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. 2023 Mar;13(3):a041297. doi: 10.1101/cshperspect.a041297

The Importance of Natural History Studies in Inherited Retinal Diseases

Allison Ayala 1, Janet Cheetham 2, Todd Durham 2, Maureen Maguire 1
PMCID: PMC9979848  PMID: 36690461

Abstract

Natural history studies of inherited retinal diseases (IRDs) play a critical role in the design and implementation of treatment trials. Study objectives ideally encompass (1) understanding the time course and pattern of disease progression, (2) within genotypic and phenotypic subtypes of patient populations, and (3) characterizing a range of measures of vision function, retinal structure, and functional vision that may serve as endpoints. In rare disease, data quality standards are paramount to optimizing smaller sample sizes, including a prospective, standardized, and longitudinal approach to data collection. Multicenter studies additionally facilitate strength in numbers and generalizability, and multidisciplinary collaboration ensures a holistic approach to study design and knowledge-building. Dissemination of natural history study results, data sets, and lessons learned will stimulate further innovation and progress in IRD therapeutic research, including setting up future trial designs for their best chance of success.

Inherited retinal diseases (IRDs) represent a genetically heterogeneous group of progressive retinal disorders caused by variants in at least 280 genes confirmed to date (sph.uth.edu/retnet). Despite progress in therapy development (Sahel et al. 2019), and a growing number of interventional trials for IRDs (Thompson et al. 2020; www.clinicaltrials.gov), there remain significant hurdles to designing trials and advancing therapies. Recent papers have reviewed the unmet needs and identified top priorities to move the promise of treatment forward among a complex landscape of IRD research (Csaky et al. 2017; Duncan et al. 2018; Thompson et al. 2020). A common theme is the vital need for natural history studies, the foundation for trial design and therapy development.

ROLE OF NATURAL HISTORY STUDIES IN THERAPY DEVELOPMENT

A natural history study is an observational data collection that summarizes the course of a disease in the absence of intervention. This may also include tracking the course of disease in the presence of a standard care therapy (e.g., arginine-restricted dietary regimens to reduce ornithine levels in patients with gyrate atrophy) and is recognized by the Food and Drug Administration (FDA) as a potential part of understanding natural history (Food and Drug Administration 2019b). Natural history study design options include prospective versus retrospective and longitudinal versus cross-sectional, which are described in detail in the FDA guidance. This manuscript is primarily focused on considerations of prospective, longitudinal natural history studies, which allow for higher data quality and more comprehensive data collection. However, it is worth noting that retrospective or cross-sectional natural history studies may be necessary to consider due to cost, time, or other logistics.

Incorporating knowledge of a disease's natural history is an essential part of the drug development process, as it informs clinical trial design. In particular, the FDA defines “adequate and well controlled” investigations according to seven key design elements: clear research objectives, valid comparison with a control, appropriate selection of subjects, method of assignment to treatment, methods to minimize bias, well-defined and reliable methods to assess response, and adequate analysis of results (Food and Drug Administration 2010). Figure 1 summarizes how natural history studies inform many of these and is also well described in FDA guidance (Food and Drug Administration 2019a,b). One important objective of a natural history study is to evaluate the rate of disease progression, which will reveal how quickly changes beyond day-to-day variation occur in the absence of treatment. This informs trial duration and frequency of data collection in the form of visit and testing schedules and must be coupled with selection of outcome measures as potential trial endpoints. Identifying the best candidate endpoint for measuring effect of treatment on disease progression in a trial requires consideration of many properties (Fig. 2), including sensitivity to changes in disease stage, subject/tester/grader reproducibility, correlation with other measures of disease stage, how much within-person change is beyond measurement variability, and whether within-person change is clinically meaningful. Biomarkers (defined as objective measures of biological or pathological process that can predict a clinically meaningful benefit, such as the area of the ellipsoid zone [EZ] as measured from spectral-domain optical coherence tomography [SD-OCT]) may also be developed or validated as surrogate endpoints as part of exploring candidates. Understanding and describing the patient population may also influence the selection of outcome measures. Factors that could impact disease progression may include demographic, genetic, environmental, and other disease characteristics. Identifying characteristics of patients with faster progression rates identifies who may benefit most from treatment and ultimately inform trial inclusion criteria. Collectively, addressing these objectives can yield estimates of variability around rates of progression within target patient populations and on endpoints of interest, which can then be used for sample size calculations and scenario planning for a future trial.

Figure 1.

Figure 1.

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How natural history studies (NHS) inform clinical trial design: A summary of the flow from each NHS objective, to the analyses and results those objectives yield, and ultimately how those results are used in designing future trials.

Figure 2.

Figure 2.

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Desirable properties of candidate endpoints to be considered when identifying the best candidate endpoint for a future trial.

Another potential objective of natural history data may include its use as an external control group for a trial. Although the introduction of bias is a major concern (e.g., differences in enrollment criteria, outcome measurement methods), the FDA allows this in special circumstances when concurrent controls are impractical or unethical. Acceptance of an external control requires discussion with regulatory agencies and careful planning, including data collection under conditions where external control participants are similar to trial participants in all respects of disease severity and risk factors, as detailed in FDA guidance (Food and Drug Administration 2019a,b). Beyond its use as a formal external control group, a well-designed natural history study will also generally provide a good historical basis for expected changes in a trial's concurrent control group, which may aid in interpretation of missing data in the clinical trial, albeit with the same noted limitations.

Outside of these scientific objectives, there are countless intangible insights and practical groundwork to be gained from natural history studies that can be applied to future trials. Natural history studies should be designed with sufficient duration to observe clinically meaningful changes, frequent enough measures to establish timing of those changes, and from a variety of measures based on features of disease and what is currently known. Testing and data-collection methods should be standardized to reduce variability (including testing equipment, staff training, and procedures manuals) and include methods to improve accuracy and reduce bias of measurements, such as independent expert grading of images. Development of these many standards requires input from a variety of subject matter experts, the experience of real-world implementation (especially on a multicenter scale), iterations of refinement based on lessons learned, and validation of methods (such as test–retest assessments or inter-/intragrader reproducibility). This important journey of ironing out details early provides a foundation for high-quality and efficient future trials, and establishes communication pathways, identifies disease-specific centers of excellence, brings clinicians and patient communities together, and improves the understanding of standard of care practices.

SPECIAL CONSIDERATIONS IN RARE DISEASE

FDA guidance states “the natural history of rare diseases is often poorly understood … the need for prospectively designed, protocol-driven natural history studies initiated in the earliest drug development planning stages cannot be overemphasized” but acknowledges that traditional knowledge gaps are “more difficult to address in rare disease” (Food and Drug Administration 2019a). Lack of specialized clinical centers, methods for diagnosis, or knowledge about potential endpoints are hallmark challenges of rare disease research (Food and Drug Administration 2012, 2014; Ogorka and Chanchu 2017). More directly, small sample sizes inherent in rare disease impact the precision of estimates of progression rates, the cornerstone of understanding natural history. Disease heterogeneity (in terms of both genotypes and phenotypes in IRDs) further exacerbates small sample size issues, as disease subtypes may have different manifestations of progression rate or severity. This means that objectives for understanding risk factors for progression and correlation of outcomes may be exploratory or inconclusive.

To address these challenges, natural history studies in rare disease should give special emphasis to the following:

Data quality. Standardization of testing procedures and data collection are important to reduce variability. Training and certification of study staff coupled with careful monitoring of protocol adherence and data validations by a central coordinating center will also enforce implementation of standardized procedures and improve precision of data collected. Independent and standardized expert grading of key outcomes, such as reading center evaluation of the SD-OCT EZ area, can provide more accurate measures, which may result in endpoints that are more sensitive to detecting changes in disease. Centralized expert review of eligibility criteria will ensure the study cohort is limited precisely to the disease under study. An example of this in IRDs would be confirmation by a centralized genetic expert that the laboratory-reported variants of the gene under study are likely pathogenic and the cause of each participant's retinal disease. Efforts to minimize missing data is also critical. Engaging patient communities early in the protocol development process may help to understand the potential burden of study procedures and motivate protocol adjustments to maximize compliance and retention.

Statistical approaches to maximize value of data collected. Longitudinal data with multiple time points improves precision within a limited sample size. Testing and analyzing data from both eyes when evaluating ocular outcome measures will also increase the effective sample size, although high correlation between the eyes will reduce this impact. Determining the degree of symmetry between the eyes may be an objective of the natural history study. Change over time may not be linear, so efforts should be made to identify the pattern of change. For example, loss may be a constant percentage of the current value (exponential decay), or not be detectable until a specific threshold of duration of disease. Statistical objectives and methods to evaluate outcome measures should focus on exploration rather than testing prespecified hypotheses. This includes descriptive statistics and graphical displays of distributions, correlations, and estimates of variability. Visual plots of individual patient trajectories can also be helpful in understanding progression in a smaller study.

Multicenter approach: strength in numbers. A multicenter approach to rare disease is essential to achieving feasible sample sizes, especially for evaluating subgroups. The Foundation Fighting Blindness (FFB) Consortium 2021 IRD Gene Poll found, in the 48 most common genes with a total consortium-wide patient count greater than 100, the median within-center count was less than 20 for all except three genes, and the consortium-wide total for each gene is, on average, 5 times greater than the maximum single center count (source: FFB Consortium, unpubl.). In addition to the absolute numbers, the efforts for achieving data quality noted above require significant logistical and budgetary considerations to carry out, and a multicenter approach to research can facilitate efficiencies, economy of scale, and knowledge-building that is not possible in small single-center studies.

SPECIFIC KNOWLEDGE GAPS IN IRDs THAT NATURAL HISTORY STUDIES CAN ADDRESS

The specific knowledge gaps in IRDs have been extensively described in recent publications (Csaky et al. 2017; Duncan et al. 2018; Thompson et al. 2020). Each of these reports point to natural history studies to address two major gaps most urgently: the identification of optimal IRD patient populations for target therapies and the development and validation of outcome measures for IRDs.

Define IRD Patient Populations

The challenge in defining IRD patient populations for target therapies is the genetic heterogeneity of hundreds of IRDs, which manifest in vastly different phenotypes. This creates the need to identify genetic factors impacting disease severity and progression, including the impact of mutation-specific variations within a given gene. Natural history studies are thereby needed within each gene-specific population and should ideally aim to understand progression in subgroups at the finest level possible to more specifically target treatments. This includes the need to evaluate a variety of genetic subfactors (within variants or types of variants) and a range of phenotypes (including duration and severity of disease). This becomes even more challenging in ultrarare genes and highlights the desirability to develop methods to pool genes based on similar disease mechanisms. Verbakel et al. (2018) described principal pathways affected in retinitis pigmentosa (RP), including phototransduction cascade (10 RP genes), the visual cycle (7 RP genes), and ciliary structure and transport (35 RP genes). Natural history study data are needed to determine whether these or other classifications of genes may allow pooling of patients to better explore other factors related to disease progression and refined criteria for candidates for therapies.

Develop Endpoints for IRDs

Specifically in the context of gene therapy for retinal disorders, the FDA “encourages sponsors to explore a wide spectrum of potential clinical endpoints” and “to develop and propose novel endpoints to measure clinically meaningful effects” (Food and Drug Administration 2020). The concept of “clinically meaningful” is critical to evaluating endpoints. The FDA-NIH Biomarker Working Group BEST (Biomarkers, Endpoints, and other Tools) Resource (2016) defines clinical benefit as “a positive clinically meaningful effect of an intervention (i.e., a positive effect on how an individual feels, functions, or survives)” and defines a clinical outcome as a measure of this benefit, which can be assessed as (1) clinician-reported outcomes (which we refer to as “visual function measures” in the context of IRDs), or as (2) patient/observer-reported outcomes or performance outcomes (which we categorize collectively as “functional vision measures”). Regulatory agencies also support development of surrogate endpoints, which are measures that are reasonably likely to predict a clinical benefit (which we use to categorize “structural measures” of IRDs).

Other characteristics of endpoints that are acceptable to regulatory bodies to demonstrate efficacy include those that are validated, reliable, and sensitive to change, among other properties noted previously and in Figure 2 (Csaky et al. 2017; Food and Drug Administration 2020; Thompson et al. 2020). The list of IRD endpoints is large and growing, and many are nicely summarized in other recent reports (Csaky et al. 2017; Cideciyan et al. 2021; Sahel et al. 2021). Some key IRD endpoints discussed or accepted by the FDA are summarized in Figure 3, grouped by the type of outcome measure. It is important to note that, although an endpoint may have established FDA acceptance, it may not be appropriate for a specific IRD, so natural history studies are still needed to validate endpoint properties within a given genotype. An overview of the types of IRD measures and gaps to be addressed by natural history studies are as follows.

Figure 3.

Figure 3.

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A summary of (inherited retinal disease [IRD]) endpoints historically discussed or accepted by the Food and Drug Administration (FDA).

Visual function measures. Visual function measures are intended to capture performance of the various components of the visual system within the clinical environment. Traditionally, the FDA has accepted definitions for clinically meaningful changes in visual function measures based on within-person thresholds. There has been some evolution over time from emphasis on a clinically meaningful change within a person to a clinically meaningful difference in means (Beck et al. 2007). In general, smaller sample sizes are required to detect a difference between treatment groups in mean change than to detect a difference in proportions of patients achieving a specific amount of change. More work is needed to build the case for defining clinical meaningfulness of visual function measures in terms of mean changes, when applicable. Of the visual function tests available, visual acuity has gained the most widespread acceptance because of its proven simplicity and reliability. However, visual acuity testing does not provide insight into aspects of visual functioning across the entire retina. Light sensitivity captured by perimetry measures such as static perimetry and microperimetry provide more spatial information across the visual field. Improvement in microperimetry of ≥7 decibels at ≥5 prespecified points has been discussed as an endpoint acceptable to regulators and presumably has the potential to be considered clinically meaningful (Weinreb and Kaufman 2009; Yang and Dunbar 2021). Hill of vision changes have also been discussed with the FDA but require more specific criteria for clinically meaningful changes, and evidence to support any criteria developed (Csaky et al. 2017). Other potential visual function measures such as full-field stimulus threshold, low luminance visual acuity, contrast sensitivity, and color vision have been listed by the FDA as worthy of consideration and many are being used as endpoints in current IRD trials (Csaky et al. 2017; Duncan et al. 2018; Food and Drug Administration 2020; Thompson et al. 2020). However, there is still little to go by in defining the extent of change that is clinically significant and establishing evidence that may be accepted by regulatory agencies.

Functional vision measures. Functional vision measures are intended to capture how well patients perform vision-related activities of daily living. Mobility course performance tests assess this in a physical environment. One multiluminance mobility test has been used to establish efficacy for FDA approval of gene therapy for mutations in RPE65 (Russell et al. 2017) and is recognized in FDA guidance (Food and Drug Administration 2020) as an endpoint that was used to support marketing approval. Newer technology has allowed for development of virtual mobility course platforms to simulate urban environments or test object detection while reducing logistics to implement (Lombardi et al. 2018; Aleman et al. 2021). Patient-reported outcomes (PROs) are also useful tools for evaluating real-life challenges faced by patients. Although a wide range of vision function and health-related quality of life instruments for retinal diseases exist (Prem Senthil et al. 2017) and more recent instruments have been developed specifically targeted to IRD patients (Lacy et al. 2021), there is an urgent need to validate these instruments for specific IRDs, based on rod versus cone dysfunction, central or peripheral visual field progression, and other disease-specific manifestations. Continued development and validation efforts within IRD natural history studies will increase the ability of therapies to demonstrate efficacy based directly on these outcomes or the association of structure and function endpoints with these measures of impact on daily living.

Structural measures. Structural measures are intended as surrogate endpoints that are reasonably likely to predict a clinically meaningful benefit and may have the benefit to detect disease progression earlier than visual function measures. The FDA highlights the rate of photoreceptor loss as an established efficacy endpoint that can be used to evaluate clinical benefit for treatment of retinal disorders (Food and Drug Administration 2020). The EZ area captures the extent of intact photoreceptors on SD-OCT imaging and has been shown to correlate with visual field loss in RP patients (Birch et al. 2015). However, the extent and location of EZ area changes acceptable to the FDA as clinically meaningful is less clear and requires additional evidence (Csaky et al. 2017). Fundus autofluorescence allows quantification of atrophic lesion growth in Stargardt disease (Ervin et al. 2019; Strauss et al. 2019) and in geographic atrophy (Schmitz-Valckenberg et al. 2011). Further development of these and other structural outcome measures (including adaptive optics scanning laser ophthalmoscopy) (Csaky et al. 2017) is an expanding area in IRDs as newer technology, better retinal imaging acquisition, and interpretation techniques may improve sensitivity, accuracy, and objectivity. IRD research may also benefit from a photographic-based disease severity scale, in much the same way the diabetic retinopathy grading scale developed by the Early Treatment Diabetic Retinopathy Study Research Group (1991) advanced treatment assessment methods in the field. Natural history studies are needed to further define these measures, provide evidence of association with functional endpoints to support clinically meaningful criteria, and to evaluate other properties of these endpoints like reproducibility and feasibility of implementation.

EXAMPLES OF NATURAL HISTORY STUDIES IN IRDs AND LESSONS LEARNED

The history of studies of the course of disease for specific IRDs has provided the groundwork for understanding aspects of disease progression and potential endpoints, but largely includes studies from single centers using protocols that may not generalize to other centers, retrospective studies that may be biased toward individuals whose disease is progressing more rapidly and are therefore seeking care, or studies using outdated methods or technology (Berson et al. 1985; Roesch et al. 1998; Birch et al. 1999; Sandberg et al. 2008). As a recent example, three natural history studies in Usher syndrome type 1B provide in combination an overall impression of this IRD-specific disease course (Jacobson et al. 2011; Lenassi et al. 2014; Testa et al. 2017). All three were longitudinal with variable follow-up duration of as much as 10 to 15 years in some patients, and sample sizes ranging from 10 to 33. In combination, these studies suggest visual field loss in Usher 1B patients occurs at a rate of 8% to 14% per year, visual acuity loss averages 2% to 4% per year, and legal blindness occurs in half of individuals by approximately age 40. The strength of these studies is the duration of data, which provide a good sense of long-term prognosis, including a zoomed-out view of visual field progression that suggests it is not linear. Understanding where on the progression curve a particular patient is may provide insights into the therapeutic window. The limitations of small sample sizes, single centers, and lack of standardized data collection, however, leave other trial design questions unanswered. Longitudinal data collected on a larger multicenter scale with standardized methods are needed to provide more precise estimates of annual rates of change, variability, and correlation of structure and function, to better define endpoints to be used for future trials. More recent multicenter studies initiated by the FFB have begun to address many of these limitations and allow for a larger scale assessment of challenges faced and insights gained for future trial development.

Stargardt Disease and ProgStar Studies

The Progression of Atrophy Secondary to Stargardt Disease (ProgStar) studies were designed to measure progression of Stargardt disease on fundus autofluorescence imaging, OCT, and microperimetry as possible efficacy measures for clinical trials (NCT01977846). These multicenter, longitudinal natural history studies included both a retrospective (N = 251 participants) and prospective (N = 259 participants) component. The measurable impact of the ProgStar studies is extensive and includes 20 publications to date (PubMed search April 2022), the vast majority of which are from the prospective study, reflecting the inability to glean much from insufficient quality retrospective data. Deidentified data sets have been made available free of charge to 26 requestors (17 industry and nine academic) to date (source: Jaeb Center for Health Research, host of ProgStar public data set). A workshop organized by the study sponsor, FFB, was held to review key findings and lessons learned, and are summarized in Figure 4 (Ervin et al. 2019). Discussion of the most promising endpoints for treatment trials included the EZ area on OCT, area of definitely decreased autofluorescence, and sensitivity around scotoma on microperimetry, all of which were found to be sensitive to change at 2 years. The study also affirmed that visual acuity was not a sensitive outcome measure for a trial. Moreover, the lessons learned include ideas for cost and time efficiencies (e.g., OCT manual segmentation is time consuming to grade and reducing the number of boundaries for segmentation may help) as well as improving properties of candidate outcome measures (e.g., changes in transition zones may be more sensitive to disease progression and new technology for frequency tracking and autocalibrating may help with fixation issues and reliability of microperimetry measures). These shared experiences offer insights for future trial designers to fine-tune endpoints while saving valuable resources.

Figure 4.

Figure 4.

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Summarizes key findings and lessons learned of the ProgStar study, as discussed at a workshop organized by the study sponsor, and shared through publication (Ervin et al. 2019).

FFB Consortium Natural History Studies

Building on the success of the ProgStar studies, FFB sought to establish a standing infrastructure from which to launch natural history studies more efficiently (Durham et al. 2021). In 2016, FFB initiated an international consortium of clinical centers to conduct IRD research, with the goal to accelerate development of treatments for IRDs. The vision for the consortium mission includes (1) investigators collaborating on ideas for hypotheses, study designs, and publications, (2) highly standardized studies providing long-term data to evaluate disease onset, progression, and sensitive structural and functional outcome measures, and (3) an open central repository of completed data sets to stimulate further hypothesis generation and innovation (public.jaeb.org/ffb). The organizational structure of the consortium is comprised of an executive committee, operations committee, coordinating center, genetics committee, study chairs, reading centers, subject matter experts, investigators, coordinators, technicians, and patients and families. Figure 5 provides an example overview of a multidisciplinary approach to design and conduct of natural history studies in IRDs, similar to that of the consortium. The consortium's established infrastructure allows for a more efficient study startup, including master agreements for clinical centers and vendors, reusable database and case report form templates, and procedure manuals and technician certifications for standard testing modalities, which are applicable across consortium studies. An international network of IRD centers also gathers a larger pool of IRD patients together for common research goals and encourages experts across centers to share ideas, improve methods, establish standards, and more easily build upon knowledge gained along the way.

Figure 5.

Figure 5.

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An example organizational chart featuring a multidisciplinary approach for conducting natural history studies in inherited retinal diseases (IRDs).

Three prospective, longitudinal natural history studies have been launched by the FFB Consortium to date: Rate of Progression in USH2A-Related Retinal Degeneration (RUSH2A; NCT03146078), Rate of Progression in EYS-Related Retinal Degeneration (Pro-EYS; NCT04127006), and Rate of Progression in PCDH15-Related Retinal Degeneration in Usher Syndrome 1F (RUSH1F; NCT04765345). As of this publication, RUSH2A was nearing completion of the final 4-year visits, which will provide the data to evaluate annual rates of change on various outcome measures and to evaluate correlations between structure and function as well as risk factors related to change on those measures. In preparation for these analyses, the consortium leadership has developed a template to summarize and compare properties of endpoint candidates provided in Figure 6 as an example of the general type of tool that may be used in evaluating multiple outcome measures as future trial endpoints.

Figure 6.

Figure 6.

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Example tool that may be used to evaluate and compare multiple candidate endpoints for a future trial.

More recently, the FFB Consortium has considered the special challenge of studying ultrarare IRD genes, which represent a significant portion of IRD genes. Three hundred and twenty-six (326) genes out of 374 potential IRD genes listed in the FFB Consortium 2021 Gene Poll had less than 100 total patients counted consortium-wide as having IRD linked to that causal gene, across 32 sites reporting in the poll (unpubl.). Individual natural history studies for each rare IRD gene are not feasible. Many centers have as few as one or two patients for a particular IRD gene and may not be able to devote resources needed to implement each study. Individual studies also require considerable startup time (e.g., contracts, ethics committee approvals) and study management expenses regardless of the number of patients. A single, universal protocol under which all rare IRD genes may be enrolled would address these challenges. The FFB consortium is launching a Universal Rare Gene Study (Uni-Rare), featuring a registry open to all rare RD genes, to cross-sectionally characterize (genotype, structure, and function) patients so they are ready to be enrolled into the universal longitudinal natural history study, which will open up to targeted genes (full protocol available on public website public.jaeb.org/ffb/stdy). This two-phase study will eliminate repetitive processes like certification, training, regulatory approval, contract agreements, and ultimately reduce costs and accelerate timelines for the natural history studies. The longitudinal natural history objectives for each gene are to (1) characterize the natural history of retinal degeneration on various measures of structure, function, and PROs, (2) explore whether structural outcome measures can be validated as surrogates for visual function outcomes, and (3) explore possible risk factors for progression of outcome measures. For a given gene, the sample size will impact the precision around the point estimates for changes in the outcome measures of interest (objective 1) and the correlation between the outcome measures of interest (objective 2), and will also affect the power to detect differences among subgroups (objective 3). The statistical chapter of the Uni-Rare protocol provides tables showing the impact of sample size and varying assumptions on these objectives. Even with collective enrollment across consortium centers, some very rare IRD genes will have very small sample sizes and therefore limited ability to evaluate all the noted objectives. As an exploratory objective, the Uni-Rare platform will also leverage the collective sample size and standardized data collection across all IRD genes to explore the extent to which genes with common mechanisms of disease have similar clinical manifestations, with the goal to determine whether and how some genes may be pooled in some analyses.

CONCLUSION

The success of IRD treatment trials depends on natural history data. Patient populations should be evaluated based on the finest level of genetic, phenotypic, and environmental factors possible to identify who may benefit from treatment and more carefully target therapy trials. Endpoint candidates should include a wide spectrum of vision function, structure, and functional vision measures. The heterogeneity of disease manifestation across IRDs also means that accepted endpoints for one gene may need to be validated within another. Numerous endpoint properties must be considered, including accuracy and reproducibility of the measure itself, sensitivity and clinical meaningfulness, feasibility, and goals of a future trial.

Well-designed natural history studies should emphasize data quality standards, such as prospective and standardized testing procedures and independent expert grading of outcomes. Longitudinal data with multiple time points can improve precision of estimates. Multicenter studies facilitate larger sample sizes and cultivate standards with broader applicability. Input from multidisciplinary teams fosters knowledge-building and sharing of ideas for a more holistic approach to study design, data collection, and data analysis. Natural history studies are also an opportunity to iron out details, work out kinks, and uncover unknowns. Publications on lessons learned can help future trial designers fine-tune study design, endpoints, and procedures, which may expedite timelines and ensure greater chances of success of future treatment trials.

The FFB Consortium's Uni-Rare study is a unique approach to IRD natural history studies and provides the opportunity to efficiently characterize disease progression of most IRD genes in a multicenter, prospective, standardized data collection. The consortium envisions Uni-Rare will result in mutually beneficial partnerships between IRD researchers and companies, including early access to study data sets and joint development of novel outcome measures. Results from the Uni-Rare studies will be widely disseminated as deidentified public data sets and publications to stimulate further innovation and progress in IRD therapeutic research.

Footnotes

Editors: Eyal Banin, Jean Bennett, Jacque L. Duncan, Botond Roska, and José-Alain Sahel

Additional Perspectives on Retinal Disorders: Genetic Approaches to Diagnosis and Treatment available at www.perspectivesinmedicine.org

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