Frontiers in drug discovery最新文献

Transduction of hematopoietic stem and progenitor cells by an MECP2 lentiviral vector improves Rett syndrome phenotypes. MECP2慢病毒载体转导造血干细胞和祖细胞可改善Rett综合征的表型。

Frontiers in drug discovery Pub Date : 2025-01-01 Epub Date: 2025-02-20 DOI: 10.3389/fddsv.2025.1545391

Joseph S Anderson, Jill L Silverman, Alyse L Lodigiani, Camilla M Barbaduomo, Julie R Beegle

{"title":"Transduction of hematopoietic stem and progenitor cells by an MECP2 lentiviral vector improves Rett syndrome phenotypes.","authors":"Joseph S Anderson, Jill L Silverman, Alyse L Lodigiani, Camilla M Barbaduomo, Julie R Beegle","doi":"10.3389/fddsv.2025.1545391","DOIUrl":"10.3389/fddsv.2025.1545391","url":null,"abstract":"Introduction: Rett Syndrome is a genetic neurodevelopmental disorder caused by decreased levels of MeCP2. Due to mutations in the MECP2 gene, insufficient MeCP2 protein levels lead to clinical phenotypes including the loss of normal movement, decreased communication, seizures, sleep disorders, and breathing problems. Currently there is no cure for Rett Syndrome and the only means to help patients is palliative care directed to their specific symptoms. Therefore, novel therapies need to be developed to alleviate disease phenotypes by restoring normal MECP2 expression. An autologous hematopoietic stem cell and gene therapy approach for Rett syndrome may offer a benefit to affected patients by systemic delivery of functional MeCP2, including to affected neurons in the central nervous system.Methods: In our current experiments, we evaluated the therapeutic effect of MECP2 lentiviral vector transduced human CD34+ hematopoietic stem and progenitor cells after transplantation into an immunodeficient mouse model of Rett syndrome.Results: We observed improvement of Rett syndrome-related phenotypes including the reversion toward normal motor abilities in an open field assay for total activity, horizontal activity, and vertical rearing activity, and an increased latency to fall in a rotarod assay. An increased level of MeCP2 protein was also observed in the brain tissue of transplanted mice.Discussion: By providing functional MeCP2 to affected cells, our results highlight the ability of this strategy to improve Rett syndrome phenotypes. These proof-of-concept studies demonstrate the potential use of a stem cell gene therapy approach as a novel treatment for Rett syndrome patients.","PeriodicalId":73080,"journal":{"name":"Frontiers in drug discovery","volume":"5 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12393828/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144981073","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Mimicking the immunosuppressive impact of fibroblasts in a 3D multicellular spheroid model 在三维多细胞球体模型中模拟成纤维细胞的免疫抑制作用

Frontiers in drug discovery Pub Date : 2024-07-26 DOI: 10.3389/fddsv.2024.1427407

Melanie Grotz, Lieke van Gijzel, Peter Bitsch, Stefania C. Carrara, Harald Kolmar, Sakshi Garg

{"title":"Mimicking the immunosuppressive impact of fibroblasts in a 3D multicellular spheroid model","authors":"Melanie Grotz, Lieke van Gijzel, Peter Bitsch, Stefania C. Carrara, Harald Kolmar, Sakshi Garg","doi":"10.3389/fddsv.2024.1427407","DOIUrl":"https://doi.org/10.3389/fddsv.2024.1427407","url":null,"abstract":"Targeting the tumor microenvironment (TME) is an attractive strategy for cancer therapy, as tumor cells in vivo are surrounded by many different influential cell types, with complex interactions strongly affecting tumor progression and therapeutic outcome. Cancer-associated fibroblasts (CAFs) represent an abundant stromal cell type in the TME that modulate tumor development by exerting an immunosuppressive effect to influence effector immune cell activation. One promising target for TME-directed therapy is the CAF marker fibroblast activation protein-α (FAP). In this study, we employ a multicellular three-dimensional (3D) spheroid model, including tumor cells, fibroblast cells, and naïve T cells and could observe a protective effect of fibroblasts on tumor cells. Subsequently, we demonstrate that fibroblasts express FAP at differing expression levels in two-dimensional (2D) versus 3D cells. Lastly, we show that in a triple-culture of tumor cells, T cells and fibroblasts, the simultaneous assembly of fibroblasts using the high-affinity ligand oncoFAP with an engineered α-CD3-scFv-Fc-dextran-oncoFAP construct resulted in effective T cell activation to augment immunogenicity. Overall, this model can be routinely used for preclinical screening to study the effects of fibroblasts on the TME in vitro.","PeriodicalId":73080,"journal":{"name":"Frontiers in drug discovery","volume":"21 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141801446","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Alternative therapeutics to control antimicrobial resistance: a general perspective 控制抗菌药耐药性的替代疗法：一般视角

Frontiers in drug discovery Pub Date : 2024-07-17 DOI: 10.3389/fddsv.2024.1385460

Biplab Singha, Vinayak Singh, Vijay Soni

引用次数: 0

Editorial: The boulder peptide symposium 2021 scientific update 社论：2021 年博尔德多肽研讨会科学更新

Frontiers in drug discovery Pub Date : 2024-03-25 DOI: 10.3389/fddsv.2024.1394489

Yvonne Angell, John Mayer, Rebecca Nofsinger, Waleed Danho

引用次数: 0

Applying artificial intelligence to accelerate and de-risk antibody discovery 应用人工智能加速抗体发现并降低风险

Frontiers in drug discovery Pub Date : 2024-03-05 DOI: 10.3389/fddsv.2024.1339697

Astrid Musnier, Christophe Dumet, Saheli Mitra, Adrien Verdier, Raouf Keskes, Augustin Chassine, Yann Jullian, Mélanie Cortes, Yannick Corde, Z. Omahdi, Vincent Puard, T. Bourquard, A. Poupon

{"title":"Applying artificial intelligence to accelerate and de-risk antibody discovery","authors":"Astrid Musnier, Christophe Dumet, Saheli Mitra, Adrien Verdier, Raouf Keskes, Augustin Chassine, Yann Jullian, Mélanie Cortes, Yannick Corde, Z. Omahdi, Vincent Puard, T. Bourquard, A. Poupon","doi":"10.3389/fddsv.2024.1339697","DOIUrl":"https://doi.org/10.3389/fddsv.2024.1339697","url":null,"abstract":"As in all sectors of science and industry, artificial intelligence (AI) is meant to have a high impact in the discovery of antibodies in the coming years. Antibody discovery was traditionally conducted through a succession of experimental steps: animal immunization, screening of relevant clones, in vitro testing, affinity maturation, in vivo testing in animal models, then different steps of humanization and maturation generating the candidate that will be tested in clinical trials. This scheme suffers from different flaws, rendering the whole process very risky, with an attrition rate over 95%. The rise of in silico methods, among which AI, has been gradually proven to reliably guide different experimental steps with more robust processes. They are now capable of covering the whole discovery process. Amongst the players in this new field, the company MAbSilico proposes an in silico pipeline allowing to design antibody sequences in a few days, already humanized and optimized for affinity and developability, considerably de-risking and accelerating the discovery process.","PeriodicalId":73080,"journal":{"name":"Frontiers in drug discovery","volume":"19 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140263618","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Editorial: Women in anti-inflammatory and immunomodulating agents: 2022 社论：抗炎剂和免疫调节剂中的女性：2022

Frontiers in drug discovery Pub Date : 2024-02-22 DOI: 10.3389/fddsv.2024.1380166

Jamie B. Spangler, Vanina A. Medina

引用次数: 0

Editorial: Use of computational tools for designing epigenetic drugs 社论：利用计算工具设计表观遗传药物

Frontiers in drug discovery Pub Date : 2024-02-21 DOI: 10.3389/fddsv.2024.1381450

Y. Sixto-López, A. Uba, Kuldeep K. Roy

引用次数: 0

Machine learning in toxicological sciences: opportunities for assessing drug toxicity 毒理学中的机器学习：评估药物毒性的机遇

Frontiers in drug discovery Pub Date : 2024-02-08 DOI: 10.3389/fddsv.2024.1336025

L. Tonoyan, Arno G. Siraki

{"title":"Machine learning in toxicological sciences: opportunities for assessing drug toxicity","authors":"L. Tonoyan, Arno G. Siraki","doi":"10.3389/fddsv.2024.1336025","DOIUrl":"https://doi.org/10.3389/fddsv.2024.1336025","url":null,"abstract":"Machine learning (ML) in toxicological sciences is growing exponentially, which presents unprecedented opportunities and brings up important considerations for using ML in this field. This review discusses supervised, unsupervised, and reinforcement learning and their applications to toxicology. The application of the scientific method is central to the development of a ML model. These steps involve defining the ML problem, constructing the dataset, transforming the data and feature selection, choosing and training a ML model, validation, and prediction. The need for rigorous models is becoming more of a requirement due to the vast number of chemicals and their interaction with biota. Large datasets make this task possible, though selecting databases with overlapping chemical spaces, amongst other things, is an important consideration. Predicting toxicity through machine learning can have significant societal impacts, including enhancements in assessing risks, determining clinical toxicities, evaluating carcinogenic properties, and detecting harmful side effects of medications. We provide a concise overview of the current state of this topic, focusing on the potential benefits and challenges related to the availability of extensive datasets, the methodologies for analyzing these datasets, and the ethical implications involved in applying such models.","PeriodicalId":73080,"journal":{"name":"Frontiers in drug discovery","volume":"27 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139852590","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Machine learning in toxicological sciences: opportunities for assessing drug toxicity 毒理学中的机器学习：评估药物毒性的机遇

Frontiers in drug discovery Pub Date : 2024-02-08 DOI: 10.3389/fddsv.2024.1336025

L. Tonoyan, Arno G. Siraki

{"title":"Machine learning in toxicological sciences: opportunities for assessing drug toxicity","authors":"L. Tonoyan, Arno G. Siraki","doi":"10.3389/fddsv.2024.1336025","DOIUrl":"https://doi.org/10.3389/fddsv.2024.1336025","url":null,"abstract":"Machine learning (ML) in toxicological sciences is growing exponentially, which presents unprecedented opportunities and brings up important considerations for using ML in this field. This review discusses supervised, unsupervised, and reinforcement learning and their applications to toxicology. The application of the scientific method is central to the development of a ML model. These steps involve defining the ML problem, constructing the dataset, transforming the data and feature selection, choosing and training a ML model, validation, and prediction. The need for rigorous models is becoming more of a requirement due to the vast number of chemicals and their interaction with biota. Large datasets make this task possible, though selecting databases with overlapping chemical spaces, amongst other things, is an important consideration. Predicting toxicity through machine learning can have significant societal impacts, including enhancements in assessing risks, determining clinical toxicities, evaluating carcinogenic properties, and detecting harmful side effects of medications. We provide a concise overview of the current state of this topic, focusing on the potential benefits and challenges related to the availability of extensive datasets, the methodologies for analyzing these datasets, and the ethical implications involved in applying such models.","PeriodicalId":73080,"journal":{"name":"Frontiers in drug discovery","volume":" 13","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139792889","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Editorial: Discovery of EGFR tyrosine kinase inhibitors for cancer treatment 社论：发现治疗癌症的表皮生长因子受体酪氨酸激酶抑制剂

Frontiers in drug discovery Pub Date : 2024-02-07 DOI: 10.3389/fddsv.2024.1365318

Panupong Mahalapbutr, T. Rungrotmongkol

引用次数: 0