Optimal design of personalized prostate cancer therapy using Infinitesimal Perturbation Analysis

Abstract

The standard treatment for advanced prostate cancer is hormone therapy in the form of continuous androgen suppression (CAS), which unfortunately frequently leads to resistance and relapse. An alternative scheme is intermittent androgen suppression (IAS), in which patients are submitted to cycles of treatment (in the form of androgen deprivation) and off-treatment periods in an alternating manner. In spite of extensive recent clinical experience with IAS, the design of ideal protocols for any given patient remains a challenge. The level of prostate specific antigen (PSA) is frequently monitored to determine when patients will be taken off therapy and when therapy will resume. In this work, we propose a threshold-based policy for optimal IAS therapy design that is parameterized by lower and upper PSA threshold values and is associated with a cost metric that combines clinically relevant measures of therapy success. We use a Stochastic Hybrid Automaton (SHA) model of prostate cancer evolution under IAS and perform Infinitesimal Perturbation Analysis (IPA) to adaptively adjust PSA threshold values so as to improve therapy outcomes. We also apply this methodology to clinical data from real patients, and obtain promising results and valuable insights for personalized IAS therapy design.

Personalized cancer therapy design: Robustness vs. optimality

“…, 6, are minimal. In [6] the optimal therapy As it can be seen in Figs. 2-9, these settings are not located in the regions of minimum sensitivities.…”

“…Hence, based on (17) and (5), we have that z 1,i (τ + k ) = 0. From (16) and (6), it is straightforward to verify that z 2,i (τ + k ) = z 2,i (τ − k ) − τ k,i , i = 1, . .…”

“…A first attempt to define optimal personalized IAS therapy schemes by applying Infinitesimal Perturbation Analysis (IPA) to stochastic models of prostate cancer evolution was reported in [5]. An IPA-driven gradient-based optimization algorithm was subsequently implemented in [6] to adaptively adjust controllable therapy settings so as to improve IAS therapy outcomes. The advantages of these IPA-based approaches stem from the fact that IPA efficiently yields sensitivities with respect to controllable parameters in a therapy (i.e., control policy), which is arguably the ultimate goal of personalized therapy design.…”

The Impact of Intermittent Androgen Suppression Therapy in Prostate Cancer Modeling

“…In addition, a biologically detailed model with a complex biochemical network has also been developed and verified with experimental data [12]. On a more applicable note, optimal scheduling, complex algorithms to predict hormonal resistance, and stochastic models have been used to provide insights into clinical aspects of prostate cancer treatment [13][14][15][16]. For a detailed review of recent work, the authors refer to [17].…”

Optimal sliding mode control of drug delivery in cancerous tumour chemotherapy considering the obesity effects

“…In another recent work, a multi‐objective optimisation approach is used to minimise the cancerous cells concentration and the drug concentration [14]. Similar optimal approaches are applied to specific cancer models such as prostate [15] and leukaemia [16].…”