Consensus gene modules strategy identifies candidate blood-based biomarkers for primary Sjögren's disease.

Primary Sjögren disease (pSD) is an autoimmune disease characterized by lymphoid infiltration of exocrine glands leading to dryness of the mucosal surfaces and by the production of autoantibodies. The pathophysiology of pSD remains elusive and no treatment with demonstrated efficacy is available yet. To better understand the biology underlying pSD heterogeneity, we aimed at identifying Consensus gene Modules (CMs) that summarize the high-dimensional transcriptomic data of whole blood samples in pSD patients. We performed unsupervised gene classification on four data sets and identified thirteen CMs. We annotated and interpreted each of these CMs as corresponding to cell type abundances or biological functions by using gene set enrichment analyses and transcriptomic profiles of sorted blood cell subsets. Correlation with independently measured cell type abundances by flow cytometry confirmed these annotations. We used these CMs to reconcile previously proposed patient stratifications of pSD. Importantly, we showed that the expression of modules representing lymphocytes and erythrocytes before treatment initiation is associated with response to hydroxychloroquine and leflunomide combination therapy in a clinical trial. These consensus modules will help the identification and translation of blood-based predictive biomarkers for the treatment of pSD.

Radiotherapy-activated NBTXR3 nanoparticles promote ferroptosis through induction of lysosomal membrane permeabilization.

PURPOSE: Radiotherapy-activated NBTXR3 (NBTXR3 + RT) has demonstrated superior efficacy in cancer cell destruction and tumor growth control, compared to radiotherapy (RT), in preclinical and clinical settings. Previous studies highlighted the immunomodulatory properties of NBTXR3 + RT, such as modification of tumor cell immunogenicity/adjuvanticity, producing an effective local tumor control and abscopal effect, related to an enhanced antitumor immune response. Furthermore, NBTXR3 + RT has shown potential in restoring anti-PD1 efficacy in a refractory tumor model. However, the early events leading to these results, such as NBTXR3 endocytosis, intracellular trafficking and primary biological responses induced by NBTXR3 + RT remain poorly understood. METHODS: We analyzed by transmission electron microscopy endocytosis and intracellular localization of NBTXR3 nanoparticles after endocytosis in various cell lines, in vitro and in vivo. A kinetic of NBTXR3 endocytosis and its impact on lysosomes was conducted using LysoTracker staining, and a RNAseq analysis was performed. We investigated the ability of NBTXR3 + RT to induce lysosomal membrane permeabilization (LMP) and ferroptosis by analyzing lipid peroxidation. Additionally, we evaluated the recapture by cancer cells of NBTXR3 released from dead cells. RESULTS: NBTXR3 nanoparticles were rapidly internalized by cells mainly through macropinocytosis and in a less extend by clathrin-dependent endocytosis. NBTXR3-containing endosomes were then fused with lysosomes. The day following NBTXR3 addition, we measured a significant increase in LysoTracker lysosome labeling intensity, in vitro as in vivo. Following RT, a significant lysosomal membrane permeabilization (LMP) was measured exclusively in cells treated with NBTXR3 + RT, while RT had no effect. The day post-irradiation, a significant increase in lipid peroxidation, a biomarker of ferroptosis, was measured with NBTXR3 + RT compared to RT. Moreover, we demonstrated that NBTXR3 nanoparticles released from dead cells can be recaptured by cancer cells. CONCLUSIONS: Our findings provide novel insights into the early and specific biological effects induced by NBTXR3 + RT, especially LMP, not induced by RT in our models. The subsequent significant increase in lipid peroxidation partially explains the enhanced cancer cell killing capacity of NBTXR3 + RT compared to RT, potentially by promoting ferroptosis. This study improves our understanding of the cellular mechanisms underlying NBTXR3 + RT and highlights its potential as an agnostic therapeutic strategy for solid cancers treatment.

Unraveling the relationships between alpha- and beta-adrenergic modulation and the risk of heart failure.

Background: The effects of α and ß adrenergic receptor modulation on the risk of developing heart failure (HF) remains uncertain due to a lack of randomized controlled trials. This study aimed to estimate the effects of α and ß adrenergic receptors modulation on the risk of HF and to provide proof of principle for genetic target validation studies in HF. Methods: Genetic variants within the cis regions encoding the adrenergic receptors α1A, α2B, ß1, and ß2 associated with blood pressure in a 757,601-participant genome-wide association study (GWAS) were selected as instruments to perform a drug target Mendelian randomization study. Effects of these variants on HF risk were derived from the HERMES GWAS (542,362 controls; 40,805 HF cases). Results: Lower α1A or ß1 activity was associated with reduced HF risk: odds ratio (OR) 0.83 (95% CI 0.74-0.93, P = 0.001) and 0.95 (95% CI 0.93-0.97, P = 8 × 10-6). Conversely, lower α2B activity was associated with increased HF risk: OR 1.09 (95% CI 1.05-1.12, P = 3 × 10-7). No evidence of an effect of lower ß2 activity on HF risk was found: OR 0.99 (95% CI 0.92-1.07, P = 0.95). Complementary analyses showed that these effects were consistent with those on left ventricular dimensions and acted independently of any potential effect on coronary artery disease. Conclusions: This study provides genetic evidence that α1A or ß1 receptor inhibition will likely decrease HF risk, while lower α2B activity may increase this risk. Genetic variant analysis can assist with drug development for HF prevention.

Virtual patients, digital twins and causal disease models: Paving the ground for in silico clinical trials.

Computational models are being explored to simulate in silico the efficacy and safety of drug candidates and medical devices. Disease models that are based on patients' profiling data are being produced to represent interactomes of genes or proteins and to infer causality in the pathophysiology, which makes it possible to mimic the impact of drugs on relevant targets. Virtual patients designed from medical records as well as digital twins are generated to simulate specific organs and to predict treatment efficacy at the individual patient level. As the acceptance of digital evidence by regulators grows, predictive artificial intelligence (AI)-based models will support the design of confirmatory trials in humans and will accelerate the development of efficient drugs and medical devices.

Combinational Drug Repurposing from Genetic Networks Applied to Alzheimer's Disease.

BACKGROUND: Human diseases are multi-factorial biological phenomena resulting from perturbations of numerous functional networks. The complex nature of human diseases explains frequently observed marginal or transitory efficacy of mono-therapeutic interventions. For this reason, combination therapy is being increasingly evaluated as a biologically plausible strategy for reversing disease state, fostering the development of dedicated methodological and experimental approaches. In parallel, genome-wide association studies (GWAS) provide a prominent opportunity for disclosing human-specific therapeutic targets and rational drug repurposing. OBJECTIVE: In this context, our objective was to elaborate an integrated computational platform to accelerate discovery and experimental validation of synergistic combinations of repurposed drugs for treatment of common human diseases. METHODS: The proposed approach combines adapted statistical analysis of GWAS data, pathway-based functional annotation of genetic findings using gene set enrichment technique, computational reconstruction of signaling networks enriched in disease-associated genes, selection of candidate repurposed drugs and proof-of-concept combinational experimental screening. RESULTS: It enables robust identification of signaling pathways enriched in disease susceptibility loci. Therapeutic targeting of the disease-associated signaling networks provides a reliable way for rational drug repurposing and rapid development of synergistic drug combinations for common human diseases. CONCLUSION: Here we demonstrate the feasibility and efficacy of the proposed approach with an experiment application to Alzheimer's disease.

Industrializing AI-powered drug discovery: lessons learned from the Patrimony computing platform.

INTRODUCTION: As a mid-size international pharmaceutical company, we initiated 4 years ago the launch of a dedicated high-throughput computing platform supporting drug discovery. The platform named 'Patrimony' was built up on the initial predicate to capitalize on our proprietary data while leveraging public data sources in order to foster a Computational Precision Medicine approach with the power of artificial intelligence. AREAS COVERED: Specifically, Patrimony is designed to identify novel therapeutic target candidates. With several successful use cases in immuno-inflammatory diseases, and current ongoing extension to applications to oncology and neurology, we document how this industrial computational platform has had a transformational impact on our R&D, making it more competitive, as well time and cost effective through a model-based educated selection of therapeutic targets and drug candidates. EXPERT OPINION: We report our achievements, but also our challenges in implementing data access and governance processes, building up hardware and user interfaces, and acculturing scientists to use predictive models to inform decisions.

Model-based computational precision medicine to develop combination therapies for autoimmune diseases.

INTRODUCTION: The complex pathophysiology of autoimmune diseases (AIDs) is being progressively deciphered, providing evidence for a multiplicity of pro-inflammatory pathways underlying heterogeneous clinical phenotypes and disease evolution. AREAS COVERED: Treatment strategies involving drug combinations are emerging as a preferred option to achieve remission in a vast majority of patients affected by systemic AIDs. The design of appropriate drug combinations can benefit from AID modeling following a comprehensive multi-omics molecular profiling of patients combined with Artificial Intelligence (AI)-powered computational analyses. Such disease models support patient stratification in homogeneous subgroups, shed light on dysregulated pro-inflammatory pathways and yield hypotheses regarding potential therapeutic targets and candidate biomarkers to stratify and monitor patients during treatment. AID models inform the rational design of combination therapies interfering with independent pro-inflammatory pathways related to either one of five prominent immune compartments contributing to the pathophysiology of AIDs, i.e. pro-inflammatory signals originating from tissues, innate immune mechanisms, T lymphocyte activation, autoantibodies and B cell activation, as well as soluble mediators involved in immune cross-talk. EXPERT OPINION: The optimal management of AIDs in the future will rely upon rationally designed combination therapies, as a modality of a model-based Computational Precision Medicine taking into account the patients' biological and clinical specificities.

Artificial intelligence-enhanced drug design and development: Toward a computational precision medicine.

Artificial Intelligence (AI) relies upon a convergence of technologies with further synergies with life science technologies to capture the value of massive multi-modal data in the form of predictive models supporting decision-making. AI and machine learning (ML) enhance drug design and development by improving our understanding of disease heterogeneity, identifying dysregulated molecular pathways and therapeutic targets, designing and optimizing drug candidates, as well as evaluating in silico clinical efficacy. By providing an unprecedented level of knowledge on both patient specificities and drug candidate properties, AI is fostering the emergence of a computational precision medicine allowing the design of therapies or preventive measures tailored to the singularities of individual patients in terms of their physiology, disease features, and exposure to environmental risks.

Network-based repurposing identifies anti-alarmins as drug candidates to control severe lung inflammation in COVID-19.

While establishing worldwide collective immunity with anti SARS-CoV-2 vaccines, COVID-19 remains a major health issue with dramatic ensuing economic consequences. In the transition, repurposing existing drugs remains the fastest cost-effective approach to alleviate the burden on health services, most particularly by reducing the incidence of the acute respiratory distress syndrome associated with severe COVID-19. We undertook a computational repurposing approach to identify candidate therapeutic drugs to control progression towards severe airways inflammation during COVID-19. Molecular profiling data were obtained from public sources regarding SARS-CoV-2 infected epithelial or endothelial cells, immune dysregulations associated with severe COVID-19 and lung inflammation induced by other respiratory viruses. From these data, we generated a protein-protein interactome modeling the evolution of lung inflammation during COVID-19 from inception to an established cytokine release syndrome. This predictive model assembling severe COVID-19-related proteins supports a role for known contributors to the cytokine storm such as IL1ß, IL6, TNFα, JAK2, but also less prominent actors such as IL17, IL23 and C5a. Importantly our analysis points out to alarmins such as TSLP, IL33, members of the S100 family and their receptors (ST2, RAGE) as targets of major therapeutic interest. By evaluating the network-based distances between severe COVID-19-related proteins and known drug targets, network computing identified drugs which could be repurposed to prevent or slow down progression towards severe airways inflammation. This analysis confirmed the interest of dexamethasone, JAK2 inhibitors, estrogens and further identified various drugs either available or in development interacting with the aforementioned targets. We most particularly recommend considering various inhibitors of alarmins or their receptors, currently receiving little attention in this indication, as candidate treatments for severe COVID-19.

A new molecular classification to drive precision treatment strategies in primary Sjögren's syndrome.

There is currently no approved treatment for primary Sjögren's syndrome, a disease that primarily affects adult women. The difficulty in developing effective therapies is -in part- because of the heterogeneity in the clinical manifestation and pathophysiology of the disease. Finding common molecular signatures among patient subgroups could improve our understanding of disease etiology, and facilitate the development of targeted therapeutics. Here, we report, in a cross-sectional cohort, a molecular classification scheme for Sjögren's syndrome patients based on the multi-omic profiling of whole blood samples from a European cohort of over 300 patients, and a similar number of age and gender-matched healthy volunteers. Using transcriptomic, genomic, epigenetic, cytokine expression and flow cytometry data, combined with clinical parameters, we identify four groups of patients with distinct patterns of immune dysregulation. The biomarkers we identify can be used by machine learning classifiers to sort future patients into subgroups, allowing the re-evaluation of response to treatments in clinical trials.

A simple inclusion criteria combination increases the rate of cartilage loss in patients with knee osteoarthritis.

Objectives: Selection of patients with KL radiographic grade 2 and 3 is widely used in clinical trials, but this approach could have some limitations. The purpose of this study performed on OsteoArthritis Initiative (OAI) data is to assess whether adding OARSI-JSN to KL grading could select a population with increased rate of cartilage loss. Indeed, KL is not compartment-specific and not uniformly graded amongst expert readers. OARSI-JSN is another established, compartment-specific grading scale that specifically captures the joint space narrowing from radiographs. Design: 1019 knee radiographs data from the progression cohort of the OAI public database were used. Cartilage loss measured with magnetic resonance imaging was evaluated using change over 1 year from baseline in cartilage thickness in the central Medial Tibio-Femoral Compartment (cMTFC) in the KL2-3 and KL2-3+JSN1-2 populations. Results: The mean cMTFC cartilage loss over one year was -0.135 â€‹± â€‹0.29 â€‹mm (median â€‹= â€‹-0.095 â€‹mm) in the KL2-3 population and -0.176 â€‹± â€‹0.29 â€‹mm (median â€‹= â€‹-0.140 â€‹mm) in the KL2-3 +JSN1-2 population. Conclusions: OARSI-JSN appears to be an effective inclusion criterion to be considered in combination with the KL grade in future clinical trials testing the structural efficacy of DMOADs in a time window of 1-year as it contributes to identify knees in whom the disease progresses rapidly.

Association of a Schizophrenia-Risk Nonsynonymous Variant With Putamen Volume in Adolescents: A Voxelwise and Genome-Wide Association Study.

Importance: Deviation from normal adolescent brain development precedes manifestations of many major psychiatric symptoms. Such altered developmental trajectories in adolescents may be linked to genetic risk for psychopathology. Objective: To identify genetic variants associated with adolescent brain structure and explore psychopathologic relevance of such associations. Design, Setting, and Participants: Voxelwise genome-wide association study in a cohort of healthy adolescents aged 14 years and validation of the findings using 4 independent samples across the life span with allele-specific expression analysis of top hits. Group comparison of the identified gene-brain association among patients with schizophrenia, unaffected siblings, and healthy control individuals. This was a population-based, multicenter study combined with a clinical sample that included participants from the IMAGEN cohort, Saguenay Youth Study, Three-City Study, and Lieber Institute for Brain Development sample cohorts and UK biobank who were assessed for both brain imaging and genetic sequencing. Clinical samples included patients with schizophrenia and unaffected siblings of patients from the Lieber Institute for Brain Development study. Data were analyzed between October 2015 and April 2018. Main Outcomes and Measures: Gray matter volume was assessed by neuroimaging and genetic variants were genotyped by Illumina BeadChip. Results: The discovery sample included 1721 adolescents (873 girls [50.7%]), with a mean (SD) age of 14.44 (0.41) years. The replication samples consisted of 8690 healthy adults (4497 women [51.8%]) from 4 independent studies across the life span. A nonsynonymous genetic variant (minor T allele of rs13107325 in SLC39A8, a gene implicated in schizophrenia) was associated with greater gray matter volume of the putamen (variance explained of 4.21% in the left hemisphere; 8.66; 95% CI, 6.59-10.81; P = 5.35 × 10-18; and 4.44% in the right hemisphere; t = 8.90; 95% CI, 6.75-11.19; P = 6.80 × 10-19) and also with a lower gene expression of SLC39A8 specifically in the putamen (t127 = -3.87; P = 1.70 × 10-4). The identified association was validated in samples across the life span but was significantly weakened in both patients with schizophrenia (z = -3.05; P = .002; n = 157) and unaffected siblings (z = -2.08; P = .04; n = 149). Conclusions and Relevance: Our results show that a missense mutation in gene SLC39A8 is associated with larger gray matter volume in the putamen and that this association is significantly weakened in schizophrenia. These results may suggest a role for aberrant ion transport in the etiology of psychosis and provide a target for preemptive developmental interventions aimed at restoring the functional effect of this mutation.

A Rasch Analysis of the Charcot-Marie-Tooth Neuropathy Score (CMTNS) in a Cohort of Charcot-Marie-Tooth Type 1A Patients.

The Charcot-Marie-Tooth Neuropathy Score (CMTNS) was developed as a main efficacy endpoint for application in clinical trials of Charcot-Marie-Tooth disease type 1A (CMT1A). However, the sensitivity of the CMTNS for measuring disease severity and progression in CMT1A patients has been questioned. Here, we applied a Rasch analysis in a French cohort of patients to evaluate the psychometrical properties of the CMTNS. Overall, our analysis supports the validity of the CMTNS for application to CMT1A patients though with some limitations such as certain items of the CMTNS being more suitable for moderate to severe forms of the disease, and some items being disordered. We suggest that additional items and/or categories be considered to better assess mild-to-moderate patients.

Combination of acamprosate and baclofen as a promising therapeutic approach for Parkinson's disease.

Parkinson's disease (PD) is a progressive neurodegenerative disorder characterised by the loss of dopaminergic nigrostriatal neurons but which involves the loss of additional neurotransmitter pathways. Mono- or polytherapeutic interventions in PD patients have declining efficacy long-term and no influence on disease progression. The systematic analysis of available genetic and functional data as well as the substantial overlap between Alzheimer's disease (AD) and PD features led us to repurpose and explore the effectiveness of a combination therapy (ABC) with two drugs - acamprosate and baclofen - that was already effective in AD animal models, for the treatment of PD. We showed in vitro that ABC strongly and synergistically protected neuronal cells from oxidative stress in the oxygen and glucose deprivation model, as well as dopaminergic neurons from cell death in the 6-hydroxydopamine (6-OHDA) rat model. Furthermore, we showed that ABC normalised altered motor symptoms in vivo in 6-OHDA-treated rats, acting by protecting dopaminergic cell bodies and their striatal terminals. Interestingly, ABC also restored a normal behaviour pattern in lesioned rats suggesting a symptomatic effect, and did not negatively interact with L-dopa. Our results demonstrate the potential value of combining repurposed drugs as a promising new strategy to treat this debilitating disease.

A Multi-Marker Genetic Association Test Based on the Rasch Model Applied to Alzheimer's Disease.

Results from Genome-Wide Association Studies (GWAS) have shown that the genetic basis of complex traits often include many genetic variants with small to moderate effects whose identification remains a challenging problem. In this context multi-marker analysis at the gene and pathway level can complement traditional point-wise approaches that treat the genetic markers individually. In this paper we propose a novel statistical approach for multi-marker analysis based on the Rasch model. The method summarizes the categorical genotypes of SNPs by a generalized logistic function into a genetic score that can be used for association analysis. Through different sets of simulations, the false-positive rate and power of the proposed approach are compared to a set of existing methods, and shows good performances. The application of the Rasch model on Alzheimer's Disease (AD) ADNI GWAS dataset also allows a coherent interpretation of the results. Our analysis supports the idea that APOE is a major susceptibility gene for AD. In the top genes selected by proposed method, several could be functionally linked to AD. In particular, a pathway analysis of these genes also highlights the metabolism of cholesterol, that is known to play a key role in AD pathogenesis. Interestingly, many of these top genes can be integrated in a hypothetic signalling network.

Analysis of drug combinations: current methodological landscape.

Combination therapies exploit the chances for better efficacy, decreased toxicity, and reduced development of drug resistance and owing to these advantages, have become a standard for the treatment of several diseases and continue to represent a promising approach in indications of unmet medical need. In this context, studying the effects of a combination of drugs in order to provide evidence of a significant superiority compared to the single agents is of particular interest. Research in this field has resulted in a large number of papers and revealed several issues. Here, we propose an overview of the current methodological landscape concerning the study of combination effects. First, we aim to provide the minimal set of mathematical and pharmacological concepts necessary to understand the most commonly used approaches, divided into effect-based approaches and dose-effect-based approaches, and introduced in light of their respective practical advantages and limitations. Then, we discuss six main common methodological issues that scientists have to face at each step of the development of new combination therapies. In particular, in the absence of a reference methodology suitable for all biomedical situations, the analysis of drug combinations should benefit from a collective, appropriate, and rigorous application of the concepts and methods reviewed here.

A meta-analysis of randomized double-blind clinical trials in CMT1A to assess the change from baseline in CMTNS and ONLS scales after one year of treatment.

CMT1A is the most common inherited peripheral neuropathy. There is currently no approved treatment. We performed a meta-analysis including four randomized, double-blind, Placebo-controlled clinical trials to assess the disease progression after one year under Placebo, Ascorbic Acid (AA) or PXT3003, a combination of three repurposed drugs. We observed a weak deterioration in patients under Placebo, well below the reported natural disease progression. Patients treated with AA were stable after one year but not significantly different from Placebo. Patients undergoing PXT3003 treatment showed an improvement in CMTNS and ONLS, statistically significant versus Placebo and potentially precursory of a meaningful change in the disease course.

Combining two repurposed drugs as a promising approach for Alzheimer's disease therapy.

Alzheimer disease (AD) represents a major medical problem where mono-therapeutic interventions demonstrated only a limited efficacy so far. We explored the possibility of developing a combinational therapy that might prevent the degradation of neuronal and endothelial structures in this disease. We argued that the distorted balance between excitatory (glutamate) and inhibitory (GABA/glycine) systems constitutes a therapeutic target for such intervention. We found that a combination of two approved drugs - acamprosate and baclofen - synergistically protected neurons and endothelial structures in vitro against amyloid-beta (Aß) oligomers. The neuroprotective effects of these drugs were mediated by modulation of targets in GABA/glycinergic and glutamatergic pathways. In vivo, the combination alleviated cognitive deficits in the acute Aß25-35 peptide injection model and in the mouse mutant APP transgenic model. Several patterns altered in AD were also synergistically normalised. Our results open up the possibility for a promising therapeutic approach for AD by combining repurposed drugs.