Abstract PO-068: Efforts to define biochemical and pharmacologic pathways potentially beneficial to both cancer and COVID-19 patients
Bibliographic record
Abstract
Abstract Since COVID-19 infections have been frequently diagnosed in cancer patients, often associated with worst prognosis (1), an effort was conducted in order to identify biomolecular pathways and repurposable drugs, which could benefit patients for COVID-19 and cancer at the same time. Starting from over 3265 PubMed references, we have screened approximately 650 abstracts in order to identify the two most interesting pathways. Abstract were independently read and selected by UR and AV and then discussed in video conferences. Final decision on biochemical pathways or particular drugs was agreed only after identification of potential underlying molecular mechanism(s) for both COVID-19 and specific cancer types. Both impinge on the basic structure of 4-aminoquinoline, which is the alkaloid backbone of several important compounds effective in parasitology, rheumatology and cancer treatment. Their pharmacologic utilization was initiated with the discovery of quinine from the bark of the cinchona tree (1630) with further synthetic drugs developments such as chloroquine (CQ) and hydroxychloroquine (HCQ) in 1949-50, all variants of the 4-aminoquinoline family (2). 1. One pathway targets the PNP enzymes (purine nucleoside phosphorylases), dysregulation of which is often associated with apoptosis in cancer cells. PNP has been considered an oncogene, which is downregulated by tumor suppressors in PCa (miR-I; miR-133) (3). PNP inhibition has been demonstrated by strong binding of the aminoquinoline quinine through several MS technologies (4). We are investigating whether this is also true for COVID-19. 2. A second pathway is associated with HCQ usage. We have recently reviewed how HCQ at higher dosage could benefit COVID-19 patients, if they are treated in the earlier (or prophylactic) phases of the disease (5). Despite the wide publicity of HCQ negative effects, the results of Mehra et al. in Lancet have been recently questioned in an open letter on several scientific and ethical grounds and the paper has been finally retracted. Furthermore, there are still few and small RCTs testing this drug in the early disease phases. HCQ behaves as a weak base that is typically trapped inside the acidic environment of phagocytic organelle—such as lysosomes—thus raising their pH (2). Therefore, in the presence of lysosomotropic HCQ: 1. SARS-CoV-2 appears to be blocked inside lysosomes and devoid of the proteolytic cleavage required for infection (6) and 2. in cancer cells, the important mechanism of autophagy is strongly inhibited, causing tumor shrinkage or apoptosis (also in association with chemotherapy) (7). We are presently investigating HCQ effects in patients’ tumors explants or surrogate tissues. Our efforts in identifying molecular/pharmacologic pathways that could benefit patients for both COVID-19 and cancer led to the identification of at least two pathways, PNP and autophagy—both associated to 4-aminoquinoline derivatives—which are now being studied in experimental models. Citation Format: Ugo Rovigatti, Amedeo Vannacci, Simone Del Corto, Ignacio Martin-Loeches, Andrea Piccin, Nizar Naji, Carmel Mothersill. Efforts to define biochemical and pharmacologic pathways potentially beneficial to both cancer and COVID-19 patients [abstract]. In: Proceedings of the AACR Virtual Meeting: COVID-19 and Cancer; 2020 Jul 20-22. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(18_Suppl):Abstract nr PO-068.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.041 | 0.007 |
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".