Interchangeability Of Biological Drug Products-FDA Draft Guidance.

The Biological Price Competition and Innovation Act (BPCI Act) of 2009 established a pathway for the approval of biosimilars and interchangeable biosimilars in the United States. The Food Drug Administration (FDA) has issued several guidances on the development and assessment of biosimilars which implement the BPCI Act. In particular, a recent draft guidance on the interchangeability of biological products presents an overview of scientific considerations on the demonstration of interchangeability with a reference product. The present communication provides a general summary of the draft guidance and briefly observes a few current issues on interchangeability.

Panos Macheras: a pioneering scientist in pharmaceutical science.

Professor Panos Macheras is a pioneering scientist in pharmacokinetics, pharmacodynamics and biopharmaceutics. His many important contributions to pharmaceutical science are reviewed.

Bioequivalence for highly variable drugs: regulatory agreements, disagreements, and harmonization.

Regulatory authorities introduced procedures in the last decade for evaluating the bioequivalence (BE) for highly variable drugs. These approaches are similar in principle but differ in details. For example, the Food and Drug Administration (FDA) and the European Medicines Agency (EMA) recommend differing regulatory constants. The constant suggested by FDA results in discontinuity of the BE limits around the switching variation at 30% observed within-subject variation of the reference product. The regulatory constant of EMA does not have these problems. The Type I error reaches 6-7% around the switching variation with the EMA constant but 16-17% with the FDA constant. Various procedures were recently suggested, especially for the EMA approach, to eliminate the inflation of the Type I error. Notably, the so-called Exact algorithms try to amalgamate the positive features of both EMA and FDA procedures without their negative sides. The computational procedure for the EMA approach is simple and has a straightforward interpretation. The procedure for the FDA approach is based on an approximation, has a bias at small degrees of freedom, and requires a suitable computer program. All regulatory agencies impose a second requirement constraining the point estimate of the ratio of geometric means. In addition, EMA and Health Canada impose an upper limit for applying the recommended procedures. These expectations have psychological motivation and political rationale but no scientific foundations. Their inclusion results in incorrect and misleading interpretation of the principal criterion which involves confidence intervals. Different regulatory authorities expect to apply their approaches either to both AUC and Cmax or only to AUC or only to Cmax. Rational resolution of the disharmonization is needed.

Algorithms for evaluating reference scaled average bioequivalence: power, bias, and consumer risk.

The determination of the bioequivalence between highly variable drug products involves the evaluation of reference scaled average bioequivalence. The European and US regulatory authorities suggest different algorithms for the implementation of this approach. Both algorithms are based on approximations reflected in lower than the achievable power or higher than the nominal consumer risk of 5%. To overcome these deficiencies, a new class of algorithms, the so-called Exact methods, was earlier introduced. However, their applicability was limited. We propose 2 modifications which make their computation simpler and also applicable with any study design. Four algorithms were evaluated in simulated 3-period and 4-period bioequivalence studies: Hyslop's approach recommended by the US FDA, the method of average bioequivalence with expanding limits requested by the European EMA, and 2 versions of the new Exact methods. At small sample sizes, the Exact methods had substantially higher statistical power than Hyslop's algorithm and had lower consumer risk than the method of average bioequivalence with expanding limits. Similarly to the Hyslop's algorithm, higher than 5% consumer risk was observed only with either unbalanced study design or with additional regulatory requirements. The improved Exact algorithms compare favorably with the alternative procedures. They are based on the bias correction method of Hedges. The recognition that the scaled difference statistics is measured with bias has important practical implications when results of pilot bioequivalence studies are evaluated and, at the same time, calls for the revision of the statistical theory of RSABE and its related methods.

Subsequent Entry Biologics in Canada: Current State of the Science.

The Canadian Society for Pharmaceutical Sciences organized a workshop on the current state of sciences of subsequent entry biologics (SEBs, biosimilars) on December 10th 2014 in the Health Canada location in Ottawa, ON. The day-long workshop provided an opportunity to discuss recent regulatory developments and a wide range of scientific issues related to SEBs. Following a discussion on the differences between the Canadian guidance and those of other countries,  a series of presentations were made that focused on the regulatory requirements with regard to the product quality, methodology, non-clinical and clinical data. In addition, issues of extrapolation from one indication to another, interchangeability and reimbursement  were articulated. It was also highlighted that both the patients and caregivers need to be better informed regarding the safety and efficacy of articulated SEBs.

Scientific factors and current issues in biosimilar studies.

Biological drugs are much more complicated than chemically synthesized, small-molecule drugs; for instance, their size is much larger, their structure is more complicated, they can be sensitive to environmental conditions such as temperature or pressure, and they may expose patients to immunogen reactions. Consequently, the assessment of biosimilarity calls for greater circumspection than the evaluation of bioequivalence. The present communication discusses scientific factors and some current issues related to biosimilarity and the interchangeability of drug products. The scientific factors include questions involving endpoint selection, the one-size-fits-all criterion, and the need for a more flexible approach, e.g., evaluation of the degree of similarity (i.e., responding to the question of "how similar is similar?"; a review of study designs that are useful for the assessment of biosimilarity and drug interchangeability; and tests for the comparability of critical quality attributes at various stages of the manufacturing process). Current issues include the choice of reference standards and the relevant study designs; criteria for biosimilarity, as well as for interchangeability and for comparability; the determination of the noninferiority margin; and the concepts of the stepwise approach to biosimilarity studies and of their assessment by the totality of the evidence. The calculation of sample sizes is discussed for crossover (including some higher-order schemes) and parallel designs.

On the interchangeability of biologic drug products.

Interchangeability of drug products has very different features with small molecules and with biologicals. With small-molecule drugs, a statement of bioequivalence generally indicates therapeutic equivalence and interchangeability. In contrast, with the much more sensitive and complicated biological drugs, a declaration of biosimilarity emphatically does not imply that a patient could be switched from one product to another. Both formulations may be prescribed and administered to subjects who have not received yet the drug in any of its forms. However, regulatory agencies have been very cautious about enabling and permitting interchangeability. Notably, the Biologics Price Competition and Innovation Act of the USA sets very formidable and severe conditions for enabling the interchangeability of biological drug products. The background and conditions for the interchangeability of both small-molecule and biologic drug products are presented in detail.

Comments on the FDA draft guidance on biosimilar products.

The Food and Drug Administration issued on February 9, 2012, drafts of three new guidance documents about the demonstration of biosimilarity. One of these deals with scientific considerations. It suggests, among others, that demonstration of biosimilarity be developed by a stepwise (step-by-step) approach and that it be assessed by considering the totality of the evidence. This communication provides comments on some scientific factors and issues that still remain unanswered or unsolved. They include the question 'how similar is considered to be highly similar?' considerations of criteria for and the degree of biosimilarity; alternatives of study design and sample size requirements; statistical methods for achieving the totality of the evidence needed for biosimilarity; and methods needed for the assessment of drug interchangeability. It is anticipated that the comments will assist the revision of the guidance documents.

Impact of variability on the choice of biosimilarity limits in assessing follow-on biologics.

With larger variation in biological products compared with small molecular drugs, it is suggested that the assessment of biosimilarity of follow-on biologics (FOBs) should take variability into consideration in addition to average as standard in bioequivalence tests in small molecule drugs. Recent research on assessing variability in biosimilarity of FOBs has focused on direct assessment of variances, individual biosimilar index aggregating average and variability, and comparison of the entire distributions. However, the choice of biosimilarity limits for evaluating FOBs has not been investigated in the literature. In this article, we first explore the impact of variability on biosimilarity limits for the average biosimilarity assessment. On the basis of the derived relationship between variability and biosimilarity limit that result in the same power given all other parameters fixed, we propose several scaled biosimilarity limits to incorporate highly variable biological products.

Scientific considerations for assessing biosimilar products.

The problem for assessing biosimilarity and drug interchangeability of follow-on biologics (biosimilar products) is studied. Unlike the generic products, the development of biosimilar products is much more complicated because of fundamental differences in functional structures and manufacturing processes. As a result, the criteria and standard methods for the design and analysis of bioequivalence assessment of generic drug products may not be directly applicable to assessing biosimilarity of biosimilar products. In this article, we provide some scientific considerations for criteria, design, and analysis regarding the assessment of biosimilarity and drug interchangeability of biosimilar products. In addition, we discuss scientific and practical issues raised at the 2010 FDA public hearing and the 2011 FDA public meeting on biosimilar products.

Determination of bioequivalence for drugs with narrow therapeutic index: reduction of the regulatory burden.

The US Food and Drug Administration (FDA) has recently suggested that the bioequivalence (BE) for products of drugs with narrow therapeutic indices (NTI) be assessed by the approach of reference-scaled average BE (SABE). Subsequently, in December, 2012, the FDA issued draft guidances for the comparison of products of warfarin sodium and of tacrolimus. The guidances expect that 4-period studies be performed, that the results be evaluated by SABE, and that the analysis include also unscaled average BE as well as the comparison of the estimated within-subject variations (sW) of the test and reference drug products. This communication discusses the new guidances and suggests considerations to reduce the regulatory burden. It is demonstrated that SABE could be applied when the within-subject variation of the reference product is not higher than 21.42%. Beyond this variation, the BE limits would remain 80% to 125%, as usual. No further testing by unscaled average BE is needed. It is also suggested that a comparison of the within-subject variations of the two drug products although interesting for both NTI and other drugs, is not essential for the determination of BE. In addition, when the within-subject variabilities are low then their ratio depends mainly on the non-product dependent factors. Moreover, introduction of an additional test would affect the probabilities involved in the primary comparison of the two means. Therefore, the test of comparing variances is not needed and replicate measurements of the test formulation need not be performed. Alternative considerations and approaches, including the use of partial AUC's, are suggested for the determination of BE for NTI drugs.

Approvable generic carbamazepine formulations may not be bioequivalent in target patient populations.

OBJECTIVES: To demonstrate that with carbamazepine (CBZ), positive results of bioequivalence trials in healthy volunteers cannot be extrapolated to patients because metabolic enzyme induction fundamentally changes the pharmacokinetics of CBZ. METHODS: Bioequivalence trials were simulated assuming normal and induced metabolic clearance. The relevant pharmacokinetic parameters were drawn from published studies. RESULTS: The Cmax ratio depends on the clearance. A generic product which is fully compliant with the regulatory requirements in healthy volunteers could be non-bioequivalent in patients with enhanced elimination. CONCLUSION: A statement of bioequivalence of CBZ formulations, based on a single-dose study performed in healthy subjects, may not hold for a target patient population in the steady state.

Sample sizes for designing bioequivalence studies for highly variable drugs.

PURPOSE: To provide tables of sample sizes which are required, by the European Medicines Agency (EMA) and the U.S. Food and Drug Administration (FDA), for the design of bioequivalence (BE) studies involving highly variable drugs. To elucidate the complicated features of the relationship between sample size and within-subject variation. METHODS: 3- and 4-period studies were simulated with various sample sizes. They were evaluated, at various variations and various true ratios of the two geometric means (GMR), by the approaches of scaled average BE and by average BE with expanding limits. The sample sizes required for yielding 80% and 90% statistical powers were determined. RESULTS: Because of the complicated regulatory expectations, the features of the required sample sizes are also complicated. When the true GMR = 1.0 then, without additional constraints, the sample size is independent of the intrasubject variation. When the true GMR is increased or decreased from 1.0 then the required sample sizes rise at above but close to 30% variation. An additional regulatory constraint on the point estimate of GMR and a cap on the use of expanding limits further increase the required sample size at high variations. Fewer subjects are required by the FDA than by the EMA procedures. CONCLUSIONS: The methods proposed by EMA and FDA lower the required sample sizes in comparison with unscaled average BE. However, each additional regulatory requirement (applying the mixed procedure, imposing a constraint on the point estimate of GMR, and using a cap on the application of expanding limits) raises the required number of subjects.

Rethinking Clinical Trials and Personalized Medicine with Placebogenomics and Placebo Dose.

Pharmacogenomics, nutrigenomics, vaccinomics, and the nascent field of plant omics are examples of variability science. They are embedded within an overarching framework of personalized medicine. Across these public health specialties, the significance and biology of the placebo response have been historically neglected. A placebo is any substance such as a sugar pill administered in the guise of medication, but one that does not have pharmacological activity. Placebos do have clinical effects, however, that can be substantive in magnitude and vary markedly from person-to-person depending, for example, on the type of disease, symptoms, or clinical trial design. Research over the past several decades attests to a genuine neurobiological basis for placebo effects. All drugs have placebo components that contribute to their overall treatment effect. Placebos are used in clinical trials as control groups to ascertain the net pharmacological effect of a drug candidate. Not only less well known but also relevant to rational therapeutics and personalized medicine is the nocebo. A nocebo effect occurs when an inert substance is administered in a context that induces negative expectations, worsening patients' symptoms. With the COVID-19 pandemic, there are high public expectations for new vaccines and medicines to end the contagion, while at the same time antiscience, post-truth, and antivaccine movements are worrisomely on the rise. These social movements, changes in public health cultures, and conditioned behavioral responses can trigger both placebo and nocebo effects. Hence, in clinical trials, forecasting and explaining placebo and nocebo variability are more important than ever for robust science and personalized health care. Against this overarching context, this article provides (1) a brief history of placebo and (2) a discussion on biology, mechanisms, and variability of placebo effects, and (3) discusses three emerging new concepts: placebogenomics, nocebogenomics, and augmented placebo, that is, the notion of a "placebo dose." We conclude with a roadmap for placebogenomics, its synergies with the nascent field of social pharmacology, and the ways in which a new taxonomy of drug and placebo variability can be anticipated in the next decade.

Do regulatory bioequivalence requirements adequately reflect the therapeutic equivalence of modified-release drug products?

PURPOSE: To demonstrate that current regulatory requirements for bioequivalence (BE) do not always reflect therapeutic equivalence. To investigate the potential usefulness of an additional metric, the partial AUC. METHODS: Pharmacokinetic information was reviewed and evaluated on the pharmacokinetics of modified-release methylphenidate and nifedipine products. RESULTS: In studies of modified-release products of methylphenidate as well as of nifedipine, traditional regulatory criteria found two formulations to be bioequivalent even though their concentration profiles strongly diverged during the period of absorption. An additional metric, partial AUC, discriminated strongly between the concentrations of the drug products. CONCLUSIONS: The current regulatory criteria for the acceptance of BE do not always reflect the therapeutic equivalence of modified-release drug products. With some modified-release products, the application of an additional metric, the partial AUC, yields an improved discriminatory representation.

Residuals and outliers in replicate design crossover studies.

Outliers in bioequivalence trials may arise through various mechanisms, requiring different interpretation and handling of such data points. For example, regulatory authorities might permit exclusion from analysis of outliers caused by product or process failure, while exclusion of outliers caused by subject-by-treatment interaction generally is not acceptable. In standard 2 x 2 crossover studies it is not possible to distinguish between relevant types of outliers based on statistical criteria alone. However, in replicate design (2-treatment, 4-period) crossover studies three types of outliers can be distinguished: (i) Subject outliers are usually unproblematic, at least regarding the analysis of bioequivalence, and may require no further action; (ii) Subject-by-formulation outliers may affect the outcome of the bioequivalence test but generally cannot simply be removed from analysis; and (iii) Removal of single-data-point outliers from analysis may be justified in certain cases. As a very simple but effective diagnostic tool for the identification and classification of outliers in replicate design crossover studies we propose to calculate and plot three types of residual corresponding to the three different types of outliers that can be distinguished. The residuals are obtained from four mutually orthogonal linear contrasts of the four data points associated with each subject. If preferred, outlier tests can be applied to the resulting sets of residuals after suitable standardization.

Digging Deeper into Precision/Personalized Medicine: Cracking the Sugar Code, the Third Alphabet of Life, and Sociomateriality of the Cell.

Precision/personalized medicine is a hot topic in health care. Often presented with the motto "the right drug, for the right patient, at the right dose, and the right time," precision medicine is a theory for rational therapeutics as well as practice to individualize health interventions (e.g., drugs, food, vaccines, medical devices, and exercise programs) using biomarkers. Yet, an alien visitor to planet Earth reading the contemporary textbooks on diagnostics might think precision medicine requires only two biomolecules omnipresent in the literature: nucleic acids (e.g., DNA) and proteins, known as the first and second alphabet of biology, respectively. However, the precision/personalized medicine community has tended to underappreciate the third alphabet of life, the "sugar code" (i.e., the information stored in glycans, glycoproteins, and glycolipids). This article brings together experts in precision/personalized medicine science, pharmacoglycomics, emerging technology governance, cultural studies, contemporary art, and responsible innovation to critically comment on the sociomateriality of the three alphabets of life together. First, the current transformation of targeted therapies with personalized glycomedicine and glycan biomarkers is examined. Next, we discuss the reasons as to why unraveling of the sugar code might have lagged behind the DNA and protein codes. While social scientists have historically noted the importance of constructivism (e.g., how people interpret technology and build their values, hopes, and expectations into emerging technologies), life scientists relied on the material properties of technologies in explaining why some innovations emerge rapidly and are more popular than others. The concept of sociomateriality integrates these two explanations by highlighting the inherent entanglement of the social and the material contributions to knowledge and what is presented to us as reality from everyday laboratory life. Hence, we present a hypothesis based on a sociomaterial conceptual lens: because materiality and synthesis of glycans are not directly driven by a template, and thus more complex and open ended than sequencing of a finite length genome, social construction of expectations from unraveling of the sugar code versus the DNA code might have evolved differently, as being future-uncertain versus future-proof, respectively, thus potentially explaining the "sugar lag" in precision/personalized medicine diagnostics over the past decades. We conclude by introducing systems scientists, physicians, and biotechnology industry to the concept, practice, and value of responsible innovation, while glycomedicine and other emerging biomarker technologies (e.g., metagenomics and pharmacomicrobiomics) transition to applications in health care, ecology, pharmaceutical/diagnostic industries, agriculture, food, and bioengineering, among others.

Regulatory and study conditions for the determination of bioequivalence of highly variable drugs.

PURPOSE: The FDA Working Group on Highly Variable (HV) Drugs recently presented interim procedures and conditions for determining the bioequivalence (BE) of HV drug products. They included analysis by the method of scaled average BE (SABE), a switching coefficient of variation of CV S = 30% and a regulatory standardized variation of CV 0 = 25% for applying SABE, and the use of a secondary regulatory criterion restricting to 0.80-1.25 the point estimate for the ratio of estimated geometric means (GMR) of the two formulations. These conditions are scrutinized in the present communication. METHODS: 3-period BE studies were simulated with various statistical and regulatory assumptions. Power curves, obtained by gradually increasing the true GMR, compared performances of the methods of SABE, a constrained point estimate of GMR (PE/GMR), and the composite of these two approaches. The consumer risk of each procedure was evaluated. RESULTS: With CV 0 = 30% and PE/GMR = 0.80-1.25, the composite criterion of BE relied on the confidence limits of SABE. In contrast, with CV 0 = 25% and/or PE/GMR = 0.87-1.15, the composite criterion approached almost completely the features of the GMR point estimate, especially at high within-subject variation. The consumer risk was near 5% with CV 0 = 30% but much higher when CV 0 = 25%. CONCLUSIONS: A constraint on GMR is difficult to justify scientifically. Still, if it is introduced then the condition of CV S = CV 0 = 30% and PE/GMR = 0.80-1.25 is recommended as a composite regulatory criterion. With alternative settings of the conditions, such as the recommended CV 0 = 25% and/or PE/GMR = 0.87-1.15, the composite criterion would reflect almost entirely the GMR point estimate. Also, with CV 0 = 25%, the BE limits are discontinuous at CVS = 30% and, as consequences, the consumer risk is substantially larger than 5%, and the regulatory uncertainty for making a decision about acceptance or rejection is enhanced. These would be undesirable outcomes.

A New Approach to Measure Adherence to Medicines Using Biomarkers and Sensors.

Approximately one in two patients with a chronic disease does not take their medicines as prescribed. Poor adherence is a worldwide epidemic and a major source of variability in pharmacokinetics (PK) and pharmacodynamics. Without addressing adherence, precision medicine is unlikely to come to fruition. In drug development, poor adherence confounds the estimates for efficacy and safety of drug candidates. Accurate and high-resolution measurement of adherence is a first step toward effective interventions against poor adherence. We describe a new cross-technology platform to measure adherence. The approach involves, first, building PK models to explain dose-exposure relationships. The model incorporates PK biomarkers by genotyping or phenotyping of drug metabolism, transport and other drug clearance pathways. Importantly, dose-exposure data for model building are obtained in healthy volunteer and/or patient cohorts who are ascertained for full adherence, using edible ingestion sensors (IS) that digitize orally administered medicines. Second, the built model is harnessed to back calculate the dose actually ingested by patients, given the empirically observed drug exposure, PK biomarker, demographic, and other patient data. The proposed platform is envisioned to result in development of both drug and drug-specific companion software for adherence measurement. In terms of feasibility, the new approach overlaps with current drug development timelines spanning the Phase 1 to 4 clinical trial continuum, and thus, could conceivably be implemented without requiring significant changes to the time sensitive clinical trial processes. For the IS-powered tools, the proposed platform creates a new space for applications in clinical trials to ensure adherence.