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Deep learning-based design and experimental validation of a medicine-like human antibody library.

Brief Bioinform

November 2024

Biotherapeutics Molecule Discovery, Boehringer Ingelheim Pharmaceutical Inc., 900 Ridgebury Road, Ridgefield, CT 06877, United States.

Antibody generation requires the use of one or more time-consuming methods, namely animal immunization, and in vitro display technologies. However, the recent availability of large amounts of antibody sequence and structural data in the public domain along with the advent of generative deep learning algorithms raises the possibility of computationally generating novel antibody sequences with desirable developability attributes. Here, we describe a deep learning model for computationally generating libraries of highly human antibody variable regions whose intrinsic physicochemical properties resemble those of the variable regions of the marketed antibody-based biotherapeutics (medicine-likeness).

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Background: Females with hypertrophic cardiomyopathy present at a more advanced stage of the disease and have a higher risk of heart failure and death. The factors behind these differences are unclear. We aimed to investigate sex-related differences in clinical and genetic factors affecting adverse outcomes in the Sarcomeric Human Cardiomyopathy Registry.

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Adeno-associated viral (AAV) vectors are increasingly used for preclinical and clinical cardiac gene therapy approaches. However, gene transfer to cardiomyocytes poses a challenge due to differences between AAV serotypes in terms of expression efficiency and . For example, AAV9 vectors work well in rodent heart muscle cells but not in cultivated neonatal rat ventricular cardiomyocytes (NRVCMs), necessitating the use of AAV6 vectors for studies.

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Objective: We aimed to evaluate microbiome and microbiota-derived C18 dietary polyunsaturated fatty acids (PUFAs), such as conjugated linoleic acid (CLA), and to investigate their differences that correlate with arthritis severity in collagen-induced arthritis (CIA) mice.

Methods: On day 84 after induction, during the chronic phase of arthritis, cecal samples were analyzed using 16S rRNA sequencing, and plasma and cecal digesta were evaluated using liquid chromatography-tandem mass spectrometry. Differences in microbial composition between 10 control (Ctrl) and 29 CIA mice or between the mild and severe subgroups based on arthritis scores were identified.

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Liver cancer is a leading cause of cancer-related deaths worldwide, highlighting the need for innovative approaches to understand its complex biology and develop effective treatments. While traditional animal models have played a vital role in liver cancer research, ethical concerns and the demand for more human-relevant systems have driven the development of advanced models. Spheroids and organoids have emerged as powerful tools due to their ability to replicate tumor microenvironment and facilitate preclinical drug development.

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