The original study, published online in November 2025 in the same journal, used real-world data to track how clinicians and patients actually behaved once a biosimilar alternative to the long-acting insulin analog became available. Real-world data studies of this kind occupy a crucial middle ground between randomized controlled trials and everyday clinical practice. Trials can establish that a biosimilar is therapeutically equivalent to its reference product, but they cannot fully capture how prescribing habits, formulary decisions, rebate negotiations, pharmacy benefit designs, and patient cost-sharing arrangements shape uptake in the messy reality of the American health system. The letter writers engage with exactly this gap, using their comment to probe what the utilization and spending patterns observed in the data actually reveal about whether biosimilar competition is delivering on its economic promise.

Insulin glargine is a long-acting basal insulin analog that has been a cornerstone of diabetes management for millions of people with type 1 and type 2 diabetes. As a biologic medicine, it is produced in living cell systems rather than through straightforward chemical synthesis, which means that even a functionally identical copy cannot be a molecular clone in the way a generic small-molecule drug can. Biosimilars are instead demonstrated to be highly similar to their reference products, with no clinically meaningful differences in safety, purity, or potency. This regulatory framework, established in the United States through the Biologics Price Competition and Innovation Act of 2010, was designed to open the door to competition while maintaining rigorous scientific standards. The arrival of the first biosimilar insulin glargine was therefore watched closely as a test case for whether the framework could translate regulatory approval into real savings at the pharmacy counter.

The stakes of this question are difficult to overstate. Insulin pricing in the United States has been the subject of sustained public outrage, legislative hearings, and policy reform efforts for more than a decade. List prices for analog insulins rose dramatically over the 2000s and 2010s, forcing many patients to ration doses, and the resulting human toll made insulin the emblematic case study of pharmaceutical pricing dysfunction. Biosimilars were held up by policymakers and health economists as one of the most credible market-based levers for change, because unlike price negotiation or direct regulation, they promised to lower prices through the familiar mechanism of competition. Whether that mechanism actually functions for biologics, where switching behavior is slower, prescriber inertia is stronger, and rebate structures can lock in incumbent products, is precisely the empirical question the original study addressed.

The commentary authors, based at institutions in India including Saveetha Medical College and Hospital in Chennai, Graphic Era Hill University in Dehradun, and Dr. D. Y. Patil Medical College in Pune, bring a pharmacoeconomic and global health perspective to the debate. Their letter engages with the methodological choices embedded in the original real-world analysis, and their response reflects a broader international interest in how biosimilar policy performs in the world’s largest pharmaceutical market. The authors report no funding for their work and declare no conflicts of interest, and they note that no new data were generated or analyzed in the comment itself, positioning it squarely as an interpretive and critical contribution to the literature rather than a new empirical study.

One of the central tensions in biosimilar economics is the distinction between list price and net price. When a biosimilar launches at a lower list price than the reference biologic, headlines often celebrate the discount. Yet the net price paid by insurers and patients is shaped by a web of rebates that manufacturers of the originator product offer to pharmacy benefit managers and health plans. These rebate arrangements can make the originator product cheaper for a plan’s formulary than the nominally cheaper biosimilar, a phenomenon that has been documented across several biologic drug classes. Real-world utilization data, such as those analyzed in the original study, are therefore essential for cutting through this opacity, because actual dispensing patterns reveal whether the biosimilar is genuinely displacing the originator or merely occupying a marginal share of the market.

Prescriber and patient behavior adds a second layer of complexity. Decades of research on generic substitution show that even for chemically identical small-molecule drugs, switching rates depend heavily on prescriber trust, electronic health record defaults, pharmacy-level substitution laws, and patient familiarity with a brand name. For biosimilars, the psychological barriers can be even higher, because patients may perceive a biologic copy as somehow less reliable than the original, and clinicians may harbor questions about immunogenicity or device differences even when regulatory data show equivalence. The pace of biosimilar insulin glargine uptake observed in real-world data thus reflects not only price dynamics but also the slow work of building clinical confidence in a new category of medicine.

The letter also situates the insulin glargine case within the wider arc of biosimilar policy. In Europe, where biosimilar uptake was encouraged earlier through national policies, tendering systems, and physician education campaigns, several biologic markets saw substantial price erosion and high biosimilar penetration within a few years of launch. The United States experience has been more uneven, with uptake varying dramatically by drug class, by payer type, and by the design of pharmacy benefit incentives. Comparing the American insulin glargine trajectory against these international benchmarks is one of the analytical lenses through which commentaries like this one help readers judge whether slow uptake is an inevitable feature of a fragmented system or a policy failure that could be corrected with better incentives.

What makes this exchange between the original researchers and the commenting authors scientifically valuable is that it exemplifies the self-correcting machinery of health services research. Real-world data studies rely on large claims databases and utilization records, and the interpretation of spending trends in such data is sensitive to choices about study windows, comparator groups, adjustments for rebate-driven price changes, and the treatment of patients who switch between products. A letter to the editor is a formal venue for surfacing those interpretive choices, questioning whether observed spending changes can be causally attributed to biosimilar entry, and proposing alternative readings of the same evidence. The published reply and the surrounding dialogue allow the field to converge on more robust conclusions than any single analysis could deliver alone.

As insulin policy in the United States enters a new phase, with out-of-pocket caps for Medicare beneficiaries, manufacturer price reductions, and continued biosimilar entries reshaping the landscape, the questions raised in this commentary will only grow in importance. The first biosimilar insulin glargine was a landmark test of whether competition can discipline biologic drug prices, and the real-world evidence examined by Watanabe and colleagues, together with the critical scrutiny it has attracted, suggests that the answer is neither a simple success nor a simple failure. Savings appear to depend on the fine print of formulary design, rebate negotiations, and the behavioral economics of prescribing. For the millions of patients who depend on basal insulin, and for the policymakers designing the next generation of drug pricing reforms, the lesson of this scholarly exchange is that regulatory approval of a biosimilar is only the beginning of the story; the economics of competition must be actively engineered, monitored, and debated in the open, one peer-reviewed analysis at a time.

Subject of Research: Real-world utilization and spending outcomes of the first biosimilar insulin glargine

Article Title: Comment on “Insulin Glargine Utilization and Spending Before and After the First Biosimilar Insulin Glargine: A Real-World Data Study”

Article References: Comment on “Insulin Glargine Utilization and Spending Before and After the First Biosimilar Insulin Glargine: A Real-World Data Study”. (n.d.). https://doi.org/10.1007/s11606-026-10834-4

Image Credits: AI Generated

DOI: 10.1007/s11606-026-10834-4

Keywords: insulin glargine, biosimilars, drug pricing, pharmacoeconomics, type 2 diabetes, real-world data, health policy, biologics, prescription drug spending, Journal of General Internal Medicine, pharmacy benefit design, diabetes care

Insulin glargine is a long-acting basal insulin analog that millions of people with type 1 and type 2 diabetes rely on to control blood glucose around the clock. For years, the market was dominated by a single originator product, making it one of the classic examples of a biologic drug with no direct competition. Biosimilars are copies of biologic medicines that are demonstrated to be highly similar to their reference products in quality, safety, and efficacy, yet without being clinically identical in the way generic small molecules are. Because insulin glargine is manufactured in living systems, its copies cannot be verified by a simple chemical assay; instead, regulators require extensive analytical, pharmacokinetic, and clinical comparative data. That regulatory architecture, designed to protect patients, has also slowed market entry and shaped how payers, pharmacies, and manufacturers respond once a biosimilar finally does arrive.

The clinical foundation for biosimilar insulin glargine was laid in randomized controlled trials conducted well before real-world uptake could be measured. The ELEMENT 2 study, published in Diabetes, Obesity and Metabolism, compared the investigational biosimilar LY2963016 with the originator insulin glargine in patients with type 2 diabetes who were either insulin-naive or already treated with glargine, and found similar efficacy and safety between the two products. Later, the phase III INSTRIDE 2 trial reported comparable results for a different glargine biosimilar candidate, MYL-1501D, versus insulin glargine over 24 weeks in patients with type 2 diabetes. These trials established that biosimilar glargine could, in principle, substitute for the originator without compromising glycemic control or safety. The question the California team pursued was whether that clinical equivalence translated into economic benefit in actual practice, outside the controlled conditions of a trial.

To answer it, the researchers turned to real-world data, examining patterns of insulin glargine utilization and spending across the period surrounding the biosimilar’s debut. Real-world data studies of drug utilization occupy a distinctive methodological niche. Unlike randomized trials, they cannot assign exposure; they must instead reconstruct a credible counterfactual: what would spending and use have looked like had the biosimilar never entered the market? The authors’ response letter makes clear that their analytical strategy leaned heavily on formal time-series methods, the statistical machinery developed for forecasting and intervention analysis in economics and engineering, adapted here to pharmaceutical claims data.

That machinery is not trivial. In their references, the authors cite the foundational work of George Box and Gwilym Jenkins on time series analysis, forecasting, and control, the framework that underlies modern autoregressive integrated moving average modeling. They also cite Rob Hyndman and Yeasmin Khandakar’s automatic time series forecasting tools for the R statistical environment, which allow analysts to fit and select forecasting models systematically rather than by hand. Most tellingly, they cite the 1987 Econometrica paper by Whitney Newey and Kenneth West on heteroskedasticity- and autocorrelation-consistent covariance matrices. That citation signals a careful attention to a problem that plagues interrupted time-series analyses of drug spending: the errors in monthly utilization and spending series are rarely independent, and their variance often shifts over time. Without Newey-West style corrections, standard errors can be badly underestimated, leading researchers to declare statistically significant changes that are really just noise in a serially correlated series.

The core finding that the authors defend in their response is sobering: the arrival of the first biosimilar insulin glargine did not produce the dramatic reduction in spending that generic entry typically delivers. Utilization and spending patterns for insulin glargine, analyzed with these rigorous time-series techniques, did not show the kind of sharp discontinuity that would indicate biosimilar competition reshaping the market. This result stands in stark contrast to the aggregate savings story told by the generic and biosimilar industry itself. The Association for Accessible Medicines, whose 2022 savings report the authors cite, has documented hundreds of billions of dollars in cumulative savings from generic and biosimilar medicines across the United States. The disconnect between that aggregate narrative and the insulin glargine case is precisely what makes the study, and the authors’ defense of it, so consequential.

Why would a biosimilar fail to move the needle where generics so often succeed? The economics of biologics differ in several structural ways. First, a single biosimilar entering a market dominated by one originator creates a duopoly, not the crowded field of many generic manufacturers that drives generic prices toward marginal cost. Second, rebate and formulary dynamics in the United States give pharmacy benefit managers and payers powerful reasons to keep preferred formulary positions with established products whose rebate structures are already negotiated; a biosimilar must often offer deep discounts simply to gain placement, and even then may capture only a fraction of volume. Third, insulin is frequently subject to patient cost-sharing arrangements, deductibles, and assistance programs that decouple the price paid by insurers from the price felt by patients, muting the demand-side response to list price changes. Fourth, switching friction matters: patients stable on a familiar insulin, and prescribers wary of any change in a life-sustaining therapy, may resist substitution even when regulators deem the products interchangeable or highly similar.

The authors’ response letter also addresses the inevitable methodological challenges that such an analysis invites. Real-world claims data are messy: utilization measured in units of volume must be reconciled with spending measured in dollars, and both are affected by changes in formulation sizes, packaging, rebates invisible to the analyst, and shifts in the insured population over time. Time-series intervention analysis requires specifying the date of the intervention, modeling pre-existing trends, and deciding whether to allow gradual or abrupt change. By grounding their approach in the Box-Jenkins tradition and using automatic forecasting procedures to establish a robust pre-intervention baseline, the authors can compare observed post-biosimilar trajectories against what the pre-existing trend predicted. The Newey-West correction then ensures that the statistical inference attached to any deviation is trustworthy. This is the kind of methodological hygiene that separates credible pharmacoeconomic research from casual before-and-after comparisons, and the authors’ willingness to defend it in print reflects the scrutiny that any claim about drug spending inevitably attracts.

The policy implications ripple outward. Insulin pricing has become one of the most visible flashpoints in American health care, prompting state-level copayment caps, federal action on insulin out-of-pocket costs for Medicare beneficiaries, and intense scrutiny of the rebate system. If biosimilar entry alone cannot discipline prices in this market, then the policy toolkit may need to focus on the intermediaries: the formulary and rebate negotiations that determine which products patients can actually access, and the cost-sharing design that determines what they pay at the counter. The insulin glargine experience suggests that simply adding a cheaper copy to the market is not sufficient; the structural incentives of the supply chain must be aligned so that savings reach patients rather than being absorbed in negotiations upstream.

There is also a forward-looking dimension. Insulin glargine was the first biosimilar in this therapeutic class, but it will not be the last biosimilar opportunity in diabetes care, and the lessons from this real-world analysis will inform expectations for every subsequent biologic that loses exclusivity. Pharmacists, health economists, and statisticians such as Watanabe, Strand, and Shen are building the evidentiary base that policymakers will need: careful, transparent, statistically defensible measurements of what actually happens when biosimilars enter complex markets. Their response letter, published in the Journal of General Internal Medicine, is a reminder that in pharmacoeconomics, as in clinical medicine, the details of method determine the credibility of the conclusion. If the United States wants biosimilars to deliver on their promise of affordable biologics, the insulin glargine experience shows that market entry is only the first step; the harder work lies in redesigning the incentives that decide whether a cheaper, clinically equivalent medicine actually changes what patients pay.

Subject of Research: Real-world utilization and spending outcomes for insulin glargine following the introduction of the first biosimilar insulin glargine in the United States

Article Title: Authors Respond: Insulin Glargine Utilization and Spending Before and After the First Biosimilar Insulin Glargine: A Real-World Data Study

Article References: Watanabe, J. H., Strand, M. W., & Shen, W. (2026). Authors Respond: Insulin Glargine Utilization and Spending Before and After the First Biosimilar Insulin Glargine: A Real-World Data Study. Journal of General Internal Medicine. https://doi.org/10.1007/s11606-026-10837-1

Image Credits: AI Generated

DOI: 10.1007/s11606-026-10837-1

Keywords: insulin glargine, biosimilars, drug spending, pharmacoeconomics, type 2 diabetes, real-world data, time-series analysis, drug utilization, Journal of General Internal Medicine, health policy, insulin pricing, biologics