Using androgen deprivation therapy to reduce tumour growth is a significant advancement in treating prostate cancer. Nevertheless, a majority of patients eventually progress to a refractory state, leading to the development of castration-resistant prostate cancer (CRPC). Recently, second-generation drugs and their combinations have received approval for CRPC treatment. Despite this, there are reported cases of tumours resisting these new drugs. Over the past few years, numerous mathematical models have been proposed to describe the dynamics of prostate cancer during treatment. One of the predominant challenges has been formulating mathematical models capable of accurately representing experiments conducted under in vivo conditions (on individuals), which is crucial for their suitability in clinical applications (see, for example, [1, 2]). This talk will present an interdisciplinary study on drug resistance in prostate cancer. We will show how integrating experimental data, statistical analysis, and mathematical and computational methods has enabled us to understand the underlying causes of resistance, and devise potential therapeutic strategies to address it. The proposed models explore neuroendocrine transdifferentiation in prostate cancer in vivo under multiple drug therapies and account for the heterogeneity of the tumour microenvironment. We will see how, through the computational analysis of their solutions, it was possible to examine the spatial dynamics of tumour cells, assess the efficacy of various drug therapies in inhibiting prostate cancer growth, and find optimal drug combination strategies and treatment schedules to eradicate cancer cells and prevent metastases formation. References [1] A. Burbanks et al. A hybrid spatiotemporal model of PCa dynamics and insights into optimal therapeutic strategies. Mathematical Biosciences, 355, 108940, 2023. [2] M. Cerasuolo et al. Modeling acquired resistance to the second-generation androgen receptor antagonist enzalutamide in the tramp model of prostate cancer, Cancer Res. 80 (7), 1564–157, 2020.