Real World Experience on Patterns of Usage and Toxicity Profile of Immunotherapy Drugs in Indian Patients: A Prospective Observational Study
Medical Journal Armed Forces India(2023)
Classified Specialist (Medicine) & Medical Oncologist
Abstract
Background:Immune checkpoint inhibitors (ICIs) are now considered revolutionary agents in the treatment of various cancers. Prospective data are limited on the patterns of usage and toxicity profile of these drugs. We planned this study for addressing the same in Indian patients. Methods:This prospective study was conducted over a period of 2 years. All patients who were treated with Nivolumab, pembrolizumab, atezolizumab, and durvalumab were included. Immune-related adverse events were recorded. Toxicities were graded and number of patients experiencing dose limiting toxicities was recorded. Results:A total of 53 patients received one of the above four agents. Majority of patients were less than 60 years of age. Carcinoma lung was the most frequent malignancy followed by renal cell carcinoma, Hodgkin's Lymphoma, Urinary Bladder cancers, Malignant Melanoma, and Recurrent/Metastatic Head and neck cancer. Nivolumab was used in most of the study population followed by pembrolizumab. Majority of agents were used in second line. The frequency of all grade adverse events for fatigue, anemia, pneumonitis, skin rash, dyspnea, diarrhea, and hypothyroidism were (in %) 73.58, 62.26, 16.9, 11.32, 9.43, 9.43, and 7.55, respectively. No grade 5 toxicity was observed. None of the grade 3 or 4 toxicities led to treatment discontinuation. Statistically, no difference was found for all grade toxicities among ICI drugs and among the various lines of use. Conclusion:Nivolumab was the commonest drug used in our cohort. Most of ICIs were used in second-line setting. Toxicities are in line with the published literature.
MoreTranslated text
Key words
Malignancy,Immunotherapy,Immune Chekpoint Inhibitors,Immune related adverse events
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined