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The attrition rate of drugs in clinical trials is generally quite high, with estimates suggesting that approximately 90% of drugs fail to make it through the process. The identification of unexpected toxicity issues during preclinical stages is a significant factor contributing to this high rate of failure. These issues can have a major impact on the success of a drug and must be carefully considered throughout the development process. These late-stage rejections or withdrawals of drug candidates significantly increase the costs associated with drug development, particularly when toxicity is detected during clinical trials or after market release. Understanding drug-biological target interactions is essential for evaluating compound toxicity and safety, as well as predicting therapeutic effects and potential off-target effects that could lead to toxicity. This will enable scientists to predict and assess the safety profiles of drug candidates more accurately. Evaluation of toxicity and safety is a critical aspect of drug development, and biomolecules, particularly proteins, play vital roles in complex biological networks and often serve as targets for various chemicals. Therefore, a better understanding of these interactions is crucial for the advancement of drug development. The development of computational methods for evaluating protein-ligand interactions and predicting toxicity is emerging as a promising approach that adheres to the 3Rs principles (replace, reduce, and refine) and has garnered significant attention in recent years. In this review, we present a thorough examination of the latest breakthroughs in drug toxicity prediction, highlighting the significance of drug-target binding affinity in anticipating and mitigating possible adverse effects. In doing so, we aim to contribute to the development of more effective and secure drugs.

Motivation: Cancer is currently one of the most notorious diseases, with over 1 million deaths in the European Union alone in 2022. As each tumor can be composed of diverse cell types with distinct genotypes, cancer cells can acquire resistance to different compounds. Moreover, anticancer drugs can display severe side effects, compromising patient well-being. Therefore, novel strategies for identifying the optimal set of compounds to treat each tumor have become an important research topic in recent decades., Results: To address this challenge, we developed a novel drug response prediction algorithm called Drug Efficacy Leveraging Forked and Specialized networks (DELFOS). Our model learns from multi-omics data from over 65 cancer cell lines, as well as structural data from over 200 compounds, for the prediction of drug sensitivity. We also evaluated the benefits of incorporating single-cell expression data to predict drug response. DELFOS was validated using datasets with unseen cell lines or drugs and compared with other state-of-the-art algorithms, achieving a high prediction performance on several correlation and error metrics. Overall, DELFOS can effectively leverage multi-omics data for the prediction of drug responses in thousands of drug-cell line pairs., Availability and Implementation: The DELFOS pipeline and associated data are available at github.com/MoreiraLAB/delfos., (© The Author(s) 2023. Published by Oxford University Press.)

Cells suffer from perturbations by different stimuli, which, consequently, rise to individual alterations in their profile and function that may end up affecting the tissue as a whole. This is no different if we consider the effect of a therapeutic agent on a biological system. As cells are exposed to external ligands their profile can change at different single-omics levels. Detecting how these changes take place through different sequencing technologies is key to a better understanding of the effects of therapeutic agents. Single-cell RNA-sequencing stands out as one of the most common approaches for cell profiling and perturbation analysis. As a result, single-cell transcriptomics data can be integrated with other omics data sources, such as proteomics and epigenomics data, to clarify the perturbation effects and mechanism at the cell level. Appropriate computational tools are key to process and integrate the available information. This chapter focuses on the recent advances on ligand-induced perturbation and single-cell omics computational tools and algorithms, their current limitations, and how the deluge of data can be used to improve the current process of drug research and development., (Copyright © 2022 Elsevier Inc. All rights reserved.)

Mitochondria are highly dynamic organelles capable of adapting their network, morphology, and function, playing a role in oxidative phosphorylation and many cellular processes in most cell types. Skeletal muscle is a very plastic tissue, subjected to many morphological changes following diverse stimuli, such as during myogenic differentiation and regenerative myogenesis. For some time now, mitochondria have been reported to be involved in myogenesis by promoting a bioenergetic remodeling and assisting myoblasts in surviving the process. However, not much is known about the interplay between mitochondrial quality control and myogenic differentiation. Sestrin2 (SESN2) is a well described regulator of autophagy and antioxidant responses and has been gaining attention due to its role in aging-associated pathologies and redox signaling promoted by reactive oxygen species (ROS) in many tissues. Current evidence involving SESN2-associated pathways suggest that it can act as a potential regulator of mitochondrial quality control following induction by ROS under stress conditions, such as during myogenesis. Yet, there are no studies directly assessing SESN2 involvement in myogenic differentiation. This review provides novel insights pertaining the involvement of SESN2 in myogenic differentiation by analyzing the interactions between ROS and mitochondrial remodeling., (Copyright © 2021. Published by Elsevier B.V.)