Introduction

Few challenges in global health carry the human, societal, and financial cost of Alzheimer’s disease. More than 50 million people worldwide live with dementia, a number expected to triple by 2050. Yet Alzheimer’s, which is the most common form of dementia, is usually diagnosed only after significant, irreversible brain damage has occurred.

Despite the arrival of disease-modifying therapies (DMTs), these drugs are effective only in the early or even pre-symptomatic stages. Early detection is therefore not just a medical goal but a market and policy imperative. Current diagnostic methods, however, remain prohibitively expensive or invasive. PET scans and cerebrospinal fluid (CSF) analyses - today’s “gold standards” - require specialised infrastructure that is inaccessible to most of the world’s population.

As Dr Tamás Letoha, CEO of Pharmacoidea Ltd., points out: “Basically, all this is the culmination of about 20 years of research. Our invention is about detecting Alzheimer’s disease at its earliest stages, before neurodegeneration takes over.”

The Breakthrough: A Simple Blood Test with Deep Molecular Insight

Pharmacoidea’s SynDecAlz Detect represents a significant shift in Alzheimer's diagnostics. The test measures the expression of syndecan-3 (SDC3), a transmembrane protein on circulating monocytes, the immune cells that mirror processes occurring in the brain.

Unlike existing blood biomarkers such as phosphorylated tau (pTau217), which primarily reflect later-stage neuronal damage, syndecan-3 captures earlier immune and molecular changes linked to amyloid aggregation and tau pathology.

“Syndecan-3 is a mechanistically relevant biomarker,” Letoha explains. “It’s involved in the early aggregation and spread of neurodegenerative proteins such as amyloid-β and tau.”

In effect, SDC3 functions as a window into the earliest pathogenic phase of Alzheimer’s and detects disruption before cognitive symptoms manifest.

From Lab to Clinic: Validation and Performance

Recent research published in the International Journal of Molecular Sciences (Hudák, Letoha et al., 2025) demonstrated that SDC3 levels are significantly elevated in Alzheimer’s patients compared with cognitively healthy controls[1]. In a pilot study of 42 participants, PBMC-expressed SDC3 correlated strongly with disease status and remained independent of age or systemic inflammation markers like CRP and D-dimer

The same study found that while pTau217 levels were unexpectedly high in some non-Alzheimer’s subjects, most likely reflecting systemic inflammatory effects, SDC3 showed a cleaner, disease-specific signal. The combination of SDC3, age, and pTau217 achieved an area under the ROC curve (AUC) of 0.85, indicating high diagnostic accuracy[2]

As Letoha summarises: "In our prospective human pilot study, syndecan-3 expression in peripheral blood monocytes was significantly elevated in Alzheimer's patients. Importantly, our biomarker outperformed plasma pTau217 in diagnostic sensitivity, especially for identifying early, preclinical stages.”

Studies in Alzheimer's mouse models supported these findings: mice with higher levels of SDC3 on their monocytes also had more amyloid plaques in their brains, confirming that SDC3 plays a significant role in the disease mechanism.

Competitive Advantage: Accessible, Affordable, and Scalable

Unlike PET scans and CSF tests that require multimillion-euro equipment and specialised staff, SynDecAlz Detect only needs a routine blood sample and uses standard ELISA technology[3] that's already widely available in hospitals and labs globally

“Our test is minimally invasive, low cost, and compatible with standard laboratory infrastructure,” Letoha says. “That makes it suitable even for low-cost hospital settings.”

This design enables fast adoption and integration across healthcare systems, from high-income countries seeking to optimise specialist workflows to primary care environments in low-resource settings, where early identification has the greatest impact.

Global Scalability and Market Entry

Pharmacoidea built SynDecAlz Detect for global scalability from day one. The company's deployment model combines strategic licensing to diagnostic providers, regional manufacturing partnerships, and alignment with international regulatory frameworks, including CE-IVD, FDA, and WHO prequalification pathways.

Pharmacoidea’s roadmap aligns scientific innovation with equitable access, and that ensures that affordability and supply resilience are central to its strategy. This approach reflects the growing global expectation that diagnostic breakthroughs must be socially as well as technically scalable.

The Road Ahead: Validation, Regulation, and Growth

The company is now moving toward large-scale, multicenter validation across Europe in collaboration with pharmaceutical and academic partners. As Letoha puts it, "We're a small biotech, but we're involved in major pharmaceutical collaborations—collecting blood samples across Europe to enable multicentre clinical studies. Once that’s complete, we’ll apply for regulatory approval. And then, the sky is the limit.”

If successful, SynDecAlz Detect could become the first affordable, globally scalable blood test for Alzheimer's, transforming patient stratification, clinical trial recruitment, and early intervention worldwide.

A New Era in Alzheimer’s Diagnosis

SynDecAlz Detect could represent a strategic inflection point in how healthcare systems will identify, treat, and manage neurodegenerative disease. By lowering costs and broadening access, it has the potential to redefine how early-stage Alzheimer’s is detected, bringing advanced diagnostics from elite clinics to everyday healthcare.

[1] https://doi.org/10.3390/ijms26146587
[2] An AUC of 0.85 means that if you randomly picked one person with Alzheimer's and one person without it, this combination of measurements would correctly identify which person has the disease about 85% of the time. That's considered "high diagnostic accuracy" - good enough to be clinically useful.
[3] Enzyme-Linked Immunosorbent Assay - a widely used laboratory technique for detecting and measuring specific proteins, antibodies, or other substances in blood or other samples.

"Talking to our customers is a competitive advantage... We're trying to do the work faster but not necessarily eliminate people because people are the gold dust here. It's relationships that matter."

This perspective represents a significant evolution in thinking about AI implementation. Rather than pursuing complete automation, leading organizations are focusing on using AI to handle routine tasks while empowering humans to make critical decisions and maintain customer relationships.

Data-Driven Customer Profiling and Engagement

AI is revolutionizing how pharmaceutical companies understand and engage with healthcare professionals. Berkan Aysan, Global Head of Medical Engagement Excellence at Merck, described their approach: "We came up with a three-step framework. First, we make sure that the data we are giving into is right. Then through different poly data sources, we take the insights ourselves, we mine it. Third, we take action."

This methodical approach ensures AI-driven engagement remains relevant and valuable. Companies are increasingly able to create comprehensive HCP profiles that consider multiple factors, resulting in more personalized interactions.

Nataliya Andreychuk, CEO of Viseven, highlighted how AI is connecting previously siloed data: "The beauty is because the technology is generative, it is growing. The more we work in that direction, the more accurate mechanism we will have in place." This evolution enables increasingly sophisticated personalization while maintaining compliance.

Streamlining Operations Through Predictive Intelligence

One of the most concrete applications of AI involves optimizing supply chain and forecasting capabilities. At Pfizer, which ships approximately 1.4 billion product packs annually across 200 countries to 130,000 customers, AI has transformed operations.

"We have about 16,000 SKUs that we have to forecast. We have about 700 brands we sell, and at this point in time, about 75%—about 12,000 of 16,000—we are now forecasting using AI,"

explained Riordan. More importantly, "The AI forecasts are better than the human forecasts," resulting in fewer manufacturing disruptions and improved on-time delivery.

This operational excellence directly impacts customer satisfaction, as Riordan noted their customer experience metrics show that "the number one key differentiator in terms of net promoter score in CSAT is the metric on time in full."

The Critical Role of Data Stewardship

All panelists emphasized that effective AI implementation requires robust data governance. Pfizer's "System Care" process exemplifies this approach, defining "which data objects are critical to decision making within the process" and implementing "an annual review process on that data object."

This foundation enables what Riordan described as the "wisdom lifecycle"—converting "data into information, information into knowledge, and then knowledge into action." While AI excels at the first conversion, humans remain essential for the critical step from knowledge to action.

Andreychuk echoed this sentiment regarding content compliance: "The human is in the driver's seat. But being in the driver's seat doesn't mean that we do not need a very experienced assistant." She described how AI can streamline MLR (Medical, Legal, Regulatory) processes by checking references, regulations, and previous approvals, allowing humans to focus their attention on areas requiring critical judgment.

Bridging Silos Through Integrated Intelligence

AI is helping pharmaceutical companies discover unexpected synergies across traditionally separate business areas. Aysan shared how at Merck, AI has revealed connections across their diverse portfolio: "Many of these patients are under polypharmacy. By using AI and different data sources, we realize that there are a lot of synergies within our portfolio."

This integration enables more holistic approaches to patient care and HCP engagement, aligning with the industry's move toward more patient-centered models.

The Future of Pharmaceutical Engagement

As AI tools continue to evolve, pharmaceutical leaders are rethinking fundamental aspects of customer engagement. Andreychuk challenged conventional thinking: "Let us imagine the email of the future. Will this email have interactive elements of communication? Questions, answers? Maybe this will be a future email we will be crafting."

Aysan emphasized that AI should ultimately enhance the most important healthcare relationship: "As a medical doctor myself, we lack soft skills speaking with patients. As we know, patients are strong emerging stakeholders in the industry, and we are not talking about the communication between HCPs and patients enough. We as pharma have the responsibility to enhance that communication as well."

The panel concluded with Andreychuk's reminder about maintaining human direction: "The content needs to be relevant. This is what is important. And relevancy of our communication is driven by us, humans... We need just to make sure we are in the driver's seat."

Key Takeaways:

1) Human-AI collaboration delivers the best results: "People are the gold dust here. It's relationships that matter," emphasized Riordan, highlighting that AI should enhance rather than replace human interactions.
2) Data quality determines AI effectiveness: Proper data stewardship and governance are essential foundations for any AI implementation, requiring systematic processes to maintain data integrity.
3) AI excels at forecasting and operations: "The AI forecasts are better than the human forecasts," noted Riordan, demonstrating how AI can deliver measurable improvements in supply chain management.
4) Customer-centricity requires both operational metrics and experience measurement: Organizations must combine internal performance indicators with direct customer feedback to understand the full picture.
5) Content relevance remains paramount: "The content needs to be relevant. This is what is important. And relevancy of our communication is driven by us, humans," reminded Andreychuk, emphasizing that AI tools must ultimately serve human objectives.

In order to get you the highlights of Pharma 25 faster, we are using generative AI technology to summarise the transcripts of the sessions.  If you have any feedback about the summary please contact lucy.fisher@thomsonreuters.com.

 "It's all about people. That's our biggest barrier to success and our key to success," Fenoglio emphasized. "You have to surround the entire program with culture and capability to change mindsets and behaviors."

Sustainable Implementation Strategies

As organizations look to embed AI sustainability in their strategy, panelists offered clear guidance: "Start now. If you haven't started now, you're already behind," urged Fenoglio. "As leaders, we need to role model the behavior and encourage experimentation and innovation in our teams, create psychological safety, make sure teams can experiment in a safe way to minimize risk."

Ferraro emphasized the importance of measurable outcomes: "You need to be really thoughtful about the challenges you're taking on and ultimately tie it back to business value first. Then make these things measurable."

DeYoung stressed the foundational importance of data infrastructure: "AI requires modern interoperable, real-time, trusted data to be able to move from isolated insights to enterprise value. We need to move from custodians of data to connectors of data so data sets can flow across functions and departments."

Key takeaways:

·      Scale beyond pilots: Most GenAI strategies lack cross-unit integration and transformative impact.

·      Focus on business problems: Target specific challenges where AI delivers measurable value.

·      Prepare for agentic AI: It will reduce operational friction, enhancing agility and personalization.

·      Fix data foundations: Modern, interoperable data systems are essential for enterprise-wide AI value.

·      Build human capabilities: Surround technology with culture that changes mindsets and behaviours.

In order to get you the highlights of Pharma EU faster, we are using generative AI technology to summarise the transcripts of the sessions. If you have any feedback about the summary and the post-event report, please contact lucy.fisher@thomsonreuters.com. 

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