Dr. Abelson credits his undergraduate training in experimental psychology as being invaluable for his career in clinical research. It taught him the fundamentals of writing a protocol, analyzing data, and identifying flaws in a study—skills he directly applied to drug development decades later.
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Instead of waiting for allergy patients to have symptoms on study days, Dr. Abelson’s team created a model to induce the allergic reaction in a controlled way. This 'Conjunctival Allergy Challenge' allowed for precise, predictable testing of new drugs, dramatically speeding up development.
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.
Beyond scientific rigor, designing a truly effective clinical trial protocol is a creative process. It involves artfully controlling for variables, selecting novel endpoints, and structuring the study to answer the core question in the most elegant and precise way possible, much like creating a piece of art.
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.
Dr. Vibha Jawa's career shows a powerful strategy: learning drug development fundamentals in large companies (Amgen, Merck) and applying them in nimble startups. This cycle across different environments accelerates learning and deepens expertise in a specialized field like immunogenicity.
Dr. Abelson’s career spans the transformation of clinical research from an endeavor led by a single physician-scientist to a complex industry with specialized companies for statistics, patient recruitment, and regulatory affairs. This specialization has enabled the current rapid pace of drug development.
After reacquiring a "failed" ALS drug, Neuvivo's team re-analyzed the 200,000 pages of trial data. They discovered a programming error in the original analysis. Correcting this single mistake was a key step in reversing the trial's outcome from failure to success.
AI will create jobs in unexpected places. As AI accelerates the discovery of new drugs and medical treatments, the bottleneck will shift to human-centric validation. This will lead to significant job growth in the biomedical sector, particularly in roles related to managing and conducting clinical trials.
To develop your "people sense," actively predict the outcomes of A/B tests and new product launches before they happen. Afterward, critically analyze why your prediction was right or wrong. This constant feedback loop on your own judgment is a tangible way to develop a strong intuition for user behavior and product-market fit.
The most impactful medical advances come from 'clinical scientists' who both see patients and work in the lab. This dual perspective provides a deep understanding of disease mechanisms and how to translate research into treatments, a model that Dr. Abelson believes is now under threat due to economic pressures.