Not every patient who could theoretically benefit from a therapy actually should receive it. Some will not respond. Others will respond but experience side effects severe enough to outweigh the benefit. Precision medicine exists to solve exactly this problem: narrowing a broad population of eligible patients down to the specific segment in which a therapy delivers a strong response with an acceptable safety profile.

This is not a philosophical exercise. It is an operational discipline with direct consequences for clinical trial design, regulatory strategy, and — increasingly — commercial viability.

Narrowing the Universe of Patients

Precision medicine helps narrow the universe of patients down to the segment in which a specific therapy will have a strong response while minimizing the risk of serious or adverse side effects. In its simplified form, the process moves through three steps:

Step 1: Identify the segment of the universe that will respond to therapy.

Step 2: Identify the segments likely to have mild, serious, or adverse side effects.

Step 3: Use that information to identify the segment with a strong response, without serious or adverse side effects.

This is a deliberately simplified illustration of stratification for therapeutic targeting. The real process draws on genomic biomarkers, clinical history, comorbidities, and increasingly on companion diagnostics developed alongside the therapy itself — but the underlying logic is the same at every level of sophistication: narrow from the broad population to the population the therapy is actually built for.

Why the Value of Stratification Is Immense

The value of doing this kind of stratification, even in its most oversimplified form, is immense — for three separate audiences.

For patients, it means avoiding exposure to a therapy that will not help them and might harm them. For clinical development teams, it means smaller, more targeted trials with a higher probability of demonstrating efficacy, since the trial population is enriched for responders rather than diluted by non-responders. For regulators, it supports the core logic behind accelerated and conditional approval pathways, which increasingly depend on a well-defined target population rather than a broad indication.

The Payor Pressure Behind Stratification

As more biopharma companies seek approval for high-cost oncology therapies — many valued upwards of $100,000 per course of treatment — payors are demanding more rigorous healthcare outcomes data to define reimbursement strategy.

This is where stratification stops being a purely clinical exercise and becomes a commercial one. A payor evaluating a six-figure therapy is not asking whether it works in general. They are asking whether it works reliably enough, in a well-enough-defined population, to justify the cost relative to alternatives. A company that cannot articulate its target population with precision — who responds, who does not, and who responds but shouldn’t receive it — is negotiating reimbursement from a position of weakness, regardless of how strong the underlying clinical data is.

Companion diagnostics have become the practical mechanism for operationalizing this at scale: a test that identifies, before treatment begins, whether a given patient falls into the target population the therapy was actually developed and approved for.

The MKA Perspective

We work with life sciences companies to translate stratification frameworks like this one into clinical development plans, regulatory strategy, and market access positioning that hold up under payor scrutiny.

MKA Insights brings market research, clinical strategy, and commercial planning expertise to life sciences and healthcare organizations. To discuss how this applies to your program, contact us.