About PepPal
Last updated: April 15, 2026
Meet the Researcher
Garret Grant
Founder & Lead Researcher - PepPal and PeptideDosingProtocols.com
B.S. Engineering, UCLA (Class of 2022)
I built PepPal and PeptideDosingProtocols.com because I kept running into the same problem: peptide dosing information was scattered across supplier marketing pages, anonymous forums, and paywalled journals, and almost none of it showed its work. I wanted a single place where every number was traceable to a source and every reconstitution calculation was verifiable.
My background spans engineering and software development. I graduated from UCLA with a B.S. in Civil Engineering (Class of 2022), where I spent four years learning quantitative analysis, technical literature review, and systematic problem-solving. After graduating, I moved into software engineering, building web applications, working with databases, and developing the technical infrastructure behind both PepPal and PeptideDosingProtocols.com. I designed and built both sites from the ground up: the reconstitution calculator logic, the protocol database architecture, the supplier rating framework, and every page template.
That combination, engineering rigor plus hands-on software development, is what makes these sites work. I read clinical trial publications and extract dosing data from peer-reviewed journals. Then I build the tools and page structures that turn complex pharmacological math into clear, usable outputs. Every reconstitution table, every titration schedule, every comparison chart was built by me and verified against primary sources.
I am not a doctor, pharmacist, or licensed medical professional. I do not provide medical advice. What I do is read primary research, the same PubMed, NEJM, and Lancet publications that clinicians reference, and organize that data into accessible, evidence-graded formats so researchers and informed readers can make their own decisions.
What I've Built
PepPal (peppal.app) is a peptide reconstitution calculator and research hub. The free calculator handles vial-to-syringe math for any peptide, any vial size, and any BAC water volume. The paid version saves your calculations so you never redo the same math twice. The blog and supplier directory provide evidence-based guides with clinical trial citations and independent quality testing data.
PeptideDosingProtocols.com (peptidedosingprotocols.com) is an independent research database I built and maintain with 24 indexed peptide protocols, 9 multi-peptide stack guides, and 31+ reconstitution references. I designed the standardized 14-section format after reviewing how clinical reference databases organize pharmacological data, then adapted it for a general audience. Every protocol page includes clinical trial citations, titration schedules sourced from published data, and reconstitution math I've verified manually and cross-checked against primary sources. I personally research, write, and review every protocol page on the site.
By the Numbers
- 24 peptide protocols researched, written, and maintained
- 9 multi-compound stack protocols with synergy analysis
- 150+ clinical trials reviewed and cited across both sites
- 12 blog guides covering fat loss, muscle recovery, skin health, supplier quality, reconstitution, stacking, and safety
- Both sites designed and built from scratch, calculator logic, database architecture, page templates, and deployment infrastructure
- Every reconstitution table verified with step-by-step math showing concentration calculations and syringe unit conversions
How We Research
Every piece of content on PepPal and PeptideDosingProtocols.com follows the same research process. This section explains exactly how data goes from a clinical trial publication to a dosing table on our site, and how you can verify every claim yourself.
Source Hierarchy
Not all evidence is equal. We grade every data point by where it comes from and label it accordingly:
| Evidence Tier | Source Type | How We Use It | How You Can Verify |
|---|---|---|---|
| Tier 1 - Clinical Trial Data | Published Phase 2/3 results from NEJM, The Lancet, Nature Medicine, JAMA | Primary source for all efficacy percentages, dosing schedules, titration protocols, and side effect incidence rates | Search the trial name or NCT ID on PubMed or ClinicalTrials.gov |
| Tier 2 - Systematic Reviews | PubMed/PMC meta-analyses and peer-reviewed review articles | Used to contextualize individual trial results and compare compounds across multiple studies | Search the DOI or title on PubMed |
| Tier 3 - Manufacturer Data | FDA filings, press releases, investor presentations from pharmaceutical developers | Used for regulatory timelines, pipeline status, and mechanism-of-action descriptions | Check FDA.gov, SEC filings, or the manufacturer's clinical trial page |
| Tier 4 - Community Protocols | Practitioner reports, published case series, established community dosing conventions | Used only when clinical trial data does not exist for a specific compound (e.g., BPC-157, TB-500), always explicitly labeled as community-derived | We note "community protocol" or "not from clinical trials" in every instance |
When you see a dosing number, side effect percentage, or efficacy claim on our sites, it comes from Tier 1 or Tier 2 unless explicitly stated otherwise. We do not use supplier marketing pages, anonymous forum posts, or social media influencer claims as evidence sources.
How AI Fits Into Our Process
I use AI tools, specifically Anthropic's Claude, as part of my content workflow. Here's exactly how that works, and where the guardrails are.
What AI does:
- First-draft generation: After I complete the research phase, reading clinical trials, extracting data points, and building reconstitution math, I use Claude to help draft prose sections from my research notes. This is faster than writing 4,000 words from scratch, and it lets me focus more time on data verification and quality control.
- Structural consistency: PDP's protocol pages follow a standardized 14-section format. AI helps maintain that consistency across 24+ protocol pages, ensuring every page covers the same data categories in the same order.
- Math verification: I use AI as a second check on reconstitution calculations. If the math doesn't match between my manual calculation and the AI's output, I investigate before publishing.
What AI does not do:
- Research decisions. I choose which clinical trials to cite, which data points matter, and how to grade evidence quality. The source hierarchy on this page is my framework, not an AI's.
- Dosing recommendations. Every dose range, titration schedule, and cycle length comes from a published trial or a clearly labeled community protocol. AI does not generate dosing numbers.
- Supplier evaluations. Finnrick Analytics testing data is independent and quantitative. AI does not assign supplier ratings.
- Editorial judgment. I decide what gets published, what gets cut, and what needs a stronger disclaimer. Every page is reviewed by me before it goes live.
The guardrails in practice: Every piece of content goes through the same pipeline: I do the research -> I compile structured notes with source citations -> AI helps draft from those notes -> I review, fact-check every claim against primary sources, verify all math, and edit for accuracy and tone -> I publish. If something doesn't trace back to a citable source, it doesn't make the page.
This isn't AI generating content at scale. It's a researcher and a software engineer using AI as a writing tool inside a rigorous, human-controlled editorial process. The data is mine. The judgment is mine. The accountability is mine.
I'm transparent about this because I believe readers, especially in a YMYL space like peptide research, deserve to know how content is produced. Google's own guidance says the same thing: what matters is content quality, not whether AI was involved in creating it. I agree, and I'd rather show you exactly how the process works than pretend AI isn't part of it.
Reconstitution Math Methodology
Every reconstitution table on PepPal and PDP is built from three verified inputs:
- Vial size - confirmed against common grey-market supplier listings and, where available, clinical trial formulation data
- BAC water volume - selected to produce clean, practical concentrations (typically 1-2 mg/mL for larger peptides, 100-200 mcg/mL for smaller peptides)
- Resulting concentration - calculated as:
Total peptide (mcg) / BAC water volume (mL) = Concentration (mcg/mL)
From the concentration, we calculate dose volumes and syringe units:
Dose volume (mL) = Target dose (mcg) / Concentration (mcg/mL)Syringe units (U-100) = Dose volume (mL) * 100
Every table on the site shows this math so you can check it yourself. The free PepPal calculator automates this for any vial size and water volume combination.
Supplier Quality Assessment
Supplier recommendations on PepPal reference independent testing data from Finnrick Analytics, a third-party peptide testing service. We do not accept payment from suppliers to influence ratings. Our supplier evaluation looks at. For the live ranked application of that framework, see our full supplier comparison:
- Independent COA (Certificate of Analysis) verification - Does a third party confirm the supplier's own COA claims?
- Testing depth - How many samples have been tested? More samples tested means a more representative quality picture.
- Purity consistency - Do results stay consistent across batches, or is quality variable?
- Contamination screening - Are samples tested for bacterial endotoxins, heavy metals, and residual solvents?
We carry affiliate relationships with some listed suppliers, which means PepPal may earn a referral commission on purchases. This is disclosed on every page where affiliate links appear. Affiliate status does not affect quality ratings or rankings, suppliers are ranked by Finnrick data, not by commission rates.
What We Don't Do
To be clear about the boundaries of this site:
- We do not provide medical advice.Nothing on PepPal or PeptideDosingProtocols.com is a recommendation to use, purchase, or administer any compound.
- We do not conduct laboratory testing ourselves. Supplier quality data comes from Finnrick Analytics, an independent third party.
- We do not run clinical trials. All efficacy and safety data is sourced from published research by credentialed scientists at academic institutions and pharmaceutical companies.
- We do not claim compounds are safe or effective for any purpose. We report what clinical trials found, with specific numbers and citations, and let readers evaluate the evidence.
Every page on both sites includes a disclaimer stating that content is for educational and research purposes only.
Contact
For corrections, data updates, or research inquiries:
If you find an error in any dosing table, reconstitution calculation, or clinical citation on either site, please let me know. Accuracy is the foundation of everything here, and I take corrections seriously.
PepPal and PeptideDosingProtocols.com are independent educational resources. All content is for research and informational purposes only. Nothing on these sites constitutes medical advice. Consult a qualified healthcare provider before considering any compound.