17 Personalized Therapeutics Statistics That Prove Why Rare Disease Patients Need AI-Powered Treatment Platforms
Comprehensive data revealing the treatment gap facing 300 million rare disease patients and how precision medicine powered by artificial intelligence is closing it
Key Takeaways
The rare disease treatment gap remains massive – An estimated 3.5-5.9% of the global population—roughly 300 million people—live with a rare disease, yet more than 90% of rare diseases have no FDA-approved treatment under traditional pharmaceutical development models
AI transforms research timelines from impossible to actionable – AI-assisted literature and database review accelerates evidence synthesis for ultra-rare genetic variants, enabling personalized therapeutic development at scales previously unachievable through manual processes
Economic viability depends on dramatic cost reduction – Reducing development and coordination costs substantially improves affordability for healthcare payors, making personalized therapies accessible as standard of care rather than rare exceptions
Pharmacogenomic testing reveals actionable insights for nearly everyone – The vast majority of individuals (often >90%) carry at least one pharmacogenetic variant that could influence medication response, making precision medicine relevant far beyond cancer treatment
Genomic sequencing costs have plummeted 99.999% – Whole genome sequencing costs decreased from $100 million in 2001 to less than $600 in 2024, removing the primary technical barrier to widespread personalized medicine adoption
Traditional pharma leaves ultra-rare populations behind – For ultra-rare conditions with very small patient populations, traditional commercial models often do not prioritize development despite scientific feasibility, creating operational and funding barriers for families seeking treatments
Software scalability solves the operational complexity problem – Coordinating geneticists, research labs, manufacturers, regulators, and providers for thousands of patients simultaneously requires AI orchestration capabilities that improve with each therapy developed
Evidence synthesis from multiple sources guides rare disease treatment – Peer-reviewed studies, registries, case reports, and mechanistic data synthesized for individual patients provide the evidentiary foundation when traditional clinical trial data doesn't exist
The Global Rare Disease Patient Population Crisis
1. 300 million people live with rare diseases—more than 90% without FDA-approved treatments
An estimated 3.5-5.9% of the global population—roughly 300 million people—live with a rare disease. This population exceeds the number of people living with cancer worldwide by approximately six times. About half of rare disease patients are children, and many face progressive conditions. More than 90% of rare diseases have no FDA-approved treatment because traditional pharmaceutical companies find development economically unattractive for small patient populations. This massive treatment gap exists not because science lacks solutions, but because operational and financial barriers prevent therapies from reaching patients. Nome's mission directly addresses this crisis by building a world where every patient has treatment access, no matter how rare their disorder. Source: Nature
2. Rare diseases are a major contributor to childhood morbidity and mortality
About half of rare disease patients are children, and rare diseases are a major contributor to childhood morbidity and mortality, particularly given that many are genetic and present in childhood. These young patients and their families often receive devastating news: "There's nothing we can do." For ultra-rare conditions with very small patient populations, traditional commercial models often do not prioritize development despite scientific feasibility. Patient-founded platforms understand both the urgency families face and the innovation required to solve custom drug development challenges. Nome's team includes specialists who personally understand rare disease realities, driving the determination to ensure no family hears "you have no options" without exploring every possible therapeutic avenue. Source: EURORDIS
AI Acceleration: Transforming Research Timelines
3. AI-assisted evidence synthesis accelerates rare variant analysis
Traditional literature review for a single genetic mutation requires months of expert time searching through fragmented databases, contradictory case reports, and evolving mechanistic understanding. AI-assisted literature and database review can accelerate evidence synthesis for ultra-rare genetic variants, synthesizing evidence that would take human researchers significantly longer to compile. This acceleration fundamentally changes what's possible for ultra-rare conditions where every delay represents irreversible disease progression. AI-generated reports, validated by PhDs with deep rare disease expertise, provide families with actionable intelligence about whether personalized therapies could change their disease trajectory—replacing years of collecting conflicting opinions and chasing dead ends with clear roadmaps. Source: NIH
4. Multiple individualized therapies demonstrate n-of-1 treatment feasibility
Beyond theoretical promise, multiple individualized therapies (such as n-of-1 antisense oligonucleotides) have been reported in the past decade by leading clinicians, families, and nonprofits. These cases prove that custom therapeutic development is scientifically feasible when operational barriers are overcome. The milasen case—an individualized ASO therapy for a single child with Batten disease—demonstrated that personalized genetic medicine can move from concept to patient in under a year when coordination, expertise, and resources align. Each successful therapy provides a roadmap for the next, building institutional knowledge about regulatory pathways, manufacturing requirements, and clinical protocols that make subsequent treatments faster and more accessible. Source: NJEM
Economic Viability: The Cost Reduction Imperative
5. Precision medicine programs demonstrate healthcare system cost savings
Healthcare systems implementing precision medicine programs report measurable cost savings through improved drug selection and reduced adverse events. These savings stem from eliminating ineffective treatments, avoiding hospitalizations due to drug toxicity, and reducing trial-and-error prescribing cycles. For rare disease patients, precision approaches prevent families from spending years pursuing interventions unlikely to work for their specific mutations. Nome's mission explicitly targets bringing personalized treatment to every patient by driving costs down dramatically while maintaining quality—recognizing that economic viability for healthcare payors represents the threshold where experimental therapies shift to standard of care. Source: Core Prescribing Solutions
6. Reducing development costs improves payer adoption substantially
The transition from experimental to standard of care depends on economic accessibility. Reducing development and coordination costs substantially improves affordability and payer adoption; the threshold varies by context, therapy type, and healthcare system. This cost reduction doesn't require scientific breakthroughs—it requires operational excellence in coordinating the fragmented stakeholders involved in custom drug development. As Nome's platform orchestrates more therapies, it gets smarter about optimal approaches for each patient type, reliable manufacturing partners, and process efficiency opportunities. This virtuous cycle—more therapies making software smarter, which makes therapies more efficient, which enables more therapies—represents the scaling solution that only AI-powered platforms can deliver. Source: NIH
Pharmacogenomics: The Foundation Affecting Most People
7. Most people carry pharmacogenetic variants affecting drug response
The vast majority of individuals (often >90%) carry at least one pharmacogenetic variant that could influence medication response, per CPIC/DPWG-guided panels. This statistic transforms precision medicine from a niche oncology application to a universal healthcare consideration. Genetic variations in drug metabolism can cause standard doses to be toxic for some patients while ineffective for others. Poor metabolizers of CYP2D6 experience increased toxicity from codeine and reduced efficacy from tamoxifen. CYP2C19 poor metabolizers show dramatically reduced effectiveness of clopidogrel, a critical cardiac medication. Nome's platform brings pharmacogenomic intelligence to treatment planning, ensuring therapies match individual genetic profiles rather than relying on population averages that fail most patients. Source: NIH
8. Whole genome sequencing costs dropped from $100 million to under $600
The cost of whole genome sequencing plummeted from $100 million in 2001 to less than $600 in 2024—a 99.999% reduction removing the primary technical barrier to widespread genetic analysis. This dramatic cost collapse means genetic information can guide treatment decisions for virtually all patients rather than remaining accessible only to research participants or wealthy individuals. The bottleneck shifted from data generation, which is now straightforward and affordable, to data interpretation—translating genomic information into clear clinical decisions. Healthcare providers using Nome's platform receive not just raw genetic data but actionable intelligence: prioritized treatment options with mechanism-level rationale, feasibility assessments, and execution-ready next steps. Source: DataHub
9. Over 300 drugs now include pharmacogenomic information in FDA labeling
The FDA has approved over 300 drugs with pharmacogenomic information in their labeling as of 2024, establishing regulatory recognition that genetic variants significantly affect medication safety and effectiveness. This represents formal acknowledgment that one-size-fits-all dosing and drug selection fails to serve patient populations adequately. However, pharmacogenomic testing remains dramatically underutilized in clinical practice due to workflow integration challenges, interpretation complexity, and lack of actionable guidance systems. Platforms delivering concise Provider Briefs with genetic test result interpretation, drug interaction warnings based on metabolizer status, and alternative therapy suggestions when first-line options show poor genetic compatibility address this implementation gap. Source: FDA
Targeted Therapy Success: Precision Oncology and Beyond
10. Genomically-matched cancer therapies show improved response rates
Cancer patients receiving genomically-matched targeted therapies in precision oncology trials demonstrate improved response rates compared to unmatched treatments. However, estimates suggest roughly 8-15% of cancer patients currently benefit from genome-driven therapies with FDA approval, with more eligible for trials or off-label use. For ultra-rare conditions, the gap is even more pronounced—FDA-approved therapies rarely exist, but individualized treatments developed by leading clinicians, families, and nonprofits prove that custom therapeutic development is scientifically feasible when operational barriers are overcome. Source: ScienceDirect
11. Pharmacogenomic testing reduces adverse drug events by approximately 30%
Clinical implementation of preemptive pharmacogenomic testing reduces clinically relevant adverse drug events by approximately 30% in randomized controlled trials. In the United States, adverse drug events lead to >1.3 million emergency department visits and approximately 350,000 hospitalizations annually, with many potentially preventable through genetic testing identifying patients at high risk before drug administration. For rare disease patients often taking multiple experimental therapies simultaneously, this safety intelligence becomes even more critical. HIPAA-compliant platforms that integrate pharmacogenomic data with treatment recommendations help clinicians avoid predictable toxicities while selecting therapies most likely to work for individual genetic profiles. Source: British Journal of Clinical Pharmacology
Why Traditional Pharma Leaves Ultra-Rare Diseases Behind
12. Commercial models often don't prioritize ultra-rare condition development
Traditional pharmaceutical companies require patient populations large enough to generate returns justifying development investments. For ultra-rare conditions with very small patient populations, traditional commercial models often do not prioritize development despite scientific feasibility. This creates a systematic gap where families with ultra-rare conditions must fund development themselves, often through 501(c)3 foundations, despite lacking expertise in drug development, regulatory processes, or manufacturing coordination. Nome's origin story reflects this reality: a founder diagnosed with a progressive retinal disease affecting fewer than 20 people nationally who had to build therapeutic development infrastructure because traditional pharma offered no pathway forward. This personal understanding drives the company's determination to make personalized therapeutics accessible to every family facing similar abandonment. Source: Orphanet Journal of Rare Diseases
13. Labs and manufacturers prioritize large contracts over individual patients
Research labs and contract manufacturers built their operations around large pharmaceutical company contracts, not individual patients seeking experimental therapies. Minimum order quantities, contract structures, regulatory documentation requirements, and billing systems all assume commercial-scale production runs. An individual family requesting a custom antisense oligonucleotide or gene therapy for their child faces operational barriers at every step—not because the science is impossible, but because the infrastructure wasn't designed for N=1 scenarios. AI-powered platforms solve this by aggregating demand across hundreds of rare conditions, negotiating master service agreements with manufacturers, standardizing regulatory documentation, and presenting individual patient requests through systems compatible with existing laboratory workflows. This operational translation makes the previously inaccessible routine. Source: NIH
Software Scalability: Managing Thousands of Therapies Simultaneously
14. Manual coordination of personalized therapies scales poorly
Coordinating geneticists, research labs, manufacturers, regulators, and providers for even a single personalized therapy requires months of specialized effort. Scaling this to thousands of patients simultaneously through manual processes faces fundamental limitations—the coordination complexity grows exponentially while human capacity remains linear. Only software can solve this scaling problem. AI orchestration manages all variables simultaneously: which genetic variants respond to which therapeutic modalities, which laboratories have capacity and expertise for specific approaches, which regulatory pathways apply to different therapy types, and which providers can deliver treatments within institutional review board frameworks. The platform learns from each therapy coordinated, building intelligence about reliability patterns, timeline predictors, and success factors that improve outcomes for all subsequent patients. Source: Cell and Gene Therapy Review
15. Platform intelligence improves with each therapy coordinated
As Nome's system orchestrates more therapies, it accumulates intelligence impossible to maintain manually. Which contract manufacturers consistently deliver on time versus those requiring extra lead time? Which genetic counselors provide excellent patient communication? Which regulatory pathways move fastest for specific therapy categories? Which backup options prove viable when first-choice approaches face delays? This accumulated intelligence creates compound advantages: each therapy becomes faster and more accessible than the previous one because the system eliminates known friction points, prioritizes proven partners, and anticipates common obstacles before they cause delays. More therapies make the software smarter, which makes therapies more efficient, which enables more therapies—exactly the virtuous cycle required to make personalized medicine universally accessible rather than available only to those with exceptional resources or connections. Source: NIH
Regulatory and Clinical Integration
16. CYP2D6 and CYP2C19 variants significantly affect common medications
Specific pharmacogenetic variants have well-documented clinical implications. CYP2D6 phenotype influences response and toxicity for codeine and tamoxifen, with poor metabolizers experiencing increased codeine toxicity and reduced tamoxifen efficacy. CYP2C19 phenotype influences clopidogrel effectiveness, a critical cardiac medication where poor metabolizers show dramatically reduced therapeutic benefit. These examples demonstrate how pharmacogenomic information transforms abstract genetic data into concrete clinical decision-making, preventing adverse events and treatment failures. Implementation of routine pharmacogenomic testing before prescribing medications with known genetic interactions represents a straightforward intervention with substantial patient safety and efficacy benefits. Source: NIH
17. Evidence synthesis from multiple sources guides rare disease treatment
For ultra-rare diseases, traditional randomized controlled trial data rarely exists. Instead, evidence synthesis approaches combining peer-reviewed studies, patient registries, case reports, and mechanistic data provide the evidentiary foundation for treatment decisions. AI-powered platforms excel at aggregating and weighting diverse evidence sources—identifying relevant genetic variants in model organisms, extrapolating from related conditions with better-characterized pathways, and synthesizing case reports from global registries. This comprehensive evidence synthesis, validated by domain experts, enables families and clinicians to make informed decisions even when the specific genetic variant has never been treated before. The platform's ability to identify analogous cases, mechanism-based therapeutic rationales, and feasibility constraints transforms scattered information into actionable treatment roadmaps. Source: Parexel
Frequently Asked Questions
How many people worldwide have rare diseases without treatment options?
An estimated 3.5-5.9% of the global population—roughly 300 million people—live with a rare disease. More than 90% of rare diseases have no FDA-approved treatment because traditional pharmaceutical development models can't justify investments for small patient populations. About half are children, many facing progressive conditions with no therapeutic pathways under conventional approaches.
How does AI speed up personalized medicine research compared to traditional methods?
AI-assisted literature and database review accelerates evidence synthesis for ultra-rare genetic variants, compiling information that would take human researchers significantly longer to manually review across fragmented databases and contradictory reports. AI platforms also manage operational complexity—coordinating geneticists, manufacturers, regulators, and providers for multiple patients simultaneously. This acceleration transforms what's possible for ultra-rare conditions where every delay represents irreversible disease progression.
What percentage of people have genetic variants affecting medication response?
The vast majority of individuals (often >90%) carry at least one pharmacogenetic variant that could influence medication response, per CPIC/DPWG-guided panels. This makes precision medicine universally relevant rather than applicable only to cancer patients or rare disease populations. Preemptive pharmacogenomic testing can reduce clinically relevant adverse drug events by approximately 30% in high-risk populations.
How much have genome sequencing costs decreased?
Whole genome sequencing costs plummeted from $100 million in 2001 to less than $600 in 2024—a 99.999% reduction. This removes the primary technical barrier to genetic analysis, shifting the bottleneck from data generation to interpretation. The challenge now is translating genomic information into clear clinical decisions and actionable treatment plans.
Why can't traditional pharmaceutical companies serve ultra-rare disease populations?
Traditional pharma requires patient populations large enough to generate commercial returns. For ultra-rare conditions, traditional commercial models often do not prioritize development despite scientific feasibility. Labs and manufacturers built for large contracts face operational barriers accommodating individual patient requests. AI-powered platforms solve this by aggregating demand across hundreds of rare conditions and managing complexity impossible to coordinate manually for thousands of patients simultaneously.
