Establishment of reference standards in biosimilar studies

Author byline as per print journal: Aijing Zhang, PhD, Associate Professor Jung-Ying Tzeng, Professor Shein-Chung Chow, PhD

Abstract:
When an innovative biological product goes off-patent, biopharmaceutical or biotechnological companies may file an application for regulatory approval of biosimilar products. In practice, however, important information on the innovative (reference) product may not be available for assessment. Thus, it is important to first establish a reference standard while assessing biosimilarity between a biosimilar product and the reference product. In this paper, reference standard is established through the biosimilarity index approach based on a reference-replicated study (or R-R study), in which the reference product is compared with itself under various scenarios. The reference standard can then be used for assessing the degree of similarity between the test and reference drugs in biosimilar studies.

Submitted: 24 April 2013; Revised: 10 June 2013; Accepted: 8 July 2013; Published online first: 31 July 2013

Introduction

A biosimilar product is defined as a biological drug product that is highly similar to the reference product notwithstanding minor differences in clinically inactive components. No clinically meaningful differences exist in terms of safety, purity, and potency (Biologics Price Competition and Innovation [BPCI] Act 2009). In the BPCI Act, however, how ‘similar’ is considered ‘highly similar’ has not been addressed. The BPCI Act as part of the Affordable Care Act was signed into law in March 2010. It gave FDA the authority to approve similar biological drug products. Currently, FDA has not set out clear standards for biosimilar approvals [1].

Kang and Chow [2] proposed a method that uses the relative difference, i.e. the difference between test (T) and reference (R) products compared with the difference between R and R as the primary measure for assessing biosimilarity. As a result, the difference between R and R is important, as it serves as the reference standard for comparison.

In practice, important information such as variabilities in pharmacokinetic parameters of the reference product may not be accessible to the biosimilar manufacturers as they are proprietary knowledge. Thus, it is important to conduct a reference-replicated study (R-R study), i.e. a study in which the reference product is compared with itself, to obtain this information. As biosimilar products are made by living cells or organisms that are sensitive to small changes or variations in environmental factors, such as light or temperature, it is expected that a small difference will occur between two reference products resulting from different manufacturing processes or locations, or different batches from the same manufacturing process. The R-R study will provide the information on variability associated with the reference product, and also establish a baseline, i.e. similarity between R and R, for comparison in biosimilarity testing.

The biosimilarity index approach was proposed by Chow et al. [3, 4] to assess the level of similarity. The index is based on the concept of reproducibility probability and is evaluated as the estimated power of the testing procedure when the alternative hypothesis is true. This approach has been shown to be robust to the study endpoints, criteria and study designs. Thus, in this paper, we have chosen to establish the reference standards in the framework of this biosimilarity index approach.

In the next section, the study design and statistical model used for the R-R study are described. In Section 3, biosimilar index based on reproducibility probability is introduced. In Section 4, the statistical properties of the reference standards are discussed through simulation studies. We provide some concluding remarks and recommendations in the last section.

Study design and statistical model

Study design
Crossover designs are often recommended for bioequivalence studies. Biosimilarity is often assessed in a parallel design in practice [5], however, because of the relatively long half-life and the possibility of immunogenicity of biological products, concerns are increasing over interchangeability in biosimilars, which can only be assessed in crossover designs. Thus, we have chosen a standard two-sequence, two-period (2 × 2) crossover design for the establishment of reference standards. A 2 × 2 crossover study can be viewed as a two-arm parallel study with replicates; the advantage of using a crossover design is that the intra-subject variability could be estimated for the reference standards.

In a 2 × 2 standard crossover study design, participants are randomly assigned to either sequence 1 (R1R2) or sequence 2 (R2R1) in the R-R study. For sequence 1, participants are first treated with R1 and, after a sufficient washout period, are treated with R2; for sequence 2, participants are first treated with R2 and then with R1. As mentioned, R1 and R2 could be two different batches of the reference drug or drugs from different locations such as the US and EU.

Statistical model
The interval hypotheses of the similarity testing written in terms of parameters are:

where ? is the study parameter chosen to assess biosimilarity; ?L and ?U are the biosimilarity limits, i.e. the accepted lower and upper bounds for declaring biosimilarity. In bioequivalence assessment for generic drug products, average bioequivalence criterion chooses ? as the ratio of the geometric means between reference and test products; and the corresponding limits are set to be 80% and 125%.

The hypotheses in equation (1) are usually decomposed into two sets of one-sided hypotheses:

Schuirmann’s two one-sided tests procedure is commonly used for testing the hypotheses in equation (2) [6]. The overall p value is the larger of the p values from these two tests.

For a standard 2 x 2 crossover study design, we consider the following statistical model with the assumption of no carryover effects:

where Yijk is the response for subject i in the kth sequence at the jth period, where i = 1, … , nk indicates subject, j = 1, 2 indicates period, k = 1, 2 indicates sequence; µ represents the overall mean; Sik represents the random effect of ith subject in kth sequence, assumed independently and identically distributed (i.i.d.) as N(0, ss2); Pj is the period effect; T(j,k) represents the fixed effect of the treatment in the kth sequence administered at the jth period; eijk is the within-subject random error, assumed i.i.d. as N(0, se2). Finally Sik’s and eijk’s are assumed to be mutually independent.

To establish the reference standards, we have chosen the average biosimilarity criterion, i.e. ? = µR2 – µR1. One concludes average biosimilarity if

and

where and are the least square means for the two reference products; they can be obtained from the sequence-by-period means: and ; n1 and n2 are the number of subjects in sequence 1 and sequence 2; is the pooled sample variance of period difference from both sequences, and can be obtained from , where .

Biosimilarity index

Definition
To reflect the characteristics and effect of variability on the therapeutic effect of biological products, Chow et al. [3, 4] in 2011 proposed the development of an index based on the concept of the reproducibility probability to assess ‘highly biosimilar’.

Reproducibility probability in clinical trials was first proposed in 2002 by Shao and Chow [7] to provide assessment on the strength of evidence against null hypothesis for regulatory agencies in deciding whether a single clinical trial is sufficient for approval. Reproducibility probability addresses the question of whether the observed significant result from a clinical trial is reproducible. Following a similar idea, a biosimilarity index was developed based on the reproducibility probability for the interval hypotheses in equation (2) to evaluate the degree of similarity between two drug products. That is, we define a reproducibility probability for evaluating the biosimilarity of two drug products as follows:

PBI = concluding average biosimilarity between two products in a future trial given that the average biosimilarity based on a chosen criterion has been established in the first trial.

Evaluation
To evaluate the biosimilarity index, the index is expressed as the estimated power of the testing procedure when the alternative hypothesis is true, replacing the parameter by its estimate based on the data observed. By this estimated power approach, the biosimilarity index for the 2 × 2 crossover design and average biosimilarity criterion can be obtained from the following equation:

where TL and TU are the test statistics given in equation (4) and equation (5). Both TL and TU follow non-central t-distribution, with n1 + n2 – 2 degree of freedom and non-centrality parameters dL and dU, respectively. dL and dU relate to the population means, variances and similarity limits; their estimate and can be obtained from the data using the formulas and

Steps
To apply the proposed biosimilarity index approach to assess biosimilarity, Chow et al. in 2013 proposed the following steps [8], see Table 1.

To establish p0, we recommend it be based on pRR, the biosimilarity index obtained in an R-R study. By basing p0 on pRR, the biosimilarity index approach allows us to assess the degree of similarity relative to the reference product.

Merits
From the definition of the biosimilarity index and the testing steps illustrated above, we can see that this approach has the following advantages. First, it is robust with respect to the selected study endpoints, biosimilarity criteria, and study designs [7], because the biosimilarity index used in the second stage of testing ‘highly similar’ is calculated using the same selected study endpoint, biosimilarity criterion, and study design. Second, it takes variability into consideration in the calculation of the index, and is sensitive to the variance of the test products. Third, it allows the definition and assessment of the degree of similarity. Or, in other words, it provides an answer to the question of ‘how similar is considered highly similar?’

Establishing reference standard

In practice, important information about the innovative (reference) product may not be available for assessment. Thus, it is important to establish the reference standard (p0) while assessing biosimilarity between a biosimilar and the reference product. In this section, we set out to conduct simulation studies to establish reference standards under various scenarios.

Simulation design
Simulation studies were conducted to demonstrate how to establish reference standards. Standard 2 × 2 crossover design and average biosimilarity criterion are used. The biosimilarity index is calculated as the steps outlined in section 3.

The study parameter ? is the mean difference, i.e. ? = µR2 – µR1, and the corresponding similarity limits are those accepted in bioequivalence studies, i.e. (?L, ?U) = (log0.8, log1.25) = (-0.223, 0.223). The data are assumed to following normal distribution, and the means and variances of the two reference products are assumed to be the same in the simulation, i.e. µR1 = µR2 = µ and sR1 = sR2 = s. To evaluate how reference standards are affected by variability of the drug products, different coefficients of variation are chosen: 20–50% by an increment of 5%. Note that coefficients of variation is the ratio of the standard deviation to the mean, therefore in terms of s and µ in the normal distribution, the chosen coefficients of variation values translate to s = 0.2µ, 0.25µ, 0.3µ, 0.35µ, 0.4µ, 0.45µ or 0.5µ. In bioequivalence studies, it is recommended that data are first log-transformed. For lognormal distribution, coefficients of variation of 20%, 25%, 30%, 35%, 40%, 45% and 50% translate to s = 0.2, 0.25, 0.294, 0.340, 0.385, 0.429 and 0.472 (). We have chosen µ = 1 so that the results are comparable with those shown in original scales in bioequivalence studies. The effects of different sample sizes (n = n1 + n2 = 40, 50, 60, 70, 80, 90 and 100) with equal allocation are also investigated. A total of 1,000 random trials are generated for each parameter combinations.

Results

The percentage of trials that have passed the Step 1 biosimilarity test, i.e. the probability of claiming biosimilarity on the basis of the average biosimilarity criterion, are shown in Table 2 and Figure 1. The results are in accordance with results observed in other studies [9]. As the variability increases, the probability decreases. Even with no mean difference between the two drug products, the probability of concluding biosimilarity can be quite low when the coefficients of variation are high. For example, when the coefficients of variation is 50%, with a total sample size of 40, biosimilarity is concluded only about 30% of the times. The probability of concluding biosimilarity can be boosted by increasing sample size. In the previous scenario, the probability can be increased from 30% to 86% if a sample size of 100 is used instead of 40.

For those trials that have passed the Step 1 test, the p values from the Schuirmann’s two one-sided tests procedure are recorded and the averages are reported in Table 3 and Figure 2. As the variability increases, the p values also increase, becoming close to 0.05. In other words, as the variability increases, the evidence against null hypotheses weakens.

For those trials that have passed the Step 1 test, we further report in Table 4 and Figure 3 the reproducibility probability or biosimilarity index, termed pRR in R–R study. The pRR is calculated from equation (6). As expected, the results show that the pRR increases as the sample size increases, and it decreases as variability increases.

Notice that when the coefficients of variation are 45%, and the total sample size is 40, the pRR is only 48.5%. When a reference drug compared with itself can only have a 48.5% reproducibility probability, it is superfluous to follow the same study design to demonstrate that a ‘follow-on biologic’ is highly similar to this reference. There are two ways to correct a low pRR: one is to choose a different criterion as in bioequivalence studies with the use of a scaled criterion; the other is to increase sample size. This also demonstrates the importance of conducting an R–R study: one is able to choose an appropriate criterion, sample size, or both, to establish reference standards.

An example
As shown above in the simulation studies, as coefficients of variation increase, it is less likely that similarity can be declared even when R is compared with R. To further illustrate the effect of the high variability on the conclusion of biosimilarity, and how the biosimilarity index addresses the degree of similarity, we consider example data with a total sample size of 40 and coefficients of variation of 45%, again assuming no true mean difference.

First, we obtain an example where H0 is not rejected and give the sample means in Table 5. The observed mean difference and the observed variability . The 90% confidence interval of the mean difference is (-0.065, 0.327); similarity is not declared.

Under the same setting, another set of data is generated. Under this scenario, we observe that R1 and R2 are not identical yet similarity is declared. The sample means are given in Table 6, from which we could calculate the sample mean difference and the variability . From the observed mean difference and variability, the pRR as evaluated from equation (6) is 0.563. This example shows that when coefficients of variation is high, even though biosimilarity is declared based on the average similarity criterion, the degree of similarity as measured by biosimilarity index is low, so is the confidence that we have in the biosimilarity conclusion.

Conclusion

The simulation studies have shown that, as variance increases, the probability of declaring biosimilarity in the Step 1 test decreases; and, for those trials that pass the Step 1 test, the degree of similarity, quantified by biosimilarity index, decreases. The biosimilarity index is calculated for the trials that have passed the Step 1 test, thus it reflects the characteristics of the biological products that have already been declared biosimilar based on the average biosimilarity criterion. To apply the biosimilar index approach, an appropriate p0, see Table 1 for details of Step 3 in the testing procedure, needs to be determined. We recommend p0 = 0.7pRR or 0.8pRR, such that the comparison is relative to the reference products. If we define d = p0/pRR, then d can be used to address the degree of similarity and the question of ‘how similar is highly similar?’ Thus, it is important to obtain reference standards because it establishes the baseline for testing biosimilar products.

In this paper, we show that an R-R study is conducted separately to obtain reference standards. Reference standards can also be established concurrently using the three-arm study as proposed by Kang and Chow [2]. In the three-arm study, participants are randomly assigned to be treated with either test product (T) in one arm, or two different reference products (R1, R2) in the other two arms, such that reference standards are established concurrently in the two reference arms and used as baseline for the test product.

The idea as outlined in this paper can be applied to establish reference standards for biosimilarity index developed based on other criteria for the assessment of biosimilarity. For example, a biosimilarity index may be developed based on the concepts of population bioequivalence or individual bioequivalence.

Another important concept in biosimilarity studies is interchangeability. To assess interchangeability, it needs to be shown that the risk, i.e. safety or diminished efficacy of alternating or switching between use of the biosimilar and the reference product, are not greater than the risk of using the reference product without such alternation or switch. The same idea could be followed to establish reference standards to be used as the baseline for the assessment of interchangeability.

Acknowledgement

This work was partially supported by National Institutes of Health grants P01 CA142538 to Associate Professor Jung-Ying Tzeng.

Competing interests: None.

Provenance and peer review: Not commissioned; externally peer reviewed.

Co-authors

Associate Professor Jung-Ying Tzeng, Department of Statistics and Bioinformatics Research Center, North Carolina State University, Raleigh, NC 27607, USA

Professor Shein-Chung Chow, Professor of Biostatistics and Bioinformatics, Department of Bioinformatics and Biostatistics, Duke University School of Medicine, Suite 1102, Hock Plaza, 2424 Erwin Road, Durham, NC 27705, USA

References
1. U.S. Food and Drug Administration. Draft guidance for industry. Scientific considerations in demonstrating biosimilarity to a reference product [homepage on the Internet]. 2012 Feb [cited 2013 Jun 10]. Available from: http://www.fda.gov/downloads/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/UCM291128.pdf
2. Kang SH, Chow SC. Statistical assessment of biosimilarity based on relative distance between follow-on biologics. Stat Med. 2013;32(3):382-92.
3. Chow SC, Endreny L, Lachenbruch PA, Yang LY, Chi E. Scientific factors for assessing biosimilarity and drug interchangeability of follow-on biologics. Biosimilars. 2011;1:13-26.
4. Chow SC, Ju C. Assessing biosimilarity and interchangeability of biosimilar products under the Biologics Price Competition and Innovation Act. Generics and Biosimilars Initiative Journal (GaBI Journal). 2013;2(1):20-5. doi:10.5639/gabij.2013.0201.004
5. Hsieh TV, Chow SC, Yang LY, Chi E. The evaluation of biosimilarity index based on reproducibility probability for assessing follow-on biologics. Stat Med. 2013;32(3):406-13.
6. Schuirmann DJ. A comparison of the two one-sided tests procedure and the power approach for assessing the equivalence of average bioavailability. J Pharmacokinet Biopharm. 1987;15(6):657-80.
7. Shao J, Chow SC. Reproducibility probability in clinical trials. Stat Med. 2002; 21(12):1727-42.
8. Chow SC, Yang LY, Starr A, Chiu ST. Statistical methods for assessing interchangeability of biosimilars. Stat Med. 2013;32(3):442-8.
9. Chow SC, Liu PB. Design and analysis of bioavailability and bioequivalence studies. 3rd ed. New York: Taylor and Francis; 2008.

Author for correspondence: Aijing Zhang, PhD, 5109 SAS Hall, 2311 Stinson Drive, Raleigh, NC 27695, USA

Disclosure of Conflict of Interest Statement is available upon request.

Copyright © 2013 Pro Pharma Communications International

Permission granted to reproduce for personal and non-commercial use only. All other reproduction, copy or reprinting of all or part of any ‘Content’ found on this website is strictly prohibited without the prior consent of the publisher. Contact the publisher to obtain permission before redistributing.


Last update: 01/08/2022

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Biosimilar development and regulation in Japan

Abstract:
In Japan, biosimilars guidelines following the principles of the EU framework were established by Japan’s Ministry of Health, Labor and Welfare in March 2009. The guidelines cover the manufacturing process, characterization of quality attributes, and clinical and non-clinical studies for biosimilars. In this paper, the requirements for regulatory approval of biosimilars in Japan are outlined.

In Japan, a biosimilar product is defined as a biotechnological drug product developed by a different company, which is comparable to an approved biotechnology-derived product (reference product) of an innovator company [1, 2].

Biosimilars may be recombinant proteins and polypeptide products, their derivatives, and products of which they are components, e.g. conjugates. Biosimilars are proteins and polypeptide products produced from recombinant expression systems using micro-organisms or cultured cells and that can be highly purified and well-characterized using an appropriate set of analytical procedures.

A biosimilar product can generally be developed on the basis of data that demonstrates the comparability between the biosimilar product and the reference product with respect to quality, safety and efficacy, or other relevant data, see Figure 1.

The reference products should be drugs approved in Japan and be the same product throughout the development period of the biosimilar products.

Manufacturing process

It should be established that the manufacturing process for the biosimilar is highly consistent and robust. If the host cell line used for the production of the reference product is disclosed by the innovator company, it is preferred that the same cell line is used. The cell banks should be established and characterized following ICH Q5A, Q5B and Q5D guidelines. The manufacturing processes should be adapted to improve the safety of the biosimilar, as long as this does not affect efficacy [1].

Quality characterization

The biosimilar should be fully characterized using state of the art analytical methods, and should include:

1) structural characterization
2) physicochemical properties
3) biological activity
4) immunological properties
5) impurities

Formulation

The dosage form and route of administration of the biosimilar product should be the same as that of the reference product. However, it is not essential for the biosimilar product to have the same formulation as the reference product, provided that the safety and efficacy are not affected.

Stability

Long-term, real-time, real-condition stability studies following the ICH Q5C guideline are required. The expiry date for the biosimilar product should be determined based on data from real-time/real-temperature studies. A comparison of stability with the reference product will not necessarily be required. Accelerated and stress stability studies are recommended to obtain useful data for evaluating the properties of the biosimilar product.

Comparability studies on quality attributes

In addition to elucidating the quality attributes of the biosimilar, comparability exercises comparing quality attributes between the biosimilar and the reference product should be conducted [1].

Non-clinical studies

Non-clinical studies that can ensure the safety for administration to humans should be performed and completed prior to initiation of clinical studies.

Both comparability and individual assessments are applicable, depending on the purpose of the study.

Pharmacokinetics
It is useful to compare the non-clinical pharmacokinetics in some glycoproteins.

Pharmacological studies
Comparability of the pharmacological action should be directly evaluated.

Toxicity studies

  • Repeated-dose toxicity studies may be useful to evaluate both single-dose and repeated-dose toxicity.
  • Local tolerance could be evaluated – in repeated-dose toxicity studies.
  • A direct comparative study of the toxicity profile may not always be necessary, e.g. for process-related impurities.
  • Other general non-clinical safety studies are generally unnecessary.

Clinical studies

Where the data from quality characterization and non-clinical studies is insufficient to demonstrate comparability with the reference product clinical studies are required. Clinical studies should be designed based on the data from quality characterization, non-clinical studies and comparability studies [1].

It is necessary to carry out comparability studies between the biosimilar and the reference product for pharmacokinetic, pharmacodynamic, efficacy and safety parameters.

Where comparability has been demonstrated through clinical pharmacokinetic, pharmacodynamic or pharmacokinetic/pharmacodynamic studies, further clinical studies may be reduced in some cases. In general, a well-designed crossover study should be considered in order to evaluate the comparability between the biosimilar and the reference product. However, a crossover study may not be suitable for long half-life products or products which may cause formation of antibodies.

Competing interests:None.

Provenance and peer review: Article prepared based on extensive research, internally peer reviewed.

Michelle Derbyshire, PhD, GaBI Online Editor

References
1. Arato T. Recent regulations of biosimilars in Japan. Pharmaceuticals and Medical Devices Agency. 47th Annual Meeting, DIA 2011; 2011 Jun 19–23, Chicago, USA.
2. Arato T. Regulatory guidelines for biosimilars in Japan. 10th EGA International Symposium on Biosimilar Medicines; 2012 April 20; London, UK.

Disclosure of Conflict of Interest Statement is available upon request.

Copyright © 2013 Pro Pharma Communications International

Permission granted to reproduce for personal and non-commercial use only. All other reproduction, copy or reprinting of all or part of any ‘Content’ found on this website is strictly prohibited without the prior consent of the publisher. Contact the publisher to obtain permission before redistributing.


Last update: 29/03/2017

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Understanding the components of pharmaceutical expenditure—overview of pharmaceutical policies influencing expenditure across European countries

Aim: To explain the components of pharmaceutical expenditure and illustrate the strengths and limitations of this indicator. In particular, we explore policies applied in European countries that affect the price and volume of medicines.
Methodology: This paper is based on a presentation held at the European Drug Utilization Research Group (EuroDURG)/International Society for Pharmacoepidemiology (ISPE) meeting in Antwerp, Belgium on 1 December 2011. The aim is to raise awareness of the limitations of pharmaceutical expenditure and explain how to interpret pharmaceutical expenditure data. Updated pharmaceutical policies in European countries are based on surveys by the WHO Collaborating Centre for Pharmaceutical Pricing and Reimbursement Policies (Vienna, Austria).
Results: Pharmaceutical expenditure consists of price and volume components. Prices may be indicated at different levels: key price types are ex-factory price (manufacturer level), pharmacy purchasing price (wholesale), and the pharmacy retail price (pharmacy). Taxes, such as value-added tax, are likely to be added. In the hospital sector, different price types, e.g. official hospital price and actual hospital price, apply. Volume may be expressed in sales units, e.g. standard units; prescriptions, or defined daily doses. Pharmaceutical policies, e.g. reference price system, prescription monitoring, discounts and rebates, influence prices or volume and thus pharmaceutical expenditure.
Conclusion: When analysing pharmaceutical expenditure data, our advice is to check which components are included and which markets the data refer to. One particular limitation of total pharmaceutical expenditure data is that they often only cover the out-patient sector.

Submitted: 10 April 2013; Revised: 7 June 2013; Accepted: 18 June 2013; Published online first: 5 July 2013

Introduction

Pharmaceutical expenditure is a major indicator used in national and international statistics on pharmaceutical markets and for pharmaceutical policy analyses. Knowledge of pharmaceutical expenditure and its development is of major interest to policymakers and researchers. Baseline information on the status quo of pharmaceutical expenditure is needed to compare with other macroeconomic indicators, such as health expenditure or gross domestic product in a country or region. Pharmaceutical expenditure is also a key indicator in international comparisons. The development of pharmaceutical expenditure at national level or in international comparisons is subject to several analyses, such as assessing the effect of pharmaceutical policies or forecasting future trends [18].

Although pharmaceutical expenditure is a standard indicator, worldwide data are not easily accessible. The World Health Organization (WHO) World Medicines Situation Report 2011 [9] devoted an entire chapter to presenting national pharmaceutical expenditure. Data on pharmaceutical spending were extracted from national health accounts where available. Large differences in pharmaceutical expenditure among the regions of the world were confirmed: pharmaceutical expenditure per capita ranged from US$7.61 in low-income countries to US$431.6 in high-income countries in 2005 and 2006, with considerable variation between income groups in each country. On average, 24.9% of total pharmaceutical expenditure was spent on medicines, ranging from 7.7 to 67.6% [9].

The total pharmaceutical bill across the European Union reached more than Euros 190 billion in 2010 [10]. On average, pharmaceutical expenditure accounted for almost one-fifth (19%) of all health expenditure in European Union Member States in 2010, making it the third biggest spending component after in-patient (hospital) and out-patient (ambulatory) care. Pharmaceutical expenditure rose by more than 50% in real terms between 2000 and 2009 in Organisation for Economic Co-operation and Development (OECD) countries, despite negative growth rates in several countries in 2010 [10].

Other major repositories of pharmaceutical expenditure data in high-income countries include the OECD Health database [11] and the Eurostat Health database [12]. The databases are populated by respective Member States according to clear definitions and sound methodologies. In order to reduce the burden of data collection for national authorities, and to increase the use of international standards and definitions in the field of health accounting, increased cooperation between OECD, Eurostat and WHO was agreed for the collection of health expenditure data based on the System of Health Accounts methodology. This resulted in the launch of the Joint OECD–Eurostat–WHO Health Accounts Data Collection [13].

Methodological challenges of health expenditure accounting have been addressed [1416]. Despite the wide use of pharmaceutical expenditure in science and practice, it is much less of a focus.

In this paper, we explain which components determine pharmaceutical expenditure. In particular, we explore which pharmaceutical policies able to influence price or volume are relevant to the European context. We flag up possible limitations of the pharmaceutical expenditure indicator, and highlight ways to interpret, analyse and compare pharmaceutical expenditure data.

Methodology

This paper is based on a presentation held at the European Drug Utilization Research Group (EuroDURG)/International Society for Pharmacoepidemiology (ISPE) meeting ‘Better public health through pharmaco-epidemiology and quality use of medicine’ in Antwerp, Belgium, on 1 December 2011. We have since added pharmaceutical policy updates. We have also added further examples, such as price data, not previously included because of time constraints.

Data sources
We present pharmaceutical expenditure data extracted from the major health indicator databases OECD Health Data [11] and Eurostat Health Data [12]. As these data are not fully comprehensive, we have included supplemental data obtained from a primary survey of national authorities. The data-collection exercise was carried out within the framework of the Pharmaceutical Health Information System (PHIS) project [17], established on completion of the PHIS database of indicators to analyse pharmaceutical systems [18].

Updated information for pharmaceutical policy mapping in European countries was provided by the WHO Collaborating Centre for Pharmaceutical Pricing and Reimbursement Policies (Vienna, Austria), to which we are affiliated. This WHO Collaborating Centre has been regularly and systematically collecting pharmaceutical policy information in European countries [19].

We obtained price data from the Pharma Price Information (PPI) service of the Austrian Health Institute [20]. The PPI service provides information on prices of medicines set by European Union Member States, covering all price types. It was established to support, as stipulated in the Austrian General Social Insurance Law [21], the Austrian Pricing Committee located at the Austrian Federal Ministry of Health, which calculates the European Union average price. This average price is required for price setting, as Austria applies external price referencing and bases its prices on the average prices of all other European Union Member States [22].

To ensure clarity of meaning and comprehension, we explain technical terms with reference to the pharmaceutical policy glossary of the WHO Collaborating Centre for Pharmaceutical Pricing and Reimbursement Policies [23]. We also use the terms ‘out-patient care’, i.e. ambulatory care, community care, without hospital out-patient departments, and ‘in-patient’ or ‘hospital care’, as defined in this and other glossaries.

We begin by addressing price components of pharmaceutical expenditure and underlying policies influencing price, and then we present volume components and policies, and the different methodological approaches to measurement units. Different sub-markets that pharmaceutical expenditure may refer to are then analysed, and further methodological limitations are finally discussed.

Price components and policies

Official out-patient price types
Pharmaceutical expenditure is determined by a value component and a volume component, i.e. price multiplied by volume in the (sub-)markets in question.

The usual price types for the out-patient sector are as follows:

  • Ex-factory price (price set at the level of the manufacturer)
  • Pharmacy purchasing price (price set at the level of the wholesaler)
  • Pharmacy retail price, also called ‘consumer price’ or ‘end price’ (price set at the level of the pharmacy)

In some cases, the latter is also referred to as ‘public price’, to indicate the price set for the public, i.e. consumers. We advise not using the term ‘public price’ because it could be misconstrued as a reference to the funding source of a ‘public payer’. The pharmacy retail price (net) can be increased by further add-ons, such as duties and taxes, which results in the pharmacy gross retail price.

These price types are shown in Figures 1 and 2, with real-life examples for four medicines from different indications (oncology, rheumatoid arthritis, diabetes, antipsychotic); generics are available for two of them. In Figure 1, prices of medicines are presented as unit prices, i.e. the price per tablet or vial, for the different price types in 16 European countries. Existing variations are highlighted among products and countries.

The different price types are attributed to a regulation in place in several high-income countries, including European countries. Medicines, or a large proportion of medicines on the market, are funded by public payers (so-called reimbursable medicines). At the manufacturer level (ex-factory prices), prices of reimbursable medicines are usually statutorily set, i.e. based on a legal act such as a law or decree, and can be negotiated between the state and the manufacturer; sometimes a combination of both procedures is applied. Different methods for setting an ex-factory price can be applied.

A frequently used approach to determine the price of new medicines is external price referencing, i.e. the practice of using the price(s) of a medicine in one or several countries to derive a benchmark or reference price for the purposes of setting or negotiating the price of the product in a given country [23]. As of 2013, 24 out of the 27 European Union Member States (all but Denmark, Sweden and UK) apply external price referencing, at least as a supportive tool, for new medicines [19, 24, 24]. At the same time, generic medicines prices are either set at a specific percentage lower than the price of the generic medicine (‘generic price link’), or countries work competitively to decrease prices, or a mixture of the two [3, 8, 26, 27].

Pharmacy purchasing and retail price types are determined through regulations stipulating maximum remuneration granted to the stakeholders involved in pharmaceutical distribution. Wholesale and pharmacy mark-ups usually apply to all medicines, but are limited to reimbursable and prescription-only medicines in some countries [18, 28]. Some European countries set a medicine price at the pharmacy level and do not have statutory wholesale mark-ups. For those countries, ex-factory prices can, at best, be calculated on the basis of an estimated average wholesale margin. The design of the distribution remuneration in the form of fixed mark-ups or regressive schemes influences pharmacy purchasing and pharmacy retail prices. Several European countries, for example, have opted for regressive wholesale and pharmacy margin schemes, which gradually decrease the mark-ups for high-cost medicines. The share of the price attributable to wholesalers, and particularly pharmacies, tends to be higher for low-price medicines in regressive margin schemes. Pharmacy remuneration may also be designed independently from the price, e.g. by providing a fee for service, such as in The Netherlands or in the UK [1, 3, 2830].

The price of a medicine and, as a result, pharmaceutical expenditure, may be increased by duties and taxes, which are known to account for a considerable part of the medicine price in middle- and low-income countries [3133]. In European countries, only value-added tax is applied to medicines. Most European countries apply a value-added tax rate on medicines that is lower than the standard rate, see Figure 3. When comparing pharmaceutical expenditure between countries, we strongly advise checking whether data are indicated net or gross.

‘Reimbursed price’ or ‘reimbursement price’ is important for public payers. It refers to the maximum amount covered by public payers (usually social insurance or a national health service) [23]. In European countries, the term ‘reimbursement price’ is not explicitly indicated, except for Austria, which uses the term ‘sickness fund price’. In other countries, the reimbursement price can be derived from the medicine price, usually the pharmacy retail price, by deducting the percentage of co-payments incurred by the consumers. Most European countries (all European Union Member States except Austria, Germany, Italy, The Netherlands and UK [29, 30]) ask the patient to co-pay a specific percentage of the medicine price: specific medicines, whose therapeutic (added) value was assessed to be lower compared with essential or life-saving medicines, are not fully funded by the public payers, but they are reimbursed to a specific extent. For instance, if the pharmacy retail price is Euros 100 and the reimbursement rate is 80%, then the reimbursement price amounts to Euros 80.

In this case, the patient is required to co-pay Euros 20 plus a prescription fee, if applicable, unless they are exempted from co-payment or are granted reduced co-payment.

A particular reimbursement price is called the reference price. A reference price system is a reimbursement system in which identical or similar medicines, e.g. originator medicines and generics, are clustered to a reference group, and a public payer defines the maximum price (amount) that is reimbursed for all medicines clustered to this group [23, 34]. A reference group can be clustered for the molecule (Anatomical Therapeutic Classification [ATC] Level 5), the class (ATC Level 4) or the therapeutic area (ATC Level 3) [35]. The patient is required to pay the difference between the reference price and the pharmacy retail price, plus any further co-payments, e.g. in Portugal, the percentage co-payment is also applicable to the reference price [36]. As of 2013, 20 European Union Member States have a reference price system in place [19], and Ireland decided in May 2013 to introduce it.

Price studies can be carried out for each of these price types, or a combination of them. It depends on the perspective of the analysis and which price type is selected.

Official in-patient price types
Hospitals are usually directly supplied by the pharmaceutical industry and, in some cases, by wholesalers [37, 38]. In contrast to the out-patient sector, only one official price type is applicable for the in-patient sector, and that is the ‘official hospital price’. It generally corresponds to the ex-factory price or, in the case of delivery by a wholesaler, to the wholesale price, which includes the wholesale mark-up.

Discounted prices
Discounted prices are known from the hospital sector. Large discounts and rebates are offered on specific medicines to the hospitals. In some European countries, cost-free medicines are permitted, so hospitals obtain medicines at a price of zero Euro. Discounts and rebates, including a cost-free supply, have been observed, particularly for medicines, where competitors have come onto the market, and whose use might be required for long-term treatment [37, 39]. As a result, the actual hospital price is of greater relevance than official hospital prices, as the hospital’s pharmaceutical bill is composed of what the hospital actually spends on medicines.

Discounted prices also play a role in the out-patient pharmaceutical sector. Along the supply chain, e.g. from wholesaler to pharmacies, from pharmacies to consumers, discounts can be granted, if not forbidden by law. Legal provisions may specify the maximum amount of discounts and rebates granted. In France, for example, pharmacies are permitted to obtain a maximum discount of 2.5% on reimbursable medicines from wholesalers, whereas the limit is 17% for reimbursable generics [40].

Furthermore, discounts and rebates granted to public payers are less well known, but have increasingly been gaining importance. According to a survey of European countries [41], discounts and rebates are granted to public payers by pharmaceutical companies in 25 of the 31 European countries surveyed (out-patient sector in 21 countries and in-patient sector in all 25 countries). The most common discounts and rebates are price reductions and refunds linked to sales volume, but types such as in-kind support, price-volume and risk-sharing agreements are also in place. A mix of various types of discounts and rebates is common. Risk-sharing and further managed-entry agreements that attempt to manage uncertainty are on the rise in several European countries, such as UK (patient access schemes), Italy, Poland and the Baltic States [42, 43].

Volume components and policies

Volume measurement and analysis
Different methodological approaches can be applied to measure utilization. Pharmaceutical utilization can be assessed by the number of medicine packs sold, dispensed, or actually consumed by patients. Sales data can also be indicated by ‘Standard Units’, which are defined as the smallest doses of a product, equivalent to one tablet or capsule for an oral dosage form, one teaspoon, i.e. 5 mL, for a syrup, and one ampoule or vial for an injectable product [44].

A major discipline dealing with the volume component is drug-utilization research, which WHO has defined as ‘the marketing, distribution, prescription, and use of drugs in a society, with special emphasis on the resulting medical, social and economic consequences’ [45]. From a public health perspective, a major aim of drug-utilization research is to enhance a more rational use of medicines by ensuring that patients receive ‘medications appropriate to their clinical needs, in doses that meet their own individual requirements for an adequate period of time, and at the lowest cost to them and their community’ [46]. Drug-utilization research describes patterns of pharmaceutical use, indicates early signals of irrational use of medicines, assesses interventions to improve the use of medicines, and benchmarks the use of the medicines in different populations [45]. This discipline has contributed greatly to statistics, and a measurement unit (Defined Daily Dose [DDD]) was developed to allow international comparisons of volume data to be made: the DDD is defined as the average maintenance dose of the medicine when used on its major indication in adults [47]. Today DDD is a standard measurement for pharmaceutical consumption; drug-utilization research would not be possible without it.

From a public payer’s perspective, the ‘items prescribed’ tend to be a focal point of analyses, as prescriptions are likely to be monitored. For this purpose, the relevant scientific measurement unit, again developed to allow comparisons to be made, is the ‘Prescribed Daily Dose’ [PDD]. It is defined as the average daily dose prescribed, as obtained from a representative sample of prescriptions [45]. The number of items prescribed might differ from the number of items actually dispensed, as not all prescriptions might be filled. It is more difficult to obtain information on the number of items dispensed [18].

Volume control policies
In European countries, volume control policies are usually targeted at medicines prescribed by physicians, and therefore prescribers are the key target group of this measure. Furthermore, demand-side measures can also be targeted at patients and pharmacists [48, 49].

In some European countries, such as the Czech Republic, Latvia, Slovakia, UK, and some regions in Spain, and Sweden, pharmaceutical budgets for prescribers are in place [19, 5052]. Budgets can be combined with financial incentives. For example, in Ireland, doctors were permitted to plough back some of their savings into their practice under the ‘Indicative Drug Target Scheme’; this financial incentive was, however, subsequently scrapped [53]. Where budgets are exceeded, penalties may be imposed, e.g. Latvia. In the 1990s pharmaceutical budgets of countries such as France, Germany and Italy yielded the savings forecast during the first year but not in subsequent years. Sanctions were imposed but could not be executed, partly because of court proceedings. Eventually, the budgets were abolished [54].

Physician prescription patterns are routinely monitored in all European countries [1, 50] to assess volume and quality of prescribing. For instance, public payers check whether doctors prescribe less expensive generics, write prescriptions by International Nonproprietary Name (INN), or both, if that is the policy in place. Prescription monitoring has been implemented in different ways and to differing extents in Europe, and also depends on the electronic support system in place. The Danish electronic monitoring system, ‘Ordiprax’, is considered to be an example of good practice. Ordiprax allows the authorities to assess pharmaceutical consumption at the level of the prescribing doctors and at aggregated local and central levels. Doctors also have access to the Ordiprax system and can compare their prescription pattern to the average of other physicians in the region [55].

Some European countries supplement their prescription monitoring by specific agreements with physicians. For example, physicians are financially rewarded for prescribing a specific target within a therapeutic class, such as statins of generics, and less expensive medicines in France [56]; doctors in Belgium have to prescribe a minimum target of ‘non-expensive medicines’ whose exact amount depends on their medical specialisation [57, 58]. Further, prescriber information also plays a major role [59]. In France, sickness fund representatives (Délégués de l’Assurance Maladie) visit physicians to provide feedback on their prescribing activity and to inform them about campaigns [40].

The patient is another key target group, and several European countries have targeted campaigns at patients [1, 50, 59]. Although these measures might have contributed to containing public pharmaceutical expenditure, the primary aim was to encourage a more rational use of medicines. Well-known examples are the antibiotics campaign in France and Italy, and generics campaigns in France and Portugal [35, 50].

Initiatives aimed at increasing generics uptake play an important role because pharmaceutical expenditure can be reduced by using lower priced medicines, where available, instead of expensive originator medicines. Key policies to encourage generics use have been devised for different stakeholders: prescribers (INN prescribing), pharmacists (generics substitution) and patients (campaigns to raise awareness about the role and benefits of generics). At the beginning of 2013, 22 out of the 27 European Member States had generics substitution in place, and 23 allowed INN prescribing [19, 34]. In recent years, Lithuania and Slovakia, for example, decided to switch from voluntary to obligatory INN prescribing [19, 60, 61] on the basis that previous voluntary policies had not been fully enforced and tended to be less successful in achieving planned outcome than mandatory policies [1, 36]. On the other hand, high generics uptake has been achieved in the UK with voluntary INN prescribing, and a decision was made against the introduction of generics substitution planned for 2010 following a public consultation [34, 62, 63].

An econometric model has been used in 12 European countries to assess the relevance of different volume components that influence pharmaceutical expenditure. The study concluded that the following three volume control policy measures had significantly decreased pharmaceutical expenditure: an electronic prescribing system; pharmaceutical budgets; and, with some delayed effect, the introduction of generics substitution [64].

Understanding pharmaceutical expenditure

As discussed, price and volume components influencing pharmaceutical expenditure have to be considered when analysing pharmaceutical expenditure. An increase in pharmaceutical expenditure resulting from the ‘price effect’ might be attributable to market entry of new expensive medicines, and ‘volume effect’ can be linked to demographic developments, such as an ageing population.

Further methodological challenges need to be addressed, including the scope of pharmaceutical expenditure.

Total pharmaceutical expenditure and sub-markets
Pharmaceutical expenditure may refer to the total market or to specific sub-markets. Major distinctions are public and private pharmaceutical expenditure, and out-patient and in-patient pharmaceutical expenditure.

Pharmaceutical expenditure from these sub-markets should ideally amount to total pharmaceutical expenditure. Other relevant sub-markets include the generics market, the prescription medicines market, the over-the-counter market, and the self-medication market. Expenditure data for a sub-market might be available for another sub-market, e.g. out-patient, only. For instance, pharmaceutical expenditure spent on generics is likely to be indicated only for the out-patient sector, for those medicines reimbursed by social health insurance, or both, because of limited data for other segments.

These gaps in data availability are also relevant to public/private pharmaceutical expenditure, and particularly out-patient/in-patient pharmaceutical expenditure. Some countries might have difficulties in indicating private pharmaceutical expenditure, or all components of it. Private pharmaceutical expenditure typically includes expenses of private households for non-reimbursed medicines, co-payments to reimbursable medicines, including co-payments under the reference price system, and expenses for medicines, which are, in principle, reimbursable but can be borne out-of pocket by the patient in specific cases; this may be because the price of the medicines is lower than the prescription fee. Although data for public pharmaceutical expenditure can easily be derived from the payers’ databases, separate data collection and assessment for private expenditure might be required. For instance, Bulgaria provides no information on private pharmaceutical expenditure, and what is indicated as total pharmaceutical expenditure in databases such as Eurostat is in fact public expenditure only.

Hospital (or in-patient) pharmaceutical expenditure is even more difficult to collect. It is not routinely surveyed at national levels. This is attributable to the financing system in European countries: pharmaceutical expenditure in hospitals are usually funded by hospital budgets [37]. As a result, what is indicated in the standard databases as total pharmaceutical expenditure refers, in practice, only to out-patient pharmaceutical expenditure. The definition of ‘hospital’ is challenged, as hospitals may also include hospital out-patient departments [23].

Data for total pharmaceutical expenditure in European countries as of 2009 are presented in Figure 4. For out-patient pharmaceutical expenditure, we referred to OECD health data [11]; if these were unavailable, Eurostat was used [12]. For hospital pharmaceutical expenditure, we conducted a separate survey. We asked competent authorities to provide hospital pharmaceutical expenditure data, or an estimate, for their countries. Some countries were able to provide the data; however, extra effort was required in most countries because the data were not easily available [18, 37]. Although we aimed to produce a complete picture for all 27 European Union Member States, representatives of several countries failed to provide these data, despite their efforts. Methodological difficulties and limitations are presented in Figure 4.

Out-patient pharmaceutical expenditure per inhabitant in European countries is shown in Figure 5. Here, a higher number of countries was able to provide data. In many cases, information on out-patient pharmaceutical expenditure was misinterpreted as total pharmaceutical expenditure, see Figure 5.

In addition, national pharmaceutical expenditure data cover expenditure for pharmaceuticals and further medical devises.

Other methodological challenges
Inter-country comparisons of pharmaceutical expenditure pose methodological challenges, specifically exchange rates and a possible weighting for the different economic situations of the countries. For the latter, use of the power purchasing parities (PPP) can eliminate the effects of differences in price levels between countries, thus reflecting the relative price level in relation of the purchasing power of a country [23, 65]. OECD, for instance, uses the PPP concept in their Health database, and they indicate pharmaceutical expenditure and other monetary indicators in US dollar PPP. In the European context, the use of Euros PPP would be preferred to US dollar PPP.

Another challenge is analysing the development of pharmaceutical expenditure over the years. It may be that the methodological approach applied to pharmaceutical expenditure data, even from the same source, might have changed at a particular time point for various reasons. This can explain breaks in the data series.

Finally, we would like to highlight the use of recent data. Many countries first publish preliminary expenditure data, based on estimations, and will ‘correct’ this estimate a few months later when all required data are available.

In this paper, we are not advising against the use of pharmaceutical expenditure data. Use of pharmaceutical expenditure data for analyses, and as a basis for policy decisions, is highly recommended despite limitations in the data sets. We must be aware of these limitations, however, and consider them when interpreting the data. As a minimum requirement, we advise checking the detailed notes accompanying the data set. One example is a note from the 2012 OECD Health at a Glance study: Pharmaceutical expenditure covers spending on prescription medicines and self-medication, often referred to as over-the-counter products. In some countries, the data also include other medical non-durable goods (adding approximately 5% to the spending). The expenditure also includes pharmacists’ remuneration when the latter is separate from the price of medicines. Pharmaceuticals consumed in hospitals are excluded (their inclusion would add another 15% to pharmaceutical spending approximately). Final expenditure on pharmaceuticals includes wholesale and retail margins and value-added tax [10]. The explanation is long but precise, and assists in understanding the coverage, quality and limitations of the data set.

Conclusion

In this paper, we show that pharmaceutical expenditure is influenced by several price and volume components. Therefore, policymakers are strongly recommended to focus on all aspects rather than on a single policy if they want to contain expenditure.

The interpretation and analysis of pharmaceutical expenditure can be compromised by limited availability of expenditure data in some segments. In some countries, pharmaceutical expenditure data refer to the public sector only. A particular challenge is the collection of hospital pharmaceutical expenditure which, as a rule, has not been routinely surveyed. What is presented under the heading ‘total pharmaceutical expenditure’ in many databases reflects only the out-patient sector. Research projects, such as PHIS, which aimed to fill gaps in data availability, provided valuable contributions to the evidence base.

Pharmaceutical expenditure is a major macroeconomic indicator for pharmaceutical and health-policy analyses. We support the use of this indicator but strongly advise studying which components are included in the given data set of pharmaceutical expenditure, and which are not, and considering the limitations appropriately in the analysis.

For patients

It is key for patients that policy makers base their decisions about policy measures on sound evidence. Pharmaceutical policy analysis is a major supportive tool for policy making. Analyses and forecasts, however, may be impaired by limitations in availability and comparability of data.

Although we acknowledge the perils of the pharmaceutical policy analyses as a result of methodological limitations and gaps in data availability, it should not defer us from undertaking such studies. Researchers and policy makers, however, are strongly advised to take caution when interpreting the data. In this study, we examined the components of pharmaceutical expenditure, which is a key indicator, and explored its pitfalls and limitations. By raising awareness and increasing knowledge of this indicator, we intend to contribute to better understanding and more careful interpretation of pharmaceutical expenditure data. This eventually benefits society as a whole, including patients.

Acknowledgements

We would like to thank the organizers of the EuroDURG/ISPE meeting ‘Better public health through pharmaco-epidemiology and quality use of medicine’ in Antwerp, Belgium, from 30 November to 3 December 2011, and particularly Dr Brian Godman, organizer of the session ‘Exploring units of expenditure in drug utilization studies’ on 1 December 2011 for inviting Dr Vogler to present on pharmaceutical expenditure and suggesting a paper based on the presentation.

We would like to thank our colleagues at the WHO Collaborating Centre for Pharmaceutical Pricing and Reimbursement Policies for surveying updated information on pharmaceutical policies. We also thank the members of the Pharmaceutical Pricing and Reimbursement Information (PPRI) network (http://whocc.goeg.at/Networks/ListOfMembers) for regularly providing information about changes in pharmaceutical policies in their countries. Further thanks go to the Pharma Price Information (PPI) service at the Austrian Health Institute for sharing price data.

Funding sources

The authors received no specific funding for this manuscript.

Competing interests: None.

Provenance and peer review: Not commissioned; externally peer reviewed.

Co-authors

Nina Zimmermann, MA
Claudia Habl

WHO Collaborating Centre for Pharmaceutical Pricing and Reimbursement Policies, Health Economics Department, Gesundheit Österreich GmbH (Austrian Health Institute)/Geschäftsbereich ÖBIG, 6 Stubenring, AT-1010 Vienna, Austria

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Author for correspondence: Sabine Vogler, PhD, Head of WHO Collaborating Centre for Pharmaceutical Pricing and Reimbursement Policies, Health Economics Department, Gesundheit Österreich GmbH (Austrian Health Institute)/Geschäftsbereich ÖBIG, 6 Stubenring, AT-1010 Vienna, Austria

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