Clinical trial protocols become overly complex because teams copy and paste from previous studies, accumulating unnecessary data points and criteria. Merck advocates for "protocol lean design," which starts from the core research question and rigorously challenges every data collection point to reduce site and patient burden.

Novo Nordisk ran a nearly 4,000-patient Phase 3 Alzheimer's trial despite publicly stating it had a low probability of success. This strategy consumes valuable patient resources, raising ethical questions about whether a smaller, definitive Phase 2 study would have been a more responsible approach for the broader research ecosystem.

Despite compelling data from trials like PATINA, some patients with ER+/HER2+ breast cancer refuse maintenance endocrine therapy due to side effects. This highlights a real-world gap between clinical trial evidence and patient adherence, forcing oncologists to navigate patient preferences against optimal treatment protocols.

A COVID-19 trial struggled for patients because its sign-up form had 400 questions; the only person who could edit the PHP file was a grad student. This illustrates how tiny, absurd operational inefficiencies, trapped in silos, can accumulate and severely hinder massive, capital-intensive research projects.

Don't wait until Phase 3 to think about commercialization. Biotech firms must embed secondary endpoints in Phase 2 trials that capture quality of life and patient journey insights. This data is critical for building a compelling value proposition that resonates with payers and secures market access.

The most valuable lessons in clinical trial design come from understanding what went wrong. By analyzing the protocols of failed studies, researchers can identify hidden biases, flawed methodologies, and uncontrolled variables, learning precisely what to avoid in their own work.

The traditional drug-centric trial model is failing. The next evolution is trials designed to validate the *decision-making process* itself, using platforms to assign the best therapy to heterogeneous patient groups, rather than testing one drug on a narrow population.

A critical distinction exists between a clinical adverse event (AE) and its impact on a patient's quality of life (QOL). For example, a drop in platelet count is a reportable AE, but the patient may be asymptomatic and feel fine. This highlights the need to look beyond toxicity tables to understand the true patient experience.

The process of testing drugs in humans—clinical development—is a massive, under-studied bottleneck, accounting for 70% of drug development costs. Despite its importance, there is surprisingly little public knowledge, academic research, or even basic documentation on how to improve this crucial stage.