stage and tissue subtype. Results: The MEL38 score can stratify higher-risk melanomas (MPATH-Dx class V or more advanced) from lower-risk skin lesions (class I-IV) with an area under the curve of 0.97 (p < 0.001). The genomic score ranges from 0 to 10 and is positively correlated with melanoma progression, with an intraclass correlation coefficient of 0.85 with stage 0-IV disease. Using an optimized classification threshold of ≥2.7 accurately identifies higher-risk melanomas with 89% sensitivity and 94% specificity. Multivariate analysis showed the score to be a significant predictor of malignancy, independent of technical and clinical covariates. Application of the MEL38 signature to Spitz nevi reveals an intrasubtype profile, with elements in common to both nevi and melanoma. Conclusion: Melanoma-specific circulating miRNAs maintain their association with malignancy when measured in the hypothesized tissue of origin. The MEL38 signature is an accurate and reproducible metric of melanoma status, based on changes in miRNA expression that occur as the disease develops and spreads. Inclusion of the MEL38 score into routine practice would provide physicians with previously unavailable, personalized genomic information about their patient's skin lesions. Combining molecular biomarker data with conventional histopathology data may improve diagnostic accuracy, healthcare resource utilization and patient outcomes.

Validating ADME QSAR Models Using Marketed Drugs.

Problems with drug ADME are responsible for many clinical failures. By understanding the ADME properties of marketed drugs and modeling how chemical structure contributes to these inherent properties, we can help new projects reduce their risk profiles. Kinetic aqueous solubility, the parallel artificial membrane permeability assay (PAMPA), and rat liver microsomal stability constitute the Tier I ADME assays at the National Center for Advancing Translational Sciences (NCATS). Using recent data generated from in-house lead optimization Tier I studies, we update quantitative structure-activity relationship (QSAR) models for these three endpoints and validate in silico performance against a set of marketed drugs (balanced accuracies range between 71% and 85%). Improved models and experimental datasets are of direct relevance to drug discovery projects and, together with the prediction services that have been made publicly available at the ADME@NCATS web portal (https://opendata.ncats.nih.gov/adme/), provide important tools for the drug discovery community. The results are discussed in light of our previously reported ADME models and state-of-the-art models from scientific literature.Graphical Abstract[Figure: see text].

Application of Machine Learning in Translational Medicine: Current Status and Future Opportunities.

The exponential increase in our ability to harness multi-dimensional biological and clinical data from experimental to real-world settings has transformed pharmaceutical research and development in recent years, with increasing applications of artificial intelligence (AI) and machine learning (ML). Patient-centered iterative forward and reverse translation is at the heart of precision medicine discovery and development across the continuum from target validation to optimization of pharmacotherapy. Integration of advanced analytics into the practice of Translational Medicine is now a fundamental enabler to fully exploit information contained in diverse sources of big data sets such as "omics" data, as illustrated by deep characterizations of the genome, transcriptome, proteome, metabolome, microbiome, and exposome. In this commentary, we provide an overview of ML applications in drug discovery and development, aligned with the three strategic pillars of Translational Medicine (target, patient, dose) and offer perspectives on their potential to transform the science and practice of the discipline. Opportunities for integrating ML approaches into the discipline of Pharmacometrics are discussed and will revolutionize the practice of model-informed drug discovery and development. Finally, we posit that joint efforts of Clinical Pharmacology, Bioinformatics, and Biomarker Technology experts are vital in cross-functional team settings to realize the promise of AI/ML-enabled Translational and Precision Medicine.

Application of MCAT questions as a testing tool and evaluation metric for knowledge graph-based reasoning systems.

"Knowledge graphs" (KGs) have become a common approach for representing biomedical knowledge. In a KG, multiple biomedical data sets can be linked together as a graph representation, with nodes representing entities, such as "chemical substance" or "genes," and edges representing predicates, such as "causes" or "treats." Reasoning and inference algorithms can then be applied to the KG and used to generate new knowledge. We developed three KG-based question-answering systems as part of the Biomedical Data Translator program. These systems are typically tested and evaluated using traditional software engineering tools and approaches. In this study, we explored a team-based approach to test and evaluate the prototype "Translator Reasoners" through the application of Medical College Admission Test (MCAT) questions. Specifically, we describe three "hackathons," in which the developers of each of the three systems worked together with a moderator to determine whether the applications could be used to solve MCAT questions. The results demonstrate progressive improvement in system performance, with 0% (0/5) correct answers during the first hackathon, 75% (3/4) correct during the second hackathon, and 100% (5/5) correct during the final hackathon. We discuss the technical and sociologic lessons learned and conclude that MCAT questions can be applied successfully in the context of moderated hackathons to test and evaluate prototype KG-based question-answering systems, identify gaps in current capabilities, and improve performance. Finally, we highlight several published clinical and translational science applications of the Translator Reasoners.

Extracellular vesicles derived from mesenchymal stem cells: A platform that can be engineered.

Mesenchymal stem cells play an important role in tissue damage and repair. This role is mainly due to a paracrine mechanism, and extracellular vesicles (EVs) are an important part of the paracrine function. EVs play a vital role in many aspects of cell homeostasis, physiology, and pathology, and EVs can be used as clinical biomarkers, vaccines, or drug delivery vehicles. A large number of studies have shown that EVs derived from mesenchymal stem cells (MSC-EVs) play an important role in the treatment of various diseases. However, the problems of low production, low retention rate, and poor targeting of MSC-EVs are obstacles to current clinical applications. The engineering transformation of MSC-EVs can make up for those shortcomings, thereby improving treatment efficiency. This review summarizes the latest research progress of MSC-EV direct and indirect engineering transformation from the aspects of improving MSC-EV retention rate, yield, targeting, and MSC-EV visualization research, and proposes some feasible MSC-EV engineering methods of transformation.

Translational cognitive neuroscience of dementia with touchscreen operant chambers.

Translational cognitive neuroscience of dementia involves mainly two areas: the validation of newly developed dementia animal models and the preclinical assessment of novel drug candidates in such model animals. To validate new animal models, a multidomain panel (battery) approach is essential in that dementia is, by definition, not merely a memory disorder but rather a multidomain cognitive/behavior disorder: animal modeling with a certain type of dementia would develop cognitive impairments in multiple (two at minimum) domains in a specific order according to unique spreading patterns of its neuropathology. In new drug development, the availability of highly sensitive tools assessing animal cognition is crucial to the detection of cognitive decline at the earliest stage of the disease, which may be an optimal time point to test a drug candidate. Using interspecies translatable (analogous) cognitive tasks would also be necessary to successfully predict the efficacy of drug candidates in subsequent clinical trials. Currently, this translational prediction is seriously limited given discrepancies in behavioral assessment methods between animals and humans in the preclinical and clinical trials, respectively. Since neurodegenerative diseases are often accompanied by not only cognitive but also affective and movement disorders, simultaneous assessment of task-relevant locomotor behavior and motivation is also important to rule out the effects of potential confounders. The touchscreen operant platform may satisfy these needs by offering several advantages over conventional methodology. In this review, we discuss the touchscreen operant chamber system and highlight some of its qualities as a promising and desirable tool for translational research of dementia.

New frontiers in translational research: Touchscreens, open science, and the mouse translational research accelerator platform.

Many neurodegenerative and neuropsychiatric diseases and other brain disorders are accompanied by impairments in high-level cognitive functions including memory, attention, motivation, and decision-making. Despite several decades of extensive research, neuroscience is little closer to discovering new treatments. Key impediments include the absence of validated and robust cognitive assessment tools for facilitating translation from animal models to humans. In this review, we describe a state-of-the-art platform poised to overcome these impediments and improve the success of translational research, the Mouse Translational Research Accelerator Platform (MouseTRAP), which is centered on the touchscreen cognitive testing system for rodents. It integrates touchscreen-based tests of high-level cognitive assessment with state-of-the art neurotechnology to record and manipulate molecular and circuit level activity in vivo in animal models during human-relevant cognitive performance. The platform also is integrated with two Open Science platforms designed to facilitate knowledge and data-sharing practices within the rodent touchscreen community, touchscreencognition.org and mousebytes.ca. Touchscreencognition.org includes the Wall, showcasing touchscreen news and publications, the Forum, for community discussion, and Training, which includes courses, videos, SOPs, and symposia. To get started, interested researchers simply create user accounts. We describe the origins of the touchscreen testing system, the novel lines of research it has facilitated, and its increasingly widespread use in translational research, which is attributable in part to knowledge-sharing efforts over the past decade. We then identify the unique features of MouseTRAP that stand to potentially revolutionize translational research, and describe new initiatives to partner with similar platforms such as McGill's M3 platform (m3platform.org).