Pub Date : 2025-11-05DOI: 10.1038/d41573-025-00182-z
Asher Mullard
{"title":"Novartis pays US$12 billion for Avidity’s lead antibody–oligonucleotide drugs","authors":"Asher Mullard","doi":"10.1038/d41573-025-00182-z","DOIUrl":"10.1038/d41573-025-00182-z","url":null,"abstract":"","PeriodicalId":19068,"journal":{"name":"Nature Reviews. Drug Discovery","volume":"24 12","pages":"896-896"},"PeriodicalIF":101.8,"publicationDate":"2025-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145440869","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-30DOI: 10.1038/d41573-025-00177-w
Sarah Crunkhorn
{"title":"PROTAC for p300/CBP in prostate cancer","authors":"Sarah Crunkhorn","doi":"10.1038/d41573-025-00177-w","DOIUrl":"10.1038/d41573-025-00177-w","url":null,"abstract":"","PeriodicalId":19068,"journal":{"name":"Nature Reviews. Drug Discovery","volume":"24 12","pages":"906-906"},"PeriodicalIF":101.8,"publicationDate":"2025-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145397245","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-30DOI: 10.1038/d41573-025-00176-x
Sarah Crunkhorn
{"title":"Targeting the ECM in melanoma","authors":"Sarah Crunkhorn","doi":"10.1038/d41573-025-00176-x","DOIUrl":"10.1038/d41573-025-00176-x","url":null,"abstract":"","PeriodicalId":19068,"journal":{"name":"Nature Reviews. Drug Discovery","volume":"24 12","pages":"906-906"},"PeriodicalIF":101.8,"publicationDate":"2025-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145397241","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-27DOI: 10.1038/s41573-025-01308-z
Christopher E. Goldring, Giusy Russomanno, Carmen Pin, Panuwat Trairatphisan, Kylie A. Beattie, Ciarán P. Fisher, Janet Piñero, Richard J. Brennan, Diana Clausznitzer, Ian M. Copple, Theo M. de Kok, Carrie A. Duckworth, Laura I. Furlong, Barbara Füzi, Attila Gabor, Louis Gall, Jan Hengstler, Henning Hermjakob, Fiona Hunter, Danyel Jennen, Mikko Koskinen, Steven J. Kunnen, Lieve Lammens, Sebastian Lobentanzer, Marcel Mohr, Elisa Passini, D. Mark Pritchard, Rahuman S. Malik-Sheriff, Blanca Rodríguez, Eric I. Rossman, Julio Saez-Rodríguez, Friedemann Schmidt, Rowena Sison-Young, Inari Soininen, Sean Turner, Bob van de Water, Johan G. C. van Hasselt, Filippo Venezia, Jeffrey A. Willy, Derek J. Leishman, James L. Stevens, Loic Laplanche
Reliable prediction and prevention of adverse drug reactions (ADRs) remains a key challenge in the development of new medicines. Advanced mathematical and computational modelling approaches, which incorporate cutting-edge mechanistic understanding of ADRs in concert with systematically collected data addressing knowledge gaps, are integral components of model-informed drug discovery and development (MID3). These approaches provide a precise, quantitative framework for predicting and mitigating safety risks in the earliest phases of drug development. Here, we highlight recent developments in the burgeoning field of quantitative systems toxicology (QST), including insights into the current state-of-the-art, as well as outcomes from the Innovative Medicines Initiative (IMI) 2 TransQST project. QST models that describe the disruption of cardiovascular, gastrointestinal, hepatic and renal physiological functions following drug exposure are presented, along with recommendations for their application in drug discovery and development. Quantitative systems toxicology (QST) models have the potential to increase confidence in the safety assessment of drug candidates and to inform project progression decisions. This article overviews the fundamentals of constructing and using QST models, presents the state-of-the-art for models of cardiovascular, gastrointestinal, hepatic and renal toxicities, and it provides recommendations for their application in drug discovery and development.
{"title":"Quantitative systems toxicology: modelling to mechanistically understand and predict drug safety","authors":"Christopher E. Goldring, Giusy Russomanno, Carmen Pin, Panuwat Trairatphisan, Kylie A. Beattie, Ciarán P. Fisher, Janet Piñero, Richard J. Brennan, Diana Clausznitzer, Ian M. Copple, Theo M. de Kok, Carrie A. Duckworth, Laura I. Furlong, Barbara Füzi, Attila Gabor, Louis Gall, Jan Hengstler, Henning Hermjakob, Fiona Hunter, Danyel Jennen, Mikko Koskinen, Steven J. Kunnen, Lieve Lammens, Sebastian Lobentanzer, Marcel Mohr, Elisa Passini, D. Mark Pritchard, Rahuman S. Malik-Sheriff, Blanca Rodríguez, Eric I. Rossman, Julio Saez-Rodríguez, Friedemann Schmidt, Rowena Sison-Young, Inari Soininen, Sean Turner, Bob van de Water, Johan G. C. van Hasselt, Filippo Venezia, Jeffrey A. Willy, Derek J. Leishman, James L. Stevens, Loic Laplanche","doi":"10.1038/s41573-025-01308-z","DOIUrl":"10.1038/s41573-025-01308-z","url":null,"abstract":"Reliable prediction and prevention of adverse drug reactions (ADRs) remains a key challenge in the development of new medicines. Advanced mathematical and computational modelling approaches, which incorporate cutting-edge mechanistic understanding of ADRs in concert with systematically collected data addressing knowledge gaps, are integral components of model-informed drug discovery and development (MID3). These approaches provide a precise, quantitative framework for predicting and mitigating safety risks in the earliest phases of drug development. Here, we highlight recent developments in the burgeoning field of quantitative systems toxicology (QST), including insights into the current state-of-the-art, as well as outcomes from the Innovative Medicines Initiative (IMI) 2 TransQST project. QST models that describe the disruption of cardiovascular, gastrointestinal, hepatic and renal physiological functions following drug exposure are presented, along with recommendations for their application in drug discovery and development. Quantitative systems toxicology (QST) models have the potential to increase confidence in the safety assessment of drug candidates and to inform project progression decisions. This article overviews the fundamentals of constructing and using QST models, presents the state-of-the-art for models of cardiovascular, gastrointestinal, hepatic and renal toxicities, and it provides recommendations for their application in drug discovery and development.","PeriodicalId":19068,"journal":{"name":"Nature Reviews. Drug Discovery","volume":"25 2","pages":"138-153"},"PeriodicalIF":101.8,"publicationDate":"2025-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145374176","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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