Decision Science for Generic Drug Development and Review.

Herein, we present the US Food and Drug Administration (FDA) Office of Research and Standards' current thinking, challenges, and opportunities for comparative clinical endpoint bioequivalence (BE) studies of orally inhaled drug products (OIDPs). Given the product-associated complexities of OIDPs, the FDA currently uses an aggregate weight-of-evidence approach to demonstrate that a generic OIDP is bioequivalent to its reference listed drug. The approach utilizes comparative clinical endpoint BE or pharmacodynamic BE studies, pharmacokinetic BE studies, and in vitro BE studies to demonstrate equivalence, in addition to formulation sameness and device similarity. For the comparative clinical endpoint BE studies, metrics based on forced expiratory volume in the first second (FEV1 ) are often the recommended clinical endpoints. However, the use of FEV1 can pose a challenge due to its large variability and a relatively flat dose-response relationship for most OIDPs. The utility of applying dose-scale analysis was also investigated by the FDA but often not recommended, due to either flat dose-response relationships or insufficient clinical study data. As a potential way to reduce sample size, we found adapting covariate analysis only explained a limited portion of the variation based on further investigation. The FDA continues to develop alternative methods to make BE assessment of OIDPs more cost- and time-efficient. Prospective generic drug applicants and academia are encouraged to participate in this effort by proposing new approaches in pre-abbreviated new drug application meeting requests and collaborating in the form of grants and contracts under the Generic Drug User Fee Amendments (GDUFA) Regulatory Science and Research Program.

Generics and Biosimilars: Barriers and Opportunities

New, brand-name, ophthalmology drug products are developed, investigated, and submitted for marketing approval through premarket interactions with the Food and Drug Administration (FDA). These drug applications for novel drugs are reviewed by FDA for safety and effectiveness before being allowed on the market. Many brand-name drugs are allowed a period of marketing exclusivity and/or have patent protections that can delay generic competition. When these exclusivity periods or patents expire or are challenged (in the case of patents), generic competitors may then market equivalent products, as allowed by U.S. law (eg, Drug Price Competition and Patent Term Restoration Act, often referred to as "the Hatch-Waxman Act"). To be approved as a therapeutic equivalent, a generic product must demonstrate that it is both pharmaceutically equivalent and bioequivalent to the brand-name drug product, which can involve innovative analytical methods and study designs. To facilitate generic drug assessment and approval, the FDA has negotiated the Generic Drug User Fee Amendments (GDUFA) program that funds a rigorous generic drug development program that includes pre-Abbreviated New Drug Application (pre-ANDA) correspondence and meetings, targeted bioequivalence research, and publication of product-specific guidances (PSGs) to support generic drug research and development for manufacturers interested in developing generic drugs for the U.S. market. FDA's regulatory practices include the monitoring of quality and postapproval adverse events of all marketed products, including those for use in and around the eyes.

Preamble . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1291 1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1291 1. Purpose of This Document . . . . . . . . . . . . . . .1291 2. Document Development Process . . . . . . . . . .1291 3. Definitions, Terminology, and Regulations . . .1292 1. Terminology . . . . . . . . . . . . . . . . . . . .1292 2. Generics . . . . . . . . . . . . . . . . . . . . . . . .1293 3. Bioequivalence . . . . . . . . . . . . . . . . . .1293 4. Biologics and Biosimilars. . . . . . . . . . .1294 2. Pharmacogenomics . . . . . . . . . . . . . . . . . . . . . . . .1295 3. Federal Regulations and State Laws . . . . . . . . . . .1296 4. Therapeutic Approaches . . . . . . . . . . . . . . . . . . . . .1296 1. Therapeutic Interchange . . . . . . . . . . . . . . . . .1296 2. Therapeutic Substitution . . . . . . . . . . . . . . . . .1297 3. Generic Substitution . . . . . . . . . . . …

Clinical Pharmacology in Women's Health: Current Status and Opportunities.

It has been reported that 88% of prescriptions filled in the United States are for generic drugs, and this has saved the US health system $1.68 trillion from 2005 to 2014.1 Over the same time period, the number of approved Abbreviated New Drug Applications for generic drugs increased by 44%.2 Generic drugs are considered safe, effective, and substitutable for the reference-listed drug under all clinical use conditions. This is because the Food and Drug Administration (FDA) mandates that they be pharmaceutically equivalent and bioequivalent through vigorous testing; therefore, they are considered to be therapeutically equivalent to the reference-listed drug (or brand-name) product.3, 4 However, the FDA's Office of Generic Drugs will occasionally receive complaints from patients and/or healthcare providers that a generic drug was either not as effective or safe as the brand name product that they were taking or prescribing previously. Such was the case with a generic version of bupropion.5 Under these circumstances, because many diseases are difficult to treat, and these reports are lacking controls, it is extremely challenging for the FDA to determine if complaints about benefits or risks of generic drugs are real or due to other factors related to disease progression or to some other factors. A related point is that the FDA interprets drug safety as a benefit-to-risk ratio. A drug is safe if its benefits favorably outweigh its risks. If patients receiving a bioinequivalent generic drug product fail to gain the benefits provided by a brand-name product, then its benefit-to-risk ratio is overstated, and failure to provide benefits may be considered a safety issue. Therefore, it is imperative that complaints about generic drug substitution be investigated thoroughly to determine if the generic drug is actually meeting bioequivalence standards and if there is a rational, mechanistic explanation for the purported reduction in benefits or increase in risks attributed to the generic drug. Through this process of investigation, the regulatory science supporting the approval criteria for all drugs, including both generic and brand-name products, continues to evolve and remain rigorous and comprehensive. In the commitment letter of the Generic Drug User Fee Act (GDUFA) of 2012, the FDA committed to consult with industry and the public to create an annual list of regulatory science initiatives specific to research on generic drugs. The research studies conducted under these initiatives will advance the public health by providing access to safe and effective generic drugs. The regulatory science results will provide new tools for the FDA to evaluate generic drug equivalence and for industry to efficiently develop new generic products in all product categories. The Office of Generic Drugs implements the GDUFA Regulatory Science Research Program by collaborating within the FDA as well as externally through grants or contracts. GDUFA's authorization of support for regulatory science research activities illustrates the importance of regulatory science innovation in the generic drug program.6 With public input, postmarket evaluation of generic drugs has been identified as a GDUFA research priority.7 Furthermore, GDUFA regulatory science priority areas include quantitative modeling and simulation tools, such as physiologically based absorption and pharmacokinetic models (PBPK) and population pharmacokinetic-pharmacodynamic (PK/PD) models as well as bioinformatics approaches for benefit-to-risk ratio assessments.8, 9 The University of Florida Center for Pharmacometrics and Systems Pharmacology and FDA's Office of Generic Drugs have collaborated on a research project to develop a mechanism- and risk-based strategy to evaluate reported postmarketing complaints about orally administered generic drugs. This strategy, starting with a patient or provider complaint about a generic drug, has 3 integrated components: (1) bioinformatics tools, (2) PBPK models, and (3) PK/PD models. Figure 1 illustrates the approach graphically that can be used to evaluate postmarketing complaints. The first step in enabling this integrated approach is to use software tools for bioinformatics that analyze the underlying molecular mechanism of the purported safety signal or reduction in effectiveness of the generic drug. This is achieved, for example, with adverse events (AEs) by using bioinformatics software tools to interrogate AE: Drug pairs from the public, online FDA Adverse Event Reporting System using bioinformatics tools such as SAS® Platform (SAS Institute, Cary, North Carolina), or the Molecular Analysis of Side Effects platform (Molecular Health, Heidelberg, Germany). These analyses use disproportionality analysis measures (eg, proportional reporting ratio) to identify a positive risk or safety signal along with a drug target–AE association. These results are supplemented by examination of a web-based deidentified, individual-level healthcare claims data warehouse that links drug prescriptions with clinical outcomes (Truven Health Analytics Marketscan® Research Database, Ann Arbor, Michigan). These databases, in turn, are complemented by additional software, EvidexTM Web-based Platform (Advera Health Analytics, Santa Rosa, California) to categorize and interrogate AEs in the FDA Adverse Event Reporting System using a proprietary computer algorithm in order to generate a rank order of AEs and associated downstream costs for a given drug that are either on-label or off-label AEs. This extensive bioinformatics approach provides the user with a pharmacological hypothesis for an AE-drug pair that is then used to evaluate the likelihood that the generic drug can plausibly cause the purported AE. This approach provides a more deterministic way for the FDA to prioritize, stratify, and categorize purported generic drug reduced efficacy or AEs into those with biological plausibility that are likely to be caused by a generic drug (ie, a signal) vs those that are unlikely to be caused by the generic drug (ie, noise). The AE-drug hypothesis generated by the bioinformatics approach is then evaluated from a bottom-up and top-down approach using a PBPK and PK/PD models, respectively, to evaluate the biological, physiological, and drug- and/or formulation-related causes of the AE-drug pair report. The second step of the integrated approach is to build a PBPK model that simulates PK profiles from, for example, oral immediate- or extended-release test and reference products and provide in silico results of virtual bioequivalence studies among different formulations. These simulations are inputted into the PK/PD model in step 3 and also are used to conduct a PBPK-based sensitivity analysis that may differentially influence the bioavailability of a generic vs brand-name drug product. PBPK modeling allows various physical, chemical, and physiological factors that influence the in vitro dissolution and in vivo absorption to be included in the sensitivity analysis. These include particle size, excipients, solubility-permeability attributes (biopharmaceutical classification system), pH-dependent solubility of the active ingredient, pH-dependent dissolution of the formulation, effect of dose, and variations in gut transit time and motility. PBPK models that incorporate key physical-chemical properties of the drug and its formulation and their associated variability may then be used to investigate possible in vitro–in vivo correlations as well as to compare in vitro dissolution profiles of test and reference drug products. This may support evaluation of new mechanistic model-based parameters to predict bioequivalence by comparing dissolution curves for differences or similarity as an alternative to the empirical factors f1 and f2 used by regulatory agencies. The third step of the integrated strategy is to build a quantitative PK/PD model of the generic drug to investigate in silico the impact of variability or differences in PK associated with potentially bioinequivalent drug products on the PD of the drug. The clinical impact of PK variability depends on the respective shapes of the PK/PD relationship for benefit and risk. Simulations of PK profiles for different degrees of differences in bioavailability between test (generic drug) and reference (brand-name drug) products generated by the related PBPK models are used to determine how different test and reference products would have to be in order for their area under the concentration-time curves and peak concentrations to render the AE (or loss of efficacy) that was the focus of the bioinformatics approach. The significance of this research project is that an integrated bioinformatics and pharmacometrics model-based strategy allows for a thorough risk-based evaluation of purported claims of bioinequivalence of generic drugs in the postapproval marketplace. The bioinformatics approach enables the identification of pharmacological pathways, including targets, that lead to AE-drug relationships and provide compelling evidence that the generic drug can or cannot cause the reduced effectiveness or AE reported by patients and/or healthcare providers. The PBPK modeling process allows one to deconstruct the PK profiles of the genetic and brand-name products and identify formulation-related differences between the products in terms of a sensitivity analysis of drug and formulation factors that have the highest likelihood of providing insights into relative bioavailability of generic and brand-name products. On confirmation that the generic drug can, in fact, cause a reported complaint, the PK/PD modeling process provides insight into the extent to which a generic drug must differ in terms of drug exposure (peak concentration and/or area under the curve), from its brand-name counterpart in order to yield the reported complaint. The bioinformatics and pharmacometrics model-based processes to evaluate marketplace performance and questions related to substitution of approved generic drug products for brand-name products was evaluated with several exemplar research projects that are reported in more detail in the individual manuscripts in this journal. These include an assessment of potential bioinequivalence of high-risk (ie, biopharmaceutical classification system type II drugs) generic products such as oral, immediate-release, and modified-release antiepileptic drugs and extended-release metoprolol, and an assessment of potential liability of bioinequivalence of generic products not yet off patent, using new oral anticoagulants as an example. Although the objective of this research is to establish a rigorous mechanistic workflow to assess complaints about generic drug bioinequivalence (ie, substitutability), this same process is equally useful for comparing brand-name drugs used in pivotal clinical trials preapproval to their to-be-marketed market formulations (ie, prescribability). The integrated approach can also be applied to other situations, such as (1) to identify and prioritize the selection of postmarketing surveillance of specific generic products that are thought to have the greater probability of bioinequivalence or whose bioinequivalence would have the greatest impact on clinical outcomes, and (2) to provide new hypotheses for future GDUFA regulatory science priority projects. Furthermore, the integrated strategy of this research can be extended to include assessment of potential food effects and bioinequivalence and factors influencing comparative bioavailability of generic vs brand-name drugs among healthy volunteers and intended patients. The authors thank Dr. Yehua Xie (Office of Generic Drugs, CDER, FDA) for his project management and administrative assistance with this article. FDA Grant No. 1U01FD005210-01 funded this study.

Regulation can influence the structure, conduct and performance of consumer product markets and the structure of product markets can influence regulation. Since the vast majority of prescription drugs consumed by Americans are generic, the structure of the U.S. generic prescription drug market is of wide interest. The supply of prescription drugs in the U.S. is also heavily regulated by the U.S. Food and Drug Administration (FDA). We describe events leading up to the passage and implementation of the Generic Drug User Fee Amendments in 2012 (GDUFA I), and compare its FDA commitments, provisions, goals and fee structure to that of the 1992 Prescription Drug User Fee Act (PDUFA) for branded drugs. Although GDUFA I expires September 30, 2017, reauthorization for GDUFA II is currently underway and is likely to shift the user fee structure away from annual facility fees to annual program fees. We explain how the fee structure of GDUFA I, and that being considered for GDUFA II, erects barriers to entry and creates scale and scope economies for incumbent manufacturers of generic drugs. Furthermore, in order to implement fees under GDUFA I, FDA required the submission of self-reported data on generic manufacturing practices including domestic and foreign active pharmaceutical ingredient (API) and finished dosage form (FDF) facilities. These data provide an unprecedented window into the recent evolution of generic drug manufacturing markets. Our analyses of these data suggest that generic drug manufacturing in 2017 is quite concentrated: a very large portion of ANDA holders have small portfolios consisting of less than five ANDAs, while a small number of very large ANDA holders have portfolios consisting of hundreds or even thousands of ANDAs. The number of API and FDF facilities have each declined by approximately 10-11% between 2013 and 2017. Furthermore, in 2017, generic manufacturing is largely foreign and has become increasingly so since 2013. We discuss the implications of the current structure of the U.S.

Regulatory Considerations of Bioequivalence Studies for Oral Solid Dosage Forms in Japan

Given the significant impacts of interindividual genetic variability on drug safety and pharmacokinetics, integrating pharmacogenetic (PGx) considerations into pharmacokinetic (PK) bioequivalence (BE) study design can improve subject safety and data robustness in generic drug development. While PGx information has been often utilized in new drug development, its use in generic drug development has not been fully considered. To understand the current landscape of its utility in generic drug development, product-specific guidances (PSGs) from the US Food and Drug Administration (FDA) containing PGx information were reviewed, along with study protocols submitted by generic drug applicants under abbreviated new drug applications (ANDAs) or controlled correspondences for the identified reference listed drugs (RLDs). Fifteen PSGs (15 RLDs) recommended PGx information as a consideration factor for subject population selection, particularly for drugs associated with inherited enzyme deficiencies or cytochrome P450 polymorphism. The PGx-based considerations in these PSGs aimed to prevent serious adverse events (60%), optimize PK BE study design (7%), or address both factors (33%). Among the 15 RLDs, 5 had submitted ANDAs or correspondences with PK BE study protocols after their respective PSGs were published. Most of these submissions aligned with the PSG recommendations, incorporating PGx-related exclusion criteria. These findings suggest that while the number of submissions is low, generic drug developers are increasingly integrating PGx considerations in PK BE studies, recognizing its potential to enhance safety and efficiency in generic drug development. Continuing efforts from both regulators and industry are critical to expand its application to other drug candidates.

Implementation of the first Generic Drug User Fee Amendments of 2012 (GDUFA I) provided funding to the US Food and Drug Administration (FDA) for modernizing review of the FDA/CDER Generic Drug Program. Under GDUFA I, FDA agreed to reduce the backlog of pending generic Abbreviated New Drug Applications (ANDAs), improve the efficiency of generic drug review, and reduce the number of review cycles with the goal of reducing overall time to approval. This study presents a preliminary analysis of initial filing and regulatory first actions on ANDAs during GDUFA I cohort year 3 (CY3) and cohort year 4 (CY4). It highlights initial successes and areas of improvement in the ANDA review process for both FDA and ANDA applicants to improve the efficiency of providing the public with high-quality, affordable generic drugs.

Since the vast majority of prescription drugs consumed by Americans are off patent (‘generic’), their regulation and supply is of wide interest. We describe events leading up to the US Congress's 2012 passage of the Generic Drug User Fee Amendments (GDUFA I) as part of the Food and Drug Administration Safety and Innovation Act (FDASIA). Under GDUFA I, generic manufacturers agreed to pay approximately $300 million in fees each year of the five-year program. In exchange, the US Food and Drug Administration (FDA) committed to performance goals. We describe GDUFA I’s FDA commitments, provisions, goals, and annual fee structure and compare it to that entailed in the authorization and implementation of GDUFA II on October 1, 2017. We explain how user fees required under GDUFA I erected barriers to entry and created scale and scope economies for incumbent manufacturers. Congress changed user fees under GDUFA II in part to lessen these incentives. In order to initiate and sustain user fees under GDUFA legislation, FDA requires the submission of self-reported data on generic manufacturers including domestic and foreign facilities. These data are public and our examination of them provides an unprecedented window into the recent organization of generic drug manufacturers supplying the US market. Our results suggest that generic drug manufacturing is increasingly concentrated and foreign. We discuss the implications of this observed market structure for GDUFA II’s implementation among other outcomes.

Regulatory science is science and research intended to improve decision making in a regulatory framework. Improvements in decision making can be in both accuracy (making better decisions) and in efficiency (making faster decisions). Science and research supported by the Generic Drug User Fee Amendments of 2012 (GDUFA) have focused on two innovative methodologies that work together to enable new approaches to development and review of generic drugs: quantitative models and advanced in vitro product characterization. Quantitative models faithfully represent current scientific understanding. They are tools pharmaceutical scientists and clinical pharmacologists use for making better and faster product development decisions. Advances in the in vitro product comparisons provide the measurements of product differences that are the critical input into the models. This paper outlines four areas where science and research funded by GDUFA support synergistic use of models and characterization at critical decision points during generic drug product development and review.

Generic drugs: are they the future for affordable medicine?

A clinical endpoint bioequivalence (BE) study aims to establish BE between a generic drug (TEST) and an innovator drug (REF). A placebo (PLB) is usually included to demonstrate the sensitivity of the study. BE is established if TEST is shown to be superior to PLB, REF superior to PLB, and TEST equivalent to REF. Therefore, an overall BE test for a clinical endpoint BE study is composed of two superiority tests (TEST vs. PLB and REF vs. PLB) and one equivalence test (TEST vs. REF).
 Previously, Chang et al (2014) calculated the sample size and power for an overall BE test based on one superiority test (TEST vs. PLB) and an equivalence test (TEST vs. REF) using the joint distribution of sample means and sample variances because ’it is not easy to derive the sample size based on the multivariate t-distribution’ (we call this a ZChiSquare method). In this paper, we propose an exact method to calculate the power and sample size for an overall BE test based on two superiority tests (TEST vs. PLB, REF vs. PLB) and one equivalence test (TEST vs. REF) using a multivariate non-central t distribution directly, which we call an Exact-t method. We also extended the Z-ChiSquare method to an overall BE test with two superiority tests and one equivalence test, rather than one superiority and one equivalence test as in Chang et al’s paper.
 Simulation shows that our proposed Exact-t method is computationally more efficient than the Z-ChiSquare method without self-writing codes to numerically calculate the conditional expectation of a multivariate normal distribution conditional upon a truncated Chi-Square distribution. When sample size is small, the Exact-t method generates more accurate results than the Z-ChiSquare method.
 The Exact-t method is recommended when calculating power and determining sample size for a three-arm clinical endpoint BE study.

A clinical endpoint bioequivalence (BE) study aims to establish BE between a generic drug (TEST) and an innovator drug (REF). A placebo (PLB) is usually included to demonstrate the sensitivity of the study. BE is established if TEST is shown to be superior to PLB, REF superior to PLB, and TEST equivalent to REF. Therefore, an overall BE test for a clinical endpoint BE study is composed of two superiority tests (TEST vs. PLB and REF vs. PLB) and one equivalence test(TEST vs. REF).Chang et al. [1] calculated the sample size and power for an overall BE test based on one superiority test (TEST vs. PLB) and an equivalence test (TEST vs. REF) using the joint distribution of sample means and sample variances (we call this a Z-ChiSquare method). Previously, we proposed an exact method to calculate the power and sample size for an overall BE test based on two superiority tests (TEST vs. PLB, REF vs. PLB) and one equivalence test (TEST vs. REF) using a multivariate non-central t distribution directly (we call this an Exact-t method) for a clinical endpoint BE study with two superiority tests and one equivalence test. Yang and Sun showed that the Exact-t method is computationally more efficient and more accurate when sample size is small as compared to the Z-ChiSquare method. These methods, however, were generally verified by simulation under thenormality assumption. In reality, data can deviate from normality (e.g., be skewed). In this paper, we test the robustness of the Exact-t method and the Z-ChiSquare method when data is mildly or severely skewed. It turns out that both methods remain accurate even when data is severely skewed as long as the mean and variance of the data are correctly specified. One thing to note is that when data is more skewed, the required sample size to attain a desired power is larger. Therefore, the Exact-t method is recommended when calculating power and determining sample size for a three-arm clinical endpoint study.