Rethinking Drug Development Decisions: How Advanced Risk – Benefit Models Can Prevent Billion-Dollar Mistakes

Bringing a new drug to market is one of the most ambitious – and riskiest – undertakings in modern science. The numbers speak for themselves: the process takes more than a decade, costs an average of $2.3 billion per approved drug, and fails 90% of the time.

For those who might not know this, drug development is highly regulated and typically goes through 3 test phases.  Phase I trials for cancer treatment test for drug safety. Doctors also look for signs that cancer actually responds to this new treatment. Phase II trials test if one particular type of cancer responds to the new treatment. And Phase III trials test if a new treatment is better than a standard or existing treatment.

Even in the final stages of development, where hundreds of millions have already been invested, drugs often falter. The leading reasons are well known: lack of efficacy accounts for 57% of failures, mostly during the earliest phases – most numerous, but cheaper – and safety issues for 17%, mostly during the latest phases – least numerous, but exponentially expansive. When these problems emerge in Phase III trials or even after regulatory submission, they represent not just a scientific setback, but an enormous financial and human cost. 

We sat down with Romain Clément, CEO & Founder of ArcaScience, to discuss one of the most expensive challenges in modern science—drug development failures—and how his company is using artificial intelligence to predict which compounds will succeed before companies invest hundreds of millions in late-stage trials.

The Problem with Late-Stage Surprises
For decades, the industry has accepted these numbers as the cost of doing business. The prevailing mindset has been to advance promising compounds into late-stage trials as quickly as possible, with the belief that clinical data will speak for itself.

But too often, the data that “speaks” in early phases tells an incomplete story. Phase I and II trials may suggest potential, but they are constrained by small sample sizes, tightly controlled environments, and short follow-up periods. The reality in the real world – where patients present with comorbidities, complex treatment histories, and varied adherence – is far messier.

This gap between early promise and actual market viability is the root cause of many costly failures. And it’s a gap that is entirely preventable.

The Case for Early, Data-Driven Risk–Benefit Analysis
Instead of treating large-scale risk–benefit analysis as an endpoint, the industry must embed it at the start of the decision-making process. That means integrating real-world evidence and encoded biological mechanisms – from electronic health records, registries, and post-market surveillance of similar drugs – alongside early trial results.

With advances in AI and computational modeling, we now have the tools to process these vast and heterogeneous data sources in a way that was impossible even a decade ago. Sophisticated algorithms can identify subtle safety signals, simulate diverse patient populations, and predict efficacy under real-world conditions.

The benefit is twofold. First it allows for earlier course corrections. If the model flags a high probability of late-stage failure, companies can refine the trial design, adjust dosing strategies, or pivot entirely – saving years and hundreds of millions.
Second, it yields overall stronger confidence in greenlighting. If the data suggests a robust risk-benefit profile across populations, companies can advance to Phase III with greater assurance.

Why This Shift Matters Now
The pressure on pharmaceutical R&D has never been greater. Rising development costs, stricter regulatory scrutiny, and growing payer demands for proof of value mean the old “fail fast, fail late” approach is no longer sustainable.

Moreover, the rise of precision medicine is changing the risk-benefit equation entirely. Targeted therapies may work brilliantly in small, genetically defined populations but fail to deliver value at scale. Without early modeling that accounts for these market realities, companies risk advancing drugs that are scientifically valid but commercially unviable.

From Data Silos to Integrated Intelligence
The challenge is that the data required for comprehensive early modeling is often siloed – spread across academic literature, proprietary clinical trial datasets, real-world registries, and even unpublished safety reports. Manually aggregating and interpreting this information is time-consuming and prone to human bias.

This is where AI-powered platforms can make the difference. By unifying disparate data sources and applying standardized frameworks for evaluation, they allow decision-makers to see a clear, quantified risk–benefit profile before committing to costly late-stage trials.

The Future: Informed Greenlights, Fewer Dead Ends
The goal is not to eliminate all failures – drug development will always involve uncertainty and risk – but to make those risks transparent, quantifiable, and manageable at the earliest possible stage.

An industry that embraces early, AI-driven risk–benefit analysis could see a profound shift in R&D efficiency: fewer late-stage dead ends, better allocation of resources, and faster delivery of effective therapies to patients who need them.

For too long, billion-dollar mistakes have been treated as an unavoidable cost of innovation. They are not. With the right data, the right tools, and the courage to act on early insights, we can ensure that the promise of a compound is matched by its real-world impact – and that every greenlight in drug development is a fully informed one.