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bioRxiv2022

Optimizing tuberculosis treatment efficacy: comparing the standard regimen with Moxifloxacin-containing regimens

Tuberculosis (TB) continues to be one of the deadliest infectious diseases in the world, causing ~1.5 million deaths every year. The World Health Organization initiated an End TB Strategy that aims to reduce TB-related deaths in 2035 by 95%. Recent research goals have focused on discovering more effective and more patient-friendly antibiotic drug regimens to increase patient compliance and decrease emergence of resistant TB. Moxifloxacin is one promising antibiotic that may improve the current standard regimen by shortening treatment time. Clinical trials and in vivo mouse studies suggest that regimens containing moxifloxacin have better bactericidal activity. However, testing every possible combination regimen with moxifloxacin either in vivo or clinically is not feasible due to experimental and clinical limitations. To identify better regimens more systematically, we simulated pharmacokinetics/pharmacodynamics of various regimens (with and without moxifloxacin) to evaluate efficacies, and then compared our predictions to both clinical trials and nonhuman primate studies performed herein. We used GranSim, our well-established hybrid agent-based model that simulates granuloma formation and antibiotic treatment, for this task. In addition, we established a multiple-objective optimization pipeline using GranSim to discover optimized regimens based on treatment objectives of interest, i.e., minimizing total drug dosage and lowering time needed to sterilize granulomas. Our approach can efficiently test many regimens and successfully identify optimal regimens to inform pre-clinical studies or clinical trials and ultimately accelerate the TB regimen discovery process. Author summaryTuberculosis (TB) is a top global health concern and treatment for TB requires multiple antibiotics taken for long periods of time, which is challenging for TB patients. Therefore, identifying regimens that are more effective and more patient-friendly than the standard treatment is urgently needed. It is also known that non-compliance leads to the development of drug resistant TB. In this work, we pair computational and experimental models to predict new regimens for the treatment of TB that optimize how fast bacteria are cleared using minimal dosage. We apply novel approaches to this goal and validate our predictions using a non-human primate model. Our findings suggest that systems pharmacological modeling should be employed as a method to narrow the design space for drug regimens for tuberculosis and other diseases as well.

pharmacology and toxicology↗

bioRxiv2023

Biological and structural analysis of new potent Integrase-LEDGF allosteric HIV-1 inhibitors

LEDGF/p75 (LEDGF) main cellular cofactor of HIV-1 integrase (IN), acts as tethering factor to target integration of HIV in actively transcribed genes. Recently a class of IN inhibitors based on inhibition of LEDGF-IN interaction has been developed. We describe here a new series of IN-LEDGF allosteric inhibitors (INLAIs), with potent anti-HIV-1 activity in the one-digit nanomolar range. These compounds inhibited IN-LEDGF interaction while enhancing IN-IN aberrant multimerization by allosteric mechanism. Compounds of this series were fully active on HIV-1 mutants resistant to IN strand transfer inhibitors (INSTIs) and other class of anti-HIV drugs, confirming that they belong to a new class of antiretrovirals. These compounds displayed most potent antiretroviral activity at post-integration due to aberrant IN polymerization responsible of infectivity defect of viral particles produced. INLAI-resistant mutants were selected by dose escalation method and the most detrimental mutation found was T174I. The impact of these resistance mutations was analyzed by fold shift EC50 with regard to wild type virus and the replication capacity of mutated viruses were determined. Crystal structure of BDM-2, the lead compound of this series in complex with IN catalytic core domain (CCD) was elucidated. No antagonism was observed between BDM-2 and a panel of 16 antiretroviral drugs from different classes. In conclusion, the overall virologic profile of BDM-2 warrants the recently completed single ascending dose phase I trial (ClinicalTrials.gov Id: NCT03634085) and supports further clinical investigation for potential use in combination with other antiretroviral drugs. Author summaryIntegrase-LEDGF allosteric inhibitors are a new class of antiretrovirals recently developed that target the interaction of HIV-1 Integrase with its cellular cofactor LEDGF/p75 required for HIV-1 integration in actively transcribed genes. The great interest of developing such new class of antiretroviral is to add to the anti-HIV drug arsenal compounds that are fully active on resistant viruses to all other classes of drugs currently used in clinic. However, none of these inhibitors are currently in clinical use or in late clinical trials. Here we describe a series of highly potent Integrase allosteric inhibitors that can be considered as precursors of a new class of antiretroviral drugs, with fully conserved activity on viruses resistant to currently used anti-HIV drugs, and a lead compound that has no antagonism with a large panel of present anti-HIV drugs and that was successfully validated in a phase I clinical trial.

microbiology↗

bioRxiv2019

The Geometric Sparse Matrix Completion Model for Predicting Drug Side effects

Pair-input associations for drug-side effects are obtained through expensive placebo-controlled experiments in human clinical trials. An important challenge in computational pharmacology is to predict missing associations given a few entries in the drug-side effect matrix, as these predictions can be used to direct further clinical trials. Here we introduce the Geometric Sparse Matrix Completion (GSMC) model for predicting drug side effects. Our high-rank matrix completion model learns non-negative sparse matrices of coefficients for drugs and side effects by imposing smoothness priors that exploit a set of pharmacological side information graphs, including information about drug chemical structures, drug interactions, molecular targets, and disease indications. Our learning algorithm is based on the diagonally rescaled gradient descend principle of non-negative matrix factorization. We prove that it converges to a globally optimal solution with a first-order rate of convergence. Experiments on large-scale side effect data from human clinical trials show that our method achieves better prediction performance than six state-of-the-art methods for side effect prediction while offering biological interpretability and favouring explainable predictions.

bioinformatics↗

bioRxiv2022

Identification and prediction of Parkinson's disease subtypes and progression using machine learning in two cohorts.

BackgroundThe clinical manifestations of Parkinsons disease (PD) are characterized by heterogeneity in age at onset, disease duration, rate of progression, and the constellation of motor versus non-motor features. There is an unmet need for the characterization of distinct disease subtypes as well as improved, individualized predictions of the disease course. The emergence of machine learning to detect hidden patterns in complex, multi-dimensional datasets provides unparalleled opportunities to address this critical need. Methods and FindingsWe used unsupervised and supervised machine learning methods on comprehensive, longitudinal clinical data from the Parkinsons Disease Progression Marker Initiative (PPMI) (n = 294 cases) to identify patient subtypes and to predict disease progression. The resulting models were validated in an independent, clinically well-characterized cohort from the Parkinsons Disease Biomarker Program (PDBP) (n = 263 cases). Our analysis distinguished three distinct disease subtypes with highly predictable progression rates, corresponding to slow, moderate, and fast disease progression. We achieved highly accurate projections of disease progression five years after initial diagnosis with an average area under the curve (AUC) of 0.92 (95% CI: 0.95 {+/-} 0.01 for the slower progressing group (PDvec1), 0.87 {+/-} 0.03 for moderate progressors, and 0.95 {+/-} 0.02 for the fast progressing group (PDvec3). We identified serum neurofilament light (Nfl) as a significant indicator of fast disease progression among other key biomarkers of interest. We replicated these findings in an independent validation cohort, released the analytical code, and developed models in an open science manner. ConclusionsOur data-driven study provides insights to deconstruct PD heterogeneity. This approach could have immediate implications for clinical trials by improving the detection of significant clinical outcomes that might have been masked by cohort heterogeneity. We anticipate that machine learning models will improve patient counseling, clinical trial design, allocation of healthcare resources, and ultimately individualized patient care.

bioinformatics↗

bioRxiv2024

Development and IND-enabling studies of a novel Cas9 genome-edited autologous CD34+ cell therapy to induce fetal hemoglobin for sickle cell disease

Sickle cell disease (SCD) is a common severe blood disorder, caused by one major point mutation in the HBB gene. Current pharmacotherapies are only partially effective and potentially curative allogeneic hematopoietic stem cell transplantation (HSCT) is associated with immune toxicities. Genome editing of autologous patient hematopoietic stem cells (HSCs) to reactivate fetal hemoglobin (HbF) in erythroid progeny offers a potentially curative approach to treat SCD and circumvents some problems associated with allogeneic HSCT. Although the FDA has released guidelines for evaluating genome editing risks, it remains unclear how to best to assess the preclinical safety and efficacy of genome-edited cellular drug products to prepare for a clinical trial. Here we describe rigorous pre-clinical characterization and optimization of a therapeutic {gamma}-globin gene promoter editing strategy that supported an investigational new drug (IND) application cleared by the FDA. We compared targets in the {gamma}-globin promoter and BCL11A erythroid-specific enhancer, identified a lead candidate that potently induces HbF, and tested our approach in mobilized CD34+ HSPCs from normal donors and individuals with SCD. We observed efficient editing, induction of HbF to levels predicted to be therapeutic, and reduction of sickling in red blood cells derived from edited HSPCs. With single-cell western and RNA-seq analyses, we defined the heterogeneity and specificity of HbF induction and HBG1/HBG2 transcription. With CHANGE-seq for sensitive and unbiased genome-wide off-target discovery followed by multiplexed targeted sequencing, we did not detect off-target activity in edited HSPCs. Our study provides a blueprint for translating new discoveries on ex vivo genome editing of HSCs towards clinical trials for treating SCD and other blood disorders.

genomics↗

bioRxiv2025

MRI-based classifier to identify close-to-onset cases in C9orf72 genetic frontotemporal dementia

Predicting symptom onset in genetic frontotemporal dementia (FTD) is crucial for advancing targeted interventions and clinical trial design. Brain changes begin years before clinical symptoms emerge, making neuroimaging a strong candidate for onset prediction. However, FTD is highly heterogeneous, encompassing diverse molecular pathologies, affected brain networks, and symptom trajectories. This variability limits the predictive power of any single imaging biomarker and underscores the need for an integrative, multimodal approach to improve prediction accuracy and generalizability. We used machine learning to integrate diverse neuroimaging features, identifying a robust signature for risk stratification. We analyzed T1-weighted and T2-weighted MRI scans from 71 symptomatic C9orf72 carriers, 90 presymptomatic carriers, and 69 healthy controls from the GENFI cohort. We used FreeSurfer to measure cortical thickness and subcortical volumes, and BISON to quantify white matter hyperintensities (WMH). We applied Principal Component Analysis for dimensionality reduction and trained a random forest classifier to distinguish symptomatic carriers from controls. The model was subsequently applied to the presymptomatic cohort to identify individuals whose brain patterns resembled those of symptomatic cases, under the hypothesis that greater similarity indicated a higher risk of conversion. We validated the model with neuropsychological data and a two-year longitudinal follow-up. The classifier distinguished symptomatic C9orf72 carriers from controls with 87.0% accuracy. When applied to presymptomatic carriers, the model identified 21.1% of the cohort as having brain features comparable to those of symptomatic cases. This "high-risk group" showed significant neuropsychological weaknesses in executive function, language and social cognition compared to the non high-risk group. The model accurately predicted clinical conversion within a two-year period with 84.5% accuracy, a 70% sensitivity and a 93.3% negative predictive value. Our findings demonstrate the utility of a machine learning approach using multi-modal MRI to identify presymptomatic C9orf72 carriers at high risk of disease onset within the next two years. By capturing subtle neuroanatomical patterns associated with disease processes, this approach offers a promising method for stratifying genetic FTD carriers prior to symptom onset. Such predictive models could optimize patient selection in future clinical trials.

neuroscience↗

bioRxiv2022

Pharmacological PP2A reactivation overcomes multikinase inhibitor tolerance across brain tumor cell models

BackgroundGlioblastoma is characterized by hyperactivation of kinase signaling pathways. Regardless, most glioblastoma clinical trials targeting kinase signaling have failed. We hypothesized that overcoming the glioblastoma kinase inhibitor tolerance requires efficient shut-down of phosphorylation-dependent signaling rewiring by simultaneous inhibition of multiple critical kinases combined with reactivation of Protein Phosphatase 2A (PP2A). MethodsLive-cell imaging and colony growth assays were used to determine long-term impact of therapy effects on ten brain tumor cell models. Immunoblotting, MS-phosphoproteomics, and Seahorse metabolic assay were used for analysis of therapy-induced signaling rewiring. BH3 profiling was used to understand the mitochondrial apoptosis mechanisms. Medulloblastoma models were used to expand the importance to other brain cancer. Intracranial xenografts were used to validate the in vivo therapeutic impact of the triplet therapy. ResultsCollectively all tested ten glioblastoma and medulloblastoma cell models were effectively eradicated by the newly discovered triplet therapy combining inhibition of AKT and PDK1-4 kinases with pharmacological PP2A reactivation. Mechanistically, the brain tumor cell selective lethality of the triplet therapy could be explained by its combinatorial effects on therapy-induced signaling rewiring, OXPHOS, and apoptosis priming. The brain-penetrant triplet combination had a significant in vivo efficacy in intracranial glioblastoma and medulloblastoma models. ConclusionThe results confirm highly heterogenous responses of brain cancer cells to mono - and doublet combination therapies targeting phosphorylation-dependent signaling. However, the brain cancer cells cannot escape the triplet therapy targeting of AKT, PDK1-4, and PP2A. The results encourage evaluation of brain tumor PP2A status for design of future kinase inhibitor combination trials. Key PointsO_LIDevelopment of triplet kinase-phosphatase targeting therapy strategy for overcoming therapy tolerance across brain tumor models. C_LIO_LIIdentification of interplay between therapy-induced signaling rewiring, OXPHOS, and BH3 protein-mediated apoptosis priming as a cause for kinase inhibitor tolerance in brain cancers. C_LIO_LIValidation of the results in intracranial in vivo models with orally bioavailable and brain penetrant triplet therapy combination. C_LI Importance of the StudyBased on current genetic knowledge, glioblastoma should be particularly suitable target for kinase inhibitor therapies, However, in glioblastoma alone over 180 clinical trials with kinase inhibitors have failed. In this manuscript, we recapitulate this clinical observation by demonstrating broad tolerance of brain cancer cell models to kinase inhibitors even when combined with reactivation of PP2A. However, we discover that the therapy-induced signaling rewiring, and therapy tolerance, can be overcome by triplet targeting of AKT, PDK1-4 and PP2A. We provide strong evidence for the translatability of the findings by orally dosed brain penetrant triplet therapy combination in intracranial brain cancer models. The results encourage biomarker profiling of brain tumors for their PP2A status for clinical trials with combination of AKT and PDK1-4 inhibitors. Further, the results indicate that rapidly developing PP2A reactivation therapies will constitute an attractive future therapy option for brain tumors when combined with multi-kinase inhibition.

cancer biology↗

bioRxiv2019

Effective buprenorphine use and tapering strategies: Endorsements and insights by people in recovery from opioid use disorder on a Reddit forum

Opioid use disorder (OUD) is a public health emergency in the United States. Over 47,000 overdose-related deaths in 2017 involved opioids. Medication-assisted treatment (MAT), in particular, buprenorphine and buprenorphine combination products such as Suboxone(R), is the most effective, evidence-based treatment for OUD. However, there are a limited number of conclusive scientific studies that provide guidance to medical professionals about strategies for using buprenorphine to achieve stable recovery. In this study, we used data-driven natural language processing methods to mine a total of 16,146 posts about buprenorphine from 1933 unique users on the anonymous social network Reddit. Analysis of a sample of these posts showed that 74% of the posts described users personal experiences and that the top three topics included advice on using Suboxone(R) (55.0%), Suboxone(R) dosage information (35.5%) and information about Suboxone(R) tapering (32.0%). Based on two models, one that incorporated upvoting by other members and one that did not, we found that Reddit users reported more successful recovery with longer tapering schedules, particularly from 2.0 mg to 0.0 mg (median: 93 days; mean: 95 days), as compared to shorter tapering schedules investigated in past clinical trials. Diarrhea, insomnia, restlessness, and fatigue were commonly reported adverse events. Physical exercise, clonidine, and Imodium(R) were frequently reported to help during the recovery process. Due to the difficulties of conducting longer-term clinical trials involving patients with OUD, clinicians should consider other information sources including peer discussions from the abundant, real-time information available on Reddit. Significance StatementOpioid use disorder (OUD) is a national crisis in the United States and buprenorphine is one of the most effective evidence-based treatments. However, few studies have explored successful strategies for using and tapering buprenorphine to achieve stable recovery, particularly due to the difficulties of conducting long-term studies involving patients with OUD. In this study, we show that discussions on the anonymous social network Reddit may be leveraged, via automatic text mining methods, to discover successful buprenorphine use and tapering strategies. We discovered that longer tapering schedules, compared to those investigated in past clinical trials, may lead to (self-reported) sustained recovery. Furthermore, Reddit posts also provide key information regarding buprenorphine withdrawal, cravings, adjunct medications for withdrawal symptoms and relapse prevention strategies.

pharmacology and toxicology↗

bioRxiv2026

Discovery of a hydrolysis operon in Lachnospiraceae provides a genetic basis to understanding castalagin metabolism by the human microbiome in a clinical study.

Gut microbes metabolize the ellagitannin castalagin to ellagic acid and urolithins, a process associated with health effects for the host. Despite the use of the castalagin-rich camu camu extracts in clinical trials, the genes and taxa responsible for castalagin hydrolysis remain elusive. We used multi-omics to discover a castalagin associated hydrolase (cah) operon sparsely distributed in Lachnospiraceae. Analysis of fecal samples from a camu camu supplementation clinical trial demonstrates functional redundancy with prevalence of cah and other ellagic acid metabolism genes in different taxa explaining the prevalence of metabolites. Employing genome mining strategies, we discovered and validated ellagitannin metabolism genes in uncharacterized and uncultured species. We further identified flavogallonic acid as a prevalent metabolite of castalagin in human fecal samples. Collectively, this work provides a genetic basis for castalagin metabolism to urolithins and flavogallonic acid end-metabolites and sets the stage to study the role of metabolism in clinical trials.

microbiology↗

bioRxiv2022

A Community Challenge to Predict Clinical Outcomes After Immune Checkpoint Blockade in Non-Small Cell Lung Cancer

PurposePredictive biomarkers of immune checkpoint inhibitors (ICIs) efficacy are currently lacking for non-small cell lung cancer (NSCLC). Here, we describe the results from the Anti-PD-1 Response Prediction DREAM Challenge, a crowdsourced initiative that enabled the assessment of predictive models by using data from two randomized controlled clinical trials (RCTs) of ICIs in first-line metastatic NSCLC. MethodsParticipants developed and trained models using public resources. These were evaluated with data from the CheckMate 026 trial (NCT02041533), according to the model-to-data paradigm to maintain patient confidentiality. The generalizability of the models with the best predictive performance was assessed using data from the CheckMate 227 trial (NCT02477826). Both trials were phase III RCTs with a chemotherapy control arm, which supported the differentiation between predictive and prognostic models. Isolated model containers were evaluated using a bespoke strategy that considered the challenges of handling transcriptome data from clinical trials. ResultsA total of 59 teams participated, with 417 models submitted. Multiple predictive models, as opposed to a prognostic model, were generated for predicting overall survival, progression-free survival, and progressive disease status with ICIs. Variables within the models submitted by participants included tumor mutational burden (TMB), programmed death ligand 1 (PD-L1) expression, and gene-expression-based signatures. The bestperforming models showed improved predictive power over reference variables, including TMB or PD-L1. ConclusionThis DREAM Challenge is the first successful attempt to use protected phase III clinical data for a crowdsourced effort towards generating predictive models for ICIs clinical outcomes and could serve as a blueprint for similar efforts in other tumor types and disease states, setting a benchmark for future studies aiming to identify biomarkers predictive of ICIs efficacy. Context summaryO_ST_ABSKey objectiveC_ST_ABSNot all patients with non-small cell lung cancer (NSCLC) eligible for immune checkpoint inhibitor (ICIs) respond to treatment, but accurate predictive biomarkers of ICIs clinical outcomes are currently lacking. This crowdsourced initiative enabled the robust assessment of predictive models using data from two randomized clinical trials of first-line ICI in metastatic NSCLC. Knowledge generatedModels submitted indicate that a combination of programmed death ligand 1 (PD-L1), tumor mutational burden (TMB), and immune gene signatures might be able to identify patients more likely to respond to ICIs. TMB and PD-L1 seemed important to predict progression-free survival and overall survival. Mechanisms including apoptosis, T-cell crosstalk, and adaptive immune resistance appeared essential to predict response. Relevance

cancer biology↗

bioRxiv2017

Large-scale phenome-wide association study of PCSK9 loss-of-function variants demonstrates protection against ischemic stroke

PCSK9 inhibitors are a potent new therapy for hypercholesterolemia and have been shown to decrease risk of coronary heart disease. Although short-term clinical trial results have not demonstrated major adverse effects, long-term data will not be available for some time. Genetic studies in large well-phenotyped biobanks offer a unique opportunity to predict drug effects and provide context for the evaluation of future clinical trial outcomes. We tested association of the PCSK9 loss-of-function variant rsll591147 (R46L) in a hypothesis-driven 11 phenotype set and a hypothesis-generating 278 phenotype set in 337,536 individuals of British ancestry in the United Kingdom Biobank (UKB), with independent discovery (n = 225K) and replication (n = 112K). In addition to the known association with lipid levels (OR 0.63) and coronary heart disease (OR 0.73), the T allele of rs11591147 showed a protective effect on ischemic stroke (OR 0.61, p = 0.002) but not hemorrhagic stroke in the hypothesis-driven screen. We did not observe an association with type 2 diabetes, cataracts, heart failure, atrial fibrillation, and cognitive dysfunction. In the phenome-wide screen, the variant was associated with a reduction in metabolic disorders, ischemic heart disease, coronary artery bypass graft operations, percutaneous coronary interventions and history of angina. A single variant analysis of UKB data using TreeWAS, a Bayesian analysis framework to study genetic associations leveraging phenotype correlations, also showed evidence of association with cerebral infarction and vascular occlusion. This result represents the first genetic evidence in a large cohort for the protective effect of PCSK9 inhibition on ischemic stroke, and corroborates exploratory evidence from clinical trials. PCSK9 inhibition was not associated with variables other than those related to low density lipoprotein cholesterol and atherosclerosis, suggesting that other effects are either small or absent.

genetics↗

bioRxiv2018

Computational Modeling of the Dynamic Biomarker Cascade in Alzheimer’s Disease

BackgroundAlzheimers disease (AD) is a major public health concern and there is an urgent need to better understand its complex biology and develop effective therapies. AD progression can be tracked in patients though validated imaging and spinal fluid biomarkers of pathology and neuronal loss. We still, however, lack a coherent quantitative model that explains how these biomarkers interact and evolve over time. Such a model could potentially help identify the major drivers of disease in individual patients and simulate response to therapy prior to entry in clinical trials. A current theory of AD biomarker progression, known as the dynamic biomarker cascade model, hypothesizes AD biomarkers evolve in a sequential, but temporally overlapping manner. A computational model incorporating assumptions about the underlying biology of this theory and its variations would be useful to test and refine its accuracy with longitudinal biomarker data from clinical trials.\n\nMethodsWe implemented a causal model to simulate time-dependent biomarker data under the descriptive assumptions of the dynamic biomarker cascade theory. We modeled pathologic biomarkers (beta-amyloid and tau), neuronal loss biomarkers and cognitive impairment as non-linear first order ordinary differential equations (ODEs) to include amyloid-dependent and non-dependent neurodegenerative cascades. We tested the feasibility of the model by adjusting its parameters to simulate three specific natural history scenarios in early-onset autosomal dominant AD and late-onset AD, and determine whether computed biomarker trajectories agreed with current assumptions of AD biomarker progression. We also simulated the effects of anti-amyloid therapy in late-onset AD.\n\nResultsThe computational model of early-onset AD demonstrated the initial appearance of amyloid, followed by biomarkers of tau and neurodegeneration, followed by onset of cognitive decline based on cognitive reserve, as predicted by prior literature. Similarly, the late-onset AD computational models demonstrated the first appearance of amyloid or non-amyloid-related tauopathy, depending on the magnitude of comorbid pathology, and also closely matched the biomarker cascades predicted by prior literature. Forward simulation of anti-amyloid therapy in symptomatic late-onset AD failed to demonstrate any slowing in progression of cognitive decline, consistent with prior failed clinical trials in symptomatic patients.\n\nConclusionWe have developed and computationally implemented a mathematical causal model of the dynamic biomarker cascade theory in AD. We demonstrate the feasibility of this model by simulating biomarker evolution and cognitive decline in early and late-onset natural history scenarios, as well as in a treatment scenario targeted at core AD pathology. Models resulting from this causal approach can be further developed and refined using patient data from longitudinal biomarker studies, and may in the future play a key role in personalizing approaches to treatment.

neuroscience↗

bioRxiv2021

The Prolyl-tRNA Synthetase Inhibitor Halofuginone Inhibits SARS-CoV-2 Infection

Summary ParagraphWe identify the prolyl-tRNA synthetase (PRS) inhibitor halofuginone1, a compound in clinical trials for anti-fibrotic and anti-inflammatory applications2, as a potent inhibitor of SARS-CoV-2 infection and replication. The interaction of SARS-CoV-2 spike protein with cell surface heparan sulfate (HS) promotes viral entry3. We find that halofuginone reduces HS biosynthesis, thereby reducing spike protein binding, SARS-CoV-2 pseudotyped virus, and authentic SARS-CoV-2 infection. Halofuginone also potently suppresses SARS-CoV-2 replication post-entry and is 1,000-fold more potent than Remdesivir4. Inhibition of HS biosynthesis and SARS-CoV-2 infection depends on specific inhibition of PRS, possibly due to translational suppression of proline-rich proteins. We find that pp1a and pp1ab polyproteins of SARS-CoV-2, as well as several HS proteoglycans, are proline-rich, which may make them particularly vulnerable to halofuginones translational suppression. Halofuginone is orally bioavailable, has been evaluated in a phase I clinical trial in humans and distributes to SARS-CoV-2 target organs, including the lung, making it a near-term clinical trial candidate for the treatment of COVID-19.

microbiology↗

bioRxiv2020

Cooperative efforts on developing vaccines and therapies for COVID-19

Health organizations have always sought partnership to join competencies in innovation, even with fierce competition in this sector. In this pandemic moment it is relevant to observe how organizations behave to seek quick and safe answers. The present research analyzes how the cooperation networks were set off considering the clinical trials on therapies and vaccines that were developed specifically to treat or prevent COVID-19. Social Network Analysis technique was used to build cooperation networks and apply metrics that characterize these connections. There was an evaluation of statistics of Strength of cooperation and Unilateral dependence of cooperation that identify the cooperation strength between two organizations, and the dependence of this relations. A total of 415 clinical trial were identified, of which 42% are in cooperation. From organizations that have partnership, firms are the first, followed by universities. We extracted the main categories that concentrate 74% of partnerships in the trials of antibody, and vaccine. Several organizations cooperate in multiple categories of trials, evidencing the efforts to focus on different strategies to treat the disease. We found high strength of cooperation and an assimetryc dependency between partners, which can be assigned to specialized models of partnership and it occurs in competitive enviroments like this pandemic moment. Cooperation were not limited to geographical proximity and the advent of Chinese players can represent a new change in the biotechnological development axis. Finally, the challenge of finding therapeutic or immunological solutions for COVID-19 demonstrates a clear composition of cooperation groups that complement their skills to manage organizational strategies to beat the pandemic. In this new paradigm, there can be partnerships not only in clinical trial but also in pre-competitive technologies development. This experience is expected to change the way of organizations define their R&D strategies and start to adopt more a collaborative innovation model.

scientific communication and education↗

bioRxiv2024

Emerging Drug Combinations for Targeting Tongue Neoplasms Associated Proteins/Genes: Employing Graph Neural Networks within the RAIN Protocol

BackgroundTongue Neoplasms is a common form of malignancy, with squamous cell carcinoma of the tongue being the most frequently diagnosed type due to regular mechanical stimulation. Its prevalence remains on the rise among neoplastic cancer cases. Finding effective combinations of drugs to target the genetic and protein elements contributing to the development of Managing Tongue Neoplasms poses a difficulty owing to the intricate and varied nature of the ailment. MethodIn this research, we introduce a novel approach using Deep Modularity Networks (DMoN) to identify potential synergistic drug combinations for the condition, following the RAIN protocol. This procedure comprises three primary phases: First, employing Graph Neural Network (GNN) to propose drug combinations for treating the ailment by extracting embedding vectors of drugs and proteins from an extensive knowledge graph containing various biomedical data types, such as drug-protein interactions, gene expression, and drug-target interactions. Second, utilizing natural language processing to gather pertinent articles from clinical trials involving the previously recommended drugs. Finally, conducting network meta-analysis to evaluate the comparative efficacy of these drug combinations. ResultWe utilized our approach on a dataset containing drugs and genes as nodes, connected by edges indicating their associated p-values. Our DMoN model identified Cisplatin, Bleomycin, and Fluorouracil as the optimal drug combination for targeting the human genes/proteins associated with this cancer. Subsequent scrutiny of clinical trials and literature confirmed the validity of our findings. Additionally, network meta-analysis substantiated the efficacy of these medications concerning the pertinent genes. ConclusionThrough the utilization of DMoN as part of the RAIN protocol, our method introduces a fresh and effective way to suggest notable drug combinations for addressing proteins/genes linked to Tongue Neoplasms. This approach holds promise in assisting healthcare practitioners and researchers in pinpointing the best treatments for patients, as well as uncovering the fundamental mechanisms of the disease. HighlightsO_LIA new method using Deep Modularity Networks and the RAIN protocol can find the best drug combinations for treating Tongue Neoplasms, a common and deadly form of cancer. C_LIO_LIThe method uses a Graph Neural Network to suggest drug pairings from a large knowledge graph of biomedical data, then searches for clinical trials and performs network meta-analysis to compare their effectiveness. C_LIO_LIThe method discovered that Cisplatin, Bleomycin, and Fluorouracil are suitable drugs for targeting the genes/proteins involved in this cancer, and confirmed this finding with literature review and statistical analysis. C_LIO_LIThe method offers a novel and powerful way to assist doctors and researchers in finding the optimal treatments for patients with Tongue Neoplasms, and to understand the underlying causes of the disease. C_LI O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=108 SRC="FIGDIR/small/598402v1_ufig1.gif" ALT="Figure 1"> View larger version (44K): org.highwire.dtl.DTLVardef@56bc74org.highwire.dtl.DTLVardef@6e9308org.highwire.dtl.DTLVardef@177144aorg.highwire.dtl.DTLVardef@d53c5e_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2022

Abnormal molecular signatures of inflammation, energy metabolism and vesicle biology in human Huntington disease peripheral tissues

BackgroundA major challenge in neurodegenerative diseases concerns identifying biological disease signatures that track with disease progression or respond to an intervention. Several clinical trials in Huntington disease (HD), an inherited, progressive neurodegenerative disease, are currently ongoing. Therefore, we examined whether peripheral tissues can serve as a source of readily accessible biological signatures at the RNA and protein level in HD patients. ResultsWe generated large, high-quality human datasets from skeletal muscle, skin and adipose tissue to probe molecular changes in human premanifest and early manifest HD patients - those most likely involved in clinical trials. In-depth single nucleotide polymorphism data across the HTT gene will facilitate the use of the generated primary- and iPSC cell lines in allele-specific targeting approaches. The analysis of the transcriptomics and proteomics data shows robust, stage-dependent dysregulation. Gene ontology analysis confirmed the involvement of inflammation and energy metabolism in peripheral HD pathogenesis. Furthermore, we observed changes in the homeostasis of extracellular vesicles, where we found consistent changes of genes and proteins involved in this process. ConclusionsOur omics data document the involvement of inflammation, energy metabolism and extracellular vesicle homeostasis. This demonstrates the potential to identify biological signatures from peripheral tissues in HD suitable as biomarkers in clinical trials. Together with the primary cell lines established from peripheral tissues and a large panel of iPSC lines that can serve as human models of HD, the generated data are a valuable and unique resource to advance the current understanding of molecular mechanisms driving HD pathogenesis.

neuroscience↗

bioRxiv2020

Research productivity and collaboration of the NIH-funded HIV Vaccine Trials Network: a bibliometric analysis

ObjectivesTo assess the scientific productivity and impact of the HIV Vaccine Trials Network (HVTN) over the last two decades and to examine how research collaboration has evolved over this time in the HIV vaccine field. DesignThis section does not apply since this is a bibliometric study. MethodsA systematic bibliometric analysis was conducted to identify all HIV vaccine and HVTN associated publications from 1999-2019. All publications were sourced from the NLM Pubmed database and funding information was obtained from the SPIRES and iSearch databases. Both the InCites and iCite databases were utilized for impact metrics. Finally, HVTN clinical trials were obtained from clinicaltrials.gov. Multiple field normalized citation metrics, such as the relative citation ratio (RCR) and number of publications in the top 1% and 10% within their respective field, were used to gauge scientific impact of publications. Network analyses were used to examine collaboration among the most prolific researchers in the HIV vaccine research. SettingThis section does not apply since this is a bibliometric study. ParticipantsThis section does not apply since this is a bibliometric study. InterventionThis section does not apply since this is a bibliometric study. Results79 clinical trials were funded by the HVTN from 1999 to 2019. These were carried out via a network of trial sites in 23 countries and 94 cities around the world. In total, 465 publications (89.5% original research articles, 7.3% reviews, and 3.2% other) acknowledged funding from the HVTN. Impact analyses using multiple field normalized metrics revealed that HVTN publications are highly cited with a mean RCR of 1.8. 10,481 HIV vaccine related publications were used to analyze collaboration in this field. Compared to the field as a whole, publications attributed to the HVTN had significantly more authors per publication (p-value < 0.001) and our network analysis found that HVTN-associated authors also had a higher degree (p-value < 0.01). ConclusionsBibliometric analysis of the last two decades of HIV vaccine research by the HVTN revealed that in addition to conducting a large number of clinical trials worldwide, the network produced high impact publications and was associated with increased collaboration among researchers. ARTICLE SUMMARYO_ST_ABSStrengths and limitations of this studyC_ST_ABSO_LITo the best of our knowledge, this is the only study that has provided a systematic bibliometric analysis of the HVTN since its inception. C_LIO_LIStudies like this can illustrate overall outcomes of large clinical network programs. C_LIO_LIAdvanced field normalized metrics were used to provide the most accurate measures of productivity, impact, and collaboration. C_LIO_LIIdentification of HVTN publications using funding acknowledgements can lead to an underestimate of the number research articles since not all research articles include grant funding information. C_LIO_LIVariations in a single authors name across publications can make name disambiguation difficult when performing network analyses. C_LI

microbiology↗

bioRxiv2025

Using gnotobiotic mice to decipher effects of gut microbiome repair in undernourished children on tuft and goblet cell function

Studies have implicated perturbations in the postnatal development of the gut microbiome as a contributing factor to childhood undernutrition. Compared to a standard ready-to-use supplementary food, a microbiome-directed complementary food (MDCF-2) designed to repair these perturbations produced superior improvements in ponderal and linear growth in clinical trials of Bangladeshi children with moderate acute malnutrition. Here, reverse translation experiments are performed where intact fecal microbiomes collected from trial participants prior to and at the end of treatment are introduced into female gnotobiotic mice just after delivery of their pups. Pups received diets designed to resemble those consumed by children in the trials to recreate "unrepaired" and "repaired" gut ecosystems. Analyses of the abundances of bacterial strains (metagenome-assembled genomes), their expressed genes and metabolic products, combined with assessments of ponderal growth and intestinal epithelial lineage transcriptomes (single-nucleus RNA-Seq with follow-up immunocytochemistry) disclosed effects of MDCF-2 associated microbiome repair that cannot be determined, in part because no treatment control arms cannot be ethically incorporated into these trials. Specifically, microbiome repair in these mice produced significant increases in ponderal growth, changes microbial gene expression consistent with a less virulent gut ecosystem and changes in expression of (i) components of gut epithelial cell junctions in the enterocytic and goblet cell lineages, (ii) pathways for synthesis and secretion of eicosanoid immune effectors in chemosensory tuft cells, and (iii) goblet cell pathways involved in glycosylation and secretion of mucin. Experiments of the type described can help formulate and test hypotheses about how microbiome repair affects host biology. SIGNIFICANCE STATEMENTUndernutrition is a global health problem. Recent clinical trials of a gut microbiome-directed complementary food (MDCF-2) designed to repair the perturbed gut microbiomes of undernourished Bangladesh children produced superior growth outcomes versus a standard nutritional supplement. Given ethical considerations and tissue sampling constraints associated with these types of studies, we colonized gnotobiotic mice postnatally with microbiome samples obtained from trial participants before and after treatment to model "unrepaired" and "repaired" gut ecosystems. Using a multi-omics approach, we uncover heretofore unappreciated changes in expressed chemosensory tuft cell, mucus-producing goblet cell and absorptive enterocytic functions and interactions accompanying microbiome repair. Extending microbiome clinical trials back to preclinical models ( reverse translation) provides mechanistic insights that can inform design/interpretation of future interventions.

microbiology↗