Deciphering RNA to Predict if a Patient Will Respond to a Targeted Drug

Ineffective drugs have a profound impact on patients. First of all, the patient could be taking a medication that not only doesn't make them feel better, but could also result in unwanted adverse effects. Establishing a way to ensure patients are prescribed optimal treatment from day one while avoiding adverse effects and the billions of dollars that payers and patients waste each year on ineffective treatments is key to combating this dilemma.

We recently spoke to Alif Saleh, CEO of Scipher Medicine to learn how RNA data from a patient’s blood sample can be used to determine the underlying molecular processes driving a disease, and establish how well a patient will respond to a particular therapy.

Laura Lansdowne (LL): Could you tell us more about Scipher Medicine, the company history, mission and goals?

Alif Saleh (AS): Scipher Medicine holds a fundamental conviction that patients with autoimmune diseases deserve a simple answer to their treatment plans using scientifically-backed data. It was founded by Dr Joseph Loscalzo, Hersey Professor of the Theory and Practice of Medicine at Harvard Medical School, Chairman of the Department of Medicine, and Physician-in-Chief at Brigham and Women’s Hospital and Dr Albert-Laszlo Barabasi, Robert Gray Dodge Professor of Network Science and a Distinguished University Professor at Northeastern University.

Drs Loscalzo and Barabasi have spent more than 10 years building and interpreting the map of human biology that explains how proteins expressed from the human genome interact to cause specific disease phenotypes. This map was initially applied to pharmaceutical research in the early days of Scipher. Then the team approached the medical insurance payers about the potential of building a drug response test. They unanimously agreed the ability to predict response to anti-tumor necrosis factor (TNF) would be most beneficial to them, patients, and medical professionals. Scipher developed Prism in collaboration with payers and physicians in order to meet this clear market need. The company is determined to bring precision medicine to autoimmune diseases by ensuring patients are prescribed optimal treatment from day one while avoiding side effects and the billions of dollars that payers and patients waste each year on ineffective treatments.

LL: What impact do ineffective drugs have on both patients and the pharma industry?

AS: Ineffective drugs have a profound impact on patients. First of all, the patient is taking drugs that not only don’t make them feel better, but also potentially expose him or her to dangerous side effects and disease progression, making the patient sicker. The patient is also wasting money on the ineffective drug through copays and doctor visits. Aside from the financial and physical impact, the patient could suffer psychological effects of taking drugs that don’t make them feel better, leaving them feeling hopeless with their treatment plan and the conversations with their doctors.

Ineffective drugs also impact the pharma industry as physicians are able to prescribe to a broader patient base than would be the case if they had to identify the optimal responders in the first instance. Other groups impacted negatively by this lack of response include the insurers who must pay for the ineffective drugs, and the tax payers if the payer is Medicare or Medicaid.

LL: How can RNA be used to develop more effective drugs?

AS: Scipher Medicine’s platform analyzes RNA data from a patient’s blood sample to determine the underlying molecular processes driving the disease, generating a patient’s specific disease signature. Drugs are then screened to predict accurately whether a patient will respond to a particular therapy. For patients who do not respond to existing drugs, the platform identifies new targets that can be used to develop more effective therapies.

LL: How have you harnessed the power of RNA and how have you translated this into the development of a platform?

AS: DNA is static and a poor predictor of disease, however RNA is dynamic and reveals real-time insight into one’s health. By deciphering RNA, we have unlocked the potential to understand an individual patient’s disease at a molecular level. We combine artificial intelligence (AI) and a proprietary map of all protein interactions in human cells to develop our platform.

You recently announced trial results for Prism.

LL: What is Prism and what was the aim of the trial?

AS: Prism is a test that predicts non-response to anti-tumor necrosis factor (anti-TNF) therapies. Our first indication is for patients with rheumatoid arthritis. The aim of our retrospective trial was to validate the test after our initial point of concept (POC) study.

LL: What were the key trial findings and what impact could these findings have for patients and physicians?

AS: The test reached the performance goals established by medical insurance payers for clinical utility. This is very exciting because it will help the 65% of patients who do not respond to anti-TNFs avoid these drugs and be prescribed alternative approved targeted therapies instead.

LL: Considering the positive conclusion of the retrospective study for Prism, what are the next steps?

AS: The positive results encourage us to continue to develop Prism by launching a prospective, randomized trial in 2019. As our study will mark the first prospective clinical trial conducted to determine response to a therapeutic class not sponsored by a pharmaceutical company, we’re pleased to be bringing a new era of precision medicine to the autoimmune disease market and look forward to our continued advancement for the industry. 

Alif Saleh was speaking to Laura Elizabeth Lansdowne, Science Writer for Technology Networks.