Chester B. Good, MD, MPH

 To evaluate how changes in generic drug prices and the incidence of abrupt price increases varied with the number of manufacturers supplying each drug.

 Analysis of 2005 to 2016 monthly wholesale acquisition costs (WACs) and University of Pittsburgh Medical Center Health Plan counts of pharmacy claims for National Drug Codes (NDCs) for generic drugs.

 Each year, NDCs were categorized according to the number of manufacturers offering each combination of active ingredient and dosage form: 1 to 3, 4 to 7, and more than 7. For every month from January 2006 to January 2017, we estimated the 12-month change in WAC (eg, 12-month change in January 2006 was calculated as the difference in WAC between January 2006 and January 2005, divided by the WAC in January 2005), before and after weighting each NDC by counts of pharmacy claims. We evaluated the proportion of NDCs that had large price increases, greater than 20%, 50%, 100%, and 500% within a year.

 Before 2010, price changes were higher for drugs supplied by a lower number of manufacturers; however, after 2010, prices increased sharply, and drugs supplied by 4 to 7 manufacturers showed increases similar to or higher than those supplied by 1 to 3. In 2013, prices increased by an average of 29% for drugs supplied by 1 to 3 and 4 to 7 manufacturers, and 10% for more than 7. Price changes increased after weighting by counts of pharmacy claims, demonstrating that price increases disproportionately affected widely used drugs. The proportion of NDCs from drugs supplied by 1 to 3 manufacturers that doubled in price within a year was 3.6 times higher in 2012 to 2015 than in 2005 to 2009 (4.6% vs 1.3%, respectively).

 Increases in generic drug prices are concerning because they affected widely used drugs and suggest that generic drug prices may be increasingly insensitive to competition.

Before 2010, changes in prices of generic drugs were higher for drugs supplied by a lower number of manufacturers. However, after 2010, price changes increased abruptly, and drugs supplied by 4 to 7 manufacturers showed increases similar to or even higher than those supplied by 1 to 3 manufacturers.

In 2013 and 2014, price changes averaged 29% and 24%, respectively, for drugs supplied by 1 to 3 manufacturers, 29% and 28% for 4 to 7 manufacturers, and 10% and 15% for more than 7 manufacturers.

Recent increases in generic drug prices are concerning because they affected multisource, widely used drugs. Rising generic drug prices may be due not only to insufficient market competition but also to the increasing insensitivity of prices to competition.

Several studies have quantified drug price inflation over the past decade, finding that although brand name and specialty drugs experienced the largest price increases, generic prices also increased over time.

 Rising generic drug prices have been attributed to the relatively low competition present in most generic markets—the average generic is only supplied by 2 manufacturers,

 and generic drug prices are not responsive to competition until at least 3 manufacturers have entered the market.

 However, prices of widely used generic drugs supplied by numerous manufacturers have recently experienced abrupt increases, in some cases triggering allegations of price fixing.

We quantified how 12-month changes in prices of all generic drugs available from 2005 to 2016 changed with the number of generic manufacturers supplying each drug, and we also evaluated the incidence of abrupt price increases across the study period. We hypothesized that price increases would be higher for generic drugs supplied by a lower number of manufacturers.

We obtained monthly wholesale acquisition costs (WACs) for all National Drug Codes (NDCs) available from 2005 to 2016 from AnalySource (reprinted with permission by First Databank, Inc) and annual counts of pharmacy claims from the University of Pittsburgh Medical Center (UPMC) Health Plan over the same time frame. WACs represent the list price from a manufacturer to a wholesaler or a direct purchaser and do not account for any type of manufacturer rebate or discount. An NDC is a unique and universal 10-digit number that identifies human drug products in the United States. UPMC Health Plan is a health insurer offering a variety of insurance products to more than 3.2 million members in Pennsylvania and parts of Ohio, West Virginia, and Maryland. Consequently, UPMC Health Plan offers a representative expression of medication use in an insured population. We standardized annual counts of pharmacy claims for each NDC, as if enrollment had remained constant since 2005.

Using data provided by First Databank, we identified all NDCs for generic drugs with WAC information available any time from 2005 to 2016. Generics entering the market within this time frame were included in the study sample starting the first year when their list price was available. The sample included 24,944 NDCs representing 1851 combinations of active ingredient and dosage form. Each year, NDCs were categorized according to the number of generic manufacturers—1 to 3, 4 to 7, and more than 7&mdash;offering each combination of active ingredient and dosage form in January. For example, flecainide tablets were supplied by 3 manufacturers in 2006 to 2008, 4 in 2009 to 2011, 6 in 2012 to 2014, 8 in 2015, and 9 in 2016. Consequently, NDCs for flecainide tablets were categorized under the 1 to 3 manufacturer group in 2006 to 2008, the 4 to 7 group in 2009 to 2014, and the more than 7 group in 2015 and 2016. We selected 3 as a cutoff because the FDA expedites review of generic drug applications in markets served by 3 or fewer manufacturers.

For every month from January 2006 to January 2017, we estimated the 12-month change in WAC for each NDC. For example, the 12-month change measured in January 2006 was calculated as the difference in WAC between January 2006 and January 2005, divided by the WAC in January 2005. This is the reason why, although we had 2005 to 2016 data, we report 12-month changes in WAC for 2006 to 2016. Because our results could be disproportionately affected by rarely used drugs with large price increases, we further calculated the average 12-month change after weighting each NDC by the count of pharmacy claims from UPMC Health Plan in the respective year. Finally, we calculated and reported the average of the 12-month change in WAC for every year of the study period.

 To assess the association between number of manufacturers and price changes while controlling for other factors that can affect price changes,

 we constructed a linear mixed model. The linear mixed model regressed 12-month change in WAC for each NDC against indicator variables for year, for groups defined by number of manufacturers, route of administration, therapeutic classes (list in the 

]), and interactions between the variables for number of manufacturers and for years. To account for the correlation in price changes between NDCs for a given product, we specified random intercepts for generic names. An autoregressive covariance structure was used to model the correlation between repeated observations for each NDC. We constructed a second version of this linear mixed model by weighting each NDC by the count of pharmacy claims from UPMC Health Plan in the respective year.

Finally, using the 12-month change in WAC measured in January of every year, we calculated the proportion of NDCs that had abrupt price increases, which we defined as increases greater than 20% within a year. We further quantified the proportion of NDCs that had even higher price increases (ie, greater than 50%, 100%, and 500%) within a year.

Before 2010, average 12-month price changes were consistently higher for drugs supplied by a lower number of manufacturers (

). Specifically, average 12-month price changes were at least 3 percentage points higher for drugs supplied by 1 to 3 manufacturers than those supplied by 4 to 7, and price changes were negative for those supplied by more than 7. For instance, in 2008, the average 12-month price change was 7% for drugs with 1 to 3 generic manufacturers, 4% for 4 to 7 manufacturers, and —8% for more than 7 manufacturers. However, after 2010, average 12-month price changes increased sharply for all groups, and drugs supplied by 4 to 7 manufacturers showed price increases similar to or even higher than those supplied by 1 to 3 (Figure 1). Drugs supplied by more than 7 manufacturers presented lower average price increases than those supplied by 7 or fewer, yet they exceeded 10% in 2013 and 2014. For instance, in 2013 and 2014, 12-month price changes averaged 29% and 24%, respectively, for drugs supplied by 1 to 3 manufacturers, 29% and 28% for those supplied by 4 to 7 manufacturers, and 10% and 15% for those supplied by more than 7. The magnitude of these estimates increased when we weighted NDCs by counts of pharmacy claims (Figure 1).

Results from mixed models were consistent with these observations (

): Average 12-month price changes in 2007 to 2010 did not significantly differ from those observed in 2006; however, price changes in 2011 to 2016 were significantly higher compared with 2006 (

 <.01). The effect of the number of manufacturers on price was different in 2012 and 2013 compared with 2006, but not in the remaining years (eAppendix Table). To facilitate interpretation of the results from these models, Figure 1 shows the price changes predicted by linear mixed models. In 2006 to 2011, predicted price changes were consistently higher for drugs supplied by a lower number of manufacturers; however, from 2012 to 2013, predicted price changes were similar for drugs supplied by 4 to 7 manufacturers and those supplied by 1 to 3 (Figure 1). The magnitude of the predicted estimates for price changes increased when each NDC was weighted by counts of pharmacy claims, and predicted price changes for drugs supplied by 4 to 7 manufacturers in 2012 to 2015 exceeded those for drugs supplied by 1 to 3 (Figure 1).

The proportion of NDCs experiencing abrupt price increases showed a similar trend (

). In 2005 to 2009 the proportion that experienced price increases of more than 20% within a year included around 4% of NDCs from drugs supplied by 1 to 3 manufacturers, 1.5% of those supplied by 4 to 7, and 0.4% of those supplied by more than 7. In this time period, an average of 1.3% of NDCs from drugs supplied by 1 to 3 manufacturers, 0.7% for 4 to 7, and 0.01% for more than 7 doubled in price within a year. After 2010, the proportion of NDCs experiencing abrupt price increases raised considerably: In 2012 to 2015, an average of 4.6% of NDCs from drugs supplied by 1 to 3 manufacturers, 3.4% for 4 to 7, and 0.15% for more than 7 doubled in price within a year. In other words, the proportion of NDCs that doubled in price within a year was 3.6 times higher in 2012 to 2015 than in 2005 to 2009 for drugs supplied by 1 to 3 manufacturers, and 5.1 times higher for those supplied by 4 to 7. NDCs from drugs supplied by 1 to 3 manufacturers experienced particularly abrupt increases in 2012: As many as 14.7% experienced a price increase of more than 20%, 7.4% doubled in price, and 2.0% quintupled. NDCs from drugs supplied by 4 to 7 manufacturers presented the strongest increases in 2014: 11.1% experienced a price increase of more than 20%, 5.0% doubled in price, and 2.2% quintupled. In 2016, the proportion of NDCs experiencing abrupt price increases decreased to levels similar to those observed in 2005 to 2009.

Before 2010, the increased competition associated with a greater number of manufacturers consistently resulted in lower price increases. However, in 2012 to 2015, price increases were similar or even higher for drugs supplied by 4 to 7 manufacturers compared with those supplied by 1 to 3, and were as high as 25%. In this time period, increases in prices of drugs supplied by more than 7 manufacturers averaged 10%. In addition, when NDCs were weighted by utilization, prices increased more dramatically, demonstrating that price increases disproportionately affected widely used drugs. The proportions of NDCs offered by 1 to 3 and by 4 to 7 manufacturers that doubled in price within a year were 3.7 and 5.1 times higher, respectively, in 2012 to 2015 than in 2005 to 2009.

Our findings add to the existing literature because they demonstrate that large increases in prices of off-patent drugs have not been limited to single-source drugs used in rare conditions,

 but have also affected commonly used multisource drugs. These price increases are particularly concerning because they can have a large impact on spending, as illustrated by our estimates that account for the relative utilization of drugs. With generic drugs supplied by 4 to 7 and more than 7 manufacturers experiencing average increases of 25% and 10%, respectively, in 2012 to 2015, our results suggest that rising prices of generic drugs may be due not only to limited competition in the generic market,

 but also to the increasing lack of sensitivity of generic drug prices to competition.

Although our study did not assess the reasons behind this increasing insensitivity of prices to competition, the high price increases observed in 2012 to 2015 could reflect the occurrence of several cases currently under investigation for alleged price collusion.

 Alternatively, they could be related to the so-called 2012 patent cliff, when several blockbuster brand name drugs—including atorvastatin, clopidogrel, olanzapine, quetiapine, escitalopram, montelukast, esomeprazole, and methylphenidate&mdash;lost patent protection.

 Specifically, manufacturers may have increased generic drug prices because they had fewer opportunities to generate revenue through 180-day exclusivity periods after the end of the patent cliff. Regardless of the underlying reasons, the sharp increase in the incidence of abrupt price increases indicates distorted pricing dynamics in the pharmaceutical market during this time period. However, we found that trends in price changes stabilized after 2015. The reason for price stabilization is also unclear, although it could be due to increased social and political pressure or the filing of several complaints alleging collusion in rising prices of generic drugs.

Future research should further evaluate whether the price trends observed in 2012 to 2015 for generic drugs are isolated and whether the price stabilization observed after 2015 is maintained over time. This will clarify whether competition in the generic market is enough to maintain low inflation rates or whether the implementation of policy solutions designed to cap inflation is needed.

First, in our analyses, we used WACs as pricing estimates, which do not account for manufacturer discounts or rebates. Nevertheless, list prices are often used to provide directional information about drug prices, and uninsured or underinsured patients are often exposed to these prices. Additionally, our focus on generic drugs also reduces the likelihood of the impact of rebates. Second, we used counts of pharmacy claims from UPMC Health Plan to weight each NDC by its relative utilization. The use of data from only 1 insurance carrier limits the generalizability of our results; however, the use of weights was necessary to confirm that the observed average increases in prices were not driven by disproportionate increases in prices of rarely used drugs.

We observed double-digit increases in prices of all generic drugs in 2012 to 2015, regardless of the number of manufacturers. Our results signal market failures in the generic drug market and warrant the consideration of policy strategies to increase the sensitivity of generic drug prices to competition.

 Department of Pharmacy and Therapeutics, School of Pharmacy, University of Pittsburgh (IH, MH), Pittsburgh, PA; Insurance Services Division, University of Pittsburgh Medical Center (CBG, NP, WHS), Pittsburgh, PA; Division of General Internal Medicine, School of Medicine, University of Pittsburgh (WFG), Pittsburgh, PA.

 The authors acknowledge funding from the Myers Family Foundation. Dr Hernandez is funded by the National Heart, Lung, and Blood Institute (grant number K01HL142847).

 Dr Parekh and Dr Good are employed by the University of Pittsburgh Medical Center (UPMC) Insurance Service Division, Center for Value-Based Pharmacy Initiatives. Dr Shrank is employed by UPMC Health Plan. The remaining authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article. This study was presented at the International Society for Pharmacoeconomics and Outcomes Research Europe 2018 Annual Meeting in Barcelona, Spain, on November 13, 2018.

 Concept and design (IH, CBG, WFG, NP, MH, WHS); acquisition of data (IH, CBG, MH); analysis and interpretation of data (IH, CBG, WFG, NP, MH, WHS); drafting of the manuscript (IH, CBG); critical revision of the manuscript for important intellectual content (WFG, NP, MH, WHS); statistical analysis (IH); provision of patients or study materials (IH); obtaining funding (IH); and administrative, technical, or logistic support (IH).

 Inmaculada Hernandez, PharmD, PhD, Department of Pharmacy and Therapeutics, School of Pharmacy, University of Pittsburgh, 3501 Terrace St, 637 Salk Hall, Pittsburgh, PA 15261. Email: inh3@pitt.edu.

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Articles

Regardless of the number of manufacturers, generic drug prices presented double-digit average increases from 2012 to 2015.

Number of Manufacturers and Generic Drug Pricing From 2005 to 2017

 To describe the prevalence and incidence of opioid and nonsteroidal anti-inflammatory drug (NSAID) use before and since the start of the Veterans Health Administration (VHA) Opioid Safety Initiative (OSI) and to assess rates of adverse events (AEs).

 The OSI began in August 2012 and was fully implemented by the end of fiscal year (FY) 2013. The study timeframe was categorized into baseline (FY 2011-2012), transition (FY 2013), and postimplementation (FY 2014-2015) phases. Prevalence and incidence rates were calculated for opioid and NSAID users by quarter between FY 2011 and FY 2015. For AEs among new users of an NSAID or opioid, Cox proportional hazards models with inverse probability weighting were used to adjust for potential confounding.

 There were 3,315,846 regular users of VHA care with at least 1 opioid and/or NSAID outpatient prescription between FYs 2011 and 2015. The quarterly opioid prevalence rate was approximately 21% during the baseline and transition phases, then decreased to 17.3% in the postimplementation phase. NSAID prevalence remained constant at about 16%. Opioid incidence rates gradually decreased (2.7% to 2.2%) during the study, whereas NSAID incidence rates remained about 2.2%. After inverse probability weighting, patients receiving opioids had a greater risk of cardiovascular events (hazard ratio [HR], 1.41; 95% CI, 1.36-1.47), acute kidney injury (HR, 2.60; 95% CI, 2.51-2.68), gastrointestinal bleeding (HR, 1.68; 95% CI, 1.56-1.81), and all-cause mortality (HR, 3.73; 95% CI, 3.60-3.87) than NSAID users.

 Opioid use declined following implementation of the OSI, whereas NSAID use remained constant. Rates of AEs were higher among opioid users, which provides additional rationale for efforts to use NSAIDs for pain management when appropriate.

The percentages of patients receiving new and continuing opioid prescriptions decreased with implementation of an opioid safety initiative (OSI), whereas the percentages of nonsteroidal anti-inflammatory drug (NSAID) users remained constant. However, patients receiving opioids had greater risks of cardiovascular events, acute kidney injury, gastrointestinal bleeding, and all-cause mortality than NSAID users.

Our concern was the potential for expanded NSAID use among patients at increased risk for NSAID-related adverse drug events (eg, older patients) as an unintended consequence of the OSI; it is reassuring that an increase in adverse events was not identified among incident NSAID users.

The results provide support for ongoing efforts to use nonopioid strategies, such as NSAIDs, for pain management as appropriate.

Further research is needed to examine the incidence of serious adverse outcomes, and causes of death, with opioids.

Opioid safety continues to receive widespread attention given the ongoing increase in the number of opioid-related deaths.

 In August 2012, the Veterans Health Administration (VHA) implemented its Opioid Safety Initiative (OSI) to improve the safe and effective use of opioids. This comprehensive initiative included implementation of a national dashboard to help providers identify patients at risk of serious adverse events (AEs) related to high-dose opioid use and practice guideline-concordant use of opioid therapy in the management of chronic pain. The OSI dashboard includes data on pharmacy patients dispensed an opioid, patients on long-term opioids who received a urine drug screen, patients who received an opioid and a benzodiazepine in the same quarter of a fiscal year (FY), and the average morphine-equivalent daily dose of opioids. In addition, the VHA National Pain Management Program Office developed an OSI Toolkit that includes advice for tapering opioids and benzodiazepines, nonpharmacologic and nonopioid treatment alternatives, consent for long-term opioid therapy, and patient educational materials.

 Given the emphasis on decreasing high-risk opioid use, prescribing of nonopioid analgesics, such as nonsteroidal anti-inflammatory drugs (NSAIDs), may have increased after the OSI. NSAIDs have their own risks, however, including adverse cardiovascular, renal, and gastrointestinal (GI) effects.

 Because the VHA patient population includes a high proportion of elderly patients with multiple comorbidities, NSAID-related AEs might increase if these medications are used inappropriately either to replace an existing opioid or to avoid initiating an opioid.

Various multifaceted interventions (eg, stakeholder involvement, education, and audit and feedback) have been effective in improving the safety of opioid prescribing.

 However, it is unclear how these safety initiatives may affect the utilization of other analgesics, such as NSAIDs, and the subsequent rate of AEs. We examined VHA databases to describe the prevalence and incidence of opioid and NSAID use and assess the rates of adverse outcomes typically associated with NSAIDs among incident opioid and NSAID users, before and since the OSI.

Study patients were 18 years or older and regular users of VHA care. We further identified patients with at least 1 prescription for an opioid or NSAID during the study timeframe of FY 2011 (October 1, 2010-September 30, 2011) through FY 2015. “Regular users” were defined as patients with at least 2 VHA outpatient visits and/or inpatient stays during the FY containing their index date (date of first opioid or NSAID prescription during the study period) and the prior FY. The OSI started in August 2012 and was fully implemented by the end of FY 2013; therefore, the study timeframe was categorized into baseline (FY 2011-2012), transition (FY 2013), and postimplementation (FY 2014-2015) phases (

). The study was approved by the institutional review board for the Hines/North Chicago VA Medical Centers; protected health information was used.

To characterize patients at the index date, we obtained data from the Outpatient and Inpatient National Medical SAS datasets during the year prior to the index date for demographics, VHA care utilization, and diagnosis codes. Comorbidities were defined using the Deyo et al adaptation of the Charlson Comorbidity Index

 and included other disease states that could influence opioid or NSAID use. We linked patient zip code to Federal Information Processing Standard county code and then mapped county code to the Area Health Resource File for describing the patient’s county of residence as urban, rural, or highly rural and classifying its Census region. Outpatient opioid and NSAID use and concomitant medications at index date were obtained from the VHA Pharmacy Benefits Management (PBM) Services database (version 3.0). Adverse outcomes typically associated with NSAIDs were primarily based on 

International Classification of Diseases, Ninth Revision

) codes from the National Medical SAS datasets; serum creatinine values from PBM laboratory data were also included to identify acute kidney injury (AKI). Mortality data were obtained from the Vital Status file.

 Prevalence (ie, continuing users) and incidence (ie, new users) rates were individually calculated for opioids and NSAIDs (

]) by quarter between FY 2011 and FY 2015. Quarterly-specific prevalent users were patients who had a supply of the drug(s) during the quarter of interest. Patients receiving an NSAID and opioid appeared in both groups. Quarterly-specific incident users were subsets of prevalent users with no opioids and/or NSAIDs during the year prior to the index date. The denominator was regular users of VHA care during both the FY containing the quarter of interest and the preceding FY.

 Adverse outcomes included cardiovascular events, AKI, GI bleeding requiring hospitalization or an emergency department (ED) visit, and all-cause mortality. Serious events associated with opioids (eg, overdose) were not included because the focus was on adverse outcomes commonly seen with NSAIDs. All were defined by 

 diagnosis codes associated with the hospitalization or ED visit, except death, and coronary revascularization was defined by Current Procedural Terminology and 

). To improve the identification of AKI not coded during an encounter, serum creatinine lab values were used as defined by Lafrance and Miller (eAppendix B).

For patients who were regular VHA users with a prescription(s) for opioids or NSAIDs during the study timeframe, we described patient characteristics at the index date. Analysis of variance or χ

 tests were used to compare differences across groups. Over the 5-year timeframe, we calculated the quarterly prevalence and incidence rates of opioid and NSAID users. We computed 95% CIs for all rates, and the lower and upper bounds were the same as the point estimates to the first decimal place, given the huge sample size. Therefore, we focused on rate changes with clinical significance rather than statistical significance.

AEs were identified among incident users of opioids or NSAIDs only when the patient was receiving the medication (ie, release date of the prescription + the day-supply). Results were calculated for the entire study period, at baseline prior to implementation of the OSI (FY 2011), and after implementation (FY 2014, to ensure time for follow-up). Only incident users were included, because prevalent users were tolerating the treatment.

 Patients were followed for a maximum of 1 year after the index date and were censored when a drug from the other group was initiated (eg, on an opioid, then NSAID started) or an AE occurred, including death. The exposure time was the number of days the patient received the medication during the follow-up period, which could vary across AEs. Patients could have multiple types of AEs, but they were censored when the first event of the same outcome occurred (eg, first admission for AKI). For the aggregate outcomes of total AEs and cardiovascular events, patients were censored when the first adverse outcome of any type or first cardiovascular event occurred, respectively. In sensitivity analyses, we extended the medication exposure window to 1.25 times the day-supply of the medication or the day-supply plus 15 days, whichever was less.

In order to address factors that may confound the effect of treatment on the occurrence of an AE, we used inverse probability of treatment weighting (IPW) with normalized weights.

 Weights were estimated using a logistic regression model, with receipt of an NSAID as the dependent variable and patient demographics, VHA healthcare utilization, comorbidities, concomitant medications, and FY at index date as independent variables. We assessed the range of weights; less than 0.2% were above 20. We then conducted a sensitivity analysis with weights truncated at 20 to prevent an inflated influence of outliers. The results of this analysis were very similar, with less than a 4% change in hazard ratio (HR) estimates (data not shown). After weighting, we assessed the balance of baseline characteristics between NSAID and opioid groups using standardized difference. A standard difference of less than 0.1 was considered negligible.

 Then, we used Cox proportional hazards models to assess the association between NSAID and opioid use and AEs, with and without application of IPW. We assessed whether the proportional hazards assumption held true. Analyses were carried out using SAS version 9.4 (SAS Institute Inc; Cary, North Carolina). Statistical tests were 2-sided, with a 

 value <.05 considered statistically significant.

Of the 3,315,846 opioid and/or NSAID users during the study period, 50.4% had prescriptions for opioids only at the index date; 42.3% received NSAIDs only, and 7.4% received both opioids and NSAIDs (

). Both prevalent and incident opioid and NSAID users had clinically similar characteristics; therefore, their characteristics are not presented separately in Table 1.

On average, opioid-only users were older than NSAID-only users (mean age of 62 vs 56 years) (Table 1). Patients in all groups were predominantly male (92%). Compared with NSAID users, opioid-only users had a higher proportion of white patients (67% vs 60%); more utilization of VHA care; a higher Charlson Comorbidity Index score (1.5 vs 0.7); greater proportions with diagnoses of cancer, AKI, GI bleeding, and cardiovascular comorbidities; and higher percentages receiving medications that could influence the decision to prescribe an opioid or NSAID (eg, potential decrease in NSAID use among patients receiving antithrombotic therapy). NSAID users had higher proportions with behavioral health conditions. The characteristics of opioid and NSAID users did not change much between FYs 2011 and 2015 (

Between FYs 2011 and 2015, VHA users increased annually from 5.5 million to 6 million. Of these, there were 3,986,683 (72% in FY 2011) to 4,392,545 (73% in FY 2015) patients who were regular users of VHA and 18 years or older who were assessed for opioid and NSAID use.

Opioid prevalence rates remained constant during the baseline phase (20.8% in the first quarter [Q1] of FY 2011), then started to gradually decrease during the transition phase in FY 2013 (

). Opioid prevalence decreased more sharply during the second year of the postimplementation phase to 17.3% by Q4 of FY 2015. From the beginning to the end of the study period, opioid prevalence decreased by 3.5%, or 16.8% of the original rate. NSAID prevalence essentially remained constant (15.8% in Q1 of FY 2011; 16.0% in Q4 of FY 2015).

The opioid incidence rate gradually decreased during the study period, from 2.7% in Q1 of FY 2011 to 2.2% in Q4 of FY 2015, a decrease of 0.5%, or 18.5% of the original rate (Figure 2). NSAID incidence remained relatively constant throughout the study period (2.2% in Q2 of FY 2011; 2.1% in Q4 of FY 2015).

Adverse Outcomes Among Incident Opioid and NSAID Users

There were no significant differences in the measured characteristics of incident opioid and NSAID users after applying IPW; all standardized differences were less than 0.1 (

). Of the 1,155,420 incident opioid-only users and 979,277 incident NSAID-only users in the entire study period, opioid users had higher incidence rates for all adverse outcomes than NSAID users, with unadjusted HRs ranging from 2.24 to 9.48 (

). The proportional hazards assumption was satisfied. After IPW, the hazard for all adverse outcomes evaluated (except acute coronary syndrome) remained significantly greater for incident opioid users, with HRs ranging from 1.32 to 3.73. Specifically, incidence rates for total AEs were 118 per 1000 person-years for opioid users versus 23 per 1000 person-years for NSAID users, with an unadjusted HR of 5.13 (95% CI, 4.97-5.28) and an HR of 2.05 (95% CI, 2.00-2.10) after IPW. The incidence rates for all-cause mortality were 85 per 1000 person-years for opioid users versus 9 per 1000 person-years for NSAID users, with an unadjusted HR of 9.48 (95% CI, 9.06-9.93) and an HR of 3.73 (95% CI, 3.60-3.87) after IPW. The results were similar in sensitivity analyses, although the incidence rates increased about 10% for both NSAID and opioid users when we extended the medication exposure window (data not shown). When comparing adverse outcomes in incident opioid versus NSAID users in FY 2011 with FY 2014, the HRs in both years after IPW were similar to the results from the entire sample (

Similar to studies on other initiatives addressing high-risk opioid use,

 we found that opioid prevalence and incidence rates declined following implementation of the VHA OSI. Mosher and colleagues identified a sharp increase in opioid prevalence (18.9% to 33.4%) from FY 2004 to FY 2012 and a modest increase in opioid incidence (8.7% to 9.6%) among patients with regular VHA medication use.

 Their rates and ours were essentially identical in the overlapping study years (ie, FY 2011 and FY 2012). Moving forward, we found that opioid prevalence rates started to gradually decrease during the transition phase in FY 2013 and dropped more sharply beginning in Q4 of FY 2014 post implementation of the OSI. The steeper decline could be related to some medical centers focusing more intently on opioid safety. These trends reflect the earlier emphasis on managing chronic pain and the more recent focus on promoting safe opioid use to guard against harm and abuse.

 Recent study findings show that the VHA OSI also led to decreases in the use of high-dose opioids and concurrent prescribing of opioids with benzodiazepines.

In contrast with opioid use, the prevalence and incidence of NSAID use remained constant, possibly indicating that fewer patients used opioids without moving to NSAIDs. The lack of an increase in prevalence and incidence after the OSI was unanticipated, especially because nonacetylated salicylates and selective cyclooxygenase-2 inhibitors were included as NSAIDs in our study. There are several possible explanations, including OTC NSAID use. Also, we did not assess whether pain went untreated or whether the use of other medications (eg, acetaminophen, duloxetine) or nonpharmacologic therapies (eg, complementary and alternative medicine, physical therapy) may have substituted for opioid use. Providers may have avoided NSAIDs in patients who were elderly; had a history of cardiovascular disease, AKI, or GI bleeding; or were taking medications that could interact with an NSAID. As seen in the baseline characteristics, a higher proportion of opioid users had these attributes, and the characteristics of opioid and NSAID users did not change over time.

Because the patients who received opioids differed from those who received NSAIDs and likely confounded the effect of treatment on the occurrence of an AE, we used IPW. There were no significant differences in the measured characteristics of incident opioid and NSAID users after applying IPW. Using methods similar to ours, Solomon and colleagues compared the safety outcomes of opioids, selective cyclooxygenase-2 inhibitors, and nonselective NSAIDs in elderly patients with arthritis.

 After propensity score matching, they also found that patients on opioids experienced a higher risk of cardiovascular events (HR, 1.77; 95% CI, 1.39-2.24), AKI (HR, 1.53; 95% CI, 1.12-2.09), and all-cause mortality (HR, 1.87; 95% CI, 1.39-2.53) in comparison with patients on nonselective NSAIDs.

 In 3 short-term randomized studies of celecoxib versus an opioid, a significantly higher percentage of patients in the opioid groups experienced AEs.

 However, no treatment-related serious AEs were reported in these studies.

We evaluated whether the risk of AEs with opioids versus NSAIDs changed from baseline to FY 2014. If higher-risk patients were receiving NSAIDs after implementation of the OSI, then the HRs would move toward the null or possibly indicate a decreased risk of AEs with opioids. However, the HRs were similar in both years, except that there was an increase in the hazard of AKI, all-cause mortality, and total AEs among opioid versus NSAID users in FY 2014. The increase could be related to a rise in the proportion of opioid users with more severe disease/pain in the years after implementation of the OSI because of the emphasis on decreasing opioid use. Those with more severe disease may be at greater risk of AEs, especially all-cause mortality. In addition, perhaps there was improved documentation of AEs among opioid users due to heightened awareness of the harms.

Although our findings of higher rates of cardiovascular, renal, and GI AEs with opioids are unexpected, they are consistent with prior reports. In addition to the study by Solomon and colleagues,

 LoCosale et al reported that among patients initiating chronic opioid therapy, rates of myocardial infarction (MI), stroke, and heart failure per 1000 person-years in the United States were 10.7, 9.3, and 37.2, respectively.

 Carman et al estimated incidence rate ratios (IRRs) for MI and MI/coronary revascularization in a cohort of chronic opioid users versus a matched cohort from the general population.

 The adjusted IRRs in opioid users versus the general cohort were 2.66 (95% CI, 2.30-3.08) and 2.39 (95% CI, 2.15-2.63) for MI and MI/coronary revascularization, respectively.

 In a nested case-control study using a UK General Practice Research Database, the authors found the odds of an MI were increased among current opioid users versus nonusers (adjusted odds ratio, 1.28; 95% CI,1.19-1.37).

 However, unmeasured confounding cannot be ruled out in these studies. Regarding adverse renal events, a recent review discussed the effects of opioids on the kidneys, including AKI and chronic kidney disease.

 Although the incidence is unknown, AKI due to opioids can result from dehydration, urinary retention, and rhabdomyolysis.

 Most dramatically, overall mortality was greater in incident opioid versus NSAID users, including the IPW cohorts. Other studies have found higher mortality rates in users of opioids relative to NSAIDs and other medications for pain; this was mainly due to cardiovascular and respiratory causes, as well as overdoses.

Although our study included a large national population of opioid and NSAID users with cohorts that were well balanced across measured baseline characteristics after IPW, there were limitations. We did not include AEs outside of the VHA. However, we only included regular VHA users and we do not anticipate differential use of non-VHA hospitals between cohorts. Although we included many characteristics in the IPW that may influence the choice of an opioid versus an NSAID, there could have been unmeasured residual confounding (eg, smoking, severity of illness); sicker patients, or patients with more severe pain, still may have received opioids. A high-dimensional propensity score may have helped with this issue and is worthwhile to consider for future analyses. In addition, while patients received the medications, it was not possible to know if and when they actually took them; opioids and NSAIDs are frequently used as needed. Therefore, patients may not have been taking the medications at the time of the outcomes, and there was an increase in the AE rates when we extended the medication exposure window beyond the day-supply in the sensitivity analysis. We also did not capture OTC NSAID use, which may have increased after implementation of the OSI. Finally, we did not consider the use of other medications and nonpharmacologic therapies that may have replaced opioids and contributed to a lower-than-anticipated use of NSAIDs.

We found that the rates of new and continuing opioid users decreased following nationwide implementation of the OSI in the VHA, while the rates of NSAID users remained constant. Our concern was the potential for expanded NSAID use among patients at increased risk for NSAID-related adverse drug events (eg, older patients) as an unintended consequence of the OSI. Thus, it is reassuring that we did not identify an increase in AEs among incident NSAID users. Indeed, our findings support ongoing efforts to use nonopioid strategies, such as NSAIDs, for pain management when appropriate. Further research is needed to compare the incidence of serious adverse outcomes among users of opioid and nonopioid medications, as well as the cause of death to determine the plausibility of the analgesic’s role.

 Kansas City VA Medical Center (VF), Kansas City, MO; VA Center for Medication Safety/Pharmacy Benefits Management Services (SLA, CBG, FEC), Hines, IL; VA Center for Health Equity Research and Promotion (SLA, XZ, CBG), Pittsburgh, PA; VA Center for Healthcare Organization and Implementation Research (DRM), Bedford, MA; Birmingham VA Medical Center (JAS), Birmingham, AL; University of Alabama at Birmingham (JAS), Birmingham, AL.

 There was no funding for this study. The work was supported by VA Pharmacy Benefits Management Services (Hines, IL), and VA Pittsburgh Healthcare System (Pittsburgh, PA). The views expressed in this paper are those of the authors, and no official endorsement by the Department of Veterans Affairs or the United States Government is intended or should be inferred.

 Dr Singh has consulted for Savient, Takeda, Regeneron, Merz, Iroko, Bioiberica, Crealta/Horizon, and Allergan (pharmaceuticals); WebMD; UBM, LLC; and the American College of Rheumatology. Dr Singh has also received grants from Takeda and Savient, is a member of the executive committee of OMERACT, and is an editor of the Cochran UAB satellite center. The remaining authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

 Concept and design (VF, SLA, XZ, DRM, JAS, CBG, FEC); acquisition of data (XZ, FEC); analysis and interpretation of data (VF, SLA, XZ, DRM, JAS, CBG, FEC); drafting of the manuscript (VF, SLA, CBG); critical revision of the manuscript for important intellectual content (SLA, DRM, JAS, CBG); statistical analysis (XZ, DRM); administrative, technical, or logistic support (JAS, FEC); and supervision (CBG).

 Sherrie Aspinall, PharmD, MSc, BCPS, VA Pittsburgh Healthcare System, University Dr (151C) Bldg 30, Pittsburgh, PA 15240. Email: sherrie.aspinall@va.gov.

1. Rudd RA, Seth P, David F, Scholl L. Increases in drug and opioid-involved overdose deaths — United States, 2010-2015 [erratum in 

. 2017;66(1):35. doi: 10.15585/mmwr.mm6601a10]. 

. 2016;65(50-51):1445-1452. doi: 10.15585/mmwr.mm655051e1.

2. Jones CM, Mack KA, Paulozzi LJ. Pharmaceutical overdose deaths, United States, 2010. 

. 2013;309(7):657-659. doi: 10.1001/jama.2013.272.

3. VHA Pain Management: Opioid Safety Initiative (OSI). US Department of Veterans Affairs website. va.gov/PAINMANAGEMENT/Opioid_Safety_Initiative_OSI.asp. Updated October 1, 2017. Accessed February 22, 2017.

Opioid use incidence and prevalence rates decreased with implementation of an opioid safety initiative, whereas nonsteroidal anti-inflammatory drug rates remained constant. Rates of adverse events were higher among opioid users.

Trends in Opioid and Nonsteroidal Anti-Inflammatory Use and Adverse Events

 A “chiral switch” occurs in the pharmaceutical market when a drug made up of 2 enantiomer forms is replaced with a purified single-enantiomer version, often in the context of a patent expiration. We studied the prevalence of chiral switching in the United States over the past decade, including trends in use of, and expenditures on, these products in Medicaid.

 We used US Adopted Names prefixes (lev/levo/ar/es/dex/dextro) to identify all single-enantiomer drugs approved from 2001 to 2011. From publicly available US Food and Drug Administration (FDA) approval documents, we extracted the characteristics of the pivotal premarket trials for the single enantiomers. Specifically, we evaluated whether the single enantiomer was directly compared with the precursor racemic drug and whether there was evidence of superior efficacy. We used quarterly drug expenditure data from each state Medicaid program to chart trends in use of, and spending on, the single-enantiomer products and their racemic precursors during the study period.

 From 2001 to 2011, the FDA approved 9 single-enantiomer products: dexlansoprazole, levoleucovorin, levocetirizine, armodafinil, arformoterol, eszopiclone, escitalopram, dexmethylphenidate, and esomeprazole. Of those 9 drugs, 3 had at least 1 pre-approval randomized trial that included the racemic precursor as a direct comparator, but there was no evidence of superiority of the single enantiomer over the racemic at comparable doses. Between 2001 and 2011, US Medicaid programs spent approximately $6.3 billion on these 9 single-enantiomer drugs.

 Recently approved single-enantiomer drugs showed no evidence of superior efficacy over the older racemic precursors in the pivotal trials leading to their approval, and in a majority of cases, they were not directly compared.

From 2001 to 2011, the US Food and Drug Administration approved 9 single-enantiomer drugs with racemic precursors. Only 3 had pre-approval studies that compared the single-enantiomer with its precursor, and none showed evidence of improved patient outcomes.

		US Medicaid programs spent approximately $6.3 billion on these 9 single-enantiomer drugs during this period.

		Well-designed comparative trials are needed to clarify, soon after approval, whether a single-enantiomer drug represents a measurable, cost-effective improvement over current racemic therapy.

		Large healthcare organizations that bear the brunt of these costs should use their position to educate prescribers about appropriate use of single-enantiomer products.

Most physicians, and certainly most patients, have never heard of the “chiral switch.”

 A chiral drug is a single molecule product that exists in 2 mirror image forms, called enantiomers.

 Despite their similar chemical structures, enantiomers can have different biological properties.

 For example, some enzymatic processes can distinguish between the R- (from the Latin rectus for “right”) and S- (from the Latin sinister for “left”) enantiomers, 

 such that 1 enantiomer may be responsible for much of the pharmaceutical benefit while the other is inactive or even harmful.

The phrase “chiral switch” was coined to refer to the substitution in the marketplace of a racemic drug (the name for a 50:50 mixture of 2 enantiomers) with a single-enantiomer version.

One prominent example of a chiral switch occurred in 2001, when the US Food and Drug Administration (FDA) approved AstraZeneca’s esomeprazole (Nexium), a proton pump inhibitor (PPI) used to treat gastroesophageal reflux disease and erosive esophagitis. At that point, the PPI marketplace was dominated by AstraZeneca’s omeprazole (Prilosec), which had been approved in 1989. Marketed as “the purple pill,” omeprazole earned over $6 billion per year in the United States by 2000, although the key patents protecting its market exclusivity ended in 2001, opening the door to generic competition.

 Omeprazole is a racemic mixture of R-omeprazole and S-omeprazole, while esomeprazole, as its name implies, is isolated S-omeprazole. The S-omeprazole enantiomer was responsible for the drug’s clinical properties while the R-omeprazole enantiomer was inactive. After FDA approval, esomeprazole was marketed as “the new purple pill” to be used in place of generic omeprazole.

Chiral switching is a controversial practice. Some claim that singleenantiomer drugs offer little clinical advantage and are used by pharmaceutical manufacturers to perpetuate revenues as the original racemic pill approaches the end of its market exclusivity. The case of esomeprazole, in fact, has been cited as an example of “corporate waste” of healthcare resources.

However, in some cases, single enantiomers may offer benefit to patients, particularly if the inactive enantiomer may also be responsible for unwanted side effects; manufacturers may pursue single-enantiomer development because of this potential for improved safety or efficacy. Indeed,in guidance originally issued in 1992, and most recently updated in 2011, the FDA identified examples of drug toxicity associated with 1 member of a pair of enantiomers.

Regardless of the motivation for development of a single-enantiomer drug, the FDA review and approval process is the same. New medications are generally approved by the FDA on the basis of effectiveness in reaching a particular outcome, and an improvement over placebo is commonly accepted.

 Since the FDA cannot by law require active comparators in clinical trials leading to drug approvals,

 singleenantiomer products can reach the market by demonstrating improvement over placebo, not their closely related racemic precursors.

How prevalent is chiral switching in the United States? To address that question, we identified all single-enantiomer medications approved from 2001 to 2011 with racemic mixtures already on the market. We examined FDA documents from the approval of these medications, focusing on their pivotal clinical trials and identifying whether the single-enantiomer version was formally compared with the racemic drug in the course of FDA approval. Finally, we use national data on prescription use and expenditures in Medicaid to illustrate the costs associated with their entry and trends in their use.

We used the FDA database of drug approvals to identify all new single-enantiomer products approved for use between 2001 and 2011 that originated from an already FDAapproved racemic mixture. We systematically searched for all United States Adopted Names (USAN) prefixes assigned to drugs with a single-enantiomer formulation: lev-/levo- for the levorotatory, S-isomer form; ar- for the levorotatory, Risomer form; es- for the dextrorotatory, S-isomer form; and dex-/dextro- for the dextrorotatory, R-isomer form.

We extracted the manufacturer name and approval year for each identified drug. We then examined the FDA approval letter(s), summary review, and medical review(s) for each of the single-enantiomer drugs, along with the approved label, and collected reports of the pivotal trials leading to FDA approval. From these reports, we extracted whether the single enantiomer was compared with the racemic mixture in those trials and whether there was any evidence of superior efficacy. We also identified whether these comparisons to the racemic mixture were reported on the approved drug label. Since no pre-approval studies were powered to assess comparative safety, we focused on efficacy.

For a more detailed investigation of chiral switching, we focused on esomeprazole (Nexium) and escitalopram (Lexapro), the 2 single-isomer drugs identified in our search with the largest revenue in the past decade. These drugs were also approved early in our study period, giving us adequate time to examine trends in prescription use. Our data source was the Centers for Medicare & Medicaid Services (CMS), which provides aggregated quarterly drug expenditure data from each state Medicaid program categorized by National Drug Code (NDC).

 We used the NDC code to link the drugs and their racemic counterparts to the National Drug Data File (NDDF) (First Data Bank, National Drug Data File, San Bruno, CA: Hearst Corporation, 2012). For each drug, we identified the total number of prescriptions filled and the amount spent on the drug by state Medicaid programs in each calendar quarter. For 49 states and the District of Columbia (Arizona data were not available), we analyzed data from 1999 through the most recent quarter of Medicaid data available at the time of the analysis.

From 2001 through 2011, the FDA approved 9 singleenantiomer versions of racemic products (

). Eight of the drugs had racemic precursors available in the United States. The lone exception was eszopiclone (Lunesta), whose precursor zopiclone (Imovane, Zimovane) was only commercially available outside the United States. Three of the 9 drugs—levoleucovorin (Fusilev), levocetirizine (Xyzal), and arformoterol (Brovana)&mdash;came from manufacturers other than the one that applied for approval of the racemic drug. The drugs covered a range of indications from depression to chronic obstructive pulmonary disease (COPD).

Comparative Efficacy of Single-Enantiomer and Racemic Drugs 

Three of the 9 single-enantiomer drugs had at least 1 preapproval randomized, controlled trial that included the racemic precursor as a direct comparator (

). Esomeprazole 40 mg demonstrated statistically significant efficacy over omeprazole 20 mg in 2 studies, although it was not superior in 2 other studies. However, pharmacologically equivalent doses of the racemic and enantiomer medications were not compared in these studies, as 40 mg of the single-enantiomer product would contain at least 3 times as much of the active S-isomer as 20 mg of racemic omeprazole on a per milligram basis.

 Dexlansoprazole (Dexilant) and levocetirizine (Xyzal) demonstrated efficacy similar to that of their racemic predecessors in the pre-approval studies. Two additional drugs—escitalopram (Lexapro) and dexmethylphenidate (Focalin)&mdash;included the racemic as an active control in pre-approval studies but compared the 2 arms with placebo, not with each other.

Trends in the Use of Esomeprazole and Escitalopram 

Between 2001 and 2011, Medicaid programs nationally spent approximately $6.3 billion on the 9 single-enantiomer drugs we reviewed (

). The 2 greatest contributors to this total were esomeprazole ($3.5 billion) and escitalopram ($1.8 billion), each representing over 60% of total Medicaid spending on the single-enantiomer and racemic precursor over that time period. Dexmethylphenidate spending was $678 million from 2001 to 2011, while the single enantiomers approved after 2004 generated substantially lower Medicaid spending.

After esomeprazole’s approval in 2001, its use in Medicaid quickly surpassed omeprazole's and peaked at over 1 million prescriptions per quarter in the second half of 2005 (

). Generic omeprazole entered the market in the fourth quarter of 2002, became available as an over-the-counter drug in June 2003, and peaked in the third quarter of that year with almost 400,000 prescriptions. However, generic omeprazole did not surpass esomeprazole in number of prescriptions until early 2010. The data show an increase in the use of omeprazole between 2009 and 2011, potentially signifying enhanced coverage of over-the-counter omeprazole in Medicaid programs, which had largely excluded this version of the product in the mid-2000s.

Esomeprazole represented almost one-third (32.7%) of prescriptions even at the end of the study period in the third quarter of 2011.

Escitalopram followed a similar pattern. After the introduction of escitalopram in 2002, its use in Medicaid rapidly increased, such that by the second quarter of 2004, over 1 million prescriptions were filled per quarter (

). Generic citalopram entered the market in the fourth quarter of 2004, leading to a steep reduction in the use of brand name citalopram, and also a leveling off in the use of escitalopram, although the number of prescriptions for escitalopram continued to outnumber those for generic citalopram until the end of 2009. Prescriptions of all products fell in 2006 due to transition of dual-eligible elderly patients to Medicare Part D.

From 2001 to 2011, the FDA approved 9 single-enantiomer drugs, several of which reached blockbuster status in the United States.

 Despite substantial spending on enantion mers, most of these drugs were not directly compared with their racemic precursor in the course of FDA approval, and when they were, there was no clear evidence of any improvements in patient outcomes.

In the postapproval period, there has also been little evidence showing any substantial comparative efficacy benefits of these single enantiomers over their racemic precursors. For example, a Cochrane systematic review of escitalopram’s effectiveness identified 6 studies of varying methodological quality (both published and unpublished) directly comparing escitalopram with citalopram. Of the 6 studies, 3 showed a statistically significant advantage of escitalopram in the primary outcome of response to treatment, and each was industry funded.

 The review authors concluded that there was a potential for “overestimation of treatment effect due to sponsorship bias.” The review also found in pooled analyses that escitalopram was no more effective than 3 other antidepressants in the same class (fluoxetine, paroxetine, and sertraline). Evidence of superior effectiveness did not emerge after esomeprazole’s approval, either. The studies reviewed by the Drug Effectiveness Review Project found that comparisons between the enantiomer and racemic precursor uniformly compared esomeprazole at a pharmacologically superior dose (40 mg) with omeprazole (usually at a dose of 20 mg), limiting the possibility of making any conclusions about efficacy.

 The Cochrane review of treatment of gastroesophageal reflux concluded that different PPIs do not show statistically significant differences in effect at comparable dosages.

 Notably, the 2 studies that showed superiority of esomeprazole over omeprazole in the pre-approval RCTs were published within 1 year of approval,

 while the 2 studies that showed no superiority were not published until 5 years later,

 again raising the concern about publication bias for the postapproval comparative effectiveness studies. Although there is no requirement for comparison of single-enantiomer drugs with their racemic precursors for FDA approval, we found numerous examples in the FDA reviews of experience with the racemic product being invoked to justify the need (or lack of need) for additional testing or guidance prior to approving the drugs. For example, while the FDA does not convene external advisory committee meetings for every drug application, it elected to not do so for dexlansoprazole specifically because the “product is not a new molecular entity. It is the R-enantiomer of a product that was approved in 1995.”

 In another instance, the Division of Pulmonary and Allergy Products did not request audits by the Division of Scientific Investigation because arformoterol “is the active enantiomer of the well-characterized LABA [long-acting beta-agonist], racemic formoterol, which is already approved for treatment of both asthma and COPD, the efficacy data are robust and as would be expected for the product, and the sponsor is not making any novel claims for the product.”

 Regulatory consideration of the safety of single-enantiomer products also benefitted from prior experience with racemic precursors. In the medical review for esomeprazole, the reviewer wrote, “Because Nexium is the S-enantiomer of omeprazole and omeprazole is a PPI that is perceived as safe, Nexium is also considered safe. Omeprazole has been marketed worldwide since 1988 and is presently available in 106 countries for various acid-related gastrointestinal disorders.”

Although we did not focus on comparative safety or tolerability of the enantiomers, it is conceivable that the benefit of single enantiomers is not in superior efficacy, but superior tolerability or safety. We are not aware, however, of any strong evidence to support that hypothesis for the drugs we studied. For example, in the Cochrane review comparing escitalopram and citalopram, there was no difference between the 2 drugs in study discontinuation due to side effects or number of patients experiencing a side effect.

Similarly, prior reviews have found no evidence of any difference in adverse events (AEs) or withdrawals due to AEs across trials of PPIs.

 To be sure, there are past examples of chiral switching bringing about important safety benefits, as in the case of levodopa, a key treatment for Parkinson’s disease; initially, the racemic drug caused significant AEs, but shifting to the single enantiomer reduced the AEs and allowed for a higher dose of the active drug.

Despite unclear efficacy or safety benefits, approval of enantiomer drugs over the past decade has had substantial cost implications. In the cases of esomeprazole and escitalopram, the enantiomer rapidly absorbed market share from the racemic precursor and diverted use of generic versions of the racemic product. Increasing the cost of medications with no discernible difference in health outcomes negatively impacts the health of patients taking those drugs. Higher prescription drug costs are associated with lower adherence, higher incidence of serious AEs, greater utilization of healthcare services, and worse health outcomes

 These costs must be weighed against any theoretical, or even real, improvement in patient outcomes with use of single-enantiomer formulations.

The case of armodafinil gives further insight into the use of enantiomers to maintain market share in products losing patent protection. Cephalon received approval for modafinil (Provigil) by the FDA in 1998. In 2002, 4 companies filed applications with the FDA for generic modafinil.

 In order to maintain its market exclusivity, in 2006, Cephalon settled with those generic manufacturers for more than $200 million, delaying entry of a generic modafinil until 2012. In 2007, Cephalon’s single-enantiomer armodafinil (Nuvigil) was approved, meaning that the company then had 5 years to “switch” market share away from modafilil.

 In order to encourage transition of modafinil to armodafinil, Cephalon steadily increased the price of modafinil, from $6.80 per pill in 2008 to $13.62 in 2009, by which time armodafinil was launched at $8.98 per pill.

From 2001 to 2011, the US Food and Drug Administration approved 9 single-enantiomer drugs with racemic precursors. None showed pre-approval evidence of improved efficacy over the racemic precursor.

Chester B. Good, MD, MPH

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