Identifying new topoisomerase II poison scaffolds by combining publicly available toxicity data and 2D/3D-based virtual screening.
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ABSTRACT: Molecular descriptor (2D) and three dimensional (3D) shape based similarity methods are widely used in ligand based virtual drug design. In the present study pairwise structure comparisons among a set of 4858 DTP compounds tested in the NCI60 tumor cell line anticancer drug screen were computed using chemical hashed fingerprints and 3D molecule shapes to calculate 2D and 3D similarities, respectively. Additionally, pairwise biological activity similarities were calculated by correlating the 60 element vectors of pGI50 values corresponding to the cytotoxicity of the compounds across the NCI60 panel. Subsequently, we compared the power of 2D and 3D structural similarity metrics to predict the toxicity pattern of compounds. We found that while the positive predictive value and sensitivity of 3D and molecular descriptor based approaches to predict biological activity are similar, a subset of molecule pairs yielded contradictory results. By simultaneously requiring similarity of biological activities and 3D shapes, and dissimilarity of molecular descriptor based comparisons, we identify pairs of scaffold hopping candidates displaying characteristic core structural changes such as heteroatom/heterocycle change and ring closure. Attempts to discover scaffold hopping candidates of mitoxantrone recovered known Topoisomerase II (Top2) inhibitors, and also predicted new, previously unknown chemotypes possessing in vitro Top2 inhibitory activity.
Project description:Computational models of the heart are increasingly being used in the development of devices, patient diagnosis and therapy guidance. While software techniques have been developed for simulating single hearts, there remain significant challenges in simulating cohorts of virtual hearts from multiple patients. To facilitate the development of new simulation and model analysis techniques by groups without direct access to medical data, image analysis techniques and meshing tools, we have created the first publicly available virtual cohort of twenty-four four-chamber hearts. Our cohort was built from heart failure patients, age 67±14 years. We segmented four-chamber heart geometries from end-diastolic (ED) CT images and generated linear tetrahedral meshes with an average edge length of 1.1±0.2mm. Ventricular fibres were added in the ventricles with a rule-based method with an orientation of -60° and 80° at the epicardium and endocardium, respectively. We additionally refined the meshes to an average edge length of 0.39±0.10mm to show that all given meshes can be resampled to achieve an arbitrary desired resolution. We ran simulations for ventricular electrical activation and free mechanical contraction on all 1.1mm-resolution meshes to ensure that our meshes are suitable for electro-mechanical simulations. Simulations for electrical activation resulted in a total activation time of 149±16ms. Free mechanical contractions gave an average left ventricular (LV) and right ventricular (RV) ejection fraction (EF) of 35±1% and 30±2%, respectively, and a LV and RV stroke volume (SV) of 95±28mL and 65±11mL, respectively. By making the cohort publicly available, we hope to facilitate large cohort computational studies and to promote the development of cardiac computational electro-mechanics for clinical applications.
Project description:UnlabelledBackgroundIdentification of prognostic biomarkers is hallmark of cancer genomics. Since miRNAs regulate expression of multiple genes, they act as potent biomarkers in several cancers. Identification of miRNAs that are prognostically important has been done sporadically, but no resource is available till date that allows users to study prognostics of miRNAs of interest, utilizing the wealth of available data, in major cancer types.DescriptionIn this paper, we present a web based tool that allows users to study prognostic properties of miRNAs in several cancer types, using publicly available data. We have compiled data from Gene Expression Omnibus (GEO), and recently developed "The Cancer Genome Atlas (TCGA)", to create this tool. The tool is called "PROGmiR" and it is available at http://www.compbio.iupui.edu/progmir. Currently, our tool can be used to study overall survival implications for approximately 1050 human miRNAs in 16 major cancer types.ConclusionsWe believe this resource, as a hypothesis generation tool, will be helpful for researchers to link miRNA expression with cancer outcome and to design mechanistic studies. We studied performance of our tool using identified miRNA biomarkers from published studies. The prognostic plots created using our tool for specific miRNAs in specific cancer types corroborated with the findings in the studies.
Project description:Differential gene expression analysis is widely used to study changes in gene expression profiles between two or more groups of samples (e.g., physiological versus pathological conditions, pre-treatment versus post-treatment, and infected versus non-infected tissues). This protocol aims to identify gene expression changes in a pre-selected set of genes associated with severe acute respiratory syndrome coronavirus 2 viral infection and host cell antiviral response, as well as subsequent gene expression association with phenotypic features using samples deposited in public repositories. For complete details on the use and outcome of this informatics analysis, please refer to Bizzotto et al. (2020).
Project description:Electrocardiography (ECG) is a key non-invasive diagnostic tool for cardiovascular diseases which is increasingly supported by algorithms based on machine learning. Major obstacles for the development of automatic ECG interpretation algorithms are both the lack of public datasets and well-defined benchmarking procedures to allow comparison s of different algorithms. To address these issues, we put forward PTB-XL, the to-date largest freely accessible clinical 12-lead ECG-waveform dataset comprising 21837 records from 18885 patients of 10 seconds length. The ECG-waveform data was annotated by up to two cardiologists as a multi-label dataset, where diagnostic labels were further aggregated into super and subclasses. The dataset covers a broad range of diagnostic classes including, in particular, a large fraction of healthy records. The combination with additional metadata on demographics, additional diagnostic statements, diagnosis likelihoods, manually annotated signal properties as well as suggested folds for splitting training and test sets turns the dataset into a rich resource for the development and the evaluation of automatic ECG interpretation algorithms.
Project description:Amsacrine (m-AMSA) is an anticancer agent that displays activity against refractory acute leukemias as well as Hodgkin's and non-Hodgkin's lymphomas. The drug is comprised of an intercalative acridine moiety coupled to a 4'-amino-methanesulfon-m-anisidide headgroup. m-AMSA is historically significant in that it was the first drug demonstrated to function as a topoisomerase II poison. Although m-AMSA was designed as a DNA binding agent, the ability to intercalate does not appear to be the sole determinant of drug activity. Therefore, to more fully analyze structure-function relationships and the role of DNA binding in the action of m-AMSA, we analyzed a series of derivatives for the ability to enhance DNA cleavage mediated by human topoisomerase II? and topoisomerase II? and to intercalate DNA. Results indicate that the 3'-methoxy (m-AMSA) positively affects drug function, potentially by restricting the rotation of the headgroup in a favorable orientation. Shifting the methoxy to the 2'-position (o-AMSA), which abrogates drug function, appears to increase the degree of rotational freedom of the headgroup and may impair interactions of the 1'-substituent or other portions of the headgroup within the ternary complex. Finally, the nonintercalative m-AMSA headgroup enhanced enzyme-mediated DNA cleavage when it was detached from the acridine moiety, albeit with 100-fold lower affinity. Taken together, our results suggest that much of the activity and specificity of m-AMSA as a topoisomerase II poison is embodied in the headgroup, while DNA intercalation is used primarily to increase the affinity of m-AMSA for the topoisomerase II-DNA cleavage complex.
Project description:Drugs capable of trapping topoisomerase II (Top2), an essential enzyme that cleaves DNA to remove naturally occurring knots and tangles, can serve as potent anticancer agents. The monofunctional platinum agent phenanthriplatin, cis-[Pt(NH3)2(phenanthridine)Cl](NO3), is shown here to trap Top2 in addition to its known modes of inhibition of DNA and RNA polymerases. Its potency therefore combines diverse modes of action by which phenanthriplatin kills cancer cells. The observation that phenanthriplatin can act as a Top2 poison highlights opportunities to design nonclassical platinum anticancer agents with this novel mechanism of action. Such complexes have the potential to overcome current limitations with chemotherapy, such as resistance, and to provide treatment options for cancers that do not respond well to classical agents. Covalent DNA-platinum lesions implicated in Top2 poisoning are distinctive from those generated by known therapeutic topoisomerase poisons, which typically exert their action by reversible binding at the interface of Top2-DNA cleavage complexes.
Project description:Each year, publicly available databases are updated with new compounds from different research institutions. Positive experimental outcomes are more likely to be reported; therefore, they account for a considerable fraction of these entries. Established publicly available databases such as ChEMBL allow researchers to use information without constrictions and create predictive tools for a broad spectrum of applications in the field of toxicology. Therefore, we investigated the distribution of positive and nonpositive entries within ChEMBL for a set of off-targets and its impact on the performance of classification models when applied to pharmaceutical industry data sets. Results indicate that models trained on publicly available data tend to overpredict positives, and models based on industry data sets predict negatives more often than those built using publicly available data sets. This is strengthened even further by the visualization of the prediction space for a set of 10,000 compounds, which makes it possible to identify regions in the chemical space where predictions converge. Finally, we highlight the utilization of these models for consensus modeling for potential adverse events prediction.
Project description:Clinical imaging relies heavily on X-ray computed tomography (CT) scans for diagnosis and prognosis. Many research applications aim to perform population-level analyses, which require images to be put in the same space, usually defined by a population average, also known as a template. We present an open-source, publicly available, high-resolution CT template. With this template, we provide voxel-wise standard deviation and median images, a basic segmentation of the cerebrospinal fluid spaces, including the ventricles, and a coarse whole brain labeling. This template can be used for spatial normalization of CT scans and research applications, including deep learning. The template was created using an anatomically-unbiased template creation procedure, but is still limited by the population it was derived from, an open CT data set without demographic information. The template and derived images are available at https://github.com/muschellij2/high_res_ct_template. Electronic supplementary material The online version of this chapter (10.1007/978-3-030-50153-2_27) contains supplementary material, which is available to authorized users.
Project description:ObjectivesTo review the nature and scope of apps targeting individuals living with and beyond cancer.DesignScoping review, searching the two largest app stores, Google Play and Apple's App store. App descriptions were exported verbatim, and summarised descriptively, thematically and by content coding.ResultsWe included 151 apps targeting individuals living with and beyond cancer. Most targeted all cancer types (n=89, 58.9%) or breast cancer (n=22, 14.6%) and originated in the USA (n=68, 45.0%). The country of origin was unclear for 31 (20.5%) apps. Most apps were developed by commercial companies/private individuals (n=64, 43%) or non-profit organisations (n=30, 19.9%) and marketed apps in terms of fighting metaphors, navigating a journey and becoming empowered to take control.App content could be summarised under five main categories: (1) imparting information about cancer; (2) planning and organising cancer care; (3) interacting with others (including others affected by cancer and healthcare professionals); (4) enacting management strategies and adjusting to life with or beyond cancer and (5) getting feedback about cancer management, for example, by sharing self-monitoring reports with professionals. We found some apps describing 'cures' for cancer or selling products, such as alkaline waters to cancer survivors.ConclusionsApps are currently available via on-line stores that cover a large spectrum of cancer survivorship activities. The effects of such apps on clinical consultations, patient work/burden and clinical outcomes merit further attention. Most apps are developed by commercial organisations, and promises of empowerment in the 'fight' against cancer are tempered by the potential for exaggerated claims and exploitation.