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bioRxiv2025

The caninized anti-canine PD-1 monoclonal antibody in canine oral malignant melanoma: Efficacy and exploratory biomarker analysis

BackgroundCanine oral malignant melanoma (OMM) is a highly aggressive tumor with few treatment options. Because of its biological similarity to human mucosal melanoma, canine OMM represents a valuable spontaneous model for translational immunotherapy studies. Anti-PD-1 antibody therapy has shown promise in canine OMM; however, predictive biomarkers for treatment response and survival have not been identified. MethodsWe conducted a multicenter, prospective, investigator-initiated clinical trial to evaluate the safety and efficacy of a caninized anti-canine PD-1 monoclonal antibody (ca-4F12-E6) in 150 dogs with advanced OMM. The dogs were administered 3 mg/kg ca-4F12-E6 intravenously every two weeks. Treatment response was assessed using cRECIST v1.0. Biomarker analyses included peripheral blood parameters, cytokine/chemokines, peripheral lymphocyte subsets, microsatellite instability (MSI), immunohistochemistry for immune cell and mismatch repair protein markers, and RNA sequencing of the tumor tissue. Associations with clinical outcomes were determined by logistic regression and Cox proportional hazard models. ResultsThe best overall response rate was 16.7%. Treatment-related adverse events occurred in 40.0% of the dogs, which were primarily grade 1-3. Increased baseline white blood cell, neutrophil count, and C-reactive protein (CRP) levels were significantly associated with poor response, shorter progression-free survival (PFS), and reduced overall survival (OS). MSI-high tumors were associated with significantly prolonged OS compared with MSI-low/microsatellite stable tumors. Transcriptome analysis revealed differentially expressed genes and enriched immune-related pathways in responders, though limited by sample size. Conclusionca-4F12-E6 exhibited durable antitumor activity and an acceptable safety profile in dogs with OMM. Baseline systemic inflammatory markers and MSI status may serve as predictive biomarkers for clinical outcomes. The results support the use of canine OMM as a comparative model for human immuno-oncology and biomarker discovery.

cancer biology↗

bioRxiv2020

Genetic Variants and Functional Pathways Associated with Resilience to Alzheimer's Disease

Approximately 30% of older adults exhibit the neuropathologic features of Alzheimers disease (AD) without signs of cognitive impairment. Yet, little is known about the genetic factors that allow these potentially resilient individuals to remain cognitively normal in the face of substantial neuropathology. We performed a large, genome-wide association study (GWAS) of two previously validated metrics of cognitive resilience quantified using a latent variable modeling approach and representing better-than-predicted cognitive performance for a given level of neuropathology. Data were harmonized across 5,108 participants from a clinical trial of AD and three longitudinal cohort studies of cognitive aging. All analyses were run across all participants and repeated restricting the sample to individuals with normal cognition to identify variants at the earliest stages of disease. As expected, all resilience metrics were genetically correlated with cognitive performance and education attainment traits (p-values<2.5x10-20), and we observed novel correlations with neuropsychiatric conditions (p-values<7.9x10-4). Notably, neither resilience metric was genetically correlated with clinical AD (p-values>0.42) nor associated with APOE (p-values>0.13). In single variant analyses, we observed a genome-wide significant locus among participants with normal cognition on chromosome 18 upstream of ATP8B1 (index SNP rs2571244, MAF=0.08, p=2.3x10-8). The top variant at this locus (rs2571244) was significantly associated with methylation in prefrontal cortex tissue at multiple CpG sites, including one just upstream of ATPB81 (cg19596477; p=2x10-13). Overall, this comprehensive genetic analysis of resilience implicates a putative role of vascular risk, metabolism, and mental health in protection from the cognitive consequences of neuropathology, while also providing evidence for a novel resilience gene along the bile acid metabolism pathway. Furthermore, the genetic architecture of resilience appears to be distinct from that of clinical AD, suggesting that a shift in focus to molecular contributors to resilience may identify novel pathways for therapeutic targets.

genetics↗

bioRxiv2021

Oral Hsp90 inhibitor, SNX-5422, attenuates SARS-CoV-2 replication and dampens inflammation in airway cells.

Currently available SARS-CoV-2 therapeutics are targeted towards moderately to severely ill patients and require intravenous infusions, with limited options for exposed or infected patients with no or mild symptoms. While vaccines have demonstrated protective efficacy, vaccine hesitancy and logistical distribution challenges will delay their ability to end the pandemic. Hence, there is a need for rapidly translatable, easy-to-administer-therapeutics, that can prevent SARS-CoV-2 disease progression, when administered in the early stages of infection. We demonstrate that an orally bioavailable Hsp90 inhibitor, SNX-5422, currently in clinical trials as an anti-cancer therapeutic, inhibits SARS-CoV-2 replication in vitro at a high selectivity index. SNX-5422 treatment of human primary airway epithelial cells dampened expression of inflammatory pathways associated with poor SARS-CoV-2 disease outcomes. Additionally, SNX-5422 interrupted expression of host factors that are crucial for SARS-CoV-2 replication machinery. Development of SNX-5422 as SARS-CoV-2-early-therapy will dampen disease severity, resulting in better clinical outcomes and reduced hospitalizations.

microbiology↗

bioRxiv2021

Variomes: a high recall search engine to support the curation of genomic variants

Precision oncology relies on the use of treatments targeting specific genetic variants. However, identifying clinically actionable variants as well as relevant information likely to be used to treat a patient with a given cancer is a labor-intensive task, which includes searching the literature for a large set of variants. The lack of universally adopted standard nomenclature for variants requires the development of variant-specific literature search engines. We develop a system to perform triage of publications relevant to support an evidence-based decision. Together with providing a ranked list of articles for a given variant, the system is also able to prioritize variants, as found in a Variant Calling Format, assuming that the clinical actionability of a genetic variant is correlated with the volume of literature published about the variant. Our system searches within three pre-annotated document collections: MEDLINE abstracts, PubMed Central full-text articles and ClinicalTrials.gov clinical trials. A variant synonym generator is used to increase the comprehensiveness of the set of retrieved documents. We then apply different strategies to rank the publications. We assess the search effectiveness of the system using different experimental settings. Experimental setting 1: The literature retrieval task is tuned and evaluated using the TREC Precision Medicine 2018 and 2019 benchmarks consisting respectively in 50 and 40 topics. Almost two thirds (62%) of the publications returned in the top-5 are relevant for clinical decision-support. Experimental setting 2: The evaluation of the variant prioritization task is based on a manually-created benchmark composed of eight patients for a total of 756 variants. For each patient, we used both their complete set of variants and tumor board reports. Our approach enabled identifying 81.8% of clinically actionable variants in the top-3. Experimental setting 3: A comparison of Variomes with LitVar, a well-known search engine for genetic variants is performed. Variomes was able to retrieve on average 90.8% of the content, while LitVar retrieved on average 58.6%. Out of the 9.2% articles, which are "missed" by Variomes, a per error analysis suggests that they are artefacts. To conclude, we are proposing here a competitive system to facilitate the curation of variants for personalized medicine.

bioinformatics↗

bioRxiv2024

A multiscale model of the action of a capsid assembly modulator for the treatment of chronic hepatitis B

Chronic hepatitis B virus (HBV) infection is strongly associated with increased risk of liver cancer and cirrhosis. While existing treatments effectively inhibit the HBV life cycle, viral rebound occurs rapidly following treatment interruption. Consequently, functional cure rates of chronic HBV infection remain low and there is increased interest in a novel treatment modality, capsid assembly modulators (CAMs). Here, we develop a multiscale mathematical model of CAM treatment in chronic HBV infection. By fitting the model to participant data from a phase I trial of the first-generation CAM vebicorvir, we estimate the drugs dose-dependent effectiveness and identify the physiological mechanisms that drive the observed biphasic decline in HBV DNA and RNA, and mechanistic differences between HBeAg-positive and negative infection. Finally, we demonstrate analytically and numerically that HBV RNA is more sensitive than HBV DNA to increases in CAM effectiveness. Author summaryCapsid assembly modulators (CAMs) are a novel class of anti-hepatitis B virus (HBV) treatments in clinical trials. These CAMs have a distinct mechanism of action from nucleos(t)ide analogues and thus represent an attractive option for the treatment of chronic HBV infection. We developed a multiscale model of the intracellular HBV lifecycle and extracellular dynamics using a time-since-infection structured partial differential equation. We fit the model to participant data from a recent phase I trial, performed a detailed parameter sensitivity analysis, identified key mechanisms driving viral response to first-generation CAM treatment, and demonstrated that HBV RNA is more sensitive than HBV DNA to changes in CAM efficacy, highlighting the potential role of HBV RNA as a biomarker for CAM effectiveness.

microbiology↗

bioRxiv2025

VEGFR2 blockade converts thermally ablative focused ultrasound into a potent driver of T cell-dependent anti-tumor immunity

Thermally ablative focused ultrasound (TFUS) can induce favorable immune signatures in solid tumors but rarely generates durable systemic immunity or enhances checkpoint inhibition. We tested whether combining TFUS with aVEGFR2, which normalizes tumor vasculature and remodels immune cell composition, triggers T cell-dependent immunity and synergizes with checkpoint inhibition. Subtotal TFUS ([~]40% volumetric ablation fraction, matching clinical trial results) was applied to EMT6 tumors in aVEGFR2-treated BALB/c mice. In EMT6 tumors, TFUS or aVEGFR2 alone had no effect on tumor growth, but their combination eradicated 50% of tumors and drove 83% rechallenge rejection, with CD4/CD8 depletion confirming that tumor eradication and rechallenge rejection were T cell-dependent. TFUS + aPD1 yielded modest benefit, whereas triple therapy (TFUS + aVEGFR2 + aPD1) cured 81% of mice and induced durable, T cell-mediated immunity. Thus, VEGFR2 blockade converts clinically relevant TFUS into a potent systemic immunotherapy and, with the addition of checkpoint inhibition, offers a rational approach to combining already-approved therapies for the treatment of solid tumors.

cancer biology↗

bioRxiv2021

Structural basis for the therapeutic advantage of dual and triple agonists at the human GIP, GLP-1 or GCG receptors

Glucose homeostasis, regulated by glucose-dependent insulinotropic polypeptide (GIP), glucagon-like peptide-1 (GLP-1) and glucagon (GCG) is critical to human health. Several multi-targeting agonists at GIPR, GLP-1R or GCGR, developed to maximize metabolic benefits with reduced side-effects, are in clinical trials to treat type 2 diabetes and obesity. To elucidate the molecular mechanisms by which tirzepatide, a GIPR/GLP-1R dualagonist, and peptide 20, a GIPR/GLP-1R/GCGR triagonist, manifest their superior efficacies over monoagonist such as semaglutide, we determined cryo-electron microscopy structures of tirzepatide-bound GIPR and GLP-1R as well as peptide 20-bound GIPR, GLP-1R and GCGR The structures reveal both common and unique features for the dual and triple agonism by illustrating key interactions of clinical relevance at the atomic level. Retention of glucagon function is required to achieve such an advantage over GLP-1 monotherapy. Our findings provide valuable insights into the structural basis of functional versatility and therapeutic supremacy of tirzepatide and peptide 20.

molecular biology↗

bioRxiv2025

Lack of synergy between AR targeted therapies and PARP inhibitors in homologous recombination-proficient prostate cancer

Recent clinical trials have explored the combination of androgen receptor (AR) pathway inhibitors and poly (ADP-ribose) polymerase (PARP) inhibitors as a potential treatment for castration-resistant prostate cancer. This combination treatment is based on the premise that AR directly regulates expression of DNA repair genes, leading to synergy between PARP and AR inhibition. Despite some promising preclinical evidence, this combination therapy has shown limited efficacy in patients with homologous recombination (HR)-proficient tumors. To investigate this discrepancy between preclinical and clinical results, we profiled the effects of PARP inhibition in prostate cancer models in the presence or absence of AR inhibition. Surprisingly, AR inhibition impaired response to PARP inhibitors in castration-sensitive cells and had no effect on response in castration-resistant cells. AR inhibition also did not regulate DNA repair in either the castration-resistant or castration-sensitive setting. Instead, we find that cell cycle progression is required for response to PARP inhibition in homologous-recombination proficient prostate cancer. STATEMENT OF SIGNIFICANCEAndrogen deprivation does not inhibit DNA repair and does not synergize with PARP inhibition in prostate cancer with intact homologous recombination repair.

cancer biology↗

bioRxiv2018

Better than maximum likelihood estimation of model-based and model-free learning style

Multiple decision making systems work together to shape the final choices in human behavior. Habitual and goal-directed systems are the two most important systems that are studied in the reinforcement learning (RL) literature by model-free and model-based learning methods. Human behavior resembles the weighted combination of these systems and such a combination is modeled by weighted summation of actions value from the model based and model free systems. Extraction of this weighted parameter, which is important for many applications and computational modeling, has been mostly based on the maximum likelihood or maximum a posteriori methods. We show these methods bring many challenges and their respective extracted values are less reliable especially in the proximity of extremes values. We propose that using a free format learning method (k-nearest neighbor) which uses more information besides the fitted values e.g. global information like stay probability instead of trial by trial information can ameliorate the estimation error. The proposed method is examined by simulation and results show the advantage of the proposed method. In addition, investigation of the human behavior data from previous researchers proved the proposed method to result in more statistically robust results in predicting other behavioral indices such as the number of gaze directions toward each target. In brief, the proposed method increases the reliability of the estimated parameters and enhances the applicability of reinforcement learning paradigms in clinical trials.

neuroscience↗

bioRxiv2026

Exact formula of the total quasi-steady state approximation in competitive target-mediated drug disposition

Competitive target-mediated drug disposition (competitive TMDD) arises when two drugs compete for the same target receptor. These dynamics can be characterized by the full competitive TMDD model; yet, its complexity motivated the use of a reduced model, which is invalid under high receptor concentrations. While this problem can be resolved by using the total quasi-steady state approximation (tQSSA), which remains valid for all receptor conditions, the exact formula of the tQSSA-based reduced model--competitive qTMDD--has remained unknown for 15 years, as it requires solving a cubic equation and the nontrivial task of identifying a biologically meaningful solution. Consequently, researchers have relied on numerical approximation methods, which impose substantial computational costs to maintain accuracy and thereby hinder their practical use in complex real-world applications. To address this problem, we derive--for the first time--a real-valued exact formula for competitive qTMDD by leveraging analytic properties of cubic equations and geometric characteristics of their roots in the complex plane. This exact formula improved computational speed by more than 11-fold compared to previous numerical approximation methods, thereby enabling Bayesian inference using competitive qTMDD, which had been impractical due to excessive computational time. When applied to real-world data from clinical trials, competitive qTMDD estimated pharmacological parameter estimates comparable to those from the full competitive TMDD model while requiring only 30-43% of the computation time. Importantly, this estimation using competitive qTMDD remained consistently accurate regardless of data sparsity, whereas the previous reduced model produced biased estimates under sparse sampling conditions. By ensuring precise biological interpretation of drug systems even in complex real-world scenarios, the exact formula of competitive qTMDD has the potential to significantly streamline the drug development and clinical testing process. Our exact formula also consists entirely of real-valued terms, allowing seamless integration into existing pharmacometrics software. Author SummaryCompetitive target-mediated drug disposition (competitive TMDD) occurs when two drugs compete for the same target receptor. Analyzing this interaction has faced a dilemma for 15 years: choosing between a full model that is accurate but computationally intensive, and a reduced model that is fast but often loses accuracy under real-world clinical conditions. Researchers tried to solve this dilemma by deriving an accurate reduced model; however, it was mathematically challenging. As a result, they had to rely on numerical approximations--which require substantial computational power to maintain the accuracy needed for clinical use, making them impractical in real-world scenarios. In this study, we derive a first-ever exact formula for the new reduced model that is accurate across all biological conditions. This formula computes more than 11 times faster than previous numerical methods, making advanced statistical analyses--such as Bayesian inference--feasible for the first time in this context. When applied to real-world clinical data for Anakinra and rhIL-7-hyFc, our method yielded parameter estimates as accurate as the full model but required significantly less computation time. This breakthrough provides a more accurate biological interpretation and better guidance for determining the right drug dose, potentially accelerating drug development and reducing associated costs.

pharmacology and toxicology↗

bioRxiv2026

Overcoming Topology Bias and Cold-Start Limita-tions in Drug Repurposing: A Clinical-Outcome-Aligned LLM Framework

Graph Neural Networks (GNNs) in drug repurposing suffer from two limitations: transductive failure in zero-shot (cold-start) scenarios and popularity bias that misidentifies high-degree nodes as effective drugs. We propose a framework that shifts the optimization objective from graph topology to clinical utility, integrating Knowledge Graph RAG (KG-RAG), Supervised Fine-Tuning (SFT), and Kahneman-Tversky Optimization (KTO) using Phase III clinical trial outcomes as rewards. We evaluated our approach on a rigorous 1:10 negative sampling benchmark derived from MiRAGE, covering Standard, Cold-Start, and Degree-Matched settings. In Cold-Start scenarios where topological signals are absent, traditional GNNs (including TxGNN) collapse (Top-10 Precision < 0.30), whereas DR-SFT model achieves 0.80, demonstrating robust semantic generalization for novel compounds. Crucially, in Degree-Matched tests dominated by "popular but ineffective" decoys, the DR-KTO acts as a clinical gatekeeper, achieving 0.90 Top-10 Precision--significantly outperforming DR-SFT (0.70) and GNNs (0.2-0.4) by effectively penalizing hard negatives. Beyond repurposing accuracy, the model achieves state-of-the-art performance on BioASQ, and increased ability in Chemprot. Orthogonal validation via DrugReAlign confirms physical plausibility, yielding significantly lower docking binding energies for KTO-recommended candidates. SPR experiments further corroborate these findings. By aligning LLM reasoning with clinical evidence, our framework successfully bridges the gap between semantic inference, topological structure, and clinical reality.

pharmacology and toxicology↗

bioRxiv2023

Novel psilocin prodrugs with altered pharmacological properties as candidate therapies for treatment-resistant anxiety disorders

The psychedelic compound psilocybin has shown therapeutic benefit in the treatment of numerous psychiatric diseases. A recent randomized clinical trial conducted at Johns Hopkins Bayview Medical Center demonstrated the efficacy of psilocybin-assisted therapy in the treatment of Major Depressive Disorder (MDD). Similarly, a phase IIb study evaluating psilocybin-assisted therapy for treatment-resistant depression (TRD) presented statistically meaningful and long-term reduction in depressive symptoms. Also, many studies have reported the successful treatment of severe anxiety after a single oral dose of psilocybin, especially in patients struggling with cancer-related distress (CRD). Despite these compelling clinical results, concerns regarding the duration of the psychedelic experience produced by psilocybin pose a significant barrier to its widespread therapeutic application. Psilocybin, derived from magic mushrooms is the naturally occurring prodrug of the neuroactive compound psilocin. When orally administered, exposure to the acidic gastrointestinal (GI) environment together with enzymatic processing by intestinal and hepatic alkaline phosphatase lead to the dephosphorylation of psilocybin producing elevated levels of systemic psilocin. These plasma levels are detectable up to 24 h and produce a psychoactive episode lasting as long as 6 h post-ingestion. In order to positively modify the kinetics of the acute psychedelic response, we have engineered a library of novel prodrug derivatives (NPDs) of psilocin, introducing a diversity of alternative metabolically cleavable moieties modified at the 4-carbon position of the core indole ring. This library consists of twenty-eight unique compounds represented by nine distinct prodrug classes. Each molecule was screened in vitro for metabolic stability using isolated human serum, and human cellular fractions derived from liver and intestinal tissues. This screen revealed fifteen prodrugs that produced measurable levels of psilocin in vitro, with ester and thiocarbonate-based prodrug derivatives significantly represented. These fifteen NPDs were further evaluated for pharmacokinetic (PK) profiles in mice, assessing plasma levels of both residual prodrug and resultant psilocin. PK results confirmed the efficiency of ester and thiocarbonate-based prodrug metabolism upon oral and intravenous administration, achieving levels reduced, albeit comparable to levels of psilocybin-derived psilocin. Of note, almost all NPDs tested maintained reduced overall exposure of psilocin relative to psilocybin, with no measurable levels detected at 24 h post-dose. Finally, all NPDs were screened for CNS bioavailability in healthy mice using the Head Twitch Response (HTR), a behavioural biomarker of 5-HT2A receptor stimulation and an established proxy for psychoactive potential. Interestingly, five NPDs produced peak HTR that approached or exceeded levels induced by an equivalent dose of psilocybin. Among these bioactive prodrugs, an ester-based and thiocarbonate-based molecule produced long-term anxiolytic benefit in chronically stressed mice evaluated in the marble burying psychiatric model. Overall, this screening campaign identified novel candidate prodrugs of psilocin with altered metabolic profiles and reduced pharmacological exposure, potentially attenuating the duration of the psychedelic response. These molecules still maintained the long-term psychiatric and physiological benefits characteristic of psilocybin therapy. Additionally, these modified parameters also offer the opportunity for altered routes of administration bypassing conventional oral dosing.

pharmacology and toxicology↗

bioRxiv2021

Massively Multiplexed Affinity Characterization of Therapeutic Antibodies Against SARS-CoV-2 Variants

Antibody therapies represent a valuable tool to reduce COVID-19 deaths and hospitalizations. Multiple antibody candidates have been granted emergency use authorization by the FDA and many more are in clinical trials. Most antibody therapies for COVID-19 are engineered to bind to the receptor-binding domain (RBD) of the SARS-CoV-2 Spike protein and disrupt its interaction with ACE2. Notably, several SARS-CoV-2 strains have accrued mutations throughout the RBD that improve ACE2 binding affinity, enhance viral transmission, and escape some existing antibody therapies. Here, we measure the binding affinity of 33 therapeutic antibodies against a large panel of SARS-CoV-2 variants and related strains of clinical significance to determine epitopic residues, determine which mutations result in loss of binding, and predict how future RBD variants may impact antibody efficacy. One-Sentence SummaryBy measuring protein binding in vitro, we identify which clinical antibodies retain binding to various mutant SARS-CoV-2 strains.

bioengineering↗

bioRxiv2026

TumorArchetypeR: A modular framework to derive signature-based tumor subtypes

MotivationThe tumor microenvironment (TME) dictates cancer progression and therapeutic response, yet translating TME subtypes into robust clinical biomarkers remains a significant challenge. Existing classification models typically rely on static gene signatures and cohort-dependent normalization, making them ill-suited for application to the small, unbalanced datasets common in early-phase clinical trials. To better guide drug development, methods are required that offer the flexibility to target specific biological contexts and bridge the gap between the discovery of tumor archetypes and their robust translation to individual patient samples. ResultsWe developed TumorArchetypeR, a modular R package that unifies unsupervised subtype discovery with the generation of rank-based, single-sample classifiers. By leveraging a systematic parameter grid search, the framework identifies stable, data-driven subtypes rather than relying on arbitrary defaults. Crucially, to ensure clinical translatability, the package includes a module to train a robust classifier using binary gene-pair rules, enabling prediction without cohort-level preprocessing. Applying TumorArchetypeR to colorectal cancer, we resolved the heterogeneity of fibrotic tumors, distinguishing an immunosuppressive "Immune-enriched/Fibrotic" state from an immune-excluded "Fibrotic/Myeloid" phenotype. Furthermore, we identified a distinct "Th/B-cell enriched" archetype associated with superior survival, a group largely obscured by existing pan-cancer models. With our rank-based classifier demonstrating robust performance on previously unseen samples, these findings highlight TumorArchetypeR as a scalable, end-to-end solution for refining patient stratification and optimizing precision oncology strategies. The TumorArchetypeR package and documentation are openly available on GitHub at https://github.com/lutgem/TumorArchetypeR.

cancer biology↗

bioRxiv2016

Toxicity Management in CAR T cell therapy for B-ALL: Mathematical modelling as a new avenue for improvement.

Advances in genetic engineering have made it possible to reprogram individual immune cells to express receptors that recognise markers on tumour cell surfaces. The process of re-engineering T cell lymphocytes to express Chimeric Antigen Receptors (CARs), and then re-infusing the CAR-modified T cells into patients to treat various cancers is referred to as CAR T cell therapy. This therapy is being explored in clinical trials - most prominently for B Cell Acute Lymphoblastic Leukaemia (B-ALL), a common B cell malignancy, for which CAR T cell therapy has led to remission in up to 90% of patients. Despite this extraordinary response rate, however, potentially fatal inflammatory side effects occur in up to 10% of patients who have positive responses. Further, approximately 50% of patients who initially respond to the therapy relapse. Significant improvement is thus necessary before the therapy can be made widely available for use in the clinic.\n\nTo inform future development, we develop a mathematical model to explore interactions between CAR T cells, inflammatory toxicity, and individual patients tumour burdens in silico. This paper outlines the underlying system of coupled ordinary differential equations designed based on well-known immunological principles and widely accepted views on the mechanism of toxicity development in CAR T cell therapy for B-ALL - and reports in silico outcomes in relationship to standard and recently conjectured predictors of toxicity in a heterogeneous, randomly generated patient population. Our initial results and analyses are consistent with and connect immunological mechanisms to the clinically observed, counterintuitive hypothesis that initial tumour burden is a stronger predictor of toxicity than is the dose of CAR T cells administered to patients.\n\nWe outline how the mechanism of action in CAR T cell therapy can give rise to such non-standard trends in toxicity development, and demonstrate the utility of mathematical modelling in understanding the relationship between predictors of toxicity, mechanism of action, and patient outcomes.

Cancer Biology↗

bioRxiv2020

Novel mTORC1 inhibitors kill Glioblastoma stem cells

Glioblastoma Multiforme (GBM) is an aggressive tumor of the brain, with an average post-diagnosis survival of 15 months. GBM stem cells (GBMSC) resist the standard-of-care therapy, temozolomide, and are considered a major contributor to tumor resistance. mTORC1 regulates cell proliferation and has been shown by others to have reduced activity in GBMSC. We recently identified a novel chemical series of human-safe piperazine-based brain-penetrant mTORC1-specific inhibitors. We assayed piperazine-mTOR binding strength by two biophysical measurements-- biolayer interferometry and field effect biosensing, and these confirmed each other and demonstrated a structure-activity relationship. Since mTORC1 is reduced in human GBMSC, and as mTORC1 inhibitors have been tested in previous GBM clinical trials, we tested the killing potency of the tightest-binding piperazines and observed these were potent GBMSC killers. GBMSCs are resistant to the standard-of-care temozolomide therapy--but temozolomide supplemented with tight-binding piperazine meclizine and flunarizine greatly enhanced GBMSC death over temozolomide alone. Lastly, we investigated IDH1-mutated GBMSC mutations that are known to affect mitochondrial and mTORC1 metabolism, the tight-binding Meclizine provoked ‘synthetic lethality’ in IDH1-mutant GBMSCs. These data tend to support a novel clinical strategy for GBM, i.e. the co-administration of meclizine or flunarizine as adjuvant therapy in the treatment of GBM, and IDH1-mutant GBM.Competing Interest StatementThe authors have declared no competing interest.View Full Text

pharmacology and toxicology↗

bioRxiv2023

Assessing vaccine-mediated protection in an ultra-low dose Mycobacterium tuberculosis murine model

Despite widespread immunization with Bacille-Calmette-Guerin (BCG), the only currently licensed tuberculosis (TB) vaccine, TB remains a leading cause of mortality globally. There are many TB vaccine candidates in the developmental pipeline, but the lack of a robust animal model to assess vaccine efficacy has hindered our ability to prioritize candidates for human clinical trials. Here we use a murine ultra-low dose (ULD) Mycobacterium tuberculosis (Mtb) challenge model to assess protection conferred by BCG vaccination. We show that BCG confers a reduction in lung bacterial burdens that is more durable than that observed after conventional dose challenge, curbs Mtb dissemination to the contralateral lung, and, in a small percentage of mice, prevents detectable infection. These findings are consistent with the ability of human BCG vaccination to mediate protection, particularly against disseminated disease, in specific human populations and clinical settings. Overall, our findings demonstrate that the ultra-low dose Mtb infection model can measure distinct parameters of immune protection that cannot be assessed in conventional dose murine infection models and could provide an improved platform for TB vaccine testing.

microbiology↗

bioRxiv2024

Persistent cognitive deficits in anti-LGI1 encephalitis are linked to a reorganization of structural brain networks

Structured AbstractO_ST_ABSImportanceC_ST_ABSDespite immunotherapy, most patients with anti-leucine-rich, glioma-inactivated 1 encephalitis (LGI1-E) develop long-term cognitive deficits that persist for years after peak illness. However, the structural brain changes that underlie these deficits remain poorly understood. ObjectiveTo study the relationship between cognitive outcomes and white matter (WM) networks in LGI1-E. DesignCross-sectional study. SettingGerman university center (Charite - Universitatsmedizin Berlin). Participants25 patients with LGI1-E (19/25 male [76%], mean age: 63 {+/-} 12 years) and 25 age- and sex-matched healthy controls (HC), recruited between January 2013 and April 2019. Main Outcomes and MeasuresClinical assessments including the modified Rankin Scale (mRS) and Clinical Assessment Scale in Autoimmune Encephalitis (CASE); comprehensive cognitive testing; WM tractography using diffusion-weighted MRI. ResultsAll patients had received first-line immunotherapy, and two-thirds underwent second-line immunotherapy. Patients showed a significant reduction in mRS scores from peak illness to post-acute follow-up (z = -3.8, p < 0.001, n = 20), with 85% presenting "good" functional outcomes (post-acute mRS [≤] 2), paralleled by a significant reduction in CASE scores (z = -3.5, p < 0.001, n = 20). Despite this overall improvement, however, cognitive symptoms were highly prevalent at peak illness (95% of patients affected) and strongly persisted into the post-acute disease stage (85% affected). Neuroimaging at post-acute follow-up (median: 12 months from onset) revealed that LGI1-E is characterized by (i) significantly reduced whole-brain structural connectivity (t = -2.16, p = 0.036, d = -0.61), (ii) a cortico-subcortical hypoconnectivity cluster that strongly affects the hippocampus but also severely impacts extra-limbic brain systems, (iii) systematic limbic and extra-limbic decreases in node degree -- a graph-theoretical measure of overall connectedness, and (iv) a "topological reorganization" of structural brain networks, marked by a bidirectional shift in the relative importance of individual brain regions in the network. Importantly, the extent of this network reorganization was significantly related to persistent cognitive deficits in the domains of verbal memory (r = -0.57, p = 0.007, n = 21), attention (r = -0.47, p = 0.030, n = 21), and executive functions (r = -0.60, p = 0.010, n = 17). Conclusion and RelevanceThis study characterizes LGI1-E as a network disease that affects both limbic and extra-limbic brain systems and shows that a reorganization of WM networks is linked to multi-domain cognitive deficits in the post-acute disease stage - despite immunotherapy and good overall recovery. These findings highlight the need for extended treatment strategies to improve long-term cognitive outcomes and propose a sensitive new neuroimaging marker to include in prospective clinical trials. Key PointsO_ST_ABSQuestionC_ST_ABSWhat structural brain changes underlie the persistent cognitive deficits observed in patients with anti-leucine-rich, glioma-inactivated 1 encephalitis (LGI1-E)? FindingsThis cross-sectional study shows that LGI1-E is characterized by a structural reorganization of white matter networks that affects both limbic and extra-limbic brain systems and correlates with persistent deficits in verbal memory, attention, and executive functions at post-acute follow-up - despite immunotherapy and good overall clinical recovery. MeaningThis study characterizes LGI1-E as a network disease -beyond focal damage to the limbic system- and shows that persistent cognitive deficits relate to immunotherapy-resistant changes in structural brain networks, highlighting the need for extended treatment strategies to improve long-term cognitive outcomes.

neuroscience↗