CYNAERA Patient Stratification Capacity

Modeling 675 Million+ Unique Recovery Paths in IACCs

Overview

CYNAERA has the first clinical intelligence system designed to simulate and stratify patient response across the full complexity of infection-associated chronic conditions (IACCs) such as ME/CFS, Long COVID, POTS, MCAS, EDS, Lyme, CRPS, and related syndromes. Unlike traditional models that flatten patients into crude categories, CYNAERA builds individualized profiles across 11 stratification axes and integrates them with six mechanistic phenotyping domains.

This dual-layer approach enables 675 million+ unique recovery path combinations, providing unmatched granularity for clinical trial design, flare-prevention protocols, and individualized care forecasting.

“Precision care demands massive granularity. What once looked like outliers were simply profiles no one had the system to stratify.”

What Defines a Patient Profile?

Each patient profile in CYNAERA is constructed by combining critical dimensions of flare behavior, physiological risk, demographic context, and environmental exposure.

The 11 stratification axes include:

1. Age group

2. Sex

3. Core phenotype

4. Sensitivity tier

5. Primary trigger cluster

6. Comorbid profile (IACCI-focused)

7. Health access modifier

8. Cycle or hormone phase

9. Geographic/environmental risk

10. Autoimmune status

11. Duration of illness

Stratification is about who the patient is and the context they live in — from their age and comorbidities to their housing stability and environmental exposures.

By contrast, phenotyping is about what is disrupted biologically and how the illness behaves.

CYNAERA maps six core mechanistic axes:

1. Mitochondrial support and energy metabolism

2. Neuroimmune modulation

3. Autonomic regulation

4. Viral persistence/reactivation control

5. Immune reset pathways

6. Oxygenation and recovery

This separation between stratification and phenotyping allows CYNAERA to model both terrain and mechanism in a way that mirrors patient reality.

Geographic and Environmental Risk

A defining feature of CYNAERA’s stratification model is its integration of environmental exposures. The engine captures how geography and climate amplify symptom severity and flare timing. Profiles include urban air pollution, mold-prone rural regions, wildfire zones, high pollen areas, high humidity, coastal salt air, high altitude, and low-AQI protective environments.

This allows CYNAERA to forecast not just who is most at risk, but where and when destabilization is most likely — an essential feature in a world where climate events increasingly drive chronic illness flare cycles.

Why It Matters

Most existing clinical systems stratify patients on age, sex, and a primary diagnosis. That leaves out the core drivers of real-world flare risk:

• Comorbidity stacking (e.g., ME/CFS + POTS + MCAS).

• Environmental reactivity (e.g., wildfire smoke triggering PEM crashes).

• Hormonal phase impacts (e.g., menstrual cycle-linked flare shifts).

• Sensory and autonomic hypersensitivity (e.g., noise, light, temperature).

CYNAERA integrates all of these into a trial-ready, NAM-compliant model (non-animal methodology, FDA 2023 guidance), positioning it as a bridge between research and patient-centered care.

FDA Modernization Act and Therapeutic Gaps

The FDA Modernization Act of 2022 (21st Century Cures 2.0) signaled a shift away from mandatory animal testing in drug development, encouraging the use of advanced modeling and simulation to accelerate clinical translation. CYNAERA directly aligns with this new regulatory pathway by offering high-fidelity, non-animal, multi-axis simulations that capture patient heterogeneity without exposing fragile populations to early-phase harms.

This is particularly urgent for IACCs. As of 2025, there are no FDA-approved treatments for ME/CFS, POTS, MCAS, or Long COVID (Komaroff & Bateman, 2023; CDC, 2023). Clinical trials for these conditions are sparse, often underpowered, and prone to collapse due to dropout or mis-stratification. CYNAERA provides the in silico safety net these fields have lacked, enabling trial arms to be stress-tested, patient dropouts to be modeled, and flare-risk overlays to be applied before a single human is enrolled.

By embedding FDA-aligned methodologies into trial design, CYNAERA lowers risk for sponsors while opening the door to long-overdue therapeutic discovery for IACCs.

Use Cases

• Precision trial design: build arms that reflect real-world heterogeneity.

• Pre-screening for safety: stratify patients likely to destabilize in standard protocols.

• Adaptive IRB overlays: flag high-risk participants and model flare-buffering interventions.

• Licensing for pharma, AI, and public health initiatives: provide modular data engines to support everything from drug discovery to climate-flare mitigation.

CYNAERA’s Quantified Edge

• 675M+ recovery path profiles, compared to <1,000 in traditional systems.

• 11 stratification axes, layered with 6 phenotyping axes.

• Five proprietary engines integrated into one platform:

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  • SymCas™ (flare forecasting),

  • VitalGuard™ (environmental overlay),

  • NeuroVerse™ (neuroimmune mapping),

  • Pathos™ (severity scoring),

  • Individualized Medicine Engine™ (treatment stack simulation).

This is not just more data. It is a structural leap in how clinical intelligence recognizes patient heterogeneity.

Conclusion

IACCs remain among the most complex and under-served conditions in medicine. Millions of patients are left without FDA-approved therapies, while research stalls under the weight of poor trial design and crude stratification tools. CYNAERA changes that equation.

By modeling 675 million+ individualized recovery paths, it exposes the hidden terrain of chronic illness, allowing researchers to see not averages, but lived-experience precision. By separating who patients are (stratification) from what systems are disrupted (phenotyping), CYNAERA provides both the breadth and the depth required for transformative clinical science.

As regulatory frameworks shift under the FDA Modernization Act, CYNAERA stands as a ready alternative: an ethical, NAM-compliant, simulation-first methodology for drug discovery and patient safety.

This is more than innovation. It is the foundation for the first generation of trials that will finally deliver safe, effective therapies for ME/CFS, Long COVID, POTS, MCAS, and related conditions.

References

1. Addis, M. E., & Mahalik, J. R. (2003). Men, masculinity, and the contexts of help seeking. American Psychologist, 58(1), 5–14.

2. Carruthers, B. M., et al. (2011). Myalgic encephalomyelitis: International Consensus Criteria. Journal of Internal Medicine, 270(4), 327–338.

3. Centers for Disease Control and Prevention (CDC). (2023). Long COVID prevalence and characteristics.

4. Courtenay, W. H. (2000). Constructions of masculinity and their influence on men's well-being: a theory of gender and health. Social Science & Medicine, 50(10), 1385–1401.

5. Komaroff, A. L., & Bateman, L. (2023). Will COVID-19 lead to ME/CFS? Frontiers in Medicine, 9, 877.

6. Nacul, L., et al. (2011). The functional status and well being of people with ME/CFS. BMC Public Health, 11(1), 402.

7. U.S. Food and Drug Administration (FDA). (2023). FDA Modernization Act 2.0 guidance.

8. World Health Organization (WHO). (2023). Global Burden of Disease database.

Author's Note:

All insights, frameworks, and recommendations in this white paper reflect the author's independent analysis and synthesis. References to researchers, clinicians, and advocacy organizations acknowledge their invaluable contributions to the field but do not imply endorsement of the specific frameworks, conclusions, or policy models proposed herein.

Applied Infrastructure Models Supporting This Analysis

Several standardized diagnostic and forecasting models developed through CYNAERA were utilized or referenced in the construction of this white paper. These tools support real-time surveillance, economic forecasting, and symptom stabilization planning for infection-associated chronic conditions (IACCs).

Note: These models were developed to bridge critical infrastructure gaps in early diagnosis, stabilization tracking, and economic impact modeling. Select academic and public health partnerships may access these modules under non-commercial terms to accelerate independent research and system modernization efforts.

Licensing and Customization

Enterprise, institutional, and EHR/API integrations are available through CYNAERA Market for organizations seeking to license, customize, or scale CYNAERA's predictive systems.

Learn More : https://www.cynaera.com/systems

About the Author 

Cynthia Adinig is an internationally recognized systems strategist, health policy advisor, and the founder of CYNAERA, an AI-powered intelligence platform advancing diagnostic reform, clinical trial simulation, and real-world modeling for infection-associated chronic conditions (IACCs). She has developed 400+ Core AI Frameworks, 1 Billion + Dynamic AI Modules. including the IACC Progression Continuum™, US-CCUC™, and RAEMI™, which reveal hidden prevalence, map disease pathways, and close gaps in access to early diagnosis and treatment.

Her clinical trial simulator, powered by over 675 million synthesized individual profiles, offers unmatched modeling of intervention outcomes for researchers and clinicians.

Cynthia has served as a trusted advisor to the US Department of Health and Human Services, coauthored research alongside experts at Yale and Mount Sinai, and influenced multiple pieces of federal legislation related to Long COVID and chronic illness. 

She has been featured in TIME, Bloomberg, USA Today, and other leading publications.

Through CYNAERA, she is building the algorithmic infrastructure that will define chronic illness care, public health resilience, and precision research for the decades ahead.