Breast cancer liver metastases (BCLM) are usually unresectable and difficult to treat with systemic chemotherapy. A major reason for chemotherapy failure is that BCLM are typically small, avascular nodules, with poor transport and fast washout of therapeutics from surrounding capillaries. We have previously shown that nanoalbumin-bound paclitaxel (nab-PTX) encapsulated in porous silicon multistage nanovectors (MSV) is preferentially taken up by tumour-associated macrophages (TAM) in the BCLM microenvironment. The TAM alter therapeutic transport characteristics and retain it in the tumour vicinity, increasing cytotoxicity. Computational modeling has shown that therapeutic regimens could be designed to eliminate single lesions. To evaluate clinically-relevant scenarios, this study develops a modeling framework to evaluate MSV-nab-PTX therapy targeting multiple BCLM. An experimental model of BCLM, splenic injection of breast cancer 4 T1 cells was established in BALB/C mice. Livers were analyzed histologically to determine size and density of BCLM. The data were used to calibrate a 3D continuum mixture model solved via distributed computing to enable simulation of multiple BCLM. Overall tumour burden was analyzed as a function of metastases number and potential therapeutic regimens. The computational model enables realistic 3D representation of metastatic tumour burden in the liver, with the capability to evaluate BCLM growth and therapy response for hundreds of lesions. With the given parameter set, the model projects that repeated MSV-nab-PTX treatment in intervals <7 days would control the tumour burden. We conclude that nanotherapy targeting TAM associated with BCLM may be evaluated and fine-tuned via 3D computational modeling that realistically simulates multiple metastases.
Keywords: breast cancer; computational simulation; liver metastases; macrophages; mathematical modeling; nanotherapy.
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