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Interviews with 10 black human resource developers exploring their career experiences indicated that they faced similar discrimination challenges as other black workers. They developed bicultural strategies for handling racial issues, such as support systems at work and outside the workplace. (Contains 35 references.) (SK)

Drug discovery and development is a complex and costly process. Machine learning approaches are being investigated to help improve the effectiveness and speed of multiple stages of the drug discovery pipeline. Of these, those that use Knowledge Graphs (KG) have promise in many tasks, including drug repurposing, drug toxicity prediction and target gene–disease prioritization. In a drug discovery KG, crucial elements including genes, diseases and drugs are represented as entities, while relationships between them indicate an interaction. However, to construct high-quality KGs, suitable data are required. In this review, we detail publicly available sources suitable for use in constructing drug discovery focused KGs. We aim to help guide machine learning and KG practitioners who are interested in applying new techniques to the drug discovery field, but who may be unfamiliar with the relevant data sources. The datasets are selected via strict criteria, categorized according to the primary type of information contained within and are considered based upon what information could be extracted to build a KG. We then present a comparative analysis of existing public drug discovery KGs and an evaluation of selected motivating case studies from the literature. Additionally, we raise numerous and unique challenges and issues associated with the domain and its datasets, while also highlighting key future research directions. We hope this review will motivate KGs use in solving key and emerging questions in the drug discovery domain. [ABSTRACT FROM AUTHOR]

Adoption of recently developed methods from machine learning has given rise to creation of drug-discovery knowledge graphs (KGs) that utilize the interconnected nature of the domain. Graph-based modelling of the data, combined with KG embedding (KGE) methods, are promising as they provide a more intuitive representation and are suitable for inference tasks such as predicting missing links. One common application is to produce ranked lists of genes for a given disease, where the rank is based on the perceived likelihood of association between the gene and the disease. It is thus critical that these predictions are not only pertinent but also biologically meaningful. However, KGs can be biased either directly due to the underlying data sources that are integrated or due to modelling choices in the construction of the graph, one consequence of which is that certain entities can get topologically overrepresented. We demonstrate the effect of these inherent structural imbalances, resulting in densely connected entities being highly ranked no matter the context. We provide support for this observation across different datasets, models as well as predictive tasks. Further, we present various graph perturbation experiments which yield more support to the observation that KGE models can be more influenced by the frequency of entities rather than any biological information encoded within the relations. Our results highlight the importance of data modelling choices, and emphasizes the need for practitioners to be mindful of these issues when interpreting model outputs and during KG composition. [ABSTRACT FROM AUTHOR]

Author(s): Mark T. Ross (corresponding author) [1]; Darren V. Grafham [1]; Alison J. Coffey [1]; Steven Scherer [2]; Kirsten McLay [1]; Donna Muzny [2]; Matthias Platzer [3]; Gareth R. Howell [...]

Many antimicrobial and anti-tumour drugs elicit hormetic responses characterised by low-dose stimulation and high-dose inhibition. While this can have profound consequences for human health, with low drug concentrations actually stimulating pathogen or tumour growth, the mechanistic understanding behind such responses is still lacking. We propose a novel, simple but general mechanism that could give rise to hormesis in systems where an inhibitor acts on an enzyme. At its core is one of the basic building blocks in intracellular signalling, the dual phosphorylation-dephosphorylation motif, found in diverse regulatory processes including control of cell proliferation and programmed cell death. Our analytically-derived conditions for observing hormesis provide clues as to why this mechanism has not been previously identified. Current mathematical models regularly make simplifying assumptions that lack empirical support but inadvertently preclude the observation of hormesis. In addition, due to the inherent population heterogeneities, the presence of hormesis is likely to be masked in empirical population-level studies. Therefore, examining hormetic responses at single-cell level coupled with improved mathematical models could substantially enhance detection and mechanistic understanding of hormesis. [ABSTRACT FROM AUTHOR]

Background: Pelvic discontinuity (PD) is a rare but devastating mechanism of failure in total hip arthroplasty. Radiographic findings have been described for the identification of PD. However, no study has specifically examined radiographic parameters and the utility of specific views in the preoperative identification of PD. Methods: We performed a retrospective review of 133 patients who underwent acetabular revision for PD. Preoperative radiographic studies were reviewed including anteroposterior pelvis (AP; n = 133), true lateral hip (n = 132), Judet (n = 47), false profile (n = 4), and computed tomography scans (n = 14). Radiographs were read by the senior authors to identify the following parameters suggestive of PD: visible fracture line, medial migration of the inferior hemipelvis, and obturator ring asymmetry. Results: Using only the AP view, the fracture line was visible in 116 (87%), medial migration of the inferior hemipelvis in 126 (95%), and obturator ring asymmetry in 114 (86%). A fracture line was visualized in 65 of 132 hips (49%) evaluated with laterals, 36 of 47 hips (77%) evaluated with Judet views, 3 of 4 (75%) evaluated with a false profile view, and 10 of 14 (71%) evaluated with computed tomography. Conclusion: Preoperative evaluation with a combination of an AP pelvis radiograph, plus a true lateral radiograph of the hip, plus Judet films in combination with the criteria for discontinuity defined in this article, allowed for identification of PD in a 100% of patients. [ABSTRACT FROM AUTHOR]

Single-cell technologies have elucidated mechanisms responsible for immune checkpoint inhibitor (ICI) response, but are not amenable to a clinical diagnostic setting. In contrast, bulk RNA sequencing (RNA-seq) is now routine for research and clinical applications. Our workflow uses transcription factor (TF)-directed coexpression networks (regulons) inferred from single-cell RNA-seq data to deconvolute immune functional states from bulk RNA-seq data. Regulons preserve the phenotypic variation in CD45+ immune cells from metastatic melanoma samples (n = 19, discovery dataset) treated with ICIs, despite reducing dimensionality by >100-fold. Four cell states, termed exhausted T cells, monocyte lineage cells, memory T cells, and B cells were associated with therapy response, and were characterized by differentially active and cell state-specific regulons. Clustering of bulk RNA-seq melanoma samples from four independent studies (n = 209, validation dataset) according to regulon-inferred scores identified four groups with significantly different response outcomes (P < 0.001). An intercellular link was established between exhausted T cells and monocyte lineage cells, whereby their cell numbers were correlated, and exhausted T cells predicted prognosis as a function of monocyte lineage cell number. The ligand-receptor expression analysis suggested that monocyte lineage cells drive exhausted T cells into terminal exhaustion through programs that regulate antigen presentation, chronic inflammation, and negative costimulation. Together, our results demonstrate how regulon-based characterization of cell states provide robust and functionally informative markers that can deconvolve bulk RNA-seq data to identify ICI responders., (©2023 The Authors; Published by the American Association for Cancer Research.)

The understanding of the mechanism-of-action (MoA) of compounds and the prediction of potential drug targets play an important role in small-molecule drug discovery. The aim of this work was to compare chemical and cell morphology information for bioactivity prediction. The comparison was performed using bioactivity data from the ExCAPE database, image data (in the form of CellProfiler features) from the Cell Painting data set (the largest publicly available data set of cell images with ∼30,000 compound perturbations), and extended connectivity fingerprints (ECFPs) using the multitask Bayesian matrix factorization (BMF) approach Macau. We found that the BMF Macau and random forest (RF) performance were overall similar when ECFPs were used as compound descriptors. However, BMF Macau outperformed RF in 159 out of 224 targets (71%) when image data were used as compound information. Using BMF Macau, 100 (corresponding to about 45%) and 90 (about 40%) of the 224 targets were predicted with high predictive performance (AUC > 0.8) with ECFP data and image data as side information, respectively. There were targets better predicted by image data as side information, such as β-catenin, and others better predicted by fingerprint-based side information, such as proteins belonging to the G-protein-Coupled Receptor 1 family, which could be rationalized from the underlying data distributions in each descriptor domain. In conclusion, both cell morphology changes and chemical structure information contain information about compound bioactivity, which is also partially complementary, and can hence contribute to in silico MoA analysis.

Inhibition of ataxia-telangiectasia mutated (ATM) during radiotherapy of glioblastoma multiforme (GBM) may improve tumor control by short-circuiting the response to radiation-induced DNA damage. A major impediment for clinical implementation is that current inhibitors have limited central nervous system (CNS) bioavailability; thus, the goal was to identify ATM inhibitors (ATMi) with improved CNS penetration. Drug screens and refinement of lead compounds identified AZ31 and AZ32. The compounds were then tested in vivo for efficacy and impact on tumor and healthy brain. Both AZ31 and AZ32 blocked the DNA damage response and radiosensitized GBM cells in vitro AZ32, with enhanced blood-brain barrier (BBB) penetration, was highly efficient in vivo as radiosensitizer in syngeneic and human, orthotopic mouse glioma model compared with AZ31. Furthermore, human glioma cell lines expressing mutant p53 or having checkpoint-defective mutations were particularly sensitive to ATMi radiosensitization. The mechanism for this p53 effect involves a propensity to undergo mitotic catastrophe relative to cells with wild-type p53. In vivo , apoptosis was >6-fold higher in tumor relative to healthy brain after exposure to AZ32 and low-dose radiation. AZ32 is the first ATMi with oral bioavailability shown to radiosensitize glioma and improve survival in orthotopic mouse models. These findings support the development of a clinical-grade, BBB-penetrating ATMi for the treatment of GBM. Importantly, because many GBMs have defective p53 signaling, the use of an ATMi concurrent with standard radiotherapy is expected to be cancer-specific, increase the therapeutic ratio, and maintain full therapeutic effect at lower radiation doses. Mol Cancer Ther; 17(8); 1637-47. ©2018 AACR ., (©2018 American Association for Cancer Research.)

While mechanisms of cytotoxicity and cytostaticity have been studied extensively from the biological side, relatively little is currently understood regarding areas of chemical space leading to cytotoxicity and cytostasis in large compound collections. Predicting and rationalizing potential adverse mechanism-of-actions (MoAs) of small molecules is however crucial for screening library design, given the link of even low level cytotoxicity and adverse events observed in man. In this study, we analyzed results from a cell-based cytotoxicity screening cascade, comprising 296 970 nontoxic, 5784 cytotoxic and cytostatic, and 2327 cytostatic-only compounds evaluated on the THP-1 cell-line. We employed an in silico MoA analysis protocol, utilizing 9.5 million active and 602 million inactive bioactivity points to generate target predictions, annotate predicted targets with pathways, and calculate enrichment metrics to highlight targets and pathways. Predictions identify known mechanisms for the top ranking targets and pathways for both phenotypes after review and indicate that while processes involved in cytotoxicity versus cytostaticity seem to overlap, differences between both phenotypes seem to exist to some extent. Cytotoxic predictions highlight many kinases, including the potentially novel cytotoxicity-related target STK32C, while cytostatic predictions outline targets linked with response to DNA damage, metabolism, and cytoskeletal machinery. Fragment analysis was also employed to generate a library of toxicophores to improve general understanding of the chemical features driving toxicity. We highlight substructures with potential kinase-dependent and kinase-independent mechanisms of toxicity. We also trained a cytotoxic classification model on proprietary and public compound readouts, and prospectively validated these on 988 novel compounds comprising difficult and trivial testing instances, to establish the applicability domain of models. The proprietary model performed with precision and recall scores of 77.9% and 83.8%, respectively. The MoA results and top ranking substructures with accompanying MoA predictions are available as a platform to assess screening collections.

The analysis of cancer genomes has benefited from the advances in technology that enable data to be generated on an unprecedented scale, describing a tumour genome's sequence and composition at increasingly high resolution and reducing cost. This progress is likely to increase further over the coming years as next-generation sequencing approaches are applied to the study of cancer genomes, in tandem with large-scale efforts such as the Cancer Genome Atlas and recently announced International Cancer Genome Consortium efforts to complement those already established such as the Sanger Institute Cancer Genome Project. This presents challenges for the cancer researcher and the research community in general, in terms of analysing the data generated in one's own projects and also in coordinating and interrogating data that are publicly available. This review aims to provide a brief overview of some of the main informatics resources currently available and their use, and some of the informatics approaches that may be applied in the study of cancer genomes.