SEARCH · Search bioRxiv

Results for “clinical trials”

Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 253 records · Page 14Linked to original sources

bioRxiv2025

In silico drug sensitivity predicts subgroup-specific therapeutics in medulloblastoma patients

BackgroundMedulloblastoma is the most common malignant pediatric brain tumor. Survival rates vary widely between subgroups, with an average overall survival of 70%. Recurrent medulloblastoma is highly aggressive, treatment-resistant, and usually fatal. In addition, current treatments are highly toxic to the developing brain and surviving patients suffer from lifelong side effects. Therefore, novel therapeutic options are urgently needed. MethodsTo inform risk-based, personalized therapy, we developed a novel platform called DrugSeq, which allows predictions of drug sensitivities in patients across medulloblastoma subgroups. We used a perturbagen-response dataset to calculate transcriptional response signatures for each drug and compared this to patient medulloblastoma tumor gene expression. We then stratified patients by molecular subgroup and used an ANOVA analysis to identify drugs that selectively targeted each subgroup. ResultsWe found distinct differences in transcriptional profiles and predicted drug sensitivity for each medulloblastoma subgroup. We identified several kinase inhibitors, epigenetic inhibitors, and several drugs that have been investigated in drug repositioning studies for cancer. ConclusionsWe posit that DrugSeq may identify novel therapies and facilitate patient stratification in clinical trials, leading to more successful targeted medulloblastoma therapies that improve tumor response while minimizing late toxicities. This computational tool can also be used for other cancers to stratify patients based on any clinical or molecular feature. Key points DrugSeq calculates drug sensitivity for medulloblastoma tumors stratified by subgroup. DrugSeq platform may inform patient stratification strategies in clinical trials. Importance of the StudyMedulloblastoma is the most common malignant pediatric brain tumor. Current standard-of-care typically includes surgical resection, multi-agent chemotherapy, and radiation. However, survival rates vary widely between subgroups, ranging from 45 to 90%, depending on age and molecular features. In addition, surviving children frequently suffer from debilitating late side effects of therapy including neurocognitive impairment, epilepsy, stroke, subsequent cancer, endocrinopathies, and early mortality. Therefore, novel therapeutic options are urgently needed. However, a one-size-fits-all approach for therapy is unlikely to be effective given the well-characterized intertumor heterogeneity of medulloblastoma.

cancer biology↗

bioRxiv2018

Subcutaneous neurotrophin-3 infusion induces corticospinal neuroplasticity and improvements in dexterity and walking in elderly rats after large cortical stroke

There is an urgent need for a therapy which reverses disability after stroke when initiated in a time frame suitable for the majority of new victims. Neurotrophin-3 (NT3) is a growth factor made by muscle spindles and skin which is required for the survival, development and function of locomotor circuits involving afferents from muscle and skin that mediate proprioception and tactile sensation. Its level declines in muscle and other tissues postnatally. We show that levels of NT-3 in the bloodstream were low in humans with ischemia stroke relative to young healthy controls. Accordingly, we set out to determine whether subcutaneous delivery of NT3 improves sensorimotor recovery after stroke in elderly rats. We show that one-month-long subcutaneous infusion of NT3 protein induces sensorimotor recovery after cortical stroke in elderly rats. Specifically, in a randomised, blinded pre-clinical trial, we show improved dexterity, walking and sensory function in rats following cortical ischemic stroke when treatment with NT3 is initiated 24 hours after stroke. Importantly, NT-3 was given in a clinically feasible time frame via this straightforward route. MRI and histology showed that recovery was not due to neuroprotection, as expected given the delayed treatment. Rather, anterograde tracing showed that corticospinal axons from the less-affected hemisphere sprouted in the spinal cord from cervical levels 2 to 8. Importantly, Phase I and II clinical trials by others show that repeated, subcutaneously administered high doses of recombinant NT-3 are safe and well tolerated in humans with other conditions. This paves the way for NT-3 as a therapy for stroke.

neuroscience↗

bioRxiv2023

A human primary airway microphysiological system infected with SARS-CoV-2 distinguishes the treatment efficacy between nirmatrelvir and repurposed compounds fluvoxamine and amodiaquine

The COVID-19 pandemic necessitated a rapid mobilization of resources toward the development of safe and efficacious vaccines and therapeutics. Finding effective treatments to stem the wave of infected individuals needing hospitalization and reduce the risk of adverse events was paramount. For scientists and healthcare professionals addressing this challenge, the need to rapidly identify medical countermeasures became urgent, and many compounds in clinical use for other indications were repurposed for COVID-19 clinical trials after preliminary preclinical data demonstrated antiviral activity against SARS-CoV-2. Two repurposed compounds, fluvoxamine and amodiaquine, showed efficacy in reducing SARS-CoV-2 viral loads in preclinical experiments, but ultimately failed in clinical trials, highlighting the need for improved predictive preclinical tools that can be rapidly deployed for events such as pandemic emerging infectious diseases. The PREDICT96-ALI platform is a high-throughput, high-fidelity microphysiological system (MPS) that recapitulates primary human tracheobronchial tissue and supports highly robust and reproducible viral titers of SARS-CoV-2 variants Delta and Omicron. When amodiaquine and fluvoxamine were tested in PREDICT96-ALI, neither compound demonstrated an antiviral response, consistent with clinical outcomes and in contrast with prior reports assessing the efficacy of these compounds in other human cell-based in vitro platforms. These results highlight the unique prognostic capability of the PREDICT96-ALI proximal airway MPS to assess the potential antiviral response of lead compounds.

bioengineering↗

bioRxiv2025

Off-target mapping enhances selectivity of machine learning-predicted CK2 inhibitors

A key challenge in drug development is identification of druggable targets, the modulation of which attenuates disease progression, while avoiding inhibition of proteins that lead to dose-limiting toxicities. Here, we investigate a drug target casein kinase 2 (CK2) - a serine/threonine kinase implicated in cancer, for which existing inhibitors have so far failed in clinical trials. Using molecular and pharmacoepidemiology approaches, we show that small molecules targeting cyclin-dependent kinase (CDK) family members CDK1/2/7/9, including the existing CK2 inhibitors, have a higher risk to induce adverse effects or fail in clinical trials. Based on this finding, we establish a machine learning (ML) assisted discovery pipeline to redesign more specific and allosteric lead compounds against CK2, with a more selective on-target binding and favourable off-target profile. Importantly, we show that such selective design is possible when standard molecular docking and ML algorithms are combined with an error prediction model. In conclusion, our study reports a simple yet efficient ML-powered drug discovery pipeline and novel submicromolar inhibitors targeting clinically relevant CK2 kinase with no clinically approved antagonists available. Our prediction pipeline was able to achieve a 90% hit-rate, significantly reducing the need for subsequent wet-lab validation.

pharmacology and toxicology↗

bioRxiv2019

Kinobead profiling reveals reprogramming of B-cell receptor signaling in response to therapy within primary CLL cells.

Signaling via the B-cell receptor (BCR) is critical for driving CLL pathobiology, promoting both malignant cell survival and disease progression. However, understanding of this pathway is limited, particularly in relation to potential changes in response to therapy. Here, we describe a kinobead-based protocol, used in conjunction with mass-spectrometry to study surface-IgM signaling in primary CLL cells. We identified a fingerprint of over 30 kinases which displayed unique, patientspecific response following sIgM stimulation. Matched analysis of CLL cells in samples taken from clinical trials showed that BCR-induced kinome responses altered between baseline and disease progression in patients who relapsed from chemoimmunotherapy. Moreover, adaptive changes to BCR signaling were also observed in CLL cells from clinical trial patients receiving ibrutinib; longitudinal profiling revealed increased signaling despite BTK inhibition. Collectively, these data comprise the first comprehensive investigation into BCR signaling response within CLL where kinobead profiling reveals unique evidence of adaptive reprogramming in response to therapy.

cancer biology↗

bioRxiv2020

Deep learning detection of informative features in tau PET for Alzheimer' s disease classification

BackgroundAlzheimers disease (AD) is the most common type of dementia, typically characterized by memory loss followed by progressive cognitive decline and functional impairment. Many clinical trials of potential therapies for AD have failed, and there is currently no approved disease-modifying treatment. Biomarkers for early detection and mechanistic understanding of disease course are critical for drug development and clinical trials. Amyloid has been the focus of most biomarker research. Here, we developed a deep learning-based framework to identify informative features for AD classification using tau positron emission tomography (PET) scans. MethodsWe analysed [18F]flortaucipir PET image data from the Alzheimers Disease Neuroimaging Initiative (ADNI) cohort. We first developed an image classifier to distinguish AD from cognitively normal (CN) older adults by training a 3D convolutional neural network (CNN)-based deep learning model on tau PET images (N=132; 66 CN and 66 AD), then applied the classifier to images from individuals with mild cognitive impairment (MCI; N=168). In addition, we applied a layer-wise relevance propagation (LRP)-based model to identify informative features and to visualize classification results. We compared these results with those from whole brain voxel-wise between-group analysis using conventional Statistical Parametric Mapping (SPM12). ResultsThe 3D CNN-based classification model of AD from CN yielded an average accuracy of 90.8% based on five-fold cross-validation. The LRP model identified the brain regions in tau PET images that contributed most to the AD classification from CN. The top identified regions included the hippocampus, parahippocampus, thalamus, and fusiform. The LRP results were consistent with those from the voxel-wise analysis in SPM12, showing significant focal AD associated regional tau deposition in the bilateral temporal lobes including the entorhinal cortex. The AD probability scores calculated by the classifier were correlated with brain tau deposition in the medial temporal lobe in MCI participants (r=0.43 for early MCI and r=0.49 for late MCI). ConclusionA deep learning framework combining 3D CNN and LRP algorithms can be used with tau PET images to identify informative features for AD classification and may have application for early detection during prodromal stages of AD.

neuroscience↗

bioRxiv2026

Deep Learning Enabled 3D Multi-Omic Analysis Reveals Molecular Signatures of Heterogeneous Response to Chemotherapy in Pancreatic Cancer

Resistance to systemic therapy is a major unmet challenge in pancreatic cancer. To identify potential mechanisms of resistance, we developed a novel 3D pipeline in clinical samples that uses deep learning to classify sensitive and persistent tumor cell populations based on morphological features, enabling subsequent molecular characterization of intratumoral heterogeneity. We applied this automated 3D pipeline to a cohort of human pancreatic cancer samples treated with neoadjuvant chemotherapy, identifying heterogeneity in response to therapy both between and within tumors. Application of spatial proteomics to these sensitive and persistent regions identified enhanced epithelial-to-mesenchymal transition and non-classical cell states in persistent cells, confirming our morphological classification. Integration of spatial transcriptomics in multiple pancreatic cancer cohorts associated fibroblast-cancer crosstalk via syndecans with resistance to cytotoxic therapy. Our validated 3D multi-omic pipeline is now poised for application to clinical trials, enabling discovery of resistance mechanisms and design of new therapeutic combinations to circumvent resistance. Statement of significanceWe developed a novel 3D multi-omic pipeline to identify mechanisms of resistance to chemotherapy in clinical samples. This approach associated fibroblast-cancer crosstalk via syndecans with resistance to cytotoxic therapy and is poised for broader application in neoadjuvant clinical trials.

cancer biology↗

bioRxiv2025

SARC028 samples reveal an interplay between TGF-beta, interferon signaling and low HLA class I expression as contributors to Ewing sarcoma checkpoint blockade resistance

PurposeEwing sarcoma, in contrast to some adult sarcoma subtypes, generally does not respond to single agent immunotherapy targeting PD1. The features of Ewing sarcoma that preclude the effectiveness of immunotherapy remain largely unknown. To address this question, we utilized biopsies from patients with Ewing sarcoma obtained pre- and post-pembrolizumab (anti-PD1) therapy from the phase 2 clinical trial SARC028 to interrogate the Ewing tumor microenvironment and features associated with resistance to checkpoint inhibition. Experimental DesignWe utilize multiplexed immunofluorescence, spatial proteomics and spatial transcriptomics to analyze paired pre- and 8 weeks post-treatment biopsy specimens from patients with Ewing sarcoma enrolled on SARC028. ResultsPembrolizumab therapy did not alter the quantity of immune cell infiltration in Ewing tumor biopsies. Analysis of tumor-associated protein markers revealed increased immunoregulatory markers after pembrolizumab. Spatial transcriptomics identified ten cellular neighborhoods (CN) across patients consisting of specific cell subsets. CN10 was consistently observed across patients with poor response. This cellular neighborhood was enriched for a tumor subpopulation with high TGF-{beta} response, low interferon (IFN) response, and low HLA class I expression. IFN response, HLA class I expression, and overall immune infiltration were correlated. ConclusionsAnalyses of the tumor microenvironment from Ewing sarcoma biopsies reveals an immunosuppressive triad, the disruption of which should be pursued to improve antitumor immunity. This work highlights the unique insight that can be gained from the analysis of paired patient Ewing sarcoma tumor biopsy samples from clinical trials. Statement of translational relevanceSingle agent checkpoint inhibitors have demonstrated limited clinical benefit in bone sarcomas, including Ewing sarcoma (EwS). While in vitro and limited patient tumor analyses have suggested mechanisms of immunotherapy resistance in EwS, tumor biopsies following checkpoint inhibitor administration have not been examined. SARC028 was a phase II study investigating blockade of programmed cell death-1 (PD-1) in patients with relapsed/refractory sarcomas, including EwS. Tumor biopsies were collected prior to and 8 weeks after anti-PD1 therapy. Here, we leverage a unique opportunity to understand EwS immunotherapy resistance through analysis of paired tumor samples from SARC028. Using a multi-omics approach, we identify features of EwS that contribute to primary resistance to anti-PD1 including TGF-{beta}, reduced interferon signaling and low HLA class I expression. This study provides an improved understanding of tumor characteristics driving poor response to immunotherapy in relapsed EwS and will guide the development of future trials to modulate this immunosuppressive axis.

cancer biology↗

bioRxiv2020

Predicting future regional tau accumulation in asymptomatic and early Alzheimer's disease

The earliest stages of Alzheimers disease (AD) involve interactions between multiple pathophysiological processes. Although these processes are well studied, we still lack robust tools to predict individualised trajectories of disease progression. Here, we employ a robust and interpretable machine learning approach to combine multimodal biological data and predict future tau accumulation, translating predictive information from deep phenotyping cohorts at early stages of AD to cognitively normal individuals. In particular, we use machine learning to quantify interactions between key pathological markers ({beta}-amyloid, medial temporal atrophy, tau and APOE 4) at early and asymptomatic stages of AD. We next derive a predictive index that stratifies individuals based on future pathological tau accumulation, highlighting two critical features for optimal clinical trial design. First, future tau accumulation provides a better outcome measure compared to changes in cognition. Second, stratification based on multimodal data compared to {beta}-amyloid alone reduces the sample size required to detect a clinically meaningful change in tau accumulation. Further, we extend our machine learning approach to derive individualised trajectories of future pathological tau accumulation in early AD patients and accurately predict regional future rate of tau accumulation in an independent sample of cognitively unimpaired individuals. Our results propose a robust approach for fine scale stratification and prognostication with translation impact for clinical trial design at asymptomatic and early stages of AD. One Sentence SummaryOur machine learning approach combines baseline multimodal data to make individualised predictions of future pathological tau accumulation at prodromal and asymptomatic stages of Alzheimers disease with high accuracy and regional specificity.

neuroscience↗

bioRxiv2023

Chemo-phosphoproteomic profiling with ATR inhibitors berzosertib and gartisertib uncovers new biomarkers and DNA damage response regulators

The ATR kinase protects cells against DNA damage and replication stress and represents a promising anti-cancer drug target. The ATR inhibitors (ATRi) berzosertib and gartisertib are in clinical trials for treatment of advanced solid tumours as monotherapy or in combination with genotoxic agents. However, the pharmacodynamic ATR biomarker phospho-CHK1 has shown limited sensitivity in for quantitative assessment of ATR activity in clinical trials. Therefore, better biomarkers are needed, and with this in mind we carried out quantitative phospho-proteomic screening for ATR biomarkers that are highly sensitive to berzosertib and gartisertib. Screening identified novel ATR-dependent targets in three broad classes: i) targets whose phosphorylation is highly sensitive to ATRi; ii) novel targets with known genome maintenance roles; iii) novel targets whose cellular roles are unclear, including SCAF1. We show that SCAF1 interacts with RNAPII in a phospho-dependent manner and suppresses homologous recombination in cells lacking the BRCA1 tumour suppressor. Taken together these data reveal potential new ATR biomarkers and new genome maintenance factors.

biochemistry↗

bioRxiv2019

Comparative Anatomical Limits of CART-Cell Delivery to Tumours in Mice and Men

BackgroundCART-cells have demonstrated clinical success for the treatment of multiple different lymphomas and leukaemias. However, comparable clinical efficacy has not been observed for various solid tumours in human clinical trials, despite promising data from murine cancer models. Lower effective CART-cell delivery rates to human solid tumours compared to haematological malignancies in humans and solid tumours in mice might partially explain these divergent clinical outcomes, requiring the development of quantitative methods to calculate effective dosing in the clinic and models. MethodsWe used anatomical and physiological data for human and rodent circulatory systems to calculate the typical value and expected variation of the perfusion of healthy and tumour tissues, and used these to estimate the upper limits of immune cell delivery rates (maximum possible under optimal conditions) across different organs, tumours types and species. ResultsEstimated maximum delivery rates were up to order 10,000-fold greater in mice than humans yet reported CART-cell doses are typically only 10-100-fold lower in mice, suggesting that the effective delivery rates of CART-cells into tumours in clinical trials are far lower than in corresponding mouse models. Estimated delivery rates were compared to published PET data and found to be consistent. ConclusionsThe results suggest that higher effective human doses may be needed to drive efficacy comparable to mouse solid tumour models. These increased doses raise safety and manufacturing concerns. We posit that quantitation of species and organ-specific delivery and homing of engineered T-cells will be key to unlocking their potential for solid tumours.

cancer biology↗

bioRxiv2020

A drug-screening platform based on organotypic cultures identifies vulnerabilities to prevent local relapse and treat established brain metastasis

Exclusion of brain metastases from clinical trials is a major cause of the limited therapeutic options for this growing population of cancer patients. Here, we report a medium-throughput drug-screening platform (METPlatform) based on organotypic cultures that allows to evaluate inhibitors against metastases growing in situ. By applying this approach to brain metastasis, we identified several hits from a library of FDA approved inhibitors and others being tested in clinical trials. A blood-brain barrier permeable HSP90 inhibitor showed high potency against mouse and human brain metastases at clinically relevant stages of the disease, including a novel model of local relapse after neurosurgery. Furthermore, in situ proteomic analysis applied to organotypic cultures with metastases treated with the chaperone inhibitor revealed novel biomarkers in human brain metastasis and actionable mechanisms of resistance. Our work validates METPlatform as a potent resource for metastasis research integrating drug-screening and unbiased omic approaches that is fully compatible with human samples. We envision that METPlatform could be established as a clinically relevant strategy to personalize the management of metastatic disease in the brain and elsewhere. SummarySystemic spread of cancer continues to be the key aspect associated with lethality. In this publication, Zhu et al. describes a drug-screening platform specifically designed to study vulnerabilities of metastasis when colonizing secondary organs and demonstrates its value in difficult-to-treat brain metastasis using new models and patient-derived samples.

cancer biology↗

bioRxiv2024

Precision Enhancement of CAR-NK Cells through Non-Viral Engineering and Highly Multiplexed Base Editing

Natural killer (NK) cells unique ability to kill transformed cells expressing stress ligands or lacking major histocompatibility complexes (MHC) has prompted their development for immunotherapy. However, NK cells have demonstrated only moderate responses against cancer in clinical trials and likely require advanced genome engineering to reach their full potential as a cancer therapeutic. Multiplex genome editing with CRISPR/Cas9 base editors (BE) has been used to enhance T cell function and has already entered clinical trials but has not been reported in human NK cells. Here, we report the first application of BE in primary NK cells to achieve both loss-of-function and gain-of-function mutations. We observed highly efficient single and multiplex base editing, resulting in significantly enhanced NK cell function. Next, we combined multiplex BE with non-viral TcBuster transposon-based integration to generate IL-15 armored CD19 CAR-NK cells with significantly improved functionality in a highly suppressive model of Burkitts lymphoma both in vitro and in vivo. The use of concomitant non-viral transposon engineering with multiplex base editing thus represents a highly versatile and efficient platform to generate CAR-NK products for cell-based immunotherapy and affords the flexibility to tailor multiple gene edits to maximize the effectiveness of the therapy for the cancer type being treated.

bioengineering↗

bioRxiv2026

Autologous Immune Cell Assay to Investigate risk of Processed- and Novel Food-Induced type 2 Inflammation in Peanut Allergy

Peanut allergy represents a major food-allergy burden, raising concerns about food processing and novel dietary products. Current diagnostics assess primarily allergic endpoints rather than immune mechanisms initiating and maintaining type 2 inflammation, particularly DC2-mediated Th2 polarization. Here, an in vitro autologous monocyte-derived dendritic cell (moDC)-T cell and B cell assay has been established to study immunomodulatory effects induced by unprocessed (P-D) and processed (P-DH (heated) or P-DHG (heated and glycated)) peanut proteins and emerging foods (protein concentrates or whole biomass), related to ex vivo DC2-T cell reactions. CD14+ monocytes, CD4+ T cells and CD19+ B cells were isolated from six peanut-allergic patients PBMCs. MoDCs generated with IL4/GM-CSF were exposed (48h) to type 2 polarizing cytokine (DC2) mix, or DC2 mix combined with the food samples. Next, DC2s were co-cultured with T cells (5d), followed by B cells incubation with DC2/T cell supernatant and food samples (10d). Supernatants and cells were analyzed for Th1/Th2/Th-regulatory (Treg) cells, IgE and IgG profiles. DC2 induced a strong Th2 phenotype and activity, P-D DC2 further enhanced IL13 secretion and %Tregs. P-DH DC2 favored Th2, whereas P-DHG DC2 increased IFN{gamma}, with neither increasing %Treg. All increased CD40L+CD25+ memory Th2 cells. Wheat, whey and seaweed biomass had little effect, whereas algae DC2 showed distinct immunomodulatory, adjuvant-like activity. In conclusion, this autologous in vitro assay captures peanut-specific and generic Th2 responses and reactivity to food samples, supporting its use as additional tool to assess type 2-driving potential and allergenicity of emerging foods and processing methods in peanut-allergic patients. Clinical trial registrationThe current in vitro study was conducted in accordance with the Declaration of Helsinki and approved by the Medical Ethics Review Committee (METC) of the Erasmus MC (NL79534.078.21 MEC-2021-0905) and registrated at International Clinical Trials Registry Platform (NL-OMON51765). Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=124 SRC="FIGDIR/small/705773v1_ufig1.gif" ALT="Figure 1"> View larger version (39K): org.highwire.dtl.DTLVardef@1f42191org.highwire.dtl.DTLVardef@c7e4baorg.highwire.dtl.DTLVardef@1798547org.highwire.dtl.DTLVardef@11d0d96_HPS_FORMAT_FIGEXP M_FIG C_FIG Immune cell illustrations were adapted (1, 2) with permission; permission conveyed through Copyright Clearance Center, Inc. Capsule summaryThe autologous moDC-T cell-B cell in vitro assay may be used to assess whether processing methods or new foods might have intrinsic capacity to affect type 2 inflammation in peanut-allergic patients.

immunology↗

bioRxiv2024

Wide-field optical redox imaging with leading-edge detection for assessment of patient-derived cancer organoids

Patient-derived cancer organoids (PDCOs) are a valuable model to recapitulate human disease in culture with important implications for drug development. However, current methods for assessing PDCOs are limited. Label-free imaging methods are a promising tool to measure organoid level heterogeneity and rapidly screen drug response in PDCOs. The aim of this study was to assess and predict PDCO response to treatments based on mutational profiles using label-free wide-field optical redox imaging (WF ORI). WF ORI provides organoid-level measurements of treatment response without labels or additional reagents by measuring the autofluorescence intensity of the metabolic co-enzymes NAD(P)H and FAD. The optical redox ratio is defined as the fluorescence intensity of [NAD(P)H / NAD(P)H +FAD] which measures the oxidation-reduction state of PDCOs. We have implemented WF ORI and developed novel leading-edge analysis tools to maximize the sensitivity and reproducibility of treatment response measurements in colorectal PDCOs. Leading-edge analysis improves sensitivity to redox changes in treated PDCOs (G{Delta} = 1.462 vs G{Delta} = 1.233). Additionally, WF ORI resolves FOLFOX treatment effects across all PDCOs better than two-photon ORI, with [~]7X increase in effect size (G{Delta} = 1.462 vs G{Delta} = 0.189). WF ORI distinguishes metabolic differences based on driver mutations in CRC PDCOs identifying KRAS+PIK3CA double mutant PDCOs vs wildtype PDCOs with 80% accuracy and can identify treatment resistant mutations in mixed PDCO cultures (G{Delta} = 1.39). Overall, WF ORI enables rapid, sensitive, and reproducible measurements of treatment response and heterogeneity in colorectal PDCOs that will impact patient management, clinical trials, and preclinical drug development. Statement of SignificanceLabel-free wide-field optical redox imaging of patient-derived cancer organoids enables rapid, sensitive, and reproducible measurements of treatment response and heterogeneity that will impact patient management, clinical trials, and preclinical drug development.

bioengineering↗

bioRxiv2021

Characterization of a vaccine-elicited human antibody with sequence homology to VRC01-class antibodies that binds the C1C2 gp120 domain

Broadly HIV-1 neutralizing VRC01-class antibodies bind the CD4-binding site of the HIV-1 envelope (Env) and contain VH1-2*02-derived heavy chains paired with light chains expressing five amino acid long CDRL3s. Their unmutated forms do not recognize Env or neutralize HIV-1. The lack of elicitation of VRC01-class antibodies in human clinical trials could potentially be due to the absence of activation of the corresponding naive B cells by the vaccine Env immunogens. To address this point directly, we examined Env-specific BCR sequences from participants in the HVTN 100 clinical trial. Of all the sequences analyzed only one displayed sequence homology to VRC01-class antibodies, but the corresponding antibody (FH1) recognized the C1C2 gp120 domain. For FH1 to switch epitope recognition to the CD4-binding site, alterations in both the CDRH3 and CDRL3 were necessary. Our findings support the use of specifically designed immunogens to activate VRC01-class B cells in future human vaccine trials.

immunology↗

bioRxiv2025

The Immunogenicity Database Collaborative (IDC): A Standardized, Publicly Available Database for Clinical Immunogenicity Observations and Insights

The incidence and impact of anti-drug antibodies (ADAs) against biotherapeutics remain difficult to predict, limiting efforts to mitigate immunogenicity risk prior to clinical trials. Existing data are fragmented across disparate sources with inconsistent definitions, representing a key barrier to progress in the field. Here, we present the Immunogenicity Database Collaborative (IDC) and its release of the Immunogenicity Database (DB) V1: a structured clinical immunogenicity dataset integrating therapeutic characteristics, sequence, and patient cohort-level data from publicly available sources. The dataset includes 4,146 ADA datapoints, 1,788 cohorts, 727 clinical trials and 218 therapeutics. We highlight trends in ADA incidence, evaluate sources of variability, and identify driving factors of immunogenicity risk. This work provides a foundational resource to standardize and support immunogenicity risk assessment across the industry. It also provides an initial data architecture and invites the research community to contribute towards future expansions of the database into key areas of interest to the field.

pharmacology and toxicology↗

bioRxiv2022

Pixel size limit of the PRIMA implants: from humans to rodents and back

ObjectiveRetinal prostheses aim at restoring sight in patients with retinal degeneration by electrically stimulating the inner retinal neurons. Clinical trials with patients blinded by atrophic Age-related Macular Degeneration (AMD) using the PRIMA subretinal implant, a 2x2 mm array of 100m-wide photovoltaic pixels, have demonstrated a prosthetic visual acuity closely matching the pixel size. Further improvement in resolution requires smaller pixels, which necessitates more intense stimulation. ApproachHere, we examine the lower limit of the pixel size for PRIMA implants by modeling the electric field, leveraging the clinical benchmarks, as well as using a preclinical animal data to assess the stimulation strength and contrast of various patterns. Visually evoked potentials were measured in RCS rats with photovoltaic implants of 100 and 75m pixels and compared to clinical thresholds with 100 m pixels. Electrical stimulation model calibrated by these clinical and rodent data was used to predict the performance of the implant with smaller pixels. Main ResultsWe found that PRIMA implants with 75m pixels under the maximum safe near-infrared (880nm) illumination of 8 mW/mm2 with 30% duty cycle (10ms pulses at 30Hz) should provide a similar perceptual brightness as with 100 m pixels under 3 mW/mm2 irradiance, used in the current clinical trials. Contrast of the Landolt C pattern scaled down to 75m pixels is also similar under such illumination to that with 100m pixels in clinical settings, increasing the maximum acuity from 20/420 to 20/300. SignificanceComputational model of the photovoltaic subretinal prosthesis defines the minimum pixel size of the PRIMA implants as 75m. Increasing the implant width from 2 to 3 mm and reducing the pixel size from 100 to 75m will nearly quadrupole the number of pixels and thereby should significantly improve the visual performance. Smaller pixels of the same bipolar flat geometry would require excessively intense illumination, and therefore a different pixel design should be considered for further improvement in resolution.

bioengineering↗