TY - JOUR
T1 - Mathematical modeling to address challenges in pancreatic cancer
AU - Dogra, Prashant
AU - Ramírez, Javier R.
AU - Peláez, María J.
AU - Wang, Zhihui
AU - Cristini, Vittorio
AU - Parasher, Gulshan
AU - Rawat, Manmeet
N1 - Funding Information:
This research has been supported in part by the National Science Foundation Grant DMS-1930583 (ZW, VC), the National Institutes of Health (NIH) Grants 1U01CA196403 (ZW, VC), 1U01CA213759 (ZW, VC), 1R01CA226537 (ZW, VC), 1R01CA222007 (ZW, VC), and U54CA210181 (ZW, VC).
Publisher Copyright:
© 2020 Bentham Science Publishers.
PY - 2020
Y1 - 2020
N2 - Pancreatic Ductal Adenocarcinoma (PDAC) is regarded as one of the most lethal cancer types for its challenges associated with early diagnosis and resistance to standard chemotherapeutic agents, thereby leading to a poor five-year survival rate. The complexity of the disease calls for a multidiscipli-nary approach to better manage the disease and improve the status quo in PDAC diagnosis, prognosis, and treatment. To this end, the application of quantitative tools can help improve the understanding of disease mechanisms, develop biomarkers for early diagnosis, and design patient-specific treatment strategies to improve therapeutic outcomes. However, such approaches have only been minimally applied towards the investigation of PDAC, and we review the current status of mathematical modeling works in this field.
AB - Pancreatic Ductal Adenocarcinoma (PDAC) is regarded as one of the most lethal cancer types for its challenges associated with early diagnosis and resistance to standard chemotherapeutic agents, thereby leading to a poor five-year survival rate. The complexity of the disease calls for a multidiscipli-nary approach to better manage the disease and improve the status quo in PDAC diagnosis, prognosis, and treatment. To this end, the application of quantitative tools can help improve the understanding of disease mechanisms, develop biomarkers for early diagnosis, and design patient-specific treatment strategies to improve therapeutic outcomes. However, such approaches have only been minimally applied towards the investigation of PDAC, and we review the current status of mathematical modeling works in this field.
KW - Desmoplasia
KW - Mathematical modeling
KW - Numerical simulation
KW - Pancreatic ductal adenocarcinoma
KW - Antineoplastic Agents/pharmacology
KW - Pancreatic Neoplasms/diagnosis
KW - Humans
KW - Apoptosis/drug effects
KW - Cell Proliferation/drug effects
KW - Models, Statistical
KW - Carcinoma, Pancreatic Ductal/diagnosis
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U2 - 10.2174/1568026620666200101095641
DO - 10.2174/1568026620666200101095641
M3 - Review article
C2 - 31893993
AN - SCOPUS:85081658009
SN - 1568-0266
VL - 20
SP - 367
EP - 376
JO - Current Topics in Medicinal Chemistry
JF - Current Topics in Medicinal Chemistry
IS - 5
ER -