I Did It My Way - N=1

It’s no secret that drug development is a complex and often frustrating process. Thousands of drugs that showed promise in clinical trials end up being shelved because they did not show broad efficacy. However, within those trials, there are often subsets of patients who experience significant benefits. So, what if we could shift the focus from mass generalization to precision?

A recent article published by The Epoch Times highlighted an interesting trend in personalized science, known as 'N=1' studies. The article discussed a Harvard medical student who consumed 720 eggs in 30 days to observe specific health impacts. This anecdote is just one example of a growing recognition of the importance of understanding how individual bodies respond differently to the same treatment. (1)

In drug development, the tendency has been to aim for a drug that works for as many people as possible. However, we know from years of clinical trial data that certain populations—defined by characteristics such as race, age, or even genetic markers—respond differently. This data is often stratified during trials, but when a drug fails to meet the overall efficacy benchmarks and therefore is not approved for market, the information on these subgroups gets lost. Thousands of potentially life-changing treatments are left on the shelf.

This raises a key question: Why aren’t more drugs approved for these identifiable subsets? If a medication demonstrates efficacy in a specific group, why not approve it for use in that group, rather than shelving it altogether?

There’s already precedent in medicine for stratified approaches to treatment. Consider how physicians frequently prescribe medications 'off-label' based on anecdotal success. For example, antidepressants like amitriptyline are often prescribed to treat chronic pain, despite being approved primarily for depression. This flexibility recognizes that medications can have multi-faceted effects on different patient groups. (2)

Incorporating more stratified data in drug approval processes could allow for a much-needed paradigm shift. Instead of dismissing a drug that works for only 20% of patients, what if we approved it for those 20%? Physicians would have more tools at their disposal to treat patients whose needs fall outside the majority, with the understanding of both the benefits and risks.

Granular Drug Approvals: The Path Forward

Here’s how this could work:

1. Stratified Data Approval: When a drug shows efficacy for a specific subset, this data should be a focal point for approval. The drug could be made available specifically for these populations (e.g., based on age, ethnicity, BMI, or genetic profiles). The efficacy rankings for each group could be included in labeling, along with clear data on likely adverse effects and risks.

2. Informed Prescribing: Doctors would have access to these stratified data points, allowing them to make informed decisions on whether to prescribe these treatments, particularly when traditional treatments are ineffective. Instead of limiting options, this opens a path for personalized, evidence-based treatment plans.

3. Building on Off-Label Prescribing Practices: Off-label prescribing already demonstrates that physicians are willing to push the boundaries of traditional approvals when they believe in the efficacy of a drug for a particular condition. What if this practice was backed by more robust clinical data for specific populations? This could reduce the trial-and-error nature of off-label prescribing.

Challenges and Considerations

The idea of N=1, or highly personalized medicine, is not without challenges. There are concerns about increased regulatory complexity and cost. More granular approvals would require more granular oversight, adding another layer to the already complex approval process. However, the trade-off is clear: better outcomes for patients who currently fall through the cracks of broad-spectrum drug development.

Moreover, personalized medicine aligns with the growing field of pharmacogenomics, which studies how genes affect a person’s response to drugs. We’re already moving toward this future in areas like oncology, where treatments like immunotherapies are tailored to the genetic profiles of specific tumors. The same principles could be applied more broadly across the pharmaceutical landscape. (3)

Let’s Take Action

The time has come to rethink our approach to drug approvals. Instead of aiming for drugs that work for the majority, let’s get granular. Let’s approve treatments that work for the 10%, 20%, or 30% of patients who can benefit. In an age of increasing precision in every other area of life, medicine should be no exception. The broader use of AI dramatically improve identification of the subsets that may benefit.

The stratification of trial data and the move toward more personalized treatment could usher in a new era where fewer drugs are shelved, and more lives are improved. N=1 could be the key to unlocking a more effective, compassionate, and scientifically sound approach to patient care.

Let’s embrace the power of precision.

#clinicalresearch #precisionmedicine #SavingAndImprovingLives #AI

The title is based on the song “My Way”, Recorded by F. Sinatra, My Way* (1969), Reprise Records.

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References

1. “Harvard Med Student Eats 720 Eggs in 30 Days, Highlighting a Trend in N=1 Science” – Epoch Times

2. “Personalized Medicine: Time for One-Size-Fits-All to Go?” – Nature Medicine

3. “Pharmacogenomics: The Promise of Tailored Treatments” – The New England Journal of Medicine