The Problem
Retail biotech traders have no systematic way to evaluate whether a drug will be approved. The process is opaque, the data is scattered, and the stakes are binary. So traders default to what's available: Twitter sentiment, gut feel, or sell-side analyst ratings that lack transparency about their methodology and track record.
Meanwhile, institutional desks run proprietary models that weigh clinical data quality, regulatory signals, competitive landscape, and company execution history. They quantify what retail traders eyeball. That asymmetry is the edge they trade on.
We built an algorithmic version of that process. The Catalyst Confidence Score organizes publicly available data — clinical trial results, FDA designations, safety signals, and company history — into a structured, transparent, quantitative framework. Every input is visible. Every weight is published. You see the work.
Bio-Score answers: "How much will this stock move?"
Catalyst Confidence answers: "How likely is the outcome positive?"
A high Bio-Score with high Confidence is a conviction long. A high Bio-Score with
low Confidence is a potential short — or a stay-away. Both data points matter.
The Four Pillars
The Catalyst Confidence Score is built on four pillars, each weighted by its predictive power for FDA approval outcomes. Each pillar scores 0–100 independently, then contributes to the base score according to its weight.
Efficacy Signal
The strongest predictor. Did prior studies meet their primary endpoint? A Phase 3 trial with p<0.001 and clinically meaningful effect size scores 100. Mixed results — hit some endpoints, missed others — scores 40. No data available defaults to 50.
Example: A drug showing 35% tumor reduction vs. 5% placebo with p<0.0001 scores maximum efficacy.
Safety Profile
The FDA kills more drugs on safety than efficacy. Clean safety comparable to approved drugs in the class scores 100. Moderate signals requiring monitoring scores 50. Prior clinical holds or deaths on trial scores 25.
Example: A drug with manageable GI side effects and no cardiac signals scores 75.
Regulatory Designation
The FDA's own signals. Breakthrough Therapy + Priority Review scores 100. Fast Track alone scores 40. Standard review with no designations scores 20. A prior Complete Response Letter on this application scores 20.
Example: An orphan drug designation signals FDA engagement but doesn't guarantee approval.
Company Track Record
The lightest weight because a good drug can come from any company. Two or more prior approvals in the same therapeutic area scores 100. First-time NDA filer scores 50. Multiple CRLs across programs scores 0.
Example: A company with two prior oncology approvals filing a third oncology NDA gets maximum track record credit.
The Three Modifiers
After the four pillars produce a base score, three modifiers adjust it based on market-aware context that the pillars alone can't capture. These are multipliers — they amplify or penalize the base score.
Indication Expansion
If the drug is already FDA-approved for another indication, risk drops substantially. Manufacturing is proven, the safety database is large, and the FDA has an existing relationship with the product. Already approved in a related indication = 1.20x. New molecular entity = 1.0x (no boost).
MOA Validation
If other drugs with the same mechanism of action are approved for the same indication, the biological hypothesis is de-risked. Two or more approved same-MOA drugs = 1.15x. Novel first-in-class mechanism = 1.0x (no boost).
Prior CRL Penalty
A CRL on Pfizer's 200th program is meaningless. A CRL on a single-asset micro-cap is devastating. Micro cap (<$300M) with CRL = 0.5x. Large cap (>$10B) with CRL = 0.85x. This market-cap-aware nuance is something no competitor models.
Most approval probability models treat all CRLs equally. But a Complete Response Letter hits differently depending on the company. A diversified large-cap can absorb it. A single-asset micro-cap may not survive it. The CRL penalty modifier accounts for this asymmetry.
The Equation
The four pillars combine into a weighted base score. The three modifiers then adjust for market context. The result is capped at 100.
What This Is NOT
This is not investment advice. Catalyst Confidence is an analytical framework, not a recommendation to buy, sell, or hold any security. Your position sizing, risk management, and conviction are yours.
This is not a prediction of FDA action. The FDA makes decisions based on a complete review of clinical, manufacturing, and labeling data — much of which is not publicly available. No external model can replicate that process with certainty.
This is a structured analytical framework that organizes publicly available data into a quantitative score. The framework is transparent — every input, every weight, every modifier is visible. You see the work. You make the decision.
Past framework performance does not guarantee future results. Biotech markets are complex, and regulatory decisions can hinge on information that no public model can access. Use Catalyst Confidence as one tool in your toolkit — not the only one.
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