Pupillometric analysis for assessment of gene therapy in Leber Congenital Amaurosis patients

Abstract

Background: Objective techniques to assess the amelioration of vision in patients with impaired visual function are needed to standardize efficacy assessment in gene therapy trials for ocular diseases. Pupillometry has been investigated in several diseases in order to provide objective information about the visual reflex pathway and has been adopted to quantify visual impairment in patients with Leber Congenital Amaurosis (LCA). In this paper, we describe detailed methods of pupillometric analysis and a case study on three Italian patients affected by Leber Congenital Amaurosis (LCA) involved in a gene therapy clinical trial at two follow-up time-points: 1 year and 3 years after therapy administration.

Methods: Pupillary light reflexes (PLR) were measured in patients who had received a unilateral subretinal injection in a clinical gene therapy trial. Pupil images were recorded simultaneously in both eyes with a commercial pupillometer and related software. A program was generated with MATLAB software in order to enable enhanced pupil detection with revision of the acquired images (correcting aberrations due to the inability of these severely visually impaired patients to fixate), and computation of the pupillometric parameters for each stimulus. Pupil detection was performed through Hough Transform and a non-parametric paired statistical test was adopted for comparison.

Results: The developed program provided correct pupil detection also for frames in which the pupil is not totally visible. Moreover, it provided an automatic computation of the pupillometric parameters for each stimulus and enabled semi-automatic revision of computerized detection, eliminating the need for the user to manually check frame by frame. With reference to the case study, the amplitude of pupillary constriction and the constriction velocity were increased in the right (treated eye) compared to the left (untreated) eye at both follow-up time-points, showing stability of the improved PLR in the treated eye.

Conclusions: Our method streamlined the pupillometric analyses and allowed rapid statistical analysis of a range of parameters associated with PLR. The results confirm that pupillometry is a useful objective measure for the assessment of therapeutic effect of gene therapy in patients with LCA.

Trial registration: ClinicalTrials.gov NCT00516477.

Figure 1

Description of the off-line pupil detection algorithm steps. (a) image acquired by the pupillometer; (b) image after the thresholding; (c) image after hole filling; (d) image after small object removing; (e) original image with the pupil border enhanced in white thanks to edge detection. (f) original image with the circle (in white) which fits the pupil border.

Figure 2

Pupil reflex of a subject involved in the study after a 0.2 second light stimulus triggered at zero second. The indicated parameters are computed as follows: BD, average of the pupil diameters 100 ms prior to the light stimulus (values to be averaged are in the green rectangular box): MA, the lowest pupil diameter after a light stimulus (represented by the orange point): RA, BD-MA (represented by the red segment); RT, t_{minimum amplitude}- t_stimulus (t = time) (represented by the light blue segment): CV, RA/RT (represented by the slope of the dark blue line).

Figure 3

Example of pupil diameter detection when the eyelashes are over the pupil (a) and on the subsequent frame in which the pupil is completely visible (b).