Gene/Protein Disease Symptom Drug Enzyme Compound
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Query: EC:2.3.1.21 (CPT)
 4,580 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

We have experienced a rare case of primary duodenal carcinoma with perforation of the duodenum. Combined CPT- 11, CDDP and DOC chemotherapy achieved a partial response. A 54-year-old man with serious abdominal pain visited our hospital with a diagnosis of acute peritonitis due to perforation of digestive tract on CT scan. An emergency operation was performed with patch for perforation of the duodenum. Endoscopic examination and biopsy after surgery showed duodenal adenocarcinoma. Abdominal CT scan revealed metastasis to the periaortic lymph nodes. Therefore, we diagnosed primary duodenal carcinoma with metastasis to the periaortic lymph nodes. Combined CPT-11, CDDP and DOC chemotherapy were performed. After two courses, endoscopic examination and biopsy showed primary lesion of the duodenum had disappeared. Metastatic lymph nodes were reduced from CT scan after three courses, and successfully controlled until nine courses. Then regimen was changed to S-1 alone and S-1/CPT-11. The patient remained alive for two years after the operation without tumor progression.
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PMID:[A case of successful control for primary duodenal cancer with combined CPT-11, CDDP and DOC chemotherapy]. 1893 82

Both molecular profiling of tumors and longitudinal tumor size data modeling are relevant strategies to predict cancer patients' response to treatment. Herein we propose a model of tumor growth inhibition integrating a tumor's genetic characteristics (p53 mutation and 1p/19q codeletion) that successfully describes the time course of tumor size in patients with low-grade gliomas treated with first-line temozolomide chemotherapy. The model captures potential tumor progression under chemotherapy by accounting for the emergence of tissue resistance to treatment following prolonged exposure to temozolomide. Using information on individual tumors' genetic characteristics, in addition to early tumor size measurements, the model was able to predict the duration and magnitude of response, especially in those patients in whom repeated assessment of tumor response was obtained during the first 3 months of treatment. Combining longitudinal tumor size quantitative modeling with a tumor''s genetic characterization appears as a promising strategy to personalize treatments in patients with low-grade gliomas.
CPT Pharmacometrics Syst Pharmacol 2015 Dec
PMID:Prediction of Response to Temozolomide in Low-Grade Glioma Patients Based on Tumor Size Dynamics and Genetic Characteristics. 2690 87

Hepatocellular carcinoma (HCC) is third in cancer-related causes of death worldwide and its treatment is a significant unmet medical need. Sunitinib is a selective tyrosine kinase inhibitor of the angiogenic biomarker: soluble vascular endothelial growth factor receptor-2 (sVEGFR2 ). Sunitinib failed its primary overall survival endpoint in patients with advanced HCC in a phase III trial compared to sorafenib. In the present study, pharmacokinetic-pharmacodynamic modeling was used to link drug-exposure to tumor-growth-inhibition (TGI) and time-to-tumor progression (TTP) through sVEGFR2 dynamics. The results suggest that 1) active drug concentration (i.e., sunitinib and its metabolite) inhibits the release of sVEGFR2 and that such inhibition is associated with TGI, and 2) daily sVEGFR2 exposure is likely a reliable predictor for the TTP in HCC patients. Moreover, the model quantitatively links the dynamics of an angiogenesis biomarker to TTP and accurately predicts observed literature-reported results of placebo treatment.
CPT Pharmacometrics Syst Pharmacol 2016 06
PMID:Bridging Sunitinib Exposure to Time-to-Tumor Progression in Hepatocellular Carcinoma Patients With Mathematical Modeling of an Angiogenic Biomarker. 2730 Feb 60

Mathematical models of tumor dynamics generally omit information on individual target lesions (iTLs), and consider the most important variable to be the sum of tumor sizes (TS). However, differences in lesion dynamics might be predictive of tumor progression. To exploit this information, we have developed a novel and flexible approach for the non-parametric analysis of iTLs, which integrates knowledge from signal processing and machine learning. We called this new methodology ClassIfication Clustering of Individual Lesions (CICIL). We used CICIL to assess similarities among the TS dynamics of 3,223 iTLs measured in 1,056 patients with metastatic colorectal cancer treated with cetuximab combined with irinotecan, in two phase II studies. We mainly observed similar dynamics among lesions within the same tumor site classification. In contrast, lesions in anatomic locations with different features showed different dynamics in about 35% of patients. The CICIL methodology has also been implemented in a user-friendly and efficient Java-based framework.
CPT Pharmacometrics Syst Pharmacol 2018 04
PMID:Assessing Similarity Among Individual Tumor Size Lesion Dynamics: The CICIL Methodology. 2938 96