Analytical Chemistry 2019

Chromatography is the first choice in separation technique for a wide selection of applications, across many fields, ranging from simple to highly complex extracts.  The main rationale for its use is the resolution power, or peak capacity, and ultimately the separation recording of an extract into its distinct components. Thus, increasing the performance of chromatography is inevitably linked to improvements in peak capacity. With this goal in mind, innovative researchers lead the introduction of sub 2 um particles (UPLC), core-shell particles, and monolithic stationary phases, and produced attainable performance of peak capacity up to a thousand for 1D LC separation.  However, from Giddings work, the actual peak capacity of any given single dimensional separation is still far below the theoretical calculation.

With new technologies, newer solutions emerge for the most difficult of applications.  The concept of hyphenated systems or multi-dimension chromatography was designed, at first, for solving complex analyses. The peak capacity, or chromatographic separation power, can be increased by combining several separation dimensions (in most cases, two) each using optimized conditions for maximum resolution.  The main challenge is the transfer of closely resolved analytes from the primary resolving dimension (PRD) to the secondary resolving dimension (SRD).  Nowadays, multi-dimensional-chromatography is gaining acceptance for both targeted and untargeted analysis of complex mixtures.  However, at its beginnings, the technique was perceived as highly complex in terms of the necessary hardware and difficulty to gain in-depth insight for practical usage.  

In recent years, advances in software control enabled hyphenated instrumentation platforms with enhanced performance and relative ease of use. Additionally, multi-fluidic circuits have been shown to decrease the cost and time of sample preparation, improve chromatographic peak shape, and enhance sensitivity.  In this presentation, several forensic and environmental applications will be discussed to fully demonstrate the capabilities of modern 2D LC/MS/MS workflows.  

Learning objectives:

1. Novel extraction techniques for wide range of applications
2. Exploring the world Multidimension liquid chromatography and the myth of a technique difficult to master.  

Mycotoxins are secondary fungal metabolites produced by mold that may be found in food or feed. They can cause severe health problems in humans and animals, and can result in significant economic losses. Among the hundreds of toxic mycotoxins, aflatoxins, fumonisins, deoxynivalenol, ochratoxin A, HT-2 toxin, zearalenone, and T-2 toxin are considered as a major concern for corn, wheat, peanuts and other agricultural products. LC-MS has become the standard and is now widely used for routine mycotoxin analysis and identification.  One of the challenges faced by LC-MS techniques is the matrix effects caused by the use of electro-spray ionization (ESI).  Generally, sample preparation, chromatographic and calibration techniques are the common strategies for reducing the negative effects of matrix effects.  Standard addition, matrix matching, and stable isotope dilution assay (SIDA) are all possible calibration solutions.  In this work, a quick “dilute-filter-shoot” method was used for sample preparation.  A seven-minute LC-MS/MS method using a biphenyl phase column was developed and verified for quantifying twelve mycotoxins in four commodities: corn, peanut butter, brown rice, and corn & wheat mixed.  Both SIDA and matrix matched calibration methods were applied, compared, and evaluated in terms of recovery, efficiency, advantages, and limitations.

The emergence of medicinal hemp represents a new frontier in medicine; one which has tremendous potential for patients struggling with a wide array of illnesses. However, in order to fully capitalize on the therapeutic benefits that are provided by the native hemp constituents, its chemical composition must be first characterized. This presentation will cover the various classes of constituents found in hemp, as well as the analytical tools that are required for their characterization.

With annual sales exceeding $145 billion, growth of biologic therapeutics is outpacing that of small-molecule drugs, and the development, manufacture, and delivery of biologics presents very different challenges. Among biologics, monoclonal antibodies (mAbs) have become particularly attractive for drug development, since it is possible to select and duplicate antibodies that bind with high affinity and specificity to most any target, and since biomanufacturing platforms for mAbs can be reused for more than one therapeutic.

Characterization of biologics and their formulations requires monitoring high order structure (HOS), since misfolding or aggregation can lead to loss of efficacy or harmful immune responses. There are many circumstances during the development and manufacturing of a biologic where high-resolution characterization of HOS is valuable, including in evaluation of similarity or stability.  Heteronuclear Nuclear Magnetic Resonance (NMR) is a practical multi-attribute method that can be applied directly to intact biologics and their formulations to meet this important measurement need.

We have previously demonstrated practical approaches to generate two-dimensional 1H/13C NMR spectra at natural isotopic abundance for molecules as large as intact monoclonal antibodies, and the robustness and precision of these methods has recently been confirmed by an international multi-laboratory study with participation from 25 academic, pharmaceutical, governmental, and regulatory organizations.

Using measurements on the IgG1k NIST reference mAb (NISTmAb), we demonstrate that small variations of structure and interaction can be revealed and classified by direct computational analysis of the shapes of such spectra, as an alternative to interactive analysis and assignment of spectral features, paving the way for NMR HOS characterization via chemometrics and machine learning that is both objective and automated.