A new recombinant factor VIII (FVIII), N8, has been produced in Chinese hamster ovary (CHO) cells. The molecule consists of a heavy chain of 88 kDa including a 21 amino acid residue truncated B-domain and a light chain of 79 kDa. The two chains are held together by non-covalent interactions. The four-step purification includes capture, affinity purification using a monoclonal recombinant antibody, anion exchange chromatography and gel filtration. The specific clotting activity of N8 was 8800-9800 IU mg(-1). Sequence and mass spectrometry analysis revealed two variants of the light chain, corresponding to two alternative N-terminal sequences also known from plasma FVIII. Two variants of the heavy chain are present in the purified product, namely with and without the B-domain linker attached. This linker is removed upon thrombin activation of N8 rendering an activated FVIII (FVIIIa) molecule similar to plasma FVIIIa. All six known tyrosine sulphations of FVIII were confirmed in N8. Two N-linked glycosylations are present in the A3 and C1 domain of the light chain and two in the A1 domain of the heavy chain. The majority of the N-linked glycans are sialylated bi-antennary structures. An O-glycosylation site is present in the B-domain linker region. This site was glycosylated with a doubly sialylated GalNAc-Gal structure in approximately 65% of the product. In conclusion, the present data show that N8 is a pure and well-characterized FVIII product with biochemical properties that equal other FVIII products.

In recent years, the development of high-throughput screening (HTS) technologies and their establishment in an industrialized environment have given scientists the possibility to test millions of molecules and profile them against a multitude of biological targets in a short period of time, generating data in a much faster pace and with a higher quality than before. Besides the structure activity data from traditional bioassays, more complex assays such as transcriptomics profiling or imaging have also been established as routine profiling experiments thanks to the advancement of Next Generation Sequencing or automated microscopy technologies. In industrial pharmaceutical research, these technologies are typically established in conjunction with automated platforms in order to enable efficient handling of screening collections of thousands to millions of compounds. To exploit the ever-growing amount of data that are generated by these approaches, computational techniques are constantly evolving. In this regard, artificial intelligence technologies such as deep learning and machine learning methods play a key role in cheminformatics and bio-image analytics fields to address activity prediction, scaffold hopping, de novo molecule design, reaction/retrosynthesis predictions, or high content screening analysis. Herein we summarize the current state of analyzing large-scale compound data in industrial pharmaceutical research and describe the impact it has had on the drug discovery process over the last two decades, with a specific focus on deep-learning technologies.

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With the increasing amount of image data collected from biomedical experiments there is an urgent need for smarter and more effective analysis methods. Many scientific questions require analysis of image subregions related to some specific biology. Finding such regions of interest (ROIs) at low resolution and limiting the data subjected to final quantification at full resolution can reduce computational requirements and save time. In this paper we propose a three-step pipeline: First, bounding boxes for ROIs are located at low resolution. Next, ROIs are subjected to semantic segmentation into sub-regions at mid-resolution. We also estimate the confidence of the segmented sub-regions. Finally, quantitative measurements are extracted at full resolution. We use deep learning for the first two steps in the pipeline and conformal prediction for confidence assessment. We show that limiting final quantitative analysis to sub-regions with full confidence reduces noise and increases separability of observed biological effects.

Turoctocog alfa (NovoEight) is a third-generation recombinant factor VIII (rFVIII) with a truncated B-domain that is manufactured in Chinese hamster ovary cells. No human or animal-derived materials are used in the process. The aim of this study is to describe the molecular design and purification process for turoctocog alfa. A five-step purification process is applied to turoctocog alfa: protein capture on mixed-mode resin; immunoaffinity chromatography using a unique, recombinantly produced anti-FVIII mAb; anion exchange chromatography; nanofiltration and size exclusion chromatography. This process enabled reduction of impurities such as host cell proteins (HCPs) and high molecular weight proteins (HMWPs) to a very low level. The immunoaffinity step is very important for the removal of FVIII-related degradation products. Manufacturing scale data shown in this article confirmed the robustness of the purification process and a reliable and consistent reduction of the impurities. The contribution of each step to the final product purity is described and shown for three manufacturing batches. Turoctocog alfa, a third-generation B-domain truncated rFVIII product is manufactured in Chinese hamster ovary cells without the use of animal or human-derived proteins. The five-step purification process results in a homogenous, highly purified rFVIII product.