Modeling of hydrophobic interaction chromatography for the separation of antibody-drug conjugates and its application towards quality by design.

Antibody-drug conjugates (ADCs) are hybrid molecules based on monoclonal antibodies (mAbs) with covalently attached cytotoxic small-molecule drugs. Due to their potential for targeted cancer therapy, they form part of the diversifying pipeline of various biopharmaceutical companies, in addition to currently seven commercial ADCs. With other new modalities, ADCs contribute to the increasing complexity of biopharmaceutical development in times of growing costs and competition. Another challenge is the implementation of quality by design (QbD), which receives a lot of attention. In order to answer these challenges, mechanistic models are gaining interest as tools for enhanced process understanding and efficient process development. The drug-to-antibody ratio (DAR) is a critical quality attribute (CQA) of ADCs. After the conjugation reaction, the DAR can still be adjusted by including a hydrophobic interaction chromatography (HIC) step. In this work, we developed a mechanistic model for the preparative separation of cysteine-engineered mAbs with different degrees of conjugation with a non-toxic surrogate drug. The model was successfully validated for varying load compositions with linear and optimized step gradient runs, applying conditions differing from the calibration runs. In two in silico studies, we then present scenarios for how the model can be applied profitably to ensure a more robust achievement of the target DAR and for the efficient characterization of the design space. For this, we also used the model in a linkage study with a kinetic reaction model developed by us previously. The combination of the two models effectively widens system boundaries over two adjacent process steps. We believe this work has great potential to help advance the incorporation of digital tools based on mechanistic models in ADC process development by illustrating their capabilities for efficient process development and increased robustness. Mechanistic models can support the implementation of QbD and eventually might be the basis for digital process twins able to represent multiple unit operations.

Kinetic reaction modeling for antibody-drug conjugate process development.

By combining the specificity of monoclonal antibodies (mAbs) and the efficacy of cytotoxic drugs in one molecule, antibody-drug conjugates (ADCs) form a promising class of anti-cancer therapeutics. This is emphasized by around 65 molecules in clinical trials and four marketed products. The conjugation reaction of mAbs with small-molecule drugs is a central step during production of ADCs. A detailed kinetic model for the conjugation reaction grants enhanced process understanding and can be profitably applied to process optimization. One example is the identification of the optimal amount of drug excess, which should be minimized due to its high toxicity and high cost. In this work, we set up six different kinetic model structures for the conjugation of a cysteine-engineered mAb with a maleimide-functionalized surrogate drug. All models consisted of a set of differential equations. The models were fit to an experimental data set, and the best model was selected based on cross-validation. The selected model was successfully validated with an external validation dataset (R² of prediction: 0.978). Based on the modeling results, process understanding was improved. The model shows that the binding of the second drug to the mAb is influenced by the attachment of the first drug molecule. Additionally, an increase in reaction rate was observed for the addition of different salts to the reaction. In a next step, the model was applied to an in silico screening and optimization, which illustrates its potential for making ADC process development more efficient. Finally, the combination of the kinetic model with a PAT tool for reaction monitoring was demonstrated. In summary, the proposed modeling approach provides a powerful tool for the investigation of ADC conjugation reactions and establishes a valuable in silico decision support for process development.

Factorization of preparative protein chromatograms with hard-constraint multivariate curve resolution and second-derivative pretreatment.

Current biopharmaceutical production heavily relies on chromatography for protein purification. Recently, research has intensified towards finding suitable solutions to monitoring the chromatographic steps by multivariate spectroscopic sensors. Here, hard-constraint multivariate curve resolution (MCR) was investigated as a calibration-free method for factorizing bilinear preparative protein chromatograms into concentrations and spectra. Protein elutions were assumed to follow exponentially modified Gaussian (EMG) curves. In three case studies, MCR was applied to chromatograms of second-derivative ultraviolet and visible (UV-vis) spectra. The three case studies consisted of the separation of a ternary mixture (ribonuclease A, cytochrome c, and lysozyme), multiple binary chromatography runs of cytochrome c and lysozyme, and the separation of an antibody-drug conjugate (ADC) from unconjugated immunoglobulin G (IgG). In all case studies, good estimates of the elution curves were obtained. R2 values compared to off-line analytics exceeded 0.90. The estimated spectra allowed for protein identification based on a protein spectral library. In summary, MCR was shown to be well able to factorize protein chromatograms without prior calibration. The method may thus substantially simplify analysis of multivariate protein chromatograms with multiple co-eluting species. It may be especially useful in process development.

Monitoring of antibody-drug conjugation reactions with UV/Vis spectroscopy.

The conjugation reaction of monoclonal antibodies (mAbs) with small-molecule drugs is a central step during production of antibody-drug conjugates (ADCs). The ability to monitor this step in real time can be advantageous for process understanding and control. Here, we propose a method based on UV/Vis spectroscopy in conjunction with partial least squares (PLS) regression for non-invasive monitoring of conjugation reactions. In experiments, the method was applied to conjugation reactions with two surrogate drugs in microplate format as well as at 20 ml scale. All calibrated PLS models performed well in cross-validation (Q2>0.975 for all models). In microplate format, the PLS models were furthermore successfully validated with an independent prediction set (Rpred2=0.9770 resp. 0.8940). In summary, the proposed method provides a quick and easily implementable tool for reaction monitoring of ADC conjugation reactions and may in the future support the implementation of Process Analytical Technologies (PAT).

Multi-step high-throughput conjugation platform for the development of antibody-drug conjugates.

Antibody-drug conjugates (ADCs) form a rapidly growing class of biopharmaceuticals which attracts a lot of attention throughout the industry due to its high potential for cancer therapy. They combine the specificity of a monoclonal antibody (mAb) and the cell-killing capacity of highly cytotoxic small molecule drugs. Site-specific conjugation approaches involve a multi-step process for covalent linkage of antibody and drug via a linker. Despite the range of parameters that have to be investigated, high-throughput methods are scarcely used so far in ADC development. In this work an automated high-throughput platform for a site-specific multi-step conjugation process on a liquid-handling station is presented by use of a model conjugation system. A high-throughput solid-phase buffer exchange was successfully incorporated for reagent removal by utilization of a batch cation exchange step. To ensure accurate screening of conjugation parameters, an intermediate UV/Vis-based concentration determination was established including feedback to the process. For conjugate characterization, a high-throughput compatible reversed-phase chromatography method with a runtime of 7 min and no sample preparation was developed. Two case studies illustrate the efficient use for mapping the operating space of a conjugation process. Due to the degree of automation and parallelization, the platform is capable of significantly reducing process development efforts and material demands and shorten development timelines for antibody-drug conjugates.

Investigation and prediction of protein precipitation by polyethylene glycol using quantitative structure-activity relationship models.

Precipitation of proteins is considered to be an effective purification method for proteins and has proven its potential to replace costly chromatography processes. Besides salts and polyelectrolytes, polymers, such as polyethylene glycol (PEG), are commonly used for precipitation applications under mild conditions. Process development, however, for protein precipitation steps still is based mainly on heuristic approaches and high-throughput experimentation due to a lack of understanding of the underlying mechanisms. In this work we apply quantitative structure-activity relationships (QSARs) to model two parameters, the discontinuity point m* and the ß-value, that describe the complete precipitation curve of a protein under defined conditions. The generated QSAR models are sensitive to the protein type, pH, and ionic strength. It was found that the discontinuity point m* is mainly dependent on protein molecular structure properties and electrostatic surface properties, whereas the ß-value is influenced by the variance in electrostatics and hydrophobicity on the protein surface. The models for m* and the ß-value exhibit a good correlation between observed and predicted data with a coefficient of determination of R2≥0.90 and, hence, are able to accurately predict precipitation curves for proteins. The predictive capabilities were demonstrated for a set of combinations of protein type, pH, and ionic strength not included in the generation of the models and good agreement between predicted and experimental data was achieved.