Pure Monograph

Will do! @PureMonograph

Worth knowing before you apply: LSVP is beta and open to teams and institutions only right now, not individual Pro or Max plans. applying through an institution or company would be the cleaner path. I work on the verification side of this: checking that every identifier and variant in a generated report resolves against source before it appears. I'm a pharmacist, so if a second pair of eyes on the drug or label parts is ever useful, happy to help. Keen to hear how the application goes. @PureMonograph

@PearlF Anyone can pitch 10 clinical-stage drugs. Fewer can name the program, keep “predicted” from becoming “shown,” and stop at the data gap. That’s the PM work I actually did. @PureMonograph is the engine to build new vaccines — mRNA and neoantigen work grounded in evidence, not slides. AI helps. The dossier is still the product.

ECHO-301's phase 2 comparator was history. Not a randomised arm. History. Then a phase 3 got powered against that delta. All-comers, no IDO1 enrichment, no PD readout confirming engagement at the dose they carried forward. A whole target class went quiet behind it. puremonograph.ai

Nine AI vendor demos this year. Every question I asked got an answer. Nine for nine. Nothing ever said it didn't know. That isn't sloppy engineering, it's a product decision. "Insufficient evidence" feels broken to whoever is running the pilot, and the pilot is what gets renewed.

We asked @PureMonograph to design an oral formulation of a full-length monoclonal antibody (~150 kDa) for rheumatoid arthritis. Instead of generating a confident 19-section dossier, the platform stopped and flagged the premise: oral administration of a full-length mAb is biologically implausible — proteins this size undergo proteolytic degradation in the gut and show negligible systemic absorption. It named the closest real precedent — oral semaglutide, a peptide ~40× smaller, which still only reaches single-digit bioavailability — and explained why a full antibody can't clear that bar. Then it laid out the studies you'd actually need to prove feasibility, and recommended holding the program until those exist. Most tools would've written polished fiction. Refusing the incoherent premise is the point. (This is the kind of early honesty we are building into PureMonograph for hard, unsolved cases.)

@jvisserlabs said on Pomp’s show that Eli Lilly could become both the world’s largest company and the largest AI company within five years. Most people heard hype. I heard someone finally saying out loud the thing I’ve been building toward for two years. $LLY isn’t dabbling in AI. They’ve stood up serious pharma-first compute, pointed it at 150 years of proprietary chemistry and clinical data, and started closing the loop between the wet lab and the molecule. That’s not a chatbot bolted onto a research org. That’s a company realizing its real moat was never the buildings or the sales force. It was the data nobody else has and the regulatory knowledge nobody else can fake. Here’s the part that keeps me up at night in a good way. Discovering the molecule is only half the race. The other half is everything that happens after, and it’s where pharma quietly bleeds out. You can design something brilliant and then lose six to twelve months and millions of dollars turning it into a submission a regulator will actually accept. The CMC work. The dossier. The CTD modules, the batch records, the tech transfer protocols. That bottleneck is invisible in the press releases and fatal in the timelines. That’s the half we built PureMonograph for. The idea was never to replace scientists. It was to give every pharma company, from the Lilly-scale giants down to a three-person biotech, the same infrastructure underneath them, so that the moment they have a candidate they don’t have to wait for the paperwork to catch up. They generate the work product, grounded in real regulatory sources and real CMC judgment, auditable, every claim traceable. And they move. What Lilly is proving in public is the whole thesis. The companies that own the full loop, from molecule design to submission-ready compliance, are going to compound faster than any pure software play, because they sit on a data moat that’s a century and a half deep and a regulatory moat almost nobody can cross. The future isn’t AI showing up in pharma. It’s the realization that the best-positioned AI companies on earth might already be pharma companies. They just haven’t all figured it out yet. Lilly figured it out. We built the missing half of the stack for everyone who comes next. @PureMonograph If you’re a pharma exec or a biotech founder tired of watching good molecules stall in documentation, that’s the conversation I want to have.

Pharm.D. and founder here — been building this at @PureMonograph for two years. CMC, CTD, tech transfer, BMRs, all machine-verifiable. But the deal-breaker isn't the documentation layer — it's pairing it with de novo molecule design. Discover the molecule and generate the submission-ready dossier in one system. That's the real OS for pharma.

Building two vertical AI companies where generic AI gets people fired or sued. @RegentForge: OCC/Fed/FDIC rules into audit-ready bank policies in hours. Not consultants. Hours. @PureMonograph : pharma-native AI that works both ends of a drug program. Upstream: trial design benchmarked against real cohorts, counterfactual positioning, and forensic teardown of why trials fail. Downstream: full CMC documentation from a single molecule. Calibrated, not hallucinating — every PMID verified, confidence scored honestly. One molecule in. Intelligence and work product out.

@PureMonograph : pharma-native AI that works both ends of a drug program. Upstream: trial design benchmarked against real cohorts, counterfactual positioning, and forensic teardown of why trials fail. Downstream: full CMC documentation from a single molecule. Calibrated, not hallucinating — every PMID verified, confidence scored honestly. One molecule in. Intelligence and work product out.

Watching this live !! the future of work software is exactly the conversation that needs to happen right now. We need infrastructure that holds up under real pressure for @PureMonograph and @RegentForge — pharma and fintech compliance don’t forgive unreliable tools. Backed Fin before when I was running Plitio Pharmacy. Exited, built new, and came back. That says everything.

Vancouver: Canada’s West Coast global tech powerhouse. This week’s momentum: Serve Robotics’ sidewalk delivery pilot, three sovereign AI data centres, and new startups @RegentForge and @PureMonograph joining the ecosystem. We’re building the future here. Proud to lead the charge.

@RegentForge and @PureMonograph run dual-stack Claude + Grok verification. o1 instant is fast. Our regulatory intel and pharma pipelines need reasoning depth + real-time synthesis Sonnet’s analysis with Grok’s speed. OpenAI’s good. Our stack’s built for domains where wrong answers cost millions. Have you tested o1 instant on high-stakes verticals yet? Curious how it handles edge cases.

Mark, Drug prices are high because drug development costs $2.5B and takes 10+ years. Most of that isn’t science. It’s process. We built an AI platform that designs novel medicines from a disease target to a complete drug design brief in hours. Biology, PK, formulation, failure modes, regulatory pathway. All cited. $199/month. Not $200K consulting engagements. If you want drugs to be cheaper, make them cheaper to design. @PureMonograph puremonograph.ai

CMC delay = $2M /month in lost revenue opportunity. A 6-month documentation delay = $12M gone. Not because of a clinical failure. Because documentation was slow. @PureMonograph generates submission-ready CMC documents in hours. Your scientists review and approve. They don't write from scratch. puremonograph.ai

We just shipped Price Benchmark. No more guessing what your molecule should cost in Canada vs India vs UAE. Real public data from 8+ countries. Traceable to source. Instant retail + tender + 4-tier CIF. Market entry, global launch, tender response — all in one module. Example: Paracetamol 500mg in Canada. puremonograph.ai @PureMonograph

This is why we built @PureMonograph . Paul Conyngham took matters into his own hands when vets gave his dog Rosie just months to live. Using AI (including Grok), tumor sequencing, and collaboration with UNSW scientists, he helped design and deliver a personalized mRNA neoantigen cancer vaccine. Tumors shrank. Rosie is still fighting. Real-world personalized medicine — happening now. Read the full @Forbes story → forbes.com/sites/johnwerner/…

Describe a drug idea in plain English. @PureMonograph covers 10 scientific dimensions — target biology, formulation, stability, PK, toxicity, manufacturing, regulatory compliance, IP, cost — and generates a complete submission-ready monograph. In ~45 seconds. Inspired by Rosie, a dog who needed a personalized neoantigen cancer vaccine. No one had a monograph for that. Now anyone can build one. puremonograph.ai

18 months ago I started with nothing but 25 years of regulatory experience and two AI models. Today I have two live platforms serving two heavily regulated industries: @RegentForge (banking compliance) @PureMonograph (pharma CMC & Tech Transfer) Zero agency. $750/month total cost. Daily execution with Grok + Claude. This is what solo founder + AI actually looks like in 2026. Not theory. Not hype. Real products. Real revenue. Real distribution. The old way is dead. The new way is just getting started. Who else is building like this?

True leadership isn’t about having all the answers, it’s about building a team that asks better questions, moves faster than the competition, and turns bold ideas into undeniable results. Stay curious, execute relentlessly, and never mistake activity for progress. @PureMonograph