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bioRxiv2021

Co-clinical FDG-PET Radiomic Signature in Predicting Response to Neoadjuvant Chemotherapy in Triple Negative Breast Cancer

PurposeWe sought to exploit the heterogeneity afforded by patient-derived tumor xenografts (PDX) to optimize robust radiomic features associated with response to therapy in the context of a co-clinical trial and implement PDX-optimized image features in the corresponding clinical study to predict and assess response to therapy using machine-learning (ML) algorithms. MethodsTNBC patients and subtype-matched PDX were recruited into a co-clinical FDG-PET imaging study to predict response to therapy. One hundred thirty-one imaging features were extracted from PDX and human segmented tumors. Robust image features were identified based on reproducibility, cross-correlation, and volume independence. A rank importance of predictors using ReliefF was used to identify predictive radiomic features in the preclinical PDX trial in conjunction with ML algorithms: classification and regression tree (CART), Naive Bayes (NB), and support vector machines (SVM). The top four PDX-optimized image features, defined as radiomic signatures (RadSig), from each task were then used to predict or assess response to therapy. Performance of RadSig in predicting/assessing response was compared to SUVmean, SUVmax, and lean body mass normalized SULpeak measures. ResultsSixty-four out of 131 preclinical imaging features were identified as robust. NB-RadSig performed highest in predicting and assessing response to therapy in the preclinical PDX trial. In the clinical study, the performance of SVM-RadSig and NB-RadSig to predict and assess response was practically identical and superior to SUVmean, SUVmax, and SULpeak, measures. ConclusionsWe optimized robust FDG-PET radiomic signatures (RadSig) to predict and assess response to therapy in a context of a co-clinical imaging trial. DECLARATIONSO_ST_ABSFundingC_ST_ABSThis work was supported by NCI grants U24CA209837, U24CA253531, and U54CA224083; U2CCA233303, and K12CA167540; Siteman Cancer Center (SCC) Support Grant P30CA091842; and Internal funds provided by Mallinckrodt Institute of Radiology. Conflicts of interest/Competing interests.None. Availability of data and materialAll the co-clinical data will be available for download through the Washington University School of Medicine Co-Clinical Imaging Research Resource web portal at https://c2ir2.wustl.edu/, co-clinical database (CCDB). Code availabilityNot applicable. Authors contributionsConceptualization: SR, FOA, KIS; Methodology: SR, TDW, SL, KIS; Formal analysis and investigation: SR, KIS; Writing - original draft preparation: SR; Writing - review and editing: RLW, FD, KIS; Funding acquisition: RWL, FOA, SL, KIS; Resources: SL; Supervision: FD, KIS. All authors read and approved the final manuscript. Ethics approvalAll studies were performed with approval from the Washington University Humans subjects research committee and animal studies committee. Consent to participateInformed consent to participate in the study was obtained from all participants. Consent for publicationNot applicable.

bioinformatics↗

bioRxiv2018

Glenohumeral Injection Using Anatomic Landmark Versus Sonographic Needle Guidance

ObjectiveWe hypothesized ultrasound (US) guidance improves outcomes of corticosteroid injection of the painful shoulder.\n\nMethods30 patients with symptomatic shoulders due to osteoarthritis were randomized to glenohumeral injection with 3 milliliters of 1% lidocaine and 60 mg of triamcinolone acetonide using the anterior approach with 1) conventional anatomic landmark palpation-guidance or 2) US-guidance. Injection pain (visual analogue pain scale (VAS)), pain at outcome (2 weeks and 6 months), therapeutic duration, time-to-next-injection, and costs were determined.\n\nResultsInjection pain was less with US (VAS: 0.3{+/-}0.6 cm) vs. landmark-guidance (VAS: 1.4{+/-}2.4 cm, 95% CI of difference: 0.5<1.1<1.7, p=0.05). Pain scores were similar at 2 weeks: US: 2.2{+/-}2.4 cm; Landmark: 1.8{+/-}2.7 cm, 95% CI of difference: -2.2<-0.4<1.4, p=0.66 and 6 months: US: 5.8{+/-}2.8 cm; Landmark: 6.4{+/-}2.9 cm, 95% CI of difference: -0.4<0.6< 1.1, p =0.71. Therapeutic duration (US: 3.9{+/-}1.5 months; Landmark: 3.0{+/-}1.2 months, 95% CI of difference: - 1.4 <-0.9<-0.4, p=0.045) and time-to-next-injection (US: 8.1{+/-}3.5 months; Landmark: 5.7{+/-}2.9 months, 95% CI of difference: -3.6<-2.4<-1.3, p=0.025) were longer, and fewer injections per year (29% less) were required: US: 1.5{+/-}0.2 injections/year; Landmark: 2.1{+/-}0.2 injections/year (p<0.037; 95% CI of difference -0.9<-0.6<-0.3). However, cost/patient/year was modestly greater with US (US: $318{+/-}89, Landmark: $301{+/-}67; p=0.28).\n\nConclusionAnatomic landmark guidance in the short-term is equally effective as US for injection of the osteoarthritic shoulder and modestly less costly, however, US may reduce the need for repetitive injections by prolonging the therapeutic effect and thus time to next injection.\n\nIRB StatementThis project was in compliance with the Helsinki Declaration, was approved by the Institutional Review Board (IRB) as ultrasound subset of a syringe safety trial (Human Research Review Committee approval 04-347), and was registered at ClinicalTrials.gov (Clinical Trial Identifier NCT00651625). The subjects gave informed consent to participate prior to all studies and interventions. Patient confidentiality was protected according to the U.S. Health Insurance Portability and Accountability Act (HIPAA) and all data was de-identified.

clinical trials↗

bioRxiv2021

Zinc2+ ion inhibits SARS-CoV-2 main protease and viral replication in vitro.

Zinc deficiency is linked to poor prognosis in COVID-19 patients while clinical trials with Zinc demonstrate better clinical outcome. The molecular target and mechanistic details of anti-coronaviral activity of Zinc remain obscure. We show that ionic Zinc not only inhibits SARS-CoV-2 main protease (Mpro) with nanomolar affinity, but also viral replication. We present the first crystal structure of Mpro-Zn2+ complex at 1.9 [A] and provide the structural basis of viral replication inhibition. We show that Zn2+ coordinates with the catalytic dyad at the enzyme active site along with two previously unknown water molecules in a tetrahedral geometry to form a stable inhibited Mpro-Zn2+ complex. Further, natural ionophore quercetin increases the anti-viral potency of Zn2+. As the catalytic dyad is highly conserved across SARS-CoV, MERS-CoV and all variants of SARS-CoV-2, Zn2+ mediated inhibition of Mpro may have wider implications.

biochemistry↗

bioRxiv2022

Cytocapsular tube network-tumor system is an integrated physical target for the highly effective and efficient pharmacotherapy of solid cancers

Cancer is a leading cause of human lethality and cancer drug pan-resistant tumor metastasis (cdp-rtm) is a major source of cancer death. The integrated cytocapsular tube (CCT) networktumor system (CNTS) is essential for cancer evolution procedures including cancer cell proliferation, migration and dissemination in CCT networks, new tumor growth in CCT terminals, conventional cancer drug pan-resistance, and tumor relapse. The preclinical screening experimentations with CCTs and networks are necessary prerequisites for the discovery and development of cancer drug candidates for efficient clinical trials, and effective and precise clinical cancer pharmacotherapy. However, it is unknown whether the popularly employed conventional mouse experimentations for preclinical therapeutic development generate CCTs and networks. Here, we comprehensively investigated the cancer cell line derived xenografts (CDX) of 8 kinds of cancer cell lines, and patient cancer cells derived-xenografts (PDX) with 16 kinds of clinical primary and metastatic malignant tumor cells in multiple cancer stages by immunohistochemistry staining assays with CCT marker protein antibodies of anti-CM-01 antibodies. We found that there are no CCTs or CCT networks in all these examined 16 CDX and 22 PDX transplanted tumors. Our data evidenced that the conventional transplantation tumors for preclinical therapeutic development do not engender CCTs or CNTS, which is consistent with and provides an explanation of the poor clinical outcomes of the marketed cancer drugs. This study demonstrated that CCT network-tumor system (CNTS) is an integrated physical target for pharmacotherapy, and that preclinical experimentations engendering CNTS (such as CCT xenograft, CCTX) should be employed for the discovery and development of highly effective and efficient cancer drugs aimed for cure of solid cancers.

cancer biology↗

bioRxiv2023

Adaptation of a Mutual Exclusivity Framework to Identify Driver Mutations within Biological Pathways

Distinguishing genomic alterations in cancer genes that have functional impact on tumor growth and disease progression from the ones that are passengers and confer no fitness advantage has important clinical implications. Evidence-based methods for nominating drivers are limited by existing knowledge on the oncogenic effects and therapeutic benefits of specific variants from clinical trials or experimental settings. As clinical sequencing becomes a mainstay of patient care, applying computational methods to mine the rapidly growing clinical genomic data holds promise in uncovering novel functional candidates beyond the existing knowledge-base and expanding the patient population that could potentially benefit from genetically targeted therapies. We propose a statistical and computational method (MAGPIE) that builds on a likelihood approach leveraging the mutual exclusivity pattern within an oncogenic pathway for identifying probabilistically both the specific genes within a pathway and the individual mutations within such genes that are truly the drivers. Alterations in a cancer gene are assumed to be a mixture of driver and passenger mutations with the passenger rates modeled in relationship to tumor mutational burden. A limited memory BFGS algorithm is used to facilitate large scale optimization. We use simulations to study the operating characteristics of the method and assess false positive and false negative rates in driver nomination. When applied to a large study of primary melanomas the method accurately identified the known driver genes within the RTK-RAS pathway and nominated a number of rare variants with previously unknown biological and clinical relevance as prime candidates for functional validation.

cancer biology↗

bioRxiv2024

Interneurons that BiTE: Harnessing the migratory capacity of cortical inhibitory interneuron precursors to treat high-grade glioma

Glioblastoma has a poor prognosis with limited therapeutic options. To date, almost all therapeutic agents that showed promise in in vitro assays, preclinical trials, or even in early clinical trials have failed to make a substantial impact on the survival of patients with high-grade glioma. One major obstacle for small molecules, therapeutic proteins, or immune cells remains the blood brain barrier, which often prevents the efficient delivery of agents to the tumor. To address this delivery issue, we developed a cellular vector where implanted modified post-mitotic Migratory Cortical Inhibitory Interneuron Precursors (MCIPs) migrate to high-grade glioma by chemoattraction to locally secrete a therapeutic protein. The inhibitory interneurons of the cerebral cortex originate predominantly in the ventral/subcortical portion of the telencephalic neural tube. During fetal brain development, MCIP migration is guided over long distances by chemorepulsant and chemoattractant factors. Remarkably, several MCIP chemoattractant factors are also secreted by high-grade gliomas. Indeed, our in vitro and in vivo data show that MCIPs robustly migrate to the majority of glioblastoma cell lines evaluated. As a proof of principle, we modified the MCIPs to secrete bispecific T-cell engagers (BiTEs), linking the EGFR tumor antigen to CD3 on T-cells to create an adaptor molecule that induces an anti-tumor response of resident and supplied T-cells by bridging a tumor antigen and the T-cell receptor. We find that implanted BITE-secreting MCIPs significantly extended survival of mice injected with high-grade glioma. Therefore, we conclude that the use of MCIPs as a delivery vector for therapeutic agents could revolutionize the way we treat glioblastoma, as they allow for the local delivery of therapeutic agents in high concentrations, bypassing the need for these agents to cross the blood brain barrier while reducing the risk for systemic toxicities outside of the brain.

cancer biology↗

bioRxiv2024

PPARalpha-ERRalpha crosstalk mitigates metabolic dysfunction-associated steatotic liver disease progression

Background and aimsMetabolic dysfunction-associated steatotic liver disease (MASLD) is now the most common liver disease worldwide. This study investigates how targeting two key nuclear receptors involved in hepatic energy metabolism, peroxisome proliferator-activated receptor alpha (PPAR) and estrogen-related receptor alpha (ERR), impacts MASLD. MethodsThe PPAR agonist pemafibrate and/or ERR inverse agonist C29 were administered in a short- and long-term Western diet plus fructose model, and a diabetic-background streptozotocin-Western diet model (STZ-WD). Liver morphology, histological samples, serum metabolites, RNA and protein levels were analysed and scanning electron microscopy was performed. In addition, we performed cell-based assays and immunohistochemistry and immunofluorescence stainings with light and super-resolution confocal microscopy of healthy, MASLD and MASH human livers. ResultsThe ligand combinations efficacy was underscored by reduced liver steatosis in all the mouse models. Both long-term models showed improvements in body weight and liver morphology, alongside reductions in inflammation and fibrosis. Additionally, tumor formation was prevented in the STZ-WD mice model. Cell-based assays demonstrated that ERR inhibits PPARs activity, likely explaining why ERR blockage improves inflammatory and lipid metabolism gene profiles and enhances lipid-lowering effects. Complementary RNA sequencing and shotgun proteomics, combined with enrichment analysis, jointly identified downregulated serum amyloid A1/A2 as an essential component underlying the combination treatments effectiveness. MASLD/MASH patient livers showed reduced PPAR and increased ERR levels supporting disrupted NR crosstalk in the hepatocyte nucleus. ConclusionOur study comprehensively supports that dual nuclear receptor targeting by simultaneously increasing PPAR and diminishing ERR activity may represent a viable novel strategy against MASLD. Lay summaryOur study using three distinct MASLD mouse models shows that a simultaneous targeting of PPAR and ERR reduces liver fat, fibrosis, inflammation, and tumor formation. The combination treatment modifies lipid metabolism pathways, and uniquely lowers levels of serum amyloid A1/A2 in the liver. Impact and implicationsOur research introduces a novel therapeutic strategy against MASLD by simultaneously increasing PPAR activity while diminishing ERR activity. With PPAR agonists already tested in phase III clinical trials, ERR ligands/modulators need further (clinical) development to make our findings applicable to both MASLD patients and physicians.

pharmacology and toxicology↗

bioRxiv2025

CAMK2D causes heart failure in RBM20 cardiomyopathy

Although heart disease can arise from different etiologies, current treatment is not tailored to the different underlying causes but is rather a one-size-fits-all approach. Importantly, not all patients benefit from this treatment regimen, which means the number needed to treat is very high. Moreover, this makes clinical trials large and costly, limiting clinical translation. Thus, there is a high medical need to develop a first etiology-specific therapy. Mutations in RBM20, a splicing factor that targets multiple pivotal cardiac genes including TTN and CAMK2D, cause a clinically aggressive form of dilated cardiomyopathy (DCM) with a high risk of malignant ventricular arrhythmias. Here, we hypothesized that CAMK2D is the heart disease-causing target of RBM20. We crossed Camk2d- to Rbm20-deficient mice and found that double knockout (DKO) mice were protected from heart failure and sudden cardiac death. Phosphorylation of multiple CAMK2D targets was increased in Rbm20-deficient mouse hearts, which was reverted in DKO hearts, confirming that CAMK2D is not only misspliced but also overactivated. AAV9-mediated re-expression of single CAMK2D splice variants in DKO mice reintroduced cardiac dysfunction irrespective of the splice variant, unmasking that overactivation rather than missplicing underlies the detrimental phenotype. To test whether heart failure could pharmacologically be reversed, we treated heterozygous Rbm20-R636Q knock-in (KI) mice with hesperadin, a potent CAMK2 inhibitor, which rescued both cardiac function and ventricular geometry. These data demonstrate that overactivation of CAMK2D underlies heart failure in RBM20 cardiomyopathy. Pharmacological inhibition of CAMK2D could therefore become the first cause-directed DCM therapy.

molecular biology↗

bioRxiv2025

Antigen-scaffolds drive preferential expansion of functional genetically engineered CAR and TCR T cells

The engineering of autologous T cells to express chimeric antigen receptors (CARs) can induce profound clinical responses in haematological malignancies, while T cell receptor-engineered T (TCR T) cells have led to durable responses in clinical trials for solid tumours. However, clinical manufacturing of engineered T cells is resource-intensive and often yields highly differentiated, exhausted effector T cells. To circumvent this, we have developed an antigen-scaffold (Ag-scaffold) technology to preferentially expand genetically engineered T cells. Such Ag-scaffolds present cognate antigen together with stimulatory factors such as cytokines. By providing a specific and receptor-engaging stimulation to CAR/TCR T cells, the expanded product is highly enriched for engineered T cells with a favourable proliferative and efficacious phenotype. We expanded TCR T cells with Ag-scaffolds presenting peptide MHC (pMHC), and anti-CD19 CAR T cells with Ag-scaffolds presenting CD19 antigen. By applying cognate pMHC Ag-scaffolds, we achieved >80% antigen-specific T cells (83.62%{+/-}9.2%) after 14 days of culture with a distinct cytotoxic, proliferative phenotypical profile. Ag-scaffold expansion enhanced initial TCR and CAR cytotoxicity; sustained control was observed after repeated rechallenges of CAR T cells. In vivo, Ag-scaffold-expanded CRISPR/Cas9-engineered anti-CD19 CAR T also showed complete tumour eradication in a B-cell lymphoma xenograft model with a low dose of CAR T cells, which was not achieved using IL2/7/15 expansion.

immunology↗

bioRxiv2019

Electronic Health Records Based Prediction of Future Incidence of Alzheimers Disease Using Machine Learning

Nationwide population-based cohort provides a new opportunity to build a completely automated risk prediction model based on individuals history of health and healthcare beyond existing risk prediction models. We tested the possibility of machine learning models to predict future incidence of Alzheimers disease (AD) using large-scale administrative health data. From the Korean National Health Insurance Service database between 2002 and 2010, we obtained de-identified health data in elders above 65 years (N=40,736) containing 4,894 unique clinical features including ICD-10 codes, medication codes, laboratory values, history of personal and family illness, and socio-demographics. To define incident AD two operational definitions were considered: "definite AD" with diagnostic codes and dementia medication (n=614) and "probable AD" with only diagnosis (n=2,026). We trained and validated a random forest, support vector machine, and logistic regression to predict incident AD in 1,2,3, and 4 subsequent years. For predicting future incidence of AD in balanced samples (bootstrapping), the machine learning models showed reasonable performance in 1-year prediction with AUC of 0.775 and 0.759, based on "definite AD" and "probable AD" outcomes, respectively; in 2-year, 0.730 and 0.693; in 3-year, 0.677 and 0.644; in 4-year, 0.725 and 0.683. The results were similar when the entire (unbalanced) samples were used. Important clinical features selected in logistic regression included hemoglobin level, age, and urine protein level. This study may shed a light on the utility of the data-driven machine learning model based on large-scale administrative health data in AD risk prediction, which may enable better selection of individuals at risk for AD in clinical trials or early detection in clinical settings.

bioinformatics↗

bioRxiv2026

Variable zinc concentrations among commercially available Mueller-Hinton Broth brands affects SIR interpretations during broth microdilution in vitro antimicrobial susceptibility testing with the novel metallo-beta-lactamase inhibitor APC148

The well-known discrepancy between the in vitro and in vivo efficacy of {beta}-lactam antibiotics in metallo-{beta}-lactamase (MBL) containing Gram-negative bacteria has during recent years been found to be at least partially explained by zinc levels at infection sites being much lower than those found in conventional cation-adjusted Mueller-Hinton Broth (caMHB) media used for in vitro susceptibility testing. Previous studies have also demonstrated that a high variability exists with respect to zinc content in caMHB from different manufacturers, potentially leading to differences in SIR interpretations for {beta}-lactam antibiotics with MBL-carrying isolates, depending on the brand of caMHB used for testing. APC148 is a zinc-chelating compound acting as an inhibitor of MBL enzymes and is currently undergoing phase one in clinical trials. In this study, ten clinical isolates of Klebsiella pneumoniae, Escherichia coli, Pseudomonas aeruginosa and Acinetobacter baumannii harbouring MBLs (NDM, n = 6; VIM, n = 4; IMP, n = 1) were tested in a broth microdilution assay with meropenem and APC148, employing caMHB of various brands. One K. pneumoniae strain carrying only a serine-{beta}-lactamase (KPC-2) was included as control. Antimicrobial susceptibility testing (AST) by broth microdilution was performed according to the European Committee on Antimicrobial Susceptibility Testing (EUCAST). MICs of meropenem alone and in combination with inhibitors were tested in four cation-adjusted Mueller Hinton II (caMHB).The four caMHBs used were analysed by ICP-MS/MS and found to have highly vaiable zinc content (in the range 0.4 to 1.9 {micro}g/mL, corresponding to 5.6 to 29.4 {micro}M). At 16{micro}g/ml APC148 lowered the MIC in nearly all strains and in caMHBs from all manufacturers. At lower concentrations of APC148 (4 or 8 {micro}g/mL), this MIC reduction could however only be retained when the zinc concentration in the broth was low, indicating that higher concentration of inhibitor is needed during in vitro Mic testing when using caMHB from certain manufacturers. The present work clearly shows that not taking the zinc concentration of the caMHB used into consideration when estimating MIC performance of compounds functioning through interactions with zinc may be a considerable source of error, and specifically when investigating potential inhibitors of metallo-{beta}-lactamase (MBL) enzymes. The present work supports the call for standardising zinc content in caMHB to be used for this purpose, to ensure that MIC results for drug combinations involving the use of zinc-chelating compounds are consistent and reproducible across laboratories.

microbiology↗

bioRxiv2018

Biomarkers of collagen synthesis predict progression in the PROFILE idiopathic pulmonary fibrosis cohort

Idiopathic pulmonary fibrosis (IPF) is characterised by excessive extracellular matrix (ECM) deposition and remodelling. Measuring this activity provides an opportunity to develop tools capable of identifying individuals at-risk of progression. Longitudinal change in markers of ECM synthesis was assessed in 145 newly-diagnosed individuals with IPF. Serum levels of collagen synthesis neoepitopes, PRO-C3 and PRO-C6 (collagen type 3 and 6), were elevated in IPF compared with controls at baseline, and progressive disease versus stable disease during follow up, (PRO-C3 p<0.001; PRO-C6 p=0.029). Assessment of rate of change in neoepitope levels from baseline to 3 months (defined as slope to month 3: HIGH slope, slope > 0 vs. LOW slope, slope <=0) demonstrated no relationship with mortality for these markers (PRO-C3 (HR 1.62, p=0.080); PINP (HR 0.76, p=0.309); PRO-C6 (HR 1.14, p=0.628)). As previously reported, rising concentrations of collagen degradation markers C1M, C3M, C6M and CRPM were associated with an increased risk of overall mortality (HR=1.84, CI 1.03 - 3.27, p=0.038, HR=2.44, CI 1.39-4.31, p=0.002; HR= 2.19, CI 1.25-3.82, p=0.006; HR= 2.13 CI 1.21-3.75, p=0.009 respectively). Elevated levels of PRO-C3 and PRO-C6 associate with IPF disease progression. Collagen synthesis and degradation biomarkers have the potential to enhance clinical trials in IPF and may inform prognostic assessment and therapeutic decision making in the clinic.

clinical trials↗

bioRxiv2026

The Virtual Biotech: A Multi-Agent AI Framework for Therapeutic Discovery and Development

Drug discovery and development requires integrating diverse evidence across biological scales and data modalities. However, relevant data, tools, and expertise remain fragmented across teams and organizations, making integration difficult. To address these challenges, we introduce the Virtual Biotech, a coordinated team of AI agents that mirrors the structure of human therapeutic research organizations to support end-to-end computational discovery. The Virtual Biotech is led by a Chief Scientific Officer agent that receives scientific queries, delegates them to domain-specialized scientist agents, and integrates their outputs through data-driven reasoning. Scientist agents leverage complementary tools and knowledge sources spanning statistical genetics, functional genomics, pathways and interactions, chemoinformatics, disease biology, and clinical data. We showcase the Virtual Biotech across three translational applications. First, the agents autonomously annotated and analyzed outcomes from 55,984 clinical trials to identify genomic features of drug targets associated with trial success. More than 37,000 clinical-trialist agents curated structured trial outcomes and linked targets to multi-omic annotations, including cell-type-specific features derived by the agents from single-cell RNA-sequencing atlases. The agents discovered that drugs targeting cell-type-specific genes were 40% more likely to progress from Phase I to Phase II and 48% more likely to reach market (Phase IV), while exhibiting 32% lower adverse event rates. Second, the Virtual Biotech evaluated B7-H3 as a lung cancer target, integrating statistical genetics, single-cell, spatial, and clinicogenomic evidence to propose an antibody-drug conjugate strategy while identifying key liabilities and differentiation opportunities. Third, the platform analyzed a terminated ulcerative colitis trial targeting OSMR{beta} to infer potential failure mechanisms and proposed biomarker-guided enrollment strategies to address precision-medicine gaps. Together, these results illustrate how the Virtual Biotech can enable more transparent, efficient, and comprehensive multi-scale therapeutic analyses, helping to accelerate early-stage drug discovery workflows while keeping human scientists in the loop.

genomics↗

bioRxiv2026

Large-Scale Assessment of Animal-to-Human Drug Translation Using Natural Language Processing

BackgroundLarge-scale estimates of animal-to-human drug translation and the study characteristics associated with successful translation remain limited. The expanding preclinical literature also challenges manual evidence synthesis. We developed a natural language processing (NLP) pipeline to structure and link preclinical and clinical evidence at scale. MethodsIn this retrospective meta-research study, we analysed more than 500,000 neuroscience-related animal drug studies from PubMed and linked them to clinical trial and regulatory approval data. NLP methods extracted drug, disease, and experimental design characteristics from abstracts and full texts. Translation was defined as progression to completed phase III/IV trials or regulatory approval. Logistic regression assessed associations between preclinical study characteristics and successful translation. FindingsAmong 291,624 drug entities identified in animal studies, 6{middle dot}7% entered clinical development and 3{middle dot}1% reached phase III/IV trials or regulatory approval. At the drug-disease level, 4{middle dot}4% entered clinical development and 1{middle dot}9% achieved translation. Restricting analyses to successfully linked ontology entities increased estimates to 11{middle dot}3% and 4{middle dot}1%, respectively. Male-only animal studies predominated, whereas reporting of randomisation, blinding, and sample size calculations remained limited. Testing across multiple species and reporting blinding were associated with higher odds of successful translation. InterpretationOnly a minority of interventions tested in animals progress to advanced clinical development or regulatory approval. Greater species diversity and blinding were associated with improved translational success. NLP-based evidence synthesis may support scalable evaluation of translational research and identification of potentially modifiable research practices. FundingSwiss National Science Foundation, UZH Digital Entrepreneurship Fellowship, Universities Federation for Animal Welfare. Research in contextO_ST_ABSEvidence before this studyC_ST_ABSWe searched the literature for studies quantifying large-scale animal-to-human translation and factors associated with successful translation. Existing work was mainly limited to specific diseases, interventions, or manually curated datasets, and large-scale linkage of animal and clinical evidence remained limited. Added value of this studyWe developed a natural language processing pipeline linking more than 500,000 animal studies to clinical trial and regulatory approval data. The study provides large-scale estimates of translation and identifies experimental characteristics associated with successful translation. Implications of all the available evidenceThe findings suggest that only a minority of interventions tested in animals progress to advanced clinical development or regulatory approval. Greater species diversity and reporting of blinding were associated with improved translation. Automated evidence synthesis may support more systematic evaluation of translational research practices.

bioinformatics↗

bioRxiv2015

Measuring the Contribution of Genomic Predictors to Improving Estimator Precision in Randomized trials

The use of genomic data in the clinic has not been as widespread as was envisioned when sequencing and genomic analysis became common techniques. An underlying difficulty is the direct assessment of how much additional information genomic data are providing beyond standard clinical measurements. This is hard to quantify in the clinical setting where laboratory tests based on genomic signatures are fairly new and there are not sufficient data collected to determine how valuable these tests have been in practice. Here we focus on the potential precision gain from using the popular MammaPrint genomic signature in a covariate-adjusted, randomized clinical trial. We describe how adjustment of an estimator for the average treatment effect using baseline measurements can improve precision. This precision gain can be translated directly into sample size reduction and corresponding cost savings. We conduct a simulation study using genomic and clinical data gathered for breast cancer patients and find that adjusting for clinical factors alone provides a gain in precision of 5-6%, adjusting for genomic factors alone provides a similar gain (5%), and combining the two yields a 2-3% additional gain over only adjusting for clinical covariates.

Bioinformatics↗

bioRxiv2018

Effects of Attentional Bias Modification on Residual Symptoms in depression. A Randomized Controlled Trial.

BackgroundFollowing treatment, many depressed patients have significant residual symptoms. However, large randomised controlled trials (RCT) in this population are lacking. When Attention bias modification training (ABM) leads to more positive emotional biases, associated changes in clinical symptoms have been reported. A broader and more transparent picture of the true advantage of ABM based on larger and more stringent clinical trials have been requested.\n\nAimsTo evaluate the early effect of two weeks ABM training on blinded clinician-rated and self-reported residual symptoms, and whether changes towards more positive attentional biases (AB) would be associated with symptom reduction.\n\nMethodA total of 321 patients with a history of depression were included in a preregistered randomized controlled double-blinded trial. Patients were randomised to an emotional ABM paradigm over fourteen days or a closely matched control condition. Symptoms based on the Hamilton Rating Scale for Depression (HRSD) and Beck Depression Inventory II (BDI-II) were obtained at baseline and after ABM training.\n\nResultsABM training led to significantly greater decrease in clinician-rated symptoms of depression as compared to the control condition. No differences between ABM and placebo were found for self-reported symptoms. ABM induced a change of AB towards relatively more positive stimuli associated with greater symptom reduction.\n\nConclusionThe current study demonstrates that ABM produces early changes in both AB and blinded clinician-rated depressive symptoms. ABM may have practical potential in the treatment of residual depression.\n\nClinicalTrials.gov ID: NCT02658682

clinical trials↗

bioRxiv2018

Prescription Opioid Analgesic Use and Mortality in Systemic Lupus Erythematosus

ObjectivesThis research investigated the prevalence of opioid analgesic use in patients with systemic lupus erythematosus (SLE).\n\nMethodsThis 5-year prospective cohort study of 275 SLE patients focused on prescription opioid use and 5-year outcome. Associations were determined with univariable regression analysis and then multivariable models were created to determine independent effects on dependent variables\n\nResultsPrescription opioid use was common in SLE with 24% using opioid analgesics chronically and 76% not using opioids. Opioid users had a higher rate of tobacco use (p<0.01), cocaine use (p<0.002), mean pain scores (p<0.001), disease activity (SLEDAI-2K) (p<0.001), disease damage (SLICC/ACRDI) (p<0.001), non-adherence to medical therapy (p<0.01), and total deaths at 5 years (opioids: 48.0%, no opioids 19.0%, p<0.001). Logistic regression analysis predicting death revealed opioid use (hazard ratio 2.6, p<0.001) and SLEDAI-2K (1.1, p<0.001) respectively; and opioid use (hazard ratio 2.5, p<0.002), SLEDAI-2K (hazard ratio 1.1, p<0.001), and non-adherence (hazard ratio 1.6, p=0.11), respectively. Multivariable Cox Model analysis estimating probability of death with covariates: opioid use (hazard ratio 2.6, p<0.001) and SLEDAI-2K (hazard ratio 1.1, p<0.001); opioid use (hazard ratios 3.0, p<0.001), and cocaine use (hazard ratio 3.2, p<0.001). The Kaplan-Meir survival analysis revealed a significantly higher probability of death for SLE patients using opioid analgesics.\n\nConclusionsPrescription opioid analgesic use is common in SLE and is associated with markedly increased mortality. Preferably, non-opioid approaches to treat chronic pain should be used in SLE patients.\n\nClinical trial registration numberThis was not a clinical trial.\n\nKEY MESSAGES1. Chronic opioid analgesic use is common in SLE (24%).\n\n2. Opioid use is associated with greater disease severity, tobacco use, non-adherence, and increased mortality.\n\n3. Opioids should be used cautiously in SLE; alternative non-opioid management of pain is recommended.\n\nACKNOWLEDGMENTS AND FUNDING INFORMATIONThis work was supported by US National Institutes of Health research grants to Dr. Sibbitt (R01 NS035708) and to the Clinical and Translational Research Center (UL1TR001449).

epidemiology↗

bioRxiv2021

Single-cell immune profiling with TCR clonotype barcoding identifies biomarker signatures that predict response to immune checkpoint blockade

The identification of predictive biomarkers for patient treatment response is urgently needed to increase the probability of success of existing and novel experimental therapies. Single-cell profiling has provided novel biological insights into drug responses in the tumor microenvironment, but its potential for biomarker discovery has not been fully explored for therapeutic purposes. We describe a novel approach to discover predictive response biomarkers from single-cell data from a small patient cohort using the T cell receptor sequence intrinsic to each T cell to match clonotypes between pre- and post-treatment tumor samples. As a result, we have identified a predictive gene expression signature for immune checkpoint blockade and validated its predictive performance using data from three larger clinical studies. Our results demonstrated that applying clonotyping with single-cell genomic profiling is a promising novel approach for biomarker identification that does not require data collected from large patient cohorts. This could increase success rates, reduce clinical trial size, and significantly impact future clinical developments of immunomodulatory therapeutics.

immunology↗