06Jun

Distracted by the COVID pandemic, clinical investigator sites that haven’t been paying attention to new privacy laws may find it difficult to conduct trials in 2021.

“Nine out of ten investigator sites in the U.S. don’t know anywhere near enough,” and “don’t have the right tools to be in compliance,” says an article on the Association Of Clinical Research Professionals blog.

An interview with Al O. Pacino II, president and CEO of BlueCloud by HealthCarePoint, discusses the details of two key privacy laws: the EU’s General Data Protection Regulation and California’s Consumer Privacy Act. Both impose significant restrictions on how personal data is collected, stored, used and shared. Both require businesses and organizations to inform individuals of the information they have and make it available to them. The California law exempts government and nonprofits.

In the European Union, the GDPR is supplemented by local privacy rules and, for clinical trials and medical data sharing, by organizational rules and governance. A study published in BMC Medical Informatics and Decision Making found enough lack of specificity and clarity among the privacy rules of each of the various study teams in just one EU program to give rise to challenges.

Noting that “Responsible data sharing in health research entails more than compliance with the GDPR,” the researchers found there was a need to reconcile local and individual investigative rules when creating “Big Data-driven translational research platforms” such as the BigData@Heart platform.

As in Europe, academic researchers in the US regularly share data. “The combination of even larger datasets into so-called ‘Big Data’ is considered to offer even greater benefits for science, medicine and society,” the Medical Informatics and Decision Making article observes.

With California’s privacy act – the toughest in the US – and other state laws now in effect and new ones under consideration, clinical researchers need to be aware of the rules, including the GDPR.

Investigator site leaders, says Pacino, must “understand modern laws and regulations that protect one’s personal data and privacy, learn how to take ownership of [their site data], and leverage modern e-vehicles that benefit healthcare professionals, sponsors, contract research organizations, and others.”    

Photo by Lianhao Qu on Unsplash

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Green Key
Jun 6, 2023

Rare Diseases Trial Network Moves Closer to Reality

As many as 30 million Americans may be living with a “rare disease,” officially defined as a condition affecting fewer than 200,000 people.

The Genetic and Rare Diseases Information Center says there are some 7,000 known rare diseases. Most are genetic. Only about 10% of all rare diseases have an approved treatment.

To hasten the development of drugs and therapies, the Food and Drug Administration in 2019 began an initiative to “to facilitate a cooperative approach and common standardized platforms to better characterize rare diseases, incorporate the patient’s perspective in clinical outcome assessment measures, and build clinical trial readiness in the pre-competitive space.”

Dubbed the Rare Disease Cures Accelerator, the initial focus of the program is building “an integrated database and analytics hub designed to promote the secure sharing of existing patient-level data and encourage the standardization of new data collection.”

A second key part was to award grants “develop standard core sets of clinical outcome assessments (COAs) and endpoints for specific disease indications.”

In May, the FDA asked for input from industry, rare disease organizations, patients and others regarding the “implementation and sustainment of global clinical trials networks.”

“As drug development for rare diseases can be challenging due to the small number of patients and limited understanding of the variability and progression of the diseases, the FDA is committed to developing bold new approaches to harness the infrastructure of global clinical trial networks,” said Anand Shah, FDA deputy commissioner for medical and scientific affairs.

A recent report on Regulatory Focus, the website of the Regulatory Affairs Professionals Society, said the stakeholders who commented “called for regulatory clarity, smart use of existing resources, and a move toward harmonized trial standards and assessments.”

Several comments addressed the need to ensure the quality of the data collected as well as reducing and eliminating some of the challenges unique to rare disease trials.

On behalf of the Biotechnology Innovation Organization, Danielle Friend, senior director of science and regulatory affairs, said the networks need to support “the collection of high-quality data that are endorsed by the agency for regulatory decision-making.” She said a rare disease network must recognize and address “heterogeneity in rare diseases, lack of harmonization among global regulators in rare disease regulatory policy, and current inconsistencies in clinical trial network operation.” The FDA plan also should consider combining rare diseases into a single trial when possible.

The National Organization for Rare Diseases echoed those comments, and, according to Regulatory Focus, said “Increased collaboration and a focus on increasing the speed and success of clinical trials can have the effect of ‘breaking down the silos of activity currently taking place in rare disease research.’”

Now the FDA will analyze the comments, incorporating them into a development plan for its trials networks program. No timeline was given for this phase of the program.

Photo by CDC on Unsplash

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Green Key
Jun 6, 2023

A Lifesciences Lab Where Robots Do All the Experiments

In the heart of Silicon Valley is a biotech laboratory run by robots. They carry out experiments ordered by scientists anywhere in the world who simply login to the lab, describe their project, set options like the cells to use or the types of analyses to perform, and go on to do other things while the robots do the rest.

The Strateos lab in Menlo Park, California is as sophisticated as many research facilities and it becomes more so all the time. In partnership with Eli Lilly, Strateos opened a second robotic cloud laboratory in San Diego this year that focuses on the drug discovery process.

Lilly is using part of this Life Sciences Studio for its own projects. The remaining capacity is available to startups in the biosciences to run their own experiments, providing them access to tools and processes few of them can afford on their own.

Though still rare, fully robotic, remote laboratories like these are the future of drug development and biological research. They’re a clear sign of just how much laboratory automation has advanced. From the early days of handling routine and basic functions like blood chemistries, immunoassay and urinalysis, the cutting edge Life Sciences Studio can synthesize, test, and optimize compounds in pursuit of new drug therapies without human help.

At the Texas Medical Center (TMC) Innovation Institute in Houston, concept automation is tested and demonstrated. One of the most futuristic is YuMi, a product of ABB Robotics, which has a research hub there. Already in use in a handful of facilities, YuMi manages viral antigen testing in one lab and handles tissue, bone, and sterile fluid samples at another.

ABB predicts that by 2025, 60,000 nonsurgical robots, many as versatile as YuMi, will be in use in healthcare. 5,000 deployed in laboratories.

Robots,says Robin Felder, PhD, professor of pathology and associate director of clinical chemistry and toxicology at the University of Virginia School of Medicine, are “beginning to swallow up all of the manual parts of the laboratory.”

But more than that, with the rapid advances in artificial intelligence, Ben Miles, PhD, head of product at Strateos, sees a future where the robots will analyze data to initiate experiments on their own.

We’re not there yet. But as Dr. Dean Ho, Provost’s chair professor of biomedical engineering at the National University of Singapore, said, “At some point, we’ll be able to move beyond solely relying on pre-existing data and algorithm training and prediction making.”

Photo by Daan Stevens on Unsplash

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