• Co-delivery of a RanGTP inhibitory peptide and doxorubicin using dual loaded liposomal carriers to combat chemotherapeutic resistance in breast cancer cells

      Haggag, Y.; Abu Ras, Bayan; El-Tanani, Yahia; Tambuwala, M.M.; McCarron, P.; Isreb, Mohammad; El-Tanani, Mohamed (2020-11)
      Multidrug resistance (MDR) limits the beneficial outcomes of conventional breast cancer chemotherapy. Ras-related nuclear protein (Ran-GTP) plays a key role in these resistance mechanisms, assisting cancer cells to repair damage to DNA. Herein, we investigate the co-delivery of Ran-RCC1 inhibitory peptide (RAN-IP) and doxorubicin (DOX) to breast cancer cells using liposomal nanocarriers. A liposomal delivery system, co-encapsulating DOX, and RAN-IP, was prepared using a thin-film rehydration technique. Dual-loaded liposomes were optimized by systematic modification of formulation variables. Real-Time-Polymerase Chain Reaction was used to determine Ran-GTP mRNA expression. In vitro cell lines were used to evaluate the effect of loaded liposomes on the viability of breast and lung cancer cell lines. In vivo testing was performed on a murine Solid Ehrlich Carcinoma model. RAN-IP reversed the Ran-expression-mediated MDR by inhibiting the Ran DNA damage repair function. Co-administration of RAN-IP enhanced sensitivity of DOX in breast cancer cell lines. Finally, liposome-mediated co-delivery with RAN-IP improved the anti-tumor effect of DOX in tumor-bearing mice when compared to single therapy. This study is the first to show the simultaneous delivery of RAN-IP and DOX using liposomes can be synergistic with DOX and lead to tumor regression in vitro and in vivo.
    • Data mining of fractured experimental data using neurofuzzy logic-discovering and integrating knowledge hidden in multiple formulation databases for a fluid-bed granulation process.

      Shao, Qun; Rowe, Raymond C.; York, Peter (2008)
      In the pharmaceutical field, current practice in gaining process understanding by data analysis or knowledge discovery has generally focused on dealing with single experimental databases. This limits the level of knowledge extracted in the situation where data from a number of sources, so called fractured data, contain interrelated information. This situation is particularly relevant for complex processes involving a number of operating variables, such as a fluid-bed granulation. This study investigated three data mining strategies to discover and integrate knowledge "hidden" in a number of small experimental databases for a fluid-bed granulation process using neurofuzzy logic technology. Results showed that more comprehensive domain knowledge was discovered from multiple databases via an appropriate data mining strategy. This study also demonstrated that the textual information excluded in individual databases was a critical parameter and often acted as the precondition for integrating knowledge extracted from different databases. Consequently generic knowledge of the domain was discovered, leading to an improved understanding of the granulation process.