A biosimilar is a biological medicinal product that is comparable to a reference medicinal product in terms of quality, safety, and efficacy. SB4 was developed as a biosimilar to Enbrel® (etanercept) and was approved as Benepali®, the first biosimilar of etanercept licensed in the European Union (EU). The quality assessment of SB4 was performed in accordance with the ICH comparability guideline and the biosimilar guidelines of the European Medicines Agency and Food and Drug Administration. Extensive structural, physicochemical, and biological testing was performed with state-of-the-art technologies during a side-by-side comparison of the products. Similarity of critical quality attributes (CQAs) was evaluated on the basis of tolerance intervals established from quality data obtained from more than 60 lots of EU-sourced and US-sourced etanercept. Additional quality assessment was focused on a detailed investigation of immunogenicity-related quality attributes, including hydrophobic variants, high-molecular-weight (HMW) species, N-glycolylneuraminic acid (NGNA), and α-1,3-galactose. This comprehensive characterization study demonstrated that SB4 is highly similar to the reference product, Enbrel®, in structural, physicochemical, and biological quality attributes. In addition, the levels of potential immunogenicity-related quality attributes of SB4 such as hydrophobic variants, HMW aggregates, and α-1,3-galactose were less than those of the reference product.
Knowledge graph embedding aims to learn distributed representations for entities and relations, and is proven to be effective in many applications. Crossover interactions -bi-directional effects between entities and relations -help select related information when predicting a new triple, but haven't been formally discussed before. In this paper, we propose CrossE, a novel knowledge graph embedding which explicitly simulates crossover interactions. It not only learns one general embedding for each entity and relation as most previous methods do, but also generates multiple triple specific embeddings for both of them, named interaction embeddings. We evaluate embeddings on typical link prediction tasks and find that CrossE achieves state-of-the-art results on complex and more challenging datasets. Furthermore, we evaluate embeddings from a new perspective -giving explanations for predicted triples, which is important for real applications. In this work, an explanation for a triple is regarded as a reliable closed-path between the head and the tail entity. Compared to other baselines, we show experimentally that CrossE, benefiting from interaction embeddings, is more capable of generating reliable explanations to support its predictions. tail entity), or ( h, r , t ) in short. They are useful resources for many AI tasks such as web search [33] and question answering [43]. Knowledge graph embedding (KGE) learns distributed representations [11] for entities and relations, called entity embeddings and relation embeddings. The embeddings are meant to preserve the information in a KG, and are represented as low-dimensional dense vectors or matrices in continuous vector spaces. Many KGEs, such as tensor factorization based RESCAL [26], translation-based TransE [4], neural tensor network NTN [30] and linear mapping method DistMult [41], have been proposed and are proven to be effective in many applications like knowledge graph completion, question answering and relation extraction.Despite their success in modeling KGs, none of existing KGEs has formally discussed crossover interactions, bi-directional effects between entities and relations including interactions from relations to entities and interactions from entities to relations. Crossover interactions are quite common and helpful for related information selection, related information selection is necessary when predicting new triples because there are various information about each entity and relation in KGs. X Z M Y Q T S Figure 1: A hypothetical knowledge graph. Nodes and edges represent entities and relations. Solid lines represent existing triples and dashed lines represent triples to be predicted.
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