6 Risks To Clinical Supply Spurred By 'Once In A Lifetime' Lp(a) Screening

By Rachel Grabenhofer, Chief Editor, Clinical Supply Leader

When cardiovascular experts recommend that all adults be tested for a given biomarker at least once in their lives, it sounds like boilerplate advice. For clinical supply teams, it represents a major shift in infrastructure — one that alters how patients enter studies, how enrollment behaves, and how inventory risks accumulate over time. This could become the reality for Lp(a), a genetic lipoprotein that indicates cardiovascular risk and that is increasingly being targeted by next‑generation RNA‑based therapies.

To provide context, unlike cholesterol or blood pressure, Lp(a) levels are almost entirely genetically determined, as reported by the American College of Cardiology, with “little to no influence from environmental or lifestyle factors.” Indeed, adult levels are reached in childhood, typically by five years of age, the source added – clearing the pathway for preventive cardiovascular interventions.

Interestingly, genetic, epidemiologic, and early interventional evidence suggests that large, targeted reductions in Lp(a) could reduce cardiovascular risk comparable to or exceeding that achieved by moderate‑intensity statins. In March 2026, the American College of Cardiology and the American Heart Association (AHA) even formally proposed that all adults undergo Lp(a) testing at least once in life – and considering that Lp(a) is primarily genetic, one test is all it would take.

Sounds clinically efficient and practical, right? But operationally, it’s disruptive. Mass screening is a significant expansion over traditional risk‑based testing. What’s more, for clinical supply professionals, the risk is not about volume alone. It’s about timing.

What appears to be a scientific breakthrough in “once‑in‑a‑lifetime” testing has quietly introduced a new risk profile for clinical supply — one that demands closer examination. Six key areas are outlined here.

1. Enrollment Preempts Disease Progression – Order Matters

Historically, clinical supply planning assumes that patient identification follows disease presentation. Lp(a) reverses that logic since testing is performed in individuals who are clinically well. Trial participation is thus triggered not by disease progression, but by screening awareness.

This distinction matters because screening initiatives could become erratic and unpredictable. For instance, updates to public guidelines could trigger testing spikes; health systems may unexpectantly add Lp(a) to lipid panels; and professional events and reference journals might temporarily skew clinician focus. Each of these waves can thereby create localized enrollment pressure that does not represent the gradual accrual curves of typical clinical supply plans.

Complicating the matter is the fact that no single function “owns” this shift. Screening strategies occur upstream while medical affairs amplifies patient awareness. As a result, enrollment accelerates – which is often a cause for celebration. In clinical supply, however, the operational consequences surface first.

2. Erratic Enrollment vs. Linear Supply Planning

Clinical supply teams are highly capable of managing complexity – when it comes in a familiar “shape.” However, broader testing for Lp(a) appears in a new form.

As screening becomes sporadic, enrollment may arrive in bursts – where one quarter appears balanced but the next one suddenly stresses depot capacity or resupply timelines. For supply operations, this creates three compounding risks:

1. Over‑buffering to support uncertain usage, which increases expiry exposure – which can be especially problematic for oligonucleotide products with narrower stability margins.
2. Underestimating mid‑trial acceleration, requiring emergency resupply, manual depot intervention, or protocol exceptions.
3. Regional disproportion, where countries with high early testing adoption consume inventory faster than global averages predict.

None of these pressures are caused by manufacturing constraints. They arise from a disconnect between how patients are identified and how supply is positioned. So while the industry has sought to decrease the enrollment “noise” generated by sites, Lp(a) screening recommendations increase enrollment noise.

3. The Higher Retention Effect

Prevention‑focused cardiovascular trials tend to enroll clinically stable patients and Lp(a) programs follow that pattern. While analyses of cardiovascular trials show that enrollment can be a dominant operational challenge (take Gargiulo, et al., for example), they also show that once eligible patients are identified and randomized, retention is generally high.

From a supply perspective, that stability comes with a cost. High retention means longer continuous treatment, lower natural attrition, and higher cumulative exposure per patient. Over time, inventory demand gradually rises, and if supply plans assume historic oncology‑like dropout rates, those assumptions can turn expiry risk and late‑stage shortages into slow‑burn problems that become visible only after initial enrollment targets are met.

Experienced clinical supply leaders will recognize this pattern: trials that feel operationally calm when it’s early often accumulate the most pressure in later maintenance phases, when course‑correction options are fewer.

4. Screening Volatility vs. IRT Logic

IRT systems are another consideration. They perform best when screening‑to‑randomization ratios are predictable; here again, Lp(a) challenges this presumption.

Since Lp(a) prevalence varies by population and early testing familiarity differs by site, screening efficiency can change rapidly. Sites may experience high screen‑fail rates initially, improve dramatically as testing becomes routine, then decline again when local screening initiatives taper off.

Static resupply rules falter in this setting. Sites can become overserved during inefficient phases and underserved just as randomization accelerates. The result is often manual intervention: overrides, depot rebalancing, or exceptions that increase operational complexity and risk management.

This should not be considered an IRT failure, but rather a reminder that in prevention‑oriented biomarker trials, screening dynamics are no longer background noise — they are a primary driver of supply behavior.

5. Protocol Amendments: The Aftershock

Despite growing enthusiasm for lowering Lp(a) to achieve specific cardiovascular outcomes, biological challenges and questions remain; for example, determining which measurement assays or threshold risks to use, among others. These uncertainties make protocol amendments more likely.

Even modest changes such as extended follow‑up, revised visit schedules, and new sub-cohort analyses (e.g., linked to testing thresholds) can quickly accumulate, impacting labeling configurations, packaging strategies, depot forecasts, and expiry assumptions. In long‑duration prevention trials, these changes can carry disproportionate supply consequences.

What makes them particularly challenging is that they often arrive as iterations of “small” adjustments. While individually manageable, they are collectively transformative and they redraw the clinical supply picture mid‑trial.

6. Hidden Disease Progression vs. Population Screening Disparities

Finally, as previously described, once‑in‑a‑lifetime testing reshapes how patients enter the clinical testing ecosystem – and changing from a disease-progression approach to a population‑level screening increases volatility before enrollment curves can reveal it. By the time disparities become visible, options are fewer and corrective actions are more costly.

Importantly, the companies most likely to struggle in the broader screening scenario will not be those experiencing a supply shortage. It will be those that continue planning as if disease progression and eligibility emerge gradually, enrollment behaves smoothly, and attrition will eventually thin treatment demand.

Organizations that adapt will realize that in Lp(a) trials, identification itself has become the most unstable variable in the system.

Achieving Controlled Complexity

Once‑in‑a‑lifetime testing does more than expand eligibility. It compresses discovery, destabilizes enrollment timing, and extends treatment exposure. For clinical supply teams, acknowledging that shift early is the difference between controlled complexity and reactive mitigation.