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bioRxiv2023

Validation of the association between MRI and gene signatures in facioscapulohumeral dystrophy muscle: implications for clinical trial design

Identifying the aberrant expression of DUX4 in skeletal muscle as the cause of facioscapulohumeral dystrophy (FSHD) has led to rational therapeutic development and clinical trials. Several studies support the use of MRI characteristics and the expression of DUX4-regulated genes in muscle biopsies as biomarkers of FSHD disease activity and progression, but reproducibility across studies needs further validation. We performed lower-extremity MRI and muscle biopsies in the mid-portion of the tibialis anterior (TA) muscles bilaterally in FSHD subjects and validated our prior reports of the strong association between MRI characteristics and expression of genes regulated by DUX4 and other gene categories associated with FSHD disease activity. We further show that measurements of normalized fat content in the entire TA muscle strongly predict molecular signatures in the mid-portion of the TA. Together with moderate-to-strong correlations of gene signatures and MRI characteristics between the TA muscles bilaterally, these results suggest a whole muscle model of disease progression and provide a strong basis for inclusion of MRI and molecular biomarkers in clinical trial design.

molecular biology↗

bioRxiv2018

Planning a future randomized clinical trial based on a network of relevant past trials

Background: The important role of network meta-analysis of randomized clinical trials in health technology assessment and guideline development is increasingly recognized. This approach has the potential to obtain conclusive results earlier than with new standalone trials or conventional, pairwise meta-analyses.\n\nMethods: Network meta-analyses can also be used to plan future trials. We introduce a four-steps framework to plan a new trial that aims to identify the optimal new design that will update the existing evidence to best serve timely clinical and public health decision making. The new trial designed within this framework does not need to include all competing interventions and comparisons of interest and can contribute direct and indirect evidence to the updated network meta-analysis. We present the method by virtually planning a new trial to compare biologics in rheumatoid arthritis and a new trial to compare two drugs for relapsing-remitting multiple sclerosis.\n\nResults: A trial design based on updating the evidence from a network meta-analysis of relevant previous trials may require a considerably smaller sample size to reach the same conclusion compared with a trial designed and analyzed in isolation. Challenges in the approach include the complexity of the methodology and the need for a coherent network meta-analysis of previous trials with little heterogeneity.\n\nConclusions: When used judiciously, conditional trial design could significantly reduce waste in clinical research.

epidemiology↗

bioRxiv2017

Exact graph-based analysis of scientific articles on clinical trials

This article describes Amorpha, a software package based on new concept of exact graph-based linguistic analysis. Analytical capabilities of Amorpha are demonstrated using analysis of scientific abstracts on clinical trials from PubMed. Current trends in therapy of breast cancer and psoriatic arthritis were analyzed using 400 abstracts on breast cancer and 131 abstracts on psoriatic arthritis. The spectrum of diseases that currently treated with paclitaxel was extracted from 400 most recent abstracts on paclitaxel.\n\nIn addition to text representation, analytical results are presented as graph images showing essential concepts of a text. Amorpha is not designed specifically to analyze clinical trials and will be also useful for analysis of biological scientific articles and regulatory documents. Amorpha does not require any preliminary knowledge base, ensures full coverage of target text and 100% accuracy of obtained results.

bioinformatics↗

bioRxiv2026

Systematic AI-Driven Drug Repurposing via Clinical Trial Data Mining: A Framework and Six Cross-Therapeutic Case Studies.

Drug repurposing -- the application of approved or shelved compounds to new therapeutic indications -- offers a cost- and time-efficient alternative to de novo drug discovery. However, the systematic identification of repurposing candidates from the rapidly expanding body of clinical trial data remains a significant challenge. Here I present a publicly accessible AI-powered tool that mines the ClinicalTrials.gov registry to identify approved drugs with under-explored therapeutic potential in high-value disease areas. The tool integrates natural language processing, mechanism-of-action pathway analysis, and trial density scoring to surface candidates where biological plausibility is high and clinical trial coverage is sparse. I demonstrate the tools utility across six cross-therapeutic case studies spanning oncology, cardiology, neurology, rare diseases, immunology, and infectious disease. Key findings include: the identification of Zonisamide as an under-explored combination candidate for obesity alongside GLP-1 receptor agonists; mechanistic validation of SGLT2 inhibitors in heart failure with preserved ejection fraction (HFpEF); and a novel cross-domain mapping of anti-TNF biologics to early-stage neurodegeneration via shared neuroinflammatory pathways. The tool is freely accessible and designed to lower the barrier for academic and industry researchers to systematically pursue repurposing opportunities.

bioinformatics↗

bioRxiv2024

Mechanistic Insights into Dengue Virus Inhibition by a Clinical Trial Compound NITD-688

Dengue, caused by the dengue virus (DENV), presents a significant public health challenge with limited effective treatments. NITD-688 is a potent pan-serotype DENV inhibitor currently in Phase II clinical trials. However, its mechanism of action is not fully understood. Here, we present the molecular details of how NITD-688 inhibits DENV. NITD-688 binds directly to the nonstructural protein 4B (NS4B) with nanomolar affinities across all four DENV serotypes and specifically disrupts the interaction between NS4B and nonstructural protein 3 (NS3) without significantly changing the interactions between NS4B and other viral or host proteins. NS4B mutations that confer resistance to NITD-688 reduce both NITD-688 binding to NS4B and disruption of the NS4B/NS3 interaction. Specifically, NITD-688 blocks the interaction of NS3 with a cytosolic loop within NS4B. This inhibits the formation of new NS4B/NS3 complexes and disrupts pre-existing complexes in vitro and DENV-infected cells, ultimately inhibiting viral replication. Consistent with this mechanism, NITD-688 retains greater potency in cellular assays with delayed treatment compared to JNJ-1802, another NS4B inhibitor that has been studied in Phase II clinical trials. Together, these findings provide critical insights into the mechanism of action of NITD-688, facilitating the development of novel flavivirus NS4B inhibitors and informing future clinical interventions against DENV.

microbiology↗

bioRxiv2019

Integrating Quality of Life and Survival Outcomes Cardiovascular Clinical Trials: Results from the PARTNER Trial

BackgroundSurvival and health status (e.g., symptoms and quality of life) are key outcomes in clinical trials of heart failure treatment. However, health status can only be recorded on survivors, potentially biasing treatment effect estimates when there is differential survival across treatment groups. Joint modeling of survival and health status can address this bias.\n\nMethods and ResultsWe analyzed patient-level data from the PARTNER 1B trial of transcatheter aortic valve replacement (TAVR) versus standard care. Health status was quantified with the Kansas City Cardiomyopathy Questionnaire (KCCQ) at randomization, 1, 6, and 12 months. We compared hazard ratios for survival and mean differences in KCCQ scores at 12 months using several models: the original growth curve model for KCCQ scores (ignoring death), separate Bayesian models for survival and KCCQ scores, and a Bayesian joint longitudinal-survival model fit to either 12 or 30 months of survival follow-up. The benefit of TAVR on 12-month KCCQ scores was greatest in the joint model fit to all survival data (mean difference = 33.7 points; 95% CrI: 24.2, 42.4), followed by the joint model fit to 12 months of survival follow-up (32.3 points; 95% CrI: 22.5, 41.5), a Bayesian model without integrating death (30.4 points; 95% CrI: 21.4, 39.3), and the original growth curve model (26.0 points; 95% CI: 18.7, 33.3). At 12 months, the survival benefit of TAVR was also greater in the joint model (hazard ratio = 0.50; 95% CrI: 0.32, 0.73) than in the non-joint Bayesian model (0.54; 95% CrI: 0.37, 0.75) or the original Kaplan-Meier estimate (0.55; 95% CI: 0.40, 0.74).\n\nConclusionsIn patients with severe symptomatic aortic stenosis and prohibitive surgical risk, the estimated benefits of TAVR on survival and health status compared with standard care were greater in joint Bayesian models than other approaches.

clinical trials↗

bioRxiv2021

COVIDrugNet: a network-based web tool to investigate the drugs currently in clinical trial to contrast COVID-19

The COVID-19 pandemic poses a huge problem of public health that requires the implementation of all available means to contrast it, and drugs are one of them. In this context, we observed an unmet need of depicting the continuously evolving scenario of the ongoing drug clinical trials through an easy-to-use, freely accessible online tool. Starting from this consideration, we developed COVIDrugNet (http://compmedchem.unibo.it/covidrugnet), a web application that allows users to capture a holistic view and keep up to date on how the clinical drug research is responding to the SARS-CoV-2 infection. Here, we describe the web app and show through some examples how one can explore the whole landscape of medicines in clinical trial for the treatment of COVID-19 and try to probe the consistency of the current approaches with the available biological and pharmacological evidence. We conclude that careful analyses of the COVID-19 drug-target system based on COVIDrugNet can help to understand the biological implications of the proposed drug options, and eventually improve the search for more effective therapies.

bioinformatics↗

bioRxiv2019

Optimal Individualized Combination Immunotherapy/Oncolytic Virotherapy Determined Through In Silico Clinical Trials Improves Late Stage Melanoma Patient Outcomes

Oncolytic virothcrapics, including the modified herpes simplex virus talimogene laherparepvec (T-VEC), have shown great promise as potent instigators of anti-tumour immune effects. The OPTiM trial in particular demonstrated the superior anti-cancer effects of T-VEC as compared to more traditional immunotherapy treatment using exogenous administration of granulocyte-macrophage colony-stimulating factor (GM-CSF). Theoretically, a combined approach leveraging immunotherapies: like exogenous cytokine administration and oncolytic virotherapv would elicit an even greater immune response and improve patient outcomes, but given that their efficacy and safety must be tested in large clinical trials, combination therapeutic regimens have yet to be established. By adopting computational biology and in silico clinical trial approaches, here we show significantly improved patient outcomes for individuals with late-stage melanoma by personalizing and optimizing combination oncolytic, virotherapv and GM-CSF therapy. Our results serve as a proof-of-concept, for interdisciplinary approaches to determining combination therapy, and suggest promising avenues of investigation towards tailored combination immunotherapy/oncolytic virotherapy.

cancer biology↗

bioRxiv2017

Identification of drug eQTL interactions from repeat transcriptional and environmental measurements in a lupus clinical trial

BackgroundCytokines are critical to human disease and are attractive therapeutic targets given their widespread influence on gene regulation and transcription. Defining the downstream regulatory mechanisms influenced by cytokines is central to defining drug and disease mechanisms. One promising strategy is to use interactions between expression quantitative trait loci (eQTLs) and cytokine levels to define target genes and mechanisms.\n\nResultsIn a clinical trial for anti-IL-6 in patients with systemic lupus erythematosus we measured interferon (IFN) status, anti-IL-6 drug exposure and genome-wide gene expression at three time points (379 samples from 157 individuals). First, we show that repeat transcriptomic measurements increases the number of cis eQTLs identified compared to using a single time point by 64%. Then, after identifying 4,818 cis-eQTLs, we observed a statistically significant enrichment of in vivo eQTL interactions with IFN status (p<0.001 by permutation) and anti-IL-6 drug exposure (p<0.001). We observed 210 and 72 interactions for IFN and anti-IL-6 respectively (FDR<20%). Anti-IL-6 interactions have not yet been described while 99 of the IFN interactions are novel. Finally, we found transcription factor binding motifs interrupted by eQTL interaction SNPs, pointing to key regulatory mediators of these environmental stimuli and therefore potential therapeutic targets for autoimmune diseases. In particular, genes with IFN interactions are enriched for ISRE binding site motifs, while those with anti-IL-6 interactions are enriched for IRF4 motifs.\n\nConclusionThis study highlights the potential to exploit clinical trial data to discover in vivo eQTL interactions with therapeutically relevant environmental variables.

genomics↗

bioRxiv2025

Proximity to an SGC-DLPFC Individualized Functional Target and outcomes in large rTMS clinical trials for Treatment-Resistant Depression

BackgroundTargeting methods for repetitive transcranial magnetic stimulation (rTMS) in patients with depression now include the use of individual functional scans to target specific functional connectivity (FC) patterns obtained from functional magnetic resonance imaging (fMRI). Potential biomarkers of rTMS response include target FC with the subgenual anterior cingulate cortex (SGC) or the causal depression circuit (CDC), each of which may be candidates for individualized functional targets (iFTs). We assessed the relationship of these two approaches to clinical outcomes in two large rTMS clinical trials. Methods501 subjects with moderate to severe depression underwent 4-6 weeks of daily rTMS to the left dorsolateral prefrontal cortex (DLPFC), targeted using neuronavigation to a common group-based functional target. Resting-state scans acquired at baseline were used to retrospectively compute iFTs using either SGC-DLPFC or CDC-DLPFC FC. The Euclidean distance from the group-based target used in the trial to the centre of gravity of each iFT was computed and correlated with outcomes. ResultsMost subjects iFTs were within 2cm of their group-based target. Proximity to either the SGC- or CDC-iFT was not associated with better outcomes. Sensitivity analyses accounting for treatment target FC, methodology, data quality, or treatment parameters did not change the results. ConclusionsProximity to SGC- or CDC-derived iFTs was not associated with better outcomes in patients who received neuronavigated rTMS to a group-based target. Prospective randomized clinical trials comparing neuronavigated group-based target to neuronavigated iFTs are needed.

neuroscience↗

bioRxiv2024

Dose escalation pre-clinical trial of novel Dok7-AAV in mouse model of DOK7 congenital myasthenia

Congenital myasthenic syndromes (CMS) are a group of inherited disorders characterised by defective neuromuscular transmission and fatigable muscle weakness. Mutations in DOK7, a gene encoding a post-synaptic protein crucial in the formation and stabilisation of the neuromuscular junction (NMJ), rank among the leading three prevalent causes of CMS in diverse populations globally. The majority of DOK7 CMS patients experience varying degrees of disability despite receiving optimised treatment, necessitating the development of improved therapeutic approaches. Here we executed a dose escalation pre-clinical trial using a DOK7-CMS mouse model to assess the efficacy of Amp-101, an innovative AAV gene replacement therapy. Amp-101 is based on AAVrh74 and contains human DOK7 cDNA under the control of a muscle-restricted promoter. We show that at doses 6x1013vg/kg and 1x1014 vg/kg, Amp-101 generated enlarged NMJs and rescued the very severe phenotype of the model. Treated mice became at least as strong as WT littermates and the diaphragm and tibialis anterior muscles displayed robust expression of DOK7. This data suggests that Amp-101 is a promising candidate to move forward to clinic trials.

neuroscience↗

bioRxiv2019

Towards development of a statistical framework to evaluate myotonic dystrophy type 1 mRNA biomarkers in the context of a clinical trial

Myotonic dystrophy type 1 (DM1) is a rare genetic disorder, characterised by muscular dystrophy, myotonia, and other symptoms. DM1 is caused by the expansion of a CTG repeat in the 3-untranslated region of DMPK. Longer CTG expansions are associated with greater symptom severity and earlier age at onset. The primary mechanism of pathogenesis is thought to be mediated by a gain of function of the CUG-containing RNA, that leads to trans-dysregulation of RNA metabolism of many other genes. Specifically, the alternative splicing (AS) and alternative polyadenylation (APA) of many genes is known to be disrupted. In the context of clinical trials of emerging DM1 treatments, it is important to be able to objectively quantify treatment efficacy at the level of molecular biomarkers. We show how previously described candidate mRNA biomarkers can be used to model an effective reduction in CTG length, using modern high-dimensional statistics (machine learning), and a blood and muscle mRNA microarray dataset. We show how this model could be used to detect treatment effects in the context of a clinical trial.

bioinformatics↗

bioRxiv2024

Reversal of contractile defects by mediating calcium homeostasis in human mini-heart models of heart failure with preserved ejection fraction (HFpEF) leads to first-in-human gene therapy clinical trial

AimsHeart failure with preserved ejection fraction (HFpEF), is a global health problem lacking disease-modifying therapeutic options, reflecting a lack of predictive models for preclinical drug testing. Aligned with FDA Modernization Act 2.0, we aimed to create the first in vitro human-specific mini-heart models of HFpEF, and to test the efficacy of a candidate gene therapy to improve cardiac kinetics and correct the disease phenotype. Methods and ResultsHealthy human pluripotent stem cell-derived ventricular cardiomyocytes were used to bioengineer beating cardiac tissue strips and pumping cardiac chambers. When conditioned with transforming growth factor-{beta}1 and endothelin-1, these mini-heart models exhibited signature disease phenotypes of significantly elevated diastolic force and tissue stiffness, and slowed contraction and relaxation kinetics, with no significant deficit in systolic force or ejection fraction versus unconditioned controls. Bioinformatic analysis of bulk RNA sequencing data from HFpEF mini-heart models and patient ventricular samples identified downregulation of SERCA2a of the calcium signalling pathway as a key differentially expressed gene. After dosage optimization, AAV-mediated expression of SERCA2a abrogated the disease phenotype and improved the cardiac kinetics in HFpEF mini-Hearts. ConclusionsThese findings contributed to FDA approval of an ongoing first-in-human gene therapy clinical trial for HFpEF, with Fast Track designation. We conclude that such human-based disease-specific mini-heart platforms are relevant for target discovery and validation that can facilitate clinical translation of novel cardiac therapies. Translational PerspectiveHeart failure with preserved ejection fraction (HFpEF) is a significant and growing global health concern lacking disease-modifying therapeutic options, reflecting inadequate preclinical models of the disease. Aligned with FDA Modernization Act 2.0, we created the first in vitro human-specific mini-heart models of HFpEF, demonstrated phenotypic disease characteristics of elevated stiffness and slowed kinetics, showed transcriptomic consistency with HFpEF patient data, identified SERCA2a as a key downregulated gene, performed dosing titration of SERCA2a gene therapy, and showed improvement of cardiac kinetics post-treatment. The findings contributed to FDA approval of an ongoing first-in-human gene therapy clinical trial for HFpEF.

bioengineering↗

bioRxiv2024

The immunological profile of RC17 hESC-derived dopaminergic neural progenitor cells in vitro: implications for the STEM-PD clinical trial.

Parkinsons Disease involves the progressive loss of dopaminergic neurons (DAn), prompting clinical trials replacing cell loss with neural grafts. This includes the transplantation of pluripotent stem cell-derived DAn progenitor cells (NPC) currently under investigation in the STEM-PD trial. To determine the likelihood of immune rejection post-grafting, we characterised the immunogenicity of the STEM-PD product (RC17-hESC-derived NPCs), comparing them to human foetal ventral mesencephalic tissue (hfVM) previously tested in trials, including our own TRANSEURO trial. Despite MHC-Class I expression, upregulated by proinflammatory cytokines, no immune response to NPCs was detected in vitro. Instead, they were immunosuppressive. Transcriptomic analysis revealed similarities between RC17-NPCs and hfVM, both strongly upregulating antigen processing and presentation pathways in response to IFN{gamma}. Furthermore, immunosuppressant mycophenolate mofetil detrimentally affected NPC survival and differentiation in vitro. Overall, our data suggest that aggressive immunosuppression is not required following hESC-NPC transplantation and that caution should be exercised when selecting the immunosuppressive regimen.

neuroscience↗

bioRxiv2018

Comprehensive Immune Monitoring of Clinical Trials to Advance Human Immunotherapy

The success of immunotherapy has led to a myriad of new clinical trials. Connected to these trials are efforts to discover biomarkers providing mechanistic insight and predictive signatures for personalization. Still, the plethora of immune monitoring technologies can face investigator bias, missing unanticipated cellular responses in limited clinical material. We here present a mass cytometry workflow for standardized, systems-level biomarker discovery in immunotherapy trials. To broadly enumerate human immune cell identity and activity, we established and extensively assessed a reference panel of 33 antibodies to cover major cell subsets, simultaneously quantifying activation and immune checkpoint molecules in a single assay. The resulting assay enumerated [≥] 98% of peripheral immune cells with [≥] 4 positively identifying antigens. Robustness and reproducibility were demonstrated on multiple samples types, across research centers and by orthogonal measurements. Using automated analysis, we monitored complex immune dynamics, identifying signatures in bone-marrow transplantation associated graft-versus-host disease. This validated and available workflow ensures comprehensive immunophenotypic analysis, data comparability and will accelerate biomarker discovery in immunomodulatory therapeutics.

immunology↗

bioRxiv2025

Herding cats: predicting immunogenicity from heterogeneous clinical trials data

Antibodies represent the largest and fastest growing class of biologic therapeutics, yet forecasting their clinical performance, particularly immunogenicity, remains a major hurdle in drug development. Despite hundreds of antibody-based drugs progressing through clinical pipelines, systematic integration of their clinical outcomes has been limited by fragmented and heterogeneous data. Here, we present the Therapeutic Antibody Database, a comprehensive and curated resource that links therapeutic antibodies to clinical trial outcomes, with a dedicated focus on immunogenicity. Our dataset is sourced from approximately 11,500 anti-drug antibody (ADA) measurements across diverse molecules and indications, offering an unprecedented view into the clinical manifestation of immune responses to biologics. In order to evaluate the main drivers of ADA, we evaluate gathered immunogenicity incidence and prevalence data against various therapeutic descriptors which includes sequence, structure and contextual features related to therapeutics. We find that most tools have very poor performance, and we pinpoint the causes of it, demonstrating the need for systems immunology approaches incorporating clinical metadata beyond biochemical properties of the molecules alone.

bioinformatics↗

bioRxiv2026

Machine-learning MRI stratification of genetic frontotemporal dementia for clinical trial enrichment

BACKGROUNDGenetic frontotemporal dementia (FTD) shows large differences in symptom profiles, brain atrophy patterns, and progression rate, making clinical trials difficult to design and power. There is a need for biomarkers that can model disease progression, identify biologically distinct groups, and support efficient trial enrichment. METHODSWe applied contrastive trajectory inference (cTI), a machine-learning method, to structural MRI, white matter hyperintensity, and demographic data from 736 participants in the GENFI cohort, including non-carriers and carriers of C9orf72, GRN, or MAPT mutations. cTI produced an individual "genetic FTD progression score" (0-1) and grouped mutation carriers into data-driven subtypes. We tested construct validity using correlations between progression score and cognitive/functional measures, examined subtype differences in brain-behavior coupling, plasma neurofilament light (NfL), and longitudinal decline, and compared cTI-based trial enrichment against age, cortical thickness and NfL using analytic and simulation-based power analyses. RESULTSGenetic FTD progression scores correlated strongly with global dementia severity and multiple cognitive domains (all p < 0.001), confirming robust clinical scoring. Two mutation-carrier subtypes emerged: a Progressive Track (Subtype 2) with strong associations between progression score and cognitive/functional impairment, rising NfL, and faster longitudinal decline; and a Dissociated Track (Subtype 3) with comparable levels of structural variation but weak or absent clinical and NfL changes, suggesting relative biological stability. Baseline subtype membership added prognostic value for future decline in processing speed and language beyond baseline severity. Notably, for C9orf72 and GRN, cTI-informed enrichment reduced required recruited sample size per arm by about 61-75% compared with unenriched designs, and outperformed enrichment using age, cortical thickness or NfL in both analytic and simulation-based power analyses. CONCLUSIONSMachine-learning stratification of genetic FTD reveals a progressive and a dissociated disease track and provides individualized progression scores that closely track clinical status. cTI progression scores offer a powerful tool for trial enrichment, enabling smaller, more efficient prevention and early-intervention trials than conventional MRI or NfL markers alone.

neuroscience↗

bioRxiv2021

Characteristics and data reporting of rare disease clinical trials: Getting better but still room for improvement

BackgroundIt is estimated that there are more than 7,000 rare diseases (RDs) worldwide, impacting the lives of approximately 400 million people and only 5% have an approved therapy. Facing special challenges, including patient scarceness, incomplete knowledge of the natural history and only few specialized clinical sites, clinical trials (CT) are limited, making the data from trials critical for research and clinical care. Despite the introduction of the U.S. Food and Drug Administration Amendment Act (FDAAA) in 2007 requiring certain CTs to post results on the registry ClinicalTrials.gov within 12 months following completion, compliance has been reportedly poor. Here, we describe general characteristics of RD CTs, identify trends, and evaluate result reporting practices under the FDAAA aiming to draw awareness to the problem of non-compliance. MethodsCTs conducted between 2008 and 2015 were extracted from the public U.S. trial registry ClinicalTrials.gov using the text mining software I2E (Linguamatics). Disease names were matched with rare disease names from the Orphanet Rare Disease Ontology (ORDO, v2.5, Orphanet). Statistical analyses and data visualization were performed using GraphPad Prism 7 and R (v3.5). The Students t-test was employed to calculate significance using p-value cut-offs of <0.05 or <0.001. ResultsWe analyzed 1,056 RD CTs of which 55.7% were phase 2, 7.7% phase 2/3 and 36.7% phase 3 trials. The studies were mostly one- and two-armed experimental CTs with the majority (60.2%) being funded by industry. Cystic fibrosis and sickle cell disease represented the most frequently investigated diseases (25.0% and 16.5%). Industry-led phase 2 RD CTs were significantly (p<0.0001) shorter than their equivalent led by academia/non-profit (22 vs. 33 months). Screening CTs completed before the end of 2015, we found that of the 725 analyzed studies, 55.2% predominantly phase 2 CTs, did not report results. Taking their potential applicability to the FDAAA into account, 25.2% industry-funded and 28.0% academia/non-profit-funded trials failed to disclose results on ClinicalTrial.gov. ConclusionRD CTs tend to be comparatively small, industry-funded studies focusing on genetic and neurologic conditions. Sponsor-related differences in study design, duration, and enrollment were observed. There are still substantial shortcomings when it comes to result publication.

scientific communication and education↗