In silico approaches in organ toxicity hazard assessment: Current status and future needs for predicting heart, kidney and lung toxicities

Bassan, Arianna; Alves, Vinicius M.; Amberg, Alexander; Anger, Lennart T.; Beilke, Lisa; Bender, Andreas; Bernal, Autumn J.; Cronin, Mark T.D.; Hsieh, Jui-Hua; Johnson, Candice; Kemper, Raymond A.; Mumtaz, Moiz; Neilson, Louise; Pavan, Manuela; Pointon, Amy; Pletz, Julia; Ruíz, Patricia; Russo, Daniel P.; Sabnis, Yogesh; Sandhu, Reena; Schaefer, Markus; Stavitskaya, Lidiya; Szabo, David T.; Valentin, Jean‐Pierre; Woolley, David; Zwickl, Craig; Myatt, Glenn J.

doi:10.1016/j.comtox.2021.100188

Cited by 18 publications

(9 citation statements)

References 181 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Some algorithms refine these data but are generally expensive and time-consuming. In addition, the computation resource for the in silico docking approaches using DNA is still limited and challenging [ 145 , 146 , 147 ].…”

Section: Challenges and Limitationsmentioning

confidence: 99%

Alternative Methods as Tools for Obesity Research: In Vitro and In Silico Approaches

Pamplona

Zoehler²,

Shigunov³

et al. 2022

Life

View full text Add to dashboard Cite

The study of adipogenesis is essential for understanding and treating obesity, a multifactorial problem related to body fat accumulation that leads to several life-threatening diseases, becoming one of the most critical public health problems worldwide. In this review, we propose to provide the highlights of the adipogenesis study based on in vitro differentiation of human mesenchymal stem cells (hMSCs). We list in silico methods, such as molecular docking for identification of molecular targets, and in vitro approaches, from 2D, more straightforward and applied for screening large libraries of substances, to more representative physiological models, such as 3D and bioprinting models. We also describe the development of physiological models based on microfluidic systems applied to investigate adipogenesis in vitro. We intend to identify the main alternative models for adipogenesis evaluation, contributing to the direction of preclinical research in obesity. Future directions indicate the association of in silico and in vitro techniques to bring a clear picture of alternative methods based on adipogenesis as a tool for obesity research.

show abstract

Section: Challenges and Limitationsmentioning

confidence: 99%

Alternative Methods as Tools for Obesity Research: In Vitro and In Silico Approaches

Pamplona

Zoehler²,

Shigunov³

et al. 2022

Life

View full text Add to dashboard Cite

show abstract

“…Hepatotoxicity data from histopathology-related findings as originating from preclinical toxicity study reports for regulatory submissions can be collated into groups of histopathology terms related to similar findings (and, possibly, potential mechanisms); general clusters are identified: tissue damage, inflammatory changes, structural alteration, and accumulative lesions with each general cluster separating into more specific groups as shown in Table 2 [145]. The same hierarchical organization is applied to other target organs (i.e., heart and kidney) [16]. [124,133,[146][147][148], and other secondary mechanisms such as those linked to the disruption of the gut-liver axis [149].…”

Section: Processes and Endpointsmentioning

confidence: 99%

“…The present work focuses on hepatotoxicity and aims at building a robust and pragmatic framework upon which IST protocols for organ toxicity can then be established; toxicity to other major target organs (heart, kidney, lung and nervous system) is discussed elsewhere [16,17]. This manuscript outlines a series of potential applications of the protocols, then summarizes general concepts concerning the state-of-thescience and organization of knowledge related to organ toxicity, and reviews liver toxicity to provide context to the endpoints requiring prediction.…”

Section: Introductionmentioning

confidence: 99%

In silico approaches in organ toxicity hazard assessment: Current status and future needs in predicting liver toxicity

Bassan

Alves

Amberg

et al. 2021

Computational Toxicology

Self Cite

View full text Add to dashboard Cite

“…Predictive models for DICT could save considerable time, resources, and human suffering, with the ultimate goal of preventing adverse events in clinical trials and the postmarket stage. However, predicting any in vivo effect is not a trivial classification task, and most predictive models are built on proxy end points (which are often reduced to binary end points) without taking into account in vivo parameters such as pharmacokinetic parameters. , While no models for DICTrank have been publicly available yet to the best of our knowledge, various studies have predicted proxy in vitro assays or side effect data from side effects resource (SIDER), some of which are related to cardiotoxicity . Studies focusing on side effects and proxy targets (such as hERG) are reasonable given that compounds that have cardiac-related indications are more likely to show related side effects as well or activity on ion channels …”

Section: Introductionmentioning

confidence: 99%

Insights into Drug Cardiotoxicity from Biological and Chemical Data: The First Public Classifiers for FDA Drug-Induced Cardiotoxicity Rank

Seal,

Spjuth,

Hosseini-Gerami

et al. 2024

J. Chem. Inf. Model.

Self Cite

View full text Add to dashboard Cite

Drug-induced cardiotoxicity (DICT) is a major concern in drug development, accounting for 10−14% of postmarket withdrawals. In this study, we explored the capabilities of chemical and biological data to predict cardiotoxicity, using the recently released DICTrank data set from the United States FDA. We found that such data, including protein targets, especially those related to ion channels (e.g., hERG), physicochemical properties (e.g., electrotopological state), and peak concentration in plasma offer strong predictive ability for DICT. Compounds annotated with mechanisms of action such as cyclooxygenase inhibition could distinguish between mostconcern and no-concern DICT. Cell Painting features for ER stress discerned most-concern cardiotoxic from nontoxic compounds. Models based on physicochemical properties provided substantial predictive accuracy (AUCPR = 0.93). With the availability of omics data in the future, using biological data promises enhanced predictability and deeper mechanistic insights, paving the way for safer drug development. All models from this study are available at https://broad.io/DICTrank_Predictor.

show abstract

In silico approaches in organ toxicity hazard assessment: Current status and future needs for predicting heart, kidney and lung toxicities

Cited by 18 publications

References 181 publications

Alternative Methods as Tools for Obesity Research: In Vitro and In Silico Approaches

Alternative Methods as Tools for Obesity Research: In Vitro and In Silico Approaches

In silico approaches in organ toxicity hazard assessment: Current status and future needs in predicting liver toxicity

Insights into Drug Cardiotoxicity from Biological and Chemical Data: The First Public Classifiers for FDA Drug-Induced Cardiotoxicity Rank

Contact Info

Product

Resources

About