A dynamic single cell-based framework for digital twins to prioritize disease genes and drug targets

Opportunities for use of neuroimaging in de-risking drug development and improving clinical outcomes in psychiatry: an industry perspective

Neuroimaging, across positron emission tomography (PET), electroencephalography (EEG), and magnetic resonance imaging (MRI), has been a mainstay of clinical neuroscience research for decades, yet has penetrated little into psychiatric drug development beyond often underpowered phase 1 studies, or into clinical care. Simultaneously, there is a pressing need to improve the probability of success in drug development, increase mechanistic diversity, and enhance clinical efficacy. These goals can be achieved by leveraging neuroimaging in a precision psychiatry framework, wherein effects of drugs on the brain are measured early in clinical development to understand dosing and indication, and then in later-stage trials to identify likely drug responders and enrich clinical trials, ultimately improving clinical outcomes. Here we examine the key variables important for success in using neuroimaging for precision psychiatry from the lens of biotechnology and pharmaceutical companies developing and deploying new drugs in psychiatry. We argue that there are clear paths for incorporating different neuroimaging modalities to de-risk subsequent development phases in the near to intermediate term, culminating in use of select neuroimaging modalities in clinical care for prescription of new precision drugs. Better outcomes through neuroimaging biomarkers, however, require a wholesale commitment to a precision psychiatry approach and will necessitate a cultural shift to align biopharma and clinical care in psychiatry to a precision orientation already routine in other areas of medicine.

E-waste: mechanisms of toxicity and safety testing

Currently, information on the toxicity profile of the majority of the identified e-waste chemicals, while extensive and growing, is admittedly fragmentary, particularly at the cellular and molecular levels. Furthermore, the toxicity of the chemical mixtures likely to be encountered by humans during and after informal e-waste recycling, as well as their underlying mechanisms of action, is largely unknown. This review paper summarizes state-of-the-art knowledge of the potential underlying toxicity mechanisms associated with e-waste exposures, with a focus on toxic responses connected to specific organs, organ systems, and overall effects on the organism. To overcome the complexities associated with assessing the possible adverse outcomes from exposure to chemicals, a growing number of new approach methodologies have emerged in recent years, with the long-term objective of providing a human-based and animal-free system that is scientifically superior to animal testing, more effective, and acceptable. This encompasses a variety of techniques, typically regarded as alternative approaches for determining chemical-induced toxicities and holds greater promise for a better understanding of key events in the metabolic pathways that mediate known adverse health outcomes in e-waste exposure scenarios. This is crucial to establishing accurate scientific knowledge on mixed e-waste chemical exposures in shorter time frames and with greater efficacy, as well as supporting the need for safe management of hazardous chemicals. The present review paper discusses important gaps in knowledge and shows promising directions for mechanistically anchored effect-based monitoring strategies that will contribute to the advancement of the methods currently used in characterizing and monitoring e-waste-impacted ecosystems.

Digital Twins of Biological Systems: A Narrative Review

The concept of Digital Twins (DTs), software models that mimic the behavior and interactions of physical or conceptual objects within their environments, has gained traction in recent years, particularly in medicine and healthcare research. DTs technology emerges as a pivotal tool in disease modeling, integrating diverse data sources to computationally model dynamic biological systems. This narrative review explores potential DT applications in medicine, from defining DTs and their history to constructing DTs, modeling biologically relevant systems, as well as discussing the benefits, risks, and challenges in their application. The influence of DTs extends beyond healthcare and can revolutionize healthcare management, drug development, clinical trials, and various biomedical research fields.

Digital twins and artificial intelligence in metabolic disease research

Digital twin technology is emerging as a transformative paradigm for personalized medicine in the management of chronic conditions. In this article, we explore the concept and key characteristics of a digital twin and its applications in chronic non-communicable metabolic disease management, with a focus on diabetes case studies. We cover various types of digital twin models, including mechanistic models based on ODEs, data-driven ML algorithms, and hybrid modeling strategies that combine the strengths of both approaches. We present successful case studies demonstrating the potential of digital twins in improving glucose outcomes for individuals with T1D and T2D, and discuss the benefits and challenges of translating digital twin research applications to clinical practice.

Transcript and protein signatures derived from shared molecular interactions across cancers are associated with mortality

BACKGROUND

Characterization of shared cancer mechanisms have been proposed to improve therapy strategies and prognosis. Here, we aimed to identify shared cell-cell interactions (CCIs) within the tumor microenvironment across multiple solid cancers and assess their association with cancer mortality.

METHODS

CCIs of each cancer were identified by NicheNet analysis of single-cell RNA sequencing data from breast, colon, liver, lung, and ovarian cancers. These CCIs were used to construct a shared multi-cellular tumor model (shared-MCTM) representing common CCIs across cancers. A gene signature was identified from the shared-MCTM and tested on the mRNA and protein level in two large independent cohorts: The Cancer Genome Atlas (TCGA, 9185 tumor samples and 727 controls across 22 cancers) and UK biobank (UKBB, 10,384 cancer patients and 5063 controls with proteomics data across 17 cancers). Cox proportional hazards models were used to evaluate the association of the signature with 10-year all-cause mortality, including sex-specific analysis.

RESULTS

A shared-MCTM was derived from five individual cancers. A shared gene signature was extracted from this shared-MCTM and the most prominent regulatory cell type, matrix cancer-associated fibroblast (mCAF). The signature exhibited significant expression changes in multiple cancers compared to controls at both mRNA and protein levels in two independent cohorts. Importantly, it was significantly associated with mortality in cancer patients in both cohorts. The highest hazard ratios were observed for brain cancer in TCGA (HR [95%CI] = 6.90[4.64-10.25]) and ovarian cancer in UKBB (5.53[2.08-8.80]). Sex-specific analysis revealed distinct risks, with a higher mortality risk associated with the protein signature score in males (2.41[1.97-2.96]) compared to females (1.84[1.44-2.37]).

CONCLUSION

We identified a gene signature from a comprehensive shared-MCTM representing common CCIs across different cancers and revealed the regulatory role of mCAF in the tumor microenvironment. The pathogenic relevance of the gene signature was supported by differential expression and association with mortality on both mRNA and protein levels in two independent cohorts.

Digital twins for health: a scoping review

The use of digital twins (DTs) has proliferated across various fields and industries, with a recent surge in the healthcare sector. The concept of digital twin for health (DT4H) holds great promise to revolutionize the entire healthcare system, including management and delivery, disease treatment and prevention, and health well-being maintenance, ultimately improving human life. The rapid growth of big data and continuous advancement in data science (DS) and artificial intelligence (AI) have the potential to significantly expedite DT research and development by providing scientific expertise, essential data, and robust cybertechnology infrastructure. Although various DT initiatives have been underway in the industry, government, and military, DT4H is still in its early stages. This paper presents an overview of the current applications of DTs in healthcare, examines consortium research centers and their limitations, and surveys the current landscape of emerging research and development opportunities in healthcare. We envision the emergence of a collaborative global effort among stakeholders to enhance healthcare and improve the quality of life for millions of individuals worldwide through pioneering research and development in the realm of DT technology.

scDrugPrio: a framework for the analysis of single-cell transcriptomics to address multiple problems in precision medicine in immune-mediated inflammatory diseases

BACKGROUND

Ineffective drug treatment is a major problem for many patients with immune-mediated inflammatory diseases (IMIDs). Important reasons are the lack of systematic solutions for drug prioritisation and repurposing based on characterisation of the complex and heterogeneous cellular and molecular changes in IMIDs.

METHODS

Here, we propose a computational framework, scDrugPrio, which constructs network models of inflammatory disease based on single-cell RNA sequencing (scRNA-seq) data. scDrugPrio constructs detailed network models of inflammatory diseases that integrate information on cell type-specific expression changes, altered cellular crosstalk and pharmacological properties for the selection and ranking of thousands of drugs.

RESULTS

scDrugPrio was developed using a mouse model of antigen-induced arthritis and validated by improved precision/recall for approved drugs, as well as extensive in vitro, in vivo, and in silico studies of drugs that were predicted, but not approved, for the studied diseases. Next, scDrugPrio was applied to multiple sclerosis, Crohn's disease, and psoriatic arthritis, further supporting scDrugPrio through prioritisation of relevant and approved drugs. However, in contrast to the mouse model of arthritis, great interindividual cellular and gene expression differences were found in patients with the same diagnosis. Such differences could explain why some patients did or did not respond to treatment. This explanation was supported by the application of scDrugPrio to scRNA-seq data from eleven individual Crohn's disease patients. The analysis showed great variations in drug predictions between patients, for example, assigning a high rank to anti-TNF treatment in a responder and a low rank in a nonresponder to that treatment.

CONCLUSIONS

We propose a computational framework, scDrugPrio, for drug prioritisation based on scRNA-seq of IMID disease. Application to individual patients indicates scDrugPrio's potential for personalised network-based drug screening on cellulome-, genome-, and drugome-wide scales. For this purpose, we made scDrugPrio into an easy-to-use R package ( https://github.com/SDTC-CPMed/scDrugPrio ).

Health Digital Twins in Life Science and Health Care Innovation

Health digital twins (HDTs) are virtual representations of real individuals that can be used to simulate human physiology, disease, and drug effects. HDTs can be used to improve drug discovery and development by providing a data-driven approach to inform target selection, drug delivery, and design of clinical trials. HDTs also offer new applications into precision therapies and clinical decision making. The deployment of HDTs at scale could bring a precision approach to public health monitoring and intervention. Next steps include challenges such as addressing socioeconomic barriers and ensuring the representativeness of the technology based on the training and validation data sets. Governance and regulation of HDT technology are still in the early stages.

scDrugPrio: A framework for the analysis of single-cell transcriptomics to address multiple problems in precision medicine in immune-mediated inflammatory diseases

Background

Ineffective drug treatment is a major problem for many patients with immune-mediated inflammatory diseases (IMIDs). Important reasons are the lack of systematic solutions for drug prioritisation and repurposing based on characterisation of the complex and heterogeneous cellular and molecular changes in IMIDs.

Methods

Here, we propose a computational framework, scDrugPrio, which constructs network models of inflammatory disease based on single-cell RNA sequencing (scRNA-seq) data. scDrugPrio constructs detailed network models of inflammatory diseases that integrate information on cell type-specific expression changes, altered cellular crosstalk and pharmacological properties for the selection and ranking of thousands of drugs.

Results

scDrugPrio was developed using a mouse model of antigen-induced arthritis and validated by improved precision/recall for approved drugs, as well as extensive in vitro, in vivo, and in silico studies of drugs that were predicted, but not approved, for the studied diseases. Next, scDrugPrio was applied to multiple sclerosis, Crohn's disease, and psoriatic arthritis, further supporting scDrugPrio through prioritisation of relevant and approved drugs. However, in contrast to the mouse model of arthritis, great interindividual cellular and gene expression differences were found in patients with the same diagnosis. Such differences could explain why some patients did or did not respond to treatment. This explanation was supported by the application of scDrugPrio to scRNA-seq data from eleven individual Crohn's disease patients. The analysis showed great variations in drug predictions between patients, for example, assigning a high rank to anti-TNF treatment in a responder and a low rank in a nonresponder to that treatment.

Conclusion

We propose a computational framework, scDrugPrio, for drug prioritisation based on scRNA-seq of IMID disease. Application to individual patients indicates scDrugPrio's potential for personalised network-based drug screening on cellulome-, genome-, and drugome-wide scales. For this purpose, we made scDrugPrio into an easy-to-use R package (https://github.com/SDTC-CPMed/scDrugPrio).

AutoCore: A network-based definition of the core module of human autoimmunity and autoinflammation

Although research on rare autoimmune and autoinflammatory diseases has enabled definition of nonredundant regulators of homeostasis in human immunity, because of the single gene-single disease nature of many of these diseases, contributing factors were mostly unveiled in sequential and noncoordinated individual studies. We used a network-based approach for integrating a set of 186 inborn errors of immunity with predominant autoimmunity/autoinflammation into a comprehensive map of human immune dysregulation, which we termed "AutoCore." The AutoCore is located centrally within the interactome of all protein-protein interactions, connecting and pinpointing multidisease markers for a range of common, polygenic autoimmune/autoinflammatory diseases. The AutoCore can be subdivided into 19 endotypes that correspond to molecularly and phenotypically cohesive disease subgroups, providing a molecular mechanism-based disease classification and rationale toward systematic targeting for therapeutic purposes. Our study provides a proof of concept for using network-based methods to systematically investigate the molecular relationships between individual rare diseases and address a range of conceptual, diagnostic, and therapeutic challenges.

The potential of the Medical Digital Twin in diabetes management: a review

Diabetes is a chronic prevalent disease that must be managed to improve the patient's quality of life. However, the limited healthcare management resources compared to the large diabetes mellitus (DM) population are an obstacle that needs modern information technology to improve. Digital twin (DT) is a relatively new approach that has emerged as a viable tool in several sectors of healthcare, and there have been some publications on DT in disease management. The systematic summary of the use of DTs and its potential applications in DM is less reported. In this review, we summarized the key techniques of DTs, proposed the potentials of DTs in DM management from different aspects, and discussed the concerns of this novel technique in DM management.

Towards systems immunology of critical illness at scale: from single cell 'omics to digital twins

Single-cell 'omics methodology has yielded unprecedented insights based largely on data-centric informatics for reducing, and thus interpreting, massive datasets. In parallel, parsimonious mathematical modeling based on abstractions of pathobiology has also yielded major insights into inflammation and immunity, with these models being extended to describe multi-organ disease pathophysiology as the basis of 'digital twins' and in silico clinical trials. The integration of these distinct methods at scale can drive both basic and translational advances, especially in the context of critical illness, including diseases such as COVID-19. Here, I explore achievements and argue the challenges that are inherent to the integration of data-driven and mechanistic modeling approaches, highlighting the potential of modeling-based strategies for rational immune system reprogramming.

Multi-organ single-cell analysis reveals an on/off switch system with potential for personalized treatment of immunological diseases

Prioritization of disease mechanisms, biomarkers, and drug targets in immune-mediated inflammatory diseases (IMIDs) is complicated by altered interactions between thousands of genes. Our multi-organ single-cell RNA sequencing of a mouse IMID model, namely collagen-induced arthritis, shows highly complex and heterogeneous expression changes in all analyzed organs, even though only joints showed signs of inflammation. We organized those into a multi-organ multicellular disease model, which shows predicted molecular interactions within and between organs. That model supports that inflammation is switched on or off by altered balance between pro- and anti-inflammatory upstream regulators (URs) and downstream pathways. Meta-analyses of human IMIDs show a similar, but graded, on/off switch system. This system has the potential to prioritize, diagnose, and treat optimal combinations of URs on the levels of IMIDs, subgroups, and individual patients. That potential is supported by UR analyses in more than 600 sera from patients with systemic lupus erythematosus.

Digital Twins for Predictive, Preventive Personalized, and Participatory Treatment of Immune-Mediated Diseases

Digital twins are computational models of complex systems, which aim to understand and optimize those systems more effectively than would be possible in real life. Ideally, digital twins can be translated to individual patients, to characterize and computationally treat their diseases with thousands of drugs, to select the drug or drugs that cure the patients. The background problem is that many patients do not respond adequately to drug treatment. This problem reflects a wide gap between the complexity of diseases and clinical practice. Each disease may involve altered interactions between thousands of genes that vary between different cell types in different organs. To our knowledge, these altered interactions have not been characterized on a genome-, cellulome-, and organ-wide scale in any disease. Thus, clinical translation of the digital twin ideal for predictive, preventive, personalized and participatory treatment involves formidable challenges, which are close to the limits of, or beyond today's technologies. Here, I discuss recent developments and challenges in relation to that ideal focusing on immune-mediated inflammatory diseases, as well as examples from other diseases.

Molecular interaction networks and drug development: Novel approach to drug target identification and drug repositioning

Conventional drug discovery requires identifying a protein target believed to be important for disease mechanism and screening compounds for those that beneficially alter the target's function. While this approach has been an effective one for decades, recent data suggest that its continued success is limited largely owing to the highly prevalent irreducibility of biologically complex systems that govern disease phenotype to a single primary disease driver. Network medicine, a new discipline that applies network science and systems biology to the analysis of complex biological systems and disease, offers a novel approach to overcoming these limitations of conventional drug discovery. Using the comprehensive protein-protein interaction network (interactome) as the template through which subnetworks that govern specific diseases are identified, potential disease drivers are unveiled and the effect of novel or repurposed drugs, used alone or in combination, is studied. This approach to drug discovery offers new and exciting unbiased possibilities for advancing our knowledge of disease mechanisms and precision therapeutics.

The Digital Twin in Medicine: A Key to the Future of Healthcare?

There is a growing need for precise diagnosis and personalized treatment of disease in recent years. Providing treatment tailored to each patient and maximizing efficacy and efficiency are broad goals of the healthcare system. As an engineering concept that connects the physical entity and digital space, the digital twin (DT) entered our lives at the beginning of Industry 4.0. It is evaluated as a revolution in many industrial fields and has shown the potential to be widely used in the field of medicine. This technology can offer innovative solutions for precise diagnosis and personalized treatment processes. Although there are difficulties in data collection, data fusion, and accurate simulation at this stage, we speculated that the DT may have an increasing use in the future and will become a new platform for personal health management and healthcare services. We introduced the DT technology and discussed the advantages and limitations of its applications in the medical field. This article aims to provide a perspective that combining Big Data, the Internet of Things (IoT), and artificial intelligence (AI) technology; the DT will help establish high-resolution models of patients to achieve precise diagnosis and personalized treatment.