Notice bibliographique
Résumé
Conspectus This Account is the first comprehensive review article on the newly developed, photochemistry-based cancer therapy near-infrared (NIR) photoimmunotherapy (PIT). NIR-PIT is a molecularly targeted phototherapy for cancer that is based on injecting a conjugate of a near-infrared, water-soluble, silicon-phthalocyanine derivative, IRdye700DX (IR700), and a monoclonal antibody (mAb) that targets an expressed antigen on the cancer cell surface. Subsequent local exposure to NIR light turns on this photochemical “death” switch, resulting in the rapid and highly selective immunogenic cell death (ICD) of targeted cancer cells. ICD occurs as early as 1 min after exposure to NIR light and results in irreversible morphologic changes only in target-expressing cells based on the newly discovered photoinduced ligand release reaction that induces physical changes on conjugated antibody/antigen complex resulting in functional damage on cell membrane. Meanwhile, immediately adjacent receptor-negative cells are totally unharmed. Because of its highly targeted nature, NIR-PIT carries few side effects and healing is rapid. Evaluation of the tumor microenvironment reveals that ICD induced by NIR-PIT results in rapid maturation of immature dendritic cells adjacent to dying cancer cells initiating a host anticancer immune response, resulting in repriming of polyclonal CD8+T cells against various released cancer antigens, which amplifies the therapeutic effect of NIR-PIT. NIR-PIT can target and treat virtually any cell surface antigens including cancer stem cell markers, that is, CD44 and CD133. A first-in-human phase 1/2 clinical trial of NIR-PIT using cetuximab-IR700 (RM1929) targeting EGFR in inoperable recurrent head and neck cancer patients successfully concluded in 2017 and led to “fast tracking” by the FDA and a phase 3 trial (https://clinicaltrials.gov/ct2/show/NCT03769506) that is currently underway in 3 countries in Asia, US/Canada, and 4 countries in EU. The next step for NIR-PIT is to further exploit the immune response. Preclinical research in animals with intact immune systems has shown that NIT-PIT targeting of immunosuppressor cells within the tumor, such as regulatory T-cells, can further enhance tumor-cell-selective systemic host-immunity leading to significant responses in distant metastatic tumors, which are not treated with light. By combining cancer-targeting NIR-PIT and immune-activating NIR-PIT or other cancer immunotherapies, NIR-PIT of a local tumor, could lead to responses in distant metastases and may also inhibit recurrences due to activation of systemic anticancer immunity and long-term immune memory without the systemic autoimmune adverse effects often associated with immune checkpoint inhibitors. Furthermore, NIR-PIT also enhances nanodrug delivery into tumors up to 24-fold superior to untreated tumors with conventional EPR effects by intensively damaging cancer cells behind tumor vessels. We conclude by describing future advances in this novel photochemical cancer therapy that are likely to further enhance the efficacy of NIR-PIT.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,003 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».