Improved assay development of pharmaceutical modalities using feedback-controlled liquid chromatography optimization.

Development of meaningful and reliable analytical assays in the (bio)pharmaceutical industry can often be challenging, involving tedious trial and error experimentation. In this work, an automated analytical workflow using an AI-based algorithm for streamlined method development and optimization is presented. Chromatographic methods are developed and optimized from start to finish by a feedback-controlled modeling approach using readily available LC instrumentation and software technologies, bypassing manual user intervention. With the use of such tools, the time requirement of the analyst is drastically minimized in the development of a method. Herein key insights on chromatography system control, automatic optimization of mobile phase conditions, and final separation landscape for challenging multicomponent mixtures are presented (e.g., small molecules drug, peptides, proteins, and vaccine products) showcased by a detailed comparison of a chiral method development process. The work presented here illustrates the power of modern chromatography instrumentation and AI-based software to accelerate the development and deployment of new separation assays across (bio)pharmaceutical modalities while yielding substantial cost-savings, method robustness, and fast analytical turnaround.

Dual-Gradient Unified Chromatography: A New Paradigm for Versatility in Simultaneous Multicomponent Analysis.

Generality in analytical chemistry can be manifested in impactful platforms that can streamline modern organic synthesis and biopharmaceutical processes. We herein introduce a hybrid separation technique named Dual-Gradient Unified Chromatography (DGUC), which is built upon an automated dynamic modulation of CO2 , organic modifier, and water blends with various buffers. This concept enables simultaneous multicomponent analysis of both small and large molecules across a wide polarity range in single experimental runs. After a careful investigation of its fundamental aspects, a DGUC-DAD-MS screening workflow that combines multiple orthogonal column and mobile phase choices across a far-reaching universal elution profile is also reported. The power of this framework is demonstrated with new analytical applications guiding academic and industrial laboratories in the development of new (bio)pharmaceutical targets (e.g. synthetic intermediates, nucleosides, cyclic and linear peptides, proteins, antibody drug conjugates).

Automated ion exchange chromatography screening combined with in silico multifactorial simulation for efficient method development and purification of biopharmaceutical targets.

Bioprocess development of increasingly challenging therapeutics and vaccines requires a commensurate level of analytical innovation to deliver critical assays across functional areas. Chromatography hyphenated to numerous choices of detection has undeniably been the preferred analytical tool in the pharmaceutical industry for decades to analyze and isolate targets (e.g., APIs, intermediates, and byproducts) from multicomponent mixtures. Among many techniques, ion exchange chromatography (IEX) is widely used for the analysis and purification of biopharmaceuticals due to its unique selectivity that delivers distinctive chromatographic profiles compared to other separation modes (e.g., RPLC, HILIC, and SFC) without denaturing protein targets upon isolation process. However, IEX method development is still considered one of the most challenging and laborious approaches due to the many variables involved such as elution mechanism (via salt, pH, or salt-mediated-pH gradients), stationary phase's properties (positively or negatively charged; strong or weak ion exchanger), buffer type and ionic strength as well as pH choices. Herein, we introduce a new framework consisting of a multicolumn IEX screening in conjunction with computer-assisted simulation for efficient method development and purification of biopharmaceuticals. The screening component integrates a total of 12 different columns and 24 mobile phases that are sequentially operated in a straightforward automated fashion for both cation and anion exchange modes (CEX and AEX, respectively). Optimal and robust operating conditions are achieved via computer-assisted simulation using readily available software (ACD Laboratories/LC Simulator), showcasing differences between experimental and simulated retention times of less than 0.5%. In addition, automated fraction collection is also incorporated into this framework, illustrating the practicality and ease of use in the context of separation, analysis, and purification of nucleotides, peptides, and proteins. Finally, we provide examples of the use of this IEX screening as a framework to identify efficient first dimension (1D) conditions that are combined with MS-friendly RPLC conditions in the second dimension (2D) for two-dimensional liquid chromatography experiments enabling purity analysis and identification of pharmaceutical targets.

Trapping-Enrichment Multi-dimensional Liquid Chromatography with On-Line Deuterated Solvent Exchange for Streamlined Structure Elucidation at the Microgram Scale.

At the forefront of chemistry and biology research, development timelines are fast-paced and large quantities of pure targets are rarely available. Herein, we introduce a new framework, which is built upon an automated, online trapping-enrichment multi-dimensional liquid chromatography platform (TE-Dt-mDLC) that enables: 1) highly efficient separation of complex mixtures in a first dimension (1 D-UV); 2) automated peak trapping-enrichment and buffer removal achieved through a sequence of H2 O and D2 O washes using an independent pump setup; and 3) a second dimension separation (2 D-UV-MS) with fully deuterated mobile phases and fraction collection to minimize protic residues for immediate NMR analysis while bypassing tedious drying processes and minimizing analyte degradation. Diverse examples of target isolation and characterization from organic synthesis and natural product chemistry laboratories are illustrated, demonstrating recoveries above 90 % using as little as a few micrograms of material.

Mapping the Separation Landscape in Two-Dimensional Liquid Chromatography: Blueprints for Efficient Analysis and Purification of Pharmaceuticals Enabled by Computer-Assisted Modeling.

Recent developments in two-dimensional liquid chromatography (2D-LC) now make separation and analysis of very complex mixtures achievable. Despite being such a powerful chromatographic tool, current 2D-LC technology requires a series of arduous method development activities poorly suited for a fast-paced industrial environment. Recent introductions of new technologies including active solvent modulation and a support for multicolumn 2D-LC are helping to overcome this stigma. However, many chromatography practitioners believe that the lack of a systematic way to effectively optimize 2D-LC separations is a missing link in securing the viability of 2D-LC as a mainstay for industrial applications. In this work, a computer-assisted modeling approach that dramatically simplifies both offline and online 2D-LC method developments is introduced. Our methodology is based on mapping the separation landscape of pharmaceutically relevant mixtures across both first (1D) and second (2D) dimensions using LC Simulator (ACD/Labs) software. Retention models for 1D and 2D conditions were built using a minimal number of multifactorial modeling experiments (2 × 2 or 3 × 3 parameters: gradient slope, column temperature, and different column and mobile phase combinations). The approach was first applied to online 2D-LC analysis involving achiral and chiral separations of complex mixtures of enantiomeric species. In these experiments, the retention models proved to be quite accurate for both the 1D and 2D separations, with retention time differences between experiments and simulations of less than 3.5%. This software-based concept was also demonstrated for offline 2D-LC purification of drug substances.

A four parameter optimization and troubleshooting of a RPLC - charged aerosol detection stability indicating method for determination of S-lysophosphatidylcholines in a phospholipid formulation.

A four parameter optimization of a stability indicating method for non-chromophoric degradation products of 1,2-distearoyl-sn-glycero-3-phosphocholine (DSPC), 1-stearoyl-sn-glycero-3-phosphocholine and 2-stearoyl-sn-glycero-3-phosphocholine was achieved using a reverse phase liquid chromatography-charged aerosol detection (RPLC-CAD) technique. Using the hydrophobic subtraction model of selectivity, a core-shell, polar embedded RPLC column was selected followed by gradient-temperature optimization, resulting in ideal relative peak placements for a robust, stability indicating separation. The CAD instrument parameters, power function value (PFV) and evaporator temperature were optimized for lysophosphatidylcholines to give UV absorbance detector-like linearity performance within a defined concentration range. The two lysophosphatidylcholines gave the same response factor in the selected conditions. System specific power function values needed to be set for the two RPLC-CAD instruments used. A custom flow-divert profile, sending only a portion of the column effluent to the detector, was necessary to mitigate detector response drifting effects. The importance of the PFV optimization for each instrument of identical build and how to overcome recovery issues brought on by the matrix effects from the lipid-RP stationary phase interaction is reported.

Cleaning verification: A five parameter study of a Total Organic Carbon method development and validation for the cleaning assessment of residual detergents in manufacturing equipment.

A Total Organic Carbon (TOC) based analytical method to quantitate trace residues of clean-in-place (CIP) detergents CIP100® and CIP200® on the surfaces of pharmaceutical manufacturing equipment was developed and validated. Five factors affecting the development and validation of the method were identified: diluent composition, diluent volume, extraction method, location for TOC sample preparation, and oxidant flow rate. Key experimental parameters were optimized to minimize contamination and to improve the sensitivity, recovery, and reliability of the method. The optimized concentration of the phosphoric acid in the swabbing solution was 0.05M, and the optimal volume of the sample solution was 30mL. The swab extraction method was 1min sonication. The use of a clean room, as compared to an isolated lab environment, was not required for method validation. The method was demonstrated to be linear with a correlation coefficient (R) of 0.9999. The average recoveries from stainless steel surfaces at multiple spike levels were >90%. The repeatability and intermediate precision results were ≤5% across the 2.2-6.6ppm range (50-150% of the target maximum carry over, MACO, limit). The method was also shown to be sensitive with a detection limit (DL) of 38ppb and a quantitation limit (QL) of 114ppb. The method validation demonstrated that the developed method is suitable for its intended use. The methodology developed in this study is generally applicable to the cleaning verification of any organic detergents used for the cleaning of pharmaceutical manufacturing equipment made of electropolished stainless steel material.

Determining the Chemical-Heterogeneity-Corrected Molar Mass Averages and Distribution of Poly(styrene-co-t-butyl methacrylate) Using SEC/MALS/UV/DRI.

Chemical heterogeneity, defined as the change (or lack thereof) across the molar mass distribution (MMD) in the monomeric ratio of a copolymer, can influence processing and end-use properties such as solubility, gas permeation, conductivity, and the energy of interfacial fracture. Given that each parent homopolymer of the copolymer monomeric components has a different specific refractive index increment (∂n/∂c) from the other component, chemical heterogeneity translates into ∂n/∂c heterogeneity. The latter, in turn, affects the accuracy of the molar mass (M) averages and distributions of the copolymers in question. Here, employing size-exclusion chromatography coupled on-line to multi-angle static light scattering, ultraviolet absorption spectroscopy, and differential refractometry detection, the chemical heterogeneity (given as mass percent styrene) was determined for a poly(styrene-co-t-butyl methacrylate) copolymer. Also determined were the chemical-heterogeneity-corrected M averages and MMD of the copolymer. In the present case, the error in molar mass incurred by ignoring the effects of chemical heterogeneity in the M calculations is seen to reach as high as 53,000 g mol-1 at the high end of the MMD. This error could be much higher, however, in copolymers with higher M or with larger difference among component ∂n/∂c values, as compared to the current analyte.