The number of Americans recommended for statins ballooned from 13 million to 56 million due to progressively lowered cholesterol thresholds. The expert committees setting these guidelines often had members with financial ties to drug makers, creating a conflict of interest.

Progress in drug development often hides inside failures. A therapy that fails in one clinical trial can provide critical scientific learnings. One company leveraged insights from a failed study to redesign a subsequent trial, which was successful and led to the drug's approval.

The idea of preventing disease by managing measurable risks like cholesterol was a paradigm shift in medicine, born from observing 5,000 residents of Framingham, MA over decades, an unprecedented study that began in 1948.

The blockbuster drug bivalirudin was discovered as an unsanctioned "20% time" project at Biogen. This policy, allowing scientists to explore personal interests, demonstrates how institutionalizing freedom for undirected research can lead to major, company-defining breakthroughs that would otherwise be missed in a rigid R&D structure.

The 'Number Needed to Treat' (NNT) for statins is around 100. This means 100 people must take the drug for five years for just one or two to avoid a heart attack. The vast majority (98%) derive no direct benefit, challenging the drug's 'miracle' status.

The long history of now-commonplace technologies like monoclonal antibodies serves as a crucial reminder for the biotech industry. What appears to be an overnight success is often the culmination of decades of hard, incremental scientific work, highlighting the necessity of patience and long-term perspective.

Modern ethical boards make certain human studies, like extended fasting, nearly impossible to conduct. This creates an opportunity to revisit older, pre-regulatory research from places like the Soviet Union. While the proposed mechanisms may be outdated, the raw data could unlock valuable modern therapeutic approaches.

Profluent CEO Ali Madani frames the history of medicine (like penicillin) as one of random discovery—finding useful molecules in nature. His company uses AI language models to move beyond this "caveman-like" approach. By designing novel proteins from scratch, they are shifting the paradigm from finding a needle in a haystack to engineering the exact needle required.

Unlike most pharmaceutical companies that focus on specific therapeutic areas, Astellas employs a 'biology-first' approach. By focusing on a biological pathway with a link to disease, rather than the disease itself, the company creates opportunities for novel discoveries outside of pre-defined, and often crowded, research fields.