Biophysical and Structural Characterization of Antibody–Drug Conjugates

TL;DR

This paper reviews biophysical methods to assess antibody-drug conjugates for cancer treatment, aiming to improve their stability and effectiveness before clinical trials.

Contribution

The paper introduces emerging biophysical techniques and AI/ML approaches for characterizing and developing antibody-drug conjugates.

Findings

Biophysical techniques like DSC, DSF, and MS are essential for assessing ADC stability and developability.

Aggregation and drug-to-antibody ratio are critical parameters measured using scattering and spectroscopic methods.

Future advancements include ex vivo biophysical assays and AI/ML to accelerate ADC development.

Abstract

Common cytotoxic agents used to treat cancers can be accompanied by harsh side effects due to their ability to kill cells and systemic circulation. Antibody–drug conjugates utilize the high target affinity of antibodies to bind and release their cytotoxic payload at the tumor site. However, in order to create an effective treatment, biophysical characterization of antibody–drug conjugates is essential. Antibodies are prone to various undesirable behaviors including aggregation due to misfolding or weak interactions, and poor pharmacokinetics and disposition. The linker and cytotoxic payload add additional potential for misbehavior such as premature linker cleavage. By implementing biophysical characterization early in drug development, the detection of poorly behaved antibody–drug conjugates before expensive clinical trials is possible.

Antibody–drug conjugates (ADCs) comprise a monoclonal antibody covalently bound to a cytotoxic payload by a linker. ADCs minimize off-target effects on healthy tissues, leveraging the specificity of monoclonal antibodies to deliver cytotoxic drugs to the intended tumor site. ADCs can be prone to poor behavior, including aggregation and misfolding, leading to poor efficacy, impaired pharmacokinetics, and immunogenicity. It is advantageous to understand the developability and potential liabilities of a protein candidate prior to costly in vivo studies or clinical trials. This review summarizes biophysical and structural techniques used to characterize ADCs and introduces emerging techniques aimed at accurately assessing the developability of protein candidates. Stability is commonly assayed using techniques like differential scanning calorimetry (DSC), differential scanning fluorimetry (DSF), or spectroscopic probes such as circular dichroism and intrinsic fluorescence. Drug-to-antibody ratio (DAR) is a critical parameter that can be measured using absorbance spectroscopy or chromatographic analysis. Aggregation and self-association can be probed using scattering techniques such as dynamic light scattering (DLS), static light scattering (SLS), and size exclusion chromatography–multi-angle light scattering (SEC-MALS), as well as more specialized approaches such as fluorescence correlation spectroscopy (FCS) and analytical ultracentrifugation (AUC). Mass spectrometry (MS) provides extremely valuable insight into stability, covalent modifications, and, through approaches like hydrogen–deuterium exchange (HDX-MS), structural dynamics of ADCs. Looking forward, the use of biophysical assays in ex vivo matrices and strategic use of artificial intelligence/machine learning (AI/ML) approaches are likely to advance the efficient and rapid development of ADCs and other next-generation protein therapeutics.

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