Asta Literature Retrieval: Pathophysiology and clinical mechanisms of Gorham-Stout disease. Core disease mechanisms, molecular and cellular path...

This report is retrieval-only and is generated directly from Asta results.

• Papers retrieved: 20
• Snippets retrieved: 20

Relevant Papers

[1] New therapeutic targets in rare genetic skeletal diseases

• Authors: M. Briggs, Peter A. Bell, M. Wright, K. A. Pirog
• Year: 2015
• Venue: Expert Opinion on Orphan Drugs
• URL: https://www.semanticscholar.org/paper/1363107f71ae6d2d60abca471cddf3da5d13644b
• DOI: 10.1517/21678707.2015.1083853
• PMID: 26635999
• PMCID: 4643203
• Citations: 37
• Influential citations: 1
• Summary: An overview of disease mechanisms that are shared amongst groups of different GSDs and potential therapeutic approaches that are under investigation are described to generate critical mass for the identification and validation of novel therapeutic targets and biomarkers.
• Evidence snippets:
• Snippet 1 (score: 0.425) > proteins of the cartilage ECM such as type II collagen [50]. However, emerging knowledge suggests that the primary genetic defect may be less important than the cells' response to the expression of the mutant gene product [107]. Moreover, the largely overlooked response of a cell (i.e. chondrocyte) to the abnormal extracellular environment is also important for disease progression as illustrated by several GSDs discussed in this review. > It is important that 'omics'-based approaches and technologies are systematically applied to the study of rare GSDs so that definitive reference profiles and disease signatures are generated for each phenotype. These can then be used in a Systems Biology approach to identify both common and dissimilar pathological signatures and disease mechanisms. This approach is entirely dependent upon relevant in vitro and in vivo models (and also novel 'disease-mechanism phenocopies' [107]) for testing new diagnostic and prognostic tools and for determining the molecular mechanisms that underpin the pathophysiology so that effective therapeutic treatments can be developed and validated. This approach will eventually lead to personalized treatments and care strategies centred on shared disease mechanisms with the use of relevant biomarkers to monitor the efficacy of treatment and disease progression. > It is vital that all relevant stakeholders are involved from the outset in defining the appropriate outcomes of any potential therapeutic regime. The perceptions of a successful therapy can differ widely between the clinical academic community and the relevant patient-support groups and it is vital that there is engagement on all these issues. > In summary, the identification of causative genes and mutations for GSDs over the last 20 years, coupled with the generation and in-depth analysis of a plethora of relevant cell and mouse models, has derived new knowledge on disease mechanisms and suggested potential therapeutic targets. The fast-evolving hypothesis that clinically disparate diseases can share common disease mechanisms is a powerful concept that will generate critical mass for the identification and validation of novel therapeutic targets and biomarkers.

[2] Changes in Serum Proteomic Profiles at Different Stages of Pregnancy Toxemia in Goats

• Authors: M. Uzti̇mür, C. N. Ünal, Gurler Akpinar
• Year: 2025
• Venue: Journal of Veterinary Internal Medicine
• URL: https://www.semanticscholar.org/paper/4b9c488b5dbd65d7b26fd2ad9aed70e8c4b59942
• DOI: 10.1111/jvim.70139
• PMID: 40492724
• PMCID: 12150350
• Summary: Understanding the serum proteome profiles of goats with pregnancy toxemia might help identify the proteomes and pathways responsible for the development of this disease and improve diagnosis and treatment.
• Evidence snippets:
• Snippet 1 (score: 0.422) > The pathophysiology and progression of this disease are not fully understood. > Traditional biomedical research has focused on the analysis of single genes, proteins, metabolites, or metabolic pathways in diseases. This molecular reductionist approach is based on the assumption that identifying genetic variations and molecular components will lead to new treatments for diseases [13][14][15][16]. However, many diseases are complex and multifactorial, and in order to determine the phenotype of such diseases, it is necessary to understand the changes that occur in more than one gene, pathway, protein, or metabolite at the cellular, tissue, and organismal levels [17][18][19]. Therefore, in recent years, proteomics, as one field of multi-omics technologies, has helped in evaluating the complex pathogenetic mechanisms of different diseases from a broad perspective and has made substantial contributions [20,21]. In veterinary medicine, proteomic analysis of metabolic diseases such as ketosis [16], hypocalcemia [22], and fatty liver [23] in dairy cows has contributed valuable insights for the definition of new pathophysiological pathways and new diagnosis and treatment protocols for these diseases. The proteomic approach can contribute importantly to a broad and detailed understanding of the changes that occur at the organismal level associated with the increase in BHBA concentration in goats with pregnancy toxemia. Our aim was to evaluate the serum protein profiles of goats with SPT or CPT using proteomic techniques to determine the proteomic profiles of these animals and to identify the relevant pathophysiological mechanisms.

[3] Organoids in gastrointestinal diseases: from bench to clinic

• Authors: Qinying Wang, Fanying Guo, Qinyuan Zhang, Tingting Hu, Yutao Jin et al.
• Year: 2024
• Venue: MedComm
• URL: https://www.semanticscholar.org/paper/9b8880d8b9d45670da950197d7e353794f51d09e
• DOI: 10.1002/mco2.574
• PMID: 38948115
• PMCID: 11214594
• Citations: 12
• Summary: A comprehensive and systematical depiction of organoids models is drawn, providing a novel insight into the utilization of organoids models from bench to clinic and clinical adhibition.
• Evidence snippets:
• Snippet 1 (score: 0.402) > Organoids models offer a robust platform for investigating the potential mechanisms of GI diseases and evaluating potential therapeutic interventions.By culturing organoids derived from patients' tissues or stem cells, researchers can delve into disease-specific cellular and molecular pathways, encompassing aberrant cell signaling, perturbed immune responses, and dysfunctional metabolic processes.These disease-specific phenotypes enable the study of disease progression, screening of prospective therapeutics, as well as identification of novel drug targets and mechanisms of action for GI diseases in a clinically relevant context.

[4] Therapies for Mitochondrial Disease: Past, Present, and Future

• Authors: Megan Ball, Nicole J. Van Bergen, A. Compton, David R Thorburn, S. Rahman et al.
• Year: 2025
• Venue: Journal of Inherited Metabolic Disease
• URL: https://www.semanticscholar.org/paper/196ee50a950f29bc4134cfb8fe6bdfa9a3a1468b
• DOI: 10.1002/jimd.70065
• PMID: 40714961
• PMCID: 12301291
• Citations: 2
• Summary: The latest developments in the pursuit to identify effective treatments for mitochondrial disease are examined and the barriers impeding their success in translation to clinical practice are discussed.
• Evidence snippets:
• Snippet 1 (score: 0.392) > Mitochondrial disease is a diverse group of clinically and genetically complex disorders caused by pathogenic variants in nuclear or mitochondrial DNA‐encoded genes that disrupt mitochondrial energy production or other important mitochondrial pathways. Mitochondrial disease can present with a wide spectrum of clinical features and can often be difficult to recognize. These conditions can be devastating; however, for the majority, there is no targeted treatment. In the last 60 years, mitochondrial medicine has experienced significant evolution, moving from the pre‐molecular era to the Age of Genomics in which considerable gene discovery and advancement in our understanding of the pathophysiology of mitochondrial disease have been made. In the last decade, in response to the urgent need for effective treatments, a wide range of emerging therapies have been developed, driven by innovative approaches addressing both the genetic and cellular mechanisms underpinning the diseases. Emerging therapies include dietary intervention, small molecule therapies aimed to restore mitochondrial function, stem cell or liver transplantation, and gene or RNA‐based therapies. However, despite these advances, translation to clinical practice is complicated by the sheer genetic and clinical complexity of mitochondrial disease, difficulty in efficient and precise delivery of therapies to affected tissues, rarity of individual genetic conditions, lack of reliable biomarkers and clinically relevant outcome measures, and the dearth of natural history data. This review examines the latest developments in the pursuit to identify effective treatments for mitochondrial disease and discusses the barriers impeding their success in translation to clinical practice. While treatment for mitochondrial disease may be on the horizon, many challenges must be addressed before it can become a reality.

[5] Pharmacometrics meets statistics—A synergy for modern drug development

• Authors: Y. Ryeznik, O. Sverdlov, E. Svensson, G. Montepiedra, A. Hooker et al.
• Year: 2021
• Venue: CPT: Pharmacometrics & Systems Pharmacology
• URL: https://www.semanticscholar.org/paper/7f3da05ef4373c4607afe634459d513b57b8ca7a
• DOI: 10.1002/psp4.12696
• PMID: 34318621
• PMCID: 8520751
• Citations: 12
• Summary: It is shown that statistics and pharmacometrics have more in common than what keeps them apart, and collectively, the synergy from these two quantitative disciplines can provide greater advances in clinical research and development, resulting in novel and more effective medicines to patients with medical need.
• Evidence snippets:
• Snippet 1 (score: 0.378) > Disease progression models use mathematical relations to quantify the time course of the disease with respect to some relevant biomarkers or clinical end points. 36 Integration of disease models with PK/PD models can further help evaluate the impact of drug on the disease trajectory; for instance, to assess how a particular drug intervention can slow down the disease progression. One can distinguish three types of disease progression models: empirical; semimechanistic; and systems biology. 36 Empirical models are frequently applied in diseases where underlying mechanisms are elusive (e.g., neuropsychiatry) and the clinical outcomes are subjective scores (e.g., patient-reported outcomes or clinician's assessments). By contrast, systems biology models provide comprehensive descriptions of the biological and/or molecular pathways (e.g., bone remodeling in osteoporosis). Semimechanistic models may utilize knowledge of both the biology and pathophysiology of the disease, but in a less comprehensive way than systems biology models. It stands to reason that both statistical and PMx expertise is essential for implementing such models. > There are several merits of disease progression models in drug development. First, they can allow prediction of individual disease trajectories, taking into account relevant covariates and uncertainties, thereby helping investigators to identify eligible subjects for clinical trials. In addition, these models can be used to optimize clinical trial designs through M&S, and they can frequently yield more powerful statistical analyses than standard statistical approaches, such as linear MMRMs. 37 Disease models are now widely adopted in drug development. One recent example is the regulatory endorsement of the clinical trials simulation tool for Alzheimer's disease progression, both by the FDA and the European Medicines Agency (EMA). 38

[6] Solving the Evidence Interpretability Crisis in Health Technology Assessment: A Role for Mechanistic Models?

• Authors: E. Courcelles, J. Boissel, J. Massol, I. Klingmann, R. Kahoul et al.
• Year: 2022
• Venue: Frontiers in Medical Technology
• URL: https://www.semanticscholar.org/paper/877d5b1b75599745f704a9c8371f74601ff17e2f
• DOI: 10.3389/fmedt.2022.810315
• PMID: 35281671
• PMCID: 8907708
• Citations: 6
• Summary: Light is shed on different stakeholder's contributions and needs in the appraisal phase and how mechanistic modeling strategies and reporting can contribute to this effort to implement mechanistic models central in the evidence generation, synthesis, and appraisal of HTA so that the totality of mechanistic and clinical evidence can be leveraged by all relevant stakeholders.
• Evidence snippets:
• Snippet 1 (score: 0.378) > A second limitation in HTA is the fact that currently population (and sometimes stratified) medicine is pursued during clinical Uncertainty not completely addressed in competent authority assessment report Example use of MIDD relevant to address uncertainty potentially also during HTA What is the optimal dosage in the clinical context? > Physiologically based pharmacokinetic models can investigate dosing-regimens relevant for regulatory review and product labels (9) and can also mimic real-life adherence to prescribed treatment regimens (see also below) or pharmacology-relevant characteristics of special populations as well as drug-drug interactions. > What is the duration of the effectiveness, especially with chronic use of a treatment? > Mechanistic models can predict the long-term disease progression by extrapolation of shorter-term findings under the constraints of how the components of the system function (and these constraints convey biological plausibility by design). An example is the use of a mechanism-based disease progression model for comparison of long-term effects of pioglitazone, metformin, and gliclazide on disease processes underlying Type 2 Diabetes Mellitus (10). Another example is prediction of long-term outcomes by short-term marker data as demonstrated by a semi-mechanistic approach in context of osteoporosis treatment (11). > What is the efficacy for relevant clinical outcomes? > Mechanistic models combined with pharmacometric approaches can translate findings for one outcome to a range of other outcomes. An example of survival modeling on the back of a mechanistic description is the modeling framework for CD19-Specific CAR-T cell immunotherapy using a quantitative systems pharmacology model (12). > What is the size of the clinical effect dependent on patient characteristics and extrinsic factors? > Data-driven modeling techniques can capture correlation within clinical data. Describing the clinical effect of a drug can also be based on mechanistic considerations. Such models either (a) link disease phenotypes to increasingly granular mathematical representations of pathophysiologic processes (top-down approach) or (b) derive functional, computable cellular networks from the molecular building blocks of genes and proteins to elucidate the impact of pathologic or therapeutic alterations on network operating states and hence clinical phenotype (bottom-up) [

[7] Novel Approaches to Studying SLC13A5 Disease

• Authors: Adriana S. Beltran
• Year: 2024
• Venue: Metabolites
• URL: https://www.semanticscholar.org/paper/8469c534cd81d96f84b61e2d963dead12088feb7
• DOI: 10.3390/metabo14020084
• PMID: 38392976
• PMCID: 10890222
• Citations: 2
• Summary: Current technologies for generating patient-specific induced pluripotent stem cells (iPSCs) and their inherent advantages and limitations are discussed, followed by a summary of the methods for differentiating iPSCs into neurons, hepatocytes, and organoids.
• Evidence snippets:
• Snippet 1 (score: 0.377) > The precise pathophysiology underlying how SLC13A5 loss-of-function results in epilepsy refractory to treatment is a subject of open and ongoing research. Several hypotheses suggest SLC13A5 alters metabolic pathways, leading to neuronal dysfunction. Conversely, therapeutic inhibition of NaCT in the liver is a target to improve metabolic diseases, including non-alcoholic fatty liver disease, obesity, and insulin resistance. Thus, functionally accurate modeling and characterization of the mechanisms involved in citrate transport disruption are critical for understanding its role in human disease. > IPSC-derived cellular systems are a powerful tool for modeling rare human genetic diseases, such as SLC13A5 (Figure 5). IPSCs derived from patients containing the genetic information of the disease can overcome the limitations of animal models, providing access to relevant human cell types that recapitulate the disease phenotype. For instance, patient-derived iPSCs differentiated into neurons or hepatocytes can be used to investigate molecular and cellular mechanisms, including citrate transport and accumulation, energy metabolism, oxidative stress, and other cellular processes. They can also be used to define the spectrum of the disease and how different mutations might lead to various disease severities, screen for potential therapeutic compounds that can restore the transporter function or ameliorate the symptoms, and enable personalized medicine approaches that can tailor treatments to individual patients based on their genetic background and disease severity. > transport disruption are critical for understanding its role in human disease. > IPSC-derived cellular systems are a powerful tool for modeling rare human genetic diseases, such as SLC13A5 (Figure 5). IPSCs derived from patients containing the genetic information of the disease can overcome the limitations of animal models, providing access to relevant human cell types that recapitulate the disease phenotype. For instance, patient-derived iPSCs differentiated into neurons or hepatocytes can be used to investigate molecular and cellular mechanisms, including citrate transport and accumulation, energy metabolism, oxidative stress, and other cellular processes.

[8] 18O-assisted dynamic metabolomics for individualized diagnostics and treatment of human diseases

• Authors: E. Nemutlu, Song Zhang, N. Juranic, A. Terzic, S. Macura et al.
• Year: 2012
• Venue: Croatian Medical Journal
• URL: https://www.semanticscholar.org/paper/880f053c7f060db4b990e447d0a22c4b69372ddb
• DOI: 10.3325/cmj.2012.53.529
• PMID: 23275318
• PMCID: 3541579
• Citations: 28
• Summary: The potential use of dynamic phosphometabolomic platform for disease diagnostics currently under development at Mayo Clinic is described and discussed briefly.
• Evidence snippets:
• Snippet 1 (score: 0.373) > Living cells represent an integrated and interacting network of genes, transcripts, proteins, small signaling molecules, and metabolites that define cellular phenotype and function. Traditionally the focus of biomedical research was on individual genes, single protein targets, single metabolites, and metabolic or signaling pathways. This "molecular reductionist" paradigm was based on the assumption that identifying genetic variations and molecular components would lead to discovery of cures for human diseases. However, most of diseases are complex and multi-factorial and the disease phenotype is determined by the alterations of multiple genes, pathways, proteins and metabolites (at cellular, tissue, and organismal levels). Therefore, an integrated "omics" approach is more viable direction for uncovering alterations in metabolic networks, disease mechanisms, and mechanisms of drug effects. > Recent advent of large-scale metabolomics and fluxomic (metabolite dynamics and metabolic flux analysis) completed the "omics revolution" (Figure 1), where genomics, transcriptomics, proteomics, metabolomics, and fluxomics all together complement phenotype determination of living organism. Such integrated "omics" cascades provide a framework for advances in system and network biology, integrative physiology, and system medicine as well as system pharmacology and regenerative medicine. Noteworthy is the "reverse omic" approach or "metabolomicsinformed pharmacogenomics, " where discovery of specific metabolite changes have led to discovery of genetic alterations (2). Therefore, bringing new "omics" technologies to clinical practice will improve disease diagnostics and treatment by targeting drugs and procedures for each unique transcriptomic and metabolomic profiles.

[9] Molecular insights into the premature aging disease progeria

• Authors: Sandra Vidak, R. Foisner
• Year: 2016
• Venue: Histochemistry and Cell Biology
• URL: https://www.semanticscholar.org/paper/60fb3b46bb7e42d5d08cc3b7cbc783b118300c31
• DOI: 10.1007/s00418-016-1411-1
• PMID: 26847180
• PMCID: 4796323
• Citations: 105
• Influential citations: 3
• Summary: Changes in mechanosignaling, altered chromatin organization and impaired genome stability, and changes in signaling pathways, leading to impaired regulation of adult stem cells, defective extracellular matrix production and premature cell senescence are discussed.
• Evidence snippets:
• Snippet 1 (score: 0.371) > The number of molecular biological studies aiming at the identification of lamin-mediated molecular disease mechanisms involved in HGPS increased tremendously following the surprising discovery that LMNA is causally linked to the premature aging disease HGPS in 2003. Despite numerous cellular pathways that were identified to be affected by the expression of the mutant lamin A protein (Fig. 2), the mechanistic details behind these effects are still unclear in most cases. Knowledge based on what was already known on lamin biology before the protein was linked to HGPS and findings on novel roles of lamins in diverse pathways in recent years allowed the launch of translational studies and the efficient search for drug targets and therapeutic approaches within a short time period. The results of the first clinical trials taught us that some improvements of the disease phenotypes can be achieved by FTI treatment, but they also made clear that we need a much better understanding of the underlying disease mechanisms to be able to tackle specific aspects of the disease in a more focused approach. It will also be important to elucidate which of the numerous pathways found to be impaired in HGPS are most relevant for and causally involved in the pathologies, and which ones are just bystanders.

[10] Role of Transcriptomics in Precision Oncology

• Authors: Ruby Srivastava
• Year: 2024
• Venue: Reports of Radiotherapy and Oncology
• URL: https://www.semanticscholar.org/paper/0bd862558bbb7286336111d9dfd232b5f905d3d9
• DOI: 10.5812/rro-142195
• Citations: 4
• Summary: : Transcriptome profiling is one of the most widely used approaches in the field of multiomics research. It plays a crucial role in the prognostic, diagnostic, and predictive treatment of cancer patients. Novel next-generation sequencing (NGS) technologies permit the identification of cancer biomarkers, gene signatures, and their abnormal expression, affecting oncogenic and molecular targets and novel biomarkers for cancer therapies. Multiomics studies have changed the overall understanding o...
• Evidence snippets:
• Snippet 1 (score: 0.371) > : Transcriptome profiling is one of the most widely used approaches in the field of multiomics research. It plays a crucial role in the prognostic, diagnostic, and predictive treatment of cancer patients. Novel next-generation sequencing (NGS) technologies permit the identification of cancer biomarkers, gene signatures, and their abnormal expression, affecting oncogenic and molecular targets and novel biomarkers for cancer therapies. Multiomics studies have changed the overall understanding of cancer and opened a precise perspective for tumor diagnostics and therapy. The use of these approaches has strengthened our understanding of disease pathophysiology and classifications at the molecular level, including specific interference with drug mechanisms of action. Still, it has limited added value in the clinical setting. The omics data on precision medicine include the application of data from genes, transcripts, and proteins for diagnosis, monitoring of diseases, risk factor determination, counseling, and development of novel therapeutics. Bioinformatics applications have expanded statistics-based analysis toward deriving molecular pathways and process models for characterizing phenotypes and drug action mechanisms. In this review, we will discuss transcriptomics and interference analysis that allows the identification of predictive biomarkers at the molecular level to test drug response and analyze the molecular process interface of disease progression-relevant pathophysiology and mechanism of action to propose predictive biomarkers.

[11] From molecular signatures to predictive biomarkers: modeling disease pathophysiology and drug mechanism of action

• Authors: A. Heinzel, P. Perco, G. Mayer, R. Oberbauer, A. Lukas et al.
• Year: 2014
• Venue: Frontiers in Cell and Developmental Biology
• URL: https://www.semanticscholar.org/paper/36d6c03a528c1358c0ae5b667cca5ce73b2fbee5
• DOI: 10.3389/fcell.2014.00037
• PMID: 25364744
• PMCID: 4207010
• Citations: 23
• Summary: This work exemplifies a computational workflow for expanding from statistics-based association analysis toward deriving molecular pathway and process models for characterizing phenotypes and drug mechanism of action, in turn providing precision medicine hypotheses utilizing predictive biomarkers.
• Evidence snippets:
• Snippet 1 (score: 0.371) > In such scenario a biomarker needs to serve as proxy of key mechanistic factors characterizing and driving a disease on a patient-specific level, combined with educating on the specific interference of disease mechanism with drug mechanism of action. For capturing these constraints a detailed molecular map of a clinical phenotype and its interference with a drug mechanism of action is needed, and here integration of Omics profiling adds to identifying such mechanisms (Fechete et al., 2011;Mühlberger et al., 2012). > An a priori stratification of patients based on an appropriately chosen biomarker panel reflecting the pathophysiology of a given patient (group) allowing to determine a match with a specific drug's mechanism of action appears as promising approach. As recently discussed by Himmelfarb et al. fresh approaches are critical in finding therapies to kidney disease benefiting patients, outlining the importance of improving the translational aspect in clinical research (Himmelfarb and Tuttle, 2013). Here, omics technologies have added significantly to the data landscape characterizing chronic kidney disease, however, in a first instance mainly expanding the candidate set of apparently relevant processes and pathways, going in hand with a large number of biomarker candidates, which individually hamper clinically relevant assessment on disease progression (Fechete et al., 2011;Hellemons et al., 2012). > Integrative approaches in the realm of Systems Biology have been proposed for reaching a consensus description of chronic kidney disease pathophysiology, including molecular models of DN as well as of the reno-cardial axis (He et al., 2012;Komorowsky et al., 2012;Mayer et al., 2012;Heinzel et al., 2013). Still, a translation process needs to be followed, joining disease pathophysiology, stratification markers allowing enrichment strategies, combined with on a molecular mechanistic level matching drugs for allowing precision medicine (Mirnezami et al., 2012). In this work we exemplify such procedure on DN being the major clinical presentation leading to end stage renal disease.

[12] LifeTime and improving European healthcare through cell-based interceptive medicine

• Authors: N. Rajewsky, G. Almouzni, S. Gorski, S. Aerts, I. Amit et al.
• Year: 2020
• Venue: Nature
• URL: https://www.semanticscholar.org/paper/d626a4acb560c1ef16ea394cb4dccf277882d119
• DOI: 10.1038/s41586-020-2715-9
• PMID: 32894860
• PMCID: 7656507
• Citations: 138
• Influential citations: 2
• Summary: The LifeTime initiative is an ambitious, multidisciplinary programme that aims to improve healthcare by tracking individual human cells during disease processes and responses to treatment in order to develop and implement cell-based interceptive medicine in Europe over the next decade.
• Evidence snippets:
• Snippet 1 (score: 0.367) > , a major challenge is a lack of understanding of the early events in disease onset to enable the development of disease-modifying therapies. The lack of access to longitudinal samples from patients necessitates the establishment of cohorts of patient-derived disease models to understand the cellular heterogeneity associated with disease. The discovery of pathways and biomarkers that will allow the stratification of patients on the basis of the cellular mechanisms that drive a disease will make it possible to design new clinical trials to reevaluate drugs that were previously tested without such stratification, and to broaden the drug target portfolio. > As seen during the coronavirus disease 2019 (COVID-19) pandemic, it is important to be able to understand infection mechanisms and the host response in order to rapidly identify the most likely effective treatment for an infection. At the same time, the continuous rise of antimicrobial resistance requires the discovery of new therapeutic strategies. A key medical challenge for infectious diseases is to understand the cellular response to infections and to develop precision, immune-based therapeutic strategies to combat infections. > Chronic inflammatory diseases impose a high burden owing to their long-term debilitating consequences, which result from the structural destruction of affected organs or tissues. Current therapies treat the symptoms but do not cure or fully control the chronic inflammatory pathophysiology. While different targeted therapies exist, they are expensive and their success is limited by high rates of non-response to treatment. Consequently, there is an urgent need to explore and understand how cellular heterogeneity contributes to the pathology of inflammatory diseases 61 and how this relates to the predicted course of disease and the response of a patient to one of the numerous available therapies. > Many cardiovascular and metabolic diseases lack effective therapies owing to a lack of knowledge of their underlying causes and the link between abnormal cardiac cell structure or function and pathophysiology. The identified medical priority is to understand the cellular and molecular mechanisms involved, in order to enable early diagnosis and the design of new mechanism-based therapies for precise clinical treatment. > The LifeTime disease roadmaps can be divided broadly into three phases 7 : first, immediate research into the identified medical challenges using established, scaled single-cell technologies, computational tools and disease models; second, the development of new technologies that are required

[13] The evolving burden of asthma and contemporary advances in management: Implications for clinical practice in Southern Africa

• Authors: A. Kiboneka
• Year: 2020
• Venue: Unknown venue
• URL: https://www.semanticscholar.org/paper/0ba536bc7dbea898dcaabe247c92c7897c7e059c
• DOI: 10.30574/wjarr.2020.8.3.0315
• Citations: 1
• Summary: The development of novel asthma phenotyping & endo typing plus better classification of patients using machine learning and big data have markedly improved asthma treatment outcomes in both children and Adults, and several research groups have developed cluster analyses of phenotypes in severe asthma.
• Evidence snippets:
• Snippet 1 (score: 0.361) > Research Program (SARP) I and II cohorts to study mechanisms differentiating severe from non-severe asthma. SARP investigators characterized severe asthma as a heterogeneous syndrome with diverse molecular, biochemical, and cellular inflammatory features and structure-function abnormalities. > Adults and children with severe asthma were further categorized by unbiased statistical methods into clusters based on distinguishing clinical features. These studies have not been done in Sub-Sahara Africa. Research performed over the past one to two decades has sought to better understand the heterogeneous clinical nature of asthma. Whereas older attempts at phenotyping asthma emphasized the duality of allergic vs. non-allergic asthma, more recent non-biased analyses have attempted to cluster patients by a multitude of possible features, including age of onset, atopy, severity of airways obstruction, and requirement for medication. Examples of these phenotypes include early-onset mild allergic asthma, later-onset asthma associated with obesity, and severe non-atopic asthma with frequent exacerbations. The elucidation of asthma phenotypes has been further refined by including information regarding pathophysiologic mechanisms present in different groups. These groups, called endo-types, include examples such as aspirin-exacerbated respiratory disease and allergic bronchopulmonary mycosis. > A phenotype covers the clinically relevant properties of the disease, but does not show the direct relationship to disease etiology and pathophysiology. Different patho-genetic mechanisms might cause similar asthma symptoms and might be operant in a certain phenotype. These putative mechanisms are addressed by the term 'endotype'. > Classification of asthma based on endo-types provides advantages for epidemiological, genetic, and drug-related studies. A successful definition of endo-types should link key pathogenic mechanisms with the asthma phenotype. Thus, the identification of corresponding molecular biomarkers for individual pathogenic-mechanism underlying phenotypes or subgroups within a phenotype is important. > The term asthma encompasses a disease spectrum with mild to very severe disease phenotypes whose traditional common characteristic is reversible airflow limitation. Unlike milder disease, severe asthma is poorly controlled by the current standard of care.

[14] Finding patterns in lung cancer protein sequences for drug repurposing

• Authors: Belén Otero-Carrasco, Paloma Tejera Nevado, Rafael Muñoz, Gema Díaz Ferreiro, Aurora Pérez et al.
• Year: 2025
• Venue: PLOS One
• URL: https://www.semanticscholar.org/paper/a40939b7bcedabfd6cf4db42cdadb0caacba73f4
• DOI: 10.1371/journal.pone.0322546
• PMID: 40334012
• PMCID: 12058034
• Citations: 2
• Summary: A novel computational framework was developed to extend this pattern-based analysis to proteins linked to other diseases, and relationships between lung cancer drug-target proteins and proteins associated with four additional cancer types were uncovered.
• Evidence snippets:
• Snippet 1 (score: 0.360) > Proteins, made up of 20 amino acids, are crucial for biological functions like structure and catalysis. Their structure is organized into four levels: the primary structure is the linear sequence of amino acids; the secondary involves shapes like alpha helix and beta sheets form through hydrogen bonds; the tertiary structure is the 3D shape resulting from protein folding; and the quaternary structure occurs when multiple polypeptide chains (subunits) combine into complex structures [1]. Protein sequences are essential for understanding diseases as they regulate cellular functions, gene expression, and immune responses. Identifying important regions within these sequences helps uncover disease mechanisms, detect patterns, mutations, or attention due to their ability to model complex interactions between drugs, targets, and disease pathways. Recent studies have demonstrated the effectiveness of these methods in identifying potential therapeutic agents by leveraging network topology and multi-omics data integration [8][9][10][11]. Their application has provided relevant insights into disease mechanisms and drug action, complementing other computational methodologies. > In this context, computational drug repurposing approaches have gained attention in recent years and utilize databases that enable gene and protein function prediction by comparing amino acid sequences, with tools like BLAST and FASTA [12]. Mutations in the DNA's protein-coding regions are often linked to human genetic disorders, providing insights into disease mechanisms through protein structure analysis. Understanding disease severity requires identifying specific basepair mutations, which vary based on protein function, the number of affected amino acids, and mutation type. For example, base pair substitutions can cause silent mutations, while insertions or deletions may result in frameshift mutations, potentially resulting in nonfunctional proteins. Additionally, mutations involving multiples of three base pairs can affect protein functionality differently [13]. > The proposed shift in genetic disease classification focuses on molecular pathways rather than traditional categories like monogenic, oligogenic, or polygenic/multifactorial. This pathway-based system organizes disease according to the affected molecular pathways that produce specific phenotypes, enhancing our understanding of disease presentation and progression [14]. Additionally, the classification highlights correlations between disease-related proteins and the sequences of charged residues, aiding in function determination and evolutionary tracking. Effective communication between distant residues is vital for protein functionality, with bioinformatics tools helping identify correlated residues.

[15] Mechanistic Models of Signaling Pathways Reveal the Drug Action Mechanisms behind Gender-Specific Gene Expression for Cancer Treatments

• Authors: C. Çubuk, F. Can, M. Peña-Chilet, J. Dopazo
• Year: 2020
• Venue: Cells
• URL: https://www.semanticscholar.org/paper/e40f7a3b8f72ba01374ba00fbf308a47a3fa5dd4
• DOI: 10.3390/cells9071579
• PMID: 32610626
• PMCID: 7408716
• Citations: 9
• Summary: Despite the existence of differences in gene expression across numerous genes between males and females having been known for a long time, these have been mostly ignored in many studies, including drug development and its therapeutic use. In fact, the consequences of such differences over the disease mechanisms or the drug action mechanisms are completely unknown. Here we applied mechanistic mathematical models of signaling activity to reveal the ultimate functional consequences that gender-s...
• Evidence snippets:
• Snippet 1 (score: 0.360) > Therefore, a proper interpretation of the effect that differences in gene expression have over phenotypes, such as drug response or disease progression, involves understanding the mechanisms of the disease or the mode of action of drugs, which can be interpreted through mechanistic models of cell signaling [12] or cell metabolism [13]. Mechanistic models have helped to understand the disease mechanisms behind different cancers [14,15], including neuroblastoma [16,17], breast cancer [18], rare diseases [19], complex diseases [20], the mechanisms of action of drugs [21,22], and other biologically interesting scenarios such as the molecular mechanisms that explain how stress-induced activation of brown adipose tissue prevents obesity [23] or the molecular mechanisms of death and the post-mortem ischemia of a tissue [24]. Among the few available proposals of mechanistic modeling algorithms that model different aspects of signaling pathway activity, Hipathia has demonstrated having superior sensitivity and specificity [12]. > Here, we propose the use of mechanistic models [13,14] of signaling activity related with cancer hallmarks [25], other cancer-related signaling pathways, and some extra relevant cellular functions to understand the functional consequences of the gender bias in gene expression. Such mechanistic models use gene expression data to produce an estimation of profiles of signaling or metabolic circuit activity within pathways [13,14]. An interesting property of mechanistic models is that they can be used not only to understand molecular mechanisms of disease or of drug action but also to predict the potential consequences of gene perturbations over the circuit activity in a given condition [26]. Actually, in a recent work, our group has successfully predicted therapeutic targets in cancer cell lines with a precision over 60% [15]. Therefore, we will use this mechanistic framework to understand what is the molecular basis of the different effects of cancer drugs by directly simulating their effect in the patients. This approach has recently been used by us to understand the generation of resistances in cancer at the single cell level in glioblastoma [27].

[16] Transcriptional profiling of Hutchinson-Gilford progeria patients identifies primary target pathways of progerin

• Authors: Sandra Vidak, Sohyoung Kim, Tom Misteli
• Year: 2026
• Venue: Nucleus
• URL: https://www.semanticscholar.org/paper/4bd99b0875508364d8672b6da5a50d024d485a53
• DOI: 10.1080/19491034.2025.2611484
• PMID: 41489464
• PMCID: 12773485
• Summary: To probe the clinical relevance of previously implicated cellular pathways and to address the extent of gene expression heterogeneity between patients, transcriptomic analysis of a comprehensive set of HGPS patients finds misexpression of several cellular pathways, including multiple signaling pathways, the UPR and mesodermal cell fate specification.
• Evidence snippets:
• Snippet 1 (score: 0.360) > Oxidative stress represents another key pathogenic mechanism in HGPS, as impaired NRF2 activity or increased reactive oxygen species (ROS) levels are sufficient to recapitulate HGPSassociated phenotypes [17,32,60]. Collectively, these findings underscore the multifactorial nature of HGPS pathogenesis, implicating interconnected signaling cascades involved in inflammation, oxidative stress, proteostasis, and vascular remodeling. Reassuringly, our findings indicate that many of the major pathways that have been described to contribute to HGPS phenotypes in mouse and cellular disease models are also misregulated in progeria patients, and targeting these pathways may provide therapeutic avenues to mitigate disease severity and improve outcomes in HGPS. > Although individuals with HGPS typically exhibit a characteristic set of clinical features, such as craniofacial abnormalities, growth retardation, and cardiovascular complications, there is notable variability in the age of onset, severity, and progression of symptoms between patients [7,9]. At the cellular level, HGPS is associated with several hallmark abnormalities, including nuclear envelope defects, decreased expression of several nuclear proteins and epigenetic marks, mitochondrial dysfunction, and increased cellular senescence [1,11,30,31,61]. These cellular phenotypes also exhibit considerable variation between patients, possibly contributing to differences in clinical outcomes. Our results indicate that even though some degree of transcriptional heterogeneity between the individual patients exists, the majority of patients exhibit misregulation of a set of shared pathways, suggesting that these pathways are universal driver mechanisms in HGPS. Further work is needed to understand the molecular and genetic factors that underlie inter-individual variability in disease expression and progression. > A limitation of pathway analysis of HGPS patient samples is to distinguish the pathways which are directly targeted by the disease-causing progerin protein and the emergence of adaptive secondary response pathways during progression of the disease in patients during their lifetime. The same caveat applies to the use of cell-based models used in the study of HGPS disease mechanisms.

[17] Targeting Hepatic Stellate Cells for the Prevention and Treatment of Liver Cirrhosis and Hepatocellular Carcinoma: Strategies and Clinical Translation

• Authors: Hao Xiong, Jinsheng Guo
• Year: 2025
• Venue: Pharmaceuticals
• URL: https://www.semanticscholar.org/paper/76e92127053136900f7e3f10e2c9278251ced5d2
• DOI: 10.3390/ph18040507
• PMID: 40283943
• PMCID: 12030350
• Citations: 8
• Summary: HSC-targeted approaches using specific surface markers and receptors may enable the selective delivery of drugs, oligonucleotides, and therapeutic peptides that exert optimized anti-fibrotic and anti-HCC effects.
• Evidence snippets:
• Snippet 1 (score: 0.358) > Significant progress has been made in elucidating the cellular and molecular mechanisms of liver fibrosis; however, only a few findings have been successfully translated into clinical applications. Firstly, the high cost of drug development and target validation necessitates prolonged timelines and substantial financial investment. Secondly, as regulatory requirements become more stringent, there is an increasing demand for drugs with well-defined clinical efficacy and safety profiles. Moreover, the efficacy observed in animal models often fails to fully translate to clinical settings due to differences in pharmacokinetics, extracellular matrix (ECM) cross-linking, and disease pathophysiology. Despite advancements in anti-fibrotic drug development, accurately identifying ideal noninvasive biomarkers for fibrotic activity and establishing consensus on optimal clinical endpoints remain significant challenges [113,114]. > Currently, addressing the underlying cause remains the only proven strategy to halt or reverse liver fibrosis progression, while the development of effective anti-fibrotic therapies continues to pose a major challenge in liver disease management. Over the past few decades, substantial progress has been made in elucidating the cellular and molecular mechanisms underlying liver fibrosis. Liver fibrosis is a complex pathological change involving multiple cells, factors, and pathways, and the study of the cellular and molecular mechanisms of its occurrence and development provides an important theoretical basis and therapeutic target for clinical drug development. It is anticipated that improved animal models and well-designed clinical trials will facilitate the successful translation of anti-fibrotic research into effective clinical treatments in the near future.

[18] Investigating the role of NPR1 in dilated cardiomyopathy and its potential as a therapeutic target for glucocorticoid therapy

• Authors: Yaomeng Huang, Tongxin Li, Shichao Gao, Shuyu Li, Xiaoran Zhu et al.
• Year: 2023
• Venue: Frontiers in Pharmacology
• URL: https://www.semanticscholar.org/paper/be229f6f2059faab4c97ec0a04bd055adab9dfe1
• DOI: 10.3389/fphar.2023.1290253
• PMID: 38026943
• PMCID: 10662320
• Citations: 3
• Summary: Natriuretic peptide receptor 1 (NPR1) was identified as a core gene associated with DCM through bioinformatics analysis and led to substantial improvements in cardiac and renal function, accompanied by an upregulation of NPR1 expression.
• Evidence snippets:
• Snippet 1 (score: 0.355) > Multiple pathways and molecules are involved in this process; however, the detailed underlying mechanisms remain unclear. In recent years, with the development of high-throughput sequencing and gene chip technologies, the use of bioinformatics technology to explore the occurrence, development, and prognosis of diseases has become a hot topic for scholars worldwide (Hwang et al., 2018;Nayor et al., 2019;Rinschen et al., 2019;Sturm et al., 2019;Montaner et al., 2020). > The present study aimed to use bioinformatics technology to screen for DCM-related genes and investigate their mechanisms, with the purpose of revealing the pathogenesis of DCM and seeking treatment methods. The GSE3586 dataset, containing expression profiles related to DCM, was selected from the Gene Expression Omnibus (GEO) database. This study aimed to predict the core genes that may play crucial roles in disease progression at the molecular level through the enrichment of relevant molecular pathways associated with DCM. Furthermore, the phenotype of the core genes was validated to further support the results of the bioinformatics analysis through basic and clinical experiments. Additionally, the role of glucocorticoids in DCM treatment is discussed in this article with the purpose of providing a theoretical and experimental basis for exploring the pathogenesis of DCM and elucidating therapeutic methods. This study also provides a theoretical reference for the interpretation, early diagnosis, and treatment of DCM.

[19] Computational Models for Clinical Applications in Personalized Medicine—Guidelines and Recommendations for Data Integration and Model Validation

• Authors: C. Collin, Tom Gebhardt, Martin Golebiewski, T. Karaderi, Maximilian Hillemanns et al.
• Year: 2022
• Venue: Journal of Personalized Medicine
• URL: https://www.semanticscholar.org/paper/6eeb2bc6c6157e98db4df4c93492b1a3b66fcbfb
• DOI: 10.3390/jpm12020166
• PMID: 35207655
• PMCID: 8879572
• Citations: 76
• Influential citations: 1
• Summary: The most relevant computational models for personalized medicine in detail are discussed in detail that can be considered as best-practice guidelines for application in clinical care and provide applicable guidelines and recommendations for study design, data acquisition, and operation.
• Evidence snippets:
• Snippet 1 (score: 0.354) > Model applications in discovery are usually mechanism-based, such as MIMs, GEMs, BMs, and ODEs, since they are frequently hypothesis driven. This is because the availability of data at this level is commonly not sufficient for purely data-driven analyses. Mechanistic models in discovery play a significant role in a wide range of clinically relevant questions ranging from representation of disease mechanisms to identification of drug targets or simulations of disease-specific phenotypes (summarized in Table 2): A recently published map on inflammation resolution provides functionality to visualize Omics data and allows making hypotheses on the role of connected molecules in a disease phenotype [83]. Another well-known example for MIMs is the disease map of Parkinson's [14]. These maps serve as a knowledge platform and represent the mechanisms of the disease in a standardized visualization. Thereby, they structure the growing knowledge of the field in a comprehensible manner. Another interesting example of MIM is the atlas of the cancer signaling network [15], which depicts in detail the molecular mechanisms involved in cancer. High-throughput data can be visualized on the map to perform functional analysis and identify dysregulated pathways. Wu et al. constructed a comprehensive molecular interaction map for rheumatoid arthritis containing detailed molecular mechanisms of the processes in patients affected by rheumatoid arthritis [84]. The map was analyzed for topological properties to suggest diagnostic and therapeutic markers for rheumatoid arthritis. > Disease-specific GEMs were used for the identification of biomarkers and drug targets in metabolism-related disorders including: cancer [17], type 2 diabetes [87], obesity [18], non-alcoholic fatty liver disease (NAFLD) [88], and Alzheimer's disease (AD) [19]. In 2017, Uhlen et al. generated GEMs of 17 types of cancer by integrating transcriptome data into a network of human metabolism using the task-driven integrative network inference for tissues (tINIT) method [91]. In addition to predicting driver genes for tumor growth, they demonstrated a widespread metabolic heterogeneity in different patients, highlighting the necessity of personalized medicine for cancer treatments [17].

[20] Direct Sarcomere Modulators Are Promising New Treatments for Cardiomyopathies

• Authors: O. Tsukamoto
• Year: 2019
• Venue: International Journal of Molecular Sciences
• URL: https://www.semanticscholar.org/paper/07467943fe92ce135b52ded5e5dea2bfc2ddf179
• DOI: 10.3390/ijms21010226
• PMID: 31905684
• PMCID: 6982115
• Citations: 16
• Summary: The direct inhibition of sarcomere contractility may be able to suppress the development and progression of HCM with hypercontractile mutations and improve clinical parameters in patients with HCM, and direct activation of sar COMs modulators that can positively influence the natural history of cardiomyopathies represent promising treatment options.
• Evidence snippets:
• Snippet 1 (score: 0.354) > Hereditary DCM can be caused by single point mutations in sarcomere proteins. However, the link between point mutations and clinical phenotypes in DCM is not thoroughly understood in most cases. Recent advances in biochemical, biophysical, stem cell, and gene editing technologies have provided a better understanding of the molecular mechanisms through which the initial insult in DCM (i.e., mutations in a sarcomere protein) induces alterations in cellular organization and contractility, resulting in disease phenotypes. In particular, hiPSC-CMs and genetically modified animals are excellent models because they can capture the initial molecular phenotype that occurs before major compensatory mechanisms mask it.

Notes

• This provider combines search_papers_by_relevance with snippet_search.
• No synthesis or second-stage model call is performed.