Revisiting column selectivity choices in ultra-high performance liquid chromatography-Using multidimensional analytical Design Spaces to identify column equivalency.

Current guides and column selection system (CSS) platforms can provide some helpful insights with regard to the selection of alternative phases. Their practical reliability however, can also turn out to be questionable, especially considering the lack of detailed specifics, such as a clear definition of points of equivalence-appropriate running conditions under which the given analytical mixture can be satisfactorily resolved on various stationary phases. In this context, the use of multivariate modeling tools can be highly beneficial. These tools, when applied systematically, are ideal for uniquely characterizing complex LC-separation systems, a fact supported by numerous peer-reviewed papers. Revisiting our earlier work [1] and the applied systematic workflow [2], we used a Design Space modeling software (DryLab), with the main focus on building and comparing 3-dimensional separation models of amlodipine and its related impurities to identify shared method conditions under which columns are conveniently interchangeable. Our study comprised 5, C18-modified ultra-high performance liquid chromatography (UHPLC) columns in total, in some cases with surprising results. We identified several equivalences between the Design Spaces (DSs) of markedly different columns. Conversely, there were cases where, despite the predicted similarities in column data, the modeled DSs demonstrated clear differences between the selected stationary phases.

The Applicability of Chromatographic Retention Modeling on Chiral Stationary Phases in Reverse-Phase Mode: A Case Study for Ezetimibe and Its Impurities.

Mechanistic modeling is useful for predicting and modulating selectivity even in early chromatographic method development. This approach is also in accordance with current analytical quality using design principles and is highly welcomed by the authorities. The aim of this study was to investigate the separation behavior of two different types of chiral stationary phases (CSPs) for the separation of ezetimibe and its related substances using the mechanistic retention modeling approach offered by the Drylab software (version 4.5) package. Based on the obtained results, both CSPs presented with chemoselectivity towards the impurities of ezetimibe. The cyclodextrin-based CSP displayed a higher separation capacity and was able to separate seven related substances from the active pharmaceutical ingredient, while the cellulose-based column enabled the baseline resolution of six impurities from ezetimibe. Generally, the accuracy of predicted retention times was lower for the polysaccharide CSP, which could indicate the presence of additional secondary interactions between the analytes and the CSP. It was also demonstrated that the combination of mechanistic modeling and an experimental design approach can be applied to method development on CSPs in reverse-phase mode. The applicability of the methods was tested on spiked artificial placebo samples, while intraday and long-term (2 years) method repeatability was also challenged through comparing the obtained retention times and resolution values. The results indicated the excellent robustness of the selected setpoints. Overall, our findings indicate that the chiral columns could offer orthogonal selectivity to traditional reverse-phase columns for the separation of structurally similar compounds.

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

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.

Expediting the chromatographic analysis of COVID-19 antibody therapeutics with ultra-short columns, retention modeling and automated method development.

The COVID-19 pandemic necessitated the emergency use authorization (EUA) of several new therapeutics and vaccines. Several monoclonal antibodies (mAbs) were among those authorized for use, and they have served a purpose to provide passive immunity and to help minimize dangerous secondary effects in at-risk and hospitalized patients infected with SARS-CoV-2. With an EUA submission, scientific data on a drug candidate is often collected near simultaneously alongside drug development. In such a situation, there is little time to allow misguided method development nor time to wait on traditional turnaround times. We have taken this dilemma as a chance to propose new means to expediting the chromatographic characterization of protein therapeutics. To this end, we have combined the use of automated, systematic modeling and ultrashort LC columns to quickly optimize high throughput RP, IEX, HILIC and SEC separations for two COVID-19-related mAbs. The development and verification of these four complementary analytical methods required only 2 days of experimental work. In the end, one chromatographic analysis can be performed with a sub-2 min run time such that it is feasible to comprehensively characterize a COVID-19 mAb cocktail by 4 different profiling techniques within a 1-hour turnaround time.

Simultaneous optimization of mobile phase composition and pH using retention modeling and experimental design.

Chromatographic methods are progressing continuously. Increasing sample complexity and safety expectations lead to higher regulatory demands, hence challenges in liquid chromatography analysis are rising, even today, when faster and faster chromatographic systems are extensively employed and become widely accessible for successful method development. The goal of this study was to investigate the impact of mobile phase influences as important factors of selectivity tuning in method development. This would mitigate mobile phase-related robustness issues throughout the method's lifecycle. To discover and understand these effects, a new module of chromatographic modeling software DryLab (ver. 4.3.4. beta) was introduced and a special experimental design (DoE) was tested, allowing the simultaneous optimization of solvent-dependent parameters, such as gradient time (tG), ternary eluent composition (tC) and pH, requiring 18 input experiments (2 × 3 × 3 = 18). Additionally, the model creation, using a UPLC system and a narrow bore column (50 × 2.1 mm), the entire experimental work could be finished in 2-3 hours. To demonstrate the applicability of this new design, amlodipine and its related pharmacopoeia impurities (A-H) were subjected to be used in a case study. Predicted vs. Experimental (or Verification) runs showed excellent agreement, average retention time deviations were typically less than 1 s. Modelled robustness testing was also performed, elucidating all important mobile phase and instrument parameters that could influence a method's lifetime performance. Furthermore, as the in silico robustness testing is the least time consuming part of the method development process, it can be used extensively to evaluate robustness even at the very early part in stage 1 of the Method Life Cycle (MLC).