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 (1
D) and second (2
D) dimensions using LC
Simulator (ACD/Labs) software. Retention models for 1
D and 2
D 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 1
D and 2
D 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.
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 (1D‐UV); 2) automated peak trapping‐enrichment and buffer removal achieved through a sequence of H2O and D2O washes using an independent pump setup; and 3) a second dimension separation (2D‐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.
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.
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