Artificial intelligence-driven drug development against autoimmune diseases

A comprehensive review of small molecule drugs approved by the FDA in 2023: Advances and prospects

In 2023, the U.S. Food and Drug Administration has approved 55 novel medications, consisting of 17 biologics license applications and 38 new molecular entities. Although the biologics license applications including antibody and enzyme replacement therapy set a historical record, the new molecular entities comprising small molecule drugs, diagnostic agent, RNA interference therapy and biomacromolecular peptide still account for over 50 % of the newly approved medications. The novel and privileged scaffolds derived from drugs, active molecules and natural products are consistently associated with the discovery of new mechanisms, the expansion of clinical indications and the reduction of side effects. Moreover, the structural modifications based on the promising scaffolds can provide the clinical candidates with the improved biological activities, bypass the patent protection and greatly shorten the period of new drug discovery. Therefore, conducting an appraisal of drug approval experience and related information will expedite the identification of more potent drug molecules. In this review, we comprehensively summarized the pertinent information encompassing the clinical application, mechanism, elegant design and development processes of 28 small molecule drugs, and expected to provide the promising structural basis and design inspiration for pharmaceutical chemists.

Revolutionary Point-of-Care Wearable Diagnostics for Early Disease Detection and Biomarker Discovery through Intelligent Technologies

Early-stage disease detection, particularly in Point-Of-Care (POC) wearable formats, assumes pivotal role in advancing healthcare services and precision-medicine. Public benefits of early detection extend beyond cost-effectively promoting healthcare outcomes, to also include reducing the risk of comorbid diseases. Technological advancements enabling POC biomarker recognition empower discovery of new markers for various health conditions. Integration of POC wearables for biomarker detection with intelligent frameworks represents ground-breaking innovations enabling automation of operations, conducting advanced large-scale data analysis, generating predictive models, and facilitating remote and guided clinical decision-making. These advancements substantially alleviate socioeconomic burdens, creating a paradigm shift in diagnostics, and revolutionizing medical assessments and technology development. This review explores critical topics and recent progress in development of 1) POC systems and wearable solutions for early disease detection and physiological monitoring, as well as 2) discussing current trends in adoption of smart technologies within clinical settings and in developing biological assays, and ultimately 3) exploring utilities of POC systems and smart platforms for biomarker discovery. Additionally, the review explores technology translation from research labs to broader applications. It also addresses associated risks, biases, and challenges of widespread Artificial Intelligence (AI) integration in diagnostics systems, while systematically outlining potential prospects, current challenges, and opportunities.

Machine learning techniques for prediction in pregnancy complicated by autoimmune rheumatic diseases: Applications and challenges

Autoimmune rheumatic diseases are chronic conditions affecting multiple systems and often occurring in young women of childbearing age. The diseases and the physiological characteristics of pregnancy significantly impact maternal-fetal health and pregnancy outcomes. Currently, the integration of big data with healthcare has led to the increasing popularity of using machine learning (ML) to mine clinical data for studying pregnancy complications. In this review, we introduce the basics of ML and the recent advances and trends of ML in different prediction applications for common pregnancy complications by autoimmune rheumatic diseases. Finally, the challenges and future for enhancing the accuracy, reliability, and clinical applicability of ML in prediction have been discussed. This review will provide insights into the utilization of ML in identifying and assisting clinical decision-making for pregnancy complications, while also establishing a foundation for exploring comprehensive management strategies for pregnancy and enhancing maternal and child health.

[The revolution of AI in drug development]

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. Based on such new capabilities, a mixed reality approach to the development of new drugs is being adopted by the pharmaceutical industry, which integrates the outputs of predictive virtual models with real-world empirical studies.

Synovial Tissue Insights into Heterogeneity of Rheumatoid Arthritis

PURPOSE OF REVIEW

Rheumatoid arthritis is one of the most common rheumatic and autoimmune diseases. While it can affect many different organ systems, RA primarily involves inflammation in the synovium, the tissue that lines joints. Patients with RA exhibit significant clinical heterogeneity in terms of presence or absence of autoantibodies, degree of permanent deformities, and most importantly, treatment response. These clinical characteristics point to heterogeneity in the cellular and molecular pathogenesis of RA, an area that several recent studies have begun to address.

RECENT FINDINGS

Single-cell RNA-sequencing initiatives and deeper focused studies have revealed several RA-associated cell populations in synovial tissues, including peripheral helper T cells, autoimmunity-associated B cells (ABCs), and NOTCH3+ sublining fibroblasts. Recent large transcriptional studies and translational clinical trials present frameworks to capture cellular and molecular heterogeneity in RA synovium. Technological developments, such as spatial transcriptomics and machine learning, promise to further elucidate the different types of RA synovitis and the biological mechanisms that characterize them, key elements of precision medicine to optimize patient care and outcomes in RA. This review recaps the findings of those recent studies and puts our current knowledge and future challenges into scientific and clinical perspective.

New investigational drugs to treat Sjogren's syndrome: lessons learnt from immunology

INTRODUCTION

Sjögren's syndrome is a heterogeneous autoimmune condition that impairs quality of life because of dryness, fatigue, pain, and systemic involvements. Current treatment largely depends on empirical evidence, with no effective therapy approved. Clinical trials on targeted drugs often fail to report efficacy due to common factors.

AREAS COVERED

This review summarizes the pathogenesis and what caused the failure of new investigational drugs in clinical trials, highlighting solutions for more effective investigations, with greater consistency between research outcomes, clinical use, and patient needs.

EXPERT OPINION

Unlinked pathobiology with symptoms resulted in misidentified targets and disappointing trials. Useful stratification tools are necessary for the heterogeneous SS patients. Composite endpoints or improvements in ESSDAI scores are needed, considering the high placebo response, and the unbalance between symptom burden and disease activity. Compared to classic biologics, targeted cell therapy will be a more promising field of investigation in the coming years.

Transforming drug discovery with a high-throughput AI-powered platform: A 5-year experience with Patrimony

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. Herein, we share our experience regarding main challenges and critical success factors faced when industrializing the platform and broadening its applications to neurological diseases. We emphasize the importance of integrating such platforms in an end-to-end drug discovery process and engaging human experts early on to ensure a transforming impact.