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<journal-meta>
<journal-id journal-id-type="publisher-id">Front. Pharmacol.</journal-id>
<journal-title>Frontiers in Pharmacology</journal-title>
<abbrev-journal-title abbrev-type="pubmed">Front. Pharmacol.</abbrev-journal-title>
<issn pub-type="epub">1663-9812</issn>
<publisher>
<publisher-name>Frontiers Media S.A.</publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="publisher-id">1619984</article-id>
<article-id pub-id-type="doi">10.3389/fphar.2025.1619984</article-id>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Pharmacology</subject>
<subj-group>
<subject>Original Research</subject>
</subj-group>
</subj-group>
</article-categories>
<title-group>
<article-title>Determinants of orphan drug health technology assessment in South Korea: an empirical analysis</article-title>
<alt-title alt-title-type="left-running-head">Lee et al.</alt-title>
<alt-title alt-title-type="right-running-head">
<ext-link ext-link-type="uri" xlink:href="https://doi.org/10.3389/fphar.2025.1619984">10.3389/fphar.2025.1619984</ext-link>
</alt-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname>Lee</surname>
<given-names>Se Hee</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
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<contrib contrib-type="author">
<name>
<surname>Lee</surname>
<given-names>Sung Kyung</given-names>
</name>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
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</contrib>
<contrib contrib-type="author" corresp="yes">
<name>
<surname>Lee</surname>
<given-names>Jong Hyuk</given-names>
</name>
<xref ref-type="aff" rid="aff3">
<sup>3</sup>
</xref>
<xref ref-type="corresp" rid="c001">&#x2a;</xref>
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<contrib contrib-type="author" corresp="yes">
<name>
<surname>Bang</surname>
<given-names>Joon Seok</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<xref ref-type="corresp" rid="c001">&#x2a;</xref>
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<aff id="aff1">
<sup>1</sup>
<institution>College of Pharmacy, Sookmyung Women&#x2019;s University</institution>, <addr-line>Seoul</addr-line>, <country>Republic of Korea</country>
</aff>
<aff id="aff2">
<sup>2</sup>
<institution>Korea Institute for Industrial Economics and Trade</institution>, <addr-line>Sejong</addr-line>, <country>Republic of Korea</country>
</aff>
<aff id="aff3">
<sup>3</sup>
<institution>College of Pharmacy, Chung-Ang University</institution>, <addr-line>Seoul</addr-line>, <country>Republic of Korea</country>
</aff>
<author-notes>
<fn fn-type="edited-by">
<p>
<bold>Edited by:</bold> <ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/45907/overview">Michael Thiede</ext-link>, Scientific Institute of the AOK (WIdO), Germany</p>
</fn>
<fn fn-type="edited-by">
<p>
<bold>Reviewed by:</bold> <ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/2859907/overview">Johanna Lister</ext-link>, Takeda Pharmaceuticals International GmbH, Switzerland</p>
<p>
<ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/3130031/overview">Seung-Mi Lee</ext-link>, Catholic University of Daegu, Republic of Korea</p>
</fn>
<corresp id="c001">&#x2a;Correspondence: Jong Hyuk Lee, <email>assajh@cau.ac.kr</email>; Joon Seok Bang, <email>jsbang@sookmyung.ac.kr</email>
</corresp>
</author-notes>
<pub-date pub-type="epub">
<day>02</day>
<month>09</month>
<year>2025</year>
</pub-date>
<pub-date pub-type="collection">
<year>2025</year>
</pub-date>
<volume>16</volume>
<elocation-id>1619984</elocation-id>
<history>
<date date-type="received">
<day>01</day>
<month>05</month>
<year>2025</year>
</date>
<date date-type="accepted">
<day>11</day>
<month>08</month>
<year>2025</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#xa9; 2025 Lee, Lee, Lee and Bang.</copyright-statement>
<copyright-year>2025</copyright-year>
<copyright-holder>Lee, Lee, Lee and Bang</copyright-holder>
<license xlink:href="http://creativecommons.org/licenses/by/4.0/">
<p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</p>
</license>
</permissions>
<abstract>
<sec>
<title>Background</title>
<p>Orphan drug pricing in South Korea, including its workings and determinants, is a complicated aspect of the country&#x2019;s healthcare system. Therefore, this study identified the factors associated with the pricing of orphan drugs in South Korea.</p>
</sec>
<sec>
<title>Methods</title>
<p>The study screened 66 orphan drugs out of the whole set of newly reimbursed drugs from 2012 to 2022. The analysis presented the characteristics of the orphan drugs and accounted for correlations between treatment cost and other seemingly related variables. Formal multivariate regression analysis indicated associated factors in multivariate settings.</p>
</sec>
<sec>
<title>Results</title>
<p>Annual treatment cost per patient displayed a weakly negative correlation with the number of patients. Meanwhile, treatment cost exhibited a strongly positive correlation with the average of adjusted prices in seven advanced countries, reaching an adjusted R-squared value of 0.99. Treatment cost also showed a positive correlation with budget impact.</p>
</sec>
<sec>
<title>Conclusion</title>
<p>The pricing of orphan drugs in South Korea is predominantly influenced by external reference pricing. Drugs targeting smaller patient populations tend to be priced higher. This finding can guide policymakers, pharmaceutical companies, and healthcare providers in their efforts to balance patient accessibility with the sustainable development of orphan drugs and the financial sustainability of public health insurance.</p>
</sec>
</abstract>
<kwd-group>
<kwd>orphan drug</kwd>
<kwd>rare diseases</kwd>
<kwd>pricing</kwd>
<kwd>health technology assessment</kwd>
<kwd>reimbursement</kwd>
<kwd>budget impact</kwd>
<kwd>external reference pricing</kwd>
</kwd-group>
<counts>
<page-count count="11"/>
</counts>
<custom-meta-wrap>
<custom-meta>
<meta-name>section-at-acceptance</meta-name>
<meta-value>Drugs Outcomes Research and Policies</meta-value>
</custom-meta>
</custom-meta-wrap>
</article-meta>
</front>
<body>
<sec id="s1">
<title>1 Introduction</title>
<p>With advances in medical and diagnostic technologies, as many as 6,000&#x2013;8,000 rare diseases have been identified, and so the number of patients diagnosed with rare diseases has been on the rise (<xref ref-type="bibr" rid="B4">Boycott and Ardig&#xf3;, 2018</xref>; <xref ref-type="bibr" rid="B33">Nguengang Wakap et al., 2020</xref>; <xref ref-type="bibr" rid="B2">Baynam et al., 2024</xref>). A significant proportion of rare diseases are chronic, progressive, debilitating, life-threatening, and/or fatal. As such, unmet medical needs with respect to rare diseases have led to strategic investments in these therapeutic areas (<xref ref-type="bibr" rid="B11">Dunoyer, 2011</xref>; <xref ref-type="bibr" rid="B36">Rodriguez-Monguio et al., 2017</xref>; <xref ref-type="bibr" rid="B4">Boycott and Ardig&#xf3;, 2018</xref>). However, rare diseases tend to have extremely low prevalence, with some occurring in a mere 1&#x2013;5 per 1,000,000 people (<xref ref-type="bibr" rid="B33">Nguengang Wakap et al., 2020</xref>). The poor prospect of commercial success, given the smaller-sized patient pool, has worked against the strategic business planning of profit-oriented pharmaceutical companies in this area (<xref ref-type="bibr" rid="B14">Gammie et al., 2015</xref>).</p>
<p>However, patients with rare diseases collectively account for 4%&#x2013;8% of the general population (<xref ref-type="bibr" rid="B33">Nguengang Wakap et al., 2020</xref>; <xref ref-type="bibr" rid="B2">Baynam et al., 2024</xref>). Moreover, disease impact reverberates throughout the lives and livelihoods of patients and their families. As many as 50% of those affected are children, 30% of whom will die before they reach the age of 5&#xa0;years (<xref ref-type="bibr" rid="B14">Gammie et al., 2015</xref>; <xref ref-type="bibr" rid="B42">The Lancet Diabetes, 2019</xref>; <xref ref-type="bibr" rid="B6">Chan et al., 2020</xref>; <xref ref-type="bibr" rid="B33">Nguengang Wakap et al., 2020</xref>).</p>
<p>After the US introduced the <italic>Orphan Drug Act</italic> in 1983, other developed countries, including the EU and Japan, have followed suit. Relevant initiatives have included the designation of orphan drugs (ODs), marketing approval and market exclusivity, and a raft of financial incentive programs particularly earmarked for rare diseases (<xref ref-type="bibr" rid="B14">Gammie et al., 2015</xref>; <xref ref-type="bibr" rid="B6">Chan et al., 2020</xref>). Presently, over half of all countries provide subsidies for the research and development (R&#x26;D) of ODs (<xref ref-type="bibr" rid="B5">Braun et al., 2010</xref>; <xref ref-type="bibr" rid="B14">Gammie et al., 2015</xref>; <xref ref-type="bibr" rid="B6">Chan et al., 2020</xref>; <xref ref-type="bibr" rid="B35">Postma et al., 2022</xref>). Subsequent market interest has led to the filing of as many as 5,099 OD applications in the US; as of 2019, 724 pipelines (14%) have successfully reached the final approval stage (<xref ref-type="bibr" rid="B32">Miller et al., 2021</xref>). Incentive legislation for orphan drugs has thus effectively contributed to their development, thereby addressing the unmet needs of patients with rare diseases (<xref ref-type="bibr" rid="B31">Michaeli et al., 2023</xref>; <xref ref-type="bibr" rid="B21">Ipe et al., 2024</xref>; <xref ref-type="bibr" rid="B30">Martins et al., 2024</xref>).</p>
<p>The sheer scale of R&#x26;D investment that has poured into the OD pipelines has naturally translated into exorbitant pricing for such drugs, in turn limiting patient access. As such, the price setting of ODs is intertwined with issues of patient accessibility and commercial viability for pharmaceutical companies. Despite the intervention of state-led initiatives, a wide range of discrepancies persist in terms of drug accessibility, with the large bulk of approved ODs remaining uninsured and stuck in reimbursement negotiation. Only approximately 65% of ODs approved in the EU are publicly insured as of 2018; in South Korea, the proportion was 56% in 2019 (<xref ref-type="bibr" rid="B40">Stawowczyk et al., 2019</xref>; <xref ref-type="bibr" rid="B28">Lee et al., 2020</xref>).</p>
<p>Reimbursement approvals are directly linked to patient accessibility&#x2014;ODs would not be financially feasible options to most patients without reimbursements (<xref ref-type="bibr" rid="B40">Stawowczyk et al., 2019</xref>). However, pricing &#x26; reimbursement (P&#x26;R) decisions require much caution as the number of ODs entering markets continue to rise and budgetary burdens become heavy (<xref ref-type="bibr" rid="B20">Hughes-Wilson et al., 2012</xref>; <xref ref-type="bibr" rid="B16">Hall and Carlson, 2014</xref>; <xref ref-type="bibr" rid="B29">Logviss et al., 2016</xref>; <xref ref-type="bibr" rid="B8">Czech et al., 2019</xref>). The pricing levels of ODs directly translate to pharmaceutical companies&#x2019; return on investment and, ultimately, long-term financial viability (<xref ref-type="bibr" rid="B10">Denis et al., 2010</xref>; <xref ref-type="bibr" rid="B7">C&#xf4;t&#xe9; and Keating, 2012</xref>; <xref ref-type="bibr" rid="B13">Franco, 2013</xref>; <xref ref-type="bibr" rid="B3">Berdud et al., 2020</xref>). Therefore, the pricing of ODs has great importance to all three parties involved&#x2014;patients, pharmaceutical companies, and policymakers.</p>
<p>Many countries have no specialized P&#x26;R pathways that apply only to ODs, opting instead for flexibilities and exceptions in their pricing systems, such as the use of conditional incremental cost-effectiveness ratio (ICER) threshold, weighted quality-adjusted life year (QALY), pharmaco-economic evaluation (PE) waivers, and risk-sharing agreements (RSAs) (<xref ref-type="bibr" rid="B14">Gammie et al., 2015</xref>; <xref ref-type="bibr" rid="B12">Felippini et al., 2024</xref>). Some countries have specialized funds that are earmarked for expensive medicines for fatal and rare diseases (<xref ref-type="bibr" rid="B14">Gammie et al., 2015</xref>; <xref ref-type="bibr" rid="B12">Felippini et al., 2024</xref>). To consider the incorporation of methods for assessing societal values and preferences within health technology assessments for ODs, P&#x26;R models such as multi-criteria decision analysis, proportional time trade-off, and discrete choice experiments are also examined (<xref ref-type="bibr" rid="B9">de Andr&#xe9;s-Nogales et al., 2021</xref>; <xref ref-type="bibr" rid="B43">V&#xe1;squez et al., 2024</xref>).</p>
<p>Four P&#x26;R pathways apply to newly approved ODs in South Korea: essential ODs, PE-waived ODs, weighted average price (WAP) ODs, and PE ODs. For serious and life-threatening rare diseases, RSAs can be applied (<xref ref-type="sec" rid="s12">Supplementary Figure S1</xref>) (<xref ref-type="bibr" rid="B27">Lee, 2021</xref>). Essential and PE-waived ODs should meet a set of prescribed criteria and can be listed based on the adjusted pricing of seven advanced countries (A7 countries: the US, United Kingdom (UK), Germany, France, Italy, Switzerland, and Japan) without proving cost-effectiveness (<xref ref-type="sec" rid="s12">Supplementary Table S1</xref>) (<xref ref-type="bibr" rid="B1">Bang and Lee, 2021</xref>). The pricing of WAP ODs is computed based on the WAP of alternatives. PE ODs can be listed up to premium prices with the support of PE studies. External reference pricing (ERP) refers to the average of adjusted A7 prices; the listing prices of newly approved drugs should not exceed average levels. Especially for PE-waived new drugs, the lowest value of adjusted A7 prices should be used as the reference price. Adjusted prices start from ex-factory rates, upon which exchange rates, value added tax, and distribution margins are applied and added (<xref ref-type="bibr" rid="B26">Kim et al., 2021</xref>).</p>
<p>In this study, we attempt to determine the factors associated with the pricing of ODs in South Korea with the goal of elucidating the workings and determinants of OD pricing there.</p>
</sec>
<sec sec-type="materials|methods" id="s2">
<title>2 Materials and methods</title>
<sec id="s2-1">
<title>2.1 Data sources</title>
<p>We screened 66 ODs out of the whole set of newly reimbursed drugs from 2012 to 2022 from the official website of the Health Insurance Review &#x26; Assessment Service (HIRA) (<xref ref-type="fig" rid="F1">Figure 1</xref>) (<xref ref-type="bibr" rid="B18">HIRA, 2024b</xref>). We picked out patient numbers, anatomical therapeutic chemical classification system (ATC), number of listed countries among the A7, modality, and the average of A7 adjusted prices as potential explanatory variables (<xref ref-type="table" rid="T1">Table 1</xref>). The respective data on each variable were obtained from HIRA Drug Reimbursement and Evaluation Committee reports (<xref ref-type="bibr" rid="B17">HIRA, 2024a</xref>). The A7-adjusted prices were calculated from official drug pricing sources of the respective A7 countries as available prior to the reimbursement decision in South Korea for each product. These sources included the Redbook Index (US), Monthly Index of Medical Specialities (UK), ROTE Liste (Germany), VIDAL (France), L&#x27;Informatore Farmaceutico (Italy), Arzneimittel Kompendium (Switzerland), and Hokenyaku Jiten published by Yakugyo Kenkyukai (Japan) (<xref ref-type="bibr" rid="B19">HIRA, 2025</xref>). In line with previous research, we used annualized treatment costs to standardize OD prices using the information found in the summary of product characteristics and Korean National Health Insurance Service reimbursement conditions (<xref ref-type="bibr" rid="B24">Jommi et al., 2021</xref>; <xref ref-type="bibr" rid="B44">Villa et al., 2022</xref>). If the recommended dosage exceeded one, we used the annualized price for multiple shots; if recommended dosage was only one for a full year, we used the price for a single administration. All cost data were converted from Korean won (KRW) to US dollars (USD) using the 2022 average annual exchange rate (1 USD &#x3d; 1,294 KRW) based on data from the Bank of Korea.</p>
<fig id="F1" position="float">
<label>FIGURE 1</label>
<caption>
<p>Flowchart for data screening HIRA, health insurance review and assessment service; OD, orphan drug; MA, marketing authorization.</p>
</caption>
<graphic xlink:href="fphar-16-1619984-g001.tif">
<alt-text content-type="machine-generated">Flowchart detailing the selection process for a study on newly reimbursed drugs from the HIRA database between 2012 and 2022. It starts with 236 drugs, narrowing to 69 orphan drugs, then 67 accessible orphan drugs, and finally, a dataset of 66 drugs used in the study. Filters include non-orphan drugs (167), marketing authorization withdrawn (2), and no reference available (1).</alt-text>
</graphic>
</fig>
<table-wrap id="T1" position="float">
<label>TABLE 1</label>
<caption>
<p>Description of variables included in the analysis.</p>
</caption>
<table>
<thead valign="top">
<tr>
<th align="left">Variables</th>
<th align="left">Data type</th>
<th align="left">Definition</th>
</tr>
</thead>
<tbody valign="top">
<tr>
<td colspan="3" align="left">Dependent variable</td>
</tr>
<tr>
<td align="left">Annual treatment cost per patient</td>
<td align="left">Continuous</td>
<td align="left">Calculated treatment cost for standardizing orphan drug prices using the information found in the summary of product characteristics and NHIS reimbursement conditions.</td>
</tr>
<tr>
<td colspan="3" align="left">Independent variable</td>
</tr>
<tr>
<td colspan="3" align="left">A. Rarity</td>
</tr>
<tr>
<td align="left">Number of patients</td>
<td align="left">Continuous</td>
<td align="left">Estimated number of prevalent patients with target disease condition.</td>
</tr>
<tr>
<td colspan="3" align="left">B. Budget impact</td>
</tr>
<tr>
<td align="left">Estimated budget impact per year</td>
<td align="left">Continuous</td>
<td align="left">Estimated total medication cost per year if all target patients were treated with the drug.</td>
</tr>
<tr>
<td colspan="3" align="left">C. Listing status of reference countries</td>
</tr>
<tr>
<td align="left">Average of A7 adjusted prices (USD)<sup>1</sup>
</td>
<td align="left">Continuous</td>
<td align="left">Average adjusted listing prices of the drug in A7 countries.</td>
</tr>
<tr>
<td align="left">Number of listed countries among A7</td>
<td align="left">Continuous</td>
<td align="left">Number of countries already listed among reference A7 countries.</td>
</tr>
<tr>
<td align="left">Rate vs. average of A7 adjusted price</td>
<td align="left">Continuous</td>
<td align="left">Ratio of listing price in South Korea compared with average of A7 adjusted prices.</td>
</tr>
<tr>
<td colspan="3" align="left">D. P&#x26;R pathways</td>
</tr>
<tr>
<td align="left">Weighted average price (WAP)</td>
<td align="left">Categorical (Yes/No)</td>
<td align="left">Pricing based on alternatives&#x2019; weighted average price.</td>
</tr>
<tr>
<td align="left">Pharmaco-economic evaluation (PE)</td>
<td align="left">Categorical (Yes/No)</td>
<td align="left">Pricing based on PE.</td>
</tr>
<tr>
<td align="left">PE waiver</td>
<td align="left">Categorical (Yes/No)</td>
<td align="left">Pricing based on average A7 adjusted prices without PE.</td>
</tr>
<tr>
<td align="left">RSA assignment</td>
<td align="left">Categorical (Yes/No)</td>
<td align="left">Risk sharing agreement application.</td>
</tr>
<tr>
<td colspan="3" align="left">E. Indication</td>
</tr>
<tr>
<td align="left">Onco drug category</td>
<td align="left">Categorical (Onco/Non-Onco)</td>
<td align="left">Whether the drug is for rare-oncology treatment.</td>
</tr>
<tr>
<td align="left">Anatomical therapeutic chemical (ATC) classification (1st Level)</td>
<td align="left">Categorical</td>
<td align="left">Anatomical or pharmacological groups of the drug&#x2019;s active substances.</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>A7: Advanced seven countries (US, UK, Germany, France, Italy, Switzerland, and Japan; P&#x26;R, pricing and reimbursement).</p>
</fn>
</table-wrap-foot>
</table-wrap>
</sec>
<sec id="s2-2">
<title>2.2 Analysis methods</title>
<p>We used descriptive analysis to present the characteristics of the ODs. We applied Fisher&#x2019;s exact test on the differences of RSA assignment patterns between oncology and non-oncology drugs. We broke down the annual treatment cost per patient, our dependent variable, by P&#x26;R pathways, RSA assignment, and the anatomical therapeutic chemical (ATC) classification system. We then looked into correlations between treatment cost and other seemingly related variables. We also conducted formal multivariate regression analyses to identify associated factors in multivariate settings. We used R version 4.3.1 to carry out statistical analysis.</p>
<p>Prior to conducting analysis, we found that both the distributions of annual treatment cost per patient in South Korea&#x2014;our variable of interest&#x2014;and the corresponding A7 adjusted average prices&#x2014;one of our explanatory variables&#x2014;displayed highly skewed patterns to the left, with a few extremely highly priced ones set apart as outliers (<xref ref-type="sec" rid="s12">Supplementary Figure S2</xref>). Hence, we used <italic>log</italic>-transformed versions of both variables to make them exhibit a normalized distribution in our regressions.</p>
</sec>
</sec>
<sec sec-type="results" id="s3">
<title>3 Results</title>
<sec id="s3-1">
<title>3.1 Descriptive analyses</title>
<p>
<xref ref-type="table" rid="T2">Table 2</xref> presents a summary of the results of the descriptive analyses. Among the distinctive characteristics, we found that ODs typically received a PE waiver, as found in 26 of the 66 ODs (39.4%), or WAP, noted in 21 ODs (31.8%). Less than a third (19, 28.8%) passed through PE. ODs could be roughly evenly classified into oncology (32, 48.5%) and non-oncology indications (34, 51.5%). The results of Fisher&#x2019;s exact test showed that all differences were significant, except for the PE-waived case (<xref ref-type="table" rid="T3">Table 3</xref>). PE-waived ODs were the most likely to receive RSA assignment compared with other pathways, regardless of oncology indication. No non-oncology drugs that passed through WAP were RSA applied.</p>
<table-wrap id="T2" position="float">
<label>TABLE 2</label>
<caption>
<p>Summary of descriptive analyses.</p>
</caption>
<table>
<thead valign="top">
<tr>
<th colspan="4" align="center">Variables (<italic>N &#x3d;</italic> 66)</th>
<th align="center">Statistics</th>
</tr>
</thead>
<tbody valign="top">
<tr>
<td rowspan="5" align="center">Continuous variables</td>
<td colspan="3" align="center">Annual treatment cost per patient (USD)</td>
<td align="center">97348.5 (&#xb1;216180.4)</td>
</tr>
<tr>
<td colspan="3" align="center">Average of A7 adjusted prices (USD)</td>
<td align="center">1288.8 (&#xb1;2553.3)</td>
</tr>
<tr>
<td colspan="3" align="center">Number of patients</td>
<td align="center">519.4 (&#xb1;1116.8)</td>
</tr>
<tr>
<td colspan="3" align="center">Budget impact/year (USD)</td>
<td align="center">6722323.8 (&#xb1;9007838.3)</td>
</tr>
<tr>
<td colspan="3" align="center">Number of listed countries among A7</td>
<td align="center">5.2 (&#xb1;1.5)</td>
</tr>
<tr>
<td rowspan="18" align="center">Categorical variables</td>
<td rowspan="2" colspan="2" align="center">Oncology (N)</td>
<td align="center">Yes</td>
<td align="center">32 (48.5%)</td>
</tr>
<tr>
<td align="center">No</td>
<td align="center">34 (51.5%)</td>
</tr>
<tr>
<td rowspan="6" align="center">P&#x26;R pathway (N)</td>
<td rowspan="2" align="center">WAP</td>
<td align="center">Yes</td>
<td align="center">21 (31.8%)</td>
</tr>
<tr>
<td align="center">No</td>
<td align="center">45 (68.2%)</td>
</tr>
<tr>
<td rowspan="2" align="center">PE</td>
<td align="center">Yes</td>
<td align="center">19 (28.8%)</td>
</tr>
<tr>
<td align="center">No</td>
<td align="center">47 (71.2%)</td>
</tr>
<tr>
<td rowspan="2" align="center">PE waiver</td>
<td align="center">Yes</td>
<td align="center">26 (39.4%)</td>
</tr>
<tr>
<td align="center">No</td>
<td align="center">40 (60.6%)</td>
</tr>
<tr>
<td rowspan="2" colspan="2" align="center">RSA (N)</td>
<td align="center">Yes</td>
<td align="center">36 (54.5%)</td>
</tr>
<tr>
<td align="center">No</td>
<td align="center">30 (45.5%)</td>
</tr>
<tr>
<td rowspan="8" align="center">ATC (N)</td>
<td colspan="2" align="center">A (alimentary tract and metabolism)</td>
<td align="center">8 (12.1%)</td>
</tr>
<tr>
<td colspan="2" align="center">B (blood and blood forming organs)</td>
<td align="center">5 (7.6%)</td>
</tr>
<tr>
<td colspan="2" align="center">C (cardiovascular system)</td>
<td align="center">1 (1.5%)</td>
</tr>
<tr>
<td colspan="2" align="center">J (general anti-infective systemic)</td>
<td align="center">5 (7.6%)</td>
</tr>
<tr>
<td colspan="2" align="center">L (antineoplastic and immunomodulating agents)</td>
<td align="center">42 (63.6%)</td>
</tr>
<tr>
<td colspan="2" align="center">M (musculoskeletal system)</td>
<td align="center">2 (3.0%)</td>
</tr>
<tr>
<td colspan="2" align="center">N (nervous system)</td>
<td align="center">2 (3.0%)</td>
</tr>
<tr>
<td colspan="2" align="center">V (various)</td>
<td align="center">1 (1.5%)</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>Note: descriptive statistics tabulate means and standard deviations for continuous variables and frequencies and percentages for categorical variables.</p>
</fn>
<fn>
<p>A7, US, UK, Germany, France, Italy, Switzerland, Japan; P&#x26;R, pricing and reimbursement; WAP, weighted average price; PE, pharmaco-economic evaluation; RSA, risk sharing agreement; ATC, anatomical therapeutic chemical classification system.</p>
</fn>
</table-wrap-foot>
</table-wrap>
<table-wrap id="T3" position="float">
<label>TABLE 3</label>
<caption>
<p>Breakdown of orphan drugs by the pricing and reimbursement pathway, oncology indications, and risk sharing agreement assignment.</p>
</caption>
<table>
<thead valign="top">
<tr>
<th align="center">P&#x0026;R pathway</th>
<th align="center">Oncology (RSA applied)</th>
<th align="center">Non-oncology (RSA applied)</th>
<th align="center">p-value</th>
<th align="center">Total</th>
</tr>
</thead>
<tbody valign="top">
<tr>
<td align="center">WAP</td>
<td align="center">5 (3, 60%)</td>
<td align="center">16 (0, 0%)</td>
<td align="center">0.00</td>
<td align="center">21 (32%)</td>
</tr>
<tr>
<td align="center">PE</td>
<td align="center">11 (8, 73%)</td>
<td align="center">8 (2, 25%)</td>
<td align="center">0.03</td>
<td align="center">19 (29%)</td>
</tr>
<tr>
<td align="center">PE waiver</td>
<td align="center">16 (14, 88%)</td>
<td align="center">10 (9, 90%)</td>
<td align="center">0.84</td>
<td align="center">26 (39%)</td>
</tr>
<tr>
<td align="center">Total</td>
<td align="center">32 (25, 78%)</td>
<td align="center">34 (11, 32%)</td>
<td align="center">0.00</td>
<td align="center">66</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>Note: Fisher&#x2019;s exact test was used to test differences in proportions when null claim was that the proportions were the same.</p>
</fn>
<fn>
<p>P&#x26;R, pricing and reimbursement; WAP, weighted average price; PE, pharmaco-economic evaluation; RSA, risk sharing agreement.</p>
</fn>
</table-wrap-foot>
</table-wrap>
<p>On average, the PE-waived ODs tended to have much higher average treatment costs, USD 178,006&#x2014;almost double the average of all ODs at USD 97,349 and quadruple the average of the not-PE-waived drugs. This variation may be attributed to the extreme outlier cases, mostly in the PE waiver pathway (<xref ref-type="table" rid="T4">Table 4</xref>; <xref ref-type="fig" rid="F2">Figure 2a</xref>). Similarly, the RSA-assigned drugs had higher average treatment costs than the not-RSA-assigned cases; the median gap tended to be much smaller (<xref ref-type="table" rid="T4">Table 4</xref>; <xref ref-type="fig" rid="F2">Figure 2b</xref>). Meanwhile, other drugs that passed through either PE or WAP had lower averages than non-PE and non-WAP cases.</p>
<table-wrap id="T4" position="float">
<label>TABLE 4</label>
<caption>
<p>Average annual treatment cost per patient by the pricing and reimbursement pathway and anatomical therapeutic chemical classification system.</p>
</caption>
<table>
<thead valign="top">
<tr>
<th colspan="3" align="center">Variables</th>
<th align="center">Mean (STD)</th>
<th align="center">Median</th>
<th align="center">Range (Min, Max)</th>
</tr>
</thead>
<tbody valign="top">
<tr>
<td rowspan="6" align="center">P&#x26;R pathway</td>
<td rowspan="2" align="center">WAP</td>
<td align="center">N (<italic>N &#x3d;</italic> 45, 68%)</td>
<td align="center">117,586 (252,725)</td>
<td align="center">42,139</td>
<td align="center">(1,815, 1,531,474)</td>
</tr>
<tr>
<td align="center">Y (<italic>N &#x3d;</italic> 21, 32%)</td>
<td align="center">53,982 (92,064)</td>
<td align="center">11,333</td>
<td align="center">(34, 315,153)</td>
</tr>
<tr>
<td rowspan="2" align="center">PE</td>
<td align="center">N (<italic>N &#x3d;</italic> 47, 71%)</td>
<td align="center">122,591 (250,808)</td>
<td align="center">40,026</td>
<td align="center">(34, 1,531,474)</td>
</tr>
<tr>
<td align="center">Y (<italic>N &#x3d;</italic> 19, 29%)</td>
<td align="center">34,905 (47,184)</td>
<td align="center">23,598</td>
<td align="center">(4,714, 222,288)</td>
</tr>
<tr>
<td rowspan="2" align="center">PE waiver</td>
<td align="center">N (<italic>N &#x3d;</italic> 40, 61%)</td>
<td align="center">44,921 (73,940)</td>
<td align="center">21,619</td>
<td align="center">(34, 315,153)</td>
</tr>
<tr>
<td align="center">Y (<italic>N &#x3d;</italic> 26, 39%)</td>
<td align="center">178,006 (319,086)</td>
<td align="center">58,987</td>
<td align="center">(1,815, 1,531,474)</td>
</tr>
<tr>
<td rowspan="2" align="center">RSA</td>
<td colspan="2" align="center">N (<italic>N &#x3d;</italic> 30, 45%)</td>
<td align="center">47,353 (78,369)</td>
<td align="center">17,551</td>
<td align="center">(34, 315,153)</td>
</tr>
<tr>
<td colspan="2" align="center">Y (<italic>N &#x3d;</italic> 36, 55%)</td>
<td align="center">139,011 (278,882)</td>
<td align="center">43,220</td>
<td align="center">(1,667, 1,531,474)</td>
</tr>
<tr>
<td rowspan="8" align="center">ATC</td>
<td colspan="2" align="center">Alimentary tract and metabolism (A) (<italic>N &#x3d;</italic> 8)</td>
<td align="center">245,916 (198,762)</td>
<td align="center">221,456</td>
<td align="center">(6,799, 604,187)</td>
</tr>
<tr>
<td colspan="2" align="center">Blood and blood forming organs (B) (<italic>N &#x3d;</italic> 5)</td>
<td align="center">73,186 (90,186)</td>
<td align="center">21,596</td>
<td align="center">(11,243, 222,288)</td>
</tr>
<tr>
<td colspan="2" align="center">Cardiovascular system (C) (<italic>N &#x3d;</italic> 1)</td>
<td align="center">4,714</td>
<td align="center">4,714</td>
<td align="center">N.A.</td>
</tr>
<tr>
<td colspan="2" align="center">Anti-infectives for systemic use (J) (<italic>N &#x3d;</italic> 5)</td>
<td align="center">13,230 (8,310)</td>
<td align="center">10,238</td>
<td align="center">(4,161, 23,254)</td>
</tr>
<tr>
<td colspan="2" align="center">Antineoplastic and immunomodulating agents (L) (<italic>N &#x3d;</italic> 42)</td>
<td align="center">49,151 (77,622)</td>
<td align="center">30,490</td>
<td align="center">(1,667, 455,041)</td>
</tr>
<tr>
<td colspan="2" align="center">Musculo-skeletal system (M) (<italic>N &#x3d;</italic> 2)</td>
<td align="center">957,326 (811,967)</td>
<td align="center">957,326</td>
<td align="center">(383,179, 1,531,474)</td>
</tr>
<tr>
<td colspan="2" align="center">Nervous system (N) (<italic>N &#x3d;</italic> 2)</td>
<td align="center">20,030 (28,279)</td>
<td align="center">20,030</td>
<td align="center">(34, 40,026)</td>
</tr>
<tr>
<td colspan="2" align="center">Various (V) (<italic>N &#x3d;</italic> 1)</td>
<td align="center">1,815</td>
<td align="center">1,815</td>
<td align="center">N.A.</td>
</tr>
<tr>
<td colspan="3" align="center">Total (<italic>N &#x3d;</italic> 66)</td>
<td align="center">97,349 (216,180)</td>
<td align="center">29,066</td>
<td align="center">(34, 1,531,474)</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>Note: units are USD (exchange &#x3d; 1,294 KRW/USD, average annual exchange rate of 2022 based on data from the Bank of Korea).</p>
</fn>
<fn>
<p>P&#x26;R, pricing and reimbursement; WAP, weighted average price; PE, pharmaco-economic evaluation; RSA, risk sharing agreement; ATC, anatomical therapeutic chemical classification system.</p>
</fn>
</table-wrap-foot>
</table-wrap>
<fig id="F2" position="float">
<label>FIGURE 2</label>
<caption>
<p>Box plots of annual treatment cost per patient by pricing and reimbursement pathway, risk-sharing agreement, and anatomical therapeutic chemical classification system. <bold>(a)</bold> Annual treatments per patient using the pricing and reimbursement pathway. <bold>(b)</bold> Annual treatment per patient with risk-sharing agreement. <bold>(c)</bold> Annual treatment per patient by anatomical therapeutic chemical classification system. X-axis represents annual treatment cost per patient in USD (exchange &#x3d; 1,294 KRW/USD, average annual exchange rate of 2022 based on data from the Bank of Korea).</p>
</caption>
<graphic xlink:href="fphar-16-1619984-g002.tif">
<alt-text content-type="machine-generated">Three boxplot panels compare different variables. (a) Boxplots for pathways: PE, PEW, and WAP, with varied value distributions. (b) Boxplots for RSA categories: NOR and RSA, illustrating spread and outliers. (c) Boxplots for ATC categories A, B, C, J, L, M, N, and V, showing diverse distributions. Each plot contains outliers. Legends are provided for categorization.</alt-text>
</graphic>
</fig>
<p>A breakdown by ATC classification indicated that a majority of ODs (42 of 66, 63.6%) fell under code L, representing antineoplastic and immunomodulating agents. The average cost of L-coded ODs, USD 49,151, was almost half that of the entire dataset of USD 97,348, but its median was slightly higher than the median of the entire set (<xref ref-type="table" rid="T4">Table 4</xref>; <xref ref-type="fig" rid="F2">Figure 2c</xref>). The mean and median of ODs classified as musculoskeletal system (M), which had only two observations, were ten and three times higher, respectively, than those of the entire dataset.</p>
</sec>
<sec id="s3-2">
<title>3.2 Regression analysis</title>
<p>Before conducting regression analysis, we drew scatterplots between the average annual treatment cost per patient in South Korea, which is our dependent variable of interest, and three seemingly associated variables: number of patients, average of A7 adjusted prices, and budget impact (<xref ref-type="fig" rid="F3">Figure 3</xref>). The annual treatment cost per patient showed a weakly negative correlation with the number of patients, a strongly positive correlation with the average of adjusted A7 prices (reaching an adjusted <italic>R</italic>-squared value of 0.99), and a positive correlation with budget impact. The scatterplots of <italic>log</italic>-transformed variables are presented in <xref ref-type="sec" rid="s12">Supplementary Figure S3</xref>.</p>
<fig id="F3" position="float">
<label>FIGURE 3</label>
<caption>
<p>Scatterplots of annual treatment cost per patient against major variables. 1. Number of patients. 2. Average adjusted prices of advanced seven countries (US, UK, Germany, France, Italy, Switzerland, and Japan). 3. Budget impact.</p>
</caption>
<graphic xlink:href="fphar-16-1619984-g003.tif">
<alt-text content-type="machine-generated">Three scatter plots compare variables with Annual Cost. The first plot relates No. Patients and shows a weak R-squared of 0.028, indicating minimal correlation. The second plot connects A7 with a strong R-squared of 0.99, indicating a tight linear relationship. The third plot shows Budget Impact with an R-squared of 0.13, indicating a weak correlation. Blue lines and gray areas indicate trend lines and confidence intervals.</alt-text>
</graphic>
</fig>
<p>Regarding the regression analysis, we set the <italic>log</italic>-transformed annual treatment cost per patient as our dependent variable and regressed it on dummy variables for oncology indication, ATC classification, P&#x26;R pathway, and RSA assignment, and continuous variables for number of patients, budget impact, number of listed countries among A7 countries, and <italic>log</italic>-transformed average of adjusted prices in A7 countries (<xref ref-type="table" rid="T5">Table 5</xref>). To avoid multicollinearity, we checked the variance inflation factor (VIF) scores. For both the regression equations of a full specification and a model without the ATC classification dummy variables, we noted that both F-statistics and adjusted R-squared values displayed fairly good signs of model fitness.</p>
<table-wrap id="T5" position="float">
<label>TABLE 5</label>
<caption>
<p>Multivariate linear regression results for annual treatment costs per patient.</p>
</caption>
<table>
<thead valign="top">
<tr>
<th colspan="2" align="center">Regressor</th>
<th align="center">
<italic>Log</italic>(Annual treatment cost per patient) (1)</th>
<th align="center">
<italic>Log</italic>(Annual treatment cost per patient) (2)</th>
</tr>
</thead>
<tbody valign="top">
<tr>
<td colspan="2" align="center">(Intercept)</td>
<td align="center">1.55 (0.000&#x2a;&#x2a;&#x2a;)</td>
<td align="center">1.276 (0.000&#x2a;&#x2a;&#x2a;)</td>
</tr>
<tr>
<td rowspan="2" align="center">Oncology</td>
<td align="center">Oncology</td>
<td align="center">&#x2212;0.148 (0.011&#x2a;)</td>
<td align="center">&#x2212;0.084 (0.122)</td>
</tr>
<tr>
<td colspan="3" align="center">Non-oncology (reference variable)</td>
</tr>
<tr>
<td rowspan="8" align="center">ATC</td>
<td align="center">A</td>
<td align="center">Reference variable</td>
<td rowspan="8" align="center">-</td>
</tr>
<tr>
<td align="center">B</td>
<td align="center">&#x2212;0.224 (0.007&#x2a;)</td>
</tr>
<tr>
<td align="center">C</td>
<td align="center">&#x2212;0.218 (0.171)</td>
</tr>
<tr>
<td align="center">J</td>
<td align="center">0.104 (0.253)</td>
</tr>
<tr>
<td align="center">L</td>
<td align="center">&#x2212;0.062 (0.382)</td>
</tr>
<tr>
<td align="center">M</td>
<td align="center">&#x2212;0.167 (0.158)</td>
</tr>
<tr>
<td align="center">N</td>
<td align="center">&#x2212;0.695 (0.000&#x2a;&#x2a;&#x2a;)</td>
</tr>
<tr>
<td align="center">V</td>
<td align="center">&#x2212;0.184 (0.274)</td>
</tr>
<tr>
<td rowspan="3" align="center">P&#x26;R pathway</td>
<td colspan="3" align="center">PE (reference variable)</td>
</tr>
<tr>
<td align="center">PE Waiver</td>
<td align="center">&#x2212;0.003 (0.952)</td>
<td align="center">&#x2212;0.033 (0.600)</td>
</tr>
<tr>
<td align="center">WAP</td>
<td align="center">&#x2212;0.072 (0.152)</td>
<td align="center">&#x2212;0.067 (0.303)</td>
</tr>
<tr>
<td rowspan="2" align="center">RSA</td>
<td align="center">RSA</td>
<td align="center">0.158 (0.002&#x2a;&#x2a;)</td>
<td align="center">0.104 (0.102)</td>
</tr>
<tr>
<td colspan="3" align="center">Non-RSA (reference variable)</td>
</tr>
<tr>
<td colspan="2" align="center">Number of patients</td>
<td align="center">&#x2212;0.00005 (0.013&#x2a;)</td>
<td align="center">&#x2212;0.00006 (0.015&#x2a;)</td>
</tr>
<tr>
<td colspan="2" align="center">Budget impact</td>
<td align="center">0.000 (0.453)</td>
<td align="center">0.000 (0.493)</td>
</tr>
<tr>
<td colspan="2" align="center">Number of listed countries among A7</td>
<td align="center">0.022 (0.066&#x2a;)</td>
<td align="center">0.024 (0.134)</td>
</tr>
<tr>
<td colspan="2" align="center">
<italic>Log</italic>(average of adjusted prices in A7)</td>
<td align="center">1.035 (0.000&#x2a;&#x2a;&#x2a;)</td>
<td align="center">1.068 (0.000&#x2a;&#x2a;&#x2a;)</td>
</tr>
<tr>
<td colspan="2" align="center">
<italic>F</italic> statistic</td>
<td align="center">121.1 (0.000)</td>
<td align="center">116.5 (0.000)</td>
</tr>
<tr>
<td colspan="2" align="center">Adjusted <italic>R</italic>-squared (<inline-formula id="inf1">
<mml:math id="m1">
<mml:mrow>
<mml:mover accent="true">
<mml:mi>R</mml:mi>
<mml:mo>&#x5e;</mml:mo>
</mml:mover>
</mml:mrow>
</mml:math>
</inline-formula>
<sup>2</sup>)</td>
<td align="center">0.965</td>
<td align="center">0.934</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>Note: Inputs are estimated beta coefficients (<inline-formula id="inf2">
<mml:math id="m2">
<mml:mrow>
<mml:mover accent="true">
<mml:mi mathvariant="bold-italic">&#x3b2;</mml:mi>
<mml:mo>&#x5e;</mml:mo>
</mml:mover>
</mml:mrow>
</mml:math>
</inline-formula>) and numbers in parentheses are p-values. &#x2a;&#x2a;&#x2a; denotes corresponding coefficient is statistically significant at a significance level (&#x3b1;) of 1%, &#x2a;&#x2a; for the case of &#x3b1; &#x3d; 5%, and &#x2a; for the case of &#x3b1; &#x3d; 10%. Since the dependent variable and one of the independent variables are log-transformed, coefficients for log-transformed independent variables are interpreted as the expected percentage change in <inline-formula id="inf3">
<mml:math id="m3">
<mml:mrow>
<mml:mi mathvariant="bold-italic">y</mml:mi>
</mml:mrow>
</mml:math>
</inline-formula> in response to a 1% change in <inline-formula id="inf4">
<mml:math id="m4">
<mml:mrow>
<mml:msub>
<mml:mi mathvariant="bold-italic">x</mml:mi>
<mml:mi mathvariant="bold-italic">j</mml:mi>
</mml:msub>
</mml:mrow>
</mml:math>
</inline-formula> and coefficients for other regressors as expected (100 &#x2a; <inline-formula id="inf5">
<mml:math id="m5">
<mml:mrow>
<mml:mover accent="true">
<mml:msub>
<mml:mi mathvariant="bold-italic">&#x3b2;</mml:mi>
<mml:mi mathvariant="bold-italic">j</mml:mi>
</mml:msub>
<mml:mo>&#x5e;</mml:mo>
</mml:mover>
</mml:mrow>
</mml:math>
</inline-formula>) % for a unit increase in <inline-formula id="inf6">
<mml:math id="m6">
<mml:mrow>
<mml:msub>
<mml:mi mathvariant="bold-italic">x</mml:mi>
<mml:mi mathvariant="bold-italic">j</mml:mi>
</mml:msub>
</mml:mrow>
</mml:math>
</inline-formula>.</p>
</fn>
<fn>
<p>P&#x26;R, pricing and reimbursement; WAP, weighted average price; PE, pharmaco-economic evaluation; RSA, risk sharing agreement; ATC, anatomical therapeutic chemical classification system; A7, US, UK, Germany, France, Italy, Switzerland, and Japan.</p>
</fn>
</table-wrap-foot>
</table-wrap>
<p>Regarding the significance of individual variables, both regression equations showed that the number of patients was negatively associated with <italic>log</italic>-transformed average treatment cost. In other words, the <italic>log</italic> of the average treatment cost could be expected to decrease by 0.005%, &#x2212;0.005% &#x3d; (100&#x2a; <inline-formula id="inf7">
<mml:math id="m7">
<mml:mrow>
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</inline-formula>)% &#x3d; (100&#x2a; &#x2013; 0.00005)%, for the full specification and by 0.006% for the model without the ATC classification in response to each additional patient increase. For both regression models, a 1% change in <italic>log</italic>-transformed adjusted average prices in the A7 was associated with a <inline-formula id="inf8">
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</inline-formula>% change in <italic>log</italic>-transformed average treatment cost. As such, a 1% change in <italic>log</italic>(average of A7 adjusted prices) was estimated to correspond to a 1.035% increase in <italic>log</italic>(average treatment cost) for the full model and a 1.068% increase for the partial model.</p>
<p>In the full model with a higher adjusted-<italic>R</italic> value and <italic>F</italic>-statistic, a classification as an oncology drug was associated with a &#x2212;14.8% change in <italic>log</italic>(average treatment cost) compared with non-oncology cases. B- and N-coded drugs (for blood and blood-forming organs and for nervous system, respectively) in the ATC classification were associated with changes of &#x2212;22.4% and &#x2212;69.5% in <italic>log</italic>(average treatment cost), respectively. Meanwhile, RSA-assigned drugs were associated with a 15.8% change in <italic>log</italic>(average treatment cost).</p>
<p>Both scatterplots and regression results indicated an almost one-to-one correspondence between average treatment cost in South Korea and adjusted average prices in A7 countries, indicating that the OD pricing in South Korea was highly influenced by the changes in adjusted average prices in A7 countries. The intrinsic characteristics of drugs, such as oncology indication and ATC classification, and RSA assignment status also had much influence. Lastly, the number of patients made a small degree of difference. Meanwhile, differences in P&#x26;R pathways and budget impact do not incur changes in average treatment cost, contrary to our expectation.</p>
<p>Therefore, the drug characteristics that qualified ODs to certain P&#x26;R pathways and other external factors collectively characterized the pricing of ODs. Notably, the pricing of ODs in South Korea did not move in tandem over different P&#x26;R categories. The P&#x26;R pathway dummy variables collectively contributed to the overall model fitness with other explanatory variables. Meanwhile, the strong statistical association with adjusted average prices in A7 countries indicated that reference prices had the greatest impact on the pricing of ODs in South Korea.</p>
</sec>
</sec>
<sec sec-type="discussion" id="s4">
<title>4 Discussion</title>
<p>P&#x26;R pathways have been widely regarded as the most important factor determining patients&#x2019; access to ODs (<xref ref-type="bibr" rid="B14">Gammie et al., 2015</xref>). Given that ODs are used by only a small fraction of the whole population, most are priced at an astronomical level to compensate for their R&#x26;D expenditure (<xref ref-type="bibr" rid="B31">Michaeli et al., 2023</xref>). Therefore, patients who need ODs face difficulties in affording such prices without the help of public reimbursement (<xref ref-type="bibr" rid="B40">Stawowczyk et al., 2019</xref>). For biopharmaceutical companies, having their drugs listed in the reimbursement system encourages consumption, which can help them recoup their hefty investment expenses in a financially viable and long-term way . Otherwise, even companies with pipelines whose safety and efficacy have passed through formal evaluation processes can go bankrupt if cashflow shortfalls occur (<xref ref-type="bibr" rid="B37">Rollet et al., 2013</xref>; <xref ref-type="bibr" rid="B40">Stawowczyk et al., 2019</xref>).</p>
<p>As more ODs enter the market, P&#x26;R decisions are becoming a major budgetary issue with which policymakers must contend. In South Korea, the expenditure share of ODs out of total pharmaceutical spending nationwide accounts for a mere 1%, below the 3% level of most European countries, and in the US or Bulgaria it can reach 5% or even to 9% (<xref ref-type="bibr" rid="B41">Teagarden et al., 2014</xref>; <xref ref-type="bibr" rid="B15">Gombocz and Vogler, 2020</xref>). Ultimately, decisions boil down to a moral dilemma between <italic>utilitarianism</italic>&#x2014;maximizing outcomes with finite healthcare resources&#x2014;and <italic>non-abandonment</italic>, which can be seen at favoring those in dire need (<xref ref-type="bibr" rid="B34">Onakpoya et al., 2015</xref>; <xref ref-type="bibr" rid="B25">Kanavos et al., 2020</xref>; <xref ref-type="bibr" rid="B45">Zimmermann et al., 2021</xref>; <xref ref-type="bibr" rid="B35">Postma et al., 2022</xref>).</p>
<p>The annual treatment costs per patient of existing ODs range between USD 1,300 and 500,000, with the average cost per patient between USD 32,000 and 35,000 (<xref ref-type="bibr" rid="B39">Schey et al., 2011</xref>; <xref ref-type="bibr" rid="B22">IQVIA, 2020</xref>). Meanwhile, the return on investment of ODs at 8.4% far exceeds that of non-orphan drugs at 2.3%&#x2014;a fact that raises suspicion that profit-oriented corporates tap patients&#x2019; willingness to pay in the absence of alternative treatments (<xref ref-type="bibr" rid="B38">Rzakhanov, 2008</xref>; <xref ref-type="bibr" rid="B7">C&#xf4;t&#xe9; and Keating, 2012</xref>; <xref ref-type="bibr" rid="B3">Berdud et al., 2020</xref>). In 2008, 43 blockbusters drugs, or drugs with sales exceeding USD 1 billion, had orphan designations (<xref ref-type="bibr" rid="B7">C&#xf4;t&#xe9; and Keating, 2012</xref>).</p>
<p>All ODs that are included in this study were developed by foreign companies and launched first in other countries and then entered the South Korean market. Since the Korean government runs the ERP system, it resulted in almost a one-to-one correspondence between adjusted A7 and Korean OD prices, leaving only limited scope for the influence of other determinants. Given that most advanced countries implement variants of the ERP system, once-determined pricings inevitably spill over to other neighboring countries and, thus, prices worldwide are aligned in some sense. Accordingly, pharmaceutical companies exploit the situation by delaying new drug launches in countries where they are highly likely to be compelled to list low prices and withdrawing marketing approvals upon unsatisfactory negotiations in the P&#x26;R review processes (<xref ref-type="bibr" rid="B37">Rollet et al., 2013</xref>; <xref ref-type="bibr" rid="B25">Kanavos et al., 2020</xref>).</p>
<p>In terms of regulatory harmonization, South Korea is placed ahead of the US, EU, and UK, but in terms of flexibility it is placed behind China (<xref ref-type="bibr" rid="B23">IQVIA, 2024</xref>). South Korean authorities have introduced layers of the abovementioned pathways in an attempt to accommodate accessibility for patients in dire need. Among the available P&#x26;R pathways in South Korea, ODs are given PE waivers the most (39%), then WAP (32%) and PE (29%). Drugs that go through the PE waiver pathway are also the most likely to be assigned an RSA compared to the other P&#x26;R pathways. Notably, orphan drugs that passed through PE waiver and are assigned an RSA tend to have higher average annual treatment costs than those that go through WAP or PE pathways.</p>
<p>Our study indicates that neither the difference in P&#x26;R pathways nor the total expected budget of an individual OD significantly influences its pricing. However, we found a strongly positive correlation with adjusted A7 prices&#x2014;in other words, the higher the adjusted A7 price, the higher the corresponding price in South Korea. Instead, the intrinsic characteristics of drugs, such as oncology status, ATC classification, and RSA assignment status, play a more substantial role in determining prices. While the expected budget of an individual OD does not appear to be statistically associated with individual OD pricing, it remains unclear whether this reflects an intentional exclusion of fiscal considerations or limitations in how such impact is operationalized in the pricing decision process.</p>
<p>Our findings suggest that while P&#x26;R pathways are structurally diverse and multi-layered in South Korea, pricing decisions are consistently driven by objective metrics such as A7 prices and drug characteristics. This indicates that a transparent and rule-based pricing environment is functioning there. In light of the global trend of increasing OD approvals and their substantial budgetary impact, South Korea&#x2019;s approach may serve as a reference for countries seeking balance between patient accessibility and fiscal sustainability in OD policy.</p>
<p>Our study has several limitations. First, in South Korea, RSAs, which require mandatory pay-back if sales exceed prespecified thresholds, represent one of the most financially supportive mechanisms for payers. As such, the listed and adjusted prices may have unrealized gaps and may not match the actual prices paid by payers and patients. In our study, as many as 54.5% of ODs included (i.e., 36 drugs) were classified as RSAs. However, we chose not to apply a sensitivity analysis to adjust for RSA refund, as similar refund-type RSAs are also common in comparator countries. Second, we calculated the annual treatment cost per patient based on the dosage regimen approved by the Korea Ministry of Food and Drug Safety (KMFDS). However, in real-world cases, treatment costs may be influenced by clinical practice guidelines or reimbursement criteria. As such, the actual prices may differ from the prices that we calculated.</p>
</sec>
<sec sec-type="conclusion" id="s5">
<title>5 Conclusion</title>
<p>Our study delineates the factors having actual influence on orphan drug (OD) pricing, highlighting the interplay between OD pricing and external reference prices, drug characteristics, and the size of the treatment-eligible patient population. In light of these results, we recommend the adoption of a flexible approach to OD pricing decisions to ensure that patients with ultra-rare diseases are not marginalized in terms of treatment access. Our findings can guide policymakers, pharmaceutical companies, and healthcare providers in their efforts to balance patient accessibility with OD development and the financial sustainability of public health insurance.</p>
</sec>
</body>
<back>
<sec sec-type="data-availability" id="s6">
<title>Data availability statement</title>
<p>The original contributions presented in the study are included in the article/<xref ref-type="sec" rid="s12">Supplementary Material</xref>; further inquiries can be directed to the corresponding authors.</p>
</sec>
<sec sec-type="author-contributions" id="s7">
<title>Author contributions</title>
<p>SeL: Data curation, Visualization, Investigation, Validation, Conceptualization, Methodology, Formal Analysis, Writing &#x2013; original draft, Writing &#x2013; review and editing. SuL: Writing &#x2013; review and editing, Formal Analysis, Writing &#x2013; original draft, Data curation, Visualization. JL: Resources, Data curation, Writing &#x2013; original draft, Project administration, Formal Analysis, Conceptualization, Methodology, Investigation, Writing &#x2013; review and editing, Supervision. JB: Writing &#x2013; original draft, Supervision, Conceptualization, Project administration, Writing &#x2013; review and editing.</p>
</sec>
<sec sec-type="funding-information" id="s8">
<title>Funding</title>
<p>The author(s) declare that no financial support was received for the research and/or publication of this article.</p>
</sec>
<sec sec-type="COI-statement" id="s9">
<title>Conflict of interest</title>
<p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p>
</sec>
<sec sec-type="ai-statement" id="s10">
<title>Generative AI statement</title>
<p>The author(s) declare that no Generative AI was used in the creation of this manuscript.</p>
<p>Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.</p>
</sec>
<sec sec-type="disclaimer" id="s11">
<title>Publisher&#x2019;s note</title>
<p>All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.</p>
</sec>
<sec sec-type="supplementary-material" id="s12">
<title>Supplementary material</title>
<p>The Supplementary Material for this article can be found online at: <ext-link ext-link-type="uri" xlink:href="https://www.frontiersin.org/articles/10.3389/fphar.2025.1619984/full#supplementary-material">https://www.frontiersin.org/articles/10.3389/fphar.2025.1619984/full&#x23;supplementary-material</ext-link>
</p>
<supplementary-material xlink:href="Supplementaryfile1.docx" id="SM1" mimetype="application/docx" xmlns:xlink="http://www.w3.org/1999/xlink"/>
</sec>
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