Drug efficacy is a primary concern for pharmaceutical research and development teams, payers, providers and patients alike. Comparing the efficacy of two therapies for a rare disease is often a matter of comparing several variables, including treatment effects, side effects and costs. You must track the right data for meaningful comparison.
When comparisons are data-driven, pharmaceutical companies can offer a clear, evidence-backed explanation of a drug’s value. They can also contextualize this value in terms of efficacy, safety, budget impacts and other key considerations.
Why It’s Time to Quantify Your Efficacy Comparisons
How well does a drug perform?
This deceptively simple question encompasses myriad factors. To answer it, pharmaceutical researchers and other interested parties need information on a medication’s treatment and side effects, administration, duration, interactions with other medications and conditions, and a host of other factors.
Some of these data points lend themselves well to being quantified. Others are more challenging to capture in numerical or electronic form. Striving for a uniform, interoperable approach to data collection and analysis is essential, however. Pharma teams that have better access to more comprehensive data on efficacy can make better comparisons, spot new avenues for research and improve their chances of a successful drug launch.
Quantifying efficacy comparisons can bolster market access strategy and its execution. A pharmaceutical company’s market access strategy and execution are essential to the success of a new orphan drug, write Brian Corvino and fellow authors at Deloitte. And the stakes are high: Pharmaceutical companies spend an average of $2 billion to bring a new pharmaceutical product to market, yet the failure rate of these launches is approximately 36 percent.
To demonstrate efficacy, pharmaceutical companies need clear, interoperable data through which to compare therapies.
Choosing and Communicating Data Points
Many information sources comparing drug efficacy are not initially quantitative in nature. Rather, these sources must be quantified in a consistent manner to allow for data analysis.
One example of this is patients’ own experiences with medications to treat a rare disease. Patients can provide valuable comparative insight into how various medications worked for them. Taken collectively, patients’ input can reveal patterns in efficacy among various therapies.
Real-world evidence (RWE), also known as real-world data (RWD), includes information about patients’ experiences. When gathered and analyzed effectively, “RDW/RWE particularly supports conditional marketing authorizations and approval of orphan medicines,” write Sini Marika Eskola and fellow researchers in a 2021 article in Clinical Pharmacology & Therapeutics.
Data sources that collect information on orphan drug efficacy have become a focus for many pharmaceutical companies and researchers. These data sources and related tools, like digital platforms, allow researchers to better understand comparative drug efficacy.
In a 2019 study, Salvatore Crisafulli and fellow researchers examined sources of rare disease patient data and orphan drug data in Italy. The researchers found that both digital sources of data and the information they contained were lacking — and that both pharmaceutical research and rare disease patients could suffer as a result.
“The establishment of large structured and integrated nationwide data sources, tailored to respond to both research as well as regulatory requirements, is necessary to provide clinically useful information on orphan drugs,” write Crisafulli, et al.
Lack of data makes comparisons difficult. Difficulties comparing rare disease therapies complicate attempts to demonstrate value and justify cost to payers and regulators. Solving the data problem makes better demonstrations of value possible, which in turn allows pharmaceutical companies to better access the positive economic impact of orphan drug research and manufacturing.
“There’s a positive economic return for rare disease therapies, even if they are expensive,” says Giacomo Chiesi, head of rare diseases at the Chiesi Group. Choosing the right data to communicate comparative efficacy can establish the value of rare disease therapies.
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