Process Mass Intensity (PMI): A Holistic Analysis of Current Peptide Manufacturing Processes Informs Sustainability in Peptide Synthesis.

Small molecule therapeutics represent the majority of the FDA-approved drugs. Yet, many attractive targets are poorly tractable by small molecules, generating a need for new therapeutic modalities. Due to their biocompatibility profile and structural versatility, peptide-based therapeutics are a possible solution. Additionally, in the past two decades, advances in peptide design, delivery, formulation, and devices have occurred, making therapeutic peptides an attractive modality. However, peptide manufacturing is often limited to solid-phase peptide synthesis (SPPS), liquid phase peptide synthesis (LPPS), and to a lesser extent hybrid SPPS/LPPS, with SPPS emerging as a predominant platform technology for peptide synthesis. SPPS involves the use of excess solvents and reagents which negatively impact the environment, thus highlighting the need for newer technologies to reduce the environmental footprint. Herein, fourteen American Chemical Society Green Chemistry Institute Pharmaceutical Roundtable (ACS GCIPR) member companies with peptide-based therapeutics in their portfolio have compiled Process Mass Intensity (PMI) metrics to help inform the sustainability efforts in peptide synthesis. This includes PMI assessment on 40 synthetic peptide processes at various development stages in pharma, classified according to the development phase. This is the most comprehensive assessment of synthetic peptide environmental metrics to date. The synthetic peptide manufacturing process was divided into stages (synthesis, purification, isolation) to determine their respective PMI. On average, solid-phase peptide synthesis (SPPS) (PMI ≈ 13,000) does not compare favorably with other modalities such as small molecules (PMI median 168-308) and biopharmaceuticals (PMI ≈ 8300). Thus, the high PMI for peptide synthesis warrants more environmentally friendly processes in peptide manufacturing.

Orientation of monoclonal antibodies in ion-exchange chromatography: A predictive quantitative structure-activity relationship modeling approach.

Chromatographic separation of biopharmaceuticals in general and monoclonal antibodies (mAbs) specifically is the bottleneck in terms of cost and throughput in preparative purification. Still, generalized platform processes are used, neglecting molecule specific characteristics, defining protein-resin interaction terms. Currently used in silico modeling approaches do not consider the orientation of the molecule towards the chromatographic resins as a result of the structural features on an atomic level. This paper describes a quantitative structure-activity relationship (QSAR) approach to model the orientation of mAbs on ion exchange chromatographic matrices as a function of property distribution and mobile phase characteristics. 6 mAbs were used to build a predictive QSAR model and to investigate the preferred binding orientations and resulting surface shielding on resins. Thereby different dominating orientations, caused by composition of Fab fragments of the mAbs, could be identified. The presented methodology is suitable to gain extended insight in molecule orientation on chromatographic resins and to tailor purification strategies based on molecule structure.

An orientation sensitive approach in biomolecule interaction quantitative structure-activity relationship modeling and its application in ion-exchange chromatography.

Quantitative structure-activity relationship (QSAR) modeling for prediction of biomolecule parameters has become an established technique in chromatographic purification process design. Unfortunately available descriptor sets fail to describe the orientation of biomolecules and the effects of ionic strength in the mobile phase on the interaction with the stationary phase. The literature describes several special descriptors used for chromatographic retention modeling, all of these do not describe the screening of electrostatic potential by the mobile phase in use. In this work we introduce two new approaches of descriptor calculations, namely surface patches and plane projection, which capture an oriented binding to charged surfaces and steric hindrance of the interaction with chromatographic ligands with regard to electrostatic potential screening by mobile phase ions. We present the use of the developed descriptor sets for predictive modeling of Langmuir isotherms for proteins at different pH values between pH 5 and 10 and varying ionic strength in the range of 10-100mM. The resulting model has a high correlation of calculated descriptors and experimental results, with a coefficient of determination of 0.82 and a predictive coefficient of determination of 0.92 for unknown molecular structures and conditions. The agreement of calculated molecular interaction orientations with both, experimental results as well as molecular dynamic simulations from literature is shown. The developed descriptors provide the means for improved QSAR models of chromatographic processes, as they reflect the complex interactions of biomolecules with chromatographic phases.

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.

Influence of structure properties on protein-protein interactions-QSAR modeling of changes in diffusion coefficients.

Information about protein-protein interactions provides valuable knowledge about the phase behavior of protein solutions during the biopharmaceutical production process. Up to date it is possible to capture their overall impact by an experimentally determined potential of mean force. For the description of this potential, the second virial coefficient B22, the diffusion interaction parameter kD, the storage modulus G', or the diffusion coefficient D is applied. In silico methods do not only have the potential to predict these parameters, but also to provide deeper understanding of the molecular origin of the protein-protein interactions by correlating the data to the protein's three-dimensional structure. This methodology furthermore allows a lower sample consumption and less experimental effort. Of all in silico methods, QSAR modeling, which correlates the properties of the molecule's structure with the experimental behavior, seems to be particularly suitable for this purpose. To verify this, the study reported here dealt with the determination of a QSAR model for the diffusion coefficient of proteins. This model consisted of diffusion coefficients for six different model proteins at various pH values and NaCl concentrations. The generated QSAR model showed a good correlation between experimental and predicted data with a coefficient of determination R2 = 0.9 and a good predictability for an external test set with R2 = 0.91. The information about the properties affecting protein-protein interactions present in solution was in agreement with experiment and theory. Furthermore, the model was able to give a more detailed picture of the protein properties influencing the diffusion coefficient and the acting protein-protein interactions. Biotechnol. Bioeng. 2017;114: 821-831. © 2016 Wiley Periodicals, Inc.

Custom-tailored adsorbers: A molecular dynamics study on optimal design of ion exchange chromatography material.

The performance of functionalized materials, e.g., ion exchange resins, depends on multiple resin characteristics, such as type of ligand, ligand density, the pore accessibility for a molecule, and backbone characteristics. Therefore, the screening and identification process for optimal resin characteristics for separation is very time and material consuming. Previous studies on the influence of resin characteristics have focused on an experimental approach and to a lesser extent on the mechanistic understanding of the adsorption mechanism. In this in silico study, a previously developed molecular dynamics (MD) tool is used, which simulates any given biomolecule on resins with varying ligand densities. We describe a set of simulations and experiments with four proteins and six resins varying in ligand density, and show that simulations and experiments correlate well in a wide range of ligand density. With this new approach simulations can be used as pre-experimental screening for optimal adsorber characteristics, reducing the actual number of screening experiments, which results in a faster and more knowledge-based development of custom-tailored adsorbers.

Light extinction and scattering by agarose based resin beads and applications in high-throughput screening.

Optimization of chromatographic processes by high-throughput screening (HTS) methodologies have become a critical part of downstream process development. Nevertheless there are still no non-invasive optical methods to characterize resin as well as protein-resin interaction on liquid-handling platforms available. Several approaches to automated resin screening in microplates are described in literature, yet all those methods involve indirect measurements by removal of, and sample quantification within, supernatant volumes. In this work, we introduce light extinction by light scattering to directly assess resin volume and bead density within microplates. Methods for this novel resin characterization are described for 96 and 384-well microplates. An example application demonstrates ligand concentration measurement in microplates with four commercial SP Sepharose™ Fast Flow batches. Further, direct quantification of adsorbent bound biomolecules is shown in an example with kinetic protein-resin interaction measurement in a batch screening process. This new approach is expected to promote batch-based resin characterization and monitoring on HTS platforms and further miniaturization and increase in throughput of chromatographic HTS processes.

Robust high-throughput batch screening method in 384-well format with optical in-line resin quantification.

High-throughput batch screening technologies have become an important tool in downstream process development. Although continuative miniaturization saves time and sample consumption, there is yet no screening process described in the 384-well microplate format. Several processes are established in the 96-well dimension to investigate protein-adsorbent interactions, utilizing between 6.8 and 50 µL resin per well. However, as sample consumption scales with resin volumes and throughput scales with experiments per microplate, they are limited in costs and saved time. In this work, a new method for in-well resin quantification by optical means, applicable in the 384-well format, and resin volumes as small as 0.1 µL is introduced. A HTS batch isotherm process is described, utilizing this new method in combination with optical sample volume quantification for screening of isotherm parameters in 384-well microplates. Results are qualified by confidence bounds determined by bootstrap analysis and a comprehensive Monte Carlo study of error propagation. This new approach opens the door to a variety of screening processes in the 384-well format on HTS stations, higher quality screening data and an increase in throughput.

A comprehensive molecular dynamics approach to protein retention modeling in ion exchange chromatography.

In downstream processing, the underlying adsorption mechanism of biomolecules to adsorbent material are still subject of extensive research. One approach to more mechanistic understanding is simulating this adsorption process and hereby the possibility to identify the parameters with strongest impact. So far this method was applied with all-atom molecular dynamics simulations of two model proteins on one cation exchanger. In this work we developed a molecular dynamics tool to simulate protein-adsorber interaction for various proteins on an anion exchanger and ran gradient elution experiments to relate the simulation results to experimental data. We were able to show that simulation results yield similar results as experimental data regarding retention behavior as well as binding orientation. We could identify arginines in case of cation exchangers and aspartic acids in case of anion exchangers as major contributors to binding.

Microfluidics on liquid handling stations (µF-on-LHS): an industry compatible chip interface between microfluidics and automated liquid handling stations.

We describe a generic microfluidic interface design that allows the connection of microfluidic chips to established industrial liquid handling stations (LHS). A molding tool has been designed that allows fabrication of low-cost disposable polydimethylsiloxane (PDMS) chips with interfaces that provide convenient and reversible connection of the microfluidic chip to industrial LHS. The concept allows complete freedom of design for the microfluidic chip itself. In this setup all peripheral fluidic components (such as valves and pumps) usually required for microfluidic experiments are provided by the LHS. Experiments (including readout) can be carried out fully automated using the hardware and software provided by LHS manufacturer. Our approach uses a chip interface that is compatible with widely used and industrially established LHS which is a significant advancement towards near-industrial experimental design in microfluidics and will greatly facilitate the acceptance and translation of microfluidics technology in industry.

Development and characterization of an automated high throughput screening method for optimization of protein refolding processes.

Optimization of protein refolding parameters by automated, miniaturized, and parallelized high throughput screening is a powerful approach to meet the demand for fast process development with low material consumption. In this study, we validated methods applicable on a standard liquid handling robot for screening of refolding process parameters by dilution of denatured lysozyme in refolding buffer systems. Different approaches for the estimation of protein solubility and folding were validated concerning resolution and compatibility with the robotic system and with the complex buffer and protein structure composition. We established an indirect method to assess soluble lysozyme concentration independent of matrix effects and protein structure varieties by automated separation of aggregated protein, resolubilization, and measurement of absorption at 280 nm. Using this nonspecific solubility assays the correlation between favorable parameters for high active and soluble lysozyme yields were evaluated. An overlap of good refolding buffer compositions was found provided that the redox environment was controlled with redox reagents. In addition, the need to control unfolding conditions like time, temperature, lysozyme, and dithiothreitol concentration was pointed out as different feedstocks resulted in different refolding yields.