Biosimilars in oncology and inflammatory diseases: current and future considerations for clinicians in Latin America

Biological therapies have revolutionized the treatment of several cancers and systemic immune-mediated inflammatory conditions. Expiry of patents protecting a number of biologics has provided the opportunity to commercialize highly similar versions, known as biosimilars. Biosimilars are approved by regulatory agencies via an independent pathway that requires extensive head-to-head comparison with the originator product. Biosimilars have the potential to provide savings to healthcare systems and expand patient access to biologics. In Latin American countries, regulatory frameworks for biosimilar approval have been introduced in recent years, and biosimilars of monoclonal antibody and fusion protein therapies are now emerging. However, the situation in this region is complicated by the presence of “non-comparable biotherapeutics” (also known as “intended copies”), which have not been rigorously compared with the originator product. We review the considerations for clinicians in Latin American countries, focusing on monoclonal antibody biosimilars relevant to oncology, rheumatology, gastroenterology, and dermatology.     

Bayesian design of biosimilars clinical programs involving multiple therapeutic indications

In this paper, we propose a Bayesian design framework for a biosimilars clinical program that entails conducting concurrent trials in multiple therapeutic indications to establish equivalent efficacy for a proposed biologic compared to a reference biologic in each indication to support approval of the proposed biologic as a biosimilar. Our method facilitates information borrowing across indications through the use of a multivariate normal correlated parameter prior (CPP), which is constructed from easily interpretable hyperparameters that represent direct statements about the equivalence hypotheses to be tested. The CPP accommodates different endpoints and data types across indications (eg, binary and continuous) and can, therefore, be used in a wide context of models without having to modify the data (eg, rescaling) to provide reasonable information-borrowing properties. We illustrate how one can evaluate the design using Bayesian versions of the type I error rate and power with the objective of determining the sample size required for each indication such that the design has high power to demonstrate equivalent efficacy in each indication, reasonably high power to demonstrate equivalent efficacy simultaneously in all indications (ie, globally), and reasonable type I error control from a Bayesian perspective. We illustrate the method with several examples, including designing biosimilars trials for follicular lymphoma and rheumatoid arthritis using binary and continuous endpoints, respectively.     

Worldwide experience with biosimilar development

Limited access for high-quality biologics due to cost of treatment constitutes an unmet medical need in the United States (US) and other regions of the world. The term “biosimilar” is used to designate a follow-on biologic that meets extremely high standards for comparability or similarity to the originator biologic drug that is approved for use in the same indications. Use of biosimilar products has already decreased the cost of treatment in many regions of the world, and now a regulatory pathway for approval of these products has been established in the US. The Food and Drug Administration (FDA) led the world with the regulatory concept of comparability, and the European Medicines Agency (EMA) was the first to apply this to biosimilars. Patents on the more complex biologics, especially monoclonal antibodies, are now beginning to expire and biosimilar versions of these important medicines are in development. The new Biologics Price Competition and Innovation Act allows the FDA to approve biosimilars, but it also allows the FDA to lead on the formal designation of interchangeability of biosimilars with their reference products. The FDA’s approval of biosimilars is critical to facilitating patient access to high-quality biologic medicines, and will allow society to afford the truly innovative molecules currently in the global biopharmaceutical industry’s pipeline.     

Biosimilars: Challenges and path forward

Development of biosimilar proteins is the fastest growing sector in the biopharmaceutical industry, as patents for the top 10 best-selling biologics will expire within one decade. The world’s first biosimilar of infliximab, Remsima® (CT-P13) made by Celltrion, was approved by the Committee for Medicinal Products for Human Use (CHMP) of European Medicine Agency (EMA) in June 2013. This has ignited competition between related companies for prior occupation of the global market on blockbuster biologics. However, to achieve approval for biosimilars, developing companies face many hurdles in process development, manufacturing, analysis, clinical trials, and CMC (chemical, manufacturing and controls) documentation. Recent evolutionary progress in science, engineering, and process technology throughout the biopharmaceutical industry supports to show similarity between originator and biosimilar products. The totality of evidence has been able to demonstrate the quality, efficacy, and safety of biosimilars whereas a lack of interchangeability and international standards has to be addressed. Further understanding of the timing importance by regulatory agencies will be key to maximizing the value of biosimilars.     

Worldwide experience with biosimilar development

Limited access for high-quality biologics due to cost of treatment constitutes an unmet medical need in the US and other regions of the world. The term “biosimilar” is used to designate a follow-on biologic that meets extremely high standards for comparability or similarity to the originator biologic drug that is approved for use in the same indications. Use of biosimilar products has already decreased the cost of treatment in many regions of the world and now a regulatory pathway for approval of these products has been established in the US. The Food and Drug Administration (FDA) led the world with the regulatory concept of comparability and the European Medicines Agency (EMA) was the first to apply this to biosimilars. Patents on the more complex biologics, especially monoclonal antibodies, are now beginning to expire and biosimilar versions of these important medicines are in development. The new Biologics Price Competition and Innovation Act (BPCIA) allows the FDA to approve biosimilars and allows the FDA to lead on the formal designation of interchangeability of biosimilars with their reference products. The FDA''s approval of biosimilars is critical to facilitating patient access to high-quality biologic medicines and will allow society to afford the truly innovative molecules currently in the global biopharmaceutical industry''s pipeline.Key words: monoclonal antibodies (mAbs), biosimilars, recombinant biopharmaceuticals     

Waiving in vivo studies for monoclonal antibody biosimilar development: National and global challenges

Biosimilars are biological medicinal products that contain a version of the active substance of an already authorised original biological medicinal product (the innovator or reference product). The first approved biosimilar medicines were small proteins, and more recently biosimilar versions of innovator monoclonal antibody (mAb) drugs have entered development as patents on these more complex proteins expire. In September 2013, the first biosimilar mAb, infliximab, was authorised in Europe. In March 2015, the first biosimilar (Zarxio?, filgrastim-sndz, Sandoz) was approved by the US Food and Drug Administration; however, to date no mAb biosimilars have been approved in the US. There are currently major differences between how biosimilars are regulated in different parts of the world, leading to substantial variability in the amount of in vivo nonclinical toxicity testing required to support clinical development and marketing of biosimilars. There are approximately 30 national and international guidelines on biosimilar development and this number is growing. The European Union's guidance describes an approach that enables biosimilars to enter clinical trials based on robust in vitro data alone; in contrast, the World Health Organization's guidance is interpreted globally to mean in vivo toxicity studies are mandatory.

We reviewed our own experience working in the global regulatory environment, surveyed current practice, determined drivers for nonclinical in vivo studies with biosimilar mAbs and shared data on practice and study design for 25 marketed and as yet unmarketed biosimilar mAbs that have been in development in the past 5y. These data showed a variety of nonclinical in vivo approaches, and also demonstrated the practical challenges faced in obtaining regulatory approval for clinical trials based on in vitro data alone. The majority of reasons for carrying out nonclinical in vivo studies were not based on scientific rationale, and therefore the authors have made recommendations for a data-driven approach to the toxicological assessment of mAb biosimilars that minimises unnecessary use of animals and can be used across all regions of the world.     

Clinical considerations for the development of biosimilars in oncology

Despite availability of biologic therapies, limited patient access to many of the most-effective cancer treatments affects overall health outcomes. To address this issue, many governments have enacted legislation for the approval of biosimilars. The term “biosimilar” refers to a biologic product that is developed to be highly similar, as opposed to identical, to a licensed biologic product (the reference or innovator product), such that, per US Food and Drug administration draft guidelines, “no clinically meaningful differences [exist] between the biological product and the reference product in terms of safety, purity, and potency.” This article presents some considerations about the development of biosimilars in cancer treatment through an overview of biosimilars from a clinical perspective. Topics covered include the development requirements and unique regulatory requirements for biosimilars, labeling considerations, potential limitations to the uptake of biosimilars, and review of some biosimilars in development for oncology indications.     

Hierarchical commensurate and power prior models for adaptive incorporation of historical information in clinical trials

Bayesian clinical trial designs offer the possibility of a substantially reduced sample size, increased statistical power, and reductions in cost and ethical hazard. However when prior and current information conflict, Bayesian methods can lead to higher than expected type I error, as well as the possibility of a costlier and lengthier trial. This motivates an investigation of the feasibility of hierarchical Bayesian methods for incorporating historical data that are adaptively robust to prior information that reveals itself to be inconsistent with the accumulating experimental data. In this article, we present several models that allow for the commensurability of the information in the historical and current data to determine how much historical information is used. A primary tool is elaborating the traditional power prior approach based upon a measure of commensurability for Gaussian data. We compare the frequentist performance of several methods using simulations, and close with an example of a colon cancer trial that illustrates a linear models extension of our adaptive borrowing approach. Our proposed methods produce more precise estimates of the model parameters, in particular, conferring statistical significance to the observed reduction in tumor size for the experimental regimen as compared to the control regimen.     

Bayesian design of noninferiority trials for medical devices using historical data

Summary We develop a new Bayesian approach of sample size determination (SSD) for the design of noninferiority clinical trials. We extend the fitting and sampling priors of Wang and Gelfand (2002, Statistical Science 17 , 193–208) to Bayesian SSD with a focus on controlling the type I error and power. Historical data are incorporated via a hierarchical modeling approach as well as the power prior approach of Ibrahim and Chen (2000, Statistical Science 15 , 46–60). Various properties of the proposed Bayesian SSD methodology are examined and a simulation‐based computational algorithm is developed. The proposed methodology is applied to the design of a noninferiority medical device clinical trial with historical data from previous trials.     

Exploring the power of Bayesian birth‐death skyline models to detect mass extinction events from phylogenies with only extant taxa

Mass extinction events (MEEs), defined as significant losses of species diversity in significantly short time periods, have attracted the attention of biologists because of their link to major environmental change. MEEs have traditionally been studied through the fossil record, but the development of birth‐death models has made it possible to detect their signature based on extant‐taxa phylogenies. Most birth‐death models consider MEEs as instantaneous events where a high proportion of species are simultaneously removed from the tree (“single pulse” approach), in contrast to the paleontological record, where MEEs have a time duration. Here, we explore the power of a Bayesian Birth‐Death Skyline (BDSKY) model to detect the signature of MEEs through changes in extinction rates under a “time‐slice” approach. In this approach, MEEs are time intervals where the extinction rate is greater than the speciation rate. Results showed BDSKY can detect and locate MEEs but that precision and accuracy depend on the phylogeny's size and MEE intensity. Comparisons of BDSKY with the single‐pulse Bayesian model, CoMET, showed a similar frequency of Type II error and neither model exhibited Type I error. However, while CoMET performed better in detecting and locating MEEs for smaller phylogenies, BDSKY showed higher accuracy in estimating extinction and speciation rates.     

Biosimilars: a regulatory perspective from America

Biosimilars are protein products that are sufficiently similar to a biopharmaceutical already approved by a regulatory agency. Several biotechnology companies and generic drug manufacturers in Asia and Europe are developing biosimilars of tumor necrosis factor inhibitors and rituximab. A biosimilar etanercept is already being marketed in Colombia and China. In the US, several natural source products and recombinant proteins have been approved as generic drugs under Section 505(b)(2) of the Food, Drug, and Cosmetic Act. However, because the complexity of large biopharmaceuticals makes it difficult to demonstrate that a biosimilar is structurally identical to an already approved biopharmaceutical, this Act does not apply to biosimilars of large biopharmaceuticals. Section 7002 of the Patient Protection and Affordable Care Act of 2010, which is referred to as the Biologics Price Competition and Innovation Act of 2009, amends Section 351 of the Public Health Service Act to create an abbreviated pathway that permits a biosimilar to be evaluated by comparing it with only a single reference biological product. This paper reviews the processes for approval of biosimilars in the US and the European Union and highlights recent changes in federal regulations governing the approval of biosimilars in the US.     

Clinical considerations for the development of biosimilars in oncology

Despite availability of biologic therapies, limited patient access to many of the most-effective cancer treatments affects overall health outcomes. To address this issue, many governments have enacted legislation for the approval of biosimilars. The term “biosimilar” refers to a biologic product that is developed to be highly similar, as opposed to identical, to a licensed biologic product (the reference or innovator product), such that, per US Food and Drug administration draft guidelines, “no clinically meaningful differences [exist] between the biological product and the reference product in terms of safety, purity, and potency.” This article presents some considerations about the development of biosimilars in cancer treatment through an overview of biosimilars from a clinical perspective. Topics covered include the development requirements and unique regulatory requirements for biosimilars, labeling considerations, potential limitations to the uptake of biosimilars, and review of some biosimilars in development for oncology indications.     

ESTIMATING DIVERSIFICATION RATES: HOW USEFUL ARE DIVERGENCE TIMES?

The dynamics of species diversification rates are a key component of macroevolutionary patterns. Although not absolutely necessary, the use of divergence times inferred from sequence data has led to development of more powerful methods for inferring diversification rates. However, it is unclear what impact uncertainty in age estimates have on diversification rate inferences. Here, we quantify these effects using both Bayesian and frequentist methodology. Through simulation, we demonstrate that adding sequence data results in more precise estimates of internal node ages, but a reasonable approximation of these node ages is often sufficient to approach the theoretical minimum variance in speciation rate estimates. We also find that even crude estimates of divergence times increase the power of tests of diversification rate differences between sister clades. Finally, because Bayesian and frequentist methods provided similar assessments of error, novel Bayesian approaches may provide a useful framework for tests of diversification rates in more complex contexts than are addressed here.     

A hybrid omnibus test for generalized semiparametric single‐index models with high‐dimensional covariate sets

Numerous statistical methods have been developed for analyzing high‐dimensional data. These methods often focus on variable selection approaches but are limited for the purpose of testing with high‐dimensional data. They are often required to have explicit‐likelihood functions. In this article, we propose a “hybrid omnibus test” for high‐dicmensional data testing purpose with much weaker requirements. Our hybrid omnibus test is developed under a semiparametric framework where a likelihood function is no longer necessary. Our test is a version of a frequentist‐Bayesian hybrid score‐type test for a generalized partially linear single‐index model, which has a link function being a function of a set of variables through a generalized partially linear single index. We propose an efficient score based on estimating equations, define local tests, and then construct our hybrid omnibus test using local tests. We compare our approach with an empirical‐likelihood ratio test and Bayesian inference based on Bayes factors, using simulation studies. Our simulation results suggest that our approach outperforms the others, in terms of type I error, power, and computational cost in both the low‐ and high‐dimensional cases. The advantage of our approach is demonstrated by applying it to genetic pathway data for type II diabetes mellitus.     

A Bayesian Approach to Assessing the Superiority of a Dose Combination

One prevalent goal within clinical trials is to determine whether or not a combination of two drugs is more effective than each of its components. Many researchers have addressed this issue for fixed-dose combination trials, using frequentist hypothesis testing techniques. In addition, several of these have incorporated prior information from sources such as Phase II trials or expert opinions. The Bayesian approach to the general selection problem naturally accomodates the need to utilize such information. It is useful in the dose combination problem because it does not rely on a nuisance parameter that affects the power of frequentist procedures. We show that hierarchical Bayesian methods may be easily applied to this problem, yielding the probability that a drug combination is superior to its components. Moreover, we present methods that may be implemented using readily available software for numerical integration as well as ones that incorporate Markov Chain Monte Carlo methods.     

Biosimilars: Key regulatory considerations and similarity assessment tools

A biosimilar drug is defined in the US Food and Drug Administration (FDA) guidance document as a biopharmaceutical that is highly similar to an already licensed biologic product (referred to as the reference product) notwithstanding minor differences in clinically inactive components and for which there are no clinically meaningful differences in purity, potency, and safety between the two products. The development of biosimilars is a challenging, multistep process. Typically, the assessment of similarity involves comprehensive structural and functional characterization throughout the development of the biosimilar in an iterative manner and, if required by the local regulatory authority, an in vivo nonclinical evaluation, all conducted with direct comparison to the reference product. In addition, comparative clinical pharmacology studies are conducted with the reference product. The approval of biosimilars is highly regulated although varied across the globe in terms of nomenclature and the precise criteria for demonstrating similarity. Despite varied regulatory requirements, differences between the proposed biosimilar and the reference product must be supported by strong scientific evidence that these differences are not clinically meaningful. This review discusses the challenges faced by pharmaceutical companies in the development of biosimilars.     

COMPARISON OF METHODS FOR ANALYZING REPLICATED PREFERENCE TESTS

Preference testing is commonly used in consumer sensory evaluation. Traditionally, it is done without replication, effectively leading to a single 0/1 (binary) measurement on each panelist. However, to understand the nature of the preference, replicated preference tests are a better approach, resulting in binomial counts of preferences on each panelist. Variability among panelists then leads to overdispersion of the counts when the binomial model is used and to an inflated Type I error rate for statistical tests of preference. Overdispersion can be adjusted by Pearson correction or by other models such as correlated binomial or beta‐binomial. Several methods are suggested or reviewed in this study for analyzing replicated preference tests and their Type I error rates and power are compared. Simulation studies show that all methods have reasonable Type I error rates and similar power. Among them, the binomial model with Pearson adjustment is probably the safest way to analyze replicated preference tests, while a normal model in which the binomial distribution is not assumed is the easiest.     

Sequential parallel comparison design for “gold standard” noninferiority trials with a prespecified margin

Three‐arm noninferiority trials (involving an experimental treatment, a reference treatment, and a placebo)—called the “gold standard” noninferiority trials—are conducted in patients with mental disorders whenever feasible, but often fail to show superiority of the experimental treatment and/or the reference treatment over the placebo. One possible reason is that some of the patients receiving the placebo show apparent improvement in the clinical condition. An approach to addressing this problem is the use of the sequential parallel comparison design (SPCD). Nonetheless, the SPCD has not yet been discussed in relation to gold standard noninferiority trials. In this article, our aim was to develop a hypothesis‐testing method and its corresponding sample size calculation method for gold standard noninferiority trials with the SPCD. In a simulation, we show that the proposed hypothesis‐testing method achieves the nominal type I error rate and power and that the proposed sample size calculation method has adequate power accuracy.     

Self-designing two-stage trials to minimize expected costs

In the design of clinical trials, the sample size for the trial is traditionally calculated from estimates of parameters of interest, such as the mean treatment effect, which can often be inaccurate. However, recalculation of the sample size based on an estimate of the parameter of interest that uses accumulating data from the trial can lead to inflation of the overall Type I error rate of the trial. The self-designing method of Fisher, also known as the variance-spending method, allows the use of all accumulating data in a sequential trial (including the estimated treatment effect) in determining the sample size for the next stage of the trial without inflating the Type I error rate. We propose a self-designing group sequential procedure to minimize the expected total cost of a trial. Cost is an important parameter to consider in the statistical design of clinical trials due to limited financial resources. Using Bayesian decision theory on the accumulating data, the design specifies sequentially the optimal sample size and proportion of the test statistic's variance needed for each stage of a trial to minimize the expected cost of the trial. The optimality is with respect to a prior distribution on the parameter of interest. Results are presented for a simple two-stage trial. This method can extend to nonmonetary costs, such as ethical costs or quality-adjusted life years.     

Exact sample size determination for a single Poisson random sample

Classical power analysis for sample size determination is typically performed in clinical trials. A “hybrid” classical Bayesian or a “fully Bayesian” approach can be alternatively used in order to add flexibility to the design assumptions needed at the planning stage of the study and to explicitly incorporate prior information in the procedure. In this paper, we exploit and compare these approaches to obtain the optimal sample size of a single-arm trial based on Poisson data. We adopt exact methods to establish the rejection of the null hypothesis within a frequentist or a Bayesian perspective and suggest the use of a conservative criterion for sample size determination that accounts for the not strictly monotonic behavior of the power function in the presence of discrete data. A Shiny web app in R has been developed to provide a user-friendly interface to easily compute the optimal sample size according to the proposed criteria and to assure the reproducibility of the results.