Navigating Neurologic Adverse Events in Immune Checkpoint Inhibitor Therapy: Challenges and Strategies
Bibliographic record
Abstract
19 th century, following surgeon William Coley's discovery that the insertion of dead bacteria into sarcomas could lead to tumour shrinkage. [1]Since then, various clinical trials have investigated a range of immunotherapy drugs for multiple cancer types.Immunotherapy boosts the immune system's ability to identify tumor-specific antigens by suppressing immune checkpoints, inhibiting immune-suppressing agents, and enhancing immune-mediated killing.To date, several immunotherapy medications have been developed, including tumor vaccines, cellular immunotherapy, immunomodulatory medications that target T-cells, and ICIs.ICIs are monoclonal antibodies that specifically target im-mune checkpoints, including programmed cell death protein 1 (PD-1), programmed cell death ligand 1 (PD-L1), and cytotoxic T-lymphocyte-associated protein 4 (CTLA-4), which act as critical regulators of the immune system.By targeting these checkpoints, ICIs enable T cells to remain activated, allowing them to attack malignant cells. [2]Eighteen ICIs have been approved for treating different types of cancer by regulatory bodies such as the United States Food and Drug Administration (FDA), the National Medical Products Administration (NMPA) of China, and the European Medicines Agency (EMA).Please refer to Table 1 for details on the approval status, indications, and dates for widely used ICIs.The advancement of immunotherapy has changed cancer treatment.Immune checkpoint inhibitors (ICIs) are one of the groundbreaking immunotherapies that have offered patients with advanced cancer new optimism by utilizing the immune system's innate ability to fight cancer with amazing success.Nevertheless, this exceptional approach has drawbacks, including the range of immune-related adverse events (irAEs) that can arise due to ICI therapy.IrAEs that impact various parts and systems of the body can be particularly problematic, and can be difficult to identify and manage.Neurological side effects of ICIs are of particular interest, as they may vary from minor sensory issues to possibly fatal neuroinflammatory reactions.The purpose of this review is to elucidate the peculiarities of neurological immune-related adverse events (n-irAEs) associated with ICIs and to provide a complete overview of the various facets of ICI therapy by exploring the mechanisms, clinical manifestations, and management strategies of n-irAEs.Furthermore, it is crucial to emphasize the importance of caution, early recognition, and collaboration between oncologists and neurologists to optimize patient outcomes while overcoming the challenges posed by these enigmatic neurologic complications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".