A phase II trial of cisplatin and irinotecan alternating with doxorubicin, cyclophosphamide and etoposide in previously untreated patients with extensive-disease small-cell lung cancer

Distinct sensitivity of CD8+ CD4- and CD8+ CD4+ leukemic cell subpopulations to cyclophosphamide and rapamycin in Notch1-induced T-ALL mouse model

The Notch1 signaling pathway plays an essential role in cell growth and differentiation. Over-expression of the intracellular Notch1 domain (ICN1) in murine hematopoietic cells is able to induce robust T-cell acute lymphoblastic leukemia (T-ALL) in mice. Here we explored the drug sensitivity of T-ALL cells in two subpopulations of CD8(+)CD4(+) and CD8(+)CD4(-) cells in Notch1-induced T-ALL mice. We found that Notch1 induced T-ALL cells could be decreased by chemotherapeutic drug cyclophosphamide (CTX). CD8(+)CD4(-) T-ALL cells were more sensitive to CTX treatment than CD8(+)CD4(+) T-ALL cells. The percentage of apoptotic cells induced by CTX treatment was higher in CD8(+)CD4(-) T-ALL cells. T-ALL cells were also inhibited by inhibitor of mTORC1 rapamycin. CD8(+)CD4(+) T-ALL cells were more susceptible to rapamycin treatment than CD8(+)CD4(-) T-ALL cells. Rapamycin treatment selectively arrested more CD8(+)CD4(+) T-ALL cells at G0 phase of cell cycle. A combination of the two drugs significantly improved overall survival of T-ALL bearing mice when compared with CTX or rapamycin alone. These results indicated that CD8(+)CD4(+) and CD8(+)CD4(-) leukemia cell populations had distinct drug sensitivity.

A classification model to predict synergism/antagonism of cytotoxic mixtures using protein-drug docking scores

Background

Safer and more effective mixtures of anticancer drugs are needed, and modeling can assist in this endeavor. This paper describes classification models that were constructed to predict which fixed-ratio mixtures created from a pool of 10 drugs would show a high degree of in-vitro synergism against H460 human lung cancer cells. One of the tested drugs was doxorubicin and the others were natural compounds including quercetin, curcumin, and EGCG. Explanatory variables were based on virtual docking profiles. Docking profiles for the 10 drugs were obtained for 1087 proteins using commercial docking software. The cytotoxicity of all 10 drugs and of 45 of the 1,013 possible mixtures was tested in the laboratory and synergism indices were generated using the MixLow method. Model accuracy was assessed using cross validation, as well as using predictions on a new set of 10 tested mixtures. Results were compared to models where explanatory variables were constructed using the pseudomolecule approach of Sheridan.

Results

On this data set, the pseudomolecule and docking data approach produce models of similar accuracy. Leave-one-out precision for the negative (highly synergistic) class and the positive (low- or non-synergistic) class was 0.73 and 0.80, respectively. Precision for a nonstandard leave-many-out cross validation procedure was 0.60 and 0.77 for the negative and positive classes, respectively.

Conclusion

Useful classification models can be constructed to predict drug synergism, even in those situations where a limited subset of component drugs can be tested. Compared to the pseudomolecule approach, the virtual docking approach has the advantage of greater potential for biologic interpretation. This distinction may become important as virtual docking software becomes more accurate and docking results more closely resemble actual binding affinities. This is the first published report of a model designed to predict the degree of in-vitro synergism based on the pseudomolecule or docking data approach.