Lena Erlach


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Erlach

First Name

Lena

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0009-0002-0630-5764

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Publications 1 - 2 of 2
  • Machine learning methods for efficient antibody discovery, engineering and optimization
    Item type: Doctoral Thesis
    Erlach, Lena (2025)
    Antibodies have emerged as one of the most important biopharmaceuticals with transformative outcomes in the treatment of various diseases including cancer, autoimmune disorders, and infectious diseases. Despite their success, the discovery, engineering and optimization of therapeutic antibodies remain limited by experimental bottlenecks along the entire development pipeline that substantially increase the cost of bringing an antibody therapeutic to the patients. Traditional in vivo discovery campaigns generate high-affinity antibodies through in vivo maturation that possess favorable developability properties as opposed to in vitro methods. However, in vivo discovery relies heavily on animal immunization and experimental screening of B cells and developability optimization is constrained by experimental low-throughput assays, and therefore costly and labor-intensive. Computational advancements, such as machine learning (ML), have the potential to transform this field, but are equally constrained by limited data availability. In this thesis, we address key challenges in antibody discovery, affinity engineering and developability optimization through three complementary studies. First, we generated a unique dataset of single-cell transcriptomes and antibody repertoires from immunized mice labeled for antigen specificity. We investigated predictive patterns in transcriptome and antibody amino acid sequences and demonstrated that gene expression-based ML models outperform sequence-based approaches in predicting antigen specificity within an antigen cohort. This work highlights the potential of single-cell gene expression patterns for in vivo antibody discovery. Second, we developed a workflow for ML-guided affinity engineering of an antigen-specific antibody variant. Using antibody repertoires from immunized mice a computational workflow aimed to select a set of antigen-binding variants was developed. The amino acid sequences and their experimentally measured affinities were used to train ML regression models and were able to accurately predict continuous affinity values. This approach enabled the ML-guided design of eight synthetic antibody variants, of which seven exhibited the desired affinities when experimentally validated. These findings highlight the feasibility of leveraging small datasets (<50) for precise affinity engineering, reducing the reliance on extensive experimental screening. Finally, we introduced a modular framework for antibody developability optimization based on Retrieval Augmented Generation (RAG). This method combines a retriever and generator to optimize antibody sequences for developability parameters, such as solubility. This framework enables flexible control over the optimization aimed at preserving antigen-binding functionality. By utilizing a generalizable database we envision this approach to be applicable across different optimization campaigns, offering a transparent and interpretable approach to improve antibody developability optimization. Together, these studies present advances along the entire therapeutic antibody development pipeline by introducing novel methodologies for antigen-specificity prediction, affinity engineering, and developability optimization. By enhancing traditional experimental approaches with computational methodologies, such as ML, this thesis provides a foundation for accelerating therapeutic antibody development while minimizing experimental efforts.
  • Antibody affinity engineering using antibody repertoire data and machine learning
    Item type: Working Paper
    Erlach, Lena; Friedensohn, Simon; Neumeier, Daniel; et al. (2025)
    bioRxiv
    Advanced antibody discovery and engineering workflows take advantage of the combination of high-throughput screening, deep sequencing and machine learning (ML). Most high-throughput methods, however, lack the resolution to provide absolute affinity values of antibody-antigen interactions, limiting their utility for precise engineering of binding kinetics. In this study, we utilize antibody repertoire data, affinity characterization and ML for antibody affinity engineering. Leveraging natural antibody sequence information from repertoires of immunized mice, we identified and experimentally measured affinities for 35 antigen-specific variants. Supervised ML models trained on these sequences achieved remarkable accuracy in predicting affinity, despite the limited dataset size. We utilized the trained ML model to in silico-design eight synthetic antibody variants, of which seven exhibited the desired affinities. Our study illustrates the potential of this streamlined and efficient approach for precise engineering of the affinity of antibodies while reducing extensive experimental screening.
Publications 1 - 2 of 2