Abstract
One of the primary barriers in treating cancer patients is the development of resistance to the available treatments. This is the case for treatment of triple negative breast cancer (TNBC) with docetaxel, which is part of the neoadjuvant treatment for TNBC. The novel compound SCO-101 is under investigation for its potential treatment effect in several types of drug resistant cancer. The aim of this study was to establish a pharmacodynamic model that captures the effect of docetaxel, SCO-101, and the combination on cell survival in docetaxel resistant MDA-MB-231 TNBC cells. Several combination models were compared and a recently published combination model, the general pharmacodynamic interaction model (GPDI), provided the best fit. The model allowed for description and quantification of the interaction between docetaxel and SCO-101 with respects to both maximal effect and potency. Based on this model, SCO-101 has a synergistic effect with docetaxel. This synergy is not present in the maximal effect, but the combination of SCO-101 and docetaxel showed an approximately 60% increase in potency compared to docetaxel alone. Furthermore, the predicted model surface for the combination provided key information regarding promising dose ratios and dose levels for further studies of the combination. Lastly, the study presents a use case for the GPDI model, which provides a way to quantify and interpret drug-drug interactions.
Highlights
The aim of this study was to establish a pharmacodynamic model that captures the effect of docetaxel, SCO-101, and the combination on cell survival in docetaxel resistant MDAMB-231 triple negative breast cancer (TNBC) cells
One of the primary barriers in current cancer treatment is the development of drug resistance, which is the main cause of cancer-related death (Housman et al, 2014, Wang et al, 2019)
The sigmoidal Imax model had a significantly better fit with a drop in objective function value (OFV) of 4.2, the hill coefficient included in the model was close to one and subsequently when fitting the model to the full data set, the parameter became statistically insignificant
Summary
Drug combination therapy is widely applied for the treatment of cancers. A major advantage of this treatment strategy, as opposed to conventional monotherapies, is a reduction in the systemic cytotoxicity as multiple pathways are targeted simultaneously (Mokhtari et al, 2017). One of the primary barriers in current cancer treatment is the development of drug resistance, which is the main cause of cancer-related death (Housman et al, 2014, Wang et al, 2019). The best approach to combat drug resistance in cancer patients is the use of combination therapies (Housman et al, 2014). The development of treatment combinations to combat cancers is of great interest. The strategy has not been successful and there is a large unmet medical need for treatment options to patients with resistant cancers
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