An experimental investigation of a novel iron chelating protoporphyrin IX prodrug for the enhancement of photodynamic therapy (article)

This is the author accepted manuscript. The final version is available from Wiley via the DOI in this recordThe dataset associated with this article is located in ORE at: http://hdl.handle.net/10871/32090Objectives: Non-melanoma skin cancers are the most frequently occurring type of cancer worldwide. They can be effectively treated using topical dermatological photodynamic therapy (PDT) employing protoporphyrin IX (PpIX) as the active photosensitising agent as long as the disease remains superficial. Novel iron chelating agents are being investigated to enhance the effectiveness and extend the applications of this treatment modality, as limiting free iron increases the accumulation of PpIX available for light activation and thus cell kill. Methods: Human lung fibroblasts (MRC-5) and epithelial squamous carcinoma (A431) cells were treated with PpIX precursors (aminolaevulinic acid (ALA) or methyl-aminolevulinate (MAL)) with or without the separate hydroxypyridinone iron chelating agent (CP94) or alternatively, the new combined iron chelator and PpIX producing agent, AP2-18. PpIX fluorescence was monitored hourly for 6 hours prior to irradiation. PDT effectiveness was then assessed the following day using the lactate dehydrogenase and neutral red assays. Results: Generally, iron chelation achieved via CP94 or AP2-18 administration significantly increased PpIX fluorescence. ALA was more effective as a PpIX-prodrug than MAL in A431 cells, corresponding with the lower PpIX accumulation observed with the latter congener in this cell type. Addition of either iron chelating agent consistently increased PpIX accumulation but did not always convey an extra beneficial effect on PpIX-PDT cell kill when using the already highly effective higher dose of ALA. However, these adjuvants were highly beneficial in the skin cancer cells when compared with MAL administration alone. AP2-18 was also at least as effective as CP94 + ALA/MAL coadministration throughout and significantly better than CP94 supplementation at increasing PpIX fluorescence in MRC5 cells as well as at lower doses where PpIX accumulation was observed to be more limited. Conclusions: PpIX fluorescence levels, as well as PDT cell kill effects on irradiation can be significantly increased by pyridinone iron chelation, either via the addition of CP94 to the administration of a PpIX precursor or alternatively via the newly synthesised combined PpIX prodrug and siderophore, AP2-18. The effect of the latter compound appears to be at least equivalent to, if not better than, the separate administration of its constituent parts, particularly when employing MAL to destroy skin cancer cells. AP2-18 therefore warrants further detailed analysis, as it may have 3 the potential to improve dermatological PDT outcomes in applications currently requiring enhancement.The authors wish to thank Professor Hider (King’s College London, UK) for synthesising CP94. The financial support of the Medical Research Council (MRC, UK) and Killing Cancer (UK) is very gratefully acknowledged

TRY plant trait database - enhanced coverage and open access

Plant traits-the morphological, anatomical, physiological, biochemical and phenological characteristics of plants-determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait-based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits-almost complete coverage for 'plant growth form'. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait-environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

TRY plant trait database - enhanced coverage and open access

Plant traits—the morphological, anatomical, physiological, biochemical and phenological characteristics of plants—determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits—almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives