Bioweaponry of the Human Immune System

Written by Dr. Simon Lowe

Abstract

The following is a literature review of the application of monoclonal antibodies in the context of pharmacometric modelling, clinical drug development and biotech. Specifically, it examines their use in the treatment of auto-immune diseases, inflammatory illnesses and their role in pharmacokinetic pharmacodynamic modelling. This is due to their monovalent affinity.

Introduction

One of the core components of the immune system is the natural creation of antibodies, Y-shaped proteins, usually found on the outer surface of white blood cells (leukocytes), which bind to the specific molecular structure of an antigen, connecting structures on microorganisms, such as bacteria or viruses, often with the aim of inhibiting or killing them. Immune response, therefore, can be likened to a biological weapon, evolved to defend an animal against microscopic attacks. Like any weapon system, however, there is always a risk of damage of a collateral nature. In allergic reactions, specific antigens (called allergens), trigger abnormal overstimulation of an immune response to otherwise harmless substances. These overreactions can result in mild to moderate adverse reactions, such as inflammation, or they can result in severe or life-threatening events, such as anaphylaxis. Whilst natural immune systems are more often than not adept at distinguishing threats and regulating responses, it is nonetheless imperfect. Immunotherapy, therefore, works by augmenting it, either through controlled activation or suppression. In allergy medicines, for instance, the aim is usually to suppress or inhibit specific antibodies which target and bind to allergens, thereby reducing the risk of overreaction from the immune system.

Monoclonals & Allergic Asthma

One way to bioengineer a controlled immune response is through the synthesis of monoclonal antibodies (mAbs). These are antibodies which have been cloned from a unique leukocyte, and have monovalent affinity, the ability to only bind to a specific epitope, the part of the antigen recognised by an antibody. This allows for the creation of antibodies on mass which lack the ability to affect anything other than the assigned target threat. It is also possible to synthesise mAbs which target and inhibit immune cells, for the purposes of controlled immune suppression. In allergic reactions in mammals, antibodies, also known as immunoglobulin (Ig), specifically the isotype immunoglobulin E (IgE), play a major role in immune hypersensitivity, manifesting in multiple diseases such as allergic asthma, sinusitis, and common food allergies. Anti-IgE mAbs work by binding to IgE proteins, to suppress and regulate their capacity to induce an immune response. Notable examples of this are drugs like omalizumab (Xolair®) and ligelizumab. Xolair® has been marketed since the late 1990s, and is a recombinant DNA-derived humanised mAb which selectively binds to IgE and interrupts the allergic cascade by causing a reversible reaction to form a biologically inert complex. This can be seen in the biological explanation provided by Hayashi, Tsukamoto, Sallas and Lowe. This complex inhibits IgE bound to FcεRI receptors on basophils and mast cells, making it harder to degranulate effector cells and release inflammatory mediators like histamine, reducing the clinical signs and symptoms of atopic allergic asthma. [1]  Ligelizumab, similarly, as seen in the works of Arm, et al and Gauvreau, et al, [2] [3] is a novel high affinity anti-IgE antibody, promising greater efficacy and safety in allergen induced early asthmatic responses versus placebo and omalizumab.

Modelling and simulation, as seen in the work of Lowe and Renard has also indicated that omalizumab reduces production of IgE. This could mean that indefinite treatment may not be necessary, as over a number of years, total IgE should provide a means to monitor production after initial accumulation and guide individual treatment decisions. This was modelled on data from epidemiological studies and randomised, double-blind, placebo-controlled trials in patients with allergic asthma. [4]

Inflammatory Diseases

Monoclonals can also activate or suppress immune responses by affecting cytokines; these are proteins which facilitate the signalling of information between immune cells, e.g. the command function to begin an inflammatory response. In Rheumatoid arthritis (RA), interleukin-1β (IL-1β), a proinflammatory cytokine, signals upregulation of inflammatory reactions. Anti-IL-1β mAbs, by contrast, inhibit IL-1β with the aim of reducing the effects of inflammation in RA. This is has been investigated in the works of, for example, Oudhia, Lowe and Mager in respect to Canakinumab. [5] Canakinumab (Ilaris®) is an IL-1β targeted mAb and plays a prominent role in the treatment of autoinflammatory diseases, such as cryopyrin-associated periodic syndromes (CAPS), including Muckle-Wells syndrome, systemic juvenile idiopathic arthritis, gout, and rheumatoid arthritis. In addition, Ilaris® has contributed to efforts to overcome epistemological difficulties in IL-1β investigations, e.g. the fact that it is virtually undetectable in human plasma, something also cited by Lachmann, et al. [6] In vivo regulation of IL-1β in CAPS patients using Ilaris®, for example, has indicated it to be the only cytokine driving disease severity and duration of response to canakinumab, the fact that canakinumab has monovalent affinity, due to it being a mAb, helping to eliminate alternate explanations.

Monoclonals in Modelling and Simulation

Virtually any antigen can be targeted with a corresponding mAb, but pinpointing which molecular combination to develop requires an understanding of the biomarkers involved in a given disease and the pharmacokinetic pharmacodynamic (PKPD) profile of a potential candidate, which can be achieved via PKPD modelling and simulation. This may involve mathematical modelling prior to a clinical trial, or it may involve empirical data from an existing clinical trial or toxicology study. Examples of this can be found in manuscripts like the work of Dudal, et al, [7] in which integrated PKPD and immunogenicity profiling of an anti-CCL21 mAb in cynomolgus monkeys was able to show that clearance of the mAb-ligand complex is the most likely explanation for the expression of monoclonals, and that in human doses, where CCL21, a chemokine that may play a role in inflammatory diseases like atherosclerosis, multiple sclerosis and obstructive pulmonary disease, is expressed at 10-fold higher concentrations, large, frequent administration of mAbs would be required to supress it in a clinical setting.

This can be a critical finding when studying the posology of new medicines and when assigning and optimising safe doses and regimens. Another advantage of PKPD modelling specifically in mAb research is that mAbs can often be studied in vitro, providing experimental data which, in combination with corresponding modelling and simulation, can inform on important factors like drug toxicity, drug interactions and human specificity without the complexity, cost and ethical difficulties of in vivo testing with animals. This can also be combined with physiologically based pharmacokinetic modelling (PBPK). An example of this can be found in the work of Hu, Datta-Mannan and D’Argenio, [8] in which PBPK was used to predict mAb pharmacokinetics in humans from in vitro physiochemical properties, such as antibody-specific differences in paracellular transport due to convection and diffusion, explained by antibody heparin relative retention time.

As with any complex system, in simplicity lies reliability and a greater capacity for repetition, whilst in convolution lies both temporal and financial cost and a greater potential for error. Therefore, due to the complex nature of PKPD and PBPK profiling, one of the most important functions of pharmacometricians is also the mathematical simplification of modelling and simulation. An example of this can be found in the works of Elmeliegy, Lowe and Krzyzanski, [9] who used a previously published PBPK model and assumptions regarding rates of processes controlling mAb disposition to reduce the complexity of the model to a simpler circular model with central, peripheral and lymph compartments specifying elimination.

In Conclusion

Monoclonals not only represent a direct solution and an efficacious treatment in the field of immunotherapy, but also a useful tool and biomarker to help inform on the drug development process via modelling and simulation. This is because the monovalent affinity of mAbs allows for a level of simplicity on a biomolecular level which can be otherwise difficult to pin down, and this can be extremely useful both from a medicinal and from a scientific perspective.

References

  1. Hayashi, N., Tsukamoto, Y., Sallas, W. M. & Lowe, P. J. A mechanism-based binding model for the population pharmacokinetics and pharmacodynamics of omalizumab. Br. J. Clin. Pharmacol. 63, 548–561 (2007).
  2. Arm, J. P. et al. Pharmacokinetics, pharmacodynamics and safety of QGE031 (ligelizumab), a novel high-affinity anti-IgE antibody, in atopic subjects. Clin. Exp. Allergy 44, 1371–1385 (2014).
  3. Gauvreau, G. M. et al. Efficacy and safety of multiple doses of QGE031 (ligelizumab) versus omalizumab and placebo in inhibiting allergen-induced early asthmatic responses. J. Allergy Clin. Immunol. 138, 1051–1059 (2016).
  4. Lowe, P. J. & Renard, D. Omalizumab decreases IgE production in patients with allergic (IgE-mediated) asthma; PKPD analysis of a biomarker, total IgE. Br. J. Clin. Pharmacol. 72, 306–320 (2011).
  5. Ait-Oudhia, S., Lowe, P. J. & Mager, D. E. Bridging clinical outcomes of canakinumab treatment in patients with rheumatoid arthritis with a population model of IL-1β kinetics. CPT Pharmacometrics Syst. Pharmacol. 1, 1–10 (2012).
  6. Lachmann, H. J. et al. In vivo regulation of interleukin 1β in patients with cryopyrin-associated periodic syndromes. J. Exp. Med. 206, 1029–1036 (2009).
  7. Dudal, S. et al. Integrated pharmacokinetic, pharmacodynamic and immunogenicity profiling of an anti-CCL21 monoclonal antibody in cynomolgus monkeys. MAbs 7, 829–837 (2015).
  8. Hu, S., Datta-Mannan, A. & D’Argenio, D. Z. Physiologically Based Modeling to Predict Monoclonal Antibody Pharmacokinetics in Humans from in vitro Physiochemical Properties. MAbs 14, 1–16 (2022).
  9. Elmeliegy, M., Lowe, P. & Krzyzanski, W. Simplification of complex physiologically based pharmacokinetic models of monoclonal antibodies. AAPS J. 16, 810–842 (2014).