Publications by authors named "E S Regalado"

128 Publications

Multifactorial Modeling for Streamlined Development and Optimization of Two-Dimensional Liquid Chromatography.

Anal Chem 2021 08 10;93(33):11532-11539. Epub 2021 Aug 10.

Analytical Research and Development, MRL, Merck & Co., Inc., Rahway, New Jersey 07065, United States.

Continued adoption of two-dimensional liquid chromatography (2D-LC) in industrial laboratories will depend on the development of approaches to make method development for 2D-LC more systematic, less tedious, and less reliant on user expertise. In this paper, we build on previous efforts in these directions by describing the use of multifactorial modeling software that can help streamline and simplify the method development process for 2D-LC. Specifically, we have focused on building retention models for second dimension (D) separations involving variables including gradient time, temperature, organic modifier blending, and buffer concentration using LC simulator (ACD/Labs) software. Multifactorial retention modeling outcomes are illustrated as resolution map planes or cubes that enable straightforward location of D conditions that maximize resolution while minimizing analysis time. We also illustrate the practicality of this approach by identifying conditions that yield baseline separation of all compounds co-eluting from a first dimension (D) separation using a single combination of D stationary phase and elution conditions. The multifactorial retention models were found to be very accurate for both the D and D separations, with differences between experimental and simulated retention times of less than 0.5%. Pharmaceutical applications of this approach for multiple heartcutting 2D-LC were demonstrated using IEC-IEC or achiral RPLC-chiral RPLC for 2D separations of multicomponent mixtures. The framework outlined here should help make 2D-LC method development more systematic and streamline development and optimization for a variety of 2D-LC applications in both industry and academia.
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http://dx.doi.org/10.1021/acs.analchem.1c01970DOI Listing
August 2021

Interlaboratory study of a supercritical fluid chromatography method for the determination of pharmaceutical impurities: Evaluation of multi-systems reproducibility.

J Pharm Biomed Anal 2021 Sep 12;203:114206. Epub 2021 Jun 12.

Analytical Research and Development, MRL, Merck & Co, Inc., 126 E. Lincoln Ave, Rahway, NJ 07065, United States.

Modern supercritical fluid chromatography (SFC) is now a well-established technique, especially in the field of pharmaceutical analysis. We recently demonstrated the transferability and the reproducibility of a SFC-UV method for pharmaceutical impurities by means of an inter-laboratory study. However, as this study involved only one brand of SFC instrumentation (Waters®), the present study extends the purpose to multi-instrumentation evaluation. Specifically, three instrument types, namely Agilent®, Shimadzu®, and Waters®, were included through 21 laboratories (n = 7 for each instrument). First, method transfer was performed to assess the separation quality and to set up the specific instrument parameters of Agilent® and Shimadzu® instruments. Second, the inter-laboratory study was performed following a protocol defined by the sending lab. Analytical results were examined regarding consistencies within- and between-laboratories criteria. Afterwards, the method reproducibility was estimated taking into account variances in replicates, between-days and between-laboratories. Reproducibility variance was larger than that observed during the first study involving only one single type of instrumentation. Indeed, we clearly observed an 'instrument type' effect. Moreover, the reproducibility variance was larger when considering all instruments than each type separately which can be attributed to the variability induced by the instrument configuration. Nevertheless, repeatability and reproducibility variances were found to be similar than those described for LC methods; i.e. reproducibility as %RSD was around 15 %. These results highlighted the robustness and the power of modern analytical SFC technologies to deliver accurate results for pharmaceutical quality control analysis.
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http://dx.doi.org/10.1016/j.jpba.2021.114206DOI Listing
September 2021

In silico method development for the reversed-phase liquid chromatography separation of proteins using chaotropic mobile phase modifiers.

J Chromatogr B Analyt Technol Biomed Life Sci 2021 Feb 24;1173:122587. Epub 2021 Feb 24.

Analytical Research & Development, Merck & Co., Inc., Rahway, NJ 07065, USA. Electronic address:

Recent advances in biomedical and pharmaceutical processes has enabled a notable increase of protein- and peptide-based drug therapies and vaccines that often contain a higher-order structure critical to their efficacy. Hyphenation of chromatographic and spectrometric techniques is at the center of all facets of biopharmaceutical analysis, purification and chemical characterization. Although computer-assisted chromatographic modeling of small molecules has reached a mature stage across the pharmaceutical industry, software-based method optimization approaches for large molecules has yet to see the same revitalization. Conformational changes of biomolecules under chromatographic conditions have been identified as the major culprit in terms of sub-optimal modeling outcomes. In order to circumvent these challenges, we herein investigate the outcomes generated via computer-assisted modeling from using different chaotropic and denaturing mobile phases (trifluoroacetic acid, sodium perchlorate and guanidine hydrochloride in acetonitrile/water-based eluents). Linear and polynomial regression retention models using ACD/Labs software were built as a function of gradient slope, column temperature and mobile phase buffer for eight different model proteins ranging from 12 to 670 kDa (holo-transferrin, cytochrome C, apomyoglobin, ribonuclease A, ribonuclease A type I-A, albumin, y-globulin and thyroglobulin bovine). Correlation between experimental and modeled outputs was substantially improved by using strong chaotropic and denaturing modifiers in the mobile phase, even when using linear regression modeling as typically observed for small molecules. On the contrary, the use of conventional TFA buffer concentrations at low column temperatures required the used of polynomial regression modeling indicating potential conformational structure changes of proteins upon chromatographic conditions. In addition, we illustrate the power of modern computer-assisted chromatography modeling combined with chaotropic agents in the developing of new RPLC assays for protein-based therapeutics and vaccines.
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http://dx.doi.org/10.1016/j.jchromb.2021.122587DOI Listing
February 2021

Expanding the range of sub/supercritical fluid chromatography: Advantageous use of methanesulfonic acid in water-rich modifiers for peptide analysis.

J Chromatogr A 2021 Apr 9;1642:462048. Epub 2021 Mar 9.

School of Pharmaceutical Sciences, University of Geneva, CMU - Rue Michel-Servet 1, 1211 Geneva 4, Switzerland; Institute of Pharmaceutical Sciences of Western Switzerland, University of Geneva, CMU - Rue Michel-Servet 1, 1211 Geneva 4, Switzerland. Electronic address:

The aim of this work was to expand the applicability range of UHPSFC to series of synthetic and commercialized peptides. Initially, a screening of different column chemistries available for UHPSFC analysis was performed, in combination with additives of either basic or acidic nature. The combination of an acidic additive (13 mM TFA) with a basic stationary phase (Torus DEA and 2-PIC) was found to be the best for a series of six synthetic peptides possessing either acidic, neutral or basic isoelectric points. Secondly, methanesulfonic acid (MSA) was evaluated as a potential replacement for TFA. Due to its stronger acidity, MSA gave better performance than TFA at the same concentration level. Furthermore, the use of reduced percentages of MSA, such as 8 mM, yielded similar results to those observed with 15 mM of MSA. The optimized UHPSFC method was, then, used to compare the performance of UHPSFC against RP-UHPLC for peptides with different pI and with increasing peptide chain length. UHPSFC was found to give a slightly better separation of the peptides according to their pI values, in few cases orthogonal to that observed in UHPLC. On the other hand, UHPSFC produced a much better separation of peptides with an increased amino acidic chain compared to UHPLC. Subsequently, UHPSFC-MS was systematically compared to UHPLC-MS using a set of linear and cyclic peptides commercially available. The optimized UHPSFC method was able to generate at least similar, and in some cases even better performance to UHPLC with the advantage of providing complementary information to that given by UHPLC analysis. Finally, the analytical UHPSFC method was transferred to a semipreparative scale using a proprietary cyclic peptide, demonstrating excellent purity and high yield in less than 15 min.
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http://dx.doi.org/10.1016/j.chroma.2021.462048DOI Listing
April 2021

Charged aerosol detection in early and late-stage pharmaceutical development: selection of regressionmodels at optimum power function value.

J Chromatogr A 2021 Mar 12;1641:461997. Epub 2021 Feb 12.

Analytical Research & Development, MRL, Merck & Co., Inc., Rahway, NJ 07065, USA. Electronic address:

In recent years, the use of quantitative liquid chromatography (LC) coupled charged aerosol detection (CAD) for poor UV absorbing analytes in multicomponent mixtures has grown exponentially across academic and industrial sectors. The ballpark of previous LC-CAD reports is focused on practical applications, as well as optimization of critical parameters such as: response dependencies on temperature, nebulization process, analyte volatility, and mobile-phase composition. However, straightforward approaches to deal with the characteristic nonlinear response of CAD still scarce. A highly overlooked parameter is the power function value (PFV), whose optimization enables a detection signal that is more linear with higher signal-to-noise ratio (S/N) and lower relative standard deviation (RSD) of area counts. Herein, a systematic investigation of different regression models (log-log, first-and second-degree polynomial) by both interpolation and extrapolation process in conjunction with PFV optimization throughout the development of LC-CAD assays is reported. The accuracy of the results via interpolation is always good (< 5%) when operating in the vicinity of the optimum PFV regardless the regression model choice. On the contrary, extrapolation process only worked when applying log-log regression at the optimum PFV (accuracy <5%). This outcome indicates that a first-order regression via interpolation can be a safe and simple choice for quantitative LC-CAD in highly regulated laboratories (GLP, GMP, etc.). Whereas a straightforward extrapolation combined with log-log regression can enable the deployment of high-throughput LC-CAD assays, especially but not limited to laboratories where the synthetic process route is undergoing rapid change and optimization (medicinal chemistry, discovery, biocatalysis, process chemistry, etc.). This approach is crucial in developing quantitative LC-CAD assays for poor UV absorbing pharmaceuticals that are sensitive, precise, accurate and robust across early and late-stage pharmaceutical development.
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http://dx.doi.org/10.1016/j.chroma.2021.461997DOI Listing
March 2021
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