23 papers · ranked by Valyu relevance
Scott Gigante, Varsha G. Raghavan, Amanda M. Robinson, Robert A. Barton + 6 more
'Robert A. Barton' 'Adeeb Rahman' 'Drausin Wulsin' 'Jacques Banchereau' 'Noam Solomon' 'Luis Voloch' 'Fabian J. Theis'] Translating the relevance of preclinical models (in vitro, animal models, or organoids) to their relevance in humans presents an important challenge during drug development. The rising abundance of…
Haochuan Guo, Xinru Xu, Jiaxi Zhang, Yajing Du + 7 more
'Zhiheng He' 'Linjie Zhao' 'Tingming Liang' 'Li Guo' 'Fuyi Wang' 'Andréia Machado Leopoldino'] The establishment and utilization of preclinical animal models constitute a pivotal aspect across all facets of cancer research, indispensably contributing to the comprehension of disease initiation and progression…
Lorenzo, Guillermo, Hormuth, David A. + 16 more
Despite advances in methods to interrogate tumor biology, the observational and population-based approach of classical cancer research and clinical oncology does not enable anticipation of tumor outcomes to hasten the discovery of cancer mechanisms and personalize disease management. To address these limitations…
Martin K. Thomsen, Morten Busk, Shunqing Liang
Simple Summary The incidence of prostate cancer is rising, primarily due to its prevalence among elderly men, and with increased life expectancy, these numbers are expected to continue increasing. Prostate cancer can remain indolent for many years, but treatment options are limited once the cancer progresses to an…
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This study presents a novel in vitro 3D hepatocyte model that contains a nanofibrous scaffold designed to mimic the extracellular matrix (ECM) of the human liver, both structurally and biochemically. A modular 3D-printed device housing the ECM scaffold was also developed, readily fitting in well plates. HepaRG…
Vibeke Fosse, Emanuela Oldoni, Chiara Gerardi, Rita Banzi + 6 more
The introduction of personalized medicine, through the increasing multi-omics characterization of disease, brings new challenges to disease modeling. The scope of this review was a broad evaluation of the relevance, validity, and predictive value of the current preclinical methodologies applied in stratified medicine…
Avery S. Williams, Elizabeth J. Wilk, Jennifer L. Fisher, Brittany N. Lasseigne
Preclinical models like cancer cell lines and patient-derived xenografts (PDXs) are vital for studying disease mechanisms and evaluating treatment options. It is essential that they accurately recapitulate the disease state of interest to generate results that will translate in the clinic. Prior studies have…
Paarth Parekh, Jason Sherfey, Begum Alaybeyoglu, Murat Cirit
Accurate clinical translation of preclinical research remains challenging, primarily due to species-specific differences and disease and patient heterogeneity. An important recent advancement has been development of microphysiological systems that consist of multiple human cell types that recapitulate key…
Grazyna Biala, Ewa Kedzierska, Marta Kruk-Slomka, Jolanta Orzelska-Gorka + 7 more
'Jolanta Orzelska-Gorka' 'Sara Hmaidan' 'Aleksandra Skrok' 'Jakub Kaminski' 'Eva Havrankova' 'Dominika Nadaska' 'Ivan Malik' 'Ziyaur Rahman'] The processes used by academic and industrial scientists to discover new drugs have recently experienced a true renaissance, with many new and exciting techniques being developed…
David Andreu-Sanz, Lisa Gregor, Emanuele Carlini, Daniele Scarcella + 2 more
Experimental mouse models are indispensable for the preclinical development of cancer immunotherapies, whereby complex interactions in the tumor microenvironment (TME) can be somewhat replicated. Despite the availability of diverse models, their predictive capacity for clinical outcomes remains largely unknown, posing…
Rachel Blomberg, Kayla Sompel, Caroline Hauer, Brisa Peña + 5 more
Lung cancer is the leading global cause of cancer-related deaths. Although smoking cessation is the best preventive action, nearly 50% of all lung cancer diagnoses occur in people who have already quit smoking. Research into treatment options for these high-risk patients has been constrained to rodent models of…
Debora Bogani, Kirsty Hooper, Owen Sansom
Summary: The call to arms for alternatives to model organisms of disease will fail without ongoing integration with - and validation against - complex animal models and support from the animal modelling community.
Qingzhi Liu, Gen Li, Veerabhadran Baladandayuthapani
The pursuit of precision oncology heavily relies on large-scale genomic and pharmacological data garnered from preclinical cancer model systems such as cell lines. While cell lines are instrumental in understanding the interplay between genomic programs and drug response, it well-established that they are not fully…
Sandrine Boulet, Moreno Ursino, Robin Michelet, Linda B. S. Aulin + 3 more
'Charlotte Kloft' 'Emmanuelle Comets' 'Sarah Zohar'] In preclinical investigations, e.g. in in vitro, in vivo and in silico studies, the pharmacokinetic, pharmacodynamic and toxicological characteristics of a drug are evaluated before advancing to firstin-man trial. Usually, each study is analyzed independently and the…
Julian Wäsche, Romina Ludwig, Irmela Jeremias, Christiane Fuchs
Dynamic models are widely used to mathematically describe biological phenomena that evolve over time. One important area of application is leukaemia research, where leukaemia cells are genetically modified in preclinical studies to explore new therapeutic targets for reducing leukaemic burden. In advanced experiments…
Caroline Hauer, Rachel Blomberg, Kayla Sompel, Chelsea M. Magin + 1 more
Lung cancer is the leading cause of global cancer death and prevention strategies are key to reducing mortality. Medical prevention may have a larger impact than treatment on mortality by targeting high-risk populations and reducing their lung cancer risk. Premalignant lesions (PMLs) that can be intercepted by…
Mingtang Zeng, Zijing Ruan, Jiaxi Tang, Maozhu Liu + 3 more
'Ping Fan' 'Xinhua Dai'] Establishing appropriate preclinical models is essential for cancer research. Evidence suggests that cancer is a highly heterogeneous disease. This follows the growing use of cancer models in cancer research to avoid these differences between xenograft tumor models and patient tumors. In recent…
Bohua Chen, Lucia Chantal Schneider, Christian Röver, Emmanuelle Comets + 14 more
'Emmanuelle Comets' 'Markus Christian Elze' 'Andrew Hooker' 'Joanna IntHout' 'Anne-Sophie Jannot' 'Daria Julkowska' 'Yanis Mimouni' 'Marina Savelieva' 'Nigel Stallard' 'Moreno Ursino' 'Marc Vandemeulebroecke' 'Sebastian Weber' 'Martin Posch' 'Sarah Zohar' 'Tim Friede'] In the context of clinical research, computational…
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Accurate modeling of drug concentration--time (C--t) profiles is central to pharmacokinetics (PK) and plays a critical role in both early-stage compound selection and late-stage individualized dosing. Traditional PK model offer mechanistic interpretability but often rely on rigid assumptions, extensive…
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This paper explores the transformative potential of Artificial Intelligence (AI) in enabling in silico clinical trials (ISCTs) for vaccine development. ISCTs, which simulate clinical trials using virtual patient cohorts, offer a cost-effective, ethical, and efficient alternative to traditional methods. AI significantly…
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Integrating machine learning (ML) into drug discovery has ushered in a new era of innovation, dramatically enhancing the efficiency and precision of identifying and developing new therapeutics. This review provides a comprehensive analysis of the current applications of machine learning in drug discovery, focusing on…
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Failures in drug discovery due to intolerable levels of adverse effects remain a significant concern. These failures can profoundly impact drug development, especially when identified during the later stages. Despite the challenges and risks posed by unexpected late-stage toxicity, publicly available data and…
Mohsen Pourmousa, Sankalp Jain, Elena Barnaeva, Wengong Jin + 15 more
Treatment regimens, especially in cancer, often include more than one medicine in order to achieve durable outcomes. Identifying the optimal combination of treatments has historically been done through clinical trial and error. And for many conditions, such as pancreatic cancer, an optimal treatment protocol has…