Home PublicationsData Innovators 5 Q’s for Emil Syundyukov, Chief Technical Officer of Longenesis

5 Q’s for Emil Syundyukov, Chief Technical Officer of Longenesis

by Christophe Carugati
Emil Syundyukov

The Center for Data Innovation spoke with Emil Syundyukov, chief technical officer of Longenesis, a startup headquartered in Latvia that helps biomedical institutions, patient organizations, and research partners share and use biomedical data. Syundyukov discusses how the firm supports greater use of health data for clinical research.

This interview has been edited.

Christophe Carugati: How does Longenesis promote collaboration among the biomedical research community?

Emil Syundyukov: Longenesis connects biomedical researchers and healthcare institutions with study sponsors, empowering direct collaboration and transparent patient engagement. We follow a three-pillar approach to streamline fully digital identification, onboarding, and engagement for biomedical institutions and study sponsors. 

Our toolkit is comprised of three interrelated modules. First, “Curator” aims at data discovery in a privacy-preserving way, and patient identification in real-time. Second, “Themis” is an end-to-end dynamic consent management tool—a privacy-by-design approach for patient-centric enrollment into research activities. Third, “Engage” is an ecosystem for launching dynamic patient-centric engagement mechanisms from treatment to research.

Our team is working towards providing a technological bridge between healthcare institutions and the biotech industry to help identify and unlock the hidden value of biomedical data to accelerate novel drug and treatment discovery and provide better help to those in need. Our track record includes working with biomedical organizations and unlocking the potential for accelerating the research and development (R&D) process around the globe, including national-level projects in the Middle East, United States, Europe, and Asia-Pacific regions.

Carugati: Which kind of biomedical data does your company manage?

Syundyukov: It differs from pillar to pillar. For the “data discovery” pillar, our team aimed towards the “data stays local” principle—believing that data should not leave or be ingested in any third-party solutions without any direct use-case outlined beforehand. We operate with “data about data”—anonymized metadata sets, describing what data points the specific institution controls. By using such a glossary, we provide an opportunity for clinical investigators, biobanks, laboratories, registries, and other institutions to showcase the scope of data that could be used and the patients that could be engaged with it, without compromising the privacy of patients or data protection regulations.

Carugati: How does the development of patient-centric tools benefit individuals?

 Syundyukov: For the “engagement” pillar, we provide a robust solution for proactive engagement of participants into prospective studies and multi-channel data gathering and personalized feedback, driving real-world data collection that enables smarter decisions. Here we serve as a facilitator of data-driven dialogue between patient groups or the broader population and scientific communities.

We help build such conversation between researchers and patients or broader populations—easing the process of launching digital studies. We offer patients tools to contribute to the research while preserving privacy by giving them the right to decide where, how, and why the data will be used within the scope of the study. 

Carugati: What hurdles do researchers face when transferring data outside Europe? Why is there a need for borderless access to biomedical data?

Syundyukov: Often launching a new research study faces a set of challenges, including the ones related to data privacy and security to protect such sensitive information. Such challenges can be mitigated using solutions offering an ethics- and privacy-by-design approach, giving the ability to grant on-demand access only to those data points that matter to the specific project.

While the challenges mentioned above can be tackled with the help of the right technology and planning, there is also a mindset level challenge due to skepticism and precaution when we speak about data utilization. Stakeholders, including clinicians, are afraid of facing regulatory issues, and sometimes it is more convenient for them to pass on the opportunity rather than potentially tackle such challenges.

Therefore, we need to invest resources into educating clinical professionals on digital technologies, including an understanding of the way how the tech works and how to use technology as a 21st-century screwdriver—a tool for problem-solving and assistance in their professional processes.

Carugati: How do you foresee collaboration around biomedical research data evolving in the future?

Syundyukov: I foresee a more proactive role for individuals in the process of research and data utilization within the patient journey, starting from prevention and screening among the broader population, and going to diagnostics, treatment, and post-treatment monitoring. 

One of the challenges is a lack of data. We can perceive a lack of data as black boxes we need to open to get more insights and to perform certain actions, leading towards earlier-stage diagnostics and prevention, as well as more efficient treatment and increasing quality of life years. To open these black boxes, we need to build a more dynamic communication between the population and clinical researchers. To achieve such conversations, digital technologies are one of the most affordable and scalable tools.

In addition to the lack of data and the need for proactive engagement, it might sound funny that we also have a challenge of oversaturation with data points. It is not “the more data we have, the better it is,” it is instead “the more sense we can bring out of data, the better it is.” By this, I mean that clinical researchers face challenges of navigating in the ocean of data, where only certain details matter among this noise. Once again, the tech that helps us generate such an amount of data can help us understand what matters by providing a better user experience, data visualizations, and analytics tools.

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