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Advancing ALS Research With Omics Analysis Technology
Industry Insight

Advancing ALS Research With Omics Analysis Technology

Advancing ALS Research With Omics Analysis Technology
Industry Insight

Advancing ALS Research With Omics Analysis Technology


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Clinical trials produce a massive amount of medically relevant and highly sensitive data. Leveraging such data could make a difference to efforts to create more personalized, effective medicines. One company attempting to use this data is New York-based Medidata. Their new collaboration with Project ALS has the complex pathology of amyotrophic lateral sclerosis (ALS) in its sights. We talked to Medidata’s Sheila Diamond to find out more.

Molly Campbell (MC): Why is omic data important in personalized medicine? Please can you tell us about Medidata's aims in the clinical omics space?

Sheila Diamond (SD):
In addition to my roles at Acorn AI and Medidata, I am a certified genetic counselor, so my training and experience in both of these clinical and research spaces have given me a front row seat to how genomic health technology can inform and guide clinical outcomes for patients. We are certainly in an exciting time of precision medicine and personalized medicine where the use of omic data and biomarkers can help us better characterize diseases. Identifying biomarkers that differentiate subsets of patients within a disease, especially in rare diseases with high unmet clinical need, presents an incredibly actionable opportunity for patients, providers and researchers. By using our technologies to integrate omic data into clinical practice, we can better understand how to use these variants to inform diagnostic criteria, prognostic factors and treatment targets. This is collectively a main goal of ours in this partnership with Project ALS, a research organization that is committed to finding a cure for ALS.

MC: Medidata has worked with a variety of biopharma companies to conduct rare disease studies. Can you tell us about some highlights from such projects?

SD:
Medidata created an end-to-end, cloud-based platform with the tools needed to accelerate the development of new therapeutics. The company launched Acorn AI by Medidata, a Dassault Systèmes company, in 2019 to leverage advanced analytics and evidence generation using AI and predictive modeling. Acorn AI is built on Medidata’s platform, which is the industry’s largest structured, standardized clinical data repository comprising nearly 20,000 clinical trials and over 5.8 million patients.

To date, Medidata has helped biopharma companies conduct more than 1,200 rare disease studies involving more than 190,000 patients. A great example is the work we’ve done in our partnership with the Castleman Disease Collaborative Network (CDCN) to advance precision medicine for patients with this rare life-threatening, lymphoproliferative disorder. These collaborative efforts used Rave Omics, our biomarker discovery solution, which is part of Acorn AI’s portfolio. The team applied Rave Omics’ machine-learning methodologies to discover new patient subgroups within a population of Castleman Disease patients. Uncovering these novel insights in treatment response can unlock potential new drug targets for these patients. This is particularly impactful for the rare disease community, where these analyses demonstrate how omic technology can be used to advance research and discover previously unknown treatment options in subpopulations within larger diseases.

MC: What challenges exist when collecting omic data in a clinical context? How can these challenges be overcome?

SD:
Rapid advancements in health technology bring not only vast volumes of data collected but also the challenge of being able to keep up the pace with translating this data into clinically meaningful results. As we make progress in defining more precise disease subtypes with omic data, obstacles arise in the development and delivery of therapies to patients. For example, the relevant population of patients available to participate in clinical trials may become more limited, making it harder to identify and enroll them in a timely manner. Or once a therapy is commercialized, finding the patients who will benefit best from treatment could be a challenge, especially in cases of rare diseases, where time from diagnosis to treatment may need to be expedited. Acorn AI is working on solutions to combat these challenges head on by leveraging quality omic data and applying machine learning algorithms to help analyze the data faster in order to drive timely go/no-go decisions and optimize the execution of a trial in order to get the right drug and treatment to the right patient.

Ruairi Mackenzie (RM) + MC: Why is ALS such a difficult disease to study? Can you explain how subtypes of ALS are distinguished?

SD:
ALS is a neurodegenerative disease that primarily affects the upper and lower motor neurons and robs one’s ability to move, speak, eat and breathe. There is currently no cure for this rare disease that affects approximately 30,000 people in the US. It is a genetically heterogeneous disorder with monogenic forms, those caused by a single gene, as well as multifactorial forms with complex etiology. Most cases of ALS are sporadic, where there is no prior family history of ALS, and about 10% of cases are considered familial. Though significant advances have been made in understanding the genetic and environmental factors of the disease, it remains a difficult disease to study and to find an effective treatment for as the causes of ALS are largely unknown. For those who seek predictive testing in ALS, the genetic counseling process is complex and similar to that of other neurodegenerative disorders, like Huntington's disease, because there is no existing preventive treatment for this devastating disease.

It’s through multifaceted collaborations, like those with Project ALS, that we can deepen our understanding of the ALS disease process for those affected and help create opportunities for the use of newly discovered biomarkers in early phase investigation.

RM: In sharing clinical trial data, security is paramount. What steps does Medidata take to maintain best data sharing practice with its clinical trial data bank?

SD:
Medidata takes stewardship of patient data very seriously and invests heavily in privacy and quality management systems. The Medidata platform is subject to rigorous privacy controls and meets the most stringent global standards for the privacy of patient and customer information.

Sheila Diamond leads scientific business development and the Medidata Institute at Acorn AI by Medidata, a Dassault Systèmes company and was speaking to Molly Campbell and Ruairi J Mackenzie, Science Writers for Technology Networks.

Meet The Authors
Ruairi J Mackenzie
Ruairi J Mackenzie
Senior Science Writer
Molly Campbell
Molly Campbell
Science Writer
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