Intermittent Androgen Suppression: Estimating Parameters for Individual Patients Based on Initial PSA Data in Response to Androgen Deprivation Therapy

Abstract

When a physician decides on a treatment and its schedule for a specific patient, information gained from prior patients and experience in the past is taken into account. A more objective way to make such treatment decisions based on actual data would be useful to the clinician. Although there are many mathematical models proposed for various diseases, so far there is no mathematical method that accomplishes optimization of the treatment schedule using the information gained from past patients or "rapid learning" technology. In an attempt to use this approach, we integrate the information gained from patients previously treated with intermittent androgen suppression (IAS) with that from a current patient by first fitting the time courses of clinical data observed from the previously treated patients, then constructing the prior information of the parameter values of the mathematical model, and finally, maximizing the posterior probability for the parameters of the current patient using the prior information. Although we used data from prostate cancer patients, the proposed method is general, and thus can be applied to other diseases once an appropriate mathematical model is established for that disease.

Fig 1. Classification of patients for intermittent androgen suppression using the mathematical model of Ref. [15].

These criteria used here were originally proposed in Ref. [30].

Fig 2. Fitting and prediction with the proposed method.

In this figure, we fitted first one and half cycles to predict the following cycles. Panels (a), (b), and (c) correspond to examples of Type (i), Type (ii), and Type (iii) patients, respectively. In each panel, the blue solid line shows the fitting and the prediction under IAS, the blue vertical dash-dotted line shows the point switching between the fitting and the prediction, and the green dashed line shows the simulation under CAS, and the red crosses show the actually observed values of PSA levels.

Fig 3. Fitting and prediction using the proposed method.

In this figure, we fitted the first half cycle of IAS. To interpret the figure, please see the caption of Fig 2.