Evaluating the Potential of Cyclodextrins in Reducing Aggregation of Antibody-Drug Conjugates with Different Payloads.

Cyclodextrins (CDs) are versatile agents used to solubilize small drugs and stabilize proteins. This dual functionality may be particularly beneficial for antibody-drug conjugates (ADCs), as CDs may "mask" the hydrophobicity of the drug payloads. In this study, we explored the effect of CDs on the physical stability of ADCs composed of the same antibody but with different payloads (maytansinoid, auristatin, and fluorophore payloads). The aggregation of ADCs was evaluated under shaking stress conditions and elevated temperatures using size-exclusion chromatography, turbidity, and backgrounded membrane imaging. Our results showed that hydroxypropyl-(HP)-CDs effectively stabilized all ADCs during shaking stress, with increasing stabilization in the order of HPαCD < HPγCD < HPßCD at concentrations of 7.5 mM and (near) complete stabilization at 75 mM. Native CDs without surface activity also stabilized certain ADCs, although less effectively than HP-CDs under agitation stress. During quiescent incubation, the HP-CD effects were small for most ADCs. However, for an ADC with a fluorophore payload that rapidly aggregated after conjugation, HPγCD substantially reduced aggregate levels, in line with fluorescence data supporting CD-ADC interactions. In contrast, sulfobutylether-ß-CD (SBEßCD) increased the aggregation rates in all ADCs under all stress conditions. In conclusion, this study highlights the potential of appropriate CD formulations to improve the physical stability of ADCs.

"Negative" Impact: The Role of Payload Charge in the Physicochemical Stability of Auristatin Antibody-Drug Conjugates.

Antibody-drug conjugates (ADCs) tend to be less stable than their parent antibodies, which is often attributed to the hydrophobic nature of their drug payloads. This study investigated how the payload charge affects ADC stability by comparing two interchain cysteine ADCs that had matched drug-to-antibody ratios and identical linkers but differently charged auristatin payloads, vcMMAE (neutral) and vcMMAF (negative). Both ADCs exhibited higher aggregation than their parent antibody under shaking stress and thermal stress conditions. However, conjugation with vcMMAF increased the aggregation rates to a greater extent than conjugation with uncharged but more hydrophobic vcMMAE. Consistent with the payload logD values, ADC-vcMMAE showed the greatest increase in hydrophobicity but minor changes in charge compared with the parent antibody, as indicated by hydrophobic interaction chromatography and capillary electrophoresis data. In contrast, ADC-vcMMAF showed a decrease in net charge and isoelectric point along with an increase in charge heterogeneity. This charge alteration likely contributed to a reduced electrostatic repulsion and increased surface activity in ADC-vcMMAF, thus affecting its aggregation propensity. These findings suggest that not only the hydrophobicity of the payload, but also its charge should be considered as a critical factor affecting the stability of ADCs.

Features in Backgrounds of Microscopy Images Introduce Biases in Machine Learning Analyses.

Subvisible particles may be encountered throughout the processing of therapeutic protein formulations. Flow imaging microscopy (FIM) and backgrounded membrane imaging (BMI) are techniques commonly used to record digital images of these particles, which may be analyzed to provide particle size distributions, concentrations, and identities. Although both techniques record digital images of particles within a sample, FIM analyzes particles suspended in flowing liquids, whereas BMI records images of dry particles after collection by filtration onto a membrane. This study compared the performance of convolutional neural networks (CNNs) in classifying images of subvisible particles recorded by both imaging techniques. Initially, CNNs trained on BMI images appeared to provide higher classification accuracies than those trained on FIM images. However, attribution analyses showed that classification predictions from CNNs trained on BMI images relied on features contributed by the membrane background, whereas predictions from CNNs trained on FIM features were based largely on features of the particles. Segmenting images to minimize the contributions from image backgrounds reduced the apparent accuracy of CNNs trained on BMI images but caused minimal reduction in the accuracy of CNNs trained on FIM images. Thus, the seemingly superior classification accuracy of CNNs trained on BMI images compared to FIM images was an artifact caused by subtle features in the backgrounds of BMI images. Our findings emphasize the importance of examining machine learning algorithms for image analysis with attribution methods to ensure the robustness of trained models and to mitigate potential influence of artifacts within training data sets.

Agitation-Induced Aggregation of Lysine- And Interchain Cysteine-Linked Antibody-Drug Conjugates.

Drug conjugation to an antibody can affect its stability, which depends on factors such as the conjugation technique used, drug-linker properties, and stress encountered. This study focused on the effects of agitation stress on the physical stability of two lysine (ADC-K) and two interchain cysteine (ADC-C) conjugates of an IgG1 monoclonal antibody (mAb) linked to either ∼4 MMAE or DM1 payloads. During agitation, all antibody-drug conjugates (ADCs) exhibited higher aggregation than the mAb, which was dependent on the conjugation technique (aggregation of ADC-Ks > ADC-Cs) and drug-linker (aggregation of ADCs with MMAE > ADCs with DM1). The aggregation propensities correlated well with higher self-interaction, hydrophobicity, and surface activity of ADCs relative to the mAb. The intermediate reduced mAb (mAb-SH) showed even higher aggregation than the final product ADC-Cs. However, blocking mAb-SH's free thiols with N-ethylmaleimide (NEM) strongly reduced its aggregation, suggesting that free thiols should be minimized in cysteine ADCs. Further, this study demonstrates that a low-volume surface tension method can be used for estimating agitation-induced aggregation of ADCs in early development phases. Identifying liabilities to agitation stress and their relationship to biophysical properties may help optimize ADC stability.

Miniaturized Forced Degradation of Therapeutic Proteins and ADCs by Agitation-Induced Aggregation Using Orbital Shaking of Microplates.

Microplate-based formulation screening is a powerful approach to identify stabilizing excipients for therapeutic proteins while reducing material requirements. However, this approach is sometimes not representative of studies conducted in relevant container closures. The present study aimed to identify critical parameters for a microplate-based orbital shaking method to screen biotherapeutic formulations by agitation-induced aggregation. For this purpose, an in-depth methodological study was conducted using different shakers, microplates, and plate seals. Aggregation was monitored by size exclusion chromatography, turbidity, and backgrounded membrane imaging. Both shaker quality and liquid-seal contact had substantial impacts on aggregation during shaking and resulted in non-uniform sample treatment when parameters were not suitably selected. The well volume to fill volume ratio (Vwell/Vfill) was identified as an useful parameter for achieving comparable aggregation levels between different microplate formats. An optimized method (2400 rpm [ac 95 m/s2], Vfill 60-100 µL [Vwell/Vfill 6-3.6], 24 h, RT, heat-sealed) allowed for uniform sample treatment independent of surface tension and good agreement with vial shaking results. This study provides valuable guidance for miniaturization of shaking stress studies in biopharmaceutical drug development, facilitating method transfer and comparability between laboratories.

Deterministic Switching in Bismuth Ferrite Nanoislands.

We report deterministic selection of polarization variant in bismuth BiFeO3 nanoislands via a two-step scanning probe microscopy procedure. The polarization orientation in a nanoisland is toggled to the desired variant after a reset operation by scanning a conductive atomic force probe in contact over the surface while a bias is applied. The final polarization variant is determined by the direction of the inhomogeneous in-plane trailing field associated with the moving probe tip. This work provides the framework for better control of switching in rhombohedral ferroelectrics and for a deeper understanding of exchange coupling in multiferroic nanoscale heterostructures toward the realization of magnetoelectric devices.

Structure-charge relationship - the case of hematite (001).

We present a multidisciplinary study on the hematite (001)-aqueous solution interface, in particular the relationship between surface structure (studied via surface diffraction in a humid atmosphere) and the macroscopic charging (studied via surface- and zeta-potential measurements in electrolyte solutions as a function of pH). Upon aging in water changes in the surface structure are observed, that are accompanied by drastic changes in the zeta-potential. Surprisingly the surface potential is not accordingly affected. We interpret our results by increasing hydration of the surface with time and enhanced reactivity of singly-coordinated hydroxyl groups that cause the isoelectric point of the surface to shift to values that are reminiscent of those typically reported for hematite particles. In its initial stages after preparation the hematite surface is very flat and only weakly hydrated. Our model links the entailing weak water structure with the observed low isoelectric point reminiscent of hydrophobic surfaces. The absence of an aging effect on the surface potential vs. pH curves is interpreted as domination of the surface potential by the doubly coordinated hydroxyls, which are present on both surfaces.

Ultra-smooth lithium niobate photonic micro-structures by surface tension reshaping.

Annealing of micro-structured lithium niobate substrates at temperatures close to, but below the melting point, allows surface tension to reshape preferentially melted surface zones of the crystal. The reshaped surface re-crystallizes upon cooling to form a single crystal again as it is seeded by the bulk which remains solid throughout the process. This procedure yields ultra-smooth single crystal superstructures suitable for the fabrication of photonic micro-components with low scattering loss.