Metrics have become the lodestar guiding pharmaceutical and biotech firms through uncertainty. Concepts like Probability of Success (POS) and Likelihood of Approval (LOA), bandied about in pitch decks and portfolio meetings, promise clarity in an industry awash with risk. These metrics are said to quantify the chances of a drug progressing from the laboratory bench to the bedside, offering investors and executives a veneer of control over a process that is inherently unpredictable. Yet, like so many quantifications in business, they are as misleading as they are illuminating.

This is precisely why I decided to make a simple tool freely available—a way for anyone, from investors to researchers, to experiment with these metrics and understand how they interconnect. It’s not a solution to all the problems inherent in drug development metrics, but it’s a start toward making these abstract concepts tangible, helping users question assumptions and uncover blind spots.

The Numbers Game

The appeal of these metrics is obvious. Drug development is a $2 billion gamble, with failure lurking at every stage. According to BIO, just 9.6% of drugs that enter clinical trials ever make it to market. To navigate this treacherous terrain, companies rely on probabilities: the likelihood that a drug will move from Phase I to Phase II, or that it will secure regulatory approval. POS, for instance, aggregates these probabilities into a single number—a deceptively tidy summary of a sprawling and chaotic process.

Metrics like PTS (Probability of Technical Success) and PPAS (Probability of Pricing and Access Success) claim to go deeper, zooming in on specific hurdles like clinical trial efficacy or payer negotiations. Armed with these figures, executives can rank candidates, optimize trial designs, and, crucially, persuade investors to part with their cash. It all seems eminently rational—until one peers behind the curtain.

This is where my tool comes in. By letting users adjust probabilities and watch how metrics like POS and LOA shift, the tool reveals the sensitivity of these calculations. It encourages users to think critically: What happens to the probability of success if Phase III trials become riskier? How does a low PPAS affect the overall likelihood of market success? These aren’t hypothetical questions—they’re the kinds of considerations that can make or break a drug’s development trajectory.

The Trouble with Certainty

The first problem with these metrics is their reliance on historical data. Most probabilities are drawn from industry-wide success rates: for example, how often drugs in a particular therapeutic area succeed in Phase III trials. But the past is not always prologue. Take the rise of CAR-T therapies or RNA-based drugs. These revolutionary treatments defy traditional models, yet their probabilities are often shoehorned into frameworks designed for conventional small molecules. It’s like trying to predict Tesla’s trajectory using data from General Motors.

Second, metrics like POS are often treated as deterministic, when they are anything but. A 20% probability of success is not a guarantee of failure four times out of five; it’s merely an estimate, contingent on countless assumptions. Investors and executives, however, are prone to mistaking precision for accuracy. When a drug with a high POS fails—or a long-shot succeeds—their faith in the metrics remains unshaken. It’s the metrics equivalent of the gambler’s fallacy.

Third, these figures often neglect the thorny question of market access. In theory, PPAS should address this blind spot, quantifying the chances of a drug being priced and reimbursed. In practice, PPAS is still nascent and woefully underused. Consider Bluebird Bio’s Zynteglo, a gene therapy priced at $1.8 million. Despite its life-changing potential, European payers balked at the cost, forcing the company to retreat from the market. Metrics like POS or LOA were irrelevant to this post-approval debacle—a reminder that the hardest battles often begin after regulatory success.

Why I Built the Tool

I built this tool to demystify these metrics, not to add to the confusion. As someone who has spent years working at the intersection of drug development, market access, and valuation, I’ve seen firsthand how these numbers can illuminate or mislead. By offering a simple, interactive way to experiment with metrics like POS, PTS, and PPAS, the tool helps users see beyond the numbers to the underlying assumptions and trade-offs.

More importantly, it encourages a critical mindset. Metrics are not gospel—they’re tools. They can help frame decisions, but they shouldn’t dictate them. By tweaking probabilities and exploring different scenarios, users can better understand the levers they control and the risks they face.

Metrics as Tools, Not Truths

Yet, despite their flaws, metrics are not entirely useless. They offer a common language for discussing risk, helping companies allocate resources and investors calibrate expectations. Used thoughtfully, they can guide decision-making without dictating it. For example:

• POS is a helpful starting point for portfolio prioritization, enabling firms to focus on the most promising candidates.
• PTS and PRS provide a lens for evaluating technical and regulatory risks, particularly in crowded therapeutic areas.
• PPAS, if taken seriously, can nudge companies to engage with payers early, avoiding the fate of Bluebird Bio.

The key is to treat metrics as one input among many, supplementing them with expert judgment and qualitative analysis. A high POS might justify investment in a drug for rare disease, but only if the market access hurdles are surmountable. Similarly, a low PTS might not dissuade investment in a first-in-class therapy with transformative potential.

A Broader View

The modern obsession with metrics reflects a desire to impose order on a messy, unpredictable world. It’s a tendency as old as management consulting, but no less pernicious for its age. Metrics provide comfort, but they can also obscure the bigger picture. In the end, drug development—like business, or life—cannot be reduced to a series of probabilities. Success requires vision, intuition, and, yes, a bit of luck. Numbers can guide the way, but they cannot show the whole map.

This tool is my small contribution to the conversation—a way to start thinking, questioning, and playing around with the probabilities that shape the future of medicine. If you’re curious or confused, feel free to reach out. The humble author is always ready to help.