Automated online deconjugation of antibody-drug conjugate for small molecule drug profiling.

Antibody-drug conjugates (ADCs) are designed by chemically linking highly potent cytotoxic small molecule drugs to monoclonal antibodies of unique specificity for targeted destruction of cancer cells. This innovative class of molecules incurs unique developmental challenges due to its structural complexity of having both small molecule and protein components. The stability of the small molecule payload on the ADC is a critical attribute as it directly relates to product efficacy and patient safety. This study describes the use of an end-to-end automated workflow for effective and robust characterization of the small molecule drug while it is conjugated to the antibody. In this approach, online deconjugation was accomplished by an autosampler user defined program and 1D size exclusion chromatography was utilized to provide separation between small molecule and protein species. The small molecule portion was then trapped and sent to the 2D for separation and quantification by reversed-phase liquid chromatography with identification of impurities and degradants by mass spectrometry. The feasibility of this system was demonstrated on an ADC with a disulfide-based linker. This fully automated approach avoids tedious sample preparation that may lead to sample loss and large assay variability. Under optimized conditions, the method was shown to have excellent specificity, sensitivity (LOD of 0.036 µg/mL and LOQ of 0.144 µg/mL), linearity (0.04-72.1 µg/mL), precision (system precision %RSD of 1.7 and method precision %RSD of 3.4), accuracy (97.4 % recovery), stability-indicating nature, and was successfully exploited to analyze the small molecule drug on a panel of stressed ADC samples. Overall, the workflow established here offers a powerful analytical tool for profiling the in-situ properties of small molecule drugs conjugated to antibodies and the obtained information could be of great significance for guiding process/formulation development and understanding pharmacokinetic/pharmacodynamic behavior of ADCs.

A proof-of-concept rat toxicity study highlights the potential utility and challenges of virtual control groups.

The virtual control group (VCG) concept provides a potential opportunity to reduce animal use in drug development by replacing concurrent control groups (CCGs) in nonclinical toxicity studies. This work investigated the feasibility and reliability of using VCGs in place of CCGs. A historical control database (HCD), constructed from Genentech Inc. rat toxicity study data, was reviewed to understand trends and sources of variability in control animals over time, and to identify data curation requirements for assembling VCGs, e.g. alignment of units of measurement. Several endpoints were investigated and stratified against different study design parameters. Sex, route of administration, fasting status, and body weight at study initiation were among the parameters that were indicated as key matching criteria. With a high-level understanding of potential sources of variability, a retrospective proof-of-concept (POC) study was designed, evaluating a historical rat pilot toxicity study for test article-related changes. A masked interpretation of the study was conducted using its CCG, and two unique VCGs that were constructed from individual animal data pulled from our HCD. While the results of the microscopic pathology assessment and most endpoints were similar across the different control groups, the POC revealed the risk of using VCGs to interpret subtle test article-related changes in clinical pathology parameters. Within the context of our POC, it appears the use of a VCG is not completely equivalent to the CCG especially with clinical pathology parameters. Additional work is needed to understand the potential utility, and thus, viability of VCGs in other contexts.

This study explored the use of virtual control groups (VCGs) as a potential method to reduce the number of living control animals in drug development. The process involves replacing concurrent control groups with historical animal data in nonclinical toxicity studies. Several parameters were identified as crucial factors that must be aligned before the construction of VCGs. The VCG concept was tested using a historical rat toxicity study, comparing results against the conventional control group as well as two unique VCGs. Although results were similar in most cases, potential risks in interpreting subtle changes in clinical pathology parameters were identified. Further work is needed to fully elucidate VCGs' potential, and whether it is a viable alternative to current methods. The significance of this work lies in the possibility of reducing the number of animals used in testing, in support of the 3Rs (replace, reduce, and refine).