Analytical quality by design-compliant retention modeling for exploring column interchangeabilities in separating ezetimibe and its related substances

https://doi.org/10.1016/j.chroma.2022.463494Get rights and content

Highlights

  • Computer-aided chromatographic method development for the separation of ezetimibe and its related substances.

  • Design space comparison - new approach for testing column interchangeability.

  • Mechanistic retention modeling and a 3D experimental design based approach via DryLab® software.

  • Baseline separation of the analytes achieved on nine different stationary phases with various chemistries.

  • Multivariate in silico robustness testing performed to identify the critical method parameters and to set up control strategy for routine applications.

  • General specification with suitable working point yielding similar results on six stationary phases.

  • Interchangeability of the columns proved in early phase method development.

Abstract

There are several potential advantages of using experimental design-based retention modeling for chromatographic method development. Most importantly, through the model-delivered systematic understanding (Design Spaces), users can benefit from increased method consistency, flexibility and robustness that can efficiently be achieved at lesser amount of development time. As a result, modeling tools have always been great supplementary assets and welcomed by both the pharmaceutical industry and the regulatory authorities. Most recently published chapters of ICH however – Q2(R2) and Q14 (both currently drafts) – evidence a further paradigm shift, specifying the elements of model-based development strategies in the so-called “enhanced approach”.

The main aim of this study was to investigate the impact of stationary phase chemistries on chromatographic method performance in the application example of ezetimibe and its related substances. A commercial modeling software package (DryLab®) was used to outline three-dimensional experimental design frameworks and acquire model Design Spaces (DSs) of 9 tested columns. This was done by performing 12 input calibration experiments per column, systematically changing critical method parameters (CMPs) as variables such as the gradient time (tG), temperature (T) and the ternary composition (tC) of the mobile phase. The constructed models allowed studying retention behaviors of selected analytes within each separation systems.

In the first part of our work, we performed single optimizations for all nine stationary phases with substantially different surface modifications based on their highest achievable critical resolution values. For these optimum points in silico robustness testing was performed, clearly showing a change of CMPs, depending on the column, and specified optimum setpoint.

In the second part of our work, we simultaneously compared the three-dimensional virtual separation models to identify all method parameter combinations that could provide at least baseline separation (Rs, crit.>1.50). These overlapping areas between the models described a common method operational design region (MODR) where columns were considered completely interchangeable – in terms of their baseline resolving capability – regardless of their exact physicochemical properties. A final optimized, column-independent working point within the common MODR was selected for verification. Indeed, experimental chromatograms showed excellent agreement with the model; all columns in the common condition were able to yield critical resolution values higher than 2.0, only their retentivity (elution window of peaks) was found different in some cases.

Our results underline that a profound understanding of the separation process is of utmost importance andthat in some cases, adequate selectivity is achievable on various stationary phases.

Introduction

Ezetimibe is an azetidinone derivative, chemically described as (3R,4S)−1-(4-fluorophenyl)−3-[(3S)−3-(4-fluorophenyl)−3-hydroxy-propyl]−4-(4-hydroxyphenyl)azetidin-2-one and acts as a cholesterol absorption inhibitor by physically interacting with cholesterol transporters at the brush border of the small intestine, decreasing the level of cholesterol in the bloodstream. Most notably, ezetimibe was the first agent of a novel class of selective cholesterol uptake inhibitors, which has been widely used since then in both oral monotherapy and in combination with statins to reduce the risk of harmful cardiovascular events [1], [2], [3].

Up to this date, several synthetic pathways have been described in the literature for the synthesis of ezetimibe [4,5] implying the possibility of forming various process-related impurities (starting materials, by-products or intermediates) in the final product. Other sources of organic impurities result from the ongoing degradation of the active pharmaceutical ingredient (API) during manufacturing and/or storage that might affect the efficacy and safety of the final drug product. Detection and quantification of impurities which may be present in the API and/or pharmaceutical product are strictly regulated by the authorities [6,7]. Stability indicating analytical procedures with high selectivity and sensitivity are therefore crucial in effective management of impurity profiling to support pharmaceutical development, but also to ensure routine quality control during manufacturing. Among other analytical methodologies, reverse-phase high performance liquid chromatography (RP-HPLC) has become one of the most popular techniques in impurity profiling [8].

Given the high number of synthetic routes and the multitude of possible impurities, there are several RP-HPLC methods for ezetimibe and its achiral impurities described in the literature, which also show a great diversity in their specifications both in their stationary- and mobile-phase conditions [9]. In this sense, the recent review by Rocha et al. provides an excellent overview of analytical methodologies developed by different groups, reflecting the huge confusion of chromatographic method parameters caused by an unsystematic, trial-and-error-based development approach. The significant differences appear due to the applied stationary phases with various chemistries such as C8, C18, pentafluorophenyl (PFP) or phenyl-hexyl types, and due to the different elution modes, including both isocratic and gradient elution with diverse profiles. There are also differences in the mobile phases applied, such as the organic modifier employed (ACN or MeOH or mixtures of these solvents in different proportions and in some cases, a low amount of tetrahydrofuran is also added), the aqueous part of the mobile phase (ultrapure water, diluted aqueous phosphoric or perchloric acid, phosphate- or acetate-based buffer systems with various pH values) [9]. There are also numerous methods that do not appear in this review, such as the method, developed by Desai et al., which could quantify six achiral related substances of ezetimibe in the presence of simvastatin and its impurities [10]. Another method described by Luo et al. was suitable for the simultaneous quantification of eleven related substances, and the effect of the stationary phase chemistry on the separation process was investigated by comparing two different columns [11].

Considering the high number of widely different analytical methods one can use, it can be difficult to justify the actual suitability of one method over another. In addition, these methods were often developed using the traditional “one-factor at a time” (OFAT) approach, which frequently lacks the model-derived systematic understanding between all system components. In contrast to the OFAT approach, experimental design-based, multivariate methodologies enable the simultaneous variation of all investigated CMPs and tracking their effect upon selected method performance indicators. As a result, deeper method understanding can be obtained with significantly fewer experimental runs while mutual interactions between variables can also be detected [12].

Drylab 4 is a commercial software suite that follows this modeling design concept by effectively integrating Design-of-Experiments (DoE) along with chromatographic fundamentals, such as the solvophobic theory and Linear Solvent Strength Model (LSSM) to model and visualize complex chromatographic interdependencies present in HPLC separation systems [13], [14], [15], [16], [17]. These virtual models are highly predictive and flexibly suited to be employed for extensive in silico studies, such as gradient optimization, robustness quantification – to identify the CMPs as sources of variability – and to facilitate method transfers. The validity of this modeling approach has extensively been described by many authors [13,14,16,18,19] and also getting a spotlight in the recently published ICH guidelines that commit to create a common platform along with well-defined terminologies for analytical quality by design (AQbD). In this sense, having observed the obvious benefits for manufacturing processes, industry practitioners have already adapted QbD-elements with success to design analytical methods “with the end in mind”[20]. Among others, a general aspect of AQbD is to include tolerance limits of the parameters involved along with other systematic elements such as a Design of Experiments (DoE) creating each DS. This greatly facilitates risk-, and knowledge-based decision making, which in the long-term can not only minimize but effectively prevent out-of-specification (OoS) investigations [21], [22], [23], [24], [25]. Regulatory intentions to support this by incorporating pharmaceutical product lifecycle elements and establish post-approval changes on a risk-, and knowledge base are clearly represented in the ICH Q12 guideline [26]. Other, current draft quality guidelines – Q14 and Q2(R2) – describe technical enablersd advantages using the “enhanced approach” in the analytical development [27,28]. By gaining understanding of the relationships between analytical variables and measured responses, the DS can be established, which enables easier validation and flexible movements within the parameter ranges. In other words, when working within this multidimensional MODR changes to the workpoint do not require additional regulatory notification. Thus, following such AQbD approaches, reduces the need of regulatory oversight, builds trust and leads to a more effective communication between applicant and regulator [27,29,30].

The other relevant chapter on Lifecycle Management, USP 〈1220〉> also points to this direction, by fostering a well-structured holistic way of analytical procedure development. It also exemplifies modeling approaches – mechanistic and empirical – also emphasizing that either may be appropriate depending on the intended use of the analytical procedure and the desired model accuracy [31].

Using such DS-modeling methodology, an alternative to the European Pharmcopoeia method for the impurity analysis of albendazole was developed and described in our earlier study [32]. Prior to that Kormány et al. in their work had already leveraged the advantages of 3D model DS to find optimum separation conditions of amlodipine and seven impurities, described in the European Pharmacopoeia (Ph. Eur.) on nine different C18-type columns. Initially, by fixing method conditions to a generic approach, only one column could offer baseline resolution. Using 3D-models however, it was then clearly shown that all columns could provide excellent baseline separations, but differences arose in their optimum setpoint conditions and their robust separation capability [33]. This methodology was extended to successfully separating multi-API (amlodipine and bisoprolol) samples along with their specified impurities on 24 out of 25 state-of-the-art Ultra-High-Performance Liquid Chromatography (UHPLC) columns [34]. Similar 3D methodology was published by Rácz et al., also visualizing column MODRs to discover batch‑to‑batch differences of commercial bridged ethylene-hybrid (BEH) columns [35].

In another work, sub-2-μm column entities differing in their residual silanol activity were subjected to 3D modeling. Great differences were observed and intelligent software algorithms – Design Space Comparison (DSC) module – were introduced allowing 2-, and 3D DSs to be simultaneously aligned and cross-sections of overlapping baseline-separating areas manifesting a common MODR were visualized. This could help identify interchangeable regions across various separation systems and alleviate the burden around replacement HPLC-column selection [36]. More recently, the same group published a new impurity profiling method for Terazosin that was developed with this approach and published as part of the official European Pharmacopeia monograph. Remarkably, with the aid of model DSs, overlapping MODRs were found and equivalent setpoints on competitive pentafluoro phases – two batches of a primary and a replacement column could be specified [14].

In the present work, Dryab® was used with the focus on building 3D separation models of ezetimibe and its related achiral impurities on nine RP columns. Based on only twelve input experiments per column, we investigated the impact of all chromatographically relevant method parameters – such as gradient time, column temperature, ternary composition of the mobile phase and other instrument factors – on the efficiency of the separation process. The acquired multivariate DSs provided in-depth characterization of each separation systems with certain tolerances of relevant method parameters, as fostered by the AQbD methodology. Furthermore, using the DSs as comparison tools, we identified both dissimilar and interchangeable areas in their MODRs. All stationary phases were first evaluated individually to determine their optimum working points for each column and finally, a common setpoint was also established and experimentally verified on all nine columns to prove the interchangeability of these stationary phases.

Section snippets

Chemicals

Gradient grade methanol (MeOH), acetonitrile (ACN) and the chromatographic grade phosphoric acid (85%) were purchased from Merck (Darmstadt, Germany). The aqueous part of the mobile phase during this study was water with 0.1% phosphoric acid. The ultrapure water was freshly prepared each day by a MilliPore MilliQ Integral 10 (Merck Millipore, USA) equipment. Ezetimibe and its impurities (ezetimibe diol, desfluoro ezetimibe, meta-fluoroaniline analog, ezetimibe ketone, ezetimibe THP

Preliminary experiments – onedimensional tC model

The first step of the study was the selection of the ideal experimental design framework. DryLab® can simultaneously handle up to three experimental variables at a time, either using a tG-T-tC or the tG-T-pH model. From the practical point of view, the only difference between the two designs is that in the first case the organic modifier composition (tC) of the mobile phase (eluent B) is investigated at three levels, while in the second the pH of the aqueous part of the mobile phase (eluent A)

Conclusion

In our work, it was shown that the experimental design approach and method modeling tools (like DryLab® ) are particularly useful tools in chromatographic method development, providing essential information to profoundly understand the complex separation processes, in accordance with existing and advocated AQbD-principles. Following a systematic development methodology, in this work, the separation of ezetimibe and its studied impurities could easily be achieved on multiple columns. Not only

CRediT authorship contribution statement

Elek Ferencz: Investigation, Validation, Software, Writing – original draft. Arnold Zöldhegyi: Software, Visualization, Writing – original draft. Éva-Katalin Kelemen: Resources, Project administration. Mona Obreja: Supervision, Resources. Melinda Urkon: Formal analysis, Visualization. Emese Sipos: Supervision, Writing – review & editing. Gergő Tóth: Methodology, Writing – review & editing. Imre Molnár: Methodology, Writing – review & editing. Zoltán-István Szabó: Conceptualization, Methodology,

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

This work was supported by the Collegium Talentum Programme of Hungary and funded by the Department of Medical and Pharmaceutical Sciences of the Transylvanian Museum Society and Semmelweis University, Faculty of Pharmacy, Hungary. This work was supported by the University of Medicine, Pharmacy, Science and Technology „George Emil Palade“ of Târgu Mureș, Research Grant number 10127/3/17.12.2020 (Z.-I. Sz.).

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