Harnessing research data for personalized healthcare

January 13, 2020

This article was reviewed by Jill Hopkins, MD

This article was reviewed by Jill Hopkins, MD

One healthcare leader is making an investment in personalized healthcare, combining the vast amounts of data being collected during clinical trials with artificial intelligence (AI) algorithms. Roche-Genentech sees a transformation on the horizon, based on the ability to use meaningful data on a large scale. 

According to Jill Hopkins, MD, global head, ophthalmology personalized health care, Roche-Genentech, the goal of the company’s personalized healthcare initiative is for patients to be identified at earlier stages of disease, and to be able to predict their most likely treatment needs and responses with a large degree of accuracy. There is also potential to build toward a preventive strategy where vision might be preserved.

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The company has targeted ophthalmology, oncology, and neuroscience as core focused therapeutic areas for the development of personalized healthcare solutions. 

The company is considering everything from digital tools to predictive and preventive algorithms of disease to change the way patients in these areas are treated. The question is how to take millions of data points being generated, and add machine learning tools for individualized disease progression and therapy.

Roche-Genentech has much legacy clinical trial data to use from the ranibizumab and lampalizumab trials. The database includes more than 13,000 patients, and around 3 million images. It continues to grow through their active phase III programs investigating ranibizumab in the Port Delivery System and faricimab.

“We have worked to optimize the value of the existing data.,” Dr. Hopkins said. “It is clinical trial grade data. It is longitudinal, with multimodal imaging, so it really provides a wonderful initial core data set on which to build some of the machine learning and deep learning proof of concept algorithms.”

Related: How artificial intelligence can transform eye care 

Dr. Hopkins explained that the first step was to get the data into a format that could be interrogated by the AI tools. 

An article published in March was the first published as part of the ophthalmology personalized healthcare initiative, and suggests that artificial intelligence could be used to provide widespread, cost-effective eye screenings.1

Related: How AI benefits patients and physicians

Dr. Hopkins said the next step is finding how to validate these algorithms on larger, more diverse datasets, which may be best accomplished through collaboration. 

One of the challenges is to determine how to build and utilize collaborations to start to build larger collections of data, not only in terms of existing disease but also in earlier stages of disease. The organization sees a need for unprecedented levels of collaboration across the healthcare space to get enough data to really be able to answer key scientific questions and to cover enough patients to be generalizable. 

One possible model could be a consortia framework, where a combination of pharmaceutical companies, academic institutions, and clinical groups could get together to look at collecting data in an ongoing fashion. The company is currently looking for the best ways to make this data useable, while always remaining sensitive to data privacy, and any other such issues.  

Related: EyePod: Episode 1: Why the fuss about AI in the practice of ophthalmology? 

The organization sees potential for personalized healthcare solutions-tools that could predict progression of disease and response to treatment as a means of delivering the right therapy at the right time, in the right dose, and with the right delivery system.

As algorithms are developed, they must be tested and validated. As the company builds collaborations to build datasets, it will become possible to test an algorithm on a set of tens of thousands of patients.

Dr. Hopkins said because it is a new field, regulatory agencies are thinking carefully about what this will look like in terms of their framework. Roche-Genentech and other companies who are doing work in similar areas are working with the regulatory agencies to determine what makes sense moving forward regarding AI, machine learning, and algorithms to bring meaningful impact to patient care, she said. 

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Jill Hopkins, MD
E: hopkins.jill@gene.com
Dr. Hopkins is global head, ophthalmology personalized health care at Roche-Genentech. She has no other financial considerations to disclose. 

References:

1. Arcadu F, Benmansour F, Maunz A, et al. Deep Learning Predicts OCT Measures of Diabetic Macular Thickening From Color Fundus Photographs. Invest Ophthalmol Vis Sci. 2019 Mar 1;60(4):852-857. doi: 10.1167/iovs.18-25634.