Determining gradient conditions for peptide purification in RPLC with machine-learning-based retention time predictions

Biomass-Based Microbial Protein Production: A Review of Processing and Properties

A rise in population and societal changes have increased pressure on resources required to meet the growing demand for food and changing dietary preferences. The increasing demand for animal protein is concerning and raises questions regarding sustainability due to its environmental impact. Subsequently, scientists seek alternative proteins, such as microbial proteins (MPs), as an environmentally friendly choice. The production of MPs promotes benefits, including reducing deforestation and CO2 emissions. Several microorganism types, such as bacteria, yeast, fungi, and algae, use a variety of substrates for MP production, from agricultural residues to lignocellulosic biomass. These complex substrates, including lignocellulosic biomass, are converted to fermentable sugar through either chemical, physical, or biological methods. Indeed, fermentation can occur through submerged cultures or other methods. However, this depends on the substrate and microorganisms being utilized. MPs have properties that make them versatile and useful ingredients in various applications. Using residues and lignocellulosic biomass as raw materials for producing MPs offers sustainability, cost-effectiveness, and waste reduction advantages. These properties are consistent with the principles established by green chemistry, which aims to conserve resources effectively and operate sustainably in all areas. This review highlights the importance of studying manufacturing aspects and the characteristics associated with MPs, which can be implemented to solve problems and encourage novel methods in the global food/feed industry.

Modelling the pH dependent retention and competitive adsorption of charged and ionizable solutes in mixed-mode and reversed-phase liquid chromatography

This study investigated the influence of pH on the retention of solutes using a mixed-mode column with carboxyl (-COOH) groups acting as weak cation exchanger bonded to the terminal of C18 ligands (C18-WCX column) and a traditional reversed-phase C18 column. First, a model based on electrostatic theory was derived and successfully used to predict the retention of charged solutes (charged, and ionizable) as a function of mobile phase pH on a C18-WCX column. While the Horváth model predicts the pH-dependent retention of ionizable solutes in reversed-phase liquid chromatography (RPLC) solely based on solute ionization, the developed model incorporates the concept of surface potential generated on the surface of the stationary phase and its variation with pH. To comprehensively understand the adsorption process, adsorption isotherms for these solutes were individually acquired on the C18-WCX and reversed-phase C18 columns. The adsorption isotherms followed the Langmuir model for the uncharged solute and the electrostatically modified Langmuir model for charged solutes. The elution profiles for the single components were calculated from these isotherms using the equilibrium dispersion column model and were found to be in close agreement with the experimental elution profiles. To enable modelling of two-component cases involving charged solute(s), a competitive adsorption isotherm model based on electrostatic theory was derived. This model was later successfully used to calculate the elution profiles of two components for scenarios involving (a) a C18 Column: two charged solutes, (b) a C18 Column: one charged and one uncharged solute, and (c) a C18-WCX Column: two charged solutes. The strong alignment between the experimental and calculated elution profiles in all three scenarios validated the developed competitive adsorption model.

Benefit of In Silico Predicted Spectral Libraries in Data-Independent Acquisition Data Analysis Workflows

Data-independent acquisition (DIA) has become a well-established method for MS-based proteomics. However, the list of options to analyze this type of data is quite extensive, and the use of spectral libraries has become an important factor in DIA data analysis. More specifically the use of in silico predicted libraries is gaining more interest. By working with a differential spike-in of human standard proteins (UPS2) in a constant yeast tryptic digest background, we evaluated the sensitivity, precision, and accuracy of the use of in silico predicted libraries in data DIA data analysis workflows compared to more established workflows. Three commonly used DIA software tools, DIA-NN, EncyclopeDIA, and Spectronaut, were each tested in spectral library mode and spectral library-free mode. In spectral library mode, we used independent spectral library prediction tools PROSIT and MS2PIP together with DeepLC, next to classical data-dependent acquisition (DDA)-based spectral libraries. In total, we benchmarked 12 computational workflows for DIA. Our comparison showed that DIA-NN reached the highest sensitivity while maintaining a good compromise on the reproducibility and accuracy levels in either library-free mode or using in silico predicted libraries pointing to a general benefit in using in silico predicted libraries.

Computer-assisted multifactorial method development for the streamlined separation and analysis of multicomponent mixtures in (Bio)pharmaceutical settings

The (bio)pharmaceutical industry is rapidly moving towards complex drug modalities that require a commensurate level of analytical enabling technologies that can be deployed at a fast pace. Unsystematic method development and unnecessary manual intervention remain a major barrier towards a more efficient deployment of meaningful analytical assay across emerging modalities. Digitalization and automation are key to streamline method development and enable rapid assay deployment. This review discusses the use of computer-assisted multifactorial chromatographic method development strategies for fast-paced downstream characterization and purification of biopharmaceuticals. Various chromatographic techniques such as reversed-phase liquid chromatography (RPLC), hydrophilic interaction liquid chromatography (HILIC), ion exchange chromatography (IEX), hydrophobic interaction chromatography (HIC), and supercritical fluid chromatography (SFC) are addressed and critically reviewed. The most significant parameters for retention mechanism modelling, as well as mapping the separation landscape for optimal chromatographic selectivity and resolution are also discussed. Furthermore, several computer-assisted approaches for optimization and development of chromatographic methods of therapeutics, including linear, nonlinear, and multifactorial modelling are outlined. Finally, the potential of the chromatographic modelling and computer-assisted optimization strategies are also illustrated, highlighting substantial productivity improvements, and cost savings while accelerating method development, deployment and transfer processes for therapeutic analysis in industrial settings.

An improved workflow for the development of MS-compatible liquid chromatography assay purity and purification methods by using automated LC Screening instrumentation and in silico modeling

The development of liquid chromatography UV and mass spectrometry (LC-UV-MS) assays in pharmaceutical analysis is pivotal to improve quality control by providing critical information about drug purity, stability, and presence and identity of byproducts and impurities. Analytical method development of these assays is time-consuming, which often causes it to become a bottle neck in drug development and poses a challenge for process chemists to quickly improve the chemistry. In this study, a systematic and efficient workflow was designed to develop purity assay and purification methods for a wide range of compounds including peptides, proteins, and small molecules with MS-compatible mobile phases (MP) by using automated LC screening instrumentation and in silico modeling tools. Initial LC MPs and chromatography column screening experiments enabled quick identification of conditions which provided the best resolution in the vicinity of the target compounds, which is further optimized using computer-assisted modeling (LC Simulator from ACD/Labs). The experimental retention times were in good agreement with the predicted retention times from LC Simulator (ΔtR < 7%). This workflow presents a practical workflow to significantly expedite the time needed to develop optimized LC-UV-MS methods, allowing for a facile, automatic method optimization and reducing the amount of manual work involved in developing new methods during drug development.

Dimethyl carbonate as a green alternative to acetonitrile in reversed-phase liquid chromatography. Part II: Purification of a therapeutic peptide

Preparative liquid chromatography in reversed phase conditions (RPLC) is the most common approach adopted in the downstream processing for the purification of therapeutic peptides at industrial level. Due to the strict requirements on the quality imposed by the Regulatory Agencies, routinary methods based on the use of aqueous buffers and acetonitrile (ACN) as organic modifier are commonly used, where ACN is practically the only available choice for the purification of peptide derivatives. However, ACN is known to suffers of many shortcomings, such as drastic shortage in the market, high costs and, most importantly, it shows unwanted toxicity for human health and environment, which led it among the less environmentally friendly ones. For this reason, the selection of a suitable alternative becomes crucial for the sustainable downstream processing of peptides and biopharmaceuticals in general. In this paper, a promising green solvent, namely dimethyl carbonate (DMC) has been used for the separation of a peptide not only in linear conditions but also for its purification through non-linear overloaded chromatography. The performance of the process has been compared to that achievable with the common method where ACN is used as organic modifier and to that obtained with two additional solvents (namely ethanol and isopropanol), already used as greener alternatives to ACN. This proof-of-concept study showed that, thanks to its higher elution strength, DMC can be considered a green alternative to ACN, since it allows to reduce method duration while reaching good purities and recoveries. Indeed, at a target purity fixed to 98.5 %, DMC led to the best productivity with respect to all the other solvents tested, confirming its suitability as a sustainable alternative to ACN for the purification of complex biopharmaceutical products.

Strategies for predictive modeling of overloaded oligonucleotide elution profiles in ion-pair chromatography

Due to their potential for gene regulation, oligonucleotides have moved into focus as one of the preferred modalities modulating currently undruggable disease-associated targets. In the course of synthesis and storage of oligonucleotides a significant number of compound-related impurities can be generated. Purification protocols and analytical methods have become crucial for the therapeutic application of any oligonucleotides, be they antisense oligonucleotides (ASOs), small interfering ribonucleic acids (siRNAs) or conjugates. Ion-pair chromatography is currently the standard method for separating and analyzing therapeutic oligonucleotides. Although mathematical modeling can improve the accuracy and efficiency of ion-pair chromatography, its application remains challenging. Simple models may not be suitable to treat advanced single molecules, while complex models are still inefficient for industrial oligonucleotide optimization processes. Therefore, fundamental research to improve the accuracy and simplicity of mathematical models in ion-pair chromatography is still a necessity. In this study, we predict overloaded concentration profiles of oligonucleotides in ion-pair chromatography and compare relatively simple and more advanced predictive models. The experimental system consists of a traditional C18 column using (dibutyl)amine as the ion-pair reagent and acetonitrile as organic modifier. The models were built and tested based on three crude 16-mer oligonucleotides with varying degrees of phosphorothioation, as well as their respective n - 1 and (P = O)1 impurities. In short, the proposed models were suitable to predict the overloaded concentration profiles for different slopes of the organic modifier gradient and column load.

Prediction of peptides retention behavior in reversed-phase liquid chromatography based on their hydrophobicity

Hydrophobicity is an important physicochemical property of peptides and proteins. It is responsible for their conformational changes, stability, as well as various chemical intramolecular and intermolecular interactions. Enormous efforts have been invested to study the extent of hydrophobicity and how it could influence various biological processes, in addition to its crucial role in the separation and purification endeavor as well. Here, we have reviewed various studies that were carried out to determine the hydrophobicity starting from (i) simple amino acids solubility behavior, (ii) experimental approach that was undertaken in the reversed-phase liquid chromatography mode, and ending with (iii) some examples of more advanced computational and machine learning models.

Precision analysis for the determination of steric mass action parameters using eight tobacco host cell proteins

Plants are advantageous as biopharmaceutical manufacturing platforms because they allow the economical and scalable upstream production of proteins, including those requiring post-translational modifications, but do not support the replication of human viruses. However, downstream processing can be more labor-intensive compared to fermenter-based systems because the product is often mixed with abundant host cell proteins (HCPs). Modeling chromatographic separation can minimize the number of process development experiments and thus reduce costs. An important part of such modeling is the sorption isotherm, such as the steric mass action (SMA) model, which describes the multicomponent protein-salt equilibria established in ion-exchange systems. Here we purified ten HCPs, including 2-Cys-peroxiredoxin, from tobacco (Nicotiana tabacum and N. benthamiana). For eight of these HCPs, we obtained sufficient quantities to determine the SMA binding parameters (KSMA and ν) under different production-relevant conditions. We studied the parameters for 2-Cys-peroxiredoxin on Q-Sepharose HP in detail, revealing that pH, resin batch and buffer batch had little influence on KSMA and ν, with coefficients of variation (COVs) less than 0.05 and 0.21, respectively. In contrast, the anion-exchange resins SuperQ-650S, Q-Sepharose FF and QAE-550C led to COVs of 0.69 for KSMA and 0.05 for ν, despite using the same quaternary amine functional group as Q-Sepharose HP. Plant cultivation in summer vs winter resulted in COVs of 0.09 for KSMA and 0.02 for ν, revealing a small impact compared to COVs of 17.15 for KSMA and 0.20 for ν when plants were grown in different settings (climate-controlled phytotron vs greenhouse). We conclude that plant cultivation can substantially affect protein properties and the resulting SMA parameters. Accordingly, plant growth but also protein purification and characterization for chromatography model building should be tightly controlled and well documented.

Overloading behavior of fenoprofen and naproxen as two model compounds on a non-porous silicon pillar array column

In this study, the adsorption behavior of naproxen and fenoprofen as two model compounds on a non-porous pillar array column (NPAC) was investigated under reverse phase liquid chromatography conditions. Band profiles of both analytes were recorded in overloaded concentrations using 30% methanol/water (v/v) as the mobile phase. Breakthrough experiments under the same chromatographic condition were carried out to measure the adsorption isotherms. Single-component adsorption isotherm data were acquired by frontal analysis for each analyte. The isotherms were found to be concave upward and downward for naproxen and fenoprofen, respectively. To find the best agreement between the experimental data points and the adsorption isotherm models, the obtained isotherms were modeled using several isotherm models. The Langmuir-Freundlich and anti-Langmuir models provided the best fitting for fenoprofen and naproxen, respectively. The solute and stationary phase properties determine the appropriate model. Adsorbate-adsorbate interaction is important in the case of naproxen, while the adsorbate- adsorbent (stationary phase) plays the main role in retention of fenoprofen on the NPAC. The validity of the selected isotherm models were checked by comparing calculated and experimental band profiles and plate heights. An excellent agreement was observed for the whole concentration range of both analytes, which confirmed the accuracy of the selected models.

Modelling of multilinear gradient retention time of bio-sweetener rebaudioside A in HPLC analysis

Artificial neural network (ANN), as one of the artificial intelligence methods, has been widely using in HPLC studies for modelling purposes. Stevia rebaudiana is an important industrial plant due to its bio-sweetener molecule, rebaudioside-a, in its leaves. Although rebaudioside-a is up to 300-fold sweeter than sucrose, its calorie is almost zero. In this study, HPLC optimization of rebaudioside-a was studied and the optimization data based on multilinear gradient retention times were modelled by ANN. The input parameters were selected as concentrations, column temperatures, initial acetonitrile percentage for the first step of gradient elution, initial acetonitrile percentage for the second step of gradient elution, slope of acetonitrile, wavelengths, flow rates. The retention time was the output. Also, dried S. rebaudiana leaves were extracted and the concentrations were evaluated by HPLC. According to the ANN results, the most effective parameters on the prediction of non-linear gradient retention time for rebaudioside-a were found as flow rate and initial acetonitrile percentage for the second step of gradient. The best back propagation was selected as Levenberg-Marquardt algorithm. The highest rebaudioside-a level was found as 96.53 ± 6.36 μg mL^-1. ANN modelling methods can be used in preparative HPLC applications to estimate the retention time of steviol glycosides.

Microporous poly(glycidyl methacrylate-co-ethylene glycol dimethyl acrylate) microspheres: synthesis, functionalization and applications

As a new kind of functional material, micron-sized porous polymer microspheres are a hot research topic in the field of polymer materials.

Machine learning guided prediction of liquid chromatography-mass spectrometry ionization efficiency for genotoxic impurities in pharmaceutical products

The limitation and control of genotoxic impurities (GTIs) has continued to receive attention from pharmaceutical companies and authorities for several decades. Because GTIs have the ability to damage deoxyribonucleic acid (DNA) and the potential to cause cancer, low-level quantitation is required to protect patients. A quick and easy method of determining the liquid chromatography-mass spectrometry (LC/MS) conditions for high-sensitivity analysis of GTIs may prospectively accelerate pharmaceutical development. In this study, a quantitative structure-property relationship (QSPR) model was developed for predicting the ionization efficiency of compounds using liquid-chromatography-mass spectrometry (LC/MS) parameters and molecular descriptors. Before implementing the QSPR prediction model, linear regression analysis was performed to model the relationship between the ionization efficiency and the LC/MS parameters for each compound. Comparison of the predicted peak areas with the experimentally observed peak areas showed good agreement based on the coefficient of determination (R² > 0.96). The machine learning-based QSPR approach begins with computation of the molecular descriptors expressing the physicochemical properties of a compound, followed by a genetic algorithm-based feature selection. Linear and nonlinear regression were performed, and support vector machine (SVM) was selected as the best machine learning algorithm for the prediction. The SVM algorithm was developed and optimized using 1031 experimental data points for nine compounds, including well-known GTIs. Validation of the model by comparison of the predicted and observed relative ionization efficiencies (RIE) showed a high coefficient of determination (R² = 0.96) and low root mean squared error value (RMSE = 0.118). Finally, this established prediction model was applied to hydrophilic interaction liquid chromatography coupled with MS for a new compound in new mobile phase compositions and new MS parameter settings. The RMSE of the predicted versus observed RIE was 0.203. This prediction accuracy was sufficient to determine the starting point of the LC/MS method development. The methodology demonstrated in this study can be used to determine the LC/MS conditions for high sensitivity analysis of GTIs.

Targeted isolation of 1,1-diphenyl-2-picrylhydrazyl inhibitors from Saxifraga atrata using medium- and high- pressure liquid chromatography combined with online high performance liquid chromatography-1,1-diphenyl-2- picrylhydrazyl detection

Traditional Tibetan medicine (TTM) is a valuable source of novel therapeutic lead molecules inspired by natural products (NPs). The health benefits of Saxifraga atrata are well documented in TTM, but reports on its chemical composition are limited, most likely due to the complicated purification process. Herein, target separation and identification of 4 main radical scavenging compounds from the methanolic extract of S. atrata was were performed using medium- and high-pressure liquid chromatography coupled with online HPLC-DPPH detection. The sample was pretreated using medium pressure liquid chromatography with MCI GELⓇ CHP20P styrene-divinylbenzene beads as a stationary phase, yielding 1.4 g of the target DPPH inhibitors (Fr4, 11.9% recovery). The compounds were further purified and isolated using HPLC on RP-C18 (ReproSil-Pur C18 AQ) followed by HILIC (Click XIon) column separation, resulting in 2.8 mg of fraction Fr4-1-1, 6.8 mg of fraction Fr4-2, 244.9 mg of the Fr4-3-1 sample, and 38.3 mg of Fr4-4-1. The structure and purity of the target compounds were determined, and four compounds (ethyl gallate, 11-O-galloylbergenin, rutin and isoquercitrin) were isolated with >95% purity. The developed methodology is efficient for targeted isolation of high-purity radical scavengers from NP extracts and could be used for rapid identification and isolation of DPPH inhibitors from various NPs.

Modern trends in downstream processing of biotherapeutics through continuous chromatography: The potential of Multicolumn Countercurrent Solvent Gradient Purification

Single-column (batch) preparative chromatography is the technique of choice for purification of biotherapeutics but it is often characterized by an intrinsic limitation in terms of yield-purity trade-off, especially for separations containing a larger number of product-related impurities. This drawback can be alleviated by employing multicolumn continuous chromatography. Among the different methods working in continuous mode, in this paper we will focus in particular on Multicolumn Countercurrent Solvent Gradient Purification (MCSGP) which has been specifically designed for challenging separations of target biomolecules from their product-related impurities. The improvements come from the automatic internal recycling of the impure fractions inside the chromatographic system, which results in an increased yield without compromising the purity of the pool. In this article, steps of the manufacturing process of biopharmaceuticals will be described, as well as the advantages of continuous chromatography over batch processes, by particularly focusing on MCSGP.

Recent advances on protein separation and purification methods

Protein, as the material basis of vita, is the crucial undertaker of life activities, which constitutes the framework and main substance of human tissues and organs, and takes part in various forms of life activities in organisms. Separating proteins from biomaterials and studying their structures and functions are of great significance for understanding the law of life activities and clarifying the essence of life phenomena. Therefore, scientists have proposed the new concept of proteomics, in which protein separation technology plays a momentous role. It has been diffusely used in the food industry, agricultural biological research, drug development, disease mechanism, plant stress mechanism, and marine environment research. In this paper, combined with the recent research situation, the progress of protein separation technology was reviewed from the aspects of extraction, precipitation, membrane separation, chromatography, electrophoresis, molecular imprinting, microfluidic chip and so on.

Residue measurement of pendimethalin in tobacco by using heart-cutting two dimensional liquid chromatography coupled with tandem mass spectrometry

A novel heart-cutting two-dimensional liquid chromatography coupled with tandem mass spectrometry method was developed for quantitative analysis of pendimethalin residue in tobacco. The strategy of reversed phase liquid chromatography coupled with another reversed-phase liquid chromatography was employed for high column efficiency and excellent compatibility of mobile phase. In the first dimensional chromatography, a cyano column with methanol/water as the eluent was applied to separate pendimethalin from thousands of interference components in tobacco. By heart-cutting technique, which effectively removed interference components, the target compound was cut to the second dimensional C18 column for further separation. The pendimethalin residue was finally determined by the tandem mass spectrometry under multiple reaction monitoring reversed-phase liquid chromatography mode. Sample pretreatment of the new method was simplified, involving only extraction and filtration. Compared with traditional methodologies, the new method showed fairly high selectivity and sensitivity with almost no matrix interference. The limit of quantitation for pendimethalin was 1.21 ng/mL, whereas the overall recoveries ranged from 95.7 to 103.3%. The new method has been successfully applied to non-stop measure of 200 real samples, without contamination of ion source. Detection results of the samples agreed well with standard method.

Introducing Multifactorial Peak Crossover in Analytical and Preparative Chromatography via Computer-Assisted Modeling

Modern pharmaceutical processes can often lead to multicomponent mixtures of closely related species that are difficult to resolve under chromatographic conditions, and even worse in preparative scale settings. Despite recent improvements in column technology and instrumentation, there remains an urgent need for creating innovative approaches that address challenging coelutions of critical pair and poor chromatographic productivity of purification methods. Herein, we overcome these challenges by introducing a simple and practical technique named multifactorial peak crossover (MPC) via computer-assisted chromatographic modeling. The approach outlined here focuses on mapping the separation landscape of pharmaceutical mixtures to quickly identify spaces of peak coelution crossings which enables one to conveniently switch the elution order of target analytes. Diverse examples of MPC diagrams as a function of column temperature, mobile phase gradient or a multifactorial combination in reversed phase and ion exchange chromatography (RPLC and IEC) modes are generated using ACD Laboratories/LC Simulator software and corroborated with experimental data match (overall retention time differences of less than 1%). This powerful MPC technique allows us to gain massive productivity increases (shorter cycle time and higher sample loading) for purification of pharmaceuticals by selectively switching the elution order of target components away from undesired tailing peaks and coelution spaces. MPC chromatography dramatically reduces the time spent developing productive analytical and preparative scale separations. In addition, we illustrate how this new MPC concept can be used to gain substantial improvements of the signal-to-noise ratio, enabling straightforward ppb detection of low-level target components with direct impact in the quantitation of metabolites and potential genotoxic impurities (PGIs). These innovations are of paramount importance in order to facilitate efficient isolation, characterization, and quantitation of drug substances in the development of new medicines.