Analysis of Manufacturing Costs in Pharmaceutical Companies

Transcription

1 J Pharm Innov (2008) 3:30 40 DOI /s PERSPECTIVE Analysis of Manufacturing Costs in Pharmaceutical Companies Prabir Basu & Girish Joglekar & Saket Rai & Pradeep Suresh & John Vernon Published online: 4 March 2008 # International Society for Pharmaceutical Engineering 2008 Abstract In the pharmaceutical industry, costs attributed to manufacturing are a major part of a company s total expenses. In this paper, trends in various expense and income categories of pharmaceutical companies have been analyzed with particular emphasis on manufacturing costs to gain an insight into their relationships and how they may differ among types of pharmaceutical companies such as brand name, generics, and biotechs. The study includes data published in the annual reports of leading pharmaceutical companies from 1994 to Twenty-two pharmaceutical companies were selected based on the annual revenues. The set was further divided into three groups: brand names, generics, and biotechs. The analysis shows that, between 1994 and 2005, manufacturing costs (as a percentage of total sales) are different for the three groups of companies listed above. Additionally, each group of companies differs in how savings are leveraged strategically. The data on brand-name pharmaceutical companies also indicate that there is a strong correlation between the reduction of the cost of goods sold (COGS) and the increase in expenditure. This suggests the validity of Vernon s theory that for brand-name companies, a reduction in COGS will P. Basu (*) : G. Joglekar : S. Rai Pharmaceutical Technology and Education Center, Purdue University, W. Lafayette, IN 47907, USA prabir1960@purdue.edu P. Suresh School of Chemical Engineering, Purdue University, W. Lafayette, IN 47907, USA J. Vernon Department of Finance, University of Connecticut, Storrs, CT, USA likely have a positive impact on investments in, presumably resulting in much needed innovations and future health benefits for the society. Keywords COGS. Pharmaceutical manufacturing Background Prescription drugs often provide effective alternatives to expensive medical procedures and hospital stays. Consequently, spending on prescription drugs as a percentage of the total national health care spending is increasing. In 1999, prescription drugs accounted for 8.2% of the total national health spending; that share was 11% in 2003 and is expected to reach 14% by 2010 [1]. Although this is still a relatively small proportion as a percentage of the total national healthcare spending, it is one of the fastest growing components of healthcare spending, increasing at double digit rates from 1995 to 2003 [2]. The cost of bringing a new drug to the market place has also been steadily increasing, with recent estimates projecting a required investment of over $2 billion to progress from a laboratory idea to successful commercialization [3]. Pharmaceutical companies are spending more money on [4], while the productivity of their investment, computed as the number of drugs introduced to the market place per year, is declining. Manufacturing costs are a substantial part of their total cost structure [5]. According to some estimates, these costs can be as high as 27 3 of sales for manufacturers of brand-name pharmaceuticals [1, 3], more than double the share of costs for research and development [1]. No such estimates are currently available in the literature for generic pharmaceuticals or drugs manufactured by biotech companies.

2 J Pharm Innov (2008) 3: The manufacturing cost of pharmaceuticals, commonly known as cost of goods sold (COGS), was approximately $90 billion in 2001 for the top 16 drug companies [1]. In 2008, it is estimated that (in absolute terms) the total COGS for all pharmaceutical products could be as much as $200 billion (estimated using 27% of estimated global pharmaceutical sales of $735 billion in 2008 [6]). In comparison, the total spending in absolute terms on by the Pharmaceutical Research and Manufacturers of America companies was $55 billion in 2007 [4]. Factors Affecting COGS of Pharmaceutical Companies The pharmaceutical industry is strictly regulated due to its direct impact on consumer health and well being. To ensure that pharmaceutical products are safe and efficacious, the Food and Drug Administration (FDA) periodically inspects the facilities and procedures of all manufacturing operations in the USA and those overseas operations that sell their products in the USA. Consequently, these facilities and procedures must be registered with the FDA and must comply with the (current) good manufacturing practices (cgmp) established by the FDA. The high COGS of pharmaceutical products is the consequence, in part, of the methods by which excellence in delivered product is achieved. Other factors contributing to the high COGS and the potential for savings in COGS have been summarized in a previous report [3]. To offset the effects of rising costs of commercialization, shorter effective exclusivity periods, and diminishing returns on investment, manufacturing costs may be a source of savings for the pharmaceutical industry [3]. Reducing manufacturing costs without sacrificing quality could be a way to effect social good in an environment where more and more investment is required to find new therapies for unmet medical needs along with a need to control or slow down the rate of price increases of prescription drugs. In fact, there are important linkages between the efficiency of pharmaceutical manufacturing, drug prices, and public health in the USA [7]. It can be predicted that reductions in manufacturing costs will lead to gains in consumer surplus (the difference between consumers willingness to pay and what consumers actually pay; the standard economic measure of social welfare) worth trillions of dollars. Objectives The main objective of this study was to assess and analyze manufacturing costs of pharmaceutical products across important industry categories. The companies comprising this sector were divided into three groups: brand name, generic, and biotech. The financial information for top companies in each group, as disclosed in the annual reports published by publicly traded companies, was used in the analysis. The main objective can be divided into the following sub-objectives: Gain a better understanding of the trends in various expense and income categories within companies of each group. Study the trends of the overall expenses and revenues. Identify the differences in the trends, if any, of various expense and income categories for each group of companies. If there are differences, identify the causes. Explore possible correlations between various expense and income categories and propose the cause of the relationships. Methodology The data used in this work were extracted from the annual reports of the pharmaceutical companies studied. The selected companies, based on annual revenues for the year 2005, are shown in Tables 1, 2, and 3. Brand-name pharmaceutical companies are the original developers of the drugs and have annual revenues of at least $10 billion. Generic pharmaceutical companies manufacture off-patent drugs, with typical annual revenues of less than $5 billion. In 2008, more than two thirds of all prescriptions written in the USA are expected to be for generics, and the generics sales is expected to grow to more than $70 billion [6]. Biotech pharmaceutical companies are the original developers of drugs made through biosynthetic processes. Brandname companies account for the greatest share of the market, though the weighted coverage of all companies is well over 5 of the total market. Table 1 Sample US brand-name pharmaceutical data set Company Pfizer Inc. 8.5 Johnson & Johnson Inc. 8.4 GlaxoSmithKline PLC 6.3 Sanofi-Aventis 5.4 Novartis 5.4 Astra Zeneca PLC 4.0 Abbott Laboratories 3.7 Merck & Co. Inc. 3.7 Bristol-Myers Squibb 3.2 Wyeth 3.1 Eli Lilly & Co. 2.4 Schering-Plough Corp % Total market share

3 32 J Pharm Innov (2008) 3:30 40 Table 2 Sample US generic pharmaceutical data set Company Teva 9.5 Ivax* (2004) 3.3 Watson 3.0 Mylan 2.3 Barr 1.9 Alpharma 1.0 Par Financial Information % Generics market share The financial information about the companies was extracted from the Wharton Research Data Services (WRDS) Compustat [8] database. The data for the following revenue and expense categories were extracted: (a) Sales (SALES) aggregate sales of a company s complete product offering. (b) Cost of goods sold (COGS) also referred to as materials and production cost. Cost of goods sold is an aggregate figure that includes all costs incurred in producing the goods including write-offs from plant, property and equipment, raw materials, inventory, etc. (c) Research and development expense () research and development expenses that are separate from the cost of goods sold. (d) Selling, general, and administrative expense (XSGA) these expenses provide for sales, marketing, as well as general expenses incurred by the product pipeline. XSGA provided by the WRDS database includes salaries, rent, and research and development () cost. In this study, and general expense were treated as separate categories. (e) General expense = XSGA. (f) Taxes taxes paid by the company. (g) Depreciation the steady loss in the value of capital goods over a specified time period. (h) Operating income (after depreciation and taxes) profits after depreciation and taxes; these are company earnings from core operations after deducting the cost of goods sold, and selling and general operating expenses. Operating income ¼ Sales COGS XSGA Depreciation Taxes For the categories given above, data were extracted for a span of 12 years, from 1994 to Data Collection and Analysis To compare data in various categories across the three company groups, the extracted data were normalized with respect to the annual sales for the corresponding year (represented as, general expense, etc.). The normalized data were used for the detailed analysis. Assumptions and Limitations The authors have assumed that the data reported by the companies in the financial statements are based on the same interpretation of various categories. For example, COGS should be truly inclusive of all costs pertaining to drug manufacturing only. Although all companies follow generally accepted accounting practices, it is difficult to enforce and monitor uniformity in their interpretations. One of the limitations of the information obtained from financial statements pertains to the lack of itemized information by product, that is, we cannot associate a certain cost to a certain product or process. From financial statements alone, we cannot determine a firm s unique or competitive advantage in manufacturing capabilities. Also, some companies are, in reality, large conglomerates reporting all business units, including non-pharmaceutical operations, under one single filing. For our analysis, we have chosen a sample space of companies. Although these companies account for a major share of the market, a significant number of companies have not been included in the study. The following procedures were applied to study the aggregate data for each group of companies, namely, brand names, generics, and biotechs: Polynomial or linear trend lines were fitted to the data set and general trends were observed. The significance of change in each category was established by using a t test, which assesses whether the means of two groups are statistically different from each other. Relationships were proposed among the Table 3 Sample US biotech pharmaceutical data set Company Amgen Inc. 24 Genentech Inc. 13 Genzyme Corp. 5 Biogen Idec Inc % Total share

4 J Pharm Innov (2008) 3: COGS % factors that showed significant changes and trends. The significance level was chosen to be 0.05 (alpha value). The period under consideration was divided into two sub-periods of 6 years each. The arithmetic averages of various expenses and incomes were calculated for each sub-period. A t test was then done to find the significance of the change from one sub-period to another. The relationships were quantified by computing the correlations among different factors. For the factors that showed significant changes, the corresponding averages were compared with Reinhardt s average from financial data collected for eight brandname companies in 1998 [1]. To provide an economic perspective on the pharmaceutical industry, Reinhardt cites a Deutsche Banc Alex Brown research report and a Banc of America Securities LLC report that shows breakdowns of the disposition of the sales revenue earned by the largest research-based pharmaceutical manufacturers (defined as brand-name companies in this paper) in Reinhardt also urges caution in interpreting the data and provides some of the reasons for that. Similar caution must be exercised in interpreting the financial data reported in this study. Key Observations Trends Analysis, %,, and General COGS Fig. 1 for different types of companies Brand Name (with merck) Generics Biotech Brandname Avg. (26%) Generics Avg. (52%) Biotech Avg. (14%) Reinhardts Avg. (27%) The trend of COGS,, operating income, and general expense as a percentage of total sales for the different types of pharmaceutical companies over a 12-year time period was analyzed. The variations over the years for different groups of companies were plotted, and the corresponding averages were compared with Reinhardt s average. Figure 1 shows the trends in the data for brand name, generics, and biotechs, along with their corresponding arithmetic averages. The average for brand-name companies is nearly equal to the average estimated by Reinhardt. However, the average COGS as a percentage of sales, is almost half the arithmetic average for COGS as a percentage of sales for generics and roughly double that of biotechs. The higher value of COGS as a percentage of sales for generics is possibly a reflection of lower expenditure on and sales, and marketing for the generic industry. However, it is also quite revealing to realize that the is significantly lower for biotechs. The COGS as a percentage of total sales () for brand names appears to have declined during the years 2000 to On the other hand, the for generics increased until the year 1996, and since then, there appears to be a gradual decline. The values of generics show a gradual reduction over the last 8 years. Biotechs show more fluctuations than either generics or brand-name companies. As a special case, the time series data for Schering- Plough are shown in Fig. 2. In the case of Schering-Plough, was significantly lower than the industry average until about However, it increased significantly over the years , growing more than the industry average and then appears to have dropped back to the level of the industry average. Correspondingly, the operating income, which was slightly above the overall industry average, dropped sharply over the years and then showed an increasing trend in The possible explanation for this is that Schering-Plough entered a consent decree with the FDA in 2002, agreeing to OIADP&T% % Schering-Plough Brand-Name Fig. 2 Schering Plough Corporation time series plot

5 34 J Pharm Innov (2008) 3:30 40 % revalidate the manufacturing processes at several sites in the USA and Puerto Rico, resulting in significant increase in since In addition, it discontinued certain older profitable products and outsourced other products. The fact that, until 2002, the at Schering-Plough was significantly lower than the industry average for brandname companies could be reflecting a lower investment in plant, equipment, and cgmp systems, which could have resulted in the findings of cgmp deficiencies. % Brand name Generics Biotech (without year 2002) Brand name Avg. (13%) Generics Avg. (8%) Biotech Avg. (26 %) Reinhardt's Avg.( 13%) Fig. 3 for different groups of companies (without 2002 data for biotechs) The trends in expenditures as a percentage of sales (%) are shown in Fig. 3. The data point for 2002 was removed for biotechs, which is considered as an outlier. The % for brand-name companies appears to be gradually increasing over the past few years. The average value is very close to Reinhardt s average. The trend for the generics is substantially flat with peaks in 2000 and The average value is slightly greater than half of the average estimated by Reinhardt and the brand-name industry average. This is expected since most generics are not expected to devote resources to discovering new drugs. As a result, their % expenditures are largely directed to developing marketable formulations of a large number of drugs that are either out of patent or may be soon. The trend for biotechs shows a record high expenditure in the year 1996 when it reached nearly 32%. Since 1996, the % has been fluctuating with an overall decreasing trend. For biotechs, the average expenditure as a percentage of sales is twice Reinhardt s average for brandname pharmaceuticals. Biotechs heavily invest in to discover new therapies; their success rates are lower, and they have a smaller portfolio of marketed products. The trend in operating income as a percentage of sales is shown in Fig. 4. The average operating income for brandname companies is around 19%. There is a gradual increase in its value until the year After that, the trend indicates a gradual decrease. However, the slope is very small, and no definitive conclusions can be drawn. The yearly average for generics is around 12%, which is lower than that of brand-name and biotechs. The operating income data for generics show a significant dip in 1997, and after that, the trend indicates that it has been gradually increasing. However, there is more fluctuation in the data for generics as compared to that for brand-name companies. For biotechs, the total number of companies in the data set is small. However, there appears to be an increasing trend of operating income in recent years. General Expense The trend in general expenses as a percentage of sales is shown in Fig. 5. The trend for brand-name companies is flat, and the average value is slightly less than the average reported by Reinhardt in The trend for generics shows more fluctuations compared to the brand-name companies, with the lowest value being 12% in the year The average value of general expenses for generics is nearly half of Reinhardt s average, which confirms that the generics spend less money in marketing and sales than brand names or biotechs. The average value of general expense for biotechs is slightly higher that that of generics but lower than that of brand-name companies. Biotech drugs are unique, have less competition from similar (metoo) drugs in the market, and treat specific unmet needs. Therefore, the biotechs may not require as much marketing and sales effort as do brand-name companies. The trend for /Sales Brand name Generics Biotech Brand name Avg. (19%) Generics Avg. (12%) Biotech Avg. (22%) 0 Fig. 4 Operating income for different types of companies

6 J Pharm Innov (2008) 3: Fig. 5 General expense for different types of companies General Expense Brand-name Generics Biotech Brand-name Avg. (31%) Generic Avg. (18%) Biotech Avg. (21%) Reinhardt's Avg. (3) biotechs, however, shows more fluctuations than brandnames as well as generics. Relationship Between COGS,,, and General Expense The data were used to study the relationships between COGS,, operating income and general expense. Only the following strong correlations were found from the data: COGS and for brand name, and COGS and operating income for generics. The results are discussed in this section. Brand-name Companies The link between manufacturing costs, price of drugs, and profits, etc. has recently attracted much attention [1, 7]. In spite of the recent work by Vernon [7], the question is often raised whether savings resulting from improvements in manufacturing efficiencies would have any impact on pharmaceutical prices or investment in for discovery of new drugs, or instead, would they merely increase the profits of these pharmaceutical companies. This question may in fact have been the main obstacle to public funding of research that would result in reduction of COGS for pharmaceutical companies. Although an in-depth answer to such questions is beyond the scope of this paper, we explore here the historical quantitative link between manufacturing costs, profits,, and general expenses. Vernon [7] discusses two scenarios in which the various expenses and incomes are related to each other. In scenario 1, Vernon has proposed that, by lowering the COGS through improvement in manufacturing efficiency of pharmaceutical companies, the market price for pharmaceutical products will decrease with all the other costs kept constant. This will lead to gains in consumer surplus [7]. In scenario 2, the market price is not lowered, and the decrease in COGS, as a result of improved manufacturing efficiency, will positively impact spending of pharmaceutical companies, which will also eventually benefit the consumers. This implies that if COGS can be reduced, the pharmaceutical companies will invest those savings into discovery of new therapies for unmet medical needs. In fact, Vernon s economic model calculates that the overall gain in consumer surplus is higher in scenario 2 than in scenario 1. Vernon also suggests that the actual effect of improving manufacturing efficiency possibly exists between the above two scenarios. Trend lines (linear or polynomial), fitted to the raw data set using the standard function of Microsoft Excel, show that there are significant changes in, operating income, general expenses, and % (Appendix 2 Fig. 13) for brand-name companies. The data exhibit a decreasing trend, while the % data exhibit an increasing trend over the time period used in the study (1994 to 2005). The operating income and general expenses, however, show rising and falling trends, respectively, with small gradients. Other expenses, such as taxes and depreciation, show a flat profile. The t test performed on the data, shown in Appendix 2 Table 4, supports the observation that, between the two subperiods of 1994 to 1999 and 2000 to 2006, there is a significant decrease in the average and general expenses, a significant increase in the average % and operating income, and no significant change in the average values of taxes and depreciation. The correlation between and % was found to be 0.93, showing a strong negative correlation between the two. Figure 6 shows the trends in and % over the years. The fitted equations along with the data points are also provided along with the R 2 values, which

7 36 J Pharm Innov (2008) 3: % Biotechs 16% 14% 12% 1 8% 6% indicate the goodness of the fit. A decreasing trend for and increasing trend for % can be observed. Thus, the strong correlation along with the trends supports Vernon s scenario 2 that, during the recent past, for brandname companies, savings in COGS has positively impacted investments in, resulting in much-needed innovations and future societal health benefits. Trend lines for brand name companies Fitted line R 2 y ¼ 0:0013x 2 þ 0:0071x þ 0: % y ¼ 0:0004x 2 þ 0:0004x þ 0: Generic Companies y = x R 2 = % 21% 23% 2 27% 29% 31% COGS Fig. 6 Relationship between COGS and brand names Similar trend lines were also fitted to the data set over the studied period ( ) for generics. A decreasing trend was observed for and general expenses, while a rising trend was observed in operating income. The trend for % shows almost a flat profile. The observations in trends were also supported by t test as shown in Appendix 2 Table 5, where significant changes were only observed in, general expenses, and operating income. During the period under study, for generics, the and general expenses decreased, while operating income increased. The correlation between and operating income was found to be 0.92, showing a strong negative correlation between the two observed in Fig. 7. From the figure, it can be observed that the showed a decreasing trend, and operating income showed an increasing trend over the years. Therefore, for generics, it appears that the reductions in may be contributing to increase in operating income. Trend lines for generics Fitted line R 2 y ¼ 0:0005x 2 þ 0:0038x þ 0: % y ¼ 0:0009x 2 þ 0:0002x þ 0: Appendix 2 Fig. 15 shows the variation in different expenses over the studied period for biotechs.. The 2002 data for general expenses and % showed unusually high deviations due to the data from AMGEN and hence were considered as outliers and removed for further analysis (Fig. 10). Trend lines were fitted to the reduced data. There is large fluctuation in the data, and the trend lines do not fit well with the data points. A t test was done to find the significance of changes in the various factors, as shown in Appendix 2 Table 6. Only depreciation increases significantly over the years. Thus, the analysis suggests that for biotechs, all the expense categories such as and % have remained almost constant, other than depreciation which has increased for the period studied. General Observations Aside from the key observations given above, the following are some general observations based on analysis of the data: The total sales of the brand names were about 9 of the total sales of all the companies in this study; thus, the performance of the brand names has a significant impact on the total market. This is in line with the IMS global pharmaceutical market forecast [6]. Therefore, an economic model, such as the one by Vernon for the brand names, will be a good predictor of the overall pharmaceutical market behavior. For generics, the manufacturing expense is nearly 5 of the total sales revenues as compared to about 27% for that for brand names. It was surprising to note that the for biotechs was the lowest. Among all the three categories of pharmaceutical companies, for biotechs, the expenditure on % as a percentage of sales is the highest. The biotechs appear to invest the most in with an average y = x R 2 = COGS Fig. 7 Relation between COGS and operating income for generics

8 J Pharm Innov (2008) 3: % of 27. As expected, % for generics is the smallest, nearly one third that of biotechs. Operating incomes for brand names and biotechs are nearly equal and higher than those for generics. The general expense for brand names is nearly equal to the industry average reported by Reinhardt in 2001 and is much higher than those for generics and biotechs. Conclusions The following conclusions can be drawn from the study: As expected, it appears that a significant difference in how the three different categories of pharmaceutical companies, viz. brand names, generics, and biotechs, spend their revenues. For brand names, COGS per unit sales showed a significant decrease, while per unit sales displayed a significant increase during 1994 to Moreover, was found to be negatively correlated with % across time, suggesting that the cost savings associated with a reduced COGS may have been budgeted for increased expenditure on research and development for the discovery of new therapies, as predicted by Vernon [7]. This work did not consider alternative hypotheses for increased expenditures during the period studied. For generics, showed a significant decrease, while operating income showed a significant increase over the years. was found to be strongly negatively correlated to operating income. Generics are not involved in the discovery of new therapies, so their expenditure is likely more geared toward developing formulations of already approved drugs whose patents are about to expire. Thus, for generics, the savings from COGS is probably not invested in research and appears to result in increased profits. For biotechs, most of the factors studied do not show a significant change over the years except for depreciation. Fluctuations were found in the data compared to brand names and generics, but the data failed to show any trend. Interestingly, depreciation is significant for these companies, which might be an effect of the high capital investment required to manufacture biotech products. Appendix Appendix 1: Anomalies in the Data The data for various categories for Merck & Co. are shown in Fig. 8. The increased steadily from 36% to Merck and Co. % 1992 Fig. 8, general expenses, operating income, and % for Merck and Co. from 1994 to 2002 but dropped sharply to 1 in 2003 and stayed close to that value for the next 2 years. Also, the value of 1 in the past 3 years is significantly different from the industry average. General expenses, operating income, and % showed an increase after Although no explanation has yet been provided for the sharp changes in the trends for Merck, the following factors may have contributed these deviations. In 2001, the divestiture of Medco Health Solutions (MHS) from Merck and Co. was completed. The data for MHS is shown in Fig. 9. As MHS is primarily focused on distribution of drugs, the most important part of its expenses is COGS (more than 9). Therefore, before the divestiture Merck s was significantly higher than the industry average and after the divestiture, it reduced significantly. However, that does not explain why the COGS % was reduced below the industry average after In 2004, Merck withdrew Vioxx from the market due to the revelation of potentially life-threatening side effects. It is also possible that, because of Vioxx withdrawal, the company may have made certain decisions to safeguard against large litigation costs. We expect that, over an extended period, the various expenses will stabilize to industrial averages. Figure 10 shows the % and general expense for biotechs. There is a sudden rise in the % and dip in general expenses in the year 2002 due to high % Medco +Merck Medco Merck 1992 Fig. 9 trend

9 38 J Pharm Innov (2008) 3:30 40 % of Sales expenses from Amgen Inc. We have not uncovered any explanation for this variation. This data point was considered as an outlier and excluded from the calculation of the overall % and general expenses for biotechs. As shown by the time series data in Fig. 11, Bristol Meyers Squibb also experienced a similar setback in 2001, resulting in higher manufacturing costs in that year. This anomaly was apparently due to accelerated depreciation, asset impairment, and restructuring expenses, as explained in the 2004 annual report. The Effect An anomaly was observed for some of the generics during the years 1996 and 1997 (Fig. 12). In this period, these companies had sharp increase in and a corresponding decrease in operating income. The plots show a dramatic change in and earnings in Some companies have provided explanations in their annual reports. For example, IVAX Corporation has provided the following explanation:. During 1996, certain national drug wholesalers instituted programs designed to provide cost savings to independent retail pharmacies on their purchases of certain Generic pharmaceutical products. Pursuant to the programs, independent retail pharmacies generally agreed to purchase their R & D % Biotech Fig. 10 and general expense per unit sales for biotechs Bristol-Meyers Squibb Fig. 11 Bristol Meyers Squibb time series plot % IVAX Corporation Par Pharmaceuticals Mylan Laboratories Inc Fig effect for three generics, IVAX Corporation, Par Pharmaceuticals, and Mylan Laboratories requirements of Generic pharmaceutical products from one wholesaler and permitted the wholesaler to select the product suppliers. Each wholesaler encouraged Generic drug suppliers to participate in its program by offering to purchase the wholesaler s requirements of particular products from a single supplier. The programs encouraged Generic drug suppliers to aggressively bid to be the exclusive supplier of products under the programs. The existence of the programs also resulted in reduced prices to non-wholesaler customers. As a result, the Generic drug industry experienced a significant reduction in the prices charged by suppliers for many of its products during the

10 J Pharm Innov (2008) 3: second and third quarters of Other wholesalers have commenced or are expected to implement similar programs, and such programs may be expanded to other product lines and customer groups. Also during 1996, the Company experienced increased competition some of its more important domestic Generic pharmaceutical products as a result of product approvals obtained by competitors. Although not explicitly stated, this explanation may be applicable to all generic drug manufacturers. For IVAX, the increase in COGS is also marked by a restructuring program, which may explain the gradual decrease in COGS over the following years. Although the above summaries appear to be reflective of what has occurred, a compelling explanation backed by numerical analysis is yet to be seen. Appendix 2: Trend Analysis Brand Name companies R & D % Taxes Deprec% 4 Poly. () Poly. () Linear () 3 Poly. (R & D %) Fig. 13 Trend analysis for various expenses and incomes for brandname companies Generics companies COGS DEPREC Fig. 14 Trend analysis for various expenses and incomes for generics TXT Table 4 The t test for the variation of the different costs as a percentage of sales between two sub-periods ( and ) for brand names % Operating income General expense Taxes Depreciation % Average (I, ) 28.1% 11.2% 17.9% 31.9% 6.6% 4. Average (II, ) 24.2% 14.3% 19.4% 30.7% 6.3% 4.9% Change (II I) 3.8% 3.1% % 0.3% 0.8% Probability (p)

11 40 J Pharm Innov (2008) 3:30 40 Table 5 The t test for the variation of the different costs as a percentage of sales between two sub-periods ( and ) for generics % Operating income General expense Taxes Depreciation % Average (I, ) 5 7% 9% 2 4% Average (II, ) 49% 8% 1 17% 6% 4% Change (II I) 2% 6% 4% 1% Probability (p) Biotech companies Taxes Depre% R & D % Poly. (R & D %) Linear () Poly. () Linear () Linear (Taxes) Linear (Depre%) Fig. 15 Trend analysis for various expenses and incomes for biotechs Table 6 The t test for the variation of the different costs as a percentage of sales between two sub-periods ( and ) for biotechs % Operating income General expense Taxes Depreciation % Average (I, ) 13% 28% 23% 21% 9% 6% Average (II, ) 14% 24% 21% 26% 9% 9% Change (II I) 1% 3% 2% 3% Probability (p) References 1. Reinhardt UE. Perspectives on the pharmaceutical industry. Health Aff. 2001;20(5): Kaiser Family Foundation Report, September Suresh P, Basu PK. Improving pharmaceutical product development and manufacturing: impact on cost of drug development and cost of goods sold of pharmaceuticals. Pharmaceutical Technology & Education Center, Purdue University, February PhRMA Report. What goes into the cost of a prescription drug? Abboud L, Hensley S. Factory shift: new prescription for drug makers. The Wall Street Journal 2003;September IMS. Global pharmaceutical market forecast Vernon JA, Keener HW, Trujillo AJ. Pharmaceutical manufacturing efficiency, drug prices, and public health: examining the casual links. Drug Inf J. 2007;41: Wharton Research Data Services (WRDS). Compustat. wharton.upenn.edu/.