scholarly journals The reimbursement of targeted cancer therapies in Bulgaria: is it evidence-based?

2015 ◽  
Vol 25 (suppl_3) ◽  
Author(s):  
T Vekov ◽  
R Koleva-Kolarova ◽  
S Aleksandrova-Yankulovska ◽  
N Veleva
2020 ◽  
pp. 71-88 ◽  
Author(s):  
Jayaram Kancherla ◽  
Shruti Rao ◽  
Krithika Bhuvaneshwar ◽  
Rebecca B. Riggins ◽  
Robert A. Beckman ◽  
...  

PURPOSE In this work, we introduce CDGnet (Cancer-Drug-Gene Network), an evidence-based network approach for recommending targeted cancer therapies. CDGnet represents a user-friendly informatics tool that expands the range of targeted therapy options for patients with cancer who undergo molecular profiling by including the biologic context via pathway information. METHODS CDGnet considers biologic pathway information specifically by looking at targets or biomarkers downstream of oncogenes and is personalized for individual patients via user-inputted molecular alterations and cancer type. It integrates a number of different sources of knowledge: patient-specific inputs (molecular alterations and cancer type), US Food and Drug Administration–approved therapies and biomarkers (curated from DailyMed), pathways for specific cancer types (from Kyoto Encyclopedia of Genes and Genomes [KEGG]), gene-drug connections (from DrugBank), and oncogene information (from KEGG). We consider 4 different evidence-based categories for therapy recommendations. Our tool is delivered via an R/Shiny Web application. For the 2 categories that use pathway information, we include an interactive Sankey visualization built on top of d3.js that also provides links to PubChem. RESULTS We present a scenario for a patient who has estrogen receptor (ER)–positive breast cancer with FGFR1 amplification. Although many therapies exist for patients with ER-positive breast cancer, FGFR1 amplifications may confer resistance to such treatments. CDGnet provides therapy recommendations, including PIK3CA, MAPK, and RAF inhibitors, by considering targets or biomarkers downstream of FGFR1. CONCLUSION CDGnet provides results in a number of easily accessible and usable forms, separating targeted cancer therapies into categories in an evidence-based manner that incorporates biologic pathway information.


2019 ◽  
Author(s):  
Jayaram Kancherla ◽  
Shruti Rao ◽  
Krithika Bhuvaneshwar ◽  
Rebecca B. Riggins ◽  
Robert A. Beckman ◽  
...  

AbstractIn this work, we introduce CDGnet, an evidence-based network approach for recommending targeted cancer therapies, available as a user-friendly informatics tool. Our approach can be used to expand the range of options of targeted therapies for cancer patients who undergo molecular profiling. It considers biological pathway information specifically by looking at downstream targets of oncogenes and is personalized for individual patients via the user-inputted molecular alterations and cancer type. CDGnet integrates disparate sources of knowledge and provides results in a number of easily-accessible and usable forms, while separating targeted cancer therapies into categories in an evidence-based manner.


Cancers ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 723
Author(s):  
Valerie J. Carpenter ◽  
Tareq Saleh ◽  
David A. Gewirtz

Senolytics represent a group of mechanistically diverse drugs that can eliminate senescent cells, both in tumors and in several aging-related pathologies. Consequently, senolytic use has been proposed as a potential adjuvant approach to improve the response to senescence-inducing conventional and targeted cancer therapies. Despite the unequivocal promise of senolytics, issues of universality, selectivity, resistance, and toxicity remain to be further clarified. In this review, we attempt to summarize and analyze the current preclinical literature involving the use of senolytics in senescent tumor cell models, and to propose tenable solutions and future directions to improve the understanding and use of this novel class of drugs.


2011 ◽  
Vol 6 (1) ◽  
pp. 24-35 ◽  
Author(s):  
Aruni S. Arachchige Don ◽  
X. F. Steven Zheng

Radiographics ◽  
2017 ◽  
Vol 37 (5) ◽  
pp. 1461-1482 ◽  
Author(s):  
Stephanie T. Chang ◽  
Christine O. Menias ◽  
Meghan G. Lubner ◽  
Vincent M. Mellnick ◽  
Amy K. Hara ◽  
...  

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