A Complete Response Letter (CRL) from the FDA due to manufacturing issues can destroy a biotech. CEO Ron Cooper warns leaders to invest heavily in Chemistry, Manufacturing, and Controls (CMC) early, even when the cost exceeds the clinical trial spend. This early investment in professionalizing CMC is critical to de-risk the company's future.

The transition to an engineering discipline in drug discovery, analogous to aeronautics, means using powerful in silico models to get much closer to a final product before physical testing. This reduces reliance on iterative, expensive, and time-consuming wet lab experiments.

The most effective way to accelerate the MLR (Medical, Legal, Regulatory) approval process is not by focusing on the review stage itself. The primary leverage point is improving the quality and compliance of the content *before* it is submitted, which dramatically simplifies and speeds up all downstream steps.

The conventional wisdom that you must sacrifice one of quality, price, or speed is flawed. High-performance teams reject this trade-off, understanding that improving quality is the primary lever. Higher quality reduces rework and defects, which naturally leads to lower long-term costs and faster delivery, creating a virtuous cycle.

AI's primary value in early-stage drug discovery is not eliminating experimental validation, but drastically compressing the ideation-to-testing cycle. It reduces the in-silico (computer-based) validation of ideas from a multi-month process to a matter of days, massively accelerating the pace of research.

Despite a threefold increase in data collection over the last decade, the methods for cleaning and reconciling that data remain antiquated. Teams apply old, manual techniques to massive new datasets, creating major inefficiencies. The solution lies in applying automation and modern technology to data quality control, rather than throwing more people at the problem.

In high-stakes fields like medtech, the "fail fast" startup mantra is irresponsible. The goal should be to "learn fast" instead—maximizing learning cycles internally through research and simulation to de-risk products before they have real-world consequences for patient safety.

Modernizing trials is less about new tools and more about adopting a risk-proportional mindset, as outlined in ICH E6(R3) guidelines. This involves focusing rigorous oversight on critical data and processes while applying lighter, more automated checks elsewhere, breaking the industry's habit of treating all data with the same level of manual scrutiny.

Don't accept the excuse that moving faster means sacrificing quality. The best performers, particularly in engineering, deliver both high speed and high quality. Leaders should demand both, framing it as an expectation for top talent, not an impossible choice.