Spatio-temporal investigation of doxorubicin in a 3D heterogeneous tumor microenvironment

Effect of tumor heterogeneity on enhancing drug delivery to vascularized tumors using thermo-sensitive liposomes triggered by hyperthermia: A multi-scale and multi-physics computational model

In this study, a novel multi-scale and multi-physics image-based computational model is introduced to assess the delivery of doxorubicin (Dox) loaded temperature-sensitive liposomes (TSLs) in the presence of hyperthermia. Unlike previous methodologies, this approach incorporates capillary network geometry extracted from images, resulting in a more realistic physiological tumor model. This model holds significant promise in advancing personalized medicine by integrating patient-specific tumor properties. The finite element method is employed to solve the equations governing intravascular and interstitial fluid flows, as well as the transport of therapeutic agents within the tissue. Realistic biological conditions and intricate processes like intravascular pressure, drug binding to cells, and cellular uptake are also considered to enhance the model's accuracy. The results underscore the significant impact of vascular architecture on treatment outcomes. Variation in vascular network pattern yielded changes of up to 38 % in the fraction of killed cells (FKCs) parameter under identical conditions. Pressure control of the parent vessels can also improve FKCs by approximately 17 %. Tailoring the treatment plan based on tumor-specific parameters emerged as a critical factor influencing treatment efficacy. For instance, changing the time interval between the administration of Dox-loaded TSLs and hyperthermia can result in a 48 % improvement in treatment outcomes. Additionally, devising a customized heating schedule led to a 20 % increase in treatment efficacy. Our proposed model highlights the significant effect of tumor characteristics and vascular network structure on the final treatment outcomes of the presented combination treatment.

Evaluation of solid tumor response to sequential treatment cycles via a new computational hybrid approach

The development of an in silico approach that evaluates and identifies appropriate treatment protocols for individuals could help grow personalized treatment and increase cancer patient lifespans. With this motivation, the present study introduces a novel approach for sequential treatment cycles based on simultaneously examining drug delivery, tumor growth, and chemotherapy efficacy. This model incorporates the physical conditions of tumor geometry, including tumor, capillary network, and normal tissue assuming real circumstances, as well as the intravascular and interstitial fluid flow, drug concentration, chemotherapy efficacy, and tumor recurrence. Three treatment approaches-maximum tolerated dose (MTD), metronomic chemotherapy (MC), and chemo-switching (CS)-as well as different chemotherapy schedules are investigated on a real tumor geometry extracted from image. Additionally, a sensitivity analysis of effective parameters of drug is carried out to evaluate the potential of using different other drugs in cancer treatment. The main findings are: (i) CS, MC, and MTD have the best performance in reducing tumor cells, respectively; (ii) multiple doses raise the efficacy of drugs that have slower clearance, higher diffusivity, and lower to medium binding affinities; (iii) the suggested approach to eradicating tumors is to reduce their cells to a predetermined rate through chemotherapy and then apply adjunct therapy.