Track global pipelines, competitors and key events to assess opportunities, anticipate change and inform portfolio strategy.
Benchmark study designs and test protocol assumptions against comparable trials and real-world evidence.
Assess where a study can succeed and identify the countries, sites and investigators to deliver it.
Understand patient populations, treatment journeys and provider networks to plan more effective recruitment.
Track regulatory obligations and manage trial registration, results disclosure and transparency across markets.
Stay ahead of the regulatory, commercial and policy developments shaping pharma and medtech.
Connect drugs, trials, companies, diseases, sites, investigators and patients to understand the relationships between them.
Specialist analysts, journalists and consultants curate and interpret the information behind our intelligence.
Predictive analytics, AI and specialist agents turn connected data into faster, more useful answers.
Suzanne Caruso
President, Citeline Strategic Intelligence, Clinical & Regulatory
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President, Citeline Strategic Intelligence, Clinical & Regulatory
Norstella
I’ve spent more than 15 years helping pharmaceutical companies plan and operationalize clinical trials, with leadership roles across Novartis, GSK, WCG, Veeva, and now Norstella. Having worked across clinical operations, data and strategic intelligence, I’ve seen firsthand where AI can remove friction from development and where human expertise remains essential.
What makes Norstella's AI different?
It starts with decades of proprietary clinical, pipeline, and real-world data already organized around the way drug development works. This provides industry-specific context rather than generic information. Transparent sourcing and expert oversight make it possible for users to understand the evidence behind an AI-generated insight.
What can pharma teams do now that was not possible two years ago?
AI agents can handle many of the manual steps between posing a question and arriving at a usable answer. Fragmented information can be turned into a sourced, evidence-based output in minutes rather than days. AI combined with real-world data also allows teams to test study assumptions, identify patient populations, and refine recruitment strategies at much greater speed and scale.
How can AI accelerate the journey from pipeline to patient?
Drug development involves numerous handoffs across systems, teams, and decisions. AI can connect those workflows, automate routine analysis, and surface relevant information at the right moment, leaving specialists to concentrate on the decisions requiring their expertise.
Francine Lane
Head of Transparency
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Head of Transparency
Citeline
Francine Lane is Head of Transparency at Citeline and former chair of the Drug Information Association (DIA) Clinical Trial Disclosure Community. Francine is responsible for helping TrialScope Disclose and TrialSummaries.com customers meet and exceed current disclosure expectations globally, giving them the tools they need to satisfy the requirements in this evolving industry. Francine also dedicates her time building relationships with external stakeholders — including sponsors, investigators, regulators, and transparency and patient advocates — to help align the goals and expectations of these groups, as well as help identify more consistent ways sponsors can meet transparency expectation and requirements.
Daniel Chancellor
VP of Thought Leadership
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VP of Thought Leadership
Norstella
Daniel has nearly 20 years of experience spanning drug development, market analysis, consulting, and pharmaceutical strategy. This gives him a broad view of what is required to bring therapies to market and where important operational and strategic pain points occur. His focus is on applying AI where it creates genuine value rather than introducing additional technology or complexity.
What makes Norstella's AI different?
The differentiator is the breadth of the underlying pharmaceutical dataset. Norstella can connect information from across the pipeline-to-patient journey, creating insights that would be difficult to generate from isolated datasets. AI extends the value that can be derived from that information.
What can pharma teams do now that was not possible two years ago?
One major advance is the ability to understand how patients appear in real-world healthcare and translate this into more effective targeting. AI can help bridge the gap between the controlled environment of a clinical trial and the much less structured reality of patient treatment.
How can AI accelerate the journey from pipeline to patient?
Trial design, portfolio investment, and market access decisions all consume substantial time. AI can help organizations make these decisions using a broader evidence base and do so more quickly, supporting faster development and potentially wider access to successful therapies.
Christina Masturzo
Head of Product, Clinical Portfolio
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Head of Product, Clinical Portfolio
Citeline
Christina leads product across Citeline's clinical and regulatory intelligence portfolio. She has more than 15 years of product experience across education technology and life sciences and joined the organization that ultimately became Citeline when it was still a small startup. Her earlier sales experience shaped a product philosophy centered on staying close to customers and solving the problems they actually face. Her current work combines Citeline's long-established trial, site, and drug intelligence with AI to improve clinical planning, recruitment, and execution.
What makes Norstella's AI different?
Norstella AI is purpose-built for life sciences rather than a general-purpose model adapted after the fact. It is embedded in workflows such as protocol design, patient feasibility, site selection, and investigator selection. Its foundation includes more than four decades of global drug R&D information and hundreds of thousands of clinical trials, and trial intelligence can be connected with real-world patient information through NorstellaLinQ. That creates a connected view across trials, sites, investigators, and actual patient populations. Traceability is another important distinction: forecasts, protocol recommendations, and site recommendations can be tied back to the underlying evidence.
What can pharma teams do now that was not possible two years ago?
Two changes stand out. Precision: Recruitment can become proactive, with AI models evaluating real-world information against trial requirements, identifying potentially appropriate patients, and supporting engagement with treating physicians or investigators earlier in the patient's treatment journey. Foresight: Trial, site, investigator, and real-world patient information that traditionally sat in separate systems can now be combined, so AI can model scenarios, forecast outcomes, and help teams evaluate study-planning decisions.
How can AI accelerate the journey from pipeline to patient?
AI can compress labor-intensive planning activities such as protocol design, feasibility, site selection, and recruitment. It can also make trial discovery and eligibility easier by translating between natural-language questions and complex medical terminology or coding. Speed must be combined with trust: Subject-matter experts continue to curate the underlying information and review and improve the models.
