Notice bibliographique
Résumé
Biologic medicines have revolutionized the treatment options for many diseases and typically carry favorable benefit–risk profiles compared with small molecule drugs. Although these innovative medicines can enable longer lifespan and improved quality of life, the high cost of these products limits access. Unlike small molecule drugs, exact copies of biologic products cannot be made, and thus it is not possible to approve “generic” biologics. To address this, legal frameworks and guidelines were established in different countries to enable approval of biosimilars (i.e., a biological product that is highly similar to, and has no clinically meaningful differences from, an approved reference product) that can rely on existing scientific knowledge about the safety, purity, and potency of the reference product. Although biosimilar development has gained momentum globally since the first approved biosimilar in Europe in 2006, as reflected in the number of reference products with approved biosimilars (Table 1), most biologics do not have approved biosimilars. As reported in this themed issue of Clinical Pharmacology & Therapeutics (CPT) on Innovations in Biosimilars (Figure 1), there is an opportunity to leverage innovations in clinical pharmacology and related disciplines to improve the efficiency of biosimilar development and approval (Figure 2). One way to do this is to leverage the use of pharmacodynamic (PD) biomarkers in clinical pharmacology studies in place of comparative clinical studies with efficacy end point(s), which is discussed in multiple articles in this issue.1-5 These articles and others6 also discuss how modeling and simulation (or model-informed drug development (MIDD)) can be used to optimize study design and analyses. Other articles focus on characterizing the relationship between product quality characteristics (e.g., structural and functional characterization) and clinical performance to further streamline clinical development.7-9 Global alignment of regulatory expectations for biosimilar approval makes biosimilar development more efficient and attractive to sponsors, and we are happy to see articles in this issue from regions around the world; in addition to the United States, this includes articles with viewpoints from Canada,10 Europe,7 India,11 and Japan.12 Finally, other articles focus on the importance of educating clinicians and patients about biosimilars and assessing the cost to patients. All of this is summarized further below. Utilization of PD biomarkers can help streamline biosimilar development programs and a limited number of biosimilar approvals has been based on pharmacokinetic (PK) and PD similarity data from clinical pharmacology studies without a large comparative clinical study using efficacy end points.13 To facilitate further uptake of PD biomarkers for biosimilars, the US Food and Drug Administration (FDA) conducted three prospective clinical trials, each with two marketed biologics with the same mechanism of action, to inform evidentiary strategies and criteria and to bring greater clarity to the FDA's expectations for the use of PD biomarkers to demonstrate biosimilarity. The primary results of these three trials are reported by Sheikhy et al. (PSCK9 inhibitors),5 Gershuny et al. (IL-5 antagonists),4 and Florian et al. (interferon beta-1a products),3 and Hyland et al. reports the results of an evaluation of the utility of proteomics for identifying PD biomarkers from the interferon clinical trial.14 In addition, Li et al.6 describe a model-based approach to inform dose selection, and Vaidyanathan et al.15 describe the evolution of regulatory requirements for insulin products to the current standard that stipulates PK and PD similarity data are sufficient to support approval of biosimilar insulin products. Broader discussions around use of PD biomarkers occurred at an FDA and Duke Margolis Center for Health Policy public workshop in 2021, which is summarized by Florian et al.2 The workshop identified opportunities for investing in PD biomarker research and clarifying regulatory expectations for PD biomarker acceptability. To this end, Strauss et al.1 discuss evidentiary considerations with practical examples to illustrate the thinking behind the recommendations in the FDA's guidance. Arato12 and Wang10 discuss perspectives from Japan and Canada, respectively. The similarity in product quality characteristics is recognized globally as the foundation for establishing biosimilarity. Given the structural and functional complexity of protein products there are numerous product quality attributes for which the clinical significance is challenging to determine; therefore, opportunities to enhance the scientific understanding on this topic exist. Guillen et al.7 evaluated a comprehensive list of quality attributes and clinical performance of approved adalimumab and bevacizumab biosimilars and report that clinical efficacy data played a limited role in addressing quality concerns. The authors call for a regulatory review of the standards and advocate for a quality-data-driven approach in facilitating tailored clinical programs for these biosimilars. Welch et al.9 provide a retrospective evaluation of a single critical quality attribute – high mannose glycans for monoclonal antibody biosimilars – that indicates analytics for glycan profiling are highly precise and reproducible to detect product differences. Enhancing knowledge of quality attributes that are drivers of clinical performance may facilitate a future state promoted by Woollett et al.5 that the quality of data from comparative analytical assessment combined with PK similarity data could be sufficient to support biosimilar approvals without the need for PD or clinical end point data. Three articles in this issue discuss efforts needed to improve biosimilar uptake and adoption. Shubow et al.16 describe the perspectives of four leading academic clinicians with specialties in oncology, rheumatology, gastroenterology, and endocrinology, which was informed by their clinical experience and real-world data on clinical use of biosimilars globally. They call for educational efforts to highlight the rigor of studies that support the approval of biosimilars—including the clinical pharmacology studies—and the benefits of biosimilars, thereby increasing the awareness, building the confidence, and ultimately improving biosimilar acceptance. Sheth et al.11 share the view on the importance of education. The issue of patient out-of-pocket costs is discussed in a research article by Feng et al.17 In summary, biosimilars are only available for a small portion of the marketed biologic products, highlighting an urgent need to ramp up biosimilar development and increase efficiency in generating high quality data to support regulatory approval. The articles in this issue identify various aspects of biosimilar development where clinical pharmacology and related disciplines can develop innovative approaches, including through the use of PD biomarkers, modeling and simulation, and analyses connecting product quality characteristics to clinical performance. We call on colleagues around the globe to continue to advance innovations in biosimilars to increase the availability of affordable, safe, and effective treatment options for patients. As an associate editor for Clinical Pharmacology & Therapeutics, David Strauss was not involved in the review or decision process for this paper. All other authors declared no competing interests for this work. This study was funded by the US Food and Drug Administration. The opinions expressed in this manuscript are those of the authors and should not be interpreted as the position of the US Food and Drug Administration.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,003 | 0,008 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,009 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».