Hallmarks of aging-based dual-purpose disease and age-associated targets predicted using PandaOmics AI-powered discovery engine

PRMT5 inhibition has a potent anti-tumor activity against adenoid cystic carcinoma of salivary glands

BACKGROUND

Adenoid cystic carcinoma (ACC) is a rare glandular malignancy, commonly originating in salivary glands of the head and neck. Given its protracted growth, ACC is usually diagnosed in advanced stage. Treatment of ACC is limited to surgery and/or adjuvant radiotherapy, which often fails to prevent disease recurrence, and no FDA-approved targeted therapies are currently available. As such, identification of new therapeutic targets specific to ACC is crucial for improved patients' outcomes.

METHODS

After thoroughly evaluating the gene expression and signaling patterns characterizing ACC, we applied PandaOmics (an AI-driven software platform for novel therapeutic target discovery) on the unique transcriptomic dataset of 87 primary ACCs. Identifying protein arginine methyl transferase 5 (PRMT5) as a putative candidate with the top-scored druggability, we next determined the applicability of PRMT5 inhibitors (PRT543 and PRT811) using ACC cell lines, organoids, and patient derived xenograft (PDX) models. Molecular changes associated with response to PRMT5 inhibition and anti-proliferative effect of the combination therapy with lenvatinib was then analyzed.

RESULTS

Using a comprehensive AI-powered engine for target identification, PRMT5 was predicted among potential therapeutic target candidates for ACC. Here we show that monotherapy with selective PRMT5 inhibitors induced a potent anti-tumor activity across several cellular and animal models of ACC, which was paralleled by downregulation of genes associated with ACC tumorigenesis, including MYB and MYC (the recognized drivers of ACC progression). Furthermore, as a subset of genes targeted by lenvatinib is upregulated in ACC, we demonstrate that addition of lenvatinib enhanced the growth inhibitory effect of PRMT5 blockade in vitro, suggesting a potential clinical benefit for patients expressing lenvatinib favorable molecular profile.

CONCLUSION

Taken together, our study underscores the role of PRMT5 in ACC oncogenesis and provides a strong rationale for the clinical development of PRMT5 inhibitors as a targeted monotherapy or combination therapy for treatment of patients with this rare disease, based on the analysis of their underlying molecular profile.

Discovery of pyrrolopyrimidinone derivatives as potent PKMYT1 inhibitors for the treatment of cancer

The protein kinase PKMYT1 is responsible for inhibitory CDK1 phosphorylation, thus playing a central role in regulating the G2/M cell cycle checkpoint. As many cancers have dysfunctional cell cycle checkpoint signaling, PKMYT1 inhibition is emerging as an attractive target in advanced tumors. PKMYT1 inhibitors, however, have encountered difficulties in balancing biological efficacy, on-target specificity, and favorable stability and other drug-like properties. Herein, we report the design and development of pyrrolopyrimidinone derivatives intended to simultaneously restrict molecular conformation and shield a metabolic site in order to optimize stability. Compound 7 demonstrated strong PKMYT1-specific inhibition, a subsequent decrease in CDK1 phosphorylation, and antitumor efficacy in vitro, as well as enhanced metabolic stability, favorable pharmacokinetic and bioavailability properties, and potent antitumor in vivo efficacy. Our findings indicate that compound 7 is a promising PKMYT1 inhibitor for the treatment of advanced cancers with cell cycle defects.

A small-molecule TNIK inhibitor targets fibrosis in preclinical and clinical models

Idiopathic pulmonary fibrosis (IPF) is an aggressive interstitial lung disease with a high mortality rate. Putative drug targets in IPF have failed to translate into effective therapies at the clinical level. We identify TRAF2- and NCK-interacting kinase (TNIK) as an anti-fibrotic target using a predictive artificial intelligence (AI) approach. Using AI-driven methodology, we generated INS018_055, a small-molecule TNIK inhibitor, which exhibits desirable drug-like properties and anti-fibrotic activity across different organs in vivo through oral, inhaled or topical administration. INS018_055 possesses anti-inflammatory effects in addition to its anti-fibrotic profile, validated in multiple in vivo studies. Its safety and tolerability as well as pharmacokinetics were validated in a randomized, double-blinded, placebo-controlled phase I clinical trial (NCT05154240) involving 78 healthy participants. A separate phase I trial in China, CTR20221542, also demonstrated comparable safety and pharmacokinetic profiles. This work was completed in roughly 18 months from target discovery to preclinical candidate nomination and demonstrates the capabilities of our generative AI-driven drug-discovery pipeline.

Utilizing AI for the Identification and Validation of Novel Therapeutic Targets and Repurposed Drugs for Endometriosis

Endometriosis affects over 190 million women globally, and effective therapies are urgently needed to address the burden of endometriosis on women's health. Using an artificial intelligence (AI)-driven target discovery platform, two unreported therapeutic targets, guanylate-binding protein 2 (GBP2) and hematopoietic cell kinase (HCK) are identified, along with a drug repurposing target, integrin beta 2 (ITGB2) for the treatment of endometriosis. GBP2, HCK, and ITGB2 are upregulated in human endometriotic specimens. siRNA-mediated knockdown of GBP2 and HCK significantly reduced cell viability and proliferation while stimulating apoptosis in endometrial stromal cells. In subcutaneous and intraperitoneal endometriosis mouse models, siRNAs targeting GBP2 and HCK notably reduced lesion volume and weight, with decreased proliferation and increased apoptosis within lesions. Both subcutaneous and intraperitoneal administration of Lifitegrast, an approved ITGB2 antagonist, effectively suppresses lesion growth. Collectively, these data present Lifitegrast as a previously unappreciated intervention for endometriosis treatment and identify GBP2 and HCK as novel druggable targets in endometriosis treatment. This study underscores AI's potential to accelerate the discovery of novel drug targets and facilitate the repurposing of treatment modalities for endometriosis.

Intestinal mucosal barrier repair and immune regulation with an AI-developed gut-restricted PHD inhibitor

Hypoxia-inducible factor prolyl hydroxylase (PHD) inhibitors have been approved for treating renal anemia yet have failed clinical testing for inflammatory bowel disease because of a lack of efficacy. Here we used a multimodel multimodal generative artificial intelligence platform to design an orally gut-restricted selective PHD1 and PHD2 inhibitor that exhibits favorable safety and pharmacokinetic profiles in preclinical studies. ISM012-042 restores intestinal barrier function and alleviates gut inflammation in multiple experimental colitis models.

Roles of hypoxia-inducible factor-prolyl hydroxylases in aging and disease

The prolyl hydroxylase domain-containing (PHD or EGL9-homologs) enzyme family is mainly known for its role in the cellular response to hypoxia. HIF-PH inhibitors can stabilize hypoxia-inducible factors (HIFs), activating transcriptional programs that promote processes such as angiogenesis and erythropoiesis to adapt to changes in oxygen levels. HIF-PH inhibitors have been clinically approved for treating several types of anaemia. While most discussions of the HIF-PH signalling axis focus on hypoxia, there is a growing recognition of its importance under normoxic conditions. Recent advances in PHD biology have highlighted the potential of targeting this pathway therapeutically for a range of aging-related diseases. In this article, we review these recent discoveries, situate them within the broader context of aging and disease, and explore current therapeutic strategies that target PHD enzymes for these indications.

Discovery of Bis-imidazolecarboxamide Derivatives as Novel, Potent, and Selective TNIK Inhibitors for the Treatment of Idiopathic Pulmonary Fibrosis

Traf2- and Nck-interacting kinase (TNIK) has been identified as a promising therapeutic target for the treatment of fibrosis-driven diseases. Utilizing a structure-based drug design workflow, we developed a series of potent TNIK inhibitors that modulate the conformation of the gatekeeper Met105 side chain and access the TNIK back pocket. The lead optimization efforts culminated in the discovery of the recently reported compound 4 (INS018_055), a novel TNIK inhibitor. This molecule demonstrated excellent activity in both enzymatic and cell-based assays, along with high selectivity in a kinome panel. Further, in vitro and in vivo preclinical studies revealed favorable in vitro and in vivo DMPK properties. Results from multiple cell-based and animal models proved that compound 4 exhibits considerable antifibrotic and anti-inflammatory efficacy. Currently, phase II clinical trials of compound 4 are underway for the treatment of idiopathic pulmonary fibrosis (IPF).

Genetic and epigenetic bases of long-term adverse effects of childhood cancer therapy

Over the past decade, genome-scale molecular profiling of large childhood cancer survivorship cohorts has led to unprecedented advances in our understanding of the genetic and epigenetic bases of therapy-related adverse health outcomes in this vulnerable population. To facilitate the integration of knowledge generated from these studies into formulating next-generation precision care for survivors of childhood cancer, we summarize key findings of genetic and epigenetic association studies of long-term therapy-related adverse effects including subsequent neoplasms and cardiomyopathies among others. We also discuss therapy-related genotoxicities including clonal haematopoiesis and DNA methylation, which may underlie accelerated molecular ageing. Finally, we highlight enhanced risk prediction models for survivors of childhood cancer that incorporate both genetic factors and treatment exposures, aiming to achieve enhanced accuracy in predicting risks for this population. These new insights will hopefully inspire future studies that harness both expanding omics resources and evolving data science methodology to accelerate the translation of precision medicine for survivors of childhood cancer.

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.

Precious2GPT: the combination of multiomics pretrained transformer and conditional diffusion for artificial multi-omics multi-species multi-tissue sample generation

Synthetic data generation in omics mimics real-world biological data, providing alternatives for training and evaluation of genomic analysis tools, controlling differential expression, and exploring data architecture. We previously developed Precious1GPT, a multimodal transformer trained on transcriptomic and methylation data, along with metadata, for predicting biological age and identifying dual-purpose therapeutic targets potentially implicated in aging and age-associated diseases. In this study, we introduce Precious2GPT, a multimodal architecture that integrates Conditional Diffusion (CDiffusion) and decoder-only Multi-omics Pretrained Transformer (MoPT) models trained on gene expression and DNA methylation data. Precious2GPT excels in synthetic data generation, outperforming Conditional Generative Adversarial Networks (CGANs), CDiffusion, and MoPT. We demonstrate that Precious2GPT is capable of generating representative synthetic data that captures tissue- and age-specific information from real transcriptomics and methylomics data. Notably, Precious2GPT surpasses other models in age prediction accuracy using the generated data, and it can generate data beyond 120 years of age. Furthermore, we showcase the potential of using this model in identifying gene signatures and potential therapeutic targets in a colorectal cancer case study.

Lnc PVT1 facilitates TGF-β1-induced human cardiac fibroblast activation in vitro and ISO-induced myocardial fibrosis in vivo through regulating MYC

Cardiac fibrosis is a commonly seen pathophysiological process in various cardiovascular disorders, such as coronary heart disorder, hypertension, and cardiomyopathy. Cardiac fibroblast trans-differentiation into myofibroblasts (MFs) is a key link in myocardial fibrosis. LncRNA PVT1 participates in fibrotic diseases in multiple organs; however, its role and mechanism in cardiac fibrosis remain largely unknown. Human cardiac fibroblasts (HCFs) were stimulated with TGF-β1 to induce myofibroblast; Immunofluorescent staining, Immunoblotting, and fluorescence in situ hybridization were used to detect the myofibroblasts phenotypes and lnc PVT1 expression. Cell biological phenotypes induced by lnc PVT1 knockdown or overexpression were detected by CCK-8, flow cytometry, and Immunoblotting. A mouse model of myocardial fibrosis was induced using isoproterenol (ISO), and the cardiac functions were examined by echocardiography measurements, cardiac tissues by H&E, and Masson trichrome staining. In this study, TGF-β1 induced HCF transformation into myofibroblasts, as manifested as significantly increased levels of α-SMA, vimentin, collagen I, and collagen III; the expression level of lnc PVT1 expression showed to be significantly increased by TGF-β1 stimulation. The protein levels of TGF-β1, TGFBR1, and TGFBR2 were also decreased by lnc PVT1 knockdown. Under TGF-β1 stimulation, lnc PVT1 knockdown decreased FN1, α-SMA, collagen I, and collagen III protein contents, inhibited HCF cell viability and enhanced cell apoptosis, and inhibited Smad2/3 phosphorylation. Lnc PVT1 positively regulated MYC expression with or without TGF-β1 stimulation; MYC overexpression in TGF-β1-stimulated HCFs significantly attenuated the effects of lnc PVT1 knockdown on HCF proliferation and trans-differentiation to MFs. In the ISO-induced myocardial fibrosis model, lnc PVT1 knockdown partially reduced fibrotic area, improved cardiac functions, and decreased the levels of fibrotic markers. In addition, lnc PVT1 knockdown decreased MYC and CDK4 levels but increased E-cadherin in mice heart tissues. lnc PVT1 is up-regulated in cardiac fibrosis and TGF-β1-stimulated HCFs. Lnc PVT1 knockdown partially ameliorates TGF-β1-induced HCF activation and trans-differentiation into MFs in vitro and ISO-induced myocardial fibrosis in vivo, potentially through interacting with MYC and up-regulating MYC.

TNIK's emerging role in cancer, metabolism, and age-related diseases

Traf2- and Nck-interacting kinase (TNIK) has emerged as a key regulator of pathological metabolic signaling in several diseases and is a promising drug target. Originally studied for its role in cell migration and proliferation, TNIK possesses several newly identified functions that drive the pathogenesis of multiple diseases. Specifically, we evaluate TNIK's newfound roles in cancer, metabolic disorders, and neuronal function. We emphasize the implications of TNIK signaling in metabolic signaling and evaluate the translational potential of these discoveries. We also highlight how TNIK's role in many biological processes converges upon several hallmarks of aging. We conclude by discussing the therapeutic landscape of TNIK-targeting drugs and the recent success of clinical trials targeting TNIK.

PandaOmics: An AI-Driven Platform for Therapeutic Target and Biomarker Discovery

PandaOmics is a cloud-based software platform that applies artificial intelligence and bioinformatics techniques to multimodal omics and biomedical text data for therapeutic target and biomarker discovery. PandaOmics generates novel and repurposed therapeutic target and biomarker hypotheses with the desired properties and is available through licensing or collaboration. Targets and biomarkers generated by the platform were previously validated in both in vitro and in vivo studies. PandaOmics is a core component of Insilico Medicine's Pharma.ai drug discovery suite, which also includes Chemistry42 for the de novo generation of novel small molecules, and inClinico─a data-driven multimodal platform that forecasts a clinical trial's probability of successful transition from phase 2 to phase 3. In this paper, we demonstrate how the PandaOmics platform can efficiently identify novel molecular targets and biomarkers for various diseases.

Innovations in Medicine: Exploring ChatGPT's Impact on Rare Disorder Management

Artificial intelligence (AI) is rapidly transforming the field of medicine, announcing a new era of innovation and efficiency. Among AI programs designed for general use, ChatGPT holds a prominent position, using an innovative language model developed by OpenAI. Thanks to the use of deep learning techniques, ChatGPT stands out as an exceptionally viable tool, renowned for generating human-like responses to queries. Various medical specialties, including rheumatology, oncology, psychiatry, internal medicine, and ophthalmology, have been explored for ChatGPT integration, with pilot studies and trials revealing each field's potential benefits and challenges. However, the field of genetics and genetic counseling, as well as that of rare disorders, represents an area suitable for exploration, with its complex datasets and the need for personalized patient care. In this review, we synthesize the wide range of potential applications for ChatGPT in the medical field, highlighting its benefits and limitations. We pay special attention to rare and genetic disorders, aiming to shed light on the future roles of AI-driven chatbots in healthcare. Our goal is to pave the way for a healthcare system that is more knowledgeable, efficient, and centered around patient needs.

N-of-1 medicine

ABSTRACT

The fields of precision and personalised medicine have led to promising advances in tailoring treatment to individual patients. Examples include genome/molecular alteration-guided drug selection, single-patient gene therapy design and synergy-based drug combination development, and these approaches can yield substantially diverse recommendations. Therefore, it is important to define each domain and delineate their commonalities and differences in an effort to develop novel clinical trial designs, streamline workflow development, rethink regulatory considerations, create value in healthcare and economics assessments, and other factors. These and other segments are essential to recognise the diversity within these domains to accelerate their respective workflows towards practice-changing healthcare. To emphasise these points, this article elaborates on the concept of digital health and digital medicine-enabled N-of-1 medicine, which individualises combination regimen and dosing using a patient's own data. We will conclude with recommendations for consideration when developing novel workflows based on emerging digital-based platforms.

Validation of biomarkers of aging

The search for biomarkers that quantify biological aging (particularly 'omic'-based biomarkers) has intensified in recent years. Such biomarkers could predict aging-related outcomes and could serve as surrogate endpoints for the evaluation of interventions promoting healthy aging and longevity. However, no consensus exists on how biomarkers of aging should be validated before their translation to the clinic. Here, we review current efforts to evaluate the predictive validity of omic biomarkers of aging in population studies, discuss challenges in comparability and generalizability and provide recommendations to facilitate future validation of biomarkers of aging. Finally, we discuss how systematic validation can accelerate clinical translation of biomarkers of aging and their use in gerotherapeutic clinical trials.

Discovery of Tetrahydropyrazolopyrazine Derivatives as Potent and Selective MYT1 Inhibitors for the Treatment of Cancer

Breast and gynecological cancers are among the leading causes of death in women worldwide, illustrating the urgent need for innovative treatment options. We identified MYT1 as a promising new therapeutic target for breast and gynecological cancer using PandaOmics, an AI-driven target discovery platform. The synthetic lethal relationship of MYT1 in tumor cell lines with CCNE1 amplification enhanced this rationale. Through structure-based drug design, we developed a series of novel, potent, and highly selective inhibitors specifically targeting MYT1. Importantly, our lead compound, featuring a tetrahydropyrazolopyrazine ring, exhibits remarkable selectivity over WEE1, a related kinase associated with bone marrow suppression upon inhibition. Optimization of potency and physical properties resulted in the discovery of compound 21, a novel MYT1 inhibitor, exhibiting optimal pharmacokinetic properties and promising in vivo antitumor efficacy.

Open Genes-a new comprehensive database of human genes associated with aging and longevity

The Open Genes database was created to enhance and simplify the search for potential aging therapy targets. We collected data on 2402 genes associated with aging and developed convenient tools for searching and comparing gene features. A comprehensive description of genes has been provided, including lifespan-extending interventions, age-related changes, longevity associations, gene evolution, associations with diseases and hallmarks of aging, and functions of gene products. For each experiment, we presented the necessary structured data for evaluating the experiment's quality and interpreting the study's findings. Our goal was to stay objective and precise while connecting a particular gene to human aging. We distinguished six types of studies and 12 criteria for adding genes to our database. Genes were classified according to the confidence level of the link between the gene and aging. All the data collected in a database are provided both by an API and a user interface. The database is publicly available on a website at https://open-genes.org/.

How is Big Data reshaping preclinical aging research?

The exponential scientific and technological progress during the past 30 years has favored the comprehensive characterization of aging processes with their multivariate nature, leading to the advent of Big Data in preclinical aging research. Spanning from molecular omics to organism-level deep phenotyping, Big Data demands large computational resources for storage and analysis, as well as new analytical tools and conceptual frameworks to gain novel insights leading to discovery. Systems biology has emerged as a paradigm that utilizes Big Data to gain insightful information enabling a better understanding of living organisms, visualized as multilayered networks of interacting molecules, cells, tissues and organs at different spatiotemporal scales. In this framework, where aging, health and disease represent emergent states from an evolving dynamic complex system, context given by, for example, strain, sex and feeding times, becomes paramount for defining the biological trajectory of an organism. Using bioinformatics and artificial intelligence, the systems biology approach is leading to remarkable advances in our understanding of the underlying mechanism of aging biology and assisting in creative experimental study designs in animal models. Future in-depth knowledge acquisition will depend on the ability to fully integrate information from different spatiotemporal scales in organisms, which will probably require the adoption of theories and methods from the field of complex systems. Here we review state-of-the-art approaches in preclinical research, with a focus on rodent models, that are leading to conceptual and/or technical advances in leveraging Big Data to understand basic aging biology and its full translational potential.

A comprehensive AI-driven analysis of large-scale omic datasets reveals novel dual-purpose targets for the treatment of cancer and aging

As aging and tumorigenesis are tightly interconnected biological processes, targeting their common underlying driving pathways may induce dual-purpose anti-aging and anti-cancer effects. Our transcriptomic analyses of 16,740 healthy samples demonstrated tissue-specific age-associated gene expression, with most tumor suppressor genes downregulated during aging. Furthermore, a large-scale pan-cancer analysis of 11 solid tumor types (11,303 cases and 4431 control samples) revealed that many cellular processes, such as protein localization, DNA replication, DNA repair, cell cycle, and RNA metabolism, were upregulated in cancer but downregulated in healthy aging tissues, whereas pathways regulating cellular senescence were upregulated in both aging and cancer. Common cancer targets were identified by the AI-driven target discovery platform-PandaOmics. Age-associated cancer targets were selected and further classified into four groups based on their reported roles in lifespan. Among the 51 identified age-associated cancer targets with anti-aging experimental evidence, 22 were proposed as dual-purpose targets for anti-aging and anti-cancer treatment with the same therapeutic direction. Among age-associated cancer targets without known lifespan-regulating activity, 23 genes were selected based on predicted dual-purpose properties. Knockdown of histone demethylase KDM1A, one of these unexplored candidates, significantly extended lifespan in Caenorhabditis elegans. Given KDM1A's anti-cancer activities reported in both preclinical and clinical studies, our findings propose KDM1A as a promising dual-purpose target. This is the first study utilizing an innovative AI-driven approach to identify dual-purpose target candidates for anti-aging and anti-cancer treatment, supporting the value of AI-assisted target identification for drug discovery.

Prediction of Clinical Trials Outcomes Based on Target Choice and Clinical Trial Design with Multi-Modal Artificial Intelligence

Drug discovery and development is a notoriously risky process with high failure rates at every stage, including disease modeling, target discovery, hit discovery, lead optimization, preclinical development, human safety, and efficacy studies. Accurate prediction of clinical trial outcomes may help significantly improve the efficiency of this process by prioritizing therapeutic programs that are more likely to succeed in clinical trials and ultimately benefit patients. Here, we describe inClinico, a transformer-based artificial intelligence software platform designed to predict the outcome of phase II clinical trials. The platform combines an ensemble of clinical trial outcome prediction engines that leverage generative artificial intelligence and multimodal data, including omics, text, clinical trial design, and small molecule properties. inClinico was validated in retrospective, quasi-prospective, and prospective validation studies internally and with pharmaceutical companies and financial institutions. The platform achieved 0.88 receiver operating characteristic area under the curve in predicting the phase II to phase III transition on a quasi-prospective validation dataset. The first prospective predictions were made and placed on date-stamped preprint servers in 2016. To validate our model in a real-world setting, we published forecasted outcomes for several phase II clinical trials achieving 79% accuracy for the trials that have read out. We also present an investment application of inClinico using date stamped virtual trading portfolio demonstrating 35% 9-month return on investment.

Potential Geroprotectors - From Bench to Clinic

Geroprotectors are substances that slow down aging process and can be used for prevention of age-related diseases. Geroprotectors can improve functioning of various organ systems and enhance their homeostatic capabilities. We have developed a system of criteria for geroprotectors and proposed their classification based on the mechanisms of their action on the aging processes. Geroprotectors are required to reduce mortality, improve human aging biomarkers, have minimal side effects, and enhance quality of life. Additionally, there are approaches based on combining geroprotectors targeted to different targets and mechanisms of aging to achieve maximum effectiveness. Currently, numerous preclinical studies are being conducted to identify new molecular targets and develop new approaches to extend healthy aging, although the number of clinical trials is limited. Geroprotectors have the potential to become a new class of preventive medicines as they prevent onset of certain diseases or slow down their progression.

Multiomic prediction of therapeutic targets for human diseases associated with protein phase separation

The phenomenon of protein phase separation (PPS) underlies a wide range of cellular functions. Correspondingly, the dysregulation of the PPS process has been associated with numerous human diseases. To enable therapeutic interventions based on the regulation of this association, possible targets should be identified. For this purpose, we present an approach that combines the multiomic PandaOmics platform with the FuzDrop method to identify PPS-prone disease-associated proteins. Using this approach, we prioritize candidates with high PandaOmics and FuzDrop scores using a profiling method that accounts for a wide range of parameters relevant for disease mechanism and pharmacological intervention. We validate the differential phase separation behaviors of three predicted Alzheimer's disease targets (MARCKS, CAMKK2, and p62) in two cell models of this disease. Overall, the approach that we present generates a list of possible therapeutic targets for human diseases associated with the dysregulation of the PPS process.

Integrative GWAS and co-localisation analysis suggests novel genes associated with age-related multimorbidity

Advancing age is the greatest risk factor for developing multiple age-related diseases. Therapeutic approaches targeting the underlying pathways of ageing, rather than individual diseases, may be an effective way to treat and prevent age-related morbidity while reducing the burden of polypharmacy. We harness the Open Targets Genetics Portal to perform a systematic analysis of nearly 1,400 genome-wide association studies (GWAS) mapped to 34 age-related diseases and traits, identifying genetic signals that are shared between two or more of these traits. Using locus-to-gene (L2G) mapping, we identify 995 targets with shared genetic links to age-related diseases and traits, which are enriched in mechanisms of ageing and include known ageing and longevity-related genes. Of these 995 genes, 128 are the target of an approved or investigational drug, 526 have experimental evidence of binding pockets or are predicted to be tractable, and 341 have no existing tractability evidence, representing underexplored genes which may reveal novel biological insights and therapeutic opportunities. We present these candidate targets for exploration and prioritisation in a web application.

Biomedical generative pre-trained based transformer language model for age-related disease target discovery

Target discovery is crucial for the development of innovative therapeutics and diagnostics. However, current approaches often face limitations in efficiency, specificity, and scalability, necessitating the exploration of novel strategies for identifying and validating disease-relevant targets. Advances in natural language processing have provided new avenues for predicting potential therapeutic targets for various diseases. Here, we present a novel approach for predicting therapeutic targets using a large language model (LLM). We trained a domain-specific BioGPT model on a large corpus of biomedical literature consisting of grant text and developed a pipeline for generating target prediction. Our study demonstrates that pre-training of the LLM model with task-specific texts improves its performance. Applying the developed pipeline, we retrieved prospective aging and age-related disease targets and showed that these proteins are in correspondence with the database data. Moreover, we propose CCR5 and PTH as potential novel dual-purpose anti-aging and disease targets which were not previously identified as age-related but were highly ranked in our approach. Overall, our work highlights the high potential of transformer models in novel target prediction and provides a roadmap for future integration of AI approaches for addressing the intricate challenges presented in the biomedical field.

Biomarkers of aging for the identification and evaluation of longevity interventions

With the rapid expansion of aging biology research, the identification and evaluation of longevity interventions in humans have become key goals of this field. Biomarkers of aging are critically important tools in achieving these objectives over realistic time frames. However, the current lack of standards and consensus on the properties of a reliable aging biomarker hinders their further development and validation for clinical applications. Here, we advance a framework for the terminology and characterization of biomarkers of aging, including classification and potential clinical use cases. We discuss validation steps and highlight ongoing challenges as potential areas in need of future research. This framework sets the stage for the development of valid biomarkers of aging and their ultimate utilization in clinical trials and practice.

Predicting lifespan-extending chemical compounds for C. elegans with machine learning and biologically interpretable features

Recently, there has been a growing interest in the development of pharmacological interventions targeting ageing, as well as in the use of machine learning for analysing ageing-related data. In this work, we use machine learning methods to analyse data from DrugAge, a database of chemical compounds (including drugs) modulating lifespan in model organisms. To this end, we created four types of datasets for predicting whether or not a compound extends the lifespan of C. elegans (the most frequent model organism in DrugAge), using four different types of predictive biological features, based on: compound-protein interactions, interactions between compounds and proteins encoded by ageing-related genes, and two types of terms annotated for proteins targeted by the compounds, namely Gene Ontology (GO) terms and physiology terms from the WormBase's Phenotype Ontology. To analyse these datasets, we used a combination of feature selection methods in a data pre-processing phase and the well-established random forest algorithm for learning predictive models from the selected features. In addition, we interpreted the most important features in the two best models in light of the biology of ageing. One noteworthy feature was the GO term "Glutathione metabolic process", which plays an important role in cellular redox homeostasis and detoxification. We also predicted the most promising novel compounds for extending lifespan from a list of previously unlabelled compounds. These include nitroprusside, which is used as an antihypertensive medication. Overall, our work opens avenues for future work in employing machine learning to predict novel life-extending compounds.

Precious1GPT: multimodal transformer-based transfer learning for aging clock development and feature importance analysis for aging and age-related disease target discovery

Aging is a complex and multifactorial process that increases the risk of various age-related diseases and there are many aging clocks that can accurately predict chronological age, mortality, and health status. These clocks are disconnected and are rarely fit for therapeutic target discovery. In this study, we propose a novel approach to multimodal aging clock we call Precious1GPT utilizing methylation and transcriptomic data for interpretable age prediction and target discovery developed using a transformer-based model and transfer learning for case-control classification. While the accuracy of the multimodal transformer is lower within each individual data type compared to the state of art specialized aging clocks based on methylation or transcriptomic data separately it may have higher practical utility for target discovery. This method provides the ability to discover novel therapeutic targets that hypothetically may be able to reverse or accelerate biological age providing a pathway for therapeutic drug discovery and validation using the aging clock. In addition, we provide a list of promising targets annotated using the PandaOmics industrial target discovery platform.

Identification of dual-purpose therapeutic targets implicated in aging and glioblastoma multiforme using PandaOmics - an AI-enabled biological target discovery platform

Glioblastoma Multiforme (GBM) is the most aggressive and most common primary malignant brain tumor. The age of GBM patients is considered as one of the disease's negative prognostic factors and the mean age of diagnosis is 62 years. A promising approach to preventing both GBM and aging is to identify new potential therapeutic targets that are associated with both conditions as concurrent drivers. In this work, we present a multi-angled approach of identifying targets, which takes into account not only the disease-related genes but also the ones important in aging. For this purpose, we developed three strategies of target identification using the results of correlation analysis augmented with survival data, differences in expression levels and previously published information of aging-related genes. Several studies have recently validated the robustness and applicability of AI-driven computational methods for target identification in both cancer and aging-related diseases. Therefore, we leveraged the AI predictive power of the PandaOmics TargetID engine in order to rank the resulting target hypotheses and prioritize the most promising therapeutic gene targets. We propose cyclic nucleotide gated channel subunit alpha 3 (CNGA3), glutamate dehydrogenase 1 (GLUD1) and sirtuin 1 (SIRT1) as potential novel dual-purpose therapeutic targets to treat aging and GBM.

Skeletal muscle gene expression dysregulation in long-term spaceflights and aging is clock-dependent

The circadian clock regulates cellular and molecular processes in mammals across all tissues including skeletal muscle, one of the largest organs in the human body. Dysregulated circadian rhythms are characteristic of aging and crewed spaceflight, associated with, for example, musculoskeletal atrophy. Molecular insights into spaceflight-related alterations of circadian regulation in skeletal muscle are still missing. Here, we investigated potential functional consequences of clock disruptions on skeletal muscle using published omics datasets obtained from spaceflights and other clock-altering, external (fasting and exercise), or internal (aging) conditions on Earth. Our analysis identified alterations of the clock network and skeletal muscle-associated pathways, as a result of spaceflight duration in mice, which resembles aging-related gene expression changes observed in humans on Earth (e.g., ATF4 downregulation, associated with muscle atrophy). Furthermore, according to our results, external factors such as exercise or fasting lead to molecular changes in the core-clock network, which may compensate for the circadian disruption observed during spaceflights. Thus, maintaining circadian functioning is crucial to ameliorate unphysiological alterations and musculoskeletal atrophy reported among astronauts.

AlphaFold accelerates artificial intelligence powered drug discovery: efficient discovery of a novel CDK20 small molecule inhibitor

The application of artificial intelligence (AI) has been considered a revolutionary change in drug discovery and development. In 2020, the AlphaFold computer program predicted protein structures for the whole human genome, which has been considered a remarkable breakthrough in both AI applications and structural biology. Despite the varying confidence levels, these predicted structures could still significantly contribute to structure-based drug design of novel targets, especially the ones with no or limited structural information. In this work, we successfully applied AlphaFold to our end-to-end AI-powered drug discovery engines, including a biocomputational platform PandaOmics and a generative chemistry platform Chemistry42. A novel hit molecule against a novel target without an experimental structure was identified, starting from target selection towards hit identification, in a cost- and time-efficient manner. PandaOmics provided the protein of interest for the treatment of hepatocellular carcinoma (HCC) and Chemistry42 generated the molecules based on the structure predicted by AlphaFold, and the selected molecules were synthesized and tested in biological assays. Through this approach, we identified a small molecule hit compound for cyclin-dependent kinase 20 (CDK20) with a binding constant Kd value of 9.2 ± 0.5 μM (n = 3) within 30 days from target selection and after only synthesizing 7 compounds. Based on the available data, a second round of AI-powered compound generation was conducted and through this, a more potent hit molecule, ISM042-2-048, was discovered with an average Kd value of 566.7 ± 256.2 nM (n = 3). Compound ISM042-2-048 also showed good CDK20 inhibitory activity with an IC50 value of 33.4 ± 22.6 nM (n = 3). In addition, ISM042-2-048 demonstrated selective anti-proliferation activity in an HCC cell line with CDK20 overexpression, Huh7, with an IC50 of 208.7 ± 3.3 nM, compared to a counter screen cell line HEK293 (IC50 = 1706.7 ± 670.0 nM). This work is the first demonstration of applying AlphaFold to the hit identification process in drug discovery.

High-confidence cancer patient stratification through multiomics investigation of DNA repair disorders

Multiple cancer types have limited targeted therapeutic options, in part due to incomplete understanding of the molecular processes underlying tumorigenesis and significant intra- and inter-tumor heterogeneity. Identification of novel molecular biomarkers stratifying cancer patients with different survival outcomes may provide new opportunities for target discovery and subsequent development of tailored therapies. Here, we applied the artificial intelligence-driven PandaOmics platform ( https://pandaomics.com/ ) to explore gene expression changes in rare DNA repair-deficient disorders and identify novel cancer targets. Our analysis revealed that CEP135, a scaffolding protein associated with early centriole biogenesis, is commonly downregulated in DNA repair diseases with high cancer predisposition. Further screening of survival data in 33 cancers available at TCGA database identified sarcoma as a cancer type where lower survival was significantly associated with high CEP135 expression. Stratification of cancer patients based on CEP135 expression enabled us to examine therapeutic targets that could be used for the improvement of existing therapies against sarcoma. The latter was based on application of the PandaOmics target-ID algorithm coupled with in vitro studies that revealed polo-like kinase 1 (PLK1) as a potential therapeutic candidate in sarcoma patients with high CEP135 levels and poor survival. While further target validation is required, this study demonstrated the potential of in silico-based studies for a rapid biomarker discovery and target characterization.

Identification of Therapeutic Targets for Amyotrophic Lateral Sclerosis Using PandaOmics – An AI-Enabled Biological Target Discovery Platform

Amyotrophic lateral sclerosis (ALS) is a severe neurodegenerative disease with ill-defined pathogenesis, calling for urgent developments of new therapeutic regimens. Herein, we applied PandaOmics, an AI-driven target discovery platform, to analyze the expression profiles of central nervous system (CNS) samples (237 cases; 91 controls) from public datasets, and direct iPSC-derived motor neurons (diMNs) (135 cases; 31 controls) from Answer ALS. Seventeen high-confidence and eleven novel therapeutic targets were identified and will be released onto ALS.AI (http://als.ai/). Among the proposed targets screened in the c9ALS Drosophila model, we verified 8 unreported genes (KCNB2, KCNS3, ADRA2B, NR3C1, P2RY14, PPP3CB, PTPRC, and RARA) whose suppression strongly rescues eye neurodegeneration. Dysregulated pathways identified from CNS and diMN data characterize different stages of disease development. Altogether, our study provides new insights into ALS pathophysiology and demonstrates how AI speeds up the target discovery process, and opens up new opportunities for therapeutic interventions.