Effective Data Sharing as a Conduit for Advancing Medical Product Development

A practical guide to data management and sharing for biomedical laboratory researchers

Effective data management and sharing have become increasingly crucial in biomedical research; however, many laboratory researchers lack the necessary tools and knowledge to address this challenge. This article provides an introductory guide into research data management (RDM), and the importance of FAIR (Findable, Accessible, Interoperable, and Reusable) data-sharing principles for laboratory researchers produced by practicing scientists. We explore the advantages of implementing organized data management strategies and introduce key concepts such as data standards, data documentation, and the distinction between machine and human-readable data formats. Furthermore, we offer practical guidance for creating a data management plan and establishing efficient data workflows within the laboratory setting, suitable for labs of all sizes. This includes an examination of requirements analysis, the development of a data dictionary for routine data elements, the implementation of unique subject identifiers, and the formulation of standard operating procedures (SOPs) for seamless data flow. To aid researchers in implementing these practices, we present a simple organizational system as an illustrative example, which can be tailored to suit individual needs and research requirements. By presenting a user-friendly approach, this guide serves as an introduction to the field of RDM and offers practical tips to help researchers effortlessly meet the common data management and sharing mandates rapidly becoming prevalent in biomedical research.

Harmonizing tau positron emission tomography in Alzheimer's disease: The CenTauR scale and the joint propagation model

INTRODUCTION

Tau-positron emission tomography (PET) outcome data of patients with Alzheimer's disease (AD) cannot currently be meaningfully compared or combined when different tracers are used due to differences in tracer properties, instrumentation, and methods of analysis.

METHODS

Using head-to-head data from five cohorts with tau PET radiotracers designed to target tau deposition in AD, we tested a joint propagation model (JPM) to harmonize quantification (units termed "CenTauR" [CTR]). JPM is a statistical model that simultaneously models the relationships between head-to-head and anchor point data. JPM was compared to a linear regression approach analogous to the one used in the amyloid PET Centiloid scale.

RESULTS

A strong linear relationship was observed between CTR values across brain regions. Using the JPM approach, CTR estimates were similar to, but more accurate than, those derived using the linear regression approach.

DISCUSSION

Preliminary findings using the JPM support the development and adoption of a universal scale for tau-PET quantification.

HIGHLIGHTS

Tested a novel joint propagation model (JPM) to harmonize quantification of tau PET. Units of common scale are termed "CenTauRs". Tested a Centiloid-like linear regression approach. Using five cohorts with head-to-head tau PET, JPM outperformed linearregressionbased approach. Strong linear relationship was observed between CenTauRs values across brain regions.

Pooling Alzheimer's disease clinical trial data to develop personalized medicine approaches is easier said than done: A proof-of-principle study and call to action

With the advent of the first generation of disease-modifying treatments for Alzheimer's disease, it is clearer now more than ever that the field needs to move toward personalized medicine. Pooling data from past trials may help identify subgroups most likely to benefit from specific treatments and thus inform future trial design. In this perspective, we report on our effort to pool data from past Alzheimer's disease trials to identify patients most likely to respond to different treatments. We delineate challenges and hurdles, from our proof-of-principle study, for which we requested access to trial datasets from various pharmaceutical companies and encountered obstacles in the process of arranging data-sharing agreements through legal departments. Six phase I-III trials from three sponsors provided access to their data (total n = 3170), which included demographic information, vital signs, primary and secondary endpoints, and in a small subset, cerebrospinal fluid amyloid (n = 165, 5.2%) and tau (n = 212, 6.7%). Data could be analyzed only within specific data access platforms, limiting potential harmonization with data provided through other platforms. Limited overlap in terms of outcome measures, clinical and biological information hindered analyses. Thus, while it is a commendable advancement that (some) trials now allow researchers to study their data, we conclude that gaining access to past trial datasets is complicated, frustrating the field's communal effort to find the best treatments for the right individuals. We provide a plea to promote harmonization and open access to data, by urging trial sponsors and the academic research community alike to remove barriers to data access and improve collaboration through practicing open science and harmonizing outcome measures, to allow investigators to learn all there is to learn from past failures and successes.

HIGHLIGHTS

Pooling data from past Alzheimer's disease clinical trials may help identify subgroups most likely to benefit from specific treatments and may help inform future trial design.Accessing past trial datasets is complicated, frustrating the field's communal effort to find the best treatments for the right individuals.We urge trial sponsors and the academic research community to remove data access barriers and improve collaboration through practicing open science and harmonizing outcome measures.

Segmentation of patients with small cell lung cancer into responders and non-responders using the optimal cross-validation technique

BACKGROUND

The timing of treating cancer patients is an essential factor in the efficacy of treatment. So, patients who will not respond to current therapy should receive a different treatment as early as possible. Machine learning models can be built to classify responders and nonresponders. Such classification models predict the probability of a patient being a responder. Most methods use a probability threshold of 0.5 to convert the probabilities into binary group membership. However, the cutoff of 0.5 is not always the optimal choice.

METHODS

In this study, we propose a novel data-driven approach to select a better cutoff value based on the optimal cross-validation technique. To illustrate our novel method, we applied it to three clinical trial datasets of small-cell lung cancer patients. We used two different datasets to build a scoring system to segment patients. Then the models were applied to segment patients into the test data.

RESULTS

We found that, in test data, the predicted responders and non-responders had significantly different long-term survival outcomes. Our proposed novel method segments patients better than the standard approach using a cutoff of 0.5. Comparing clinical outcomes of responders versus non-responders, our novel method had a p-value of 0.009 with a hazard ratio of 0.668 for grouping patients using the Cox proportion hazard model and a p-value of 0.011 using the accelerated failure time model which approved a significant difference between responders and non-responders. In contrast, the standard approach had a p-value of 0.194 with a hazard ratio of 0.823 using the Cox proportion hazard model and a p-value of 0.240 using the accelerated failure time model indicating the responders and non-responders do not differ significantly in survival.

CONCLUSION

In summary, our novel prediction method can successfully segment new patients into responders and non-responders. Clinicians can use our prediction to decide if a patient should receive a different treatment or stay with the current treatment.

Return of participant-level clinical trial results to participants: pilot of a simplified centralised approach

OBJECTIVES

Public access databases such as clinicaltrials.gov achieve dissemination of clinical trial design and aggregated study results. However, return of participant-level data is rarely done. A key barrier includes the proprietary ownership of data by the sponsor. Additionally, investigators may not have access to centralised data, and per International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) Good Clinical Practice, must maintain the confidentiality of participants. This study piloted an approach to return both individual and aggregate clinical trial data to parents of children participating in a series of open-label clinical trials.

SETTING AND DESIGN

A small biotech company obtained central ethics approval (centralised institutional review board [IRB], non-exempt). The study was advertised via parent advocacy groups. Parents of trial participants were offered the option to contact an employee (coordinator) within the company, requesting return of their child's study results. Ethics approval covered participation in six countries. The study focused on the sequential clinical trials of vamorolone VBP15-002 (NCT02760264) and VBP15-003 (NCT02760277) (post-results).

INTERVENTIONS

Contact initiated by the parent enabled the coordinator to obtain informed consent (and separate General Data Protection Regulations consent), with phone translation when needed. Using date of birth and study site location provided by the parent, the data manager reported the participant number to the coordinator. The coordinator retrieved and compiled data, along with an aggregate summary, which was mailed via a password protected and encrypted memory device to the parent. Prereturn and postreturn surveys were sent to consented parents (n=19; 40% of 48 total trial participants) and investigators.

RESULTS

Prereturn surveys indicated a request for as much data as offered, in all formats offered. Postreturn survey showed high satisfaction with the process and data returned. Survey of the physician site investigators (n=10; 100% participation of investigators) voiced general satisfaction with the process, with some reservations.

CONCLUSIONS

This pilot study demonstrates an innovative, cost-effective, centralised and labour conservative approach to return of participant-level and aggregate data to participants in studies.

Recommendations for the collection and annotation of biosamples for analysis of biomarkers in neurofibromatosis and schwannomatosis clinical trials

INTRODUCTION

Neurofibromatosis 1 and schwannomatosis are characterized by potential lifelong morbidity and life-threatening complications. To date, however, diagnostic and predictive biomarkers are an unmet need in this patient population. The inclusion of biomarker discovery correlatives in neurofibromatosis 1/schwannomatosis clinical trials enables study of low-incidence disease. The implementation of a common data model would further enhance biomarker discovery by enabling effective concatenation of data from multiple studies.

METHODS

The Response Evaluation in Neurofibromatosis and Schwannomatosis biomarker working group reviewed published data on emerging trends in neurofibromatosis 1 and schwannomatosis biomarker research and developed recommendations in a series of consensus meetings.

RESULTS

Liquid biopsy has emerged as a promising assay for neurofibromatosis 1/schwannomatosis biomarker discovery and validation. In addition, we review recommendations for a range of biomarkers in clinical trials, neurofibromatosis 1/schwannomatosis-specific data annotations, and common data models for data integration.

CONCLUSION

These Response Evaluation in Neurofibromatosis and Schwannomatosis consensus guidelines are intended to provide best practices for the inclusion of biomarker studies in neurofibromatosis 1/schwannomatosis clinical trials, data, and sample annotation and to lay a framework for data harmonization and concatenation between trials.

A Coalition to Advance Treatments for Parkinson's Disease, Dementia with Lewy Bodies, and Related Disorders

Parkinson's disease (PD) and dementia with Lewy bodies (DLB) share underlying neuropathology. Despite overlapping biology, therapeutic development has been approached separately for these clinical syndromes and there remains no treatment to slow, stop or prevent progression of clinical symptoms and development disability for people living with PD or DLB. Recent advances in biomarker tools, however, have paved new paths for biologic definition and staging of PD and DLB under a shared research framework. Patient-centered research funding organizations see the opportunity for a novel biological staging system for PD and DLB to accelerate and increase success of therapeutic development for the patient communities they serve. Amid growing momentum in the field to develop biological definitions for these neurodegenerative diseases, 7 international nonprofit organizations focused on PD and DLB came together to drive multistakeholder discussion and input on a biological staging system for research. The impact of these convenings to date can be seen in changes incorporated into a proposed biological staging system and growing alignment within the field to rapidly apply new scientific knowledge and biomarker tools to inform clinical trial design. In working together, likeminded nonprofit partners who were initially catalyzed by the significant potential for a biological staging system also realized the power of a shared voice in calling the field to action and have since worked together to establish a coalition to advance precompetitive progress and reduce hurdles to developing better treatments for PD, DLB and biologically related disorders.

Transforming Drug Development for Neurological Disorders: Proceedings from a Multidisease Area Workshop

Neurological disorders represent some of the most challenging therapeutic areas for successful drug approvals. The escalating global burden of death and disability for such diseases represents a significant worldwide public health challenge, and the rate of failure of new therapies for chronic progressive disorders of the nervous system is higher relative to other non-neurological conditions. However, progress is emerging rapidly in advancing the drug development landscape in both rare and common neurodegenerative diseases. In October 2022, the Critical Path Institute (C-Path) and the US Food and Drug Administration (FDA) organized a Neuroscience Annual Workshop convening representatives from the drug development industry, academia, the patient community, government agencies, and regulatory agencies regarding the future development of tools and therapies for neurological disorders. This workshop focused on five chronic progressive diseases: Alzheimer's disease, Parkinson's disease, Huntington's disease, Duchenne muscular dystrophy, and inherited ataxias. This special conference report reviews the key points discussed during the three-day dynamic workshop, including shared learnings, and recommendations that promise to catalyze future advancement of novel therapies and drug development tools.

Qualifying a novel clinical trial endpoint (iBOX) predictive of long-term kidney transplant outcomes

New immunosuppressive therapies that improve long-term graft survival are needed in kidney transplant. Critical Path Institute's Transplant Therapeutics Consortium received a qualification opinion for the iBOX Scoring System as a novel secondary efficacy endpoint for kidney transplant clinical trials through European Medicines Agency's qualification of novel methodologies for drug development. This is the first qualified endpoint for any transplant indication and is now available for use in kidney transplant clinical trials. Although the current efficacy failure endpoint has typically shown the noninferiority of therapeutic regimens, the iBOX Scoring System can be used to demonstrate the superiority of a new immunosuppressive therapy compared to the standard of care from 6 months to 24 months posttransplant in pivotal or exploratory drug therapeutic studies.

Qualifying a Novel Clinical Trial Endpoint (iBOX) Predictive of Long-Term Kidney Transplant Outcomes

New immunosuppressive therapies that improve long-term graft survival are needed in kidney transplant. Critical Path Institute's Transplant Therapeutics Consortium received a qualification opinion for the iBOX Scoring System as a novel secondary efficacy endpoint for kidney transplant clinical trials through European Medicines Agency's qualification of novel methodologies for drug development. This is the first qualified endpoint for any transplant indication and is now available for use in kidney transplant clinical trials. Although the current efficacy failure endpoint has typically shown the noninferiority of therapeutic regimens, the iBOX Scoring System can be used to demonstrate the superiority of a new immunosuppressive therapy compared to the standard of care from 6 months to 24 months posttransplant in pivotal or exploratory drug therapeutic studies.

Data sharing to advance gene-targeted therapies in rare diseases

Recent advancements in gene-targeted therapies have highlighted the critical role data sharing plays in successful translational drug development for people with rare diseases. To scale these efforts, we need to systematize these sharing principles, creating opportunities for more rapid, efficient, and scalable drug discovery/testing including long-term and transparent assessment of clinical safety and efficacy. A number of challenges will need to be addressed, including the logistical difficulties of studying rare diseases affecting individuals who may be scattered across the globe, scientific, technical, regulatory, and ethical complexities of data collection, and harmonization and integration across multiple platforms and contexts. The NCATS/NIH Gene-Targeted Therapies: Early Diagnosis and Equitable Delivery meeting series held during June 2021 included data sharing models that address these issues and framed discussions of areas that require improvement. This article describes these discussions and provides a series of considerations for future data sharing.

Leveraging the regulatory framework to facilitate drug development in Parkinson's disease

There is an exigent need for disease-modifying and symptomatic treatment approaches for Parkinson's disease. A better understanding of Parkinson's disease pathophysiology and new insights in genetics has opened exciting new venues for pharmacological treatment targets. There are, however, many challenges on the path from discovery to drug approval. These challenges revolve around appropriate endpoint selection, the lack of accurate biomarkers, challenges with diagnostic accuracy, and other challenges commonly encountered by drug developers. The regulatory health authorities, however, have provided tools to provide guidance for drug development and to assist with these challenges. The main goal of the Critical Path for Parkinson's Consortium, a nonprofit public-private partnership part of the Critical Path Institute, is to advance these so-called drug development tools for Parkinson's disease trials. The focus of this chapter will be on how the health regulators' tools were successfully leveraged to facilitate drug development in Parkinson's disease and other neurodegenerative diseases.

Access to medicines for rare diseases: A European regulatory roadmap for academia

Background: Novel or repurposed medicines for rare diseases often emerge from fundamental research or empirical findings in academia. However, researchers may be insufficiently aware of the possibilities and requirements to bring novel medicinal treatment options to the patient. This paper aims to provide an easily applicable, comprehensive roadmap designed for academic researchers to make medicines for rare diseases available for patients by addressing the relevant regulatory frameworks, including marketing authorization and alternative routes. Methods: Key points of the regulatory chapters "Placing on the Market" and "Scope" of Directive 2001/83/EC relating to medicinal products for human use were summarized. Provisions in EU directives regarding blood products, radiopharmaceuticals, and herbal and homeopathic medicinal products were excluded. Cross-referencing to other provisions was included. European case-law was retrieved from the InfoCuria database to exemplify the implications of alternative routes. Results: Medicines may only be placed on the market with a valid marketing authorization. To obtain such authorization in Europe, a "Common Technical Document" comprising reports on quality and non-clinical and clinical studies must be submitted to a "competent authority", a national medicine agency or the European Medicines Agency. Timely interaction of academic researchers with regulators via scientific advice may lead to better regulatory alignment and subsequently a higher chance for approval of academic inventions. Furthermore, reimbursement by national payers could be essential to ensure patient access. Apart from the marketing authorization route, we identified multiple alternative routes to provide (early) access. These include off-label use, named-patient basis, compassionate use, pharmacy compounding, and hospital exemption for Advanced Therapy Medicinal Products. Discussion: Aligning academic (non-)clinical studies on rare diseases with regulatory and reimbursement requirements may facilitate fast and affordable access. Several alternative routes exist to provide (early) pharmaceutical care at a national level, but case-law demonstrates that alternative routes should be interpreted strictly and for exceptional situations only. Academics should be aware of these routes and their requirements to improve access to medicines for rare diseases.

Early termination of pivotal trials in Alzheimer's disease-Preserving optimal value for participants and science

Participants in Alzheimer's disease late-phase clinical trials are frequently confronted with a situation of early termination. We discuss measures to protect the perceived value of study participation and to maximize the scientific value under such circumstances. A communication strategy should ensure that trial participants maintain a positive relationship with the research team and have their informational needs optimally met. Measures to maximize the scientific value may include data/sample sharing, strategies for personalized medicine, as well as scientific follow-up. Critical for the success of such a concept are networks of excellence, extending models of existing initiatives like Global Alzheimer's Platform Foundation Network (GAP-Net). These networks could fundamentally strengthen the role of clinical investigators if they decide on their involvement in trials based upon their estimation of the scientific value and benefit for the participants, actively contribute to scientific analyses, and mediate optimal communication among the relevant trial stakeholders.

Licensing of Orphan Medicinal Products—Use of Real-World Data and Other External Data on Efficacy Aspects in Marketing Authorization Applications Concluded at the European Medicines Agency Between 2019 and 2021

Background: Reference to so-called real-world data is more often made in marketing authorization applications for medicines intended to diagnose, prevent or treat rare diseases compared to more common diseases. We provide granularity on the type and aim of any external data on efficacy aspects from both real-world data sources and external trial data as discussed in regulatory submissions of orphan designated medicinal products in the EU. By quantifying the contribution of external data according to various regulatory characteristics, we aimed at identifying specific opportunities for external data in the field of orphan conditions.

Methods: Information on external data in regulatory documents covering 72 orphan designations was extracted. Our sample comprised public assessment reports for approved, refused, or withdrawn applications concluded from 2019–2021 at the European Medicines Agency. Products with an active orphan designation at the time of submission were scrutinized regarding the role of external data on efficacy aspects in the context of marketing authorization applications, or on the criterion of “significant benefit” for the confirmation of the orphan designation at the time of licensing. The reports allowed a broad distinction between clinical development, regulatory decision making, and intended post-approval data collection. We defined three categories of external data, administrative data, structured clinical data, and external trial data (from clinical trials not sponsored by the applicant), and noted whether external data concerned the therapeutic context of the disease or the product under review.

Results: While reference to external data with respect to efficacy aspects was included in 63% of the approved medicinal products in the field of rare diseases, 37% of marketing authorization applications were exclusively based on the dedicated clinical development plan for the product under review. Purely administrative data did not play any role in our sample of reports, but clinical data collected in a structured manner (from routine care or clinical research) were often used to inform on the trial design. Two additional recurrent themes for the use of external data were the contextualization of results, especially to confirm the orphan designation at the time of licensing, and reassurance of a large difference in treatment effect size or consistency of effects observed in clinical trials and practice. External data on the product under review were restricted to either active substances already belonging to the standard of care even before authorization or to compassionate use schemes. Furthermore, external data were considered pivotal for marketing authorization only exceptionally and only for active substances already in use within the specific therapeutic indication. Applications for the rarest conditions and those without authorized treatment alternatives were especially prominent with respect to the use of external data from real-world data sources both in the pre- and post-approval setting.

Conclusion: Specific opportunities for external data in the setting of marketing authorizations in the field of rare diseases were identified. Ongoing initiatives of fostering systematic data collection are promising steps for a more efficient medicinal product development in the field of rare diseases.

Innovations in Therapy Development for Rare Diseases Through the Rare Disease Cures Accelerator-Data and Analytics Platform

Rare diseases impact the lives of an estimated 350 million people worldwide, and yet about 90% of rare diseases remain without an approved treatment. New technologies have become available, such as gene and oligonucleotide therapies, that offer great promise in treating rare diseases. However, progress toward the development of therapies to treat these diseases is hampered by a limited understanding of the course of each rare disease, how changes in disease progression occur and can be effectively measured over time, and challenges in designing and running clinical trials in diseases where the natural history is poorly characterized. Data that could be used to characterize the natural history of each disease has often been collected in various ways, including in electronic health records, patient-report registries, clinical natural history studies, and in past clinical trials. However, each data source contains a limited number of subjects and different data elements, and data is frequently kept proprietary in the hands of the study sponsor rather than shared widely across the rare disease community. The Rare Disease Cures Accelerator-Data and Analytics Platform (RDCA-DAP) is an FDA-funded effort to overcome these persistent challenges. By aggregating data across all rare diseases and making that data available to the community to support understanding of rare disease natural history and inform drug development, RDCA-DAP aims to accelerate the regulatory approval of new therapies. RDCA-DAP curates, standardizes, and tags data across rare disease datasets to make it findable within the database, and contains a built-in analytics platform to help visualize, interpret, and use it to support drug development. RDCA-DAP will coordinate data and tool resources across non-profit, commercial, and for-profit entities to serve a diverse array of rare disease stakeholders that includes academic researchers, drug developers, FDA reviewers and of course patients and their caregivers. Drug development programs utilizing the RDCA-DAP will be able to leverage existing data to support their efforts and reach definitive decisions on the efficacy of their therapeutics more efficiently and more rapidly than ever.

Recommendations to Optimize the Use of Volumetric MRI in Huntington's Disease Clinical Trials

Volumetric magnetic resonance imaging (vMRI) has been widely studied in Huntington's disease (HD) and is commonly used to assess treatment effects on brain atrophy in interventional trials. Global and regional trajectories of brain atrophy in HD, with early involvement of striatal regions, are becoming increasingly understood. However, there remains heterogeneity in the methods used and a lack of widely-accessible multisite, longitudinal, normative datasets in HD. Consensus for standardized practices for data acquisition, analysis, sharing, and reporting will strengthen the interpretation of vMRI results and facilitate their adoption as part of a pathobiological disease staging system. The Huntington's Disease Regulatory Science Consortium (HD-RSC) currently comprises 37 member organizations and is dedicated to building a regulatory science strategy to expedite the approval of HD therapeutics. Here, we propose four recommendations to address vMRI standardization in HD research: (1) a checklist of standardized practices for the use of vMRI in clinical research and for reporting results; (2) targeted research projects to evaluate advanced vMRI methodologies in HD; (3) the definition of standard MRI-based anatomical boundaries for key brain structures in HD, plus the creation of a standard reference dataset to benchmark vMRI data analysis methods; and (4) broad access to raw images and derived data from both observational studies and interventional trials, coded to protect participant identity. In concert, these recommendations will enable a better understanding of disease progression and increase confidence in the use of vMRI for drug development.

Etiology of Autoimmune Islet Disease: Timing Is Everything

Life is about timing. -Carl LewisThe understanding of autoimmune type 1 diabetes is increasing, and examining etiology separate from pathogenesis has become crucial. The components to explain type 1 diabetes development have been known for some time. The strong association with HLA has been researched for nearly 50 years. Genome-wide association studies added another 60+ non-HLA genetic factors with minor contribution to risk. Insulitis has long been known to be present close to clinical diagnosis. T and B cells recognizing β-cell autoantigens are detectable prior to diagnosis and in newly diagnosed patients. Islet autoantibody tests against four major autoantigens have been standardized and used as biomarkers of islet autoimmunity. However, to clarify the etiology would require attention to time. Etiology may be defined as the cause of a disease (i.e., type 1 diabetes) or abnormal condition (i.e., islet autoimmunity). Timing is everything, as neither the prodrome of islet autoimmunity nor the clinical onset of type 1 diabetes tells us much about the etiology. Rather, the islet autoantibody that appears first and persists would mark the diagnosis of an autoimmune islet disease (AID). Events after the diagnosis of AID would represent the pathogenesis. Several islet autoantibodies without (stage 1) or with impaired glucose tolerance (stage 2) or with symptoms (stage 3) would define the pathogenesis culminating in clinical type 1 diabetes. Etiology would be about the timing of events that take place before the first-appearing islet autoantibody.