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bioRxiv2018

Postmarketing commitments for novel drugs and biologics approved by the US Food and Drug Administration: a cross-sectional analysis

BackgroundPostmarketing commitments are clinical studies that drug sponsors agree to conduct at the time of FDA approval, but which are not required by statute or regulation. The objective of this study was to determine the characteristics, completion, and dissemination of postmarketing commitments agreed upon by sponsors at first FDA approval.\n\nMethodsWe performed a cross-sectional analysis of postmarketing commitments for new drugs and biologics approved 2009-2012. Using public FDA documents, ClinicalTrials.gov, and Scopus, we determined postmarketing commitments and their characteristics known at the time of FDA approval; number of postmarketing commitments subject to reporting requirements, for which FDA is required to make study status information available to the public (\"506B studies\"), and their statuses; and rates of registration and results reporting on ClinicalTrials.gov and publication in peer-reviewed journals for all clinical trials, with follow-up through July 2018.\n\nResultsAmong 110 novel drugs and biologics approved by the FDA between 2009-2012, 61 (55.5%) had at least one postmarketing commitment at the time of first approval. Of 331 total postmarketing commitments, 271 (81.9%) were non-human subjects research, predominantly chemistry, manufacturing, and controls studies; 49 (14.8%) were clinical trials (33 new and 16 ongoing trials for which follow-up results would be reported). Study descriptions for the new clinical trials often lacked information to establish study design features. Of the 89 (26.9%) 506B studies subject to public reporting requirements, of which 42 were clinical trials, 59 (66.3%) did not have an up-to-date status provided by FDA. Nearly all new clinical trials (28 of 31, 90.3%) were registered on ClinicalTrials.gov; of the 23 registered trials that were completed or terminated, 22 (95.7%) had reported results. Only half (14 of 29, 48.3%) of completed or terminated clinical trials, registered or unregistered, were published in peer-reviewed journals. Conclusions: The majority of postmarketing commitments agreed to by sponsors at the time of FDA approval for novel drugs and biologics approved between 2009-2012 were chemistry, manufacturing, and controls studies. While only 15% were clinical trials, these trials were nearly always registered with reported results on ClinicalTrials.gov. However, despite FDA public reporting requirements, up-to-date study status information was often unavailable for 506B studies.

epidemiology↗

bioRxiv2017

Systematic interrogation of diverse Omic data reveals interpretable, robust, and generalizable transcriptomic features of clinically successful therapeutic targets

Target selection is the first and pivotal step in drug discovery. An incorrect choice may not manifest itself for many years after hundreds of millions of research dollars have been spent. We collected a set of 332 targets that succeeded or failed in phase III clinical trials, and explored whether Omic features describing the target genes could predict clinical success. We obtained features from the recently published comprehensive resource: Harmonizome. Nineteen features appeared to be significantly correlated with phase III clinical trial outcomes, but only 4 passed validation schemes that used bootstrapping or modified permutation tests to assess feature robustness and generalizability while accounting for target class selection bias. We also used classifiers to perform multivariate feature selection and found that classifiers with a single feature performed as well in cross-validation as classifiers with more features (AUROC=0.57 and AUPR=0.81). The two predominantly selected features were mean mRNA expression across tissues and standard deviation of expression across tissues, where successful targets tended to have lower mean expression and higher expression variance than failed targets. This finding supports the conventional wisdom that it is favorable for a target to be present in the tissue(s) affected by a disease and absent from other tissues. Overall, our results suggest that it is feasible to construct a model integrating interpretable target features to inform target selection. We anticipate deeper insights and better models in the future, as researchers can reuse the data we have provided to improve methods for handling sample biases and learn more informative features. Code, documentation, and data for this study have been deposited on GitHub at https://github.com/arouillard/omic-features-successful-targets.\n\nAUTHOR SUMMARYDrug discovery often begins with a hypothesis that changing the abundance or activity of a target--a biological molecule, usually a protein--will cure a disease or ameliorate its symptoms. Whether a target hypothesis translates into a successful therapy depends in part on the characteristics of the target, but it is not completely understood which target characteristics are important for success. We sought to answer this question with a supervised machine learning approach. We obtained outcomes of target hypotheses tested in clinical trials, scoring targets as successful or failed, and then obtained thousands of features (i.e. properties or characteristics) of targets from dozens of biological datasets. We statistically tested which features differed between successful and failed targets, and built a computational model that used these features to predict success or failure of targets in clinical trials. We found that successful targets tended to have more variable mRNA abundance from tissue to tissue and lower average abundance across tissues than failed targets. Thus, it is probably favorable for a target to be present in the tissue(s) affected by a disease and absent from other tissues. Our work demonstrates the feasibility of predicting clinical trial outcomes from target features.

bioinformatics↗

bioRxiv2021

COVID-19: A need for new rather than repurposed antiviral drugs.

BackgroundSARS-CoV-2 infection, the causative agent of COVID-19, has resulted in over 2,500,000 deaths to date1. Although vaccines are becoming available, treatment options remain limited. Repurposing of compounds could reduce the time, cost, and risks associated with the development of new drugs and has been the focus of many clinical studies. Here, we summarise available evidence on 29 FDA-approved compounds, from in vitro results to clinical trials, focussing on remdesivir, galidesivir and favipiravir, and test 29 antiviral compounds activity in vitro. MethodsA comprehensive search strategy was used to retrieve trials and publications related to antiviral compounds with potential efficacy to treat coronaviruses. These data were used to prioritise testing of a panel of antiviral drugs in vitro against patient isolates of SARS-CoV-2. An in vitro screen was carried out to determine the activity of 29 FDA-approved compounds. Results625 clinical trials investigated 16 repurposed antiviral candidate compounds for the treatment of COVID-19. In vitro studies identified ten drug candidates with demonstrable anti-SARS-CoV-2 activity, including favipiravir, remdesivir, and galidesivir. To validate these findings, a drug screen was conducted using two cell lines and wildtype isolates of SARS-CoV-2 isolated from patients in the UK. While eight drugs with anti-SARS-CoV-2 activity were identified in vitro, activity in clinical trials has, as yet failed to demonstrate a strong effect on mortality. ConclusionsSo far, no repurposed antiviral has shown a strong effect on mortality in clinical studies. The urgent need for novel antivirals in this pandemic is clear, despite the costs and time associated with their development. Research in ContextO_ST_ABSEvidence before this studyC_ST_ABSRepurposing of existing compounds for the treatment of COVID-19 has been the focus of many in vitro studies and clinical trials, saving time, costs and risks associated with the research and development of new compounds. Added value of this studyWe reviewed the literature for 29 FDA-approved compounds with previously reported (or suspected) anti-SARS-CoV-2 activity and found 625 clinical trials that have been undertaken on 16 different drugs. We determined if repurposed antivirals are suitable for clinical trials based on previously published data, and conducted an additional in vitro screen using locally circulating strains in the UK (PHE2 and GLA1). We report the difference in IC50 from published data using Wuhan1/Wash1 strains with PHE2 and GLA1, including IC50 values below 100M for galidesivir in wild-type virus. Given the limited success of repurposed compounds in the treatment of COVID-19, we comment on the urgent need for new antivirals specifically targeting SARS-CoV-2. Implications of all the available evidenceOur data show that most prospective compounds for repurposing show no anti-SARS-CoV-2 activity, and antiviral activity in vitro does not always translate to clinical benefit. So far, no repurposed compound has shown a strong effect on mortality in clinical studies. Drugs, including monoclonal antibody therapies, that have been developed to target SARS-CoV-2 virus itself have shown most promise.

microbiology↗

bioRxiv2022

Development of a LAG-3 Immunohistochemistry Assay for Melanoma

AimsA robust immunohistochemistry (IHC) assay was developed to detect lymphocyte-activation gene 3 (LAG-3) expression by immune cells (ICs) in tumor tissues. LAG-3 is an immuno-oncology target with demonstrable clinical benefit, and there is a need for a standardized, well-characterized assay to measure its expression. This study aims to describe LAG-3 scoring criteria and present the specificity, sensitivity, analytical precision, and reproducibility of this assay. MethodsThe specificity of the assay was investigated by antigen competition and with LAG3 knockout cell lines. A melanin pigment removal procedure was implemented to prevent melanin interference in IHC interpretation. Formalin-fixed, paraffin-embedded (FFPE) human melanoma samples with a range of LAG-3 expression levels were used to assess the sensitivity and analytical precision of the assay with a [≥]1% cutoff to determine LAG-3-positivity. Interobserver and intraobserver reproducibility were evaluated with 60 samples in intralaboratory studies and 70 samples in interlaboratory studies. ResultsThe LAG-3 IHC method demonstrated performance suitable for analysis of LAG-3 IC expression in clinical melanoma samples. The pretreatment step effectively removed melanin pigment that could interfere with interpretation. LAG-3 antigen competition and analysis of LAG3 knockout cell lines indicated that the 17B4 antibody clone binds specifically to LAG-3. The intrarun repeatability, interday, interinstrument, interoperator, and interreagent lot reproducibility demonstrated a high scoring concordance (>95%). The interobserver and intraobserver reproducibility and overall interlaboratory and intralaboratory reproducibility also showed high scoring concordance (>90%). ConclusionsWe have demonstrated that the assay reliably assesses LAG-3 expression in FFPE human melanoma samples by IHC. Key messagesO_ST_ABSWhat is already known on this topicC_ST_ABSLymphocyte-activation gene 3 (LAG-3) is an immune checkpoint receptor expressed on immune cells that limits T-cell activity and is being actively explored as a target for immunotherapy. What this study addsAn immunohistochemistry assay was developed to detect the LAG-3 protein in formalin-fixed paraffin-embedded human tumor tissue specimens. This study describes scoring criteria and shows the specificity, sensitivity, analytical precision, and reproducibility of this assay as an aid to determine LAG-3 expression in melanoma patients using a [≥]1% expression on immune cells threshold. How this study might affect research, practice or policyThe study describes a key immuno-oncology checkpoint immunohistochemistry assay that is robust and suitable for clinical trials. The assay was used in RELATIVITY-047 (NCT03470922), a phase 2/3 clinical trial that compared combined nivolumab and relatlimab treatment with nivolumab monotherapy, to stratify patients based on the percentage of LAG-3-positive immune cells within the tumor region. This assay is also being used in several ongoing clinical trials evaluating clinical response to relatlimab.

pathology↗

bioRxiv2024

Enhancing Biomarker-Based Oncology Trial Matching Using Large Language Models

Clinical trials are an essential component of drug development for new cancer treatments, yet the information required to determine a patients eligibility for enrollment is scattered in large amounts of unstructured text. Genomic biomarkers are especially important in precision medicine and targeted therapies, making them essential for matching patients to appropriate trials. Large language models (LLMs) offer a promising solution for extracting this information from clinical trial data, aiding both physicians and patients in identifying suitable matches. In this study, we explore various LLM strategies for extracting genetic biomarkers from oncology trials to improve patient enrollment rates. Our results show that open-source language models, when applied out-of-the-box, effectively capture complex logical expressions and structure genomic biomarkers in disjunctive normal form, outperforming closed-source models such as GPT-4 and GPT-3.5-Turbo. Furthermore, fine-tuning these open-source models with additional data significantly enhances their performance.

bioinformatics↗

bioRxiv2026

Leveraging Large Language Models to Extract Prognostic Pathology Features in Ewing Sarcoma

BackgroundCurrent risk stratification for Ewing sarcoma relies heavily on clinical factors such as metastatic status, failing to capture histologic heterogeneity as a potential prognostic indicator. Although pathology reports contain rich biological data, this information remains locked in unstructured narrative text, limiting large-scale retrospective analyses. We aimed to validate the utility of Large Language Models (LLMs) for scalable data abstraction and to identify prognostic histologic features from a large multi-institutional cohort. MethodsWe conducted a retrospective cohort study using data from six Childrens Oncology Group (COG) clinical trials. We utilized an LLM-based pipeline (OpenAI o3) to extract structured variables--including immunohistochemical (IHC) markers and CD99 staining patterns--from digitized, Optical Character Recognition (OCR)-processed pathology reports. Extraction accuracy was validated against a human-annotated ground truth (n=200) and cross-validated against senior experts (n=48). We assessed the association between extracted features and Overall Survival (OS) using Kaplan-Meier analysis and multivariable Cox proportional hazards regression, adjusting for metastatic status. FindingsWe analyzed 931 diagnostic pathology reports from 185 institutions spanning over 21-years. The LLM achieved a weighted average accuracy of 94% across 17 IHC markers; in a cross-validation subset, the LLM outperformed human annotators (weighted average accuracy over 15 IHC markers: LLM o3: 98.1%, a pediatric resident 91.4%, and a pediatric oncologist 95.9%). Survival analysis identified Neuron-Specific Enolase (NSE) and S100 as significant prognostic biomarkers. After adjusting for metastatic status, NSE positivity was associated with significantly inferior survival (HR 2.15, 95% CI 1.15-4.02, p=0.016); this risk was most pronounced in patients with non-metastatic disease (HR 5.64, p=0.0055). Conversely, S100 positivity was associated with improved survival (HR 0.58, 95% CI 0.34-1.00, p=0.046). InterpretationLLM-assisted extraction of pathology variables is highly accurate and scalable, capable of unlocking "dark data" from historical clinical trials. We identified NSE as a potent risk factor and S100 as a protective marker in Ewing sarcoma, particularly in localized disease. These findings suggest that AI-derived histologic data can refine risk stratification and, if validated, warrant inclusion in future prospective trials. Research in ContextO_ST_ABSEvidence before this studyC_ST_ABSWe searched PubMed and Google Scholar for articles published up to December 2025, using terms such as "Ewing sarcoma", "prognosis", "risk stratification", "large language models", "natural language processing", and "pathology report extraction". Current risk stratification for Ewing sarcoma relies predominantly on clinical variables, specifically the presence of metastatic disease and tumor size. While histologic heterogeneity is well-documented, it is rarely incorporated into risk models because extracting structured data from narrative pathology reports is labor-intensive. Small, single-institution studies have suggested potential prognostic roles for markers like Neuron-Specific Enolase (NSE) or S100, but results have been inconsistent and limited by small sample sizes. Furthermore, while Large Language Models (LLMs) have demonstrated potential for extracting clinical data, their application to "rescue" data from legacy, multi-institutional clinical trial documents (scanned PDF images) for rare disease biomarker discovery has not been validated at scale. Added value of this studyTo our knowledge, this is the largest study to utilize AI to extract histologic data from Ewing sarcoma pathology reports, aggregating 931 patients from six distinct Childrens Oncology Group (COG) clinical trials spanning over 21 years. We validated an LLM-based pipeline that converted noisy, optically character-recognized text from diverse institutions into structured data with 98.1% accuracy, outperforming human annotators. This scalable approach revealed that NSE positivity is a significant independent risk factor for mortality (HR=2.15), particularly in patients with non-metastatic disease where it confers a more than 5-fold increased risk of death. Conversely, we identified S100 positivity as a protective factor associated with improved survival (HR=0.58). Implications of all the available evidenceThis study demonstrates that LLMs can reliably unlock "dark data" from historical clinical trials, rendering vast archives of unstructured medical documents accessible for retrospective analysis. The identification of NSE and S100 as robust prognostic biomarkers suggests that these widely available immunohistochemical stains provide valuable information beyond standard diagnostic information. These findings support the integration of automated data extraction tools in research workflows and suggest that NSE and S100 status should be considered in the design of future risk-stratified clinical trials for Ewing sarcoma.

bioinformatics↗

bioRxiv2018

Reverse Engineered Virtual Patient Populations as Surrogates for Real Patient-Level Data

ObjectivesTo demonstrate a new method for generating virtual, individual-level data by testing it on a known clinical trial population. DesignVirtualization of aggregate data from a clinical trial. SettingVirtual Participants936,100 virtual patients InterventionsNone Main Outcomes MeasuresOdds ratios for adverse outcomes in virtual patient populations compared to clinical trial participants. MethodsThe replicate engineered virtual patient populations (RE-ViPPs) method, based on aggregate cross-tabulated categorical population data, does not require access to individual-level data. Using sequential regression combined with randomization, it generates virtual individual patients to comprise populations that, on average, closely resemble the real population in question. The method is validated by applying it to aggregated data from the seminal SPRINT trial, which compared intensive versus standard blood pressure treatment goals on major adverse cardiovascular events. ResultsThe method yields virtual populations, each with 9361 patients, faithfully mimicking the real SPRINT participants. Multiple logistic regression on 100 such populations shows that factors with the highest odds ratios for the primary event are, in descending order, past clinical cardiovascular disease, age [≥] 75, chronic kidney disease, high non-HDL, and smoking history. Intensive blood pressure treatment, the trials intervention, had an odds ratio of 0.74 [0.63-0.87]. On all these measures, the 100 RE-ViPPs mirrored the real SPRINT participants, including the intensive therapy result (actual SPRINT odds ratio: 0.74 [0.62-0.88]). ConclusionsClinical data dissemination has limitations. The most coveted data is descriptive at the individual level but comes with significant cost, effort, and time. There is potential for privacy breaches, and the open-data movement has progressed slowly due to data-ownership concerns. RE-ViPPs closely matched the true SPRINT population. Applied to trials, registries, and databases, RE-ViPPs could reduce open-data burdens by encouraging dissemination of aggregate cross-tabulated real data that allow investigators to generate and measure virtual patients.

bioinformatics↗

bioRxiv2022

OpenCap: 3D human movement dynamics from smartphone videos

Measures of human movement dynamics can predict outcomes like injury risk or musculoskeletal disease progression. However, these measures are rarely quantified in clinical practice due to the prohibitive cost, time, and expertise required. Here we present and validate OpenCap, an open-source platform for computing movement dynamics using videos captured from smartphones. OpenCaps web application enables users to collect synchronous videos and visualize movement data that is automatically processed in the cloud, thereby eliminating the need for specialized hardware, software, and expertise. We show that OpenCap accurately predicts dynamic measures, like muscle activations, joint loads, and joint moments, which can be used to screen for disease risk, evaluate intervention efficacy, assess between-group movement differences, and inform rehabilitation decisions. Additionally, we demonstrate OpenCaps practical utility through a 100-subject field study, where a clinician using OpenCap estimated movement dynamics 25 times faster than a laboratory-based approach at less than 1% of the cost. By democratizing access to human movement analysis, OpenCap can accelerate the incorporation of biomechanical metrics into large-scale research studies, clinical trials, and clinical practice.

bioengineering↗

bioRxiv2023

CREATION AND CHARACTERIZATION OF A RECOMBINANT MAMMALIAN ORTHOREOVIRUS EXPRESSING HUMAN EPIDERMAL GROWTH FACTOR RECEPTOR 2 PEPTIDES

Mammalian orthoreovirus (MRV) is an oncolytic virus that has been tested in over 30 clinical trials. Increased clinical success has been achieved when MRV is used in combination with other onco-immunotherapies. This has led the field to explore the creation of recombinant MRVs which incorporate immunotherapeutic sequences into the virus genome. This work focuses on creation and characterization of a recombinant MRV, S1/HER2nhd, which expresses three human epidermal growth factor receptor 2 (HER2) peptides (E75, AE36, and GP2) known to induce HER2 specific CD8+ and CD4+ T cells. We show S1/HER2nhd expresses HER2 peptides in infected cells and on the virion, and infects, replicates, and reduces HER2+ breast cancer cell survival. The oncolytic properties of MRV combined with HER2 peptide expression holds potential as a vaccine to prevent recurrences of HER2 expressing cancers.

microbiology↗

bioRxiv2024

PathOmCLIP: Connecting tumor histology with spatial gene expression via locally enhanced contrastive learning of Pathology and Single-cell foundation model

Tumor morphological features from histology images are a cornerstone of clinical pathology, diagnostic biomarkers, and basic cancer biology research. Spatial transcriptomics, which provides spatially resolved gene expression profiles overlaid on histology images, offers a unique opportunity to integrate morphological and expression features, thereby deepening our understanding of tumor biology. However, spatial transcriptomics experiments with patient samples in either clinical trials or clinical care are costly and challenging, whereas histology images are generated routinely and available for many legacy prospective cohorts of disease progression and outcomes in well-annotated cohorts. Inferring spatial transcriptomics profiles computationally from these histology images would significantly expand our understanding of tumor biology, but paired data for training multi-modal spatial-histology models remains limited. Here, we tackle this challenge by incorporating performant foundation models pre-trained on massive datasets of pathology images and single-cell RNA-Seq, respectively, which provide useful embeddings to underpin multi-modal models. To this end, we developed PathOmCLIP, a model trained with contrastive loss to create a joint-embedding space between a histopathology foundation model and a single-cell RNA-seq foundation model. We incorporate a set transformer to gather localized neighborhood tumor architecture following contrastive training, which further enhances performance and is necessary to obtain robust results. We validate PathOmCLIP across five tumor types and achieve significant performance improvements in gene expression prediction tasks over other methods. PathOmCLIP can be applied to many archived histology images, unlocking valuable clinical information and facilitating new biomarker discoveries.

bioinformatics↗

bioRxiv2025

Interventionally targeting somatic CAG expansions can be a rapid disease-modifying therapeutic avenue: Preclinical evidence.

Huntington disease (HD) is caused by inherited CAG expansions, which continue expanding somatically in affected brain regions to hasten disease onset and progression. Therapeutically diminishing somatic expansions is expected to be clinically beneficial. However, it is not known if interventionally modifying somatic CAG expansions will actually modify in vivo clinically-relevant phenotypes, what the therapeutic window is, or which phenotypes will be altered. Here we show that acute (6-week) delivery of the contraction-inducing slipped-CAG DNA ligand naphthyridine-azaquinolone to young (4-week-old) (CAG)120 HD mice, induces contractions throughout brain regions, improves motor function (locomotion, balance, coordination, muscle strength), molecular disease landmarks (mHTT aggregates, nuclear envelope morphology, nucleocytoplasmic mRNA transport, transcriptomic dysregulation, neuroinflammation), and neurodegeneration. Beneficial effects of modifying somatic expansions were also evident in muscle and blood, where blood CAG instability correlated with brain instability and blood serum had diminished levels of neurofilament light (a biomarker for neurodegeneration) -- offering blood as having elements of target engagement and efficacy. These data support that targeting somatic repeat expansions can be a rapid disease-modifying therapeutic avenue for HD and possibly other repeat expansion diseases. Our findings support an etiologic pathway interconnected to somatic CAG expansions that will inform the design of clinical trials expecting clinical benefit by modulating somatic expansions.

genetics↗

bioRxiv2018

Generating perfusion maps from structural optical coherence tomography with artificial intelligence

Despite advances in artificial intelligence (AI), its application in medical imaging has been burdened and limited by expert-generated labels. We used images from optical coherence tomography angiography (OCTA), a relatively new imaging modality that measures retinal blood flow, to train an AI algorithm to generate flow maps from standard optical coherence tomography (OCT) images, exceeding the ability and bypassing the need for expert labeling. Deep learning was able to infer flow from single structural OCT images with similar fidelity to OCTA and significantly better than expert clinicians (P < 0.00001). Our model allows generating flow maps from large volumes of previously collected OCT data in existing clinical trials and clinical practice. This finding demonstrates a novel application of AI to medical imaging, whereby subtle regularities between different modalities are used to image the same body part and AI is used to generate detailed inferences of tissue function from structure imaging.

neuroscience↗

bioRxiv2019

Deep learning detects virus presence in cancer histology

Oncogenic viruses like human papilloma virus (HPV) or Epstein Barr virus (EBV) are a major cause of human cancer. Viral oncogenesis has a direct impact on treatment decisions because virus-associated tumors can demand a lower intensity of chemotherapy and radiation or can be more susceptible to immune check-point inhibition. However, molecular tests for HPV and EBV are not ubiquitously available.\n\nWe hypothesized that the histopathological features of virus-driven and non-virus driven cancers are sufficiently different to be detectable by artificial intelligence (AI) through deep learning-based analysis of images from routine hematoxylin and eosin (HE) stained slides. We show that deep transfer learning can predict presence of HPV in head and neck cancer with a patient-level 3-fold cross validated area-under-the-curve (AUC) of 0.89 [0.82; 0.94]. The same workflow was used for Epstein-Barr virus (EBV) driven gastric cancer achieving a cross-validated AUC of 0.80 [0.70; 0.92] and a similar performance in external validation sets. Reverse-engineering our deep neural networks, we show that the key morphological features can be made understandable to humans.\n\nThis workflow could enable a fast and low-cost method to identify virus-induced cancer in clinical trials or clinical routine. At the same time, our approach for feature visualization allows pathologists to look into the black box of deep learning, enabling them to check the plausibility of computer-based image classification.

cancer biology↗

bioRxiv2026

Validation and optimisation of wearable accelerometer data pre-processing for digital measure implementation and development

The increasing use of accelerometers as digital health technologies in clinical trials and clinical care is driving the need for data processing to meet medical standards. The aim of this study was to create and test a modular pipeline for the pre-processing of high-resolution accelerometry that assures the quality, transparency and traceability of digital measures from sensor-level data. The objective is for the pipeline to be a foundational layer in the development, implementation and comparison of measures. The study developed the open GENEAcore package to meet the requirements of regulators, verifying the engineering implementation and analytically validating outputs against reference datasets. Early stages included the optimisation of calibration and non-wear detection. Data-driven detection of behavioural transitions was then validated to give direct bout outputs without the need to identify rules for epoch aggregation and interruptions. The utility for measure development was shown by comparing two algorithms for the characterisation of activity intensity in both the epoch and bout paradigms. Non-wear was detected with a balanced accuracy of 92.3% and the commonly used 13mg acceleration standard deviation threshold was empirically validated for the first time. The detection of transitions proved reliable with 99% detected, on average, within 2 seconds of their occurrence to give a mean expected event duration of 68.6s from a log-normal distribution. The different activity intensity algorithms were more than 99% concordant during movement but their outputs diverged in low movement conditions. Importantly, variable duration bouts created 31% higher daily activity durations compared to 1-second epochs. This evaluation of pre-processing steps has confirmed the attention to detail required to create robust and reproducible results for later clinical validation where small changes in an algorithm or its implementation may have clinically meaningful consequences.

animal behavior and cognition↗

bioRxiv2026

Blood-to-tissue translation in autoimmune disease: paired single-cell evidence from systemic sclerosis

AO_SCPLOWBSTRACTC_SCPLOWThe biology that governs progression and therapeutic response in autoimmune disease is organized in affected tissue, but direct molecular readout of that biology requires invasive biopsy and is rarely repeated during clinical trials or routine care. Using paired blood-skin single-cell RNA-sequencing from a systemic sclerosis (SSc) cohort of 74 individuals (57 patients and 17 matched controls, 192,809 cells across 53 annotated cell states), we show that peripheral blood carries a recoverable projection of tissue-resident molecular state. Across 63 pathways scored in both compartments, 43 same-pathway blood-skin associations reach FDR < 0.05; at cell-type resolution, 212 cross-compartment associations survive residualization for disease status and sex. Per-patient classifiers recover tissue-defined molecular states out of fold with AUCs between 0.62 and 0.79, with the strongest recoveries on fibroblast subtype programs that have no direct circulating analog: fibroblast COMP at 0.79, COCH at 0.75, MYOC2 at 0.74, POSTN at 0.74. Tissue programs route through different blood compartments at different representational levels: fibroblast programs resolve through T-cell, Treg, monocyte and B-cell axes at compositional and distributional levels, while interferon resolves through expression state across multiple cell types. Within SSc alone, a cross-validated partial least squares model learns a shared blood-skin latent axis at r = 0.486 (permutation p = 0.006); the induced patient ranking recovers tissue-interferon-high patients at 86% precision at the top-20% screening threshold against a 50% base rate. A paired multiview autoencoder, trained on module-level dependency structure under contrastive alignment, paired reconstruction, neighborhood preservation and tissue-target supervision, learns a shared latent geometry in which blood-only projections land in the same tissue-state region as their matched tissue samples and supports recovery of held-out tissue targets above simpler baselines and above two permutation null families. These results map the empirical geometry of cross-compartment inference in autoimmune disease and position peripheral blood as a substrate for tissue-state inference at trial and clinical scale.

immunology↗

bioRxiv2026

Proteomic profiling of baseline CSF and serum from HDClarity identifies signatures for Huntington disease staging and stratification

Background: Sensitive biomarkers that objectively stage Huntington disease (HD) are needed to improve participant stratification and facilitate the enrichment of clinical trials with biologically and clinically homogeneous populations. The HDClarity study, an international longitudinal biofluid collection initiative for HD, provides a unique resource for large-scale proteomic profiling of matched CSF and serum samples from healthy controls and individuals across premanifest and manifest stages of HD. Here, we leveraged baseline proteomic data from HDClarity to characterize protein signatures associated with disease stage and clinical severity, compare measurements across analytical platforms and biofluid compartments, and identify candidate multi-protein panels for disease staging. Methods: Baseline proteomic data generated using Olink Explore (~3,000 proteins) and SomaScan (~7,000 proteins) were analyzed in matched CSF and serum samples from 315 HD gene-expansion carriers and 92 non-HD controls. A total of 2,119 proteins overlapped between Olink and SomaScan, enabling assessment of cross-platform concordance, while CSF-serum relationships were evaluated using all available protein measurements within each assay. Covariate-adjusted linear regression models were used to assess disease stage-associated differences in protein abundance, while partial correlation analyses evaluated relationships between protein abundance, clinical severity in HD gene-expansion carriers, and estimated years to disease onset in premanifest participants. A nested machine-learning pipeline incorporating univariate feature ranking, penalized regression-based feature selection, and repeated cross-validation was used to derive compact multi-protein classifiers for HD staging. Results: Cross-platform and CSF-serum correlations were highly protein-dependent, with some analytes showing strong concordance and others exhibiting weak or inverse relationships. These findings highlight substantial heterogeneity in biomarker behaviour across analytical platforms and biofluids. Adjusted models identified both known HD-associated markers (NEFL, GFAP, CHI3L1) and less well-characterized proteins in CSF (TNFRSF8, TPM3, NPPB) and serum (OMG, NPTXR, NCAN) whose baseline abundance differed across clinical and/or HD-Integrated Staging System (HD-ISS) stages. Partial correlation analyses revealed additional candidate biomarkers associated with clinical severity and years to predicted disease onset. Machine-learning models identified compact CSF and serum protein panels that achieved high classification accuracy (AUC > 0.9) across all clinical contrasts, with models distinguishing the transitions from HD-ISS stage 0 to 1 and from premanifest to early manifest disease demonstrating robust generalization to unseen data. Conclusions: This study provides the first large-scale comparison of matched CSF and serum proteomic profiles from the HDClarity cohort, identifying robust baseline proteomic signatures across the HD continuum. Our findings demonstrate the importance of considering both analytical platform and biofluid when interpreting protein biomarkers and identify compact protein panels with potential utility for objective disease staging, patient stratification, and clinical trial enrichment in HD.

neuroscience↗

bioRxiv2025

Minimal impact of ivermectin on immune response and transcriptional profiles in naïve adults with mild COVID-19

Ivermectin (IVM), an antiparasitic drug, was repurposed to treat COVID-19 based on its in vitro antiviral effects. However, it was abandoned after multiple clinical trials reported a lack of efficacy. Immunomodulatory effects have been proposed but remain unclear, yet they may be relevant given IVM use for other infections. We assessed the IVM immunomodulatory effect in 24 participants from a clinical trial evaluating its potential to reduce COVID-19 transmission in mild cases within 48 hours of symptoms onset. The IVM-treated patients showed non-significant lower viral loads, and a significantly shorter duration of hyposmia/anosmia. We measured IgG, IgA, and IgM against five SARS-CoV-2 antigens, and 30 cytokines by Luminex, alongside whole blood RNA sequencing, pan-leukocyte immunophenotyping, and SARS-CoV-2-specific T cell analysis by flow cytometry from day 1 to day 28 post-treatment. All antibody responses increased from day 4, while 13 cytokines significantly decreased over time (adjusted p<0.05). IVM-treated patients had only significantly higher anti-nucleocapsid IgG levels at day 4 (adjusted p=0.041) and 7 (adjusted p=0.045) compared to placebo. SARS-CoV-2-specific CD4+ and CD8+ T cells increased over time, with significantly higher effector memory CD4+ T cells at day 7 compared to day 1 (p=0.027) and the only difference between groups was lower frequencies of spike-specific naive CD4+ T cells at day 7 in IVM-treated participants (0.006% vs 0.036% p=0.02). Transcriptomic data showed downregulation of innate and antiviral blood transcriptional modules (BTMs) over time, with an increase in adaptive immune related BTMs. While no differential gene expression was detected, the IVM-treated had upregulated innate and downregulated T cell and cell cycle BTMs compared to placebo. Overall, our comprehensive longitudinal analysis of early immune responses in mild COVID-19 revealed no robust immunological effects of IVM, consistent with clinical trials results and suggesting a lack of efficacy of IVM in COVID-19 treatment. AUTHOR SUMMARYIvermectin (IVM), an antiparasitic drug, was tested in clinical trials as a potential treatment for COVID-19 due to its in vitro antiviral properties and hypothetical immunomodulatory effects. In this study, we explored the immunomodulatory effects of IVM in a clinical trial involving 24 mild COVID-19 patients who received IVM within 48 hours of symptoms onset. The IVM group showed a trend towards lower viral loads post-treatment, and a significantly shorter duration of hyposmia/anosmia. We comprehensively analyzed immune responses by measuring antibody levels, cytokine profiles, immune cell subsets and whole blood gene expression over 28 days. The IVM group only had increased levels of IgG against the SARS-CoV-2 nucleocapsid compared to the control group. IVM did not affect the kinetics of SARS-CoV-2 T cells, despite a slight decrease in naive CD4+ T cells. Additionally, gene expression analysis showed a decrease in innate and antiviral responses and an increase of adaptive responses over time that were slightly stronger in the placebo compared to the IVM group. In conclusion, despite some differences in IVM treated participants, our detailed analyses do not support significant immunomodulatory effects that could benefit disease progression.

immunology↗

bioRxiv2021

Preclinical evaluation of a SARS-CoV-2 mRNA vaccine PTX-COVID19-B

Safe and effective vaccines are needed to end the COVID-19 pandemic caused by SARS-CoV-2. Here we report the preclinical development of a lipid nanoparticle (LNP) formulated SARS-CoV-2 mRNA vaccine, PTX-COVID19-B. PTX-COVID19-B was chosen among three candidates after the initial mouse vaccination results showed that it elicited the strongest neutralizing antibody response against SARS-CoV-2. Further tests in mice and hamsters indicated that PTX-COVID19-B induced robust humoral and cellular immune responses and completely protected the vaccinated animals from SARS-CoV-2 infection in the lung. Studies in hamsters also showed that PTX-COVID19-B protected the upper respiratory tract from SARS-CoV-2 infection. Mouse immune sera elicited by PTX-COVID19-B vaccination were able to neutralize SARS-CoV-2 variants of concern (VOCs), including the B.1.1.7, B.1.351 and P.1 lineages. No adverse effects were induced by PTX-COVID19-B in both mice and hamsters. These preclinical results indicate that PTX-COVID19-B is safe and effective. Based on these results, PTX-COVID19-B was authorized by Health Canada to enter clinical trials in December 2020 with a phase 1 clinical trial ongoing (ClinicalTrials.gov number: NCT04765436). One Sentence SummaryPTX-COVID19-B is a SARS-CoV-2 mRNA vaccine that is highly immunogenic, safe, and effective in preventing SARS-CoV-2 infection in mice and hamsters and is currently being evaluated in human clinical trials.

immunology↗