ORCID Profile
0000-0003-3023-0790
Current Organisations
Aarhus University
,
Rhodes University
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Publisher: Elsevier BV
Date: 10-2010
Publisher: Elsevier BV
Date: 07-2013
DOI: 10.1016/J.BMC.2013.04.076
Abstract: DOXP-reductoisomerase (DXR) is a validated target for the development of antimalarial drugs to address the increase in resistant strains of Plasmodium falciparum. Series of aryl- and heteroarylcarbamoylphosphonic acids, their diethyl esters and disodium salts have been prepared as analogues of the potent DXR inhibitor fosmidomycin. The effects of the carboxamide N-substituents and the length of the methylene linker have been explored using in silico docking studies, saturation transfer difference NMR spectroscopy and enzyme inhibition assays using both EcDXR and PfDXR. These studies indicate an optimal linker length of two methylene units and have confirmed the importance of an additional binding pocket in the PfDXR active site. Insights into the constraints of the PfDXR binding site provide additional scope for the rational design of DXR inhibitors with increased ligand-receptor interactions.
Publisher: Wiley
Date: 08-2023
DOI: 10.1002/PATH.6155
Abstract: The clinical significance of the tumor‐immune interaction in breast cancer is now established, and tumor‐infiltrating lymphocytes (TILs) have emerged as predictive and prognostic biomarkers for patients with triple‐negative (estrogen receptor, progesterone receptor, and HER2‐negative) breast cancer and HER2‐positive breast cancer. How computational assessments of TILs might complement manual TIL assessment in trial and daily practices is currently debated. Recent efforts to use machine learning (ML) to automatically evaluate TILs have shown promising results. We review state‐of‐the‐art approaches and identify pitfalls and challenges of automated TIL evaluation by studying the root cause of ML discordances in comparison to manual TIL quantification. We categorize our findings into four main topics: (1) technical slide issues, (2) ML and image analysis aspects, (3) data challenges, and (4) validation issues. The main reason for discordant assessments is the inclusion of false‐positive areas or cells identified by performance on certain tissue patterns or design choices in the computational implementation. To aid the adoption of ML for TIL assessment, we provide an in‐depth discussion of ML and image analysis, including validation issues that need to be considered before reliable computational reporting of TILs can be incorporated into the trial and routine clinical management of patients with triple‐negative breast cancer. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.
Publisher: Wiley
Date: 08-2023
DOI: 10.1002/PATH.6165
Abstract: Modern histologic imaging platforms coupled with machine learning methods have provided new opportunities to map the spatial distribution of immune cells in the tumor microenvironment. However, there exists no standardized method for describing or analyzing spatial immune cell data, and most reported spatial analyses are rudimentary. In this review, we provide an overview of two approaches for reporting and analyzing spatial data (raster versus vector‐based). We then provide a compendium of spatial immune cell metrics that have been reported in the literature, summarizing prognostic associations in the context of a variety of cancers. We conclude by discussing two well‐described clinical biomarkers, the breast cancer stromal tumor infiltrating lymphocytes score and the colon cancer Immunoscore, and describe investigative opportunities to improve clinical utility of these spatial biomarkers. © 2023 The Pathological Society of Great Britain and Ireland.
Publisher: Elsevier BV
Date: 02-2011
DOI: 10.1016/J.BMC.2010.11.062
Abstract: The diethyl esters and disodium salts of a range of heteroarylcarbamoylphosphonic acids have been prepared and evaluated as analogues of the highly active DOXP-reductoisomerase (DXR) inhibitor, fosmidomycin. Computer-simulated docking studies, Saturation Transfer Difference (STD) NMR analysis and enzyme inhibition assays have been used to explore enzyme-binding and -inhibition potential, while in silico analysis of the DXR active site has highlighted the importance of including a well-parameterised metal co-factor in docking studies and has revealed the availability of an additional binding pocket to guide future drug design.
No related grants have been discovered for Kevin Lobb.