Drug Development Research Articles

Application of microphysiological systems for nonclinical evaluation of cell therapies.

Microphysiological systems (MPS) are gaining broader application in the pharmaceutical industry but have primarily been leveraged in early discovery toxicology and pharmacology studies with small molecules. The adoption of MPS offers a promising avenue to reduce animal use, improve in-vitro-to-in-vivo translation of pharmacokinetics/pharmacodynamics and toxicity correlation, and provide mechanistic understanding of model species suitability. While MPS have demonstrated utility in these areas with small molecules and biologics, cell therapeutic MPS models in drug development have not been fully explored, let alone validated. Distinguishing features of MPS, including long-term viability and physiologically relevant expression of functional enzymes, receptors, and pharmacological targets make them attractive tools for nonclinical characterization. However, there is currently limited published evidence of MPS being utilized to study the disposition, metabolism, pharmacology, and toxicity profiles of cell therapies. This review provides an industry perspective on the nonclinical application of MPS on cell therapies, first with a focus on oncology applications followed by examples in regenerative medicine.

Advances in the study of gastric organoids as disease models

Gastric organoids are models created in the laboratory using stem cells and sophisticated three-dimensional cell culture techniques. These models have shown great promise in providing valuable insights into gastric physiology and advanced disease research. This review comprehensively summarizes and analyzes the research advances in culture methods and techniques for adult stem cells and induced pluripotent stem cell-derived organoids, and patient-derived organoids. The potential value of gastric organoids in studying the pathogenesis of stomach-related diseases and facilitating drug screening is initially discussed. The construction of gastric organoids involves several key steps, including cell extraction and culture, three-dimensional structure formation, and functional expression. Simulating the structure and function of the human stomach by disease modeling with gastric organoids provides a platform to study the mechanism of gastric cancer induction by Helicobacter pylori . In addition, in drug screening and development, gastric organoids can be used as a key tool to evaluate drug efficacy and toxicity in preclinical trials. They can also be used for precision medicine according to the specific conditions of patients with gastric cancer, to assess drug resistance, and to predict the possibility of adverse reactions. However, despite the impressive progress in the field of gastric organoids, there are still many unknowns that need to be addressed, especially in the field of regenerative medicine. Meanwhile, the reproducibility and consistency of organoid cultures are major challenges that must be overcome. These challenges have had a significant impact on the development of gastric organoids. Nonetheless, as technology continues to advance, we can foresee more comprehensive research in the construction of gastric organoids. Such research will provide better solutions for the treatment of stomach-related diseases and personalized medicine.

Serendipity in psychotropic drugs: Unearthing the origins

This is a dive into the history of several groundbreaking psychiatric drugs and their discoveries. The article begins by exploring the curious origin of the word “serendipity” following Horace Walpole’s encounter with a fairytale. This captivating emergence of “serendipity” is followed by an account of lithium discovered in 1817. About a century and a half later, lithium would receive FDA approval, becoming a cornerstone in the treatment of mania.The narrative meanders to other drugs that have a similar thread of serendipity woven into their beginnings— chlordiazepoxide, meprobamate, chlorpromazine, finally halting at iproniazid, an anti-tubercular agent that unexpectedly revealed its mood-enhancing potential and paved the way for the development of monoamine oxidase inhibitors as antidepressants.The authors navigate through the intricacies of chance and discovery that have influenced the trajectory of psychiatric drug development, leading to unexpected breakthroughs that have forever left their mark on the undeniably vast landscape that is mental health.

Bioguided Fractionation of Phyllanthus spp.: Unveiling Anticancer Potential through Metabolomic Correlation and ADMETox Insights.

Cancer remains a significant global health concern, with mortality rates steadily rising and prompting an urgent search for effective treatments. This study focuses on the medicinal properties of plants from the Phyllanthus genus, specifically Phyllanthus amarus and Phyllanthus niruri, which have shown promise in traditional medicine. Through bioguided fractionation using preparative high-performance liquid chromatography (HPLC), bioactive compounds were isolated and identified using ultra-performance liquid chromatography coupled to time-of-flight mass spectrometry (UHPLC-QTOF-MSE) and nuclear magnetic resonance (NMR) spectroscopy. Chemometric analyses such as principal component analysis (PCA) aided in understanding metabolite distribution. Biological assays demonstrated cytotoxic activities of specific fractions against cancer cell lines, notably the PhyN 4n fraction from P. niruri, which induced S-phase cell cycle arrest and apoptosis in HL60 cells. These findings underscore the anticancer potential of Phyllanthus species and lay the groundwork for future drug development efforts. The study's integration of advanced analytical techniques, chemometrics, and biological assays provides valuable insights for harnessing natural products in the fight against cancer.

Case Report book review: Computer-aided drug development

Case Report book review: Computer-aided drug development

Biomarkers for Precision Patient Selection in Cancer Therapy Approvals in the US, from 2011 to 2023.

During the period of 2011-2023, the US Food and Drug Administration (US FDA) granted 139 accelerated and 329 regular approvals for 86 and 152 cancer therapeutic products, respectively. The percentage of approvals for a biomarker-defined population was numerically higher in accelerated approvals in comparison to regular approvals, that is, 48% vs. 40%. From 2011-2016 to 2017-2023, there was an increasing number of approvals with biomarker-defined populations in lung and breast cancers, serving as the primary driver for the overall increase in the percentage of approvals for biomarker-defined populations in solid tumors relative to hematological malignancies. Over the years, approvals were incorporating a more diverse collection of distinct biomarkers, from 3 in 2011 to 16 in 2022. Overall, HER2, hormone receptor (HR), EGFR, ALK, BRAF, and PD-L1-defined populations received the highest numbers of approvals. The FDA decision on approving a biomarker-defined or an all-comers population may depend on a number of factors and may evolve over time based on emerging evidence. The review discusses selected FDA approvals where a pivotal trial enrolled an all-comers population but the approved indication was restricted to a biomarker-defined population, as well as challenges in clinical trial design in the context of precision medicine. The prominent role of biomarkers in optimizing trial design and identifying a population most likely to benefit from treatment underlines the significance of a comprehensive understanding of disease biology and drug mechanisms. Our review illustrates that biomarker-driven approaches enhance the likelihood of identifying optimal patient populations, potentially streamlining trials through accelerated approval pathways for cancer drug development.

Hits and misses with animal models of narcolepsy and the implications for drug discovery.

Narcolepsy is a chronic and rare neurological disorder characterized by disordered sleep. Based on animal models and further research in humans, the dysfunctional orexin system was identified as a contributing factor to the pathophysiology of narcolepsy. Animal models played a larger role in the discovery of some of the pharmacological agents with established benefit/risk profiles. In this review, the authors examine the phenotypes observed in animal models of narcolepsy and the characteristics of clinically used pharmacological agents in these animal models. Additionally, the authors compare the effects of clinically used pharmacological agents on the phenotypes in animal models with those observed in narcolepsy patients. Research in canine and mouse models have linked narcolepsy to the O×R2mutation and orexin deficiency, leading to new diagnostic criteria and a drug development focus. Advancements in pharmacological therapies have significantly improved narcolepsy management, with insights from both clinical experience and from animal models having led to new treatments such as low sodium oxybate and solriamfetol. However, challenges persist in addressing symptoms beyond excessive daytime sleepiness and cataplexy, highlighting the need for further research, including the development of diurnal animal models to enhance understanding and treatment options for narcolepsy.

AIGO-DTI: Predicting Drug-Target Interactions Based on Improved Drug Properties Combined with Adaptive Iterative Algorithms.

Artificial intelligence-based methods for predicting drug-target interactions (DTIs) aim to explore reliable drug candidate targets rapidly and cost-effectively to accelerate the drug development process. However, current methods are often limited by the topological regularities of drug molecules, making them difficult to generalize to a broader chemical space. Additionally, the use of similarity to measure DTI network links often introduces noise, leading to false DTI relationships and affecting the prediction accuracy. To address these issues, this study proposes an Adaptive Iterative Graph Optimization (AIGO)-DTI prediction framework. This framework integrates atomic cluster information and enhances molecular features through the design of functional group prompts and graph encoders, optimizing the construction of DTI association networks. Furthermore, the optimization of graph structure is transformed into a node similarity learning problem, utilizing multihead similarity metric functions to iteratively update the network structure to improve the quality of DTI information. Experimental results demonstrate the outstanding performance of AIGO-DTI on multiple public data sets and label reversal data sets. Case studies, molecular docking, and existing research validate its effectiveness and reliability. Overall, the method proposed in this study can construct comprehensive and reliable DTI association network information, providing new graphing and optimization strategies for DTI prediction, which contribute to efficient drug development and reduce target discovery costs.

Optimization of quenched fluorescent peptide substrates of SARS-CoV-2 3CLpro main protease (Mpro) from proteomic identification of P6-P6' active site specificity.

SARS-CoV-2 3C-like main protease (3CLpro) is essential for protein excision from the viral polyprotein. 3CLpro inhibitor drug development to block SARS-CoV-2 replication focuses on the catalytic non-prime (P) side for specificity and potency, but the importance of the prime (P') side in substrate specificity and for drug development remains underappreciated. We determined the P6-P6' specificity for 3CLpro from >800 cleavage sites that we identified using Proteomic Identification of Cleavage site Specificity (PICS). Cleavage occurred after the canonical P1-Gln and non-canonical P1-His and P1-Met residues. Moreover, P3 showed a preference for Arg/Lys and P3' for His. Essential H-bonds between the N-terminal Ser1 of protomer-B in 3CLpro dimers form with P1-His, but not with P1-Met. Nonetheless, cleavage occurs at P1-Met456 in native MAP4K5. Elevated reactive oxygen species in SARS-CoV-2 infection oxidize methionines. Molecular simulations revealed P1-MetOX forms an H-bond with Ser1 and notably, strong positive cooperativity between P1-Met with P3'-His was revealed, which enhanced peptide-cleavage rates. The highly plastic S3' subsite accommodates P3'-His that displays stabilizing backbone H-bonds with Thr25 lying central in a "'threonine trio" (Thr24-Thr25-Thr26) in the P'-binding domain I. Molecular docking simulations unveiled structure-activity relationships impacting 3CLpro-substrate interactions, and the role of these structural determinants was confirmed by MALDI-TOF-MS cleavage assays of P1'- and P3'-positional scanning peptide libraries carrying a 2nd optimal cut-site as an internal positive control. These data informed the design of two new and highly soluble 3CLproquenched-fluorescent peptide substrates for improved FRET monitoring of 3CLpro activity with 15× improved sensitivity over current assays.IMPORTANCEFrom global proteomics identification of >800 cleavage sites, we characterized the P6-P6' active site specificity of SARS-CoV-2 3CLpro using proteome-derived peptide library screens, molecular modeling simulations, and focussed positional peptide libraries. In P1', we show that alanine and serine are cleaved 3× faster than glycine and the hydrophobic small amino acids Leu, Ile, or Val prevent cleavage of otherwise optimal non-prime sequences. In characterizing non-canonical non-prime P1 specificity, we explored the unusual P1-Met specificity, discovering enhanced cleavage when in the oxidized state (P1-MetOX). We unveiled unexpected amino acid cooperativity at P1-Met with P3'-His and noncanonical P1-His with P2-Phe, and the importance of the threonine trio (Thr24-Thr25-Thr26) in the prime side binding domain I in defining prime side binding in SARS-CoV-2 3CLpro. From these analyses, we rationally designed quenched-fluorescence natural amino acid peptide substrates with >15× improved sensitivity and high peptide solubility, facilitating handling and application for screening of new antiviral drugs.

Antifungal activity of β-lapachone against a fluconazole-resistant Candida auris strain.

Candida spp. can be found in the human microbiome. However, immunocompromised patients are likely to develop invasive Candida infections, with mortality rates higher than 50%. The discovery of C. auris, a species that rapidly acquire antifungal resistance, increased the concern about Candida infections. The limited number of antifungal agents and the high incidence of resistance to them make imperative the development of new antifungal drugs. β-lapachone is a biological active naphthoquinone that displays antifungal activity against C. albicans and C. glabrata. The aim of this study was to evaluate if this substance affects C. auris growth and elucidate its mechanism of action. A fluconazole-resistant C. auris isolate was used in this study. The antifungal activity of β-lapachone was determined through microbroth dilution assays, and its mechanism of action was evaluated using fluorescent probes. Interaction with fluconazole and amphotericin B was assessed by disk diffusion assay and checkerboard. β-lapachone inhibited planktonic C. auris cell growth by 92.7%, biofilm formation by 84.9%, and decrease the metabolism of preformed biofilms by 87.1% at 100µg/ml. At 100µg/ml, reductions of 30% and 59% of Calcofluor White and Nile red fluorescences were observed, indicating that β-lapachone affects cell wall chitin and neutral lipids content, respectively. Also, the ratio 590nm/529nm of JC-1 decreased 52%, showing that the compound affects mitochondria. No synergism was observed between β-lapachone and fluconazole or amphotericin B. Data show that β-lapachone may be a promising candidate to be used as monotherapy to treat C. auris resistant infections.

Identification of novel single nucleotide variants in the drug resistance mechanism of Mycobacterium tuberculosis isolates by whole-genome analysis

BackgroundTuberculosis (TB) represents a major global health challenge. Drug resistance in Mycobacterium tuberculosis (MTB) poses a substantial obstacle to effective TB treatment. Identifying genomic mutations in MTB isolates holds promise for unraveling the underlying mechanisms of drug resistance in this bacterium.MethodsIn this study, we investigated the roles of single nucleotide variants (SNVs) in MTB isolates resistant to four antibiotics (moxifloxacin, ofloxacin, amikacin, and capreomycin) through whole-genome analysis. We identified the drug-resistance-associated SNVs by comparing the genomes of MTB isolates with reference genomes using the MuMmer4 tool.ResultsWe observed a strikingly high proportion (94.2%) of MTB isolates resistant to ofloxacin, underscoring the current prevalence of drug resistance in MTB. An average of 3529 SNVs were detected in a single ofloxacin-resistant isolate, indicating a mutation rate of approximately 0.08% under the selective pressure of ofloxacin exposure. We identified a set of 60 SNVs associated with extensively drug-resistant tuberculosis (XDR-TB), among which 42 SNVs were non-synonymous mutations located in the coding regions of nine key genes (ctpI, desA3, mce1R, moeB1, ndhA, PE_PGRS4, PPE18, rpsA, secF). Protein structure modeling revealed that SNVs of three genes (PE_PGRS4, desA3, secF) are close to the critical catalytic active sites in the three-dimensional structure of the coding proteins.ConclusionThis comprehensive study elucidates novel resistance mechanisms in MTB against antibiotics, paving the way for future design and development of anti-tuberculosis drugs.

Evolution of the Innovative Therapies for Children With Cancer Consortium Trial Portfolio for Drug Development for Children With Cancer.

The aim of the Innovative Therapies for Children with Cancer (ITCC) consortium is to improve access to novel therapies for children and adolescents with cancer. The evolution of the ITCC clinical trial portfolio since 2003 was reviewed. All ITCC-labeled phase I/II trials opened between January 1, 2003 and February 3, 2018 were analyzed in two periods (2003-2010 and 2011-2018), and data were extracted from the ITCC database, regulatory agencies' registries, and publications. Sixty-one trials (62% industry-sponsored) enrolled 3,198 patients. The number of trials in the second period increased by almost 300% (16 v 45). All biomarker-driven trials (n = 14) were conducted in the second period. The use of rolling six and model-based designs increased (1 of 9, 11% v 21 of 31, 68%), and that of 3 + 3 designs decreased (5 of 9, 55% v 5 of 31, 16%; P = .014). The proportion of studies evaluating chemotherapeutics only decreased (5 of 16, 31% v 4 of 45, 9%), the proportion of single-agent targeted therapies did not change (9 of 16, 56.2% v 24 of 45, 53.3%), the proportion of combination targeted therapies trials increased (2 of 16, 12%, v 17 of 45, 38%), the proportion of randomized phase II trials increased (1 of 7, 14% v 8 of 14, 57%). More trials were part of a pediatric investigation plan in the second period (4 of 16, 25% v 21 of 45, 46%). The median time for Ethics Committees' approvals was 1.7 times longer for academic compared with industry-sponsored trials. This study reports a shift in the paradigm of early drug development for childhood cancers, with more biologically relevant targets evaluated in biomarker-driven trials or in combination with other therapies and with more model-based or randomized designs and a greater focus on fulfilling regulatory requirements. Improvement of trial setup and recruitment could increase the number of patients benefiting from novel agents.

Project Optimus elicits the "holistic" benefits of PK/PD modeling of immunotherapy.

Leveraging a heterogeneous dataset, a recent article demonstrated the application of pharmacometric modeling to inform dosing of nivolumab-relatlimab fixed-dose combination in special populations, including adolescents lacking clinical data. Use of model-informed approaches during clinical drug development can be cost effective, ensuring faster access to drugs, thus enhancing patient outcomes.

Fluorescent small molecule donors.

Small molecule donors (SMDs) play subtle roles in the signaling mechanism and disease treatments. While many excellent SMDs have been developed, dosage control, targeted delivery, spatiotemporal feedback, as well as the efficiency evaluation of small molecules are still key challenges. Accordingly, fluorescent small molecule donors (FSMDs) have emerged to meet these challenges. FSMDs enable controllable release and non-invasive real-time monitoring, providing significant advantages for drug development and clinical diagnosis. Integration of FSMDs with chemotherapeutic, photodynamic or photothermal properties can take full advantage of each mode to enhance therapeutic efficacy. Given the remarkable properties and the thriving development of FSMDs, we believe a review is needed to summarize the design, triggering strategies and tracking mechanisms of FSMDs. With this review, we compiled FSMDs for most small molecules (nitric oxide, carbon monoxide, hydrogen sulfide, sulfur dioxide, reactive oxygen species and formaldehyde), and discuss recent progress concerning their molecular design, structural classification, mechanisms of generation, triggered release, structure-activity relationships, and the fluorescence response mechanism. Firstly, from the large number of fluorescent small molecular donors available, we have organized the common structures for producing different types of small molecules, providing a general strategy for the development of FSMDs. Secondly, we have classified FSMDs in terms of the respective donor types and fluorophore structures. Thirdly, we discuss the mechanisms and factors associated with the controlled release of small molecules and the regulation of the fluorescence responses, from which universal guidelines for optical properties and structure rearrangement were established, mainly involving light-controlled, enzyme-activated, reactive oxygen species-triggered, biothiol-triggered, single-electron reduction, click chemistry, and other triggering mechanisms. Fourthly, representative applications of FSMDs for trackable release, and evaluation monitoring, as well as for visible in vivo treatment are outlined, to illustrate the potential of FSMDs in drug screening and precision medicine. Finally, we discuss the opportunities and remaining challenges for the development of FSMDs for practical and clinical applications, which we anticipate will stimulate the attention of researchers in the diverse fields of chemistry, pharmacology, chemical biology and clinical chemistry. With this review, we hope to impart new understanding thereby enabling the rapid development of the next generation of FSMDs.

Chemical manipulation of an activation/inhibition switch in the nuclear receptor PXR

Nuclear receptors are ligand-activated transcription factors that can often be useful drug targets. Unfortunately, ligand promiscuity leads to two-thirds of receptors remaining clinically untargeted. PXR is a nuclear receptor that can be activated by diverse compounds to elevate metabolism, negatively impacting drug efficacy and safety. This presents a barrier to drug development because compounds designed to target other proteins must avoid PXR activation while retaining potency for the desired target. This problem could be avoided by using PXR antagonists, but these compounds are rare, and their molecular mechanisms remain unknown. Here, we report structurally related PXR-selective agonists and antagonists and their corresponding co-crystal structures to describe mechanisms of antagonism and selectivity. Structural and computational approaches show that antagonists induce PXR conformational changes incompatible with transcriptional coactivator recruitment. These results guide the design of compounds with predictable agonist/antagonist activities and bolster efforts to generate antagonists to prevent PXR activation interfering with other drugs.

Synergy of lytic phage pB23 and meropenem combination against carbapenem-resistant Acinetobacter baumannii.

Phage-antibiotic combination treatment is a novel noteworthy drug delivery method in anti-infection. In the current study, we have isolated a new phage, pB23, against carbapenem-resistant Acinetobacter baumannii 2023. Synergistic antibacterial effect between phage pB23 and meropenem combination could be more stable, using moderate doses of phage (multiplicity of infection ranging from 0.1 to 1,000) based on results of in vitro antibacterial activity. Phage pB23 and meropenem combination could effectively clear mature biofilms and prevent biofilm formation of carbapenem-resistant Acinetobacter baumannii in vitro. Phage pB23 and meropenem combination also has good synergistic antibacterial effects against carbapenem-resistant Acinetobacter baumannii in different growth phases under static culture conditions. The pig skin explant model shows that phage pB23 and meropenem combination has a synergistic effect to remove bacteria from wounds ex vivo. Phage pB23 and meropenem combination also exhibited a synergistic antibacterial effect in vivo using a zebrafish infection mode. The potential promotion of phage proliferation by meropenem and the sensitivity recovery of phage-resistant bacteria to meropenem might elucidate the mechanism of the synergistic antimicrobial activity. In summary, our study illustrates that phage pB23 and meropenem combination could produce synergistic antibacterial effects against carbapenem-resistant Acinetobacter baumannii under static growth conditions. This study also demonstrates that phage-antibiotic combination will become an effective strategy to enhance antibacterial activity of individual drug and provide a new idea of the drug development for the treatment of infections due to carbapenem-resistant Acinetobacter baumannii and other multidrug-resistant bacteria.

Large-Scale Asymmetric Synthesis of Spirocyclic Building Blocks for Drug Development

Large-Scale Asymmetric Synthesis of Spirocyclic Building Blocks for Drug Development

Potential of natural products in inflammation: biological activities, structure-activity relationships, and mechanistic targets.

A balance between the development and suppression of inflammation can always be found in the body. When this balance is disturbed, a strong inflammatory response can damage the body. It sometimes is necessary to use drugs with a significant anti-inflammatory effect, such as nonsteroidal anti-inflammatory drugs and steroid hormones, to control inflammation in the body. However, the existing anti-inflammatory drugs have many adverse effects, which can be deadly in severe cases, making research into new safer and more effective anti-inflammatory drugs necessary. Currently, numerous types of natural products with anti-inflammatory activity and distinct structural features are available, and these natural products have great potential for the development of novel anti-inflammatory drugs. This review summarizes 260 natural products and their derivatives with anti-inflammatory activities in the last two decades, classified by their active ingredients, and focuses on their structure-activity relationships in anti-inflammation to lay the foundation for subsequent new drug development. We also elucidate the mechanisms and pathways of natural products that exert anti-inflammatory effects via network pharmacology predictions, providing direction for identifying subsequent targets of anti-inflammatory natural products.

Investigating gene signatures associated with immunity in colon adenocarcinoma to predict the immunotherapy effectiveness using NFM and WGCNA algorithms.

Colon adenocarcinoma (COAD), a frequently encountered and highly lethal malignancy of the digestive system, has been the focus of intensive research regarding its prognosis. The intricate immune microenvironment plays a pivotal role in the pathological progression of COAD; nevertheless, the underlying molecular mechanisms remain incompletely understood. This study aims to explore the immune gene expression patterns in COAD, construct a robust prognostic model, and delve into the molecular mechanisms and potential therapeutic targets for COAD liver metastasis, thereby providing critical support for individualized treatment strategies and prognostic evaluation. Initially, we curated a comprehensive dataset by screening 2600 immune-related genes (IRGs) from the ImmPort and InnateDB databases, successfully obtaining a rich data resource. Subsequently, the COAD patient cohort was classified using the non-negative matrix factorization (NMF) algorithm, enabling accurate categorization. Continuing on, utilizing the weighted gene co-expression network analysis (WGCNA) method, we analyzed the top 5000 genes with the smallest p-values among the differentially expressed genes (DEGs) between immune subtypes. Through this rigorous screening process, we identified the gene modules with the strongest correlation to the COAD subpopulation, and the intersection of genes in these modules with DEGs (COAD vs COAD vs Normal colon tissue) is referred to as Differentially Expressed Immune Genes Associated with COAD (DEIGRC). Employing diverse bioinformatics methodologies, we successfully developed a prognostic model (DPM) consisting of six genes derived from the DEIGRC, which was further validated across multiple independent datasets. Not only does this predictive model accurately forecast the prognosis of COAD patients, but it also provides valuable insights for formulating personalized treatment regimens. Within the constructed DPM, we observed a downregulation of CALB2 expression levels in COAD tissues, whereas NOXA1, KDF1, LARS2, GSR, and TIMP1 exhibited upregulated expression levels. These genes likely play indispensable roles in the initiation and progression of COAD and thus represent potential therapeutic targets for patient management. Furthermore, our investigation into the molecular mechanisms and therapeutic targets for COAD liver metastasis revealed associations with relevant processes such as fat digestion and absorption, cancer gene protein polysaccharides, and nitrogen metabolism. Consequently, genes including CAV1, ANXA1, CPS1, EDNRA, and GC emerge as promising candidates as therapeutic targets for COAD liver metastasis, thereby providing crucial insights for future clinical practices and drug development. In summary, this study uncovers the immune gene expression patterns in COAD, establishes a robust prognostic model, and elucidates the molecular mechanisms and potential therapeutic targets for COAD liver metastasis, thereby possessing significant theoretical and clinical implications. These findings are anticipated to offer substantial support for both the treatment and prognosis management of COAD patients.

Envisioning the Future of Personalized Medicine: Role and Realities of Digital Twins.

Digital twins have emerged as a groundbreaking concept in personalized medicine, offering immense potential to transform health care delivery and improve patient outcomes. It is important to highlight the impact of digital twins on personalized medicine across the understanding of patient health, risk assessment, clinical trials and drug development, and patient monitoring. By mirroring individual health profiles, digital twins offer unparalleled insights into patient-specific conditions, enabling more accurate risk assessments and tailored interventions. However, their application extends beyond clinical benefits, prompting significant ethical debates over data privacy, consent, and potential biases in health care. The rapid evolution of this technology necessitates a careful balancing act between innovation and ethical responsibility. As the field of personalized medicine continues to evolve, digital twins hold tremendous promise in transforming health care delivery and revolutionizing patient care. While challenges exist, the continued development and integration of digital twins hold the potential to revolutionize personalized medicine, ushering in an era of tailored treatments and improved patient well-being. Digital twins can assist in recognizing trends and indicators that might signal the presence of diseases or forecast the likelihood of developing specific medical conditions, along with the progression of such diseases. Nevertheless, the use of human digital twins gives rise to ethical dilemmas related to informed consent, data ownership, and the potential for discrimination based on health profiles. There is a critical need for robust guidelines and regulations to navigate these challenges, ensuring that the pursuit of advanced health care solutions does not compromise patient rights and well-being. This viewpoint aims to ignite a comprehensive dialogue on the responsible integration of digital twins in medicine, advocating for a future where technology serves as a cornerstone for personalized, ethical, and effective patient care.