Significant Efficacy in a Rare Disease Trial Faster

Significant Efficacy in a Rare Disease Trial Faster

Significance Efficacy in a Rare Disease Trial Faster

A global pharmaceutical company completed their Phase III trial failing to meet primary and secondary endpoints. The Company started working with IAG after all imaging data was already collected. The imaging data in this rare disease is vital for the assessment of treatment efficacy, as the current clinical test is highly subjective and does not reflect true impact of the treatment. The disease affects growing children; however, the clinical test does not take into account the growth adjustments and children’ individual development.

After the initial assessment of the imaging data, we understood the problem. With nearly 250 sites seeing only a few patients per year, it was a great challenge to standardize image acquisition and maintain the imaging data quality.

This is a real problem for a lot of our biotech clients with assets in rare diseases. In a multi-centre trial, where data is collected at various sites, while sites might see only 1 patient in 4-6 months. If a trial lasts for several years, a trained technologist or a radiographer might leave; the imaging equipment might break or be upgraded to a newer software version and all such situations will lead to discrepancy in the image quality and in some cases, loss of the imaging end points.

The only way we can ensure that the data is of usable quality is to be ahead of the game: utilize cloud based infrastructure for data management and quality control these images in real-time, while patient is still in the hospital. Furthermore, advanced alerts to the imaging sites of the planned patient visits and regular checks on the robustness of their imaging set-up and readiness for the patient procedure are vital.

After meeting the Company, we devised a strategy to re-analyze the data for quality and then design a more specific methodology to quantify the treatment efficacy. The quantification of images provides more sensitive way of extracting intelligent information, as comparing to a radiological assessment. An image reader would normally look at the images by eye and utilize a simplified scoring system, such as yes / no or 0,1,2,3 or similar. A computer aided quantification would produce continuous numbers, allowing to capture even subtle treatment induced changes.

Every single image was scored for ‘quality’ and nearly 25% of images were classified as ‘poor’ with remaining data being borderline, acceptable and of good quality. The design of the image scoring methodology took into account that some data would be of borderline quality and we deployed the most sensitive approach to distinguish treatment effects.

When conducting statistical analysis, we weighted the results based on the image quality the image acceptability for reading. The readers and the operational team were blinded to patient data, visit and treatment regime.

Due to high degree of automation of the reading, the reproducibility of scoring has increased. Thus, we could use fewer data points to achieve statistically significant results when using images of good and acceptable quality.

Within a few months, the Company was presented with the data showing that it is possible to differentiate treatment groups and it is possible to do it with lesser patients and at earlier timepoint.

About IAG

IAG is a strategic partner to bio-pharmaceutical companies developing new treatments to improve patients’ lives. Our dynamic Strategy, Trial Solutions and Bio-Partnering divisions work closely to meet critical needs of biotechnology companies: funding, clinical development and monetization of their assets. We fuse decades of therapeutic insights, risk-sharing business model and agile culture to accelerate novel drug development. IAG broadly leverages its core imaging expertise, proprietary technology platform DYNAMIKA and capabilities to support an objective early go no/ go decision and drive excellence for tomorrow’s innovative therapeutic agents with speed.