Biological and Physical Strategies to Improve the Therapeutic Index of Photodynamic Therapy
Bibliographic record
Abstract
Photodynamic therapy (PDT) derives its tumour selectivity from preferential photosensitizer accumulation and short light penetration in tissue.\nHowever, additional strategies are needed to improve the therapeutic index of PDT in oncological applications where light is delivered interstitially to large volumes (e.g. prostate), or when adjacent normal tissue is extremely sensitive (e.g. brain).\nMuch research to improve PDT's selectivity is directed towards developing targeted photosensitizers. Here, I present two alternative strategies to improve PDT's selectivity, without compromising its efficacy. For interstitial delivery, I investigated whether customizable cylindrical diffusers can be used to deliver light doses that conform better to target geometries, specifically the prostate. Additionally, I examined whether the neuroprotectant erythropoietin, used as an adjuvant to PDT for brain tumours, can reduce the sensitivity of normal tissue, thereby improving treatment selectivity.\n\nTo determine if tailored diffusers constitute an improvement over conventional ones, I introduce a novel optimization algorithm for treatment planning.\nI also analyze the sensitivity of the resulting plans to changes in the optical properties and diffuser placement. These results are contextualized by a mathematical formalism to characterize the light dose distributions arising from tailored diffusers. In parallel, I investigate the neuroprotective effects of erythropoietin in PDT of primary cortical neurons in culture and normal rat brain in vivo.\n\nI show that the most important parameter determining prostate coverage is the number of diffusers employed. Moreover, while tailored diffusers do offer an improvement over conventional ones, the improvement is likely masked by perturbations introduced by the uncertainties of light delivery. Although these results largely discard the use of tailored diffusers in prostate PDT, significant insight has been gained into PDT treatment planning, and tailored diffusers may still be advantageous in more complicated geometries. Additionally, I show that erythropoietin does not improve survival of PDT-treated neurons PDT, nor reduces the volume of necrosis in vivo, for the ranges of conditions and doses studied. To our knowledge, this is the first time this strategy has been tested in brain PDT and deserves to be investigated further, by using later time-points, functional outcomes, and other neuroprotectants.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".