Animal cell-based expression platforms enable the production of complex biomolecules such as recombinant proteins and viral vectors. Although most biotherapeutics are produced in animal cell lines, production in human cell lines is expanding. One important advantage of using human cell lines is the increased potential that the resulting biotherapeutics would carry more “human-like” post-translational modifications. Among the human cell lines, HEK293 is widely utilized due to its high transfectivity, rapid growth rate, and ability to grow in a serum-free, suspension culture. In this review, we discuss the use of HEK293 cells and its subtypes in the production of biotherapeutics. We also compare their usage against other commonly used host cell lines in each category of biotherapeutics and summarise the factors influencing the choice of host cell lines used.

Funding Acknowledgements Type of funding sources: Other. Main funding source(s): Industry Alignment Fund – Pre-positioning Programme Background Epicardial adipose tissue (EAT) is the visceral fat deposit within the pericardium that surrounds the heart and the coronary arteries. EAT volume measured from non-contrast CT (NCCT) has been demonstrated to be significantly associated with adverse cardiovascular risk,1 particularly in patients with coronary artery disease.2 However, routine measurement of EAT volume is still challenging in clinical practice, as it is a tedious manual process and prone to human error. Purpose We aimed to develop a fully automated AI toolkit (i.e., AI EAT) for the quantification of EAT from routine NCCT scans and assess its performance in reference to clinical ground truth. Methods This is a multicenter study which performs CT scans in 5000 Asian Admixture patients (APOLLO study NCT05509010). In the current stage of this study, NCCT data analysis were conducted in 551 patients with 26,037 images. AI EAT was developed via a novel deep learning framework using an ensemble region-based UNet. The region-based UNet uses 2 component UNet models to perform segmentation of pericardium at the apex region and non-apex region (middle and basal). EAT volume was obtained by automated thresholding of the voxels (-190 to -30 Hounsfield Unit) within the pericardium (Figure 1). The network was trained in 501 patients with 23,712 NCCT images and tested in 50 patients with 2,325 NCCT images. The performance of AI EAT was evaluated with respect to clinical ground truth using Dice similarity coefficient (DSC), Pearson correlation, and Bland-Altman analysis. Results The AI EAT quantification process took less than 10 seconds per subject, compared with 20-30 minutes for expert readers. Compared to clinical ground truth, our AI EAT achieved a DSC of 0.96±0.01 and 0.91±0.02 for pericardium and EAT segmentations, respectively. There was strong agreement between the AI EAT and clinical ground truth in deriving the EAT volume (r=0.99, P<0.001) with minimal error of 7±5%. Conclusion End-to-end deep learning system accurately quantifies epicardial adipose tissue in standard NCCT images without manual segmentation.