Dataset Information

Discovery, development, and future application of senolytics: theories and predictions.

ABSTRACT: Senescent cells accumulate with aging and at etiological sites of multiple diseases, including those accounting for most morbidity, mortality, and health costs. Senescent cells do not replicate, can release factors that cause tissue dysfunction, and yet remain viable. The discovery of senolytic drugs, agents that selectively eliminate senescent cells, created a new route for alleviating age-related dysfunction and diseases. As anticipated for agents targeting fundamental aging mechanisms that are 'root cause' contributors to multiple disorders, potential applications of senolytics are protean. We review the discovery of senolytics, strategies for translation into clinical application, and promising early signals from clinical trials.

SUBMITTER: Wissler Gerdes EO

PROVIDER: S-EPMC7302972 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

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Discovery, development, and future application of senolytics: theories and predictions.

Wissler Gerdes Erin O EO Zhu Yi Y Tchkonia Tamar T Kirkland James L JL

The FEBS journal 20200317 12

Senescent cells accumulate with aging and at etiological sites of multiple diseases, including those accounting for most morbidity, mortality, and health costs. Senescent cells do not replicate, can release factors that cause tissue dysfunction, and yet remain viable. The discovery of senolytic drugs, agents that selectively eliminate senescent cells, created a new route for alleviating age-related dysfunction and diseases. As anticipated for agents targeting fundamental aging mechanisms that ar ...[more]

PMID: 32112672

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Project description:Computational toxicology models have been successfully implemented to prioritize and screen chemicals. There are numerous in silico (quantitative) structure-activity relationship ([Q]SAR) models for the prediction of a range of human-relevant toxicological endpoints, but for a given endpoint and chemical, not all predictions are identical due to differences in their training sets, algorithms, and methodology. This poses an issue for high-throughput screening of a large chemical inventory as it necessitates several models to cover diverse chemistries but will then generate data conflicts. To address this challenge, we developed a consensus modeling strategy to combine predictions obtained from different existing in silico (Q)SAR models into a single predictive value while also expanding chemical space coverage. This study developed consensus models for nine toxicological endpoints relating to estrogen receptor (ER) and androgen receptor (AR) interactions (i.e., binding, agonism, and antagonism) and genotoxicity (i.e., bacterial mutation, in vitro chromosomal aberration, and in vivo micronucleus). Consensus models were created by combining different (Q)SAR models using various weighting schemes. As a multi-objective optimization problem, there is no single best consensus model, and therefore, Pareto fronts were determined for each endpoint to identify the consensus models that optimize the multiple-criterion decisions simultaneously. Accordingly, this work presents sets of solutions for each endpoint that contain the optimal combination, regardless of the trade-off, with the results demonstrating that the consensus models improved both the predictive power and chemical space coverage. These solutions were further analyzed to find trends between the best consensus models and their components. Here, we demonstrate the development of a flexible and adaptable approach for in silico consensus modeling and its application across nine toxicological endpoints related to ER activity, AR activity, and genotoxicity. These consensus models are developed to be integrated into a larger multi-tier NAM-based framework to prioritize chemicals for further investigation and support the transition to a non-animal approach to risk assessment in Canada.

Dataset Information

Discovery, development, and future application of senolytics: theories and predictions.

Publications

Discovery, development, and future application of senolytics: theories and predictions.

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