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Introduction: Cancer refers to a group of diseases characterized by the uncontrolled growth and spread of abnormal cells in the body. Due to its complexity, it has been hard to find an ideal medicine to treat all cancer types, although there is an urgent need for it. However, the cost of developing a new drug is high and time-consuming. In this sense, drug repurposing (DR) can hasten drug discovery by giving existing drugs new disease indications. Many computational methods have been applied to achieve DR, but just a few have succeeded. Therefore, this review aims to show in silico DR approaches and the gap between these strategies and their ultimate application in oncology. Methods: The scoping review was conducted according to the Arksey and O’Malley framework and the Joanna Briggs Institute recommendations. Relevant studies were identified through electronic searching of PubMed/ MEDLINE, Embase, Scopus, and Web of Science databases, as well as the grey literature. We included peer-reviewed research articles involving in silico strategies applied to drug repurposing in oncology, published between 1 January 2003, and 31 December 2021. Results: We identified 238 studies for inclusion in the review. Most studies revealed that the United States, India, China, South Korea, and Italy are top publishers. Regarding cancer types, breast cancer, lymphomas and leukemias, lung, colorectal, and prostate cancer are the top investigated. Additionally, most studies solely used computational methods, and just a few assessed more complex scientific models. Lastly, molecular modeling, which includes molecular docking and molecular dynamics simulations, was the most frequently used method, followed by signature-, Machine Learning-, and network-based strategies. Discussion: DR is a trending opportunity but still demands extensive testing to ensure its safety and efficacy for the new indications. Finally, implementing DR can be challenging due to various factors, including lack of quality data, patient populations, cost, intellectual property issues, market considerations, and regulatory requirements. Despite all the hurdles, DR remains an exciting strategy for identifying new treatments for numerous diseases, including cancer types, and giving patients faster access to new medications. [ABSTRACT FROM AUTHOR]

Background: The emergence of multidrug-resistant Klebsiella pneumoniae is a major public health concern. Many K. pneumoniae infections can only be treated when resorting to last-line drugs such as polymyxin B (PB). However, resistance to this antibiotic is also observed, although insufficient information is described on its mode of action as well as the mechanisms used by resistant bacteria to evade its effects. We aimed to study PB resistance and the influence of abiotic stresses in a clinical K. pneumoniae strain using whole transcriptome profiling. Results: We sequenced 12 cDNA libraries of K. pneumoniae Kp13 bacteria, from two biological replicates of the original strain Kp13 (Kp13) and five derivative strains: induced high-level PB resistance in acidic pH (Kp13pH), magnesium deprivation (Kp13Mg), high concentrations of calcium (Kp13Ca) and iron (Kp13Fe), and a control condition with PB (Kp13PolB). Our results show the involvement of multiple regulatory loci that differentially respond to each condition as well as a shared gene expression response elicited by PB treatment, and indicate the participation of two-regulatory components such as ArcA-ArcB, which could be involved in re-routing the K. pneumoniae metabolism following PB treatment. Modules of co-expressed genes could be determined, which correlated to growth in acid stress and PB exposure. We hypothesize that polymyxin B induces metabolic shifts in K. pneumoniae that could relate to surviving against the action of this antibiotic. Conclusions: We obtained whole transcriptome data for K. pneumoniae under different environmental conditions and PB treatment. Our results supports the notion that the K. pneumoniae response to PB exposure goes beyond damaged membrane reconstruction and involves recruitment of multiple gene modules and intracellular targets. [ABSTRACT FROM AUTHOR]