The weight-loss drug market is a duopoly, not a monopoly, because companies cannot patent the underlying biological mechanism (mimicking GLP-1). Instead, Novo Nordisk and Eli Lilly patented distinct molecules that achieve a similar outcome, allowing both to compete directly.

Co-developing a product with just one enterprise client (N=1) is a trap. It leads to a "Frankenstein" solution tailored to their unique problems, making it nearly impossible to scale and sell to a broader market without significant rework.

Oshkosh structures partnerships to own IP developed jointly with a startup, then licenses it back. This approach, outlined in the initial NDA, gives the large corporation control over patent defense while providing the startup with usage rights, often with market-specific limitations.

Despite pancreatic cancer being notoriously difficult, Actuate prioritized it as a lead indication for strategic reasons. Strong preclinical data allowed the company to bypass later-line trials and move directly into a first-line setting, a 'leapfrog' maneuver that significantly accelerates the drug's overall development and regulatory path.

For years, Actuate's CEO has shared progress with large pharma companies, not just for early deal-making, but to get critical feedback on their development plan. This helps them understand what data potential acquirers need to see to make a compelling offer later.

Using safety and preliminary efficacy data from its lead drug for MPS1, Immusoft successfully requested an FDA waiver for definitive toxicology studies for its next program in MPS2. This platform approach saves significant time and capital, accelerating the entire pipeline without 'reinventing the wheel'.

A theoretical cardiac safety risk for Abivax's drug was first highlighted by competitor AbbVie at an investor conference. Analysts believe this risk is unlikely based on existing clinical data, suggesting such concerns can be a competitive tactic to cast doubt on a rival's asset rather than a significant clinical signal.

The bottleneck for AI in drug development isn't the sophistication of the models but the absence of large-scale, high-quality biological data sets. Without comprehensive data on how drugs interact within complex human systems, even the best AI models cannot make accurate predictions.

M&A is often framed as a win, but it can be detrimental to patients. The acquisition of an aggressive, fast-moving biotech by a large pharma company can lead to slowed development timelines and more conservative regulatory strategies, ultimately delaying access to life-saving treatments.