Paul Conyngham's Complete Process Breakdown | Can an Ordinary Person Replicate This? | 2026-04-05
Video source: Today Show Australia | Cross-referenced with multiple reports
Paul said in an interview: "Rosie is my best friend. She was there for me through breakups, through tough business negotiations, through walks in the forest. When she was given a death sentence, I felt I had to do something for her."
Below is the complete process reconstructed from cross-referencing multiple reports. This is the most detailed step-by-step breakdown available in public information.
Paul described Rosie's condition to ChatGPT (mast cell cancer, failed chemo and surgery, multiple tumors) and asked what treatment options remained.
ChatGPT suggested the immunotherapy route, specifically a personalized cancer vaccine, and directed him to get genomic sequencing done.
Key output: A complete research roadmap -- from DNA sequencing to vaccine design, step by step.
Paul contacted UNSW's (University of New South Wales) Ramaciotti Centre for Genomics and spent approximately $3,000 USD to sequence both:
Purpose of sequencing: compare the two DNA sets to identify tumor-specific genetic mutations (these mutations become the attack targets).
With sequencing results in hand, Paul used his own data pipeline and ChatGPT to:
After identifying the mutant proteins, Paul used Google DeepMind's AlphaFold (a free tool) to predict the 3D structures of these mutant proteins.
Purpose: understand the shape of mutant proteins and determine which parts are "exposed on the surface" -- most easily recognized and attacked by immune system antibodies.
From dozens of mutation candidates, he selected the most promising neoantigens as vaccine targets.
Based on the selected neoantigens, Paul designed an mRNA sequence that, when injected into the body, would instruct cells to produce fragments of these mutant proteins, teaching the immune system to recognize and attack them.
Paul spent three months preparing a 100-page ethics review document and obtained ethics approval from UNSW.
Final output: a half-page mRNA formula -- this is the vaccine's "blueprint."
UNSW RNA Institute Director Professor Pall Thordarson (a pioneer in nanomedicine) took Paul's design and synthesized the actual mRNA vaccine in the lab.
From receiving the sequence design to completing manufacturing: less than two months.
The vaccine was administered by a veterinarian, combined with a checkpoint inhibitor -- a drug that "releases the brakes" on the immune system, amplifying the immune response.
Rosie diagnosed with mast cell cancer. Chemotherapy and surgery failed. Vet gave her only months to live.
Paul used ChatGPT to develop plan -> DNA sequencing -> AI mutation analysis -> AlphaFold protein modeling -> mRNA sequence design -> prepared 100-page ethics review document
UNSW RNA Institute began manufacturing the vaccine (completed in less than two months)
Rosie received her first mRNA vaccine dose. At this point, her mobility had severely declined; she was starting to "shut down."
Six weeks later, Rosie jumped over a fence at the dog park chasing a rabbit. Paul: "I cried right there on the spot."
Second booster dose administered. Tumors continued shrinking.
The tennis-ball-sized tumor on her leg shrank by 75%. Most tumors were shrinking. However, one tumor did not respond -- Paul is sequencing that tumor and preparing to design a second vaccine.
| Tool | Purpose | Cost | Accessible to anyone? |
|---|---|---|---|
| ChatGPT | Research planning, literature review, mutation analysis, mRNA design assistance | $20/mo | Fully accessible |
| AlphaFold | Predict 3D structure of mutant proteins | Free | Free & open source |
| Genomic sequencing service | Whole genome sequencing of healthy DNA + tumor DNA | $3,000 USD | Requires external service |
| Custom data pipeline | Processing genomic data, comparing mutations | Free (self-built) | Requires coding skills |
| Bioinformatics tools VarScan / MuTect / GATK etc. | Variant detection | Free & open source | Requires learning |
| RNA laboratory | mRNA synthesis, LNP encapsulation | Undisclosed | Requires lab access |
| Checkpoint inhibitor | Enhance immune response | Undisclosed | Requires prescription |
"Design side" total: approx. $3,200 USD (~NT$100,000)
"Manufacturing + treatment side" estimate: tens of thousands to hundreds of thousands NT$
| Step | Difficulty | Feasible for individuals? | What you need |
|---|---|---|---|
| 1. Use AI to develop plan | Low | Yes | ChatGPT + good questioning skills |
| 2. DNA sequencing | Medium | Outsource needed | Find a sequencing company + vet for sampling |
| 3. Mutation analysis | Medium-High | Learning required | Python + bioinformatics basics |
| 4. AlphaFold protein modeling | Medium | Yes | AlphaFold Server + ChatGPT guidance |
| 5. mRNA sequence design | High | Verification needed | ChatGPT assistance + expert review |
| 6. Vaccine manufacturing | Very High | No -- lab required | Find a cooperative RNA laboratory |
| 7. Injection + monitoring | Medium | Vet needed | Veterinary oncologist cooperation |
Steps 1-5 (research plan -> DNA analysis -> vaccine design) can indeed be done independently given a technical background. Paul is living proof.
But Step 6 (manufacturing) is an insurmountable barrier for individuals -- you must find a lab willing to collaborate. Paul succeeded because a UNSW professor was convinced by his data.
So the real question isn't "am I capable of designing it," but "can I find a lab willing to manufacture it for me?"
Estimated cost: Whole genome sequencing NT$50,000-150,000 (depending on depth and scope)
Key challenge: Convincing a lab to synthesize an mRNA vaccine for your "pet" case. Academic labs may be more willing to frame it as a "research collaboration."
In essence, it's "teaching the immune system what the enemy looks like, then unleashing it to hunt."
Every tumor's genetic mutations are different, even within the same type of cancer. So generic vaccines have limited effectiveness. A personalized vaccine is designed based on "this specific patient, this specific tumor's" unique mutations, achieving far greater precision than any one-size-fits-all approach.
This is also the technology that Moderna and BioNTech are spending billions of dollars to develop -- and Paul used AI to accomplish the same design work in just a few months.
Hua Tuo, a legendary physician in ancient China (~200 AD), is regarded as a pioneer of surgery. But the deeper metaphor is this: the ancient Chinese already had the concept of "fighting poison with poison" -- using small amounts of the "enemy" to train the body's defense system. Variolation (the practice of inoculating with smallpox material) has centuries of history in China, and its essence is "showing the enemy's features to the immune system."
What Paul did is perfectly consistent with this ancient intuition -- the only difference is that the tools evolved from "ground-up smallpox scabs" to "AI-designed mRNA sequences." The principle hasn't changed; the tools have evolved.
Zhuge Liang (the legendary strategist of China's Three Kingdoms era, ~200 AD), facing Sima Yi's massive army with no troops of his own, used "information asymmetry" to make his opponent retreat on their own. Paul faced the multi-billion-dollar pharmaceutical industry without a lab, without medical training, without a research team -- but he had AI, a tool that made him "look like he had an entire research team."
The essence of AI is giving individuals institution-level capabilities. This is the most profound significance of this case.
The global pet healthcare market exceeds $30 billion, and pet cancer treatment is the fastest-growing segment. Yet there are virtually no "personalized gene therapy" services available. If Paul's process could be productized (design-side SaaS + manufacturing partner network), this represents a massive opportunity.
UNSW Professor Thordarson said: "This technology is democratizing the entire process." Imagine a platform where: users upload genomic sequencing data -> AI automatically analyzes mutations -> automatically designs vaccine sequences -> connects to RNA manufacturing labs. This would be "the Shopify of gene therapy."
Paul proved that vaccine design can be done by AI + an individual, while manufacturing is handed off to specialized labs. This "fabless model" is already mature in the semiconductor industry (TSMC = foundry, MediaTek = design). The fabless era of biotech is dawning.
Sources: Fortune | Newsweek | Yahoo Finance | The Conversation | Creati.ai | Today Show Australia transcript