Discovery of senolytics using machine learning

Vaccarin ameliorates osteoarthritis by suppressing the c-Jun N-terminal kinase (JNK)-serum amyloid A2 (SAA2) pathway mediating chondrocyte senescence

BACKGROUND

Osteoarthritis is a chronic degenerative joint disease marked by chondrocyte senescence and extracellular matrix degradation. Vaccarin, a flavonoid with anti-inflammatory and antioxidant properties, has not been previously investigated for its therapeutic potential in osteoarthritis.

PURPOSE

To evaluate the therapeutic potential of Vaccarin in osteoarthritis and elucidate its underlying mechanisms.

DESIGN AND METHOD

This study utilized in vitro chondrocyte cultures and RNA sequencing to identify relevant pathways, followed by validation at the genetic, protein, and metabolic levels using multiple approaches. Additionally, the therapeutic effects of Vaccarin were assessed in vivo using a destabilization of the medial meniscus (DMM)-induced osteoarthritis mouse model and human cartilage samples from osteoarthritis patients.

RESULTS

Vaccarin effectively ameliorated osteoarthritis both in vivo and in vitro. Transcriptomic sequencing indicated a significant downregulation of serum amyloid A2 (SAA2) expression following Vaccarin treatment. Multi-omics analysis, validated by human specimens, indicated that SAA2 is minimally secreted in healthy articular cartilage but serves as a crucial osteoarthritis biomarker in Asian populations. Mechanistically, Vaccarin inhibits c-Jun N-terminal kinase (JNK) phosphorylation, thereby reducing SAA2 expression and mitigating chondrocyte inflammation and senescence. Notably, inflammatory conditions upregulate SAA2 expression in chondrocytes via the JNK pathway. Elevated SAA2 levels contribute to mitochondrial dysfunction in chondrocytes, leading to increased reactive oxygen species (ROS) production and exacerbating osteoarthritis progression.

CONCLUSION

This study identifies SAA2 as a potential therapeutic target for osteoarthritis and suggests that Vaccarin presents a promising treatment avenue.

Anti-senescence therapies: a new concept to address cardiovascular disease

Accumulation of senescent cells is an increasingly recognized factor in the development and progression of cardiovascular (CV) disease (CVD). Senescent cells of different types display a pro-inflammatory and matrix remodelling molecular programme, known as the 'senescence-associated secretory phenotype' (SASP), which has roots in (epi)genetic changes. Multiple therapeutic options (senolytics, anti-SASP senomorphics, and epigenetic reprogramming) that delete or ameliorate cellular senescence have recently emerged. Some drugs routinely used in the clinics also have anti-senescence effects. However, multiple challenges hinder the application of novel anti-senescence therapeutics in the clinical setting. Understanding the biology of cellular senescence, advantages and pitfalls of anti-senescence treatments, and patients who can profit from these interventions is necessary to introduce this novel therapeutic modality into the clinics. We provide a guide through the molecular machinery of senescent cells, systematize anti-senescence treatments, and propose a pathway towards senescence-adapted clinical trial design to aid future efforts.

In vitro senescence and senolytic functional assays

A detailed understanding of aging biology and the development of anti-aging therapeutic strategies remain imperative yet inherently challenging due to the protracted nature of aging. Cellular senescence arises naturally through replicative exhaustion and is accelerated by clinical treatments or environmental stressors. The accumulation of senescent cells-defined by a loss of mitogenic potential, resistance to apoptosis, and acquisition of a pro-inflammatory secretory phenotype-has been implicated as a key driver of chronic disease, tissue degeneration, and organismal aging. Recent studies have highlighted the therapeutic promise of senolytic drugs, which selectively eliminate senescent cells. Compelling results from preclinical animal studies and ongoing clinical trials underscore this potential. However, the clinical translation of senolytics requires further pharmacological validation to refine selectivity, minimize toxicity, and determine optimal dosing. Equally important is the evaluation of senolytics' potential to restore tissue structure and function by reducing the senescent cell burden. In vitro tissue culture models offer a powerful platform to advance these efforts. This review summarizes the current landscape of in vitro systems used for inducing cellular senescence-referred to as "senescence assays"-and for screening senolytic drugs-referred to as "senolytic assays". We conclude by discussing key challenges to improving mechanistic insight, predictive accuracy, and clinical relevance in senolytic drug development, as well as emerging applications of senolytic therapies.

A Critical Appraisal of the Utility of Targeting Therapy-Induced Senescence for Cancer Treatment

Cancer chemotherapy and radiotherapy are rarely successful in eliminating the entire tumor population, often leaving behind a subpopulation of senescent cells that can contribute to disease recurrence. These senescent tumor cells also secrete various chemokines and cytokines that may be tumor promoting and immunosuppressive. Recognition of the deleterious impact of therapy-induced senescence has led to the preclinical development of senolytic compounds that eliminate senescent cells, representing a potential strategy to enhance the efficacy of conventional and targeted anticancer therapy. However, it remains uncertain whether this strategy can or will be translated to the clinic. This review provides a summary of the recent preclinical literature supporting the use of senolytics as an adjunct for cancer treatment, discusses the limitations associated with their use in the current preclinical models, and provides perspectives on the clinical development of senolytics in cancer treatment regimens. Overall, preclinical studies support the potential of senolytics to enhance efficacy and prolong the antitumor activity of current standard-of-care cancer therapies that promote senescence. However, further work is needed to develop optimal senolytic agents with the appropriate combination of properties for clinical testing, specifically, activity in the context of therapy-induced senescence with acceptable tolerability.

Hydrogen Peroxide-Releasing Hydrogel-Mediated Cellular Senescence Model for Aging Research

Cellular senescence, a process that induces irreversible cell cycle arrest in response to diverse stressors, is a primary contributor to aging and age-related diseases. Currently, exposure to hydrogen peroxide is a widely used technique for establishing in vitro cellular senescence models; however, this traditional method is inconsistent, laborious, and ineffective in vivo. To overcome these limitations, we have developed a hydrogen peroxide-releasing hydrogel that can readily and controllably induce senescence in conventional 2-dimensional cell cultures as well as advanced 3-dimensional microphysiological systems. Notably, we have established 2 platforms using our hydrogen peroxide-releasing hydrogel for investigating senolytics, which is a promising innovation in anti-geronic therapy. Conclusively, our advanced model presents a highly promising tool that offers a simple, versatile, convenient, effective, and highly adaptable technique for inducing cellular senescence. This innovation not only lays a crucial foundation for future research on aging but also markedly accelerates the development of novel therapeutic strategies targeting age-related diseases.

Circadian rhythm, hypoxia, and cellular senescence: From molecular mechanisms to targeted strategies

Cellular senescence precipitates a decline in physiological activities and metabolic functions, often accompanied by heightened inflammatory responses, diminished immune function, and impaired tissue and organ performance. Despite extensive research, the mechanisms underpinning cellular senescence remain incompletely elucidated. Emerging evidence implicates circadian rhythm and hypoxia as pivotal factors in cellular senescence. Circadian proteins are central to the molecular mechanism governing circadian rhythm, which regulates homeostasis throughout the body. These proteins mediate responses to hypoxic stress and influence the progression of cellular senescence, with protein Brain and muscle arnt-like 1 (BMAL1 or Arntl) playing a prominent role. Hypoxia-inducible factor-1α (HIF-1α), a key regulator of oxygen homeostasis within the cellular microenvironment, orchestrates the transcription of genes involved in various physiological processes. HIF-1α not only impacts normal circadian rhythm functions but also can induce or inhibit cellular senescence. Notably, HIF-1α may aberrantly interact with BMAL1, forming the HIF-1α-BMAL1 heterodimer, which can instigate multiple physiological dysfunctions. This heterodimer is hypothesized to modulate cellular senescence by affecting the molecular mechanism of circadian rhythm and hypoxia signaling pathways. In this review, we elucidate the intricate relationships among circadian rhythm, hypoxia, and cellular senescence. We synthesize diverse evidence to discuss their underlying mechanisms and identify novel therapeutic targets to address cellular senescence. Additionally, we discuss current challenges and suggest potential directions for future research. This work aims to deepen our understanding of the interplay between circadian rhythm, hypoxia, and cellular senescence, ultimately facilitating the development of therapeutic strategies for aging and related diseases.

Senescence as a molecular target in skin aging and disease

Skin aging represents a multifactorial process influenced by both intrinsic and extrinsic factors, collectively known as the skin exposome. Cellular senescence, characterized by stable cell cycle arrest and secretion of pro-inflammatory molecules, has been implicated as a key driver of physiological and pathological skin aging. Increasing evidence points towards the role of senescence in a variety of dermatological diseases, where the accumulation of senescent cells in the epidermis and dermis exacerbates disease progression. Emerging therapeutic strategies such as senolytics and senomorphics offer promising avenues to target senescent cells and mitigate their deleterious effects, providing potential treatments for both skin aging and senescence-associated skin diseases. This review explores the molecular mechanisms of cellular senescence and its role in promoting age-related skin changes and pathologies, while compiling the observed effects of senotherapeutics in the skin and discussing the translational relevance.

Rejuvenation of Bone Marrow Mesenchymal Stem Cells: Mechanisms and Their Application in Senile Osteoporosis Treatment

Bone marrow mesenchymal stromal cells (BM-MSCs) are multipotent cells present in bone marrow; they play a crucial role in the process of bone formation. Cellular senescence is defined as a stable state of cell cycle arrest that impairs the functioning of cells. Research has shown that aging triggers a state of senescence in BM-MSCs, leading to a reduced capacity for osteogenic differentiation and the accumulation of senescent cells, which can accelerate the onset of various diseases. Therefore, it is essential to explore mechanisms and strategies for the rejuvenation of senescent BM-MSCs. Senile osteoporosis (SOP) is a metabolic bone disease characterized by reduced bone formation. The senescence of BM-MSCs is considered one of the most important factors in the occurrence and development of SOP. Therefore, the rejuvenation of BM-MSCs for the treatment of SOP represents a promising strategy. This work provides a summary of the functional alterations observed in senescent BM-MSCs and a systematic review of the mechanisms that facilitate the rejuvenation of senescent BM-MSCs. Additionally, we analyze the progress in and the limitations associated with the application of rejuvenated senescent BM-MSCs to treat SOP, with the aim of providing new insights for the prevention and treatment of SOP.

The Dipeptidyl Peptidase-4 Inhibitor Saxagliptin as a Candidate Treatment for Disorders of Consciousness: A Deep Learning and Retrospective Clinical Analysis

BACKGROUND

Despite advancements in the neuroscience of consciousness, no new medications for disorders of consciousness (DOC) have been discovered in more than a decade. Repurposing existing US Food and Drug Administration (FDA)-approved drugs for DOC is crucial for improving clinical management and patient outcomes.

METHODS

To identify potential new treatments among existing FDA-approved drugs, we used a deep learning-based drug screening model to predict the efficacy of drugs as awakening agents based on their three-dimensional molecular structure. A retrospective cohort study from March 2012 to October 2024 tested the model's predictions, focusing on changes in Glasgow Coma Scale (GCS) scores in 4047 patients in a coma from traumatic, vascular, or anoxic brain injury.

RESULTS

Our deep learning drug screens identified saxagliptin, a dipeptidyl peptidase-4 inhibitor, as a promising awakening drug for both acute and prolonged DOC. The retrospective clinical analysis showed that saxagliptin was associated with the highest recovery rate from acute coma among diabetes medications. After matching patients by age, sex, initial GCS score, coma etiology, and glycemic status, brain-injured patients with diabetes on incretin-based therapies, including dipeptidyl peptidase-4 inhibitors and glucagon-like peptide-1 analogues, recovered from coma at significantly higher rates compared to both brain-injured patients with diabetes on non-incretin-based diabetes medications (95% confidence interval of 1.8-14.1% higher recovery rate, P = 0.0331) and brain-injured patients without diabetes (95% confidence interval of 2-21% higher recovery rate, P = 0.0272). Post matching, brain-injured patients with diabetes on incretin-based therapies also recovered at a significantly higher rate than patients treated with amantadine (95% confidence interval for the difference 2.4-25.1.0%, P = 0.0364). A review of preclinical studies identified several pathways through which saxagliptin and other incretin-based medications may aid awakening from both acute and chronic DOC: restoring monoaminergic and GABAergic neurotransmission, reducing brain inflammation and oxidative damage, clearing hyperphosphorylated tau and amyloid-β, normalizing thalamocortical glucose metabolism, increasing neural plasticity, and mitigating excitotoxic brain damage.

CONCLUSIONS

Our findings suggest incretin-based medications in general, and saxagliptin in particular, as potential novel therapeutic agents for DOC. Further prospective clinical trials are needed to confirm their efficacy and safety in DOC.

The immunosenescence clock: A new method for evaluating biological age and predicting mortality risk

Precisely assessing an individual's immune age is critical for developing targeted aging interventions. Although traditional methods for evaluating biological age, such as the use of cellular senescence markers and physiological indicators, have been widely applied, these methods inherently struggle to capture the full complexity of biological aging. We propose the concept of an 'immunosenescence clock' that evaluates immune system changes on the basis of changes in immune cell abundance and omics data (including transcriptome and proteome data), providing a complementary indicator for understanding age-related physiological transformations. Rather than claiming to definitively measure biological age, this approach can be divided into a biological age prediction clock and a mortality prediction clock. The main function of the biological age prediction clock is to reflect the physiological state through the transcriptome data of peripheral blood mononuclear cells (PBMCs), whereas the mortality prediction clock emphasizes the ability to identify people at high risk of mortality and disease. We hereby present nearly all of the immunosenescence clocks developed to date, as well as their functional differences. Critically, we explicitly acknowledge that no single diagnostic test can exhaustively capture the intricate changes associated with biological aging. Furthermore, as these biological functions are based on the acceleration or delay of immunosenescence, we also summarize the factors that accelerate immunosenescence and the methods for delaying it. A deep understanding of the regulatory mechanisms of immunosenescence can help establish more accurate immune-age models, providing support for personalized longevity interventions and improving quality of life in old age.

Immune therapeutic strategies for the senescent tumor microenvironment

Senescent cells can either to promote immunosuppressive tumor microenvironment or facilitate immune surveillance. Despite the revolutionary impact of cancer immunotherapy, durable responses in solid tumors, particularly in advanced stages, remain limited. Recent studies have shed light on the influence of senescent status within the tumor microenvironment (TME) on therapy resistance and major efforts are needed to overcome these challenges. This review summarizes recent advancements in targeting cellular senescence, with a particular focus on immunomodulatory approaches on the hallmarks of cellular senescence.

Improving the generalization of protein expression models with mechanistic sequence information

The growing demand for biological products drives many efforts to maximize expression of heterologous proteins. Advances in high-throughput sequencing can produce data suitable for building sequence-to-expression models with machine learning. The most accurate models have been trained on one-hot encodings, a mechanism-agnostic representation of nucleotide sequences. Moreover, studies have consistently shown that training on mechanistic sequence features leads to much poorer predictions, even with features that are known to correlate with expression, such as DNA sequence motifs, codon usage, or properties of mRNA secondary structures. However, despite their excellent local accuracy, current sequence-to-expression models can fail to generalize predictions far away from the training data. Through a comparative study across datasets in Escherichia coli and Saccharomyces cerevisiae, here we show that mechanistic sequence features can provide gains on model generalization, and thus improve their utility for predictive sequence design. We explore several strategies to integrate one-hot encodings and mechanistic features into a single predictive model, including feature stacking, ensemble model stacking, and geometric stacking, a novel architecture based on graph convolutional neural networks. Our work casts new light on mechanistic sequence features, underscoring the importance of domain-knowledge and feature engineering for accurate prediction of protein expression levels.

Aging and tumors: a dynamic interaction

Aging is an inevitable physiological process in organisms, and the development of tumors is closely associated with cellular senescence. This article initially examines the role of cellular senescence in tumorigenesis, emphasizing the correlation between telomere length-a marker of cellular senescence-and tumor risk. Concurrently, the study explores the expression levels of senescence-associated markers, such as p16, p53, and mTOR, in the context of tumor development. Additionally, the study investigates the impact of tumors on cellular and organismal senescence, including the effects on immune system function and metabolic processes. Ultimately, the discussion explores the potential application of anti-aging strategies in tumor therapy and considers the possibility of utilizing senescence mechanisms as a novel therapeutic approach for tumors. This research provides novel insights into the complex interplay between senescence and tumor development, suggesting potential strategies for future preventative measures and therapeutic interventions.

SenPred: a single-cell RNA sequencing-based machine learning pipeline to classify deeply senescent dermal fibroblast cells for the detection of an in vivo senescent cell burden

BACKGROUND

Senescence classification is an acknowledged challenge within the field, as markers are cell-type and context dependent. Currently, multiple morphological and immunofluorescence markers are required. However, emerging scRNA-seq datasets have enabled an increased understanding of senescent cell heterogeneity.

METHODS

Here we present SenPred, a machine-learning pipeline which identifies fibroblast senescence based on single-cell transcriptomics from fibroblasts grown in 2D and 3D.

RESULTS

Using scRNA-seq of both 2D and 3D deeply senescent fibroblasts, the model predicts intra-experimental fibroblast senescence to a high degree of accuracy (> 99% true positives). Applying SenPred to in vivo whole skin scRNA-seq datasets reveals that cells grown in 2D cannot accurately detect fibroblast senescence in vivo. Importantly, utilising scRNA-seq from 3D deeply senescent fibroblasts refines our ML model leading to improved detection of senescent cells in vivo. This is context specific, with the SenPred pipeline proving effective when detecting senescent human dermal fibroblasts in vivo, but not the senescence of lung fibroblasts or whole skin.

CONCLUSIONS

We position this as a proof-of-concept study based on currently available scRNA-seq datasets, with the intention to build a holistic model to detect multiple senescent triggers using future emerging datasets. The development of SenPred has allowed for the detection of an in vivo senescent fibroblast burden in human skin, which could have broader implications for the treatment of age-related morbidities. All code for the SenPred pipeline is available at the following URL: https://github.com/bethk-h/SenPred_HDF .

Natural Product-Derived Senotherapeutics: Extraction and Biological Evaluation Techniques

Selective targeting of senescent cells has been thus far considered a widespread preventive strategy, as well as a main or adjuvant therapy for age-associated diseases, fueling the research on the discovery of senotherapeutics (i.e., senolytic or senomorphic compounds). Given that until now no single senotherapeutic has been reported to exert a universal anti-senescence action due to the cell- /tissue-, and context-dependent specificity of such compounds, seeking novel selective senotherapeutics remains of great importance. In this chapter, a research strategy that could be followed to screen natural product collections for putative senotherapeutics with enhanced specificity and reduced toxicity is presented, from the extraction of the source material and the isolation and chemical characterization of the compounds of interest to their biological evaluation in vitro and in vivo.

Ginkgetin Alleviates Inflammation and Senescence by Targeting STING

Ginkgo biloba extract is reported to have therapeutic effects on aging-related disorders. However, the specific component responsible for this biological function and its mechanism of action remain largely unknown. This study finds that Ginkgetin, an active ingredient of Ginkgo biloba extract, can alleviate cellular senescence and improve pathologies in multiple tissues of aging mice. To reveal the molecular mechanism of Ginkgetin's anti-aging effect, a graph convolutional network-based drug "on-target" pathway prediction algorithm for prediction is employed. The results indicate that the cGAS-STING pathway may be a potential target for Ginkgetin. Subsequent cell biological and biophysical data confirmed that Ginkgetin directly binds to the carboxy-terminal domain of STING protein, thereby inhibiting STING activation and signal transduction. Furthermore, in vivo pharmacodynamic data showed that Ginkgetin effectively alleviates systemic inflammation in Trex1-/- mice and inhibits the abnormally activated STING signaling in aging mouse model. In summary, this study, utilizing an artificial intelligence algorithm combined with pharmacological methods, confirms STING serves as a critical target for Ginkgetin in alleviating inflammation and senescence. Importantly, this study elucidates the specific component and molecular mechanism underlying the anti-aging effect of Ginkgo biloba extract, providing a robust theoretical basis for its therapeutic use.

Artificial intelligence in drug development

Drug development is a complex and time-consuming endeavor that traditionally relies on the experience of drug developers and trial-and-error experimentation. The advent of artificial intelligence (AI) technologies, particularly emerging large language models and generative AI, is poised to redefine this paradigm. The integration of AI-driven methodologies into the drug development pipeline has already heralded subtle yet meaningful enhancements in both the efficiency and effectiveness of this process. Here we present an overview of recent advancements in AI applications across the entire drug development workflow, encompassing the identification of disease targets, drug discovery, preclinical and clinical studies, and post-market surveillance. Lastly, we critically examine the prevailing challenges to highlight promising future research directions in AI-augmented drug development.

Senescence as a therapeutic target in cancer and age-related diseases

Cellular senescence is a stress response that restrains the growth of aged, damaged or abnormal cells. Thus, senescence has a crucial role in development, tissue maintenance and cancer prevention. However, lingering senescent cells fuel chronic inflammation through the acquisition of a senescence-associated secretory phenotype (SASP), which contributes to cancer and age-related tissue dysfunction. Recent progress in understanding senescence has spurred interest in the development of approaches to target senescent cells, known as senotherapies. In this Review, we evaluate the status of various types of senotherapies, including senolytics that eliminate senescent cells, senomorphics that suppress the SASP, interventions that mitigate senescence and strategies that harness the immune system to clear senescent cells. We also summarize how these approaches can be combined with cancer therapies, and we discuss the challenges and opportunities in moving senotherapies into clinical practice. Such therapies have the potential to address root causes of age-related diseases and thus open new avenues for preventive therapies and treating multimorbidities.

Cellular senescence as a key contributor to secondary neurodegeneration in traumatic brain injury and stroke

Traumatic brain injury (TBI) and stroke pose major health challenges, impacting millions of individuals globally. Once considered solely acute events, these neurological conditions are now recognized as enduring pathological processes with long-term consequences, including an increased susceptibility to neurodegeneration. However, effective strategies to counteract their devastating consequences are still lacking. Cellular senescence, marked by irreversible cell-cycle arrest, is emerging as a crucial factor in various neurodegenerative diseases. Recent research further reveals that cellular senescence may be a potential driver for secondary neurodegeneration following brain injury. Herein, we synthesize emerging evidence that TBI and stroke drive the accumulation of senescent cells in the brain. The rationale for targeting senescent cells as a therapeutic approach to combat neurodegeneration following TBI/stroke is outlined. From a translational perspective, we emphasize current knowledge and future directions of senolytic therapy for these neurological conditions.

Emerging insights in senescence: pathways from preclinical models to therapeutic innovations

Senescence is a crucial hallmark of ageing and a significant contributor to the pathology of age-related disorders. As committee members of the young International Cell Senescence Association (yICSA), we aim to synthesise recent advancements in the identification, characterisation, and therapeutic targeting of senescence for clinical translation. We explore novel molecular techniques that have enhanced our understanding of senescent cell heterogeneity and their roles in tissue regeneration and pathology. Additionally, we delve into in vivo models of senescence, both non-mammalian and mammalian, to highlight tools available for advancing the contextual understanding of in vivo senescence. Furthermore, we discuss innovative diagnostic tools and senotherapeutic approaches, emphasising their potential for clinical application. Future directions of senescence research are explored, underscoring the need for precise, context-specific senescence classification and the integration of advanced technologies such as machine learning, long-read sequencing, and multifunctional senoprobes and senolytics. The dual role of senescence in promoting tissue homoeostasis and contributing to chronic diseases highlights the complexity of targeting these cells for improved clinical outcomes.

Therapeutic targeting of senescent cells in the CNS

Senescent cells accumulate throughout the body with advanced age, diseases and chronic conditions. They negatively impact health and function of multiple systems, including the central nervous system (CNS). Therapies that target senescent cells, broadly referred to as senotherapeutics, recently emerged as potentially important treatment strategies for the CNS. Promising therapeutic approaches involve clearing senescent cells by disarming their pro-survival pathways with 'senolytics'; or dampening their toxic senescence-associated secretory phenotype (SASP) using 'senomorphics'. Following the pioneering discovery of first-generation senolytics dasatinib and quercetin, dozens of additional therapies have been identified, and several promising targets are under investigation. Although potentially transformative, senotherapies are still in early stages and require thorough testing to ensure reliable target engagement, specificity, safety and efficacy. The limited brain penetrance and potential toxic side effects of CNS-acting senotherapeutics pose challenges for drug development and translation to the clinic. This Review assesses the potential impact of senotherapeutics for neurological conditions by summarizing preclinical evidence, innovative methods for target and biomarker identification, academic and industry drug development pipelines and progress in clinical trials.

Longevity biotechnology: bridging AI, biomarkers, geroscience and clinical applications for healthy longevity

The recent unprecedented progress in ageing research and drug discovery brings together fundamental research and clinical applications to advance the goal of promoting healthy longevity in the human population. We, from the gathering at the Aging Research and Drug Discovery Meeting in 2023, summarised the latest developments in healthspan biotechnology, with a particular emphasis on artificial intelligence (AI), biomarkers and clocks, geroscience, and clinical trials and interventions for healthy longevity. Moreover, we provide an overview of academic research and the biotech industry focused on targeting ageing as the root of age-related diseases to combat multimorbidity and extend healthspan. We propose that the integration of generative AI, cutting-edge biological technology, and longevity medicine is essential for extending the productive and healthy human lifespan.

A Fully-Automated Senescence Test (FAST) for the high-throughput quantification of senescence-associated markers

Cellular senescence is a major driver of aging and age-related diseases. Quantification of senescent cells remains challenging due to the lack of senescence-specific markers and generalist, unbiased methodology. Here, we describe the Fully-Automated Senescence Test (FAST), an image-based method for the high-throughput, single-cell assessment of senescence in cultured cells. FAST quantifies three of the most widely adopted senescence-associated markers for each cell imaged: senescence-associated β-galactosidase activity (SA-β-Gal) using X-Gal, proliferation arrest via lack of 5-ethynyl-2'-deoxyuridine (EdU) incorporation, and enlarged morphology via increased nuclear area. The presented workflow entails microplate image acquisition, image processing, data analysis, and graphing. Standardization was achieved by (i) quantifying colorimetric SA-β-Gal via optical density; (ii) implementing staining background controls; and (iii) automating image acquisition, image processing, and data analysis. In addition to the automated threshold-based scoring, a multivariate machine learning approach is provided. We show that FAST accurately quantifies senescence burden and is agnostic to cell type and microscope setup. Moreover, it effectively mitigates false-positive senescence marker staining, a common issue arising from culturing conditions. Using FAST, we compared X-Gal with fluorescent C12FDG live-cell SA-β-Gal staining on the single-cell level. We observed only a modest correlation between the two, indicating that those stains are not trivially interchangeable. Finally, we provide proof of concept that our method is suitable for screening compounds that modify senescence burden. This method will be broadly useful to the aging field by enabling rapid, unbiased, and user-friendly quantification of senescence burden in culture, as well as facilitating large-scale experiments that were previously impractical.

Targeting stress induction of GRP78 by cardiac glycoside oleandrin dually suppresses cancer and COVID-19

BACKGROUND

Despite recent therapeutic advances, combating cancer resistance remains a formidable challenge. The 78-kilodalton glucose-regulated protein (GRP78), a key stress-inducible endoplasmic reticulum (ER) chaperone, plays a crucial role in both cancer cell survival and stress adaptation. GRP78 is also upregulated during SARS-CoV-2 infection and acts as a critical host factor. Recently, we discovered cardiac glycosides (CGs) as novel suppressors of GRP78 stress induction through a high-throughput screen of clinically relevant compound libraries. This study aims to test the possibility that agents capable of blocking stress induction of GRP78 could dually suppress cancer and COVID-19.

RESULTS

Here we report that oleandrin (OLN), is the most potent among the CGs in inhibiting acute stress induction of total GRP78, which also results in reduced cell surface and nuclear forms of GRP78 in stressed cells. The inhibition of stress induction of GRP78 is at the post-transcriptional level, independent of protein degradation and autophagy and may involve translational control as OLN blocks stress-induced loading of ribosomes onto GRP78 mRNAs. Moreover, the human Na+/K+-ATPase α3 isoform is critical for OLN suppression of GRP78 stress induction. OLN, in nanomolar range, enhances apoptosis, sensitizes colorectal cancer cells to chemotherapeutic agents, and reduces the viability of patient-derived colon cancer organoids. Likewise, OLN, suppresses GRP78 expression and impedes tumor growth in an orthotopic breast cancer xenograft model. Furthermore, OLN blocks infection by SARS-CoV-2 and its variants and enhances existing anti-viral therapies. Notably, GRP78 overexpression mitigates OLN-mediated cancer cell apoptotic onset and suppression of virus release.

CONCLUSION

Our findings validate GRP78 as a target of OLN anti-cancer and anti-viral activities. These proof-of-principle studies support further investigation of OLN as a readily accessible compound to dually combat cancer and COVID-19.

Improved classification of soil As contamination at continental scale: Resolving class imbalances using machine learning approach

The identification of arsenic (As)-contaminated areas is an important prerequisite for soil management and reclamation. Although previous studies have attempted to identify soil As contamination via machine learning (ML) methods combined with soil spectroscopy, they have ignored the rarity of As-contaminated soil samples, leading to an imbalanced learning problem. A novel ML framework was thus designed herein to solve the imbalance issue in identifying soil As contamination from soil visible and near-infrared spectra. Spectral preprocessing, imbalanced dataset resampling, and model comparisons were combined in the ML framework, and the optimal combination was selected based on the recall. In addition, Bayesian optimization was used to tune the model hyperparameters. The optimized model achieved recall, area under the curve, and balanced accuracy values of 0.83, 0.88, and 0.79, respectively, on the testing set. The recall was further improved to 0.87 with the threshold adjustment, indicating the model's excellent performance and generalization capability in classifying As-contaminated soil samples. The optimal model was applied to a global soil spectral dataset to predict areas at a high risk of soil As contamination on a global scale. The ML framework established in this study represents a milestone in the classification of soil As contamination and can serve as a valuable reference for contamination management in soil science.

Deep Learning-Driven Exploration of Pyrroloquinoline Quinone Neuroprotective Activity in Alzheimer's Disease

Alzheimer's disease (AD) is a pressing concern in neurodegenerative research. To address the challenges in AD drug development, especially those targeting Aβ, this study uses deep learning and a pharmacological approach to elucidate the potential of pyrroloquinoline quinone (PQQ) as a neuroprotective agent for AD. Using deep learning for a comprehensive molecular dataset, blood-brain barrier (BBB) permeability is predicted and the anti-inflammatory and antioxidative properties of compounds are evaluated. PQQ, identified in the Mediterranean-DASH intervention for a diet that delays neurodegeneration, shows notable BBB permeability and low toxicity. In vivo tests conducted on an Aβ₁₋₄₂-induced AD mouse model verify the effectiveness of PQQ in reducing cognitive deficits. PQQ modulates genes vital for synapse and anti-neuronal death, reduces reactive oxygen species production, and influences the SIRT1 and CREB pathways, suggesting key molecular mechanisms underlying its neuroprotective effects. This study can serve as a basis for future studies on integrating deep learning with pharmacological research and drug discovery.

Revolutionizing Diabetic Foot Ulcer Care: The Senotherapeutic Approach

Diabetic foot ulcers (DFUs) are a prevalent and profoundly debilitating complication that afflicts individuals with diabetes mellitus (DM). These ulcers are associated with substantial morbidity, recurrence rates, disability, and mortality, imposing substantial economic, psychological, and medical burdens. Timely detection and intervention can mitigate the morbidity and disparities linked to DFU. Nevertheless, current therapeutic approaches for DFU continue to grapple with multifaceted limitations. A growing body of evidence emphasizes the crucial role of cellular senescence in the pathogenesis of chronic wounds. Interventions that try to delay cellular senescence, eliminate senescent cells (SnCs), or suppress the senescence-associated secretory phenotype (SASP) have shown promise for helping chronic wounds to heal. In this context, targeting cellular senescence emerges as a novel therapeutic strategy for DFU. In this comprehensive review, we look at the pathology and treatment of DFU in a systematic way. We also explain the growing importance of investigating SnCs in DFU and highlight the great potential of senotherapeutics that target SnCs in DFU treatment. The development of efficacious and safe senotherapeutics represents a pioneering therapeutic approach aimed at enhancing the quality of life for individuals affected by DFU.

Integration of graph neural networks and genome-scale metabolic models for predicting gene essentiality

Genome-scale metabolic models are powerful tools for understanding cellular physiology. Flux balance analysis (FBA), in particular, is an optimization-based approach widely employed for predicting metabolic phenotypes. In model microbes such as Escherichia coli, FBA has been successful at predicting essential genes, i.e. those genes that impair survival when deleted. A central assumption in this approach is that both wild type and deletion strains optimize the same fitness objective. Although the optimality assumption may hold for the wild type metabolic network, deletion strains are not subject to the same evolutionary pressures and knock-out mutants may steer their metabolism to meet other objectives for survival. Here, we present FlowGAT, a hybrid FBA-machine learning strategy for predicting essentiality directly from wild type metabolic phenotypes. The approach is based on graph-structured representation of metabolic fluxes predicted by FBA, where nodes correspond to enzymatic reactions and edges quantify the propagation of metabolite mass flow between a reaction and its neighbours. We integrate this information into a graph neural network that can be trained on knock-out fitness assay data. Comparisons across different model architectures reveal that FlowGAT predictions for E. coli are close to those of FBA for several growth conditions. This suggests that essentiality of enzymatic genes can be predicted by exploiting the inherent network structure of metabolism. Our approach demonstrates the benefits of combining the mechanistic insights afforded by genome-scale models with the ability of deep learning to infer patterns from complex datasets.

Therapy-induced senescence is finally escapable, what is next?

Several breakthrough articles have recently confirmed the ability of tumor cells to escape the stable cell cycle arrest imposed by Therapy-Induced Senescence (TIS). Subsequently, accepting the hypothesis that TIS is escapable should encourage serious reassessments of the fundamental roles of senescence in cancer treatment. The potential for escape from TIS undermines the well-established tumor suppressor function of senescence, proposes it as a mechanism of tumor dormancy leading to disease recurrence and invites for further investigation of its unfavorable contribution to cancer therapy outcomes. Moreover, escaping TIS strongly indicates that the elimination of senescent tumor cells, primarily through pharmacological means, is a suitable approach for increasing the efficacy of cancer treatment, one that still requires further exploration. This commentary provides an overview of the recent evidence that unequivocally demonstrated the ability of therapy-induced senescent tumor cells in overcoming the terminal growth arrest fate and provides future perspectives on the roles of TIS in tumor biology.

Prediction of heavy-section ductile iron fracture toughness based on machine learning

The preparation process and composition design of heavy-section ductile iron are the key factors affecting its fracture toughness. These factors are challenging to address due to the long casting cycle, high cost and complex influencing factors of this type of iron. In this paper, 18 cubic physical simulation test blocks with 400 mm wall thickness were prepared by adjusting the C, Si and Mn contents in heavy-section ductile iron using a homemade physical simulation casting system. Four locations with different cooling rates were selected for each specimen, and 72 specimens with different compositions and cooling times of the heavy-section ductile iron were prepared. Six machine learning-based heavy-section ductile iron fracture toughness predictive models were constructed based on measured data with the C content, Si content, Mn content and cooling rate as input data and the fracture toughness as the output data. The experimental results showed that the constructed bagging model has high accuracy in predicting the fracture toughness of heavy-section ductile iron, with a coefficient of coefficient (R2) of 0.9990 and a root mean square error (RMSE) of 0.2373.

Selective (α)-l-Rhamnosylation and Neuroprotective Activity Exploration of Cardiotonic Steroids

This work describes the studies on the direct C3-glycosylation of the C19-hydroxylated cardiotonic steroids strophanthidol, anhydro-ouabagenin, and ouabagenin using a strategy based on in situ protection of the C5 and C19 hydroxyl groups with boronic acids. While this strategy resulted in a successful one-pot C3-selective glycosylation of strophanthidol and anhydro-ouabegenin, it failed to provide ouabain from ouabagenin. The neuroprotective activity of the synthetic and natural glycosides against LPS-induced neuroinflammation was explored in neonatal mouse primary glia cells. Co-administration of natural and synthetic C3-glycosides at 200 nM concentrations resulted in the significant reduction of the LPS-induced neuroinflammatory markers IL-6, IL-1, TNFα, and IKBKE, with the anhydro-ouabagenin-3-(α)-l-rhamnoside (anhydro-ouabain) showing the most significant effect. At the same time, unglycosylated anhydro-ouabagenin enhanced rather than suppressed LPS-induced neuroinflammation.

Senolytics: from pharmacological inhibitors to immunotherapies, a promising future for patients' treatment

The involvement of cellular senescence in the initiation and propagation of diseases is clearly characterized, making the elimination of senescent cells essential to treat age-related diseases. The development of senolytic drugs demonstrated that targeting these cells limits the deterioration of patients' condition, by inducing apoptosis. Nevertheless, the first generations of senolytics which has been developed displayed their activities through specific mechanisms and demonstrated several limitations during clinical development. However, the rational to eliminate senescent cells remains evident, with the necessity to develop specific therapies in a context of diseases and tissues. The evolutions in the field of drug discovery open the way to a new generation of senolytic therapies, such as immunological approaches (CAR-T cells, Antibody-Drug Conjugated or vaccines), which require preliminary steps of research to identify markers specifically expressed on senescent cells, demonstrating promising specific effects. Currently, the preclinical development of these strategies appears more challenging to avoid strong side effects, but the expected results are commensurate with patients' hopes for treatments. In this review, we highlight the fact that the classical senolytic approach based on drug repurposing display limited efficacy and probably reached its limits in term of clinical development. The recent development of more complex therapies and the extension of interest in the domain of senescence in different fields of research allow to extend the possibility to discover powerful therapies. The future of age-related diseases treatment is linked to the development of new approaches based on cell therapy or immunotherapy to offer the best treatment for patients.

Current senolytics: Mode of action, efficacy and limitations, and their future

Senescence is a cellular state characterized by its near-permanent halted cell cycle and distinct secretory phenotype. Although senescent cells have a variety of beneficial physiological functions, progressive accumulation of these cells due to aging or other conditions has been widely shown to provoke deleterious effects on the normal functioning of the same or higher-level biological organizations. Recently, erasing senescent cells in vivo, using senolytics, could ameliorate diseases identified with an elevated number of senescent cells. Since then, researchers have struggled to develop new senolytics each with different selectivity and potency. In this review, we have gathered and classified the proposed senolytics and discussed their mechanisms of action. Moreover, we highlight the heterogeneity of senolytics regarding their effect sizes, and cell type specificity as well as comment on the exploited strategies to improve these features. Finally, we suggest some prospective routes for the novel methods for ablation of senescent cells.

Bioinformatics procedure for investigating senolytic (anti-aging) agents: A digital signal processing technique

Objective

Cell growth involves cell division. This stops after reaching a certain limit. Some cells become inactive and unable to undergo apoptosis (programmed cell death). These cells accumulate at sites of tissue damage or disease, thus accelerating aging. They are called senescent cells. Therapeutic interventions that can either eliminate senescent cells (senolytics) or suppress their harmful effects (senomorphics) have been developed. Senescence (aging) is caused by the inter- and intramolecular interactions between the domains of forkhead (FHD) and transactivation (TAD), as well as C-terminal region 3 (CR3) and DNA binding (DBD). On the other hand, anti-senescent/senolytic (anti-aging) activities are achieved by disrupting these interactions with CR3- and forkhead box protein O4 (FOXO4)-based peptides, such as ES2 and DRI, respectively. In this study, we use a computerized procedure based on digital signal processing to systematically analyze the inter-molecular interactions between senolytics and their targets.

Methods

Informational spectrum method (ISM) is engaged.

Results

We obtained the sequences of the peptides from the interacting proteins of CR3 and FOXO4 and evaluated their ability to disrupt the inter-molecular interactions between FOXO4 and DRI and CR3 and BDB, which are responsible for senescence (aging). Our results show that the peptides have different degrees of senolytic (anti-aging) activity, depending on their affinity for CR3 and BDB, or FOXO4 and DRI. We found that enhanced senescence 2 (ES2) has a higher affinity for CR3 and BDB than FOXO4 and DRI, and that the interaction between CR3 and BDB is crucial for aging. Therefore, ES2 and other CR3-based peptides are more potent senolytics than FOXO4-based peptides. Our findings are consistent with previous studies and reveal new insights into the mechanisms of senescence and senolytics. ES2 is considered the best senolytic candidate, as it is 3-7 times more effective than DRI. We verified that ES2 has a weaker interaction with FOXO4 than CR3. However, the performance of DRI has been noted to depend on its intramolecular interactions and stability. Hence, intramolecular analyses using the digital signal processing-based technique has become very vital and will follow.

Conclusion

CR3-based peptides are promising candidates for senolytic therapy. Senolytics are linear chains of amino acids that can target and eliminate senescent cells, which are cells that have stopped dividing and contribute to aging and age-related diseases. By using this proposed, novel computerized technique that is based on digital signal processing, senolytics can be easily analyzed and optimized for their effectiveness and safety. This provides a more rational approach to enhancing our longevity and well-being by offering interventions that can delay or reverse aging and insights that can advance the field of gerontology. This procedure also will compliment other approaches such as molecular stimulation, etc.

The leading global health challenges in the artificial intelligence era

Millions of people's health is at risk because of several factors and multiple overlapping crises, all of which hit the vulnerable the most. These challenges are dynamic and evolve in response to emerging health challenges and concerns, which need effective collaboration among countries working toward achieving Sustainable Development Goals (SDGs) and securing global health. Mental Health, the Impact of climate change, cardiovascular diseases (CVDs), diabetes, Infectious diseases, health system, and population aging are examples of challenges known to pose a vast burden worldwide. We are at a point known as the "digital revolution," characterized by the expansion of artificial intelligence (AI) and a fusion of technology types. AI has emerged as a powerful tool for addressing various health challenges, and the last ten years have been influential due to the rapid expansion in the production and accessibility of health-related data. The computational models and algorithms can understand complicated health and medical data to perform various functions and deep-learning strategies. This narrative mini-review summarizes the most current AI applications to address the leading global health challenges. Harnessing its capabilities can ultimately mitigate the Impact of these challenges and revolutionize the field. It has the ability to strengthen global health through personalized health care and improved preparedness and response to future challenges. However, ethical and legal concerns about individual or community privacy and autonomy must be addressed for effective implementation.

Applications of artificial intelligence and machine learning in dynamic pathway engineering

Dynamic pathway engineering aims to build metabolic production systems embedded with intracellular control mechanisms for improved performance. These control systems enable host cells to self-regulate the temporal activity of a production pathway in response to perturbations, using a combination of biosensors and feedback circuits for controlling expression of heterologous enzymes. Pathway design, however, requires assembling together multiple biological parts into suitable circuit architectures, as well as careful calibration of the function of each component. This results in a large design space that is costly to navigate through experimentation alone. Methods from artificial intelligence (AI) and machine learning are gaining increasing attention as tools to accelerate the design cycle, owing to their ability to identify hidden patterns in data and rapidly screen through large collections of designs. In this review, we discuss recent developments in the application of machine learning methods to the design of dynamic pathways and their components. We cover recent successes and offer perspectives for future developments in the field. The integration of AI into metabolic engineering pipelines offers great opportunities to streamline design and discover control systems for improved production of high-value chemicals.