Pub Date : 2024-02-05DOI: 10.3389/fddsv.2024.1324564
Ruifeng Liu, M. AbdulHameed, Zhen Xu, Benjamin Clancy, V. Desai, Anders Wallqvist
Toxidromes constitute patterns of symptoms and signs caused by specific toxic effects that guide emergency treatments. Computational identification of chemicals that cause different toxidromes allows us to rapidly screen novel compounds and compound classes as to their potential toxicity. The aim of the current study was to create a computational toolset that can map chemicals to their potential toxidromes. Hence, we evaluated the performance of a state-of-the-art deep learning method—the recently developed communicative message passing neural network (CMPNN)—for its ability to overcome the use of small datasets for training deep learning models. Our results indicated that multi-task training—a technique known for its ability to use multiple small datasets to train conventional deep neural networks—works equally well with CMPNN. We also showed that CMPNN-based ensemble learning results in more reliable predictions than those obtained using a single CMPNN model. In addition, we showed that the standard deviations of individual model predictions from an ensemble of CMPNN models correlated with the errors of ensemble predictions and could be used to estimate the reliability of ensemble predictions. For toxidromes that do not have well-defined molecular mechanisms or sufficient data to train a deep learning model, we used the similarity ensemble approach to develop molecular structural similarity-based toxidrome models. We made the toolset developed in this study publicly accessible via a web user interface at https://toxidrome.bhsai.org/.
{"title":"Rapid screening of chemicals for their potential to cause specific toxidromes","authors":"Ruifeng Liu, M. AbdulHameed, Zhen Xu, Benjamin Clancy, V. Desai, Anders Wallqvist","doi":"10.3389/fddsv.2024.1324564","DOIUrl":"https://doi.org/10.3389/fddsv.2024.1324564","url":null,"abstract":"Toxidromes constitute patterns of symptoms and signs caused by specific toxic effects that guide emergency treatments. Computational identification of chemicals that cause different toxidromes allows us to rapidly screen novel compounds and compound classes as to their potential toxicity. The aim of the current study was to create a computational toolset that can map chemicals to their potential toxidromes. Hence, we evaluated the performance of a state-of-the-art deep learning method—the recently developed communicative message passing neural network (CMPNN)—for its ability to overcome the use of small datasets for training deep learning models. Our results indicated that multi-task training—a technique known for its ability to use multiple small datasets to train conventional deep neural networks—works equally well with CMPNN. We also showed that CMPNN-based ensemble learning results in more reliable predictions than those obtained using a single CMPNN model. In addition, we showed that the standard deviations of individual model predictions from an ensemble of CMPNN models correlated with the errors of ensemble predictions and could be used to estimate the reliability of ensemble predictions. For toxidromes that do not have well-defined molecular mechanisms or sufficient data to train a deep learning model, we used the similarity ensemble approach to develop molecular structural similarity-based toxidrome models. We made the toolset developed in this study publicly accessible via a web user interface at https://toxidrome.bhsai.org/.","PeriodicalId":73080,"journal":{"name":"Frontiers in drug discovery","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139803677","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 : 2024-02-05DOI: 10.3389/fddsv.2024.1324564
Ruifeng Liu, M. AbdulHameed, Zhen Xu, Benjamin Clancy, V. Desai, Anders Wallqvist
Toxidromes constitute patterns of symptoms and signs caused by specific toxic effects that guide emergency treatments. Computational identification of chemicals that cause different toxidromes allows us to rapidly screen novel compounds and compound classes as to their potential toxicity. The aim of the current study was to create a computational toolset that can map chemicals to their potential toxidromes. Hence, we evaluated the performance of a state-of-the-art deep learning method—the recently developed communicative message passing neural network (CMPNN)—for its ability to overcome the use of small datasets for training deep learning models. Our results indicated that multi-task training—a technique known for its ability to use multiple small datasets to train conventional deep neural networks—works equally well with CMPNN. We also showed that CMPNN-based ensemble learning results in more reliable predictions than those obtained using a single CMPNN model. In addition, we showed that the standard deviations of individual model predictions from an ensemble of CMPNN models correlated with the errors of ensemble predictions and could be used to estimate the reliability of ensemble predictions. For toxidromes that do not have well-defined molecular mechanisms or sufficient data to train a deep learning model, we used the similarity ensemble approach to develop molecular structural similarity-based toxidrome models. We made the toolset developed in this study publicly accessible via a web user interface at https://toxidrome.bhsai.org/.
{"title":"Rapid screening of chemicals for their potential to cause specific toxidromes","authors":"Ruifeng Liu, M. AbdulHameed, Zhen Xu, Benjamin Clancy, V. Desai, Anders Wallqvist","doi":"10.3389/fddsv.2024.1324564","DOIUrl":"https://doi.org/10.3389/fddsv.2024.1324564","url":null,"abstract":"Toxidromes constitute patterns of symptoms and signs caused by specific toxic effects that guide emergency treatments. Computational identification of chemicals that cause different toxidromes allows us to rapidly screen novel compounds and compound classes as to their potential toxicity. The aim of the current study was to create a computational toolset that can map chemicals to their potential toxidromes. Hence, we evaluated the performance of a state-of-the-art deep learning method—the recently developed communicative message passing neural network (CMPNN)—for its ability to overcome the use of small datasets for training deep learning models. Our results indicated that multi-task training—a technique known for its ability to use multiple small datasets to train conventional deep neural networks—works equally well with CMPNN. We also showed that CMPNN-based ensemble learning results in more reliable predictions than those obtained using a single CMPNN model. In addition, we showed that the standard deviations of individual model predictions from an ensemble of CMPNN models correlated with the errors of ensemble predictions and could be used to estimate the reliability of ensemble predictions. For toxidromes that do not have well-defined molecular mechanisms or sufficient data to train a deep learning model, we used the similarity ensemble approach to develop molecular structural similarity-based toxidrome models. We made the toolset developed in this study publicly accessible via a web user interface at https://toxidrome.bhsai.org/.","PeriodicalId":73080,"journal":{"name":"Frontiers in drug discovery","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139863559","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 : 2024-02-02DOI: 10.3389/fddsv.2024.1362956
Virgilio Romanelli, Carmen Cerchia, Antonio Lavecchia
Drug discovery is a costly and time-consuming process, especially because of the significant expenses connected with the high percentage of clinical trial failures. As such, there is a need for new paradigms enabling the optimization of the various stages, from hit identification to market approval. The upsurge in the use of artificial intelligence (AI) technologies and the advent of deep learning (DL) demonstrated a lot of promise in rethinking and redesigning the traditional pipelines in drug discovery, including de novo molecular design. In this regard, generative models have greatly impacted the de novo design of molecules with desired properties and are being increasingly integrated into real world drug discovery campaigns. Herein, we will briefly appraise recent case studies utilizing generative models for chemical structure generation in the area of anticancer drug discovery. Finally, we will analyze current challenges and limitations as well as the possible strategies to overcome them, outlining potential future directions to advance this exciting field.
{"title":"Deep generative models in the quest for anticancer drugs: ways forward","authors":"Virgilio Romanelli, Carmen Cerchia, Antonio Lavecchia","doi":"10.3389/fddsv.2024.1362956","DOIUrl":"https://doi.org/10.3389/fddsv.2024.1362956","url":null,"abstract":"Drug discovery is a costly and time-consuming process, especially because of the significant expenses connected with the high percentage of clinical trial failures. As such, there is a need for new paradigms enabling the optimization of the various stages, from hit identification to market approval. The upsurge in the use of artificial intelligence (AI) technologies and the advent of deep learning (DL) demonstrated a lot of promise in rethinking and redesigning the traditional pipelines in drug discovery, including de novo molecular design. In this regard, generative models have greatly impacted the de novo design of molecules with desired properties and are being increasingly integrated into real world drug discovery campaigns. Herein, we will briefly appraise recent case studies utilizing generative models for chemical structure generation in the area of anticancer drug discovery. Finally, we will analyze current challenges and limitations as well as the possible strategies to overcome them, outlining potential future directions to advance this exciting field.","PeriodicalId":73080,"journal":{"name":"Frontiers in drug discovery","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139871303","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 : 2024-02-02DOI: 10.3389/fddsv.2024.1362956
Virgilio Romanelli, Carmen Cerchia, Antonio Lavecchia
Drug discovery is a costly and time-consuming process, especially because of the significant expenses connected with the high percentage of clinical trial failures. As such, there is a need for new paradigms enabling the optimization of the various stages, from hit identification to market approval. The upsurge in the use of artificial intelligence (AI) technologies and the advent of deep learning (DL) demonstrated a lot of promise in rethinking and redesigning the traditional pipelines in drug discovery, including de novo molecular design. In this regard, generative models have greatly impacted the de novo design of molecules with desired properties and are being increasingly integrated into real world drug discovery campaigns. Herein, we will briefly appraise recent case studies utilizing generative models for chemical structure generation in the area of anticancer drug discovery. Finally, we will analyze current challenges and limitations as well as the possible strategies to overcome them, outlining potential future directions to advance this exciting field.
{"title":"Deep generative models in the quest for anticancer drugs: ways forward","authors":"Virgilio Romanelli, Carmen Cerchia, Antonio Lavecchia","doi":"10.3389/fddsv.2024.1362956","DOIUrl":"https://doi.org/10.3389/fddsv.2024.1362956","url":null,"abstract":"Drug discovery is a costly and time-consuming process, especially because of the significant expenses connected with the high percentage of clinical trial failures. As such, there is a need for new paradigms enabling the optimization of the various stages, from hit identification to market approval. The upsurge in the use of artificial intelligence (AI) technologies and the advent of deep learning (DL) demonstrated a lot of promise in rethinking and redesigning the traditional pipelines in drug discovery, including de novo molecular design. In this regard, generative models have greatly impacted the de novo design of molecules with desired properties and are being increasingly integrated into real world drug discovery campaigns. Herein, we will briefly appraise recent case studies utilizing generative models for chemical structure generation in the area of anticancer drug discovery. Finally, we will analyze current challenges and limitations as well as the possible strategies to overcome them, outlining potential future directions to advance this exciting field.","PeriodicalId":73080,"journal":{"name":"Frontiers in drug discovery","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139811555","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 : 2024-01-31DOI: 10.3389/fddsv.2024.1347246
Lindsay J. Marshall, Kathleen M. Conlee
Creating and developing new drugs can take decades, costs millions of dollars, requires untold human effort and usually, takes thousands of animal lives. Despite regulators professing confidence in non-animal approaches and guidance documents that permit submission of non-animal data, toxicity testing is routinely carried out in animals, employing rodents (invariably mice) and non-rodents. However, extensive preclinical testing in animals is still no guarantee that drugs will be safe and/or effective. In fact, more than nine out of every ten drugs that appear safe from animal trials will fail when tested in people, often due to unexplained toxicity or a lack of efficacy. This paper will describe recent advances in drug development where non-animal approaches have been used, to explore how and where these could be applied more widely to revolutionize the drug development pipeline and accelerate the creation of safe and effective medicines. As one case study, we look at the small molecule channel modifiers developed to address the consequences of the mutated chloride channel in the fatal genetic condition, cystic fibrosis. We then take a closer look at where drug development could be accelerated by focusing on innovative, human biology-based testing methods. Finally, we put forward recommendations, targeting all stakeholders, including the public, that will be needed to put this into practice and enable drug development to become more efficient - focusing on human-biology based testing and cutting out the middle-mouse.
{"title":"The case of the missing mouse—developing cystic fibrosis drugs without using animals","authors":"Lindsay J. Marshall, Kathleen M. Conlee","doi":"10.3389/fddsv.2024.1347246","DOIUrl":"https://doi.org/10.3389/fddsv.2024.1347246","url":null,"abstract":"Creating and developing new drugs can take decades, costs millions of dollars, requires untold human effort and usually, takes thousands of animal lives. Despite regulators professing confidence in non-animal approaches and guidance documents that permit submission of non-animal data, toxicity testing is routinely carried out in animals, employing rodents (invariably mice) and non-rodents. However, extensive preclinical testing in animals is still no guarantee that drugs will be safe and/or effective. In fact, more than nine out of every ten drugs that appear safe from animal trials will fail when tested in people, often due to unexplained toxicity or a lack of efficacy. This paper will describe recent advances in drug development where non-animal approaches have been used, to explore how and where these could be applied more widely to revolutionize the drug development pipeline and accelerate the creation of safe and effective medicines. As one case study, we look at the small molecule channel modifiers developed to address the consequences of the mutated chloride channel in the fatal genetic condition, cystic fibrosis. We then take a closer look at where drug development could be accelerated by focusing on innovative, human biology-based testing methods. Finally, we put forward recommendations, targeting all stakeholders, including the public, that will be needed to put this into practice and enable drug development to become more efficient - focusing on human-biology based testing and cutting out the middle-mouse.","PeriodicalId":73080,"journal":{"name":"Frontiers in drug discovery","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140472973","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 : 2023-12-18DOI: 10.3389/fddsv.2023.1326736
Abigail Haffner, Manoel Figueiredo Neto, C. Umbaugh, Tiago J. P. Sobreira, T. Lescun, H. Sintim, M. Figueiredo
Introduction: Monocytes and macrophages are the first barrier of the innate immune system, which interact with agents causing osteoarthritis or other conditions, leading to the release of proinflammatory mediators that exacerbate inflammation.Methods: The aim of this study was to investigate the proteomic changes in THP-1 monocytes differentiated to macrophages, pre- or -post small compound treatments and in the presence or absence of a proinflammatory stimulus, Lipopolysaccharide (LPS). This study aimed to discover and isolate small compounds that mimic the interaction between Pigment derived growth factor (PEDF) and its 37/67 kDa Laminin receptor (LR) with potential anti-inflammatory activity.Results: Our results suggested that novel compounds targeting the LR-PEDF interface can be useful for modulating anti-inflammatory effects. Several compounds were selected based on in silico docking at the PEDF/LR interface and examined for their ability to reduce IL-1β expression in a macrophage cell model. Compound C3 showed the highest efficacy in reducing IL-1β expression in the presence of LPS proinflammatory stimulus. Proteomics analysis revealed that C3 treatment altered the global proteomic profile of THP-1 activated macrophages, affecting pathways such as MYC targets, oxidative phosphorylation, and mTORC1 signaling.Discussion: The analysis also highlighted the involvement of key regulators, including RPSA and MYC, and their interactions with other proteins such as ribosome proteins and cell cycle regulators. Furthermore, the downregulated proteome analysis revealed shared and unique pathways affected by the treatments, including processes related to actin cytoskeleton, translation, and the inflammatory response. Protein-protein interaction networks suggested the potential involvement of transcription factors like MYC and the interconnectedness of signaling pathways in mediating such as the effects of the treatments. Overall, these findings provide valuable insights into the potential anti-inflammatory activity and underlying mechanisms of compound C3, emphasizing its relevance for further investigation in the context of inflammatory conditions.
{"title":"Global proteomics insights for a novel small compound targeting the non-integrin Laminin Receptor in a macrophage cell model","authors":"Abigail Haffner, Manoel Figueiredo Neto, C. Umbaugh, Tiago J. P. Sobreira, T. Lescun, H. Sintim, M. Figueiredo","doi":"10.3389/fddsv.2023.1326736","DOIUrl":"https://doi.org/10.3389/fddsv.2023.1326736","url":null,"abstract":"Introduction: Monocytes and macrophages are the first barrier of the innate immune system, which interact with agents causing osteoarthritis or other conditions, leading to the release of proinflammatory mediators that exacerbate inflammation.Methods: The aim of this study was to investigate the proteomic changes in THP-1 monocytes differentiated to macrophages, pre- or -post small compound treatments and in the presence or absence of a proinflammatory stimulus, Lipopolysaccharide (LPS). This study aimed to discover and isolate small compounds that mimic the interaction between Pigment derived growth factor (PEDF) and its 37/67 kDa Laminin receptor (LR) with potential anti-inflammatory activity.Results: Our results suggested that novel compounds targeting the LR-PEDF interface can be useful for modulating anti-inflammatory effects. Several compounds were selected based on in silico docking at the PEDF/LR interface and examined for their ability to reduce IL-1β expression in a macrophage cell model. Compound C3 showed the highest efficacy in reducing IL-1β expression in the presence of LPS proinflammatory stimulus. Proteomics analysis revealed that C3 treatment altered the global proteomic profile of THP-1 activated macrophages, affecting pathways such as MYC targets, oxidative phosphorylation, and mTORC1 signaling.Discussion: The analysis also highlighted the involvement of key regulators, including RPSA and MYC, and their interactions with other proteins such as ribosome proteins and cell cycle regulators. Furthermore, the downregulated proteome analysis revealed shared and unique pathways affected by the treatments, including processes related to actin cytoskeleton, translation, and the inflammatory response. Protein-protein interaction networks suggested the potential involvement of transcription factors like MYC and the interconnectedness of signaling pathways in mediating such as the effects of the treatments. Overall, these findings provide valuable insights into the potential anti-inflammatory activity and underlying mechanisms of compound C3, emphasizing its relevance for further investigation in the context of inflammatory conditions.","PeriodicalId":73080,"journal":{"name":"Frontiers in drug discovery","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138994748","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 : 2023-12-15DOI: 10.3389/fddsv.2023.1294454
Yukun Min, Luke A. J. O’Neill
Mitochondrial metabolites and their derivatives have been the focus of recent efforts to develop new anti-inflammatory therapeutics. The widely used therapeutic agents dimethyl fumarate (DMF) and metformin have anti-inflammatory properties and have been shown to target metabolism. The mitochondrial metabolites succinate, itaconate, and fumarate have multiple immunomodulatory effects and present interesting therapeutic possibilities for immune and inflammatory diseases. Mitochondrial DNA and double-stranded RNA have also been shown to be highly inflammatory, acting via specific pattern recognition receptors (PRRs) such as cGAS and TLR9 for mitochondrial DNA, RIG-I, MDA5 for mitochondrial double stranded RNA, and TLR7 for mitochondrial single stranded RNA. These recent discoveries are changing our view of mitochondria suggesting that they are at the heart of multiple inflammatory diseases and provide opportunities for the development of new anti-inflammatory therapeutics.
线粒体代谢物及其衍生物是近年来开发新型抗炎疗法的重点。被广泛使用的治疗药物富马酸二甲酯（DMF）和二甲双胍具有抗炎特性，并已被证明可以靶向代谢。线粒体代谢产物琥珀酸盐、伊他康酸盐和富马酸盐具有多种免疫调节作用，为免疫和炎症疾病的治疗提供了有趣的可能性。线粒体 DNA 和双链 RNA 也被证明具有高度炎症性，可通过特定的模式识别受体（PRR）发挥作用，如线粒体 DNA 受体为 cGAS 和 TLR9，线粒体双链 RNA 受体为 RIG-I、MDA5，线粒体单链 RNA 受体为 TLR7。这些最新发现正在改变我们对线粒体的看法，表明线粒体是多种炎症疾病的核心，并为开发新的抗炎疗法提供了机会。
{"title":"Targeting mitochondrial metabolites and nucleic acids as an anti-inflammatory strategy","authors":"Yukun Min, Luke A. J. O’Neill","doi":"10.3389/fddsv.2023.1294454","DOIUrl":"https://doi.org/10.3389/fddsv.2023.1294454","url":null,"abstract":"Mitochondrial metabolites and their derivatives have been the focus of recent efforts to develop new anti-inflammatory therapeutics. The widely used therapeutic agents dimethyl fumarate (DMF) and metformin have anti-inflammatory properties and have been shown to target metabolism. The mitochondrial metabolites succinate, itaconate, and fumarate have multiple immunomodulatory effects and present interesting therapeutic possibilities for immune and inflammatory diseases. Mitochondrial DNA and double-stranded RNA have also been shown to be highly inflammatory, acting via specific pattern recognition receptors (PRRs) such as cGAS and TLR9 for mitochondrial DNA, RIG-I, MDA5 for mitochondrial double stranded RNA, and TLR7 for mitochondrial single stranded RNA. These recent discoveries are changing our view of mitochondria suggesting that they are at the heart of multiple inflammatory diseases and provide opportunities for the development of new anti-inflammatory therapeutics.","PeriodicalId":73080,"journal":{"name":"Frontiers in drug discovery","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138998724","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 : 2023-12-11DOI: 10.3389/fddsv.2023.1286710
J. Torzewski, S. Mattecka, W. Ries, C. Garlichs, F. Heigl, J. Fiedler, A. Sheriff
C-reactive protein (CRP) apheresis may preserve myocardial tissue after acute myocardial infarction with delayed revascularization. Ten consecutive patients with cardiogenic shock were graded using the Society of Cardiovascular Angiography and Interventions shock classification and treated with CRP apheresis. All patients tolerated CRP apheresis well and were discharged in good clinical condition.
{"title":"Case report: C-reactive protein apheresis in cardiogenic shock: case series from the C-reactive protein apheresis in acute myocardial infarction-registry","authors":"J. Torzewski, S. Mattecka, W. Ries, C. Garlichs, F. Heigl, J. Fiedler, A. Sheriff","doi":"10.3389/fddsv.2023.1286710","DOIUrl":"https://doi.org/10.3389/fddsv.2023.1286710","url":null,"abstract":"C-reactive protein (CRP) apheresis may preserve myocardial tissue after acute myocardial infarction with delayed revascularization. Ten consecutive patients with cardiogenic shock were graded using the Society of Cardiovascular Angiography and Interventions shock classification and treated with CRP apheresis. All patients tolerated CRP apheresis well and were discharged in good clinical condition.","PeriodicalId":73080,"journal":{"name":"Frontiers in drug discovery","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139010461","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 : 2023-12-11DOI: 10.3389/fddsv.2023.1261094
Diana L. Prado-Romero, Alejandro Gómez-García, Raziel Cedillo-González, Hassan Villegas-Quintero, Juan F. Avellaneda-Tamayo, E. López-López, Fernanda I. Saldívar-González, Ana L. Chávez-Hernández, J. Medina‐Franco
The structure-activity relationships data available in public databases of inhibitors of DNA methyltransferases (DNMTs), families of epigenetic targets, plus the structural information of DNMT1, enables the development of a robust structure-based drug design strategy to study, at the molecular level, the activity of DNMTs inhibitors. In this study, we discuss a consensus molecular docking strategy to aid in explaining the activity of small molecules tested as inhibitors of DNMT1. The consensus docking approach, which was based on three validated docking algorithms of different designs, had an overall good agreement with the experimental enzymatic inhibition assays reported in the literature. The docking protocol was used to explain, at the molecular level, the activity profile of a novel DNMT1 inhibitor with a distinct chemical scaffold whose identification was inspired by de novo design and complemented with similarity searching.
DNA 甲基转移酶（DNMTs）抑制剂、表观遗传靶标家族的结构-活性关系数据，加上 DNMT1 的结构信息，使得开发基于结构的药物设计策略成为可能，从而在分子水平上研究 DNMTs 抑制剂的活性。在本研究中，我们讨论了一种共识分子对接策略，以帮助解释作为 DNMT1 抑制剂测试的小分子的活性。该共识对接方法基于三种不同设计的验证对接算法，与文献报道的实验酶抑制测定结果总体上吻合良好。该对接方案在分子水平上解释了一种具有独特化学结构的新型 DNMT1 抑制剂的活性特征。
{"title":"Consensus docking aid to model the activity of an inhibitor of DNA methyltransferase 1 inspired by de novo design","authors":"Diana L. Prado-Romero, Alejandro Gómez-García, Raziel Cedillo-González, Hassan Villegas-Quintero, Juan F. Avellaneda-Tamayo, E. López-López, Fernanda I. Saldívar-González, Ana L. Chávez-Hernández, J. Medina‐Franco","doi":"10.3389/fddsv.2023.1261094","DOIUrl":"https://doi.org/10.3389/fddsv.2023.1261094","url":null,"abstract":"The structure-activity relationships data available in public databases of inhibitors of DNA methyltransferases (DNMTs), families of epigenetic targets, plus the structural information of DNMT1, enables the development of a robust structure-based drug design strategy to study, at the molecular level, the activity of DNMTs inhibitors. In this study, we discuss a consensus molecular docking strategy to aid in explaining the activity of small molecules tested as inhibitors of DNMT1. The consensus docking approach, which was based on three validated docking algorithms of different designs, had an overall good agreement with the experimental enzymatic inhibition assays reported in the literature. The docking protocol was used to explain, at the molecular level, the activity profile of a novel DNMT1 inhibitor with a distinct chemical scaffold whose identification was inspired by de novo design and complemented with similarity searching.","PeriodicalId":73080,"journal":{"name":"Frontiers in drug discovery","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138979883","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 : 2023-12-08DOI: 10.3389/fddsv.2023.1346042
Laura Alcântara, Caio Franco, Nilmar Moretti, Denise Pilger
{"title":"Editorial: Drug discovery for emerging and neglected tropical diseases: advances, challenges and perspectives","authors":"Laura Alcântara, Caio Franco, Nilmar Moretti, Denise Pilger","doi":"10.3389/fddsv.2023.1346042","DOIUrl":"https://doi.org/10.3389/fddsv.2023.1346042","url":null,"abstract":"","PeriodicalId":73080,"journal":{"name":"Frontiers in drug discovery","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138588036","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：