Deana Ferreri, PhD

An attempt to understand whether regulatory and scientific sources are consistent with each other in their reporting of quality attributes (QAs) for biosimilars has demonstrated general conformance between these 2 sources of biosimilar information.

Reporting of biosimilar quality attributes (QAs) is “inconsistent but reasonably complementary” between regulatory and scientific publications, according to the study comparing European public assessment reports (EPARs) from the European Medicines Agency (EMA) with peer-reviewed scientific publications on biosimilars.

Investigators looked at the reporting of 2 general categories of biosimilar QAs: structural and functional. Structural qualities include amino acid sequence, which must be similar between biosimilars and their reference products. They also concern purity of the drugs and physicochemical properties. Functional QAs include immunochemical activity and biological activity, such as antibody binding to the target ligands necessary to achieve a clinical response.

QAs, therefore, “describe specific physical, chemical, biological or microbial properties” of a biosimilar, and although the aims of scientific and regulatory reporting differ, data on QAs should be consistent, according to the authors. Limited information about the consistency and complementarity of QA reporting made this study necessary, they wrote.

“Along with the surge of biosimilars introduced to the European market over the last decade, the need for comprehensive and reliable information among decision makers (eg, clinicians, pharmacists, payers, and regulators) about the justification of biosimilarity has become pertinent,” the authors wrote.

The authors focused exclusively on adalimumab biosimilars for their study because the monoclonal antibody (mAb) to tumor necrosis factor alpha (TNF) “has the largest number of approved mAb biosimilars and the broadest spectrum of therapeutic indications among TNF-α inhibitors.” They studied the reporting of 77 different QAs (53 structural and 24 functional) from reports on 6 adalimumab biosimilars.

The total number of individual QAs reported for adalimumab biosimilars in regulatory and scientific sources ranged from 47 to 60, and the number of QAs reported consistently by both source types varied by biosimilar, ranging from 28% to 75%. Overall, a greater number of QAs were reported in EPARs (range 36-57) compared with scientific publications (range 14-49).

In both EPARs and scientific publications, functional QAs were reported more frequently than structural QAs (96% vs 89%) and more consistently (88% vs 62%, respectively). The authors suggested the reason functional attributes were more frequently and more consistently reported could be that “they reflect the clinically relevant” mechanisms of action of the biologic drugs and are useful in predicting the outcomes of clinical studies.

For example, binding to and neutralizing TNF-ɑ are functional attributes relevant to adalimumab’s mechanism of action, and these QAs were reported consistently in both information source types for all 6 biosimilars.

Regulatory and scientific motives also may have much to do with the type and frequency of information that gets reported. “Regulators...may be more concerned with the consistency and accountability of decisions. Researchers, frequently affiliated with biosimilar companies, might be more focused on presenting positive news—that is, QAs with favorable results in terms of biosimilarity, such as highly similar attributes,” they wrote.

The authors analyzed whether publications included test results, biosimilarity interpretation, or both for each QA. EPARs more commonly included a biosimilarity interpretation without test results compared with scientific publications. The 2 sources agreed, on average, on 90% (range 78%-100%) of QAs in their interpretations of biosimilarity.

The authors noted the QAs with agreement on biosimilarity interpretation “were frequently related to biological and immunochemical activity [functional QAs],” whereas, “the types of QAs with different biosimilarity interpretations in both sources were frequently related to [post-translational] modifications and biological activity.”

The authors advised consulting both sources of information to obtain a “more complete reporting of the biosimilarity assessment” and “an improved understanding of how biosimilarity was established at the molecular level.”

According to the authors, there have been few previous assessments of “whether and how data presented in these 2 publicly available information sources overlap.” However, studies assessing the reporting of safety and efficacy data “have found substantial discord between regulatory reports and scientific publications.” Their study, they said, was probably the first to examine whether the reporting of QAs is consistent or complementary between regulatory and scientific sources.

The authors argued bringing greater consistency to reporting of QAs between sources could ultimately contribute to increased acceptance and use of biosimilars by clinicians. “A comprehensive and consistently reported set of QAs is needed to understand the science behind regulatory approval and increase confidence in biosimilars in clinical practice,” they wrote.

Alsamil AM, Giezen TJ, Egberts TC, Leufkens HG, Gardarsdottir H. Comparison of consistency and complementarity of reporting biosimilar quality attributes between regulatory and scientific communities: an adalimumab case study. 

. 2021;S1045-1056(20)30142-1. doi:10.1016/j.biologicals.2020.12.003

Articles

Consistency of reporting quality attributes for biosimilars may differ, complicating the job of getting a full picture of biosimilarity. 

Investigators Weigh Differences in Biosimilar Quality Reporting

Switching patients with inflammatory bowel disease (IBD) from the CT-P13 infliximab biosimilar to the SB2 infliximab biosimilar appears to be safe and effective whether 1 or more switches between biosimilars and the originator product were involved, investigators concluded in a retrospective 

In seeking to elucidate the safety and efficacy of switching, the investigators noted little clinical evidence is yet available on switching patients with IBD between infliximab products. Further, they cited a lack of evidence for SB2 

, saying most of the available evidence “derives from the rheumatologic field.”

They enrolled patients who had originally been treated with CT-P13 or the originator product, but all patients had been treated with CT-P13 before switching to SB2. Clinical activity and adverse events were evaluated; the investigators also compared loss of response (LOR) between the biosimilars.

Originator infliximab (Remicade), a monoclonal antibody to tumor necrosis factor alpha, has been used to treat IBD for over 20 years. CT-P13 (Inflectra, Remsima; Celltrion) was the first approved infliximab biosimilar for IBD—Europe in 2013 and the United States in 2016)—and SB2 (Renflexis; Samsung Bioepis) was approved in Europe in 2016 and in the United States in 2017.

Multiple switches, from the reference product to CT-P13 and then to SB2 are likely to occur, the authors said, because of the lower cost of SB2 vs CT-P13. However, European guidelines do not advocate multiple switches because of a lack of clinical evidence.

Thirty-six patients, 14 with Crohn disease and 22 with ulcerative colitis, were followed for 6 months following a switch from CT-P13 to SB2. Twelve had previously switched from the infliximab reference product to CT-P13, and the rest were naïve to infliximab before treatment with CT-P13. Thirteen patients were treated with adalimumab or golimumab immunosuppressants prior to using an infliximab product.

Clinical activity was assessed 6 months before and 3 months after switching to SB2. The proportion of patients in clinical remission was 58.3% both before and after the switch, and rates of mild clinical activity were not significantly different before and after (27.8% vs 33.3%, respectively; 

 = 0.68). The percentages of patients with normal C-reactive protein levels were not significantly different 6 months before and 3 months after switching (94.4% vs 91.7%, respectively; 

The authors said SB2 treatment for 2 patients was suspended after they achieved clinical and endoscopic remission,” and 2 patients (5.5%) receiving SB2 experienced an adverse event (AE).

After switching from CT-P13 to SB2, 25 patients (69.4%) maintained clinical remission and 11 (30.5%) experienced LOR.

Comparing patients who experienced LOR with those who didn’t, the investigators found that 40% of those without LOR (n = 25) had undergone ≥2 switches (reference product to CT-P13 to SB2) compared with just 9.1% of patients who did experience LOR (n = 11), although the difference did not reach significance. Previous AEs during treatment with CT-P13 were more common among patients who experienced LOR on SB2 (27.3%) compared with those who did not (4%), although these findings also did not reach significance.

In univariate and multivariate analyses, the investigators evaluated the number of switches, AEs during CT-P13 treatment, previous LOR to CT-P13, endoscopic activity, clinical activity, clinical variables, and demographic variables, and did not find significant associations with LOR on SB2.

The authors recommended these factors be studied in a larger population to determine their potential influence on LOR, saying, “The low number of enrolled patients could explain why, at univariate, no significant variable was predictive.”

The nvestigators said it would have been “helpful to evaluate the value of infliximab trough levels and the detection of anti-infliximab antibodies,” to more precisely evaluate the potential association between previous AEs to CT-P13 and LOR to SB2. However, they were unable to perform this analysis due to the retrospective nature of the study and recommended future investigation into this potential predictive factor.

They concluded that switching from CT-P13 to SB2 is safe and effective in patients with a single switch or multiple switches among infliximab treatments. The authors described their efforts as “one of the initial studies investigating the efficacy and safety of the SB2 administration to IBD patients in a real-life setting.”

Lovero R, Losurdo G, La Fortezza RF, et al. Safety and efficacy of switching from infliximab biosimilar CT-P13 to infliximab biosimilar SB2 in patients with inflammatory bowel disease. 

. 2021;32(2):201-207. doi:10.1097/MEG.0000000000001988

A need for clinical evidence on the efficacy and safety of infliximab switching led the authors to pursue this investigation.

Infliximab Biosimilar Switching Study Yields Evidence of Safety, Efficacy

Could machine learning (artificial intelligence) be used to help predict outcomes for specific patients treated with biosimilars? Authors of a 

of studies on machine learning speculated on that potentiality and noted many other ways in which an adaptive computerized study of biosimilar use in the real world might be used to elucidate the value and qualities of individual biosimilars and inform providers to improve practice.

Because of the complex nature of biosimilars, they wrote, there are minute differences between them and their reference products that need to be more fully understood to build a practical understanding of these agents and their effects on patients. For example, patients might have immediate reactions to specific biosimilars or experience adverse events only after repeated treatments. Machine learning could track and interpret this information, although they said this relies on being able to “teach” computerized systems to interpret data collected across diverse health care systems with different protocols for recording patient data. 

“Unwanted immune reactions may be induced for numerous reasons, including product variations. However, it is challenging to assess these unwanted immune reactions because of the multiplicity of causes and potential delays before any reaction,” the authors wrote.

It has long been accepted that the analysis of drugs should not end with the clinical assessments done to achieve FDA approval for commercialization. Follow-up should continue long afterward, especially with biosimilars, regulators and patient advocates have said. Due to their complex manufacturing and derivation from living organisms, biologic drugs are mutable and batches of drug will vary over time.

the use of artificial intelligence technologies, which it says ”have the potential to transform health care by deriving new and important insights from the vast amount of data generated during the delivery of health care every day.”

Authors of the review said they hope to spur a broader discussion about machine learning with respect to biosimilars and include insight into maintaining patient safety amid the economic incentives that play a large role in which biosimilars get used. Biosimilars may offer discounts from originator products as deep as 30%; however, “economic incentives to use biosimilars and questions from the clinical community regarding unwanted immunogenicity can be incompatible,” the authors wrote.

“Machine learning, which is one type of artificial intelligence method, refers to programs that can adapt the instructions being used to produce the outcome. Here, the human aspect is its ability to learn and adapt the program’s code to experience,” the authors wrote. In particular, they suggested machine learning be used to identify risk factors for immune reactions, which they said could ensure safe biosimilar use while supporting their associated cost savings.

Machine learning, the authors explained, has the potential to detect important patterns or predict outcomes, but machine learning models require a learning process, just as humans do, to employ data in a way that enhances the accuracy of their predictions. The authors identified recent articles discussing machine learning applications in health care, but they noted that none have applied to biosimilar safety. Machine learning algorithms are a better fit for assessing immunogenicity than traditional statistical models, the authors suggested, because of the complexity and volume of the data involved. Immunogenicity assessments involve the characterization of human immune responses between biosimilars and reference products, in order to understand how patients might react to these agents.

The authors advocated for the use of real-world data to supplement clinical trial data on safety and immunogenicity. Although clinical trials measure safety metrics and antidrug antibodies, which may impair biosimilar performance, they “cannot provide a full picture of the safety profile,” the authors wrote, “because they are performed on a standardized population during a limited period.”

For purposes of informing and programming artificial intelligence systems, real-world data on biologic and biosimilar use and patient outcomes would be drawn from multiple sources, such as hospital systems and payers. Machine learning systems are best equipped to manage such a flood of data and help to make accurate and useful interpretations, the authors said. They suggested that “neural networks” would be capable of such functionality. 

Using immune reactions as an example, the authors outlined a hypothetical neural network model for identifying risk factors for developing an immune reaction after a switch from a biologic reference product to a biosimilar. For a patient treated with reference biologic and then switched to a biosimilar, the model would consider the spectrum of medications the patient has received, the biosimilar prescribed, the reference biologic previously prescribed, drug batch information, and other data. The neural model would classify patients according to predefined categories of immune reactions and hone its power to predict an immune response for an individual hypothetical patient. 

Proper handling of patient information is also a challenge of making a system like this work, the authors wrote: “Any machine learning application needs to comply with the ethical guidelines laid down by national ethics committees.” Complying with data privacy regulations in each country could be challenging, they said, but using aggregated and anonymized data would reduce the risk of violating those regulations.

One problem with neural networks, the authors wrote, is a lack of transparency, which may raise questions about how the machine learning systems arrived at their conclusions during the sifting of patient data. Neural networks use protocols that are layered one on top of another, and each of these folds involves a leap to a new level of interpretation whose workings may be hidden from clinicians trying to understand the reasoning process. “As a result, the black box might create conflict between processing by the neural network model and human interpretation,” the authors wrote. 

In addition to immunogenicity studies, machine learning models could be used to automate pharmacovigilance processes, detect safety signals, make risk assessments to facilitate decision-making on dose regimens or switching, and compare switching initiated by a clinician vs substitution at the pharmacy, which could inform policy on substitution, the authors suggested. They concluded that machine learning technology “should be seen as a tool to learn more about biologic drugs after clinical trials and the medical practices during their uses.”

Perpoil A, Grimandi G, Birklé S, et al. Public health impact of using biosimilars, is automated follow up relevant? 

. 2020;18(1):E186. doi:10.3390/ijerph18010186

Authors of a new study conclude that artificial intelligence could improve the selection of biosimilars for therapy. 

Study: Machine Learning Could Optimize Biosimilar Therapy

the “totality of evidence” for the bevacizumab biosimilar Mvasi (ABP 215) found “no clinically meaningful differences” between the biosimilar and its reference product, and they also concluded “robust data” from multiple trials support extrapolation to all indications of the reference product.

Mvasi is approved in the United States and European Union for several oncology indications, including metastatic colorectal cancer, metastatic cervical cancer, and metastatic nonsquamous non–small cell lung cancer (NSCLC). The authors stated they had no role in any of the human or animal studies included in their review.

Mvasi, an Amgen product, is a monoclonal antibody that binds with vascular endothelial growth factor A (VEGF-A) to retard blood vessel growth that contributes to tumor development. Mvasi was the first bevacizumab biosimilar approved in the United States (2017) and European Union (2018). In the United States, the biosimilar was launched in July 2019.

According to the authors, analytical tests that compared Mvasi with reference product samples from the United States and European Union showed biosimilarity in physicochemical properties, including primary structure, secondary structure, and thermal stability.

Functional properties also were similar. The authors outlined studies that demonstrated Mvasi’s comparable binding affinity to VEGF, inhibition of VEGF binding to its receptor, and inhibition of proliferation of endothelial cells in vitro. “These findings indicate that [Mvasi] and the [reference product] have the same mechanism of action (MOA),” the authors wrote.

The authors cited 2 studies in healthy individuals that indicated comparable pharmacokinetic properties between Mvasi and its reference product. Safety and tolerability in these studies also were similar. None of the enrollees developed binding or neutralizing antidrug antibodies, investigators said.

A comparative clinical trial (MAPLE) involving 642 patients with stage IV or recurrent nonsquamous NSCLC who also received carboplatin and paclitaxel also demonstrated biosimilarity. The primary efficacy end point was the risk ratio of objective response rate (ORR, defined as best overall response). Objective responses were observed in 39.0% and 41.7% of patients receiving Mvasi and reference product, respectively, and the 2-sided confidence interval for ORR fell within the prespecified margin, indicating similar clinical efficacy.

Safety and immunogenicity indicators were also similar between groups. Among patients receiving Mvasi, 26.2% experienced a serious adverse event (AE), vs 23.0% for the reference product cohort; 18.8% and 17.2% of patients on Mvasi and the reference product, respectively, experienced an AE leading to discontinuation.

Anti-VEGF toxicities, such as hypertension, gastrointestinal perforation, or wound healing complications were similar between groups. Immunogenicity was low in both treatment groups, with 1.4% in the Mvasi group and 2.5% in the reference product group developing antidrug antibodies, which were transient in 1.0% and 1.1% of subjects, respectively. No patients tested positive for neutralizing antibodies.

The authors concluded the totality of evidence supports the extrapolation of Mvasi to all approved indications of the reference product, as “no clinically meaningful differences were found in function, purity, potency, PK, clinical efficacy, safety, or immunogenicity.”

They said these studies “should provide oncologists with assurance about using [Mvasi] in treating all approved indications per the prescribing information in their country or region.”

Providers should feel confident using Mvasi, a bevacizumab biosimilar, for all indications of the reference product (Avastin), reviewers stated. 

Deana Ferreri, PhD

Investigators Weigh Differences in Biosimilar Quality Reporting

Infliximab Biosimilar Switching Study Yields Evidence of Safety, Efficacy

Study: Machine Learning Could Optimize Biosimilar Therapy

Reviewers Consider "Totality of Evidence" for Mvasi