Expert Rev Clin Immunol
October 2024
Artificial intelligence and machine learning enable the construction of predictive models, which are currently used to assist in decision-making throughout the process of drug discovery and development. These computational models can be used to represent the heterogeneity of a disease, identify therapeutic targets, design and optimize drug candidates, and evaluate the efficacy of these drugs on virtual patients or digital twins. By combining detailed patient characteristics with the prediction of potential drug-candidate properties, artificial intelligence promotes the emergence of a "computational" precision medicine, allowing for more personalized treatments, better tailored to patient specificities with the aid of such predictive models.
View Article and Find Full Text PDFObjective: While the involvement of IL-7/IL-7R axis in pSS has been described in relation to T cells, little is known about the contribution of this pathway in relationship with other immune cells, and its implication in autoimmunity. Using high-content multiomics data, we aimed at characterizing IL-7R expressing cells and the involvement of IL-7/IL-7R pathway in pSS pathophysiology.
Methods: An IL-7 signature established using RNA-sequencing of human PBMCs incubated with IL-7 was applied to 304 pSS patients, and on RNA-Seq datasets from tissue biopsies.
High-throughput computational platforms are being established to accelerate drug discovery. Servier launched the Patrimony platform to harness computational sciences and artificial intelligence (AI) to integrate massive multimodal data from internal and external sources. Patrimony has enabled researchers to prioritize therapeutic targets based on a deep understanding of the pathophysiology of immuno-inflammatory diseases.
View Article and Find Full Text PDFTrends Pharmacol Sci
July 2023
Artificial intelligence (AI)-based predictive models are being used to foster a precision medicine approach to treat complex chronic diseases such as autoimmune and autoinflammatory disorders (AIIDs). In the past few years the first models of systemic lupus erythematosus (SLE), primary Sjögren syndrome (pSS), and rheumatoid arthritis (RA) have been produced by molecular profiling of patients using omic technologies and integrating the data with AI. These advances have confirmed a complex pathophysiology involving multiple proinflammatory pathways and also provide evidence for shared molecular dysregulation across different AIIDs.
View Article and Find Full Text PDFComputational 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.
View Article and Find Full Text PDFIntroduction: Auto-immune diseases are complex and heterogeneous. Various types of biomarkers can be used to support precision medicine approaches to autoimmune diseases, ensuring that the right patient receives the most appropriate therapy to improve treatment outcomes.
Areas Covered: We review the recent progress made in modeling several autoimmune diseases such as Systemic Lupus Erythematosus, primary Sjogren Syndrome, and Rheumatoid Arthritis following extensive molecular profiling of large cohorts of patients.
Osteoarthr Cartil Open
December 2021
Objective: Understanding the heterogeneity and pathophysiology of osteoarthritis (OA) is critical to support the development of tailored disease-modifying treatments. To this aim, transcriptomics tools are highly relevant to delineate dysregulated molecular pathways and identify new therapeutic targets.
Methods: We review the methodology and outcomes of transcriptomics studies conducted in OA, based on a comprehensive literature search of the PubMed and Google Scholar databases using the terms "osteoarthritis", "OA", "knee OA", "hip OA", "genes", "RNA-seq", "microarray", "transcriptomic" and "PCR" as key words.
Expert Opin Drug Discov
August 2022
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 ' 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, is designed to identify novel therapeutic target candidates.
Expert Rev Proteomics
January 2022
Introduction: Proteomics encompasses a wide and expanding range of methods to identify, characterize, and quantify thousands of proteins from a variety of biological samples, including blood samples, tumors, and tissues. Such methods are supportive of various forms of immunotherapy applied to chronic conditions such as allergies, autoimmune diseases, cancers, and infectious diseases.
Areas Covered: In support of immunotherapy, proteomics based on mass spectrometry has multiple specific applications related to (i) disease modeling and patient stratification, (ii) antigen/ autoantigen/neoantigen/ allergen identification, (iii) characterization of proteins and monoclonal antibodies used for immunotherapeutic or diagnostic purposes, (iv) identification of biomarkers and companion diagnostics and (v) monitoring by immunoproteomics of immune responses elicited in the course of the disease or following immunotherapy.
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.
Background: The identification of patients with knee osteoarthritis (OA) likely to progress rapidly in terms of structure is critical to facilitate the development of disease-modifying drugs.
Methods: Using 9280 knee magnetic resonance (MR) images (3268 patients) from the Osteoarthritis Initiative (OAI) database , we implemented a deep learning method to predict, from MR images and clinical variables including body mass index (BMI), further cartilage degradation measured by joint space narrowing at 12 months.
Results: Using COR IW TSE images, our classification model achieved a ROC AUC score of 65%.
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.
View Article and Find Full Text PDFWhile 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.
View Article and Find Full Text PDFInterferon (IFN)-α has emerged as a major therapeutic target for several autoimmune rheumatic diseases. In this review, we focus on clinical and preclinical advances in anti-IFN-α treatments in systemic lupus erythematosus (SLE), primary Sjögren syndrome (pSS), systemic sclerosis (SSc), and dermatomyositis (DM), for which a high medical need persists. Promising achievements were obtained following direct IFN-α neutralization, targeting its production through the cytosolic nucleic acid sensor pathways or by blocking its downstream effects through the type I IFN receptor.
View Article and Find Full Text PDFThere 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.
View Article and Find Full Text PDFIncreased interferon-α (IFN-α) production is a critical component in the pathophysiology of systemic lupus erythematosus (SLE) and other rheumatic autoimmune diseases. Herein, we report the characterization of S95021, a fully human IgG1 anti-IFN-α monoclonal antibody (mAb) as a novel therapeutic candidate for targeted patient populations. S95021 was expressed in CHOZN GS-/- cells, purified by chromatography and characterized by using electrophoresis, size exclusion chromatography and liquid chromatography-mass spectrometry.
View Article and Find Full Text PDFArtificial intelligence (AI) encompasses technologies recapitulating four dimensions of human intelligence, i.e. sensing, thinking, acting and learning.
View Article and Find Full Text PDFA Correction to this paper has been published: https://doi.org/10.1038/s41591-020-01186-5.
View Article and Find Full Text PDFAn amendment to this paper has been published and can be accessed via a link at the top of the paper.
View Article and Find Full Text PDFImproved understanding and management of COVID-19, a potentially life-threatening disease, could greatly reduce the threat posed by its etiologic agent, SARS-CoV-2. Toward this end, we have identified a core peripheral blood immune signature across 63 hospital-treated patients with COVID-19 who were otherwise highly heterogeneous. The signature includes discrete changes in B and myelomonocytic cell composition, profoundly altered T cell phenotypes, selective cytokine/chemokine upregulation and SARS-CoV-2-specific antibodies.
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