Dr. Richard L. Easton, Technical Director, BioPharmaSpec
The FDA  and EMA  guidelines are both comprehensive articles giving clear guidance on what the regulatory agencies expect from structural characterization studies. The use of state-of the-art instrumentation and techniques is expected but what is also clearly required is the use of orthogonal techniques in the structural comparability assessments.
This requirement comes from the need to give a firm structural foundation to any claims, such as the ability to produce consistent batches or biosimilarity between your biosimilar and the reference drug. Thus, the use of characterization techniques that can verify and support conclusions drawn from other techniques will result in a robust package of structural data in which both the manufacturer and the regulatory authorities can have confidence.
In Part 2 of this blog, we describe the second and third of three examples of orthogonality within required analyses, each examining different aspects of protein structure. Read Part 1, discussing orthogonality in glycan analysis, here.
The C-terminal Lysine on the heavy chain of monoclonal antibodies is a very common post translational modification that takes place to varying degrees on different mAbs . This modification can readily be detected in a peptide map through the presence of C-terminal heavy chain peptides with and without Lysine. Fragment ion information that may be concurrently generated in the peptide mapping exercise (through the use of, for example, Q-TOF type mass spectrometers) serves to support this assignment.
As an orthogonal identification of this modification, imaging capillary isoelectric focussing (icIEF) can be performed. In this experiment, charged isoforms of the sample will migrate to their isoelectric points. Since Lysine is a basic residue, species that have the C-terminal Lysine will migrate to a more basic pI compared to those that do not. Since mAbs contain two heavy chains this can result in three peaks: 1) mAb with no C-terminal Lysine, 2) mAb with a C-terminal Lysine on one heavy chain and 3) mAb with a C-terminal Lysine on both heavy chains. Furthermore, pre-treatment of the sample with the enzyme carboxypeptidase B will remove any remaining Lysine and thus eliminate the “with Lysine” signals when run in a second icIEF experiment, thus not only confirming the C-terminal Lysine modification but also serving to identify the nature of the basic peaks seen in the original icIEF analysis (Figure 2).
Figure 2: Imaged capillary IsoElectric focussing (icIEF) electropherogram of an IgG prior to (A) and following (B) treatment with carboxypeptidase B to remove C-terminal Lysine from the heavy chain. The Lyine containing components (arrowed) are absent following treatment with the enzyme. The components at more acidic pI are deamidated species. The peaks labelled “I” and “M” are intact monoclonal antibody with no heavy chain C-terminal Lysine and a pI marker respectively.
A final example of the use of orthogonality is in the area of Higher Order Structure (HOS) analysis. The assessment of secondary and tertiary structure is a regulatory requirement and is examined using techniques that will assess different aspects of these higher orders of structure such as Circular Dichroism (CD), Fourier Transform-Infra Red (FT-IR) spectroscopy and Nuclear Magnetic Resonance (NMR). Each technique will probe different aspects of the higher order structure and generate data dependent on the nature of the technique used, each having its own area of strength (such as alpha helix or beta sheet detection)
HOS analysis across these various techniques is a case of the sum being more than the parts i.e. a combination of the data obtained provides a very robust orthogonal assessment of secondary and tertiary structure and an in-depth assessment of comparability. In any structural study, regardless of whether it is an assessment of biosimilarity, data generation in this manner is becoming the regulatory expectation in terms of investigating HOS.
In summary, the use of orthogonal techniques for structural characterization not only serves to give a robust overall package of information on the nature of the product but will cross-verify conclusions drawn from within different data sets, adding to the robustness of the package as a whole.