INNODIA Master Protocol for the evaluation of investigational medicinal products in children, adolescents and adults with newly diagnosed type 1 diabetes

Leveraging artificial intelligence and machine learning to accelerate discovery of disease-modifying therapies in type 1 diabetes

Progress in developing therapies for the maintenance of endogenous insulin secretion in, or the prevention of, type 1 diabetes has been hindered by limited animal models, the length and cost of clinical trials, difficulties in identifying individuals who will progress faster to a clinical diagnosis of type 1 diabetes, and heterogeneous clinical responses in intervention trials. Classic placebo-controlled intervention trials often include monotherapies, broad participant populations and extended follow-up periods focused on clinical endpoints. While this approach remains the 'gold standard' of clinical research, efforts are underway to implement new approaches harnessing the power of artificial intelligence and machine learning to accelerate drug discovery and efficacy testing. Here, we review emerging approaches for repurposing agents used to treat diseases that share pathogenic pathways with type 1 diabetes and selecting synergistic combinations of drugs to maximise therapeutic efficacy. We discuss how emerging multi-omics technologies, including analysis of antigen processing and presentation to adaptive immune cells, may lead to the discovery of novel biomarkers and subsequent translation into antigen-specific immunotherapies. We also discuss the potential for using artificial intelligence to create 'digital twin' models that enable rapid in silico testing of personalised agents as well as dose determination. To conclude, we discuss some limitations of artificial intelligence and machine learning, including issues pertaining to model interpretability and bias, as well as the continued need for validation studies via confirmatory intervention trials.

Investigating the effect of verapamil on preservation of beta-cell function in adults with newly diagnosed type 1 diabetes mellitus (Ver-A-T1D): protocol for a randomised, double-blind, placebo-controlled, parallel-group, multicentre trial

INTRODUCTION

Type 1 diabetes mellitus (T1DM) is a disorder that arises following the selective autoimmune destruction of the insulin-producing beta cells. Beta-cell protective or beta-cell regenerative approaches have gained wider attention, and pharmacological approaches to protect the patient's own insulin-producing beta-cell mass have been proposed. Verapamil is an L-type calcium channel blocker that has been reported to effectively lowers beta-cell thioredoxin-interacting protein expression in rodent beta cells and islets, as well as in human islets, and thus promotes functional beta-cell mass.

METHODS AND ANALYSIS

The trial is a multicentre, randomised, double-blind, placebo-controlled trial in participants with T1DM, investigating the effect of verapamil on preservation of beta-cell function (Ver-A-T1D). A total of 120 participants will be randomised in a 2:1 ratio between 360 mg verapamil and placebo, administered orally once daily. T1DM patients aged ≥18 and <45 years will be eligible for recruitment within 6 weeks of diagnosis (defined as day of starting insulin therapy). The primary objective will be to determine the changes in stimulated C-peptide response during the first 2 hours of a mixed meal tolerance test at baseline and after 12 months for 360 mg verapamil administered orally once daily versus placebo. Secondary objectives include the effects of 360 mg verapamil on (1) fasting C-peptide, (2) dried blood spot C-peptide, (3) glycated haemoglobin, (4) daily total insulin dose, (5) time in range by intermittent continuous glucose monitoring measures, (6) other biomarkers related to immunological changes and beta-cell death and (6) safety (vital signs, ECG).

ETHICS AND DISSEMINATION

Ethics approval was sought from the research ethics committee of all participating countries. All participants provided written informed consent before joining the study. Ver-A-T1D received first regulatory and ethical approvals in Austria. The publication policy is set in the innovative approach towards understanding and arresting type 1 diabetes grant agreement (www.innodia.eu).

TRIAL REGISTRATION NUMBER

EudraCT, 2020-000435-45; ClinicalTrials.gov, NCT04545151.

PROTOCOL VERSION

Version 8.0 (08 November 2021).

Presentation and characteristics of children with screen-detected type 1 diabetes: learnings from the ELSA general population pediatric screening study

INTRODUCTION

We describe the identification and management of general population screen-detected type 1 diabetes (T1D) and share learnings for best practice.

RESEARCH DESIGN AND METHODS

Children diagnosed with T1D through a general population screening initiative, the EarLy Surveillance for Autoimmune diabetes (ELSA) study, were reviewed and described.Parents provided written, informed consent for inclusion in the case series.

RESULTS

14 children with insulin requiring (stage 3) T1D are described. These cases offer unique insights into the features of screen-detected T1D. T1D is identified sooner through screening programs, characterized by absent/short symptom duration, median presenting glycated hemoglobin 6.6% (49 mmol/mol) and insulin requirements<0.5 units/kg/day. ELSA identified four children at stage 3 and another 4 progressed within 4 months of ELSA completion, including two single seropositive children. Six children developed stage 3 T1D prior to ELSA completion, including two children (14%, n=2/14) with diabetic ketoacidosis prior to confirmed antibody status.

CONCLUSIONS

There are three main learnings from this case series. First, T1D identified through screening is at an earlier stage of its natural history and requires personalized insulin regimens with lower total daily insulin doses. Second, single autoantibody seropositivity can rapidly progress to stage 3. Finally, insulin requirement can manifest at any stage of the T1D screening pathway, and therefore early education around symptom recognition is essential for families participating in screening programs.

Consensus guidance for monitoring individuals with islet autoantibody-positive pre-stage 3 type 1 diabetes

Given the proven benefits of screening to reduce diabetic ketoacidosis (DKA) likelihood at the time of stage 3 type 1 diabetes diagnosis, and emerging availability of therapy to delay disease progression, type 1 diabetes screening programmes are being increasingly emphasised. Once broadly implemented, screening initiatives will identify significant numbers of islet autoantibody-positive (IAb+) children and adults who are at risk of (confirmed single IAb+) or living with (multiple IAb+) early-stage (stage 1 and stage 2) type 1 diabetes. These individuals will need monitoring for disease progression; much of this care will happen in non-specialised settings. To inform this monitoring, JDRF in conjunction with international experts and societies developed consensus guidance. Broad advice from this guidance includes the following: (1) partnerships should be fostered between endocrinologists and primary-care providers to care for people who are IAb+; (2) when people who are IAb+ are initially identified there is a need for confirmation using a second sample; (3) single IAb+ individuals are at lower risk of progression than multiple IAb+ individuals; (4) individuals with early-stage type 1 diabetes should have periodic medical monitoring, including regular assessments of glucose levels, regular education about symptoms of diabetes and DKA, and psychosocial support; (5) interested people with stage 2 type 1 diabetes should be offered trial participation or approved therapies; and (6) all health professionals involved in monitoring and care of individuals with type 1 diabetes have a responsibility to provide education. The guidance also emphasises significant unmet needs for further research on early-stage type 1 diabetes to increase the rigour of future recommendations and inform clinical care.

Gut microbiome shifts in people with type 1 diabetes are associated with glycaemic control: an INNODIA study

AIMS/HYPOTHESIS

The gut microbiome is implicated in the disease process leading to clinical type 1 diabetes, but less is known about potential changes in the gut microbiome after the diagnosis of type 1 diabetes and implications in glucose homeostasis. We aimed to analyse potential associations between the gut microbiome composition and clinical and laboratory data during a 2 year follow-up of people with newly diagnosed type 1 diabetes, recruited to the Innovative approaches to understanding and arresting type 1 diabetes (INNODIA) study. In addition, we analysed the microbiome composition in initially unaffected family members, who progressed to clinical type 1 diabetes during or after their follow-up for 4 years.

METHODS

We characterised the gut microbiome composition of 98 individuals with newly diagnosed type 1 diabetes (ND cohort) and 194 autoantibody-positive unaffected family members (UFM cohort), representing a subgroup of the INNODIA Natural History Study, using metagenomic sequencing. Participants from the ND cohort attended study visits within 6 weeks from the diagnosis and 3, 6, 12 and 24 months later for stool sample collection and laboratory tests (HbA1c, C-peptide, diabetes-associated autoantibodies). Participants from the UFM cohort were assessed at baseline and 6, 12, 18, 24 and 36 months later.

RESULTS

We observed a longitudinal increase in 21 bacterial species in the ND cohort but not in the UFM cohort. The relative abundance of Faecalibacterium prausnitzii was inversely associated with the HbA1c levels at diagnosis (p=0.0019). The rate of the subsequent disease progression in the ND cohort, as assessed by change in HbA1c, C-peptide levels and insulin dose, was associated with the abundance of several bacterial species. Individuals with rapid decrease in C-peptide levels in the ND cohort had the lowest gut microbiome diversity. Nineteen individuals who were diagnosed with type 1 diabetes in the UFM cohort had increased abundance of Sutterella sp. KLE1602 compared with the undiagnosed UFM individuals (p=1.2 × 10-4).

CONCLUSIONS/INTERPRETATION

Our data revealed associations between the gut microbiome composition and the disease progression in individuals with recent-onset type 1 diabetes. Future mechanistic studies as well as animal studies and human trials are needed to further validate the significance and causality of these associations.

Consensus Guidance for Monitoring Individuals With Islet Autoantibody-Positive Pre-Stage 3 Type 1 Diabetes

Given the proven benefits of screening to reduce diabetic ketoacidosis (DKA) likelihood at the time of stage 3 type 1 diabetes diagnosis, and emerging availability of therapy to delay disease progression, type 1 diabetes screening programs are being increasingly emphasized. Once broadly implemented, screening initiatives will identify significant numbers of islet autoantibody-positive (IAb+) children and adults who are at risk for (confirmed single IAb+) or living with (multiple IAb+) early-stage (stage 1 and stage 2) type 1 diabetes. These individuals will need monitoring for disease progression; much of this care will happen in nonspecialized settings. To inform this monitoring, JDRF, in conjunction with international experts and societies, developed consensus guidance. Broad advice from this guidance includes the following: 1) partnerships should be fostered between endocrinologists and primary care providers to care for people who are IAb+; 2) when people who are IAb+ are initially identified, there is a need for confirmation using a second sample; 3) single IAb+ individuals are at lower risk of progression than multiple IAb+ individuals; 4) individuals with early-stage type 1 diabetes should have periodic medical monitoring, including regular assessments of glucose levels, regular education about symptoms of diabetes and DKA, and psychosocial support; 5) interested people with stage 2 type 1 diabetes should be offered trial participation or approved therapies; and 6) all health professionals involved in monitoring and care of individuals with type 1 diabetes have a responsibility to provide education. The guidance also emphasizes significant unmet needs for further research on early-stage type 1 diabetes to increase the rigor of future recommendations and inform clinical care.

A set of circulating microRNAs belonging to the 14q32 chromosome locus identifies two subgroups of individuals with recent-onset type 1 diabetes

Circulating microRNAs (miRNAs) are linked to the onset and progression of type 1 diabetes mellitus (T1DM), thus representing potential disease biomarkers. In this study, we employed a multiplatform sequencing approach to analyze circulating miRNAs in an extended cohort of prospectively evaluated recent-onset T1DM individuals from the INNODIA consortium. Our findings reveal that a set of miRNAs located within T1DM susceptibility chromosomal locus 14q32 distinguishes two subgroups of individuals. To validate our results, we conducted additional analyses on a second cohort of T1DM individuals, confirming the identification of these subgroups, which we have named cluster A and cluster B. Remarkably, cluster B T1DM individuals, who exhibit increased expression of a set of 14q32 miRNAs, show better glycemic control and display a different blood immunomics profile. Our findings suggest that this set of circulating miRNAs can identify two different T1DM subgroups with distinct blood immunomics at baseline and clinical outcomes during follow-up.

The INNODIA Type 1 Diabetes Natural History Study: a European cohort of newly diagnosed children, adolescents and adults

AIMS/HYPOTHESIS

Type 1 diabetes is an heterogenous condition. Characterising factors explaining differences in an individual's clinical course and treatment response will have important clinical and research implications. Our aim was to explore type 1 diabetes heterogeneity, as assessed by clinical characteristics, autoantibodies, beta cell function and glycaemic outcomes, during the first 12 months from diagnosis, and how it relates to age at diagnosis.

METHODS

Data were collected from the large INNODIA cohort of individuals (aged 1.0-45.0 years) newly diagnosed with type 1 diabetes, followed 3 monthly, to assess clinical characteristics, C-peptide, HbA1c and diabetes-associated antibodies, and their changes, during the first 12 months from diagnosis, across three age groups: <10 years; 10-17 years; and ≥18 years.

RESULTS

The study population included 649 individuals (57.3% male; age 12.1±8.3 years), 96.9% of whom were positive for one or more diabetes-related antibodies. Baseline (IQR) fasting C-peptide was 242.0 (139.0-382.0) pmol/l (AUC 749.3 [466.2-1106.1] pmol/l × min), with levels increasing with age (p<0.001). Over time, C-peptide remained lower in participants aged <10 years but it declined in all age groups. In parallel, glucose levels progressively increased. Lower baseline fasting C-peptide, BMI SD score and presence of diabetic ketoacidosis at diagnosis were associated with lower stimulated C-peptide over time. HbA1c decreased during the first 3 months (p<0.001), whereas insulin requirement increased from 3 months post diagnosis (p<0.001).

CONCLUSIONS/INTERPRETATION

In this large cohort with newly diagnosed type 1 diabetes, we identified age-related differences in clinical and biochemical variables. Of note, C-peptide was lower in younger children but there were no main age differences in its rate of decline.

Pleconaril and ribavirin in new-onset type 1 diabetes: a phase 2 randomized trial

Previous studies showed a low-grade enterovirus infection in the pancreatic islets of patients with newly diagnosed type 1 diabetes (T1D). In the Diabetes Virus Detection (DiViD) Intervention, a phase 2, placebo-controlled, randomized, parallel group, double-blind trial, 96 children and adolescents (aged 6-15 years) with new-onset T1D received antiviral treatment with pleconaril and ribavirin (n = 47) or placebo (n = 49) for 6 months, with the aim of preserving β cell function. The primary endpoint was the mean stimulated C-peptide area under the curve (AUC) 12 months after the initiation of treatment (less than 3 weeks after diagnosis) using a mixed linear model. The model used longitudinal log-transformed serum C-peptide AUCs at baseline, at 3 months, 6 months and 1 year. The primary endpoint was met with the serum C-peptide AUC being higher in the pleconaril and ribavirin treatment group compared to the placebo group at 12 months (average marginal effect = 0.057 in the linear mixed model; 95% confidence interval = 0.004-0.11, P = 0.037). The treatment was well tolerated. The results show that antiviral treatment may preserve residual insulin production in children and adolescent with new-onset T1D. This provides a rationale for further evaluating antiviral strategies in the prevention and treatment of T1D. European Union Drug Regulating Authorities Clinical Trials identifier: 2015-003350-41 .

Targeted serum proteomics of longitudinal samples from newly diagnosed youth with type 1 diabetes distinguishes markers of disease and C-peptide trajectory

AIMS/HYPOTHESIS

There is a growing need for markers that could help indicate the decline in beta cell function and recognise the need and efficacy of intervention in type 1 diabetes. Measurements of suitably selected serum markers could potentially provide a non-invasive and easily applicable solution to this challenge. Accordingly, we evaluated a broad panel of proteins previously associated with type 1 diabetes in serum from newly diagnosed individuals during the first year from diagnosis. To uncover associations with beta cell function, comparisons were made between these targeted proteomics measurements and changes in fasting C-peptide levels. To further distinguish proteins linked with the disease status, comparisons were made with measurements of the protein targets in age- and sex-matched autoantibody-negative unaffected family members (UFMs).

METHODS

Selected reaction monitoring (SRM) mass spectrometry analyses of serum, targeting 85 type 1 diabetes-associated proteins, were made. Sera from individuals diagnosed under 18 years (n=86) were drawn within 6 weeks of diagnosis and at 3, 6 and 12 months afterwards (288 samples in total). The SRM data were compared with fasting C-peptide/glucose data, which was interpreted as a measure of beta cell function. The protein data were further compared with cross-sectional SRM measurements from UFMs (n=194).

RESULTS

Eleven proteins had statistically significant associations with fasting C-peptide/glucose. Of these, apolipoprotein L1 and glutathione peroxidase 3 (GPX3) displayed the strongest positive and inverse associations, respectively. Changes in GPX3 levels during the first year after diagnosis indicated future fasting C-peptide/glucose levels. In addition, differences in the levels of 13 proteins were observed between the individuals with type 1 diabetes and the matched UFMs. These included GPX3, transthyretin, prothrombin, apolipoprotein C1 and members of the IGF family.

CONCLUSIONS/INTERPRETATION

The association of several targeted proteins with fasting C-peptide/glucose levels in the first year after diagnosis suggests their connection with the underlying changes accompanying alterations in beta cell function in type 1 diabetes. Moreover, the direction of change in GPX3 during the first year was indicative of subsequent fasting C-peptide/glucose levels, and supports further investigation of this and other serum protein measurements in future studies of beta cell function in type 1 diabetes.

A Plasma miR-193b-365 Signature Combined With Age and Glycemic Status Predicts Response to Lactococcus lactis-Based Antigen-Specific Immunotherapy in New-Onset Type 1 Diabetes

Immunomodulation combined with antigen therapy holds great promise to arrest autoimmune type 1 diabetes, but clinical translation is hampered by a lack of prognostic biomarkers. Low-dose anti-CD3 plus Lactococcus lactis bacteria secreting proinsulin and IL-10 reversed new-onset disease in nonobese diabetic (NOD) mice, yet some mice were resistant to the therapy. Using miRNA profiling, six miRNAs (i.e., miR-34a-5p, miR-125a-3p, miR-193b-3p, miR-328, miR-365-3p, and miR-671-3p) were identified as differentially expressed in plasma of responder versus nonresponder mice before study entry. After validation and stratification in an independent cohort, plasma miR-193b-3p and miR-365-3p, combined with age and glycemic status at study entry, had the best power to predict, with high sensitivity and specificity, poor response to the therapy. These miRNAs were highly abundant in pancreas-infiltrating neutrophils and basophils with a proinflammatory and activated phenotype. Here, a set of miRNAs and disease-associated parameters are presented as a predictive signature for the L. lactis-based immunotherapy outcome in new-onset type 1 diabetes, hence allowing targeted recruitment of trial participants and accelerated trial execution.

ARTICLE HIGHLIGHTS

Low-dose anti-CD3 combined with oral gavage of genetically modified Lactococcus lactis bacteria secreting human proinsulin and IL-10 holds great promise to arrest autoimmune type 1 diabetes, but the absence of biomarkers predicting therapeutic success hampers clinical translation. A set of cell-free circulation miRNAs together with age and glycemia at baseline predicts a poor response after L. lactis-based immunotherapy in nonobese mice with new-onset diabetes. Pancreas-infiltrating neutrophils and basophils are identified as potential cellular sources of discovered miRNAs. The prognostic signature could guide targeted recruitment of patients with newly diagnosed type 1 diabetes in clinical trials with the L. lactis-based immunotherapy.

Gene expression signature predicts rate of type 1 diabetes progression

BACKGROUND

Type 1 diabetes is a complex heterogenous autoimmune disease without therapeutic interventions available to prevent or reverse the disease. This study aimed to identify transcriptional changes associated with the disease progression in patients with recent-onset type 1 diabetes.

METHODS

Whole-blood samples were collected as part of the INNODIA study at baseline and 12 months after diagnosis of type 1 diabetes. We used linear mixed-effects modelling on RNA-seq data to identify genes associated with age, sex, or disease progression. Cell-type proportions were estimated from the RNA-seq data using computational deconvolution. Associations to clinical variables were estimated using Pearson's or point-biserial correlation for continuous and dichotomous variables, respectively, using only complete pairs of observations.

FINDINGS

We found that genes and pathways related to innate immunity were downregulated during the first year after diagnosis. Significant associations of the gene expression changes were found with ZnT8A autoantibody positivity. Rate of change in the expression of 16 genes between baseline and 12 months was found to predict the decline in C-peptide at 24 months. Interestingly and consistent with earlier reports, increased B cell levels and decreased neutrophil levels were associated with the rapid progression.

INTERPRETATION

There is considerable individual variation in the rate of progression from appearance of type 1 diabetes-specific autoantibodies to clinical disease. Patient stratification and prediction of disease progression can help in developing more personalised therapeutic strategies for different disease endotypes.

FUNDING

A full list of funding bodies can be found under Acknowledgments.

The importance of biomarker development for monitoring type 1 diabetes progression rate and therapeutic responsiveness

Type 1 diabetes (T1D) is an autoimmune condition of children and adults in which immune cells target insulin-producing pancreatic β-cells for destruction. This results in a chronic inability to regulate blood glucose levels. The natural history of T1D is well-characterized in childhood. Evidence of two or more autoantibodies to the islet antigens insulin, GAD, IA-2 or ZnT8 in early childhood is associated with high risk of developing T1D in the future. Prediction of risk is less clear in adults and, overall, the factors controlling the progression rate from multiple islet autoantibody positivity to onset of symptoms are not fully understood. An anti-CD3 antibody, teplizumab, was recently shown to delay clinical progression to T1D in high-risk individuals including adults and older children. This represents an important proof of concept for those at risk of future T1D. Given their role in risk assessment, islet autoantibodies might appear to be the most obvious biomarkers to monitor efficacy. However, monitoring islet autoantibodies in clinical trials has shown only limited effects, although antibodies to the most recently identified autoantigen, tetraspanin-7, have not yet been studied in this context. Measurements of beta cell function remain fundamental to assessing efficacy and different models have been proposed, but improved biomarkers are required for both progression studies before onset of diabetes and in therapeutic monitoring. In this mini-review, we consider some established and emerging predictive and prognostic biomarkers, including markers of pancreatic function that could be integrated with metabolic markers to generate improved strategies to measure outcomes of therapeutic intervention.

Islet autoantibody screening in at-risk adolescents to predict type 1 diabetes until young adulthood: a prospective cohort study

BACKGROUND

Screening for islet autoantibodies in children and adolescents identifies individuals who will later develop type 1 diabetes, allowing patient and family education to prevent diabetic ketoacidosis at onset and to enable consideration of preventive therapies. We aimed to assess whether islet autoantibody screening is effective for predicting type 1 diabetes in adolescents aged 10-18 years with an increased risk of developing type 1 diabetes.

METHODS

Data were harmonised from prospective studies from Finland (the Diabetes Prediction and Prevention study), Germany (the BABYDIAB study), and the USA (Diabetes Autoimmunity Study in the Young and the Diabetes Evaluation in Washington study). Autoantibodies against insulin, glutamic acid decarboxylase, and insulinoma-associated protein 2 were measured at each follow-up visit. Children who were lost to follow-up or diagnosed with type 1 diabetes before 10 years of age were excluded. Inverse probability censoring weighting was used to include data from remaining participants. Sensitivity and the positive predictive value of these autoantibodies, tested at one or two ages, to predict type 1 diabetes by the age of 18 years were the main outcomes.

FINDINGS

Of 20 303 children with an increased type 1 diabetes risk, 8682 were included for the analysis with inverse probability censoring weighting. 1890 were followed up to 18 years of age or developed type 1 diabetes between the ages of 10 years and 18 years, and their median follow-up was 18·3 years (IQR 14·5-20·3). 442 (23·4%) of 1890 adolescents were positive for at least one islet autoantibody, and 262 (13·9%) developed type 1 diabetes. Time from seroconversion to diabetes diagnosis increased by 0·64 years (95% CI 0·34-0·95) for each 1-year increment of diagnosis age (Pearson's correlation coefficient 0·88, 95% CI 0·50-0·97, p=0·0020). The median interval between the last prediagnostic sample and diagnosis was 0·3 years (IQR 0·1-1·3) in the 227 participants who were autoantibody positive and 6·8 years (1·6-9·9) for the 35 who were autoantibody negative. Single screening at the age of 10 years was 90% (95% CI 86-95) sensitive, with a positive predictive value of 66% (60-72) for clinical diabetes. Screening at two ages (10 years and 14 years) increased sensitivity to 93% (95% CI 89-97) but lowered the positive predictive value to 55% (49-60).

INTERPRETATION

Screening of adolescents at risk for type 1 diabetes only once at 10 years of age for islet autoantibodies was highly effective to detect type 1 diabetes by the age of 18 years, which in turn could enable prevention of diabetic ketoacidosis and participation in secondary prevention trials.

FUNDING

JDRF International.

Innovative Designs and Logistical Considerations for Expedited Clinical Development of Combination Disease-Modifying Treatments for Type 1 Diabetes

It has been 100 years since the life-saving discovery of insulin, yet daily management of type 1 diabetes (T1D) remains challenging. Even with closed-loop systems, the prevailing need for persons with T1D to attempt to match the kinetics of insulin activity with the kinetics of carbohydrate metabolism, alongside dynamic life factors affecting insulin requirements, results in the need for frequent interventions to adjust insulin dosages or consume carbohydrates to correct mismatches. Moreover, peripheral insulin dosing leaves the liver underinsulinized and hyperglucagonemic and peripheral tissues overinsulinized relative to their normal physiologic roles in glucose homeostasis. Disease-modifying therapies (DMT) to preserve and/or restore functional β-cell mass with controlled or corrected autoimmunity would simplify exogenous insulin need, thereby reducing disease mortality, morbidity, and management burdens. However, identifying effective DMTs for T1D has proven complex. There is some consensus that combination DMTs are needed for more meaningful clinical benefit. Other complexities are addressable with more innovative trial designs and logistics. While no DMT has yet been approved for marketing, existing regulatory guidance provides opportunities to further "de-risk" development. The T1D development ecosystem can accelerate progress by using more innovative ways for testing DMTs for T1D. This perspective outlines suggestions for accelerating evaluation of candidate T1D DMTs, including combination therapies, by use of innovative trial designs, enhanced logistical coordination of efforts, and regulatory guidance for expedited development, combination therapies, and adaptive designs.