Nitrosoureas: a reappraisal of clinical trials
Abstract
The nitrosoureas (BCNU, CCNU, methyl CCNU) represent a new class of antineoplastic agents with a broad spectrum of antitumor activity. They are cell-cycle nonspecific cytotoxic agents. Postulated modes of action and pharmacology of these nitrosoureas are reviewed. Their therapeutic effectiveness as single agents and in combinations have been recognized in malignant lymphomas, multiple myeloma, melanoma, glioblastoma multiforme, gastric and colorectal carcionma, and small-cell carcinoma of the lung. The nitrosoureas are administered on an intermittent 6--8-week schedule because of delayed and frequently severe bone marrow toxicity which may be cumulative in nature.
How strong is the evidence?
Evidence Hub reads this as a clinical trial, done in people. Its evidence level is 3 of 5 (Moderate · trial / cohort / secondary analysis). Trials without a large randomized design, cohorts and secondary analyses give moderate evidence.
Read the paper
Related studies
- Comparison of the Effects of Glucagon-Like Peptide Receptor Agonists and Sodium-Glucose Cotransporter 2 Inhibitors for Prevention of Major Adverse Cardiovascular and Renal Outcomes in Type 2 Diabetes Mellitus: Systematic Review and Meta-Analysis of Cardiovascular Outcomes Trials
- Efficacy of personalized psychological interventions: A systematic review and meta-analysis
- Effects of Lifestyle Interventions That Include a Physical Activity Component in Class II and III Obese Individuals: A Systematic Review and Meta-Analysis
- Blood pressure lowering and risk of new-onset type 2 diabetes: an individual participant data meta-analysis
- Topical treatments for atopic dermatitis (eczema): systematic review and network meta-analysis of randomized trials
- Comparative efficacy and tolerability of pharmacological interventions for acute bipolar depression in adults: a systematic review and network meta-analysis
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.