For manufacturers, the success of a new brand hinges on its uptake in the first few months after launch. Many pharma companies are surprised to realize that payer coverage is more limited than anticipated, especially in highly competitive markets. A poor launch can quickly lead to a disappointing performance trajectory.

To predict payer behavior with accuracy, many manufacturers analyze analogous market access scenarios to better anticipate the payer policies that might apply to their therapy. Analog analyses can reveal the details of policy development timelines and likely restrictions for both commercially insured patients and Medicare and Medicaid beneficiaries.

So, how do manufacturers choose which market analogs to analyze? In this post, we’ll explain how to select an analog that helps you uncover the key dynamics at play for your launch.

Selecting the ideal market analog

Typically, a launch analog analysis tracks coverage for a previously released drug throughout the first year of approval, within all payer segments. The chosen analog should be comparable to the manufacturer’s drug in at least one factor, such as the disease area, competitive landscape, or market entry timing. Although there is no set template for choosing the best analogs, there are a few key attributes to consider:

• Timing is of critical importance. The analog should be fairly recent, ideally within the last one to two years. recent years, more payers have instituted new-to-market blocks, which enable pre-determined coverage policies immediately after FDA approval for a set period of time. As payers also make coverage decisions a lot faster than they did historically, it’s important to stay within the current timeframe.
• The benefit type of the market analog is crucial. Payers manage medical benefit and pharmacy benefit drugs differently, and there may be significant discrepancies in areas such as the timing of policy development and release. It’s important to note that a therapy’s route of administration helps payers determine its benefit type. Though intravenous and oral therapies are clearly delineated, therapies which necessitate subcutaneous administration or intramuscular injections might be covered either or both benefit types, depending on payer preference. Some agents, such as cell and gene therapies, must be administered at certain sites, which also contributes to payer preferences. For products covered under both categories, it’s important to understand how the growing prevalence of white or brown bagging might affect reimbursement and utilization.
• Category dynamics play a huge role in analog selection. If you’re planning to launch an agent in a competitive category, at least one of your analogs should be from a competitive market. The type and number of competitors is also important: Are they typically branded or generics? Do payers manage this class chiefly with step edits, diagnostic criteria, authorization duration, reauthorization criteria, or other means?

  It’s also key to understand payer behavior in competitive non-oncology categories, as the profusion of more targeted therapies can lead to increased contracting. Payers in these scenarios often prefer agents with a broader label, as manufacturers of therapies approved for multiple indications can offer payers more favorable discounts.

  Choosing an analog with a similar mechanism of action, testing requirements, line of therapy, and/or future pipeline can also be a smart move, as these factors can play a role in payer coverage. It’s also important to consider the segment mix for each analog, and to choose an analog that targets the same segments as your therapy.

  Although FDA designations—including fast-tracked drugs, breakthrough therapies and those with orphan status—can lead to accelerated approval, our data shows that they aren’t good criteria for analog selection. According to MMIT’s policy and restrictions data, such FDA designations do not influence payer preference. Qualitative findings from MMIT’s Message Monitor indicate that payers only evaluate therapies once they receive full approval—not conditional approval.

• Pricing dynamics within a category also plays an important role in analog selection. The analog’s price should be relatively similar to your brand’s price, as that will allow you to anticipate payer reactions and expectations on rebating.

  By overlaying net sales data with claims data, manufacturers also get a better understanding of the typical rebate percentages offered within the category. For example, categories like immunology are managed more tightly than a category like rare diseases. The types of contracts within these two categories vary dramatically, with the former more focused on contracting by rebates or by portfolio, while the latter tends to have warranty-based or value-based contracts.

  In categories like oncology, pricing also contributes to whether an agent is available via a regular pharmacy or a specialty pharmacy. Generally, high-cost drugs will have much more intensive utilization management in place. Expensive products are more likely to end up on a specialty tier and have additional restrictions compared to cheaper alternatives.

By considering all these factors, you can choose relevant analogs which closely mimic your therapy’s market entry scenario. Real-world payer preference and behavior data can also augment an analog analysis, providing further insights.

For example, primary research from MMIT’s Message Monitor and Index solutions can reveal payer reactions to factors such as dosing frequency or the product’s use in combination therapy. Supplementing quantitative data with such qualitative insights will provide you with a more holistic understanding of what payers are saying—and how they will likely respond to your drug at launch.

To see how we can help you predict uptake of your new therapy, learn more about MMIT’s Strategic Launch Report & Evaluate Forecast.

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