Who Actually Matches DNA to Peptides? We Checked 15 Companies. (2026)

This Review at a Glance

• **15 companies** reviewed across 5 categories
• **$99 to $25,000/year:** the price range for "personalized" health
• **3** companies accept raw DNA uploads (23andMe, AncestryDNA)
• **2** companies offer automated genetics-to-peptide matching
• **1** company provides per-peptide published citations with DOI/PubMed links
• **0** companies have published prospective clinical outcome data

Read that last line again.

Zero. Not one company in this space, including ThePeptideList, has published a prospective study showing that genetically matched peptide protocols produce better results than unmatched ones. The entire category is early. We will come back to that, because it matters.

The Cost of Getting It Wrong

A patient prescribed semaglutide at $1,000 per month who turns out to be a genetic non-responder has spent $3,000 before the three-month assessment point reveals the drug is not working. Three months of nausea, injections, and hope. GLP-1 receptor agonists cause nausea in roughly 44% of patients during titration. If your genetics could have flagged that tirzepatide was a better fit for your phenotype, those three months were not just expensive. They were avoidable.

Now extend that across the peptide landscape. A patient cycles through sermorelin, ipamorelin, and tesamorelin over six months before finding the one that moves their IGF-1 levels. Someone trying BPC-157 for a rotator cuff injury does not respond because their inflammatory genetics point to TB-500 as a better pathway match. A clinician prescribes thymosin alpha-1 for immune support without knowing the patient carries APOE4/4.

Trial and error is not free. It costs money, side effects, and patient trust. Who is building the tools to reduce that error rate?

Almost nobody.

What Counts as "Genetics-to-Peptide Matching"?

Before grading anyone, we need to define the category. The term "personalized peptides" gets thrown around like confetti at longevity conferences. Clinics say it. Supplement brands say it. Influencers with 5-gene tests say it. Most of them mean something different.

For this review, we defined genetics-to-peptide matching as a product or service that meets **all** of the following:

Review Methodology

Companies were assessed on seven dimensions based on publicly available materials, product documentation, FAQs, and published pricing as of March 2026.

1. **Genetic input:** Does the product accept or generate genetic data?
2. **Peptide output:** Does it produce peptide-specific recommendations?
3. **Algorithmic link:** Are recommendations programmatically connected to genotype?
4. **Evidence stratification:** Does it distinguish between strong and weak genetic associations?
5. **Citation transparency:** Are published studies cited per peptide recommendation?
6. **Safety integration:** Are CYP metabolism, APOE, MTHFR, or other safety markers included?
7. **Consumer accessibility:** Can a consumer access this without a $5,000 membership?

This review assesses **product architecture and transparency**. It does not assess the clinical efficacy of any peptide or the medical validity of any recommendation. We are mapping who builds what, not who is right.

With that framework, the 15 companies we reviewed fell into five distinct archetypes. Some are doing excellent work in their lane. Some are making promises their products cannot keep. And a few are sitting on the line between real science and expensive wellness theater.

!Dozens of empty pharmaceutical vials scattered on a dark lab surface, one glowing teal vial standing upright in the center_Fifteen companies reviewed. Most are empty promises. A few have something real inside._

Archetype 1: The Legacy PGx Players

GeneSight, Genomind, OneOme, ClarityX

These are the established pharmacogenomics companies. They are good at what they do. GeneSight, owned by Myriad Genetics, covers 60+ psychotropic medications. Genomind recently expanded beyond psychiatry to cover 1,000+ medications across cardiology, pain, and primary care. OneOme's RightMed panel handles 340+ drugs across 30 therapeutic areas, including oncology. ClarityX covers 275+ FDA-approved medications.

They analyze how your CYP enzymes metabolize specific drugs. They tell your doctor whether you are a normal, intermediate, poor, or ultrarapid metabolizer. For antidepressants, blood thinners, and pain medications, this is genuinely valuable clinical information. GeneSight alone has been used by over 5 million patients and validated in the randomized, controlled GUIDED trial, which demonstrated that PGx-guided treatment led to a 50% greater improvement in depression outcomes compared to unguided treatment at 8 weeks.

Genomind's Genecept Assay was one of the first multi-gene PGx tests on the market, and their recent expansion beyond psychiatry signals where the industry is heading: broader clinical coverage across more drug categories and therapeutic areas. OneOme, founded at Mayo Clinic, brings pharmacogenomic testing directly into hospital systems and EHRs. ClarityX differentiates with a consumer-friendly interface and rapid turnaround.

These are established, profitable companies with real clinical validation.

For peptides? Nothing.

Zero peptide coverage across all four companies. No BPC-157. No sermorelin. No thymosin alpha-1. Not even the GLP-1 receptor agonists that are arguably the most commercially significant peptides in pharmaceutical history.

This is not a criticism. These platforms were built for a world where medicine comes in pills and goes through the liver. Most peptides do not. They are cleared through proteolytic degradation in tissue and plasma, not hepatic CYP metabolism. The traditional pharmacogenomics framework was never designed for them.

But that is exactly the point. The PGx industry built a $7 billion market around drug-gene interactions for small molecules. Nobody expanded the model to cover the fastest-growing class of therapeutics in modern medicine.

Access model: clinician-ordered only. UnitedHealthcare restricted coverage for multi-gene PGx panels effective January 2025, adding a reimbursement headwind on top of the scope limitation.

Archetype 2: The GLP-1 Specialists

Phenomix Sciences, Sequencing.com (PGxAI)

If the legacy PGx players are ignoring peptides entirely, these two are paying attention to exactly one: semaglutide.

**Phenomix Sciences** is the standout. Founded by Andres Acosta, M.D., Ph.D. and Michael Camilleri, M.D., D.Sc. at the Mayo Clinic after a decade of clinical research into obesity treatment variability, Phenomix holds an exclusive technology license from Mayo Clinic. Their test, MyPhenome, uses a machine-learning genetic risk score to classify patients into obesity phenotypes.

The concept is elegant. If you have "Hungry Gut" (rapid gastric emptying, hungry between meals), you respond best to GLP-1 receptor agonists. Phenomix validated this in a June 2025 Cell Metabolism publication: up to 84% accuracy predicting GLP-1 response, with Hungry Gut patients achieving 6.4% body weight loss at 16 weeks versus 3.3% for the wrong phenotype. If you have "Hungry Brain" (defective satiation signaling), phentermine-topiramate works better. Named TIME's 200 Best Inventions of 2024.

This is real science. Published. Peer-reviewed. Validated in diverse populations.

What makes Phenomix credible is the study design. Validation across multiple cohorts with significant Hispanic and Black population representation, addressing the European-ancestry bias that plagues most genomics research. The four obesity phenotypes are clinically defined metabolic profiles, not marketing labels.

But the scope is narrow by design. MyPhenome answers one question: which obesity drug class fits your genotype? It does not cover growth hormone secretagogues, tissue repair peptides, immune modulators, cognitive peptides, or mitochondrial peptides. A precision tool for a single therapeutic decision. Excellent at what it does. Silent on everything else.

Clinician-gated. Available in roughly 400 clinics nationwide. Pricing not publicly disclosed.

**Sequencing.com** takes a different approach. Through a partnership with PGxAI announced in August 2025, they offer a $99 consumer-facing GLP-1 genetic report. It accepts raw DNA uploads from 23andMe and AncestryDNA. It covers 18 genes including GLP1R, GIPR, TCF7L2, CYP3A4/5, and POMC across seven medication categories for obesity.

At $99, it is the cheapest genetics-to-peptide product on the market. But like Phenomix, the scope is limited to weight loss medications. If your question is "will Ozempic or Mounjaro work for me?", this is a credible, affordable answer. If your question is about anything else in the peptide universe, silence.

Archetype 3: The Elite Walled Gardens

Fountain Life, Wild Health, Cenegenics, Human Longevity Inc.

This is where the money lives. And where the integration gap is most glaring.

**Fountain Life**, founded by Peter Diamandis and Tony Robbins, raised $18 million in Series B funding in August 2025. Their APEX membership costs $21,500 per year. You get full-body MRI with AI analysis, brain imaging, liquid biopsies, comprehensive blood panels, DNA and epigenetic testing, and access to regenerative therapies including peptides and NAD+ infusions. Locations in Dallas, New York, Naples, and Orlando, with LA and Miami planned for Q2 2026.

They have the data. Genetics and peptides sit under the same roof. But the matching happens inside a physician's head, not in software. There is no automated engine connecting your CYP2D6 status to your sermorelin protocol. The integration is clinical judgment, which is valuable but unscalable and unreproducible.

**Wild Health** is the surprise. Their Clarity platform, marketed as "the world's first precision medicine algorithm," analyzes 700,000+ SNPs alongside 200+ blood biomarkers. They published a retrospective study on 871 patients showing meaningful improvements in HbA1c, triglycerides, and CRP. Standard membership starts at $495 per month. Premium Elite is custom-priced with a cap of 50 patients per physician.

But here is the fact that changes everything: Wild Health's FAQ explicitly states they do **not** prescribe peptides. Their exact language: "We do not prescribe scheduled medications requiring a DEA license at this time, which includes but is not limited to: testosterone, amphetamines, opiates, benzodiazepines, peptides, etc."

Wild Health was acquired by LivePerson for $30 million in February 2022, divested roughly two years later, and now operates independently. Their precision medicine platform recommends nutrition, supplements, exercise, and sleep interventions. Not peptides.

**Cenegenics**, founded in 1997, runs 20 U.S. locations and 6 international clinics. They actually prescribe peptides, including GLP-1 agonists, BPC-157, TB-500, MOTS-c, semax, and PT-141. Their "Explore+ Pathways" program is one of the most comprehensive clinical peptide offerings in the market. Performance health assessments start at $4,495 and annual programs run $14,000 to $21,000.

But their genetic testing is limited and optional. Protocols are adjusted based on biomarker data (VO2 Max, hs-CRP, A1c), not SNP profiles. A physician reviews your bloodwork and builds a plan. Your DNA may or may not enter the conversation.

**Human Longevity Inc.**, founded by J. Craig Venter (the man who first sequenced the human genome), has one of the largest private genomic databases on Earth. Tens of thousands of whole genomes in its database. Executive health packages start at $8,000. They do whole genome sequencing, full-body MRI, 120+ blood biomarkers, and AI-driven risk profiling.

Peptide recommendations? Not part of the product. Risk profiling and disease detection. The most sophisticated genomic infrastructure in this review, pointed at a completely different question.

The pattern across all four: genetics here, peptides there, and a physician in between doing the integration manually.

This is not a technology problem. It is an incentive problem. When your business model charges $20,000 per patient per year for a physician to manually interpret genetic data and build a protocol, there is no economic reason to automate that interpretation. The manual process is the product. The physician's time is what you are selling.

The result is that the most data-rich organizations in longevity medicine, the ones with whole genome sequences and comprehensive biomarker panels and access to every peptide on the market, are also the ones with the least incentive to build the software that would make their insights accessible to anyone who cannot afford a five-figure annual membership.

This creates a strange inversion. The companies with the best raw data have the worst scalability. And the companies trying to scale affordable genetic analysis have the smallest datasets to build from.

That gap is the entire story of this article. It does not work for the person who wants to understand why BPC-157 might be worth discussing with their provider based on their inflammatory genetics, without spending more than their monthly rent on a consultation.

Archetype 4: Peptide Clinics Without Genetics

Marek Health, Superpower

**Marek Health**, founded by Derek of "More Plates More Dates" (2M+ YouTube subscribers), is a telehealth clinic specializing in hormone optimization and peptide therapy. BPC-157, CJC-1295, ipamorelin, tesamorelin, PT-141, GHK-Cu, semaglutide. Comprehensive bloodwork through Quest Diagnostics. Available in all 50 states. 5 stars on Trustpilot with 879 reviews.

Genetic testing: zero. Not offered. Not accepted. Not mentioned anywhere on their website, marketing materials, or service descriptions.

Personalization is based on blood panels, symptoms, and clinician assessment. One independent reviewer noted that protocols are "clearly a template with personalized swaps" rather than deeply individualized. This is worth understanding. The consult-and-lab model is the standard of care in the peptide telehealth space. A patient fills out an intake form, gets bloodwork through Quest Diagnostics, has a telehealth consult with a licensed provider, and receives a peptide protocol based on symptoms and lab values. It is a legitimate medical service. The physicians are real. The prescriptions are real.

But blood panels tell you where you are today. They do not tell you why. They do not tell you which pathways are genetically predisposed toward certain dysfunctions, or which peptides your body is most likely to respond to based on receptor variant expression. Two patients with identical IGF-1 levels may respond completely differently to the same growth hormone secretagogue because their GH1 and GHRHR variants create different signaling dynamics. Bloodwork alone cannot see that.

It is not bad medicine. But it is 2026's version of the educated guess.

**Superpower** is a biomarker testing membership ($199/year) offering 100+ blood biomarkers through Quest Labs at 2,000+ locations. AI-generated health plans. Biological age tracking. Their own website states: "Superpower's standard biomarker panels do not include DNA or genetic testing." They sell some peptide-adjacent products through a marketplace (NAD+, semaglutide), but these are add-on purchases, not biomarker-driven protocols.

Superpower represents a growing category of companies that use AI to interpret biomarker data and generate health recommendations. The AI layer is the product differentiator. But biomarker-only AI, no matter how sophisticated, is working with half the picture. Blood panels show you the current state. Genetics show you the predisposition. A person with normal IGF-1 levels today may still carry GH1 variants that predict suboptimal growth hormone signaling long-term. A person with excellent inflammatory markers may carry IL-6 promoter variants that put them at higher risk during periods of stress or injury. Biomarkers without genetics is like looking at today's weather without knowing the climate.

A March 2026 report signals ambitions to become "AI-peptide infrastructure," but today the product does not touch genetics. Worth watching. Not yet a player in this category.

Archetype 5: The Data Platforms

SelfDecode, The DNA Company, 10X Health

This is where the landscape gets interesting.

**SelfDecode** ($418 to $927, plus $120/year renewal) is the most relevant competitor. They accept raw DNA uploads from 23andMe, AncestryDNA, and 20+ other providers, analyzing 200 million+ genetic variants through imputation. 200,000+ users. $12 million raised. A methodology paper in Nature Scientific Reports.

They now have a peptide report covering approximately 43 peptides. Each peptide gets a predicted response: **better, typical, or worse**. This is meaningful progress. SelfDecode is the first major consumer genomics platform to address peptides by name.

The problems are structural.

Their CYP pharmacogenomics panel exists in the $927 "Ultimate" tier, completely **disconnected** from the peptide predictions. If your CYP3A4 status affects how you process semaglutide, the platform does not make that connection. Two independent reports in the same subscription that do not talk to each other.

No per-peptide citations. No DOI links. No PubMed references on individual recommendations. No safety flags, no contraindication alerts, no APOE risk stratification, no MTHFR context. Every peptide recommendation carries the same apparent confidence, whether backed by a 4,571-patient Lancet GWAS or a single rodent study. A consumer has no way to tell the difference. A clinician has no citation trail to follow.

When every prediction looks equal, nobody can make an informed decision about which ones to take seriously. That is not a UX problem. It is an evidence communication failure with real consequences.

SelfDecode built an impressive genomics platform. But for peptides specifically: no evidence tiers, no safety integration, no citation transparency. Personalization without guardrails.

**The DNA Company** ($499) offers "DNA 360," analyzing 4.7 billion data points across 38 health reports covering hormones, methylation, detox, aging, nutrition, sleep, fitness, and immune function. Founded by Kashif Khan. Well-designed reports with audio explainers. Zero peptide coverage of any kind. Recommendations focus on diet, supplements, and lifestyle.

**10X Health** is a different story.

Gary Brecka, the public face of 10X Health, was fired on November 5, 2024 by co-owner Grant Cardone. Dueling lawsuits followed. Brecka filed a $100 million defamation claim. Cardone accused Brecka of $13 million in competing revenue through side entities. All lawsuits were settled by April 2025. Brecka now operates independently under "The Ultimate Human" brand. 10X Health continues under Cardone's ownership.

The product: a methylation genetic test ($599) covering 5 genes and roughly 9 SNPs (MTHFR, MTR, MTRR, COMT, AHCY). A "Precision" test ($1,299) covers 54 genes and 66 SNPs. No CYP enzymes. No pharmacogenomics. No automated peptide matching. Peptides are offered as a separate service line through clinic consultations.

For context: 23andMe's raw data captures most of those same methylation SNPs for $199. Genetic Genie provides the same methylation analysis for free from existing raw data. The entire methylation panel that 10X Health charges $599 for is a subset of what most consumer genomics platforms include as one section of a much larger report.

The broader issue with 10X Health is not the price. It is the gap between the marketing and the product. The brand was built on viral social media content: dramatic methylation reveals, supplement stacks presented with clinical authority, and health claims that sounded precise but lacked the evidence infrastructure to support them.

McGill University's Office for Science and Society published a detailed critique characterizing Brecka's broader health recommendations as "ranging from unproven to pseudoscientific." The article specifically flagged the presentation of MTHFR variants as causal explanations for a wide range of health conditions, a framing that genetic researchers have consistently pushed back on. MTHFR variants are real. They do affect folate metabolism. But the distance between "this SNP affects methylation" and "this SNP explains your anxiety, fatigue, and weight gain" is enormous, and 10X Health was not careful about communicating that distance.

Trustpilot ratings have fluctuated between 1.7 and 3.9 stars depending on the time period. We will leave it there.

The Outlier: Neo7Bioscience

One company sits outside all five archetypes. **Neo7Bioscience** claims to custom-engineer peptides based on individual genomes using whole exome sequencing and a patented AI platform called PBIMA. Available through partner clinics (Austin Regenerative Therapy, Dayspring Cancer Clinic, Omni Medix). Targeting longevity, cancer, autoimmune, and chronic disease.

This is the only company besides ThePeptideList that we found explicitly claiming to bridge genetics and peptides in a systematic way. The model is fundamentally different: bespoke peptide engineering from your exome versus matching existing peptides to your genetic profile. Boutique scale. Premium pricing. Interesting to watch, but not a consumer product.

The Price of Personalization

Here is what each tier actually gives you.

**$99** (Sequencing.com): Will this weight loss drug work for your genotype? Eighteen genes, seven medication categories, all scoped to obesity. If GLP-1 response is your only question, this is the best value in the market.

**$250** (ThePeptideList DNA Kit): The broadest genetics-to-peptide report at the lowest price point. Twelve biological categories. Seven CYP enzymes. Twenty-nine peptides across three evidence tiers with per-peptide citations. Safety flags. APOE. MTHFR. No physician interpretation included. It is designed to give your clinician something to work with, not replace the clinician.

**$418 to $927** (SelfDecode): The broadest consumer genomics platform overall, 200+ health reports. Peptides are one module. CYP is another. They do not talk to each other. No per-peptide citations. Annual renewal required.

**$4,495 to $21,000** (Cenegenics, Fountain Life): A physician reads your genetics, your bloodwork, and your health history, and builds a protocol manually. For complex cases, this delivers something software cannot: judgment applied to your specific context.

The market failed in the middle. Between a $99 single-drug report and a $5,000 physician visit, there was nothing that combined broad genetic analysis with broad peptide coverage at a price a normal person could justify. That gap is the whole story.

!Dark laboratory workbench with a genetic sequencing screen on the left, peptide vials on the right, and an empty gap of darkness between them_Genetics on one side. Peptides on the other. The space between them is where the market failed._

Where ThePeptideList Genetics Sits

We built ThePeptideList Genetics because nobody else would.

Here is what $250 buys you. A saliva collection kit shipped to your door. You provide a saliva sample, mail it back, and your DNA gets sequenced. No 23andMe account required. No old data files to dig up. No clinic visit. No $5,000 membership. A brand new DNA sample, processed through an automated engine that was purpose-built for one thing: connecting your genetics to peptide science.

The engine scores 12 biological categories across your genetic profile: growth hormone axis, GLP-1 and metabolic pathways, drug metabolism, recovery and repair, immune response, melanocortin system, sleep and circadian rhythm, cognitive and neurological function, longevity markers, body composition, athletic performance, and pain sensitivity.

Seven CYP enzymes profiled: CYP2D6, CYP2C19, CYP2C9, CYP3A4, CYP3A5, CYP1A2, CYP2B6. APOE haplotype. MTHFR status. Safety flags that trigger automatically when your variants warrant clinical attention: APOE4 carriers see specific guidance about inflammatory peptides, MTHFR compound heterozygotes see methylation context, CYP poor metabolizers see alerts relevant to co-prescribed medications.

29 peptides matched to your genetic profile. Not 29 predictions at equal confidence. 29 peptides grouped into three evidence tiers, because the science is not uniform and pretending otherwise would be dishonest:

• **Tier 1: Evidence-Supported.** Published pharmacogenomic trials have studied these specific peptide-gene interactions. Scored numerically. Every recommendation includes the study title, journal, year, DOI link, and PubMed ID. You can click through and read the paper yourself. Today this covers semaglutide, tirzepatide, and liraglutide, backed by studies including the 4,571-patient GLP-1 RA pharmacogenomics GWAS published in The Lancet Diabetes & Endocrinology in 2023.
• **Tier 2: Pathway-Informed.** The genetic pathway is well-studied, but the peptide-specific response is inferred from mechanism of action, not validated in direct PGx trials. No numeric score. A "Pathway Match" badge with a rationale explaining the connection. Includes BPC-157, TB-500, sermorelin, ipamorelin, GHK-Cu, thymosin alpha-1.
• **Tier 3: Exploratory.** Theoretical genetic association based on shared pathway biology. Insufficient direct evidence. Clearly separated. Listed as areas of interest, not recommendations. DSIP, selank, semax, epithalon, MOTS-c.

!A genetics report partially illuminated by teal light on a dark surface, with a peptide vial and pen beside it_The report tells you what it knows, labels what it doesn't, and gives you the citations to check both._

That tiering system is the entire point. SelfDecode tells you "better" or "worse" for 43 peptides, and every prediction carries the same apparent weight whether it is backed by a Lancet GWAS or a rodent study. Phenomix answers one question about one drug class. The concierge clinics give you a physician's opinion for $20,000. We built the only system that covers 29 peptides across the full breadth of peptide therapy, assigns evidence tiers that distinguish between "published clinical data supports this" and "the biology is plausible but nobody has run the trial," and provides per-peptide citations so you or your clinician can evaluate the evidence yourselves.

A clinician version exists with additional data points: raw SNP-level detail, extended pathway analysis, and interaction flags that are not appropriate for consumer-facing materials but are useful in a prescribing context. The consumer report is educational. It connects published genetic research to peptide science. It is not a clinical test, a diagnosis, or a treatment recommendation. But it is the most comprehensive genetic-to-peptide report available to a consumer at any price point. And it costs less than a single month of the semaglutide prescription it might help you avoid.

The CYP Question: An Honest Reckoning

A fair critique of including CYP enzymes in a peptide genetics report: most peptides are not metabolized through hepatic CYP pathways the way small-molecule drugs are. BPC-157, TB-500, semax, epithalon. These are cleared primarily through proteolytic degradation in tissue and plasma. Your CYP2D6 status is not going to change how your body processes a pentadecapeptide.

So why include it?

Three reasons.

First, a subset of peptides do interact with CYP pathways. GLP-1 receptor agonists, the most commercially significant peptides on Earth, have documented CYP3A4 interactions. When you are recommending semaglutide or tirzepatide based on genetic data, CYP context is directly relevant.

Second, the report serves a person, not a molecule. People considering peptide protocols are almost always on other medications. Statins. Antidepressants. Blood pressure drugs. A CYP profile that tells you you are a CYP2C19 intermediate metabolizer is useful context for any clinician building a comprehensive protocol, even if the peptide itself bypasses the liver.

Third, and most importantly: this is exactly why evidence tiers exist. Tier 1 peptides (semaglutide, tirzepatide, liraglutide) have published pharmacogenomic trial data linking specific genetic variants to differential response. The CYP data integrates meaningfully there. Tier 3 peptides ( DSIP, selank, epithalon) have theoretical pathway associations only. The report labels them accordingly. No numeric score. No false confidence.

The honest answer is that peptide pharmacogenomics is a young field. The evidence base for most peptides is pathway-level, not outcomes-level. There is no CPIC guideline for BPC-157. There is no DPWG recommendation for sermorelin dosing based on GH1 genotype. The field is pre-guideline. We built the tiering system specifically so the report communicates the difference between "published clinical data supports this connection" and "the biology is plausible but nobody has run the trial yet."

This is a philosophical design choice, not just a technical one. Most health tech companies optimize for confidence. They want every recommendation to feel definitive because definitive feels premium. Uncertainty feels like a weakness. But in a field this early, false confidence is more dangerous than honest uncertainty. A consumer who believes that every peptide recommendation on their report carries equal evidentiary weight will make different decisions than one who understands that semaglutide matching is backed by a 4,571-patient GWAS while DSIP matching is based on pathway biology with no human trial data.

If we presented every recommendation with the same confidence regardless of evidence depth, that would be misleading. We chose transparency over polish.

What Nobody Has

No company in this review, including ThePeptideList, has published a prospective clinical study demonstrating that genetically matched peptide protocols produce superior outcomes compared to standard prescribing. Not Phenomix. Not SelfDecode. Not us. That is the honest state of the science.

CPIC has published guidelines for over 400 drug-gene pairs. The number of peptide-gene pairs with equivalent clinical evidence? Functionally zero, unless you count GLP-1 receptor agonists. Most genomic studies underpinning these recommendations were conducted in European-ancestry cohorts. Effect sizes may differ across populations.

The companies in this review fall into two camps on this fact. Some present all recommendations with equal confidence, letting consumers assume the evidence is uniform. Others build tiering systems that explicitly communicate where strong evidence ends and biological inference begins.

That is not a reason to avoid the category. It is a reason to build it with radical honesty. The company that treats evidence gaps as a design problem to solve, rather than a fact to obscure, will earn the trust that compounds over time.

What This Means for Clinicians

If you prescribe peptides, the practical takeaway is simple: your patients are going to start bringing you genetic reports. The question is whether those reports help you or waste your time.

A report that says "better" or "worse" without citations or evidence context creates more confusion than clarity. You cannot evaluate a recommendation you cannot trace to its source. Look for per-peptide citations, evidence stratification that distinguishes clinical trial data from pathway inference, and safety flags that account for CYP metabolizer status, APOE risk, and MTHFR context.

A Tier 1 recommendation backed by a multi-thousand-patient GWAS is a different clinical input than a Tier 3 exploratory association. Both may be worth discussing with a patient. Neither should be presented as equivalent certainty. The absence of CPIC/DPWG guidelines for peptide-gene interactions means this gray zone requires more clinical judgment, not less.

ThePeptideList's clinician version includes raw SNP-level detail, extended pathway analysis, and interaction flags not appropriate for consumer reports. Available to licensed practitioners through a separate access pathway.

How a Solo Founder Got Here First

If this gap is so obvious, why did a solo technical founder fill it instead of a $100 million longevity company?

Because the $100 million companies do not want to fill it. Fountain Life charges $21,500 per year. Cenegenics has been profitable at $14,000 to $21,000 per patient for three decades. When your revenue model depends on a physician manually connecting genetics to protocols, you have zero incentive to build software that does it for $250. The manual process is not a bug. It is the product.

So the gap stayed open. And it got filled by a founder who used AI as an engineering force multiplier to build what would have cost a traditional health tech startup $10 million and a team of bioinformaticians. One person built the engine: 12 biological categories, 7 CYP enzymes, 29 peptides across three evidence tiers, per-peptide citations, automated safety flags. ThePeptideList. 51 articles published. 2,062 peptide providers indexed. 1,700 newsletter subscribers. All built in public, week by week, on Substack and X.

To be clear about what "built with AI" means: AI wrote the code. It did not invent the science. The engine is deterministic and rules-based. It runs structured queries against a curated knowledge base of published research. Every recommendation traces back to a specific genetic variant, a specific biological pathway, and a specific published study. No language model generates conclusions. No hallucinated peptide matches. The system is as transparent as the citations it outputs.

Building in public is not a marketing strategy. It is accountability. 1,700 subscribers includes practitioners, clinicians, biohackers, and researchers who will find the holes in your science faster than any peer reviewer. When a subscriber flagged stale GLP-1 pricing, it was corrected the same day. When a clinician questioned a panel choice, the rationale was written and published. That feedback loop is not possible behind closed doors.

The asymmetric leverage is real. The longevity protocols that executives pay Fountain Life five figures a year for come down to three things: bloodwork, genomics, and a smart person connecting the dots to interventions. In 2026, the connecting-the-dots part can be software. The question is who builds it honestly. A $250 saliva kit that tells you what it knows, labels what it does not, and gives your clinician the citations to evaluate both.

Where This Goes

The market is moving. SelfDecode added a peptide report in 2025. Phenomix validated GLP-1 genetic matching through Mayo Clinic. Sequencing.com partnered with PGxAI for a $99 GLP-1 report. These are signals that the genetics-to-peptide category is real. But signals are not products. And products without evidence tiers, safety logic, and citation transparency are predictions wearing a lab coat.

Three things will reshape this landscape within 18 months.

The FDA is reshaping the compounding pharmacy landscape. Companies that cover the full peptide spectrum will need to adapt recommendations dynamically as compound availability changes. Platforms built only around FDA-approved drugs will have a structural advantage if compounded peptides face further restrictions. Platforms that already distinguish between evidence tiers will not need to rebuild their architecture when the regulatory ground shifts. They will just update which tier a compound sits in.

Whole genome sequencing has dropped below $200. When sequencing costs less than a doctor visit, the bottleneck is entirely interpretation. Having a million SNPs on file means nothing without an engine that knows what to do with them. The 23andMe bankruptcy created millions of orphaned genetic datasets. The data exists. The interpretation layer is what is missing.

And the first prospective peptide pharmacogenomics outcome study will get published. When it does, the platform that already separates Tier 1 evidence from Tier 3 will absorb the new data without breaking. Every other platform will need to retrofit.

Five years from now, prescribing a peptide without considering the patient's genetic profile will feel as incomplete as prescribing an antidepressant without checking CYP2D6 status does today. That infrastructure is being built right now. By almost nobody. And we intend to stay ahead of the almost.

For Investors and Partners

Everything in this review was built by one person. The genetics engine, the knowledge graph, the evidence tiering system, the report pipeline, the 51 articles, the 2,062 provider database, this article. One founder with ingenuity, creativity, and grit, using AI as an engineering force multiplier to build what funded teams have not.

Now imagine what this becomes with capital and compute.

If you are serious about equity participation in a company that is defining the genetics-to-peptide category, not following it, reach out: [partnerships@thepeptidelist.com](mailto:partnerships@thepeptidelist.com).

FAQ

#### What is genetics-to-peptide matching?

It is the practice of using a person's genetic data to identify which peptides are most likely to align with their biology. This can range from broad pathway analysis (linking inflammatory genetics to tissue repair peptides) to specific pharmacogenomic evidence (linking GLP-1 receptor variants to semaglutide response). The depth and rigor vary enormously across companies.

#### Can my 23andMe data tell me which peptides to take?

Raw DNA data from 23andMe or AncestryDNA contains hundreds of thousands of SNPs, including many that are relevant to peptide-related biological pathways. However, the raw data file alone does not provide peptide recommendations. It needs to be processed through an interpretation engine that maps those variants to peptide science. Very few platforms currently do this. ThePeptideList Genetics and SelfDecode both accept raw DNA uploads and provide peptide-related analysis.

#### What is the difference between pharmacogenomics and peptide genetics?

Traditional pharmacogenomics (PGx) focuses on how genetic variants affect your metabolism of FDA-approved drugs, primarily through CYP liver enzymes. Peptide genetics is a newer application that maps genetic variants to peptide-relevant biological pathways, including growth hormone signaling, inflammatory response, immune function, circadian biology, and metabolic regulation. Most peptides are not metabolized through the same CYP pathways as traditional drugs, which is why legacy PGx platforms do not cover them.

#### Why do traditional PGx companies not cover peptides?

Two reasons. First, most peptides are cleared through proteolytic degradation rather than hepatic CYP metabolism, so the traditional drug-gene interaction framework does not apply directly. Second, many peptides used in longevity and wellness contexts are compounded or research-use compounds, not FDA-approved drugs with established CPIC/DPWG prescribing guidelines. The PGx industry was built for a pharmaceutical model that peptides do not neatly fit into.

#### What are evidence tiers in a peptide genetics report?

Evidence tiers distinguish between the strength of the scientific connection linking a genetic variant to a peptide response. Tier 1 (Evidence-Supported) means published pharmacogenomic trials have studied the association. Tier 2 (Pathway-Informed) means the genetic pathway is well-studied but the peptide-specific link is inferred from mechanism. Tier 3 (Exploratory) means the association is theoretical. Not all companies use this system. Most present all peptide recommendations with equal confidence regardless of evidence depth.

#### Is a genetics-to-peptide report a medical test?

No. Consumer-facing reports in this space, including ThePeptideList's, are educational tools that connect published genetic research to peptide science. They are not clinical tests, diagnoses, or treatment recommendations. Always consult a qualified healthcare provider before making health decisions based on any genetic report.

#### How much does genetic peptide matching cost?

The range is extreme. Sequencing.com's GLP-1 report costs $99 but only covers weight loss medications. SelfDecode's peptide report requires a subscription ($418 to $927 plus annual renewal). ThePeptideList's DNA kit is $250 for a full report. Concierge clinics that perform manual genetic-peptide integration (Fountain Life, Cenegenics, Wild Health) range from $6,000 to $25,000 per year.

#### What happens if I am a CYP poor metabolizer?

CYP poor metabolizer status means your body processes certain substances slower than average. For traditional drugs (antidepressants, blood thinners, pain medications), this can lead to higher-than-expected blood levels and increased side effect risk. For peptides specifically, the relevance depends on the peptide. GLP-1 receptor agonists have documented CYP3A4 interactions, making metabolizer status clinically meaningful. Most other peptides ( BPC-157, TB-500, semax) are cleared through proteolytic degradation, not hepatic CYP metabolism, so your CYP status is less directly relevant to the peptide itself. However, because most peptide users are also taking other medications, a CYP profile provides valuable safety context for the overall protocol.

#### Why do most genomics companies not cover peptides?

The genomics industry was built around FDA-approved small molecule drugs with established CPIC and DPWG prescribing guidelines. These guidelines define how specific genetic variants should influence dosing for specific drugs. No equivalent guidelines exist for compounded peptides. Building a genetics-to-peptide platform requires working from primary research literature, GWAS data, and pathway-level evidence rather than established clinical guidelines. This is harder, less standardized, and carries more scientific uncertainty, which is why most companies in the genomics space have avoided it.

#### Should I get a genetics report before starting peptide therapy?

A genetic report is not a prerequisite for peptide therapy, and no regulatory body currently requires one. However, genetic information can provide useful context for both you and your clinician. If you are considering GLP-1 receptor agonists, the pharmacogenomic evidence is strong enough to meaningfully inform which medication and dosing approach may work best for your genotype. For other peptide categories, the evidence is earlier-stage but can still highlight relevant biological pathway strengths and weaknesses. The most practical approach: get the genetic data, share it with your prescribing clinician, and use it as one input alongside bloodwork, symptoms, and clinical assessment. No single data source tells the whole story.

#### Which peptides have the strongest genetic evidence?

GLP-1 receptor agonists ( semaglutide, tirzepatide, liraglutide) have the deepest pharmacogenomic literature. Multiple GWAS studies, including a 4,571-patient study published in The Lancet Diabetes & Endocrinology in 2023, have validated associations between GLP1R, TCF7L2, and FTO variants and differential treatment response. Beyond GLP-1s, the evidence thins considerably. Most other peptide-gene connections are at the pathway level, not the clinical trial level.

_Educational Content Notice. This article is for informational and educational purposes only. It is not medical advice, a clinical recommendation, or a diagnostic tool. The competitive assessments are based on publicly available information as of March 2026 and may not reflect unreleased products or internal capabilities. Always consult a qualified healthcare provider before making health decisions. ThePeptideList does not practice medicine._

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This website is for informational purposes only and does not constitute medical advice. Consult a licensed physician before using any peptides. Provider listings do not constitute endorsements. None of the statements on this site have been evaluated by the FDA.