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I see a lot of new faces coming in since Moderna blew up and I thought I should share my Due Diligence checklists. I have one for Phase II and another for Phase III. I don't do Phase I because of reasons.

Don't get nervous. I plug a LOT of data and variables into a [Skills.md](http://Skills.md) file which I use across two separate AI models to both run reports and then confirm reports. It takes a while but the text output can and will seem like AI slop. The point is to have a checklist. I'm sharing mine.
Before I do, let me offer up some of what I've found to be best AI biotech best practices...

I limit my DD to a top 10 set of questions because there are way too many tickers out there.
1. Don't use Gemini. At least not yet. It will go off of outdated data from its training days and you'll be very sorry. Gemini will get better but it's not there right now.
2. Do use ChatGPT set on Sol High. I've found it to be the best at getting actual non-hallucinated answers. A lot of people prefer Claude and that's fine if you can afford Fable tokens. If not, you're likely better off with ChatGPT or even the latest Grok. You will need to pay for AI to get the real answers and research you seek. Sorry.
3. You need to learn how to set up skills within whichever AI bot you choose. You can turn the following 10 question due diligence Phase II checklist into a [skills.md](http://skills.md) file and call it with a ticker that you want to pursue.
4. You must always take whatever results you get from AI and run those results through another AI to confirm what you have is real. For instance, take the ChatGPT Sol high results and plug them into Claude Opus and ask the following question: "I think there may be at least one, if not two, mistakes in the following due diligence. FIND THEM and tell me whey they're incorrect." That will almost always uncover at least one or two mistakes which you will either take back to the original (or third) AI or you can always, you know, do real due diligence and find the actual answers.
5. Your due diligence and a checklist is table stakes to get into a ticker. It is NOT the same checklist you'll use to determine whether it's time to get OUT of a ticker. UNDERSTAND THIS. Entry and exit are two different things. Your thesis may break for reasons that you didn't foresee. One thing I've done - that I'm not sharing here yet - is my scanning [skills.md](http://skills.md) that goes through all of my holdings each day to determine whether anything fundamental has broken. Entry is easy'ish. Exit is hard. Always.

With that out of the way, you can copy/paste the following into your AI along with a ticker you're considering (make sure they're in or on the verge of Phase II trials) and see whether or not the following AI slop can make your ENTRY point a bit more informed. Good luck!
AI SLOP: GIVE ME THE PHASE II CHECKLIST...
Phase II is where the question changes from **“can the science translate?”** to **“is the human signal real, reproducible, and strong enough to justify a pivotal program?”**
At this stage, human data should dominate the diligence. Preclinical evidence still matters, but it should no longer be used to explain away weak clinical results.
Use three classifications:
**GATE** — failure can invalidate the investment thesis.
**MAJOR DISCOUNT** — does not necessarily kill the program, but materially reduces what you should pay.
**VALUATION INPUT** — affects probability-adjusted upside and downside.
* **1. Is the drug demonstrably biologically active in humans? — GATE** Look for a coherent translational chain:
**dose → exposure → target engagement → biological effect → clinical effect**
Evaluate human PK, pharmacodynamics, tissue penetration, target engagement, biomarkers, dose-response, and evidence that the proposed mechanism is actually occurring in patients.
The more directly the company can demonstrate mechanism in humans, the less you need to rely on animal biology.
**Red flag:** management responds to weak human efficacy by returning repeatedly to preclinical models or mechanistic theory.
* **2. How large and credible is the efficacy signal versus the correct comparator? — GATE** Effect size and comparator quality must be evaluated together.
For randomized trials, examine:
* treatment versus control performance
* absolute and relative effect size
* confidence intervals
* response durability
* subgroup consistency
* missing data and censoring
* investigator versus central review
For single-arm studies, historical controls must match the treated population in:
* disease severity
* prior lines of therapy
* refractory status
* biomarker status
* age and performance status
* geography
* assessment methodology
* scan or evaluation schedule
* supportive care
* calendar period and evolving standard of care
A 40% response rate is meaningless until you know whether comparable patients normally achieve 5%, 20%, or 35%.
**Red flags:** the company emphasizes responder subsets while avoiding all-comers data; repeatedly changes the historical comparator; or compares against a population materially easier to treat than the study population.
* **3. Is the endpoint clinically meaningful and suitable for a pivotal program? — GATE** Determine whether the Phase II endpoint:
* measures something patients or regulators care about
* is validated or well accepted
* is objective where possible
* can be reproduced in Phase III
* has a credible relationship to eventual clinical benefit
* has been discussed with regulators
Distinguish a true surrogate endpoint from a biomarker that merely changes.
Review [ClinicalTrials.gov](http://ClinicalTrials.gov) history for endpoint changes after enrollment began.
**Red flag:** the primary endpoint changes after meaningful enrollment without a convincing scientific or regulatory explanation, especially if the new endpoint favors the emerging data.
* **4. Is the selected dose actually the right dose? — GATE** Phase III dose selection should be evidence-based, not inherited from the highest tolerated dose.
Evaluate:
* exposure-response
* dose-response
* target saturation
* efficacy by dose
* toxicity by dose
* dose reductions
* interruptions
* discontinuations
* duration of exposure
* interpatient PK variability
Ask whether a lower dose preserves efficacy with better tolerability, or whether higher exposure produces additional benefit.
**Red flag:** the company enters pivotal development while still uncertain which dose provides the optimal benefit-risk profile.
* **5. Is the safety profile acceptable at the exposure required for efficacy? — GATE / MAJOR DISCOUNT** Focus on whether patients can remain on treatment long enough to benefit.
Examine:
* treatment-related serious adverse events
* deaths
* discontinuations
* dose reductions
* interruptions
* cumulative toxicity
* organ-specific toxicity
* class-specific risks
* monitoring burden
* interactions with standard-of-care therapy
Safety must be judged relative to disease severity and available alternatives.
**Red flag:** efficacy depends on exposure that produces frequent dose reductions, discontinuations, or cumulative toxicity.
* **6. How much Phase II effect degradation can the pivotal program tolerate? — GATE** Assume the Phase II point estimate is too optimistic.
Model what happens if efficacy deteriorates by:
* 10%
* 20%
* 30%
* 40%
Stress-test:
* smaller true treatment effect
* worse control-arm performance
* broader Phase III population
* additional geographies
* less experienced sites
* more heterogeneous patients
* lower adherence
* more missing data
The strongest Phase II programs do not require Phase III to reproduce an unusually favorable early result exactly.
**Red flag:** a modest reduction in effect size makes the proposed pivotal study statistically unlikely to succeed.
* **7. Can the Phase II product be bridged cleanly into Phase III and commercial manufacturing? — GATE / MAJOR DISCOUNT** Determine whether the product used in Phase II is materially representative of what will be used in Phase III and ultimately sold commercially.
Ask about changes in:
* manufacturing site
* scale
* formulation
* device or delivery system
* raw materials
* cell line
* purification
* analytical methods
* release specifications
* storage
* stability
* container closure
For biologics, cell therapies, gene therapies, and complex products, assess the comparability package carefully.
For small molecules, determine whether formulation changes alter exposure.
**Red flag:** Phase III will use materially different product and a substantial comparability or bridging package remains unresolved.
* **8. Has FDA agreed with the pivotal-development strategy? — GATE** Determine what regulators have actually agreed to regarding:
* indication
* patient population
* dose
* endpoint
* control group
* statistical analysis
* trial size
* duration
* safety database
* accelerated versus traditional approval
* confirmatory requirements
* manufacturing expectations
Distinguish:
**“FDA provided feedback”**
from:
**“FDA agreed that this program could support registration if successful.”**
Review actual regulatory disclosures rather than relying solely on management characterization.
**Red flag:** the company is preparing an expensive pivotal program while major questions about endpoint, population, dose, or registration strategy remain unsettled.
* **9. Can the company enter Phase III from a position of financial strength? — MAJOR DISCOUNT** The key Phase II financing question is not simply whether cash lasts until Phase III.
Ask:
**Can management start or negotiate Phase III on its own terms, or must it raise or partner from weakness?**
Evaluate:
* current cash
* quarterly burn
* cost to complete Phase II
* cost to prepare Phase III
* manufacturing commitments
* debt
* milestones
* warrants
* converts
* ATM capacity
* fully diluted shares
Then assess strategic leverage:
* Can the company fund initial Phase III work independently?
* Is the Phase II package strong enough to attract competitive partnering interest?
* Must the company accept whatever terms are available?
* Is a highly dilutive raise unavoidable immediately after data?
**Red flag:** the company must finance or partner regardless of the quality of the Phase II result and has insufficient cash to create negotiating leverage.
* **10. Does today's valuation compensate you for the probability of pivotal success? — VALUATION INPUT** Build a probability-adjusted value rather than simply assigning a successful drug a peak-sales multiple.
Model:
* probability Phase II signal is real
* probability Phase III succeeds
* probability of approval
* dilution before approval
* partnership economics
* royalties
* milestones
* remaining patent life
* regulatory exclusivity
* addressable population
* pricing
* reimbursement
* competition
* manufacturing cost
* likely strategic value
At minimum model:
**Strong Phase III success**
**Marginal or mixed Phase III result**
**Pivotal failure**
The investment question is not:
**“Is the Phase II data exciting?”**
It is:
**“After allowing for regression to the mean, regulatory risk, manufacturing risk, financing, and dilution, does today's enterprise value still leave asymmetric upside?”**
# Phase II decision rule
Before investing, you should be able to say:
**“The drug is demonstrably active in humans, the efficacy signal is large relative to the correct comparator, the dose and endpoint are defensible, the result can deteriorate meaningfully and still support Phase III success, the commercial product can be manufactured comparably, regulators support the pivotal path, and the company has enough leverage to finance or partner the next stage without destroying shareholder value.”**
If several of those statements require optimistic assumptions, the Phase II program is not yet de-risked enough to deserve a Phase III-style valuation.

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