Lorenzo Angiolini,Fabrizio Manetti,Ottavia Spiga,Andrea Tafi,Anna Visibelli,Elena Petricci
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Machine Learning for Predicting the Drug-to-Antibody Ratio (DAR) in the Synthesis of Antibody-Drug Conjugates (ADCs).
The pharmaceutical industry faces challenges in developing efficient and cost-effective drug delivery systems. Among various applications, antibody-drug conjugates (ADCs) stand out by combining cytotoxic or bioactive agents with monoclonal antibodies (mAbs) for targeted therapies. However, bioconjugation methods can produce different outcomes, including no bioconjugation, depending on the mAb, the amino acid residues, and the linker-payload (LP) system used. In this work, we developed a machine learning (ML) algorithm capable of predicting bioconjugation outcomes, allowing the design of the best mAb, LP systems, and conditions for the development of efficient ADCs. In particular, we exploited the potential of the XGBoost algorithm in predicting the drug-to-antibody ratio (DAR) in the synthesis of ADCs. Our model demonstrated high predictive accuracy, with R2 scores of 0.85 and 0.95 for lysine and cysteine data sets, respectively. The integration of ML algorithms into bioconjugation processes for ADC synthesis offers a promising approach to streamlining ADC development.
期刊介绍:
The Journal of Chemical Information and Modeling publishes papers reporting new methodology and/or important applications in the fields of chemical informatics and molecular modeling. Specific topics include the representation and computer-based searching of chemical databases, molecular modeling, computer-aided molecular design of new materials, catalysts, or ligands, development of new computational methods or efficient algorithms for chemical software, and biopharmaceutical chemistry including analyses of biological activity and other issues related to drug discovery.
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