In this episode, Nijat from Lindus Health shares their vision for building the future of clinical trials by creating what they call the "anti-CRO." He dives into Lindus Health's mission and his personal passion for driving change in this space. Nijat explores the challenges of innovation in clinical trials, particularly the complexity of aligning trials with patient needs while navigating the demands of various stakeholders. Nijat sheds light on how Lindus Health is addressing these challenges and redefining the clinical trial process to better serve patients and the industry alike.
Guest: Nijat Hasanli, head of product at Lindus Health, an end-to-end CRO; PhD in chemistry from the University of Oxford (prior research on materials for semiconductors); background as a founding team member at several startups.
Host: Tomás Sabat Stöfsel, CEO & Co-Founder, Verisian
Topics: Lindus Health's mission to accelerate clinical trials, the "anti-CRO" philosophy and product approach, building unified technology across clinical operations and patient experience, patient-centric trial design, using accumulated trial data to inform future study design, balancing innovation with regulatory compliance, designing for SDTM compliance at the point of data collection, breaking down silos between data management and statistical programming, current technical and organizational challenges, what changed Nijat's mind about automating biostatistics
The full episode can be found on YouTube, Apple Podcasts or Spotify.
Nijat frames Lindus Health's "anti-CRO" tagline not as a marketing gimmick but as a description of how the company structures, prices, and delivers studies differently from conventional CROs, built around genuinely caring about the research study's outcome more than typical CRO incentive structures allow. The company's value proposition combines two halves: a clinical operations team built around fast, tailored team formation for each study, and a unified technology platform that keeps all patient and research data in a single system rather than requiring multiple disconnected tools to reconstruct a coherent study narrative, something Nijat identifies as one of the industry's most persistent, and solvable, structural problems.
Nijat is explicit that Lindus isn't solving exotic technical problems, no language models or chip-level engineering, but rather focusing on the unglamorous, high-leverage problem of research data management done well. The company's real differentiation, in his framing, is productization: building EDC- and ePRO-equivalent functionality into a single, well-designed user experience shared by clinicians, patients, and data managers, prioritized around the trial itself rather than any one function's isolated needs. This was a deliberate early bet against feature-by-feature pressure, and Nijat credits an underlying clinical database management system, which absorbs the complexity of getting disparate systems to talk to each other, as the compounding architectural decision that made this approach scale.
Discussing Lindus's approach to CDISC and SDTM, Nijat describes learning the standard early ("FDA is mandating this, there must be something here") and deliberately designing the platform's data collection experience to be SDTM-informed at the point of entry rather than requiring later reprogramming into compliance. A concrete example: a disposition-related CRF form is built so that every new record entered automatically constructs the study's disposition dataset in the background, rather than needing separate downstream SDTM programming work. He's careful to note this doesn't produce perfect, fully validated compliance automatically, but meaningfully reduces the distance between raw data collection and a compliant deliverable. The company also actively engages with CDISC initiatives and tools like Pinnacle 21 and Core to stay current with evolving standards.
A recurring theme is Lindus's effort to eliminate the traditional separation between data management and statistical programming, disciplines Nijat notes are often siloed even within the same company. Practically, this means data managers, trial managers, and biostatisticians are encouraged to communicate directly and immediately (a Slack DM away) about decisions like how to structure a schedule of activities, since a choice that seems reasonable from a data collection standpoint can create significant downstream programming difficulty if made without input from the team that will eventually process that data. Nijat notes some team members don't initially realize how unusual this level of cross-functional access is, since many arrive from environments (technology and clinical operations alike) where these functions were historically separated by both organizational structure and physical distance.
Asked how Lindus reconciles a fast-moving product culture with a heavily regulated, life-and-death domain, Nijat describes embedding compliance directly into the development lifecycle rather than treating it as an external check: every code change and feature scope requires a documented GCP risk assessment (who was consulted, what was decided, why), making compliance a built-in step of the process rather than a bolt-on audit function. He also describes actively challenging ambiguous regulatory language when the company believes there's a better-justified interpretation, consulting subject matter experts internally, and participating in feedback to regulators on how guidance itself could evolve, rather than treating regulation purely as a fixed constraint to work around.
Looking at current priorities, Nijat cites the continuous work of keeping SDTM-related tooling current (new exposure models, randomization schemes) as studies grow more complex, avoiding any lapse into "maintenance mode" given the inherent urgency of live trials. He also flags execution challenges around bigger strategic bets, like making adaptive trial design the default rather than the exception, which require cross-functional coordination that gets structurally harder as the company scales, and navigating evolving regulatory guidance (citing FDA and MHRA positions questioning the value of 100% source data verification) into concrete product and customer recommendations.
Asked what he'd changed his mind about over the past year, Nijat points to his understanding of the dynamics between data management, statistical programmers, and biostatisticians. He'd previously found the idea of "automated biostatistics" exciting in the abstract, but after directly engaging with each stakeholder group's actual pain points, concluded that while meaningful automation and abstraction of redundant work is achievable, the deeper complexity involved, particularly in adaptive trial design and statistical medical writing, means the realistic goal isn't removing these functions but making the people doing them substantially more efficient. He frames this as a shift away from pitching a single "out-of-the-box" technology solution toward genuinely reinventing how the underlying process works.
"Having a technology platform that is also end to end with regards to the trial life cycle... everything that touches patient research data, if we keep it in the same place and give it a good user experience, we believe it can seriously transformationally change how study's conducted." — Nijat Hasanli
"Can we be SDTM compliant at point of collection versus needing to reprogram it later?" — Nijat Hasanli
"You reduce the risk of random things going wrong as you go down the pipeline." — Nijat Hasanli, on breaking down silos between data management and statistical programming
"I think you can make it automated. But I think you can't really make it automatic... it's not about removing that piece, it's about making that piece more efficient." — Nijat Hasanli, on statistical programming and biostatistics
"The first two trials we ran at Lindus Health are two different treatments, and each of my individual parents could have benefited from each of those two treatments." — Nijat Hasanli
What does Lindus Health mean by calling itself the "anti-CRO"?
It describes a deliberate approach to structuring, pricing, and delivering clinical trials differently from conventional CROs, combining tailored clinical operations team-building with a unified, end-to-end technology platform that keeps all patient and research data in one place.
How does Lindus Health approach SDTM compliance?
Rather than collecting data and reprogramming it into SDTM compliance afterward, Lindus designs its data collection forms to be informed by SDTM standards from the point of entry, for example structuring disposition-related forms so the disposition dataset effectively builds itself as data is entered.
How does Lindus Health balance fast product development with regulatory compliance?
By embedding compliance directly into its development process: every code change or feature requires a documented GCP risk assessment as a required step, rather than treating regulatory compliance as a separate, bolt-on review function.
Why does Nijat say data management and statistical programming shouldn't be siloed?
Because decisions made early in data collection, like how a schedule of activities is structured, can create significant downstream programming difficulty if made without input from the teams that will eventually process that data; direct, immediate communication between these functions reduces that risk.
What did Nijat change his mind about regarding automating biostatistics?
He moved away from believing biostatistics and statistical programming work could become fully "automatic," concluding instead that the realistic and valuable goal is making these functions meaningfully more efficient ("automated"), not replacing the people doing them.
The Verisian Community Podcast brings together experts in clinical trials to exchange innovative ideas and best practices central to clinical reporting, submission and review. Aligned with Verisian's mission to accelerate the evaluation and market launch of new medical treatments, each episode features expert insights, with guests ranging from statistical programmers to medical writers, to discuss the challenges and opportunities of the latest software and technology.
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