A Mathematical Skew-Logistic Model for G-Flip is An Effective Treatment for Chemotherapy Refractory Metastatic Pancreatic Cancer

In this paper we introduce the skew logistic distribution. It is observed that the probability density function of the skew logistic distribution is always unimodal. In this paper, we mainly consider the generalization of the logistic distribution by introducing skewness parameters. The main idea is to introduce the skewness parameter, so that the generalized logistic distribution can be used to model data exhibiting a unimodal density function having some skewness present in the data, a feature which is very common in practice. Patients with Single agents have only modest activity as treatment for metastatic pancreatic cancer with response rates of less than 10 % and median survivals of less than 6 months. Evaluations of single-agent gemcitabine and rubitecan as second-line treatment for relapsed pancreatic cancer have reported good patient tolerability and median survivals of 3.85 months and 4.7 months respectively. Adding a single new drug such as irinotecan to the same first-line chemotherapy combination upon disease progression may be an important alternative to switching to different drug classes for treatment of relapsed/resistant cancer. The outcomes and moderate toxicity associated with G-FLIP in this heavily pretreated group warrant development of this regimen including tests as first-line therapy in patients with diseases likely to be responsive to the drugs contained in this combination. The combination of G-FLIP is a generally well-tolerated and effective regimen that provided sustained disease control in intensively metastatic pancreatic cancer patients. Our mathematical results shows G-FLIP drugs combinations are well tolerated treatment option. The medical curve and mathematical curve for disease control is higher than the probability density functions which are monotonically increasing function.