Pub Date : 2024-07-26DOI: 10.3389/fddsv.2024.1427407
Melanie Grotz, Lieke van Gijzel, Peter Bitsch, Stefania C. Carrara, Harald Kolmar, Sakshi Garg
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.
{"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}
Pub Date : 2024-07-17DOI: 10.3389/fddsv.2024.1385460
Biplab Singha, Vinayak Singh, Vijay Soni
Antimicrobial Resistance (AMR) is a critical global health challenge, and in this review article, we examine the limitations of traditional therapeutic methods and the emerging role of alternative therapies. By examining the reasons behind the failure of conventional treatments, including the inadequacy of one-drug-one-enzyme approaches, the complex evolution of AMR, and the impact of drug biotransformation, we better understand why conventional treatments failed. Moreover, the review discusses several alternative therapies, including RNA-based treatments, aptamers, peptide-based therapies, phage therapy, and probiotics, discussing their applications, advantages, and limitations. Additionally, we discuss the obstacles to develop these therapies, including funding shortages, regulatory barriers, and public perception. This comprehensive analysis aims to provide insight into the future of AMR, emphasizing the need for innovative strategies and practical approaches.
抗菌药耐药性（AMR）是全球健康面临的严峻挑战，在这篇综述文章中，我们探讨了传统治疗方法的局限性和替代疗法的新兴作用。通过研究传统疗法失败的原因，包括一药一酶方法的不足、AMR 的复杂演变以及药物生物转化的影响，我们更好地理解了传统疗法失败的原因。此外，综述还讨论了几种替代疗法，包括基于 RNA 的疗法、适配体、基于肽的疗法、噬菌体疗法和益生菌，并讨论了它们的应用、优势和局限性。此外，我们还讨论了开发这些疗法的障碍，包括资金短缺、监管障碍和公众认知。这一全面分析旨在为 AMR 的未来提供洞察力，强调创新战略和实用方法的必要性。
{"title":"Alternative therapeutics to control antimicrobial resistance: a general perspective","authors":"Biplab Singha, Vinayak Singh, Vijay Soni","doi":"10.3389/fddsv.2024.1385460","DOIUrl":"https://doi.org/10.3389/fddsv.2024.1385460","url":null,"abstract":"Antimicrobial Resistance (AMR) is a critical global health challenge, and in this review article, we examine the limitations of traditional therapeutic methods and the emerging role of alternative therapies. By examining the reasons behind the failure of conventional treatments, including the inadequacy of one-drug-one-enzyme approaches, the complex evolution of AMR, and the impact of drug biotransformation, we better understand why conventional treatments failed. Moreover, the review discusses several alternative therapies, including RNA-based treatments, aptamers, peptide-based therapies, phage therapy, and probiotics, discussing their applications, advantages, and limitations. Additionally, we discuss the obstacles to develop these therapies, including funding shortages, regulatory barriers, and public perception. This comprehensive analysis aims to provide insight into the future of AMR, emphasizing the need for innovative strategies and practical approaches.","PeriodicalId":73080,"journal":{"name":"Frontiers in drug discovery","volume":" 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141830491","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}
Pub Date : 2024-03-25DOI: 10.3389/fddsv.2024.1394489
Yvonne Angell, John Mayer, Rebecca Nofsinger, Waleed Danho
{"title":"Editorial: The boulder peptide symposium 2021 scientific update","authors":"Yvonne Angell, John Mayer, Rebecca Nofsinger, Waleed Danho","doi":"10.3389/fddsv.2024.1394489","DOIUrl":"https://doi.org/10.3389/fddsv.2024.1394489","url":null,"abstract":"","PeriodicalId":73080,"journal":{"name":"Frontiers in drug discovery","volume":"8 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140381394","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}
Pub Date : 2024-03-05DOI: 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
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.
{"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}
Pub Date : 2024-02-22DOI: 10.3389/fddsv.2024.1380166
Jamie B. Spangler, Vanina A. Medina
{"title":"Editorial: Women in anti-inflammatory and immunomodulating agents: 2022","authors":"Jamie B. Spangler, Vanina A. Medina","doi":"10.3389/fddsv.2024.1380166","DOIUrl":"https://doi.org/10.3389/fddsv.2024.1380166","url":null,"abstract":"","PeriodicalId":73080,"journal":{"name":"Frontiers in drug discovery","volume":"13 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140441132","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}
Pub Date : 2024-02-21DOI: 10.3389/fddsv.2024.1381450
Y. Sixto-López, A. Uba, Kuldeep K. Roy
{"title":"Editorial: Use of computational tools for designing epigenetic drugs","authors":"Y. Sixto-López, A. Uba, Kuldeep K. Roy","doi":"10.3389/fddsv.2024.1381450","DOIUrl":"https://doi.org/10.3389/fddsv.2024.1381450","url":null,"abstract":"","PeriodicalId":73080,"journal":{"name":"Frontiers in drug discovery","volume":"33 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140445528","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}
Pub Date : 2024-02-08DOI: 10.3389/fddsv.2024.1336025
L. Tonoyan, Arno G. Siraki
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.
毒理学中的机器学习（ML）正呈指数级增长，这带来了前所未有的机遇，也为在这一领域使用 ML 带来了重要的考虑因素。本综述讨论了监督学习、无监督学习和强化学习及其在毒理学中的应用。科学方法的应用是开发 ML 模型的核心。这些步骤包括定义 ML 问题、构建数据集、转换数据和特征选择、选择和训练 ML 模型、验证和预测。由于化学品及其与生物群的相互作用种类繁多，对严格模型的要求也越来越高。大型数据集使这项任务成为可能，但选择具有重叠化学空间的数据库也是一个重要的考虑因素。通过机器学习预测毒性可以产生重大的社会影响，包括增强风险评估、确定临床毒性、评估致癌特性以及检测药物的有害副作用。我们将简明扼要地概述这一课题的现状，重点关注与大量数据集的可用性相关的潜在优势和挑战、分析这些数据集的方法以及应用此类模型所涉及的伦理问题。
{"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}
Pub Date : 2024-02-08DOI: 10.3389/fddsv.2024.1336025
L. Tonoyan, Arno G. Siraki
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.
毒理学中的机器学习（ML）正呈指数级增长，这带来了前所未有的机遇，也为在这一领域使用 ML 带来了重要的考虑因素。本综述讨论了监督学习、无监督学习和强化学习及其在毒理学中的应用。科学方法的应用是开发 ML 模型的核心。这些步骤包括定义 ML 问题、构建数据集、转换数据和特征选择、选择和训练 ML 模型、验证和预测。由于化学品及其与生物群的相互作用种类繁多，对严格模型的要求也越来越高。大型数据集使这项任务成为可能，但选择具有重叠化学空间的数据库也是一个重要的考虑因素。通过机器学习预测毒性可以产生重大的社会影响，包括增强风险评估、确定临床毒性、评估致癌特性以及检测药物的有害副作用。我们将简明扼要地概述这一课题的现状，重点关注与大量数据集的可用性相关的潜在优势和挑战、分析这些数据集的方法以及应用此类模型所涉及的伦理问题。
{"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}
Pub Date : 2024-02-07DOI: 10.3389/fddsv.2024.1365318
Panupong Mahalapbutr, T. Rungrotmongkol
{"title":"Editorial: Discovery of EGFR tyrosine kinase inhibitors for cancer treatment","authors":"Panupong Mahalapbutr, T. Rungrotmongkol","doi":"10.3389/fddsv.2024.1365318","DOIUrl":"https://doi.org/10.3389/fddsv.2024.1365318","url":null,"abstract":"","PeriodicalId":73080,"journal":{"name":"Frontiers in drug discovery","volume":"21 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139795953","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}
Pub Date : 2024-02-07DOI: 10.3389/fddsv.2024.1365318
Panupong Mahalapbutr, T. Rungrotmongkol
{"title":"Editorial: Discovery of EGFR tyrosine kinase inhibitors for cancer treatment","authors":"Panupong Mahalapbutr, T. Rungrotmongkol","doi":"10.3389/fddsv.2024.1365318","DOIUrl":"https://doi.org/10.3389/fddsv.2024.1365318","url":null,"abstract":"","PeriodicalId":73080,"journal":{"name":"Frontiers in drug discovery","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139855822","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}
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