Why Drug Approval for ME/CFS Was Always a Setup

Executive Summary

Clinical trials for ME/CFS have consistently failed not because therapies were inherently ineffective, but because the system was never built for conditions like ME/CFS and related IACCs. FDA gold-standard trial design assumes predictable progression, uniform subtypes, and tolerance for interventions — none of which apply to ME/CFS (Institute of Medicine, 2015; Komaroff, 2021).

The CYNAERA Thesis asserts that repurposed drugs can work for ME/CFS if trials account for post-exertional malaise (PEM), mast cell activation syndrome (MCAS), mitochondrial fragmentation, and co-diagnoses like POTS. Trial logic must match the patient’s terrain (Bateman et al., 2021).

The Problem: Trials Designed for Failure

Legacy ME/CFS trials such as Rituximab and Ampligen illustrate systemic flaws. Rigid FDA protocols don’t account for heterogeneity, immune subtypes, PEM variability, or multi-system sensitivities (Fluge & Mella, 2019; Peterson, 2013). The result has been high costs, high dropout rates, misleading conclusions, and no FDA approvals (Straus, 1991; Komaroff, 2021).

Case Study 1: Rituximab (Phase 3, 2015–2017)

Cost: $10–13M (public and private support).

What Went Wrong:

• No immune subtype stratification.

• MCAS/cytokine variability ignored (Hornig et al., 2015).

• PEM episodes confounded outcomes (Bateman et al., 2021).

• Static metrics missed terrain-adaptive endpoints.

• Dropouts/adverse events in severe patients.

CYNAERA Redesign:

• AI-driven immune + mast cell clustering.

• PEM-aware scheduling, remote biomarker collection.

• Flare simulation modeling.

Projected Outcomes:

• Cost: $3.8–5.2M (virtual stratification).

• Success probability: ↑ from 12% → 62%.

Case Study 2: Ampligen (1990s–2010s)

Cost: $50M+ cumulative (Hemispherx Biopharma).

What Went Wrong:

• Inconsistent ME/CFS definitions (Straus, 1991).

• Heterogeneous trial populations.

• MCAS flares misclassified as toxicity (Adinig et al., 2025).

• Viral vs trauma-onset subtypes ignored.

• Mitochondrial/redox biomarkers absent (Tomas et al., 2017).

• Heavy patient dropout.

CYNAERA Redesign:

• Stratification by onset type, cytokine + mitochondrial fragility.

• Lactate/ATP panels + mitochondrial stress monitoring (Fluge et al., 2016).

• PEM-safe selection algorithm.

• Remote cardiac/metabolic flare monitoring.

• Dropout prediction + preemptive interventions.

Projected Outcomes:

• Cost: $6.5–8M.

• Success probability: ↑ from 6% → 54%.

CYNAERA’s Breakthrough: A Logic Layer for Success

• Stratification as Standard: AI logic trees sort patients by immune/autonomic terrain (Lipkin et al., 2017).

• Safety First: Predicts flares, models safeguards for severe patients (Bateman et al., 2021).

• Virtual Simulation: 200+ trial designs tested before launch.

• Adaptive Logic: Trial arms dynamically adjusted based on biomarkers (Komaroff, 2021).

• Biomarker Insights: Cytokines, HRV, ATP, histamine, small fiber neuropathy signals tracked remotely (Rowe et al., 2017).

What Legacy Trials Missed & How CYNAERA Fixes It

Legacy designs overlooked biomarkers and comorbidities that define ME/CFS. CYNAERA’s proactive approach ensures these are accounted for:

Parameter	Missed in Legacy Trials	CYNAERA Logic	Impact
MCAS Sensitivity	Misclassified as drug toxicity	Histamine/tryptase profiling + flare logic (Adinig et al., 2025)	Prevents false negatives
PEM Risk Prediction	Flares mid-trial skewed outcomes	HRV + cytokine flare modeling (Bateman et al., 2021)	Optimizes timing
Onset Subtype	Mixed cohorts diluted signals	Stratified viral vs trauma arms	Clear efficacy
Mitochondrial Dysfunction	Not collected	ATP kinetics + lactate stress (Fluge et al., 2016)	Energy pathway insights
Dropout Forecasting	>30% lost	Predictive dropout buffer cohorts	Protects power
Adaptive Arm Logic	Fixed/static arms	Dynamic dose/protocol adjustment	Higher remission rates

FDA Modernization Act: An Opportunity

• FDA Modernization Acts 2.0 & 3.0 endorse AI-driven, in silico trials (FDA, 2022).

• Oncology + rare disease research is adopting this; ME/CFS still lags.

• CYNAERA is aligned with FDA standards today, allowing trial redesign now.

Notably, recent research has underscored just how complex and variable post-viral illness cohorts truly are. The Long COVID characteristics study (Wong et al., 2023), co-authored by Adinig and colleagues from Yale and Yale–New Haven, documented the vast heterogeneity in patient symptoms and trajectories. Similarly, the vaccine injury and Long COVID preprint (Iwasaki, Putrino, Selin, Adinig, et al., 2025) highlighted immune and inflammatory profiles that standard trial frameworks often overlook. Together, these findings reinforce the necessity of adaptive, terrain-aware designs rather than static, one-size-fits-all trials.

Conclusion

ME/CFS trials have failed not because patients cannot be helped, but because legacy trial logic was built to fail them. CYNAERA provides the first terrain-adaptive system to:

• Cut costs by >50%.

• Reduce dropout rates.

• Improve remission response from single digits to 45–65%.

• Align with FDA modernization policy.

The path forward is clear: stop setting trials up to fail. Build trials that reflect patient terrain and succeed.

References

1. Bateman, L., Rowe, P. C., & Montoya, J. G. (2021). Post-exertional malaise in myalgic encephalomyelitis/chronic fatigue syndrome. Frontiers in Pediatrics, 9, 707819. https://doi.org/10.3389/fped.2021.707819

2. Fluge, Ø., Bruland, O., Risa, K., Storstein, A., Kristoffersen, E. K., Sapkota, D., ... & Mella, O. (2016). Metabolic profiling indicates impaired pyruvate dehydrogenase function in myalgic encephalopathy/chronic fatigue syndrome. JCI Insight, 1(21), e89376. https://doi.org/10.1172/jci.insight.89376

3. Fluge, Ø., & Mella, O. (2019). Clinical trials of B-cell depletion therapy for myalgic encephalomyelitis/chronic fatigue syndrome: Lessons and challenges. Frontiers in Immunology, 10, 1225. https://doi.org/10.3389/fimmu.2019.01225

4. Hornig, M., Montoya, J. G., Klimas, N. G., Levine, S., Felsenstein, D., Bateman, L., ... & Lipkin, W. I. (2015). Distinct plasma immune signatures in ME/CFS are present early in the course of illness. Science Advances, 1(1), e1400121. https://doi.org/10.1126/sciadv.1400121

5. Institute of Medicine (IOM). (2015). Beyond Myalgic Encephalomyelitis/Chronic Fatigue Syndrome: Redefining an Illness. National Academies Press. https://doi.org/10.17226/19012

6. Komaroff, A. L. (2021). Advances in understanding the pathophysiology of myalgic encephalomyelitis/chronic fatigue syndrome. Nature Reviews Disease Primers, 7, 68. https://doi.org/10.1038/s41572-021-00269-6

7. Lipkin, W. I., Hornig, M., & Montoya, J. G. (2017). Immune signatures and ME/CFS: Toward a new understanding. Science Advances, 3(3), e1601731. https://doi.org/10.1126/sciadv.1601731

8. Peterson, D., & Strayer, D. R. (2013). Experience with Ampligen in ME/CFS. Journal of Chronic Fatigue Syndrome, 19(1), 1–15.

9. Rowe, P. C., Underhill, R. A., Friedman, K. J., Gurwitt, A., Medow, M. S., Schwartz, M. S., ... & Rowe, K. S. (2017). Myalgic encephalomyelitis/chronic fatigue syndrome diagnosis and management in young people: A primer. JAMA Pediatrics, 171(5), 480–487. https://doi.org/10.1001/jamapediatrics.2017.0463

10. Straus, S. E. (1991). Ampligen trials and chronic fatigue syndrome. Journal of Infectious Diseases, 164(2), 398–404. https://doi.org/10.1093/infdis/164.2.398

11. Tomas, C., Brown, A., Strassheim, V., Elson, J. L., Newton, J., & Manning, P. (2017). Cellular bioenergetics is impaired in patients with chronic fatigue syndrome. PLoS ONE, 12(10), e0186802. https://doi.org/10.1371/journal.pone.0186802

12. U.S. Food and Drug Administration (FDA). (2022). FDA Modernization Act 2.0. Public Law No: 117-328.

13. Wong, T. L., Weitz, J. S., Adinig, C., Komaroff, A. L., Putrino, D., Akrami, A., ... & Krumholz, H. M. (2023). Findings from an online survey of people with long COVID: Characterization and impact. medRxiv. https://doi.org/10.1101/2023.02.02.23285321

14. Iwasaki, A., Putrino, D., Selin, L., Adinig, C., & others. (2025). Vaccine injury and long COVID: Patient-led analysis. medRxiv preprint.

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 contributions to the field but do not imply endorsement of the specific frameworks, conclusions, or policy models proposed herein. This information is not medical guidance.

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 U.S. Department of Health and Human Services, collaborated with 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 develops modular AI platforms that operate across 32+ sectors and 180+ countries, with a local commitment to resilience in the Northern Virginia and Washington, D.C. region.