Jesse Mendelsohn, Model N

Developing effective lifesaving drugs requires significant research and development (R&D) investments. New medications cost an average of $2.3 billion to develop,

 largely funded by existing drug sales. At the same time, pharmaceutical companies are under increasing pressure from the government to reduce prescription prices, which presents a significant hurdle to maintaining the R&D pipeline.

Industry leaders and economic experts are concerned that the Inflation Reduction Act (IRA),

 with its price negotiations and caps, could further stifle research investments. According to Model N’s 2025 State of Revenue Report,

 nearly two-thirds of pharmaceutical leaders are worried about how the IRA will impact their pricing strategies.

Pharma manufacturers must adjust legacy pricing processes to maintain medication access while still supporting drug development. While difficult, this is doable. Effective revenue management and artificial intelligence (AI)-powered data analysis can help manufacturers navigate the pricing pressure.

Under the IRA, pharma manufacturers must negotiate Medicare drug prices for specific medications with the federal government. The parties reached an agreement on the first 10 drugs in 2024, and the Centers for Medicare and Medicaid Services announced the next 15 drugs up for negotiation earlier this year,

 which includes revenue-driving GLP-1s. The larger number of drugs included in the second set signals the federal government’s intent to accelerate cost reductions across a broader spectrum of therapeutic areas.

In addition to price negotiations, the IRA requires manufacturers to pay rebates to the government if they raise drug list prices faster than inflation, and it caps out-of-pocket expenses for Medicare recipients.

Some experts and industry leaders worry these provisions could potentially have a chilling effect on R&D in several ways:

The IRA’s pricing actions will likely compress revenue streams and reduce capital for R&D investments. After Medicare-negotiated prices take effect, manufacturers lose pricing flexibility, resulting in lower revenues for products that traditionally help fund research for new therapies. Inflation price caps restrict manufacturers’ ability to offset increasing operational costs. Maintaining compliance also requires dedicated resources that could detract from research initiatives. Research from the University of Chicago estimates IRA provisions could lead to a 31% decrease in pharmaceutical revenues through 2039 and result in 135 fewer drug approvals.

The price limits may discourage companies from pursuing new therapies, particularly those targeting Medicare beneficiaries. The potentially low—or even negative—return on investment makes R&D difficult to justify fiscally. With reduced revenue certainty, manufacturers may deprioritize high-risk or long-term projects, focusing instead on safer or more immediately profitable therapeutic areas.

The reduced incentive is particularly pronounced for biosimilar and generic development. If government negotiations significantly reduce brand-name drug prices, the profit margin for potential generic and biosimilar versions also drops. Combined with rebate structures that favor brand names, spending time developing and launching these lower-cost alternatives may not make financial sense.

The IRA's negotiated prices cover all of a drug’s indications, regardless of when the new uses are approved. This means pharma manufacturers cannot price the drug differently for the new treatment, further limiting their ability to recoup research investments. Additionally, a drug with multiple indications may attract government interest in negotiations more so than one with a single therapeutic use.

These factors disincentivize post-approval research, resulting in fewer treatment options for patients and curtailing evidence generation to track long-term health outcomes. Postmarketing studies generate more than just new indications; data from these kinds of studies demonstrates a drug’s safety and efficacy and informs prescribing practices. Research in 

demonstrates the IRA’s potential impact on this research.

 The market uncertainty generated by the IRA increases financial risks and complicates forecasting. If manufacturers don’t know which drugs will be included in future negotiations, they can’t predict revenue or prioritize research areas. The IRA also shortens the period during which manufacturers can charge market-driven prices, forcing companies to estimate the value of a drug over a shortened time frame. Pharma companies may hesitate to pursue long-term projects if they can’t accurately assess potential returns.

After just one year of negotiations, the IRA is already impacting pharma companies’ strategies. In Model N’s aforementioned State of Revenue Report, 87% of surveyed executives say they’ve already altered their launch plans for specific diseases or therapeutic areas.

Reducing the costs of R&D is difficult given the focus on safety, increasing sophistication of modern therapies, and specific requirements for FDA approvals. Despite scientists’ best efforts, many new compounds fail, and the ones that succeed often take years or even decades to bring to market.

 However, there are some reasons to be hopeful.

AI-powered drug discovery may soon expedite research timelines and reduce development costs. AI technologies enable comprehensive analysis of vast molecular databases to identify the most promising compounds, reducing failure risks and accelerating preclinical timelines. Additionally, genomic analysis and sequencing innovations will enable researchers to identify disease targets and biomarkers for more precise therapies.

Advanced analytics can enhance data quality by identifying ideal clinical trial candidates, quickly evaluating trial outcomes and monitoring adverse events. The result is more effective medications delivered quickly, increasing revenue opportunities.

However, these technologies are relatively new and require investments to implement. In the meantime, pharma manufacturers must overhaul their revenue management processes to maintain funding for drug R&D in this new pricing environment.

Enhancing revenue management processes to protect R&D

Legacy pricing strategies will no longer suffice for pharma companies under pressure for the IRA. They must improve their data gathering, aggregation, and analysis to develop optimized pricing tactics while also reducing revenue leakage.

Successful data analysis requires manufacturers to break down organizational silos and incorporate external sources to obtain and consolidate data from drug portfolios; R&D efforts; current contracts; sales; the supply chain; and competitor prices, along with market preferences and trends.

Manual data gathering no longer suffices in the complex market. Pharma companies should invest in automation to ensure they have comprehensive, current, and actionable data. AI-powered platforms can analyze this information to model different scenarios, such as the impact of IRA price negotiations on revenue, inflation penalties on Medicare segments versus commercial market revenue, rebate strategies for maintaining market access, and gross-to-net implications across different payer channels.

Such insights can inform launch and pricing strategies to maintain market access and margins. In many cases, success may require sophisticated rebate and contracting approaches. A robust revenue management platform enables manufacturers to develop optimal structures and accurately calculate rebates and payments to avoid unnecessary losses.

While the IRA’s long-term impacts on pharma manufacturers are yet to be seen, maintaining market access for drugs and R&D funding will be a delicate balance over the next few years. These companies will need to complement new R&D strategies with more nuanced pricing strategies to continue the steady development of new lifesaving drugs.

By integrating advanced analytics and AI-driven tools into their revenue management processes, manufacturers can anticipate market changes, mitigate regulatory risks, and optimize their portfolios while continuing to support innovation. More efficient and effective revenue management processes will shore up income to support short-term revenue goals while continuing to fund the long-term development efforts that both patients and the industry depend on.

Jesse Mendelsohn is a senior vice president at Model N, a revenue optimization and compliance provider for life sciences and high-tech companies.

1. Deloitte Pharma Study: R&D Returns are Improving – Regulation Could Stifle Innovation. Deloitte. June 24, 2024. 

https://www.deloitte.com/ch/en/about/press-room/deloitte-pharma-study-r-and-d-returns-are-improving.html

2. Inflation Reduction Act and Medicare. CMS.gov. 

https://www.cms.gov/inflation-reduction-act-and-medicare

3. 2025 State of Revenue Report. Model N. 

https://www.modeln.com/state-of-revenue-report-2025/

4. HHS Announces 15 Additional Drugs Selected for Medicare Drug Price Negotiations in Continued Effort to Lower Prescription Drug Costs for Seniors. CMS.gov. January 17, 2025. 

https:// www.cms.gov/newsroom/press-releases/hhs-announces-15-additional-drugs-selected-medicare-drug-price-negotiations-continued-effort-lower

5. Philipson, T.J.; Durie, T. The Impact of HR 5376 on Biopharmaceutical Innovation and Patient Health. The University of Chicago. November 29, 2021. 

https://cpb-us-w2.wpmucdn.com/voices.uchicago.edu/dist/d/3128/files/2021/08/Issue-Brief-Drug-Pricing-in-HR-5376-11.30.pdf

6. O’Brien, J.M.; Motyka, J.; Patterson, J.A. How the IRA Could Delay Pharmaceutical Launches, Reduce Indications, and Chill Evidence Generation. 

https://www.healthaffairs.org/content/forefront/ira-could-delay-pharmaceutical-launches-reduce-indications-and-chill-evidence

7. Van Norman, G.A. Drugs, Devices, and the FDA: Part 1: An Overview of Approval Processes for Drugs. 

https://doi.org/10.1016/j.jacbts.2016.03.002

Articles

How data-driven insights can help pharma companies balance their revenue management and innovation strategies—including resource allocation, pricing, and market access.

Maintaining R&D Investments in the IRA Era

 prescription drug products as approved for marketing, and there are approximately 

 active physicians in the US who can prescribe those drugs. Accounting for all of the pharmaceutical products that ultimately reach patients requires a massive effort, and this complexity directly impacts pharmaceutical manufacturers who contract with, and sell to, middlemen and prescriber organizations.

The key to untangling the web of complexity between drug companies, wholesalers and prescribers is effective master data management (MDM). For pharmaceutical companies, “master data” goes well beyond clean and up-to-date records of products, prices and customers. In order to effectively and profitably contract with prescribers, pharmaceutical manufacturers must maintain complicated sets of master data, including ever-changing group membership rosters. They must track group purchasing 

organizations (GPOs), integrated delivery networks (IDNs) and health systems and ascertain their eligibility across highly regulated government health insurance programs such as the 340B drug pricing program, Department of Defense (Tricare) and the Veteran’s Administration (VA).

Mismanaging master data can mean lost revenue for companies, incorrectly calculated rebates and government prices and even legal non-compliance. In other words, the consequences are significant and can directly affect the success of an organization.

Complexity drives the need for master data management

For the purposes of this article, master data management (MDM) means a true understanding of the parties a company is doing business with, what it sells and what price is charged. This “master data” information is also sometimes referred to as the “material master,” and there are also associated “masters” for identifying and tracking related data, including customer and pricing masters.

However, for pharmaceutical manufacturers, things are much more complicated, because basic customer or product information reflects only “ground-level,” or baseline, information required to manage complex prescriber and payment data.

This complexity is driven by the numerous different entities that purchase drugs, including independent and community pharmacies, large chains, hospitals, independent doctor’s offices and clinics, VA facilities, long-term care facilities and even military bases. And given the regulations that govern pharma companies, the way each of these discrete customers purchases drugs can be very different. As for government customers, regulations are even more strict and, therefore, complex.

When it comes to drug purchasing, larger entities obviously have more power, and, for that reason, many medical practices will join with others to obtain group and volume discounts. A common example is GPOs, which leverage the buying power of multiple entities to obtain better pricing, as well as relevant and legally-appropriate discounts. However, keeping track of which purchasers are eligible for what discounts, as well as ensuring they’re correctly enrolled in various discount programs, requires an incredible amount of tracking and management. Many pharma companies try— and fail—to manage their master data using manual methods like spreadsheets, and this critical business function often gets deprioritized.

In addition, manufacturers must keep track of wholesalers and their relationships with various provider organizations. For example, a long-term care facility might be working with a wholesaler that is unaware of the discounts that the facility is entitled to, or that wholesaler might be giving the facility an excessive and/or incorrect discount based on incorrect data.

Unfortunately, the onus is on the manufacturer to manage customer data and appropriate discounts, because there is little financial incentive for wholesalers to carefully track class of trade and other master data; their prime motivation is to make sales. Their lack of concern for the financial consequences that manufacturers face is because, in cases where manufacturers do not have strong MDM, these middlemen are made financially whole, whether they sell a given drug at full price or discount it and recover costs via chargeback.

Ultimately, pharma companies must deal more effectively with MDM. This is because mismanaging master data can lead to unhappy customers, as well as lost revenue, a result of incorrectly calculated rebates and government prices. In addition, failure to manage master data creates compliance risk and potential legal liability. What’s more, drug pricing has become a politically-charged issue, and the need for highly-scrutinized pharma companies to “get it right” has never been more important from a public relations standpoint.

Best practices and technology: Taming master data

Effective management of master data is critical to mitigating business risk, and, most important to manufacturers, to capturing maximum revenue. Given the complexity of the overall system of prescribers and payers, the most effective way to handle MDM and capture all of a company’s rightfully-earned revenue is by leveraging best practices and technology.

Comprehensive master data is far more than basic customer names, addresses and billing information. Proper master data is built on accurate and up-to-date visibility into products and what pricing and discount terms a provider organization should receive, taking into account factors such as relevant memberships and government regulations. Beyond a basic grasp of “what pills we sell,” firms must have visibility into the solutions they offer, as well as the critical details associated with those products, such as their size, shape, dimensions and weight. Comprehensive master data must also capture which regulatory processes those products have gone through, including their availability or lack thereof.

While it might sound counterintuitive, one of the most important best practices around MDM is to “start at the end.” What this means is that companies should establish a solid understanding of their customer base and then leverage technology to create a program of extremely solid data management built around the right pricing and privileges that each customer enjoys.

Systems should understand, for example, what providers and facilities are eligible for which pricing and discounts, based on specific criteria such as membership in a given GPO, as well as geography and other factors. Digital tools that manage this information also empower pharma companies to make discounting decisions using software that enhances speed and accuracy. These better-informed decisions, based on shared information, can then be used to drive wholesaler compliance while ensuring that customers get the right pricing and that sales are reported properly, ultimately reducing revenue leakage.

Effective oversight of master data can trigger early warnings that prevent losses. Technology solutions can provide manufacturers with an early warning system that can help ensure contract compliance. For example, a manufacturer might observe a transaction with a new or unknown customer who claims eligibility for VA or 340B pricing or one who might be attempting to incorrectly use a GPO discount. This transaction would then be automatically flagged in the system, giving the company the opportunity to adjust pricing, notify the impacted parties, and possibly reject an inappropriate chargeback request. The benefit of today’s revenue management solutions that integrate master data is that these pricing interventions can be done at scale, and in near real time, given the capabilities of cloud computing and data analysis.

Tech can facilitate proactive management of pricing and discounts through tight integration with membership organizations such as GPOs and wholesalers.

Driven by technology, more accurate reporting means that pharmas can leverage master data for increased sales. For example, if a company is reviewing a “heat map” of its sales and subsequently learns that a given geography is falling behind when compared to other regions of similar populations, it can rapidly implement new sales incentives and direct account management teams to extend special pricing that can lead to market share gains.

Manufacturers may have strong customer categorization and discount strategy, but if they are not in lockstep with their wholesalers regarding which entities should receive which pricing, they aren’t reaping the benefits. Data exchange with wholesalers and membership organizations is another area where systems and processes can drive increased contract compliance, while potentially recapturing lost or missed revenue opportunities. Optimizing data exchange with wholesalers means ensuring its absolute accuracy.

In addition, technology can improve support for membership practices, enabling companies to not only manage their membership but also to manage offices, subgroups and classes of trade. Tight synchronization gives companies better visibility into what providers they are reaching and serve as an effective roadmap for sales management efforts—enabling master data to become a tool not only for reducing revenue leakage but also for driving incremental revenue.

In the end, it is understandable that drug purchasers wish to gain every possible discount, and it is similarly understandable that pharma companies want to collect as much money as possible for their products. And through effective management of master data that integrates accurate and timely information, companies can ensure that their industry trading partners receive the pricing and compensation that they deserve in a fair, ethical and legally-compliant manner, ultimately mitigating business risk for all parties.

Jesse Mendelsohn is Vice President at Model N

Go-to safeguards for manufacturers in avoiding mismanagement of their business-central records