Vittorio Cristini

Nanotechnology in Cancer Drug Therapy: A Biocomputational Approach

Although the clinical arsenal in treating cancer has been greatly extended in recent years with the application of new drugs and therapeutic modalities, the three basic approaches continue to be (in order of success) surgical resection, radiation, and chemotherapy. The latter treatment modality is primarily directed at metastatic cancer, which generally has a poor prognosis. A significant proportion of research investment is focused on improving the efficacy of chemotherapy, which is often the only hope in treating a cancer patient. Yet the challenges with chemotherapy are many. They include drug resistance by tumor cells, toxic effects on healthy tissue, inadequate targeting, and impaired transport to the tumor. Determination of proper drug dosage and scheduling, and optimal drug concentration can also be difficult. Finally, drug release kinetics at the tumor site is an important aspect of chemotherapy.

Agent-Based Modeling of Ductal Carcinoma In Situ: Application to Patient-Specific Breast Cancer Modeling

Ductal carcinoma in situ (DCIS) of the breast is the most common precursor to invasive carcinoma (IC), the second-leading cause of death in women in USA. There has been great progress in modeling DCIS at both the cellular scale (e.g., using cellular automata and agent-based models) and the population scale (e.g., using partial differential equations or systems of ordinary differential equations), but these past efforts have been difficult to calibrate with patient-specific molecular and cellular measurements. We develop a biophysically justified, agent-based cellular model of DCIS that is well-suited to patient-specific calibration. The model is modular in nature and can thus be readily extended to incorporate more advanced biology. We give an example of recently developed, patient-specific calibration of the model and conduct parameter studies that generate testable biological hypotheses.