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By: Chee M Ng

Chee M NgDrug discovery and development are very complex, highly interactive, and expensive processes. Translational science plays the essential role of moving molecules from discovery to development and eventually to patients: bench to bedside. However, translational science is not a miracle machine that will automatically turn junk into gold. Garbage in and garbage out!  It simply cannot develop the discovered molecules with undesirable properties into drugs for patients.

Furthermore, the mostly forward unidirectional view of translational science has its own limitation. For example, there is no systematic way to transform the data accumulating during the development process into knowledge for guiding the discovery of new molecules with better drug-like properties. Ideally, drug discovery and development should consist of highly interacting translational and reverse translational science approaches where the cycle continues back and forth between discovery and development, ad infinitum (Figure 1).

Figure 1: Highly interactive drug discovery and development processes consist of translational/reverse translational science approaches.

Figure 1: Highly interactive drug discovery and development processes consist of translational/reverse translational science approaches.

Therapeutic monoclonal immunoglobulin G (IgG) antibody (TMAb) is the best-selling class of biologics with global annual revenue of nearly $75 billion, which represents approx. half of the total sales of all biopharmaceutical products. It is anticipated that there will be approx. 70 TMAb products on the market by 2020, with combined worldwide sales of nearly $125 billion. However, TMAbs are complex large molecules produced by an expensive live-cell manufacturing process, and they require repeated intravenous or subcutaneous injection.  Therefore, improving the pharmacokinetic (PK) properties of TMAbs via extending circulating half-life is highly desirable in order to lower the cost and increase the patient’s quality of life by reducing the dose and frequency of repetitive injection.

The neonatal Fc Receptor (FcRn) plays an important role in the maintaining the homeostasis of IgG in mammals. It has been demonstrated that the PK of IgG antibodies can be influenced by altering FcRn binding affinity at acidic and physiological pH in preclinical and clinical studies. However, there is no clear mechanism of translating these findings in preclinical and clinical studies to guide the optimal design of the TMAb with desirable PK properties.

In our recent AAPS Journal paper, we elucidated a novel concept of model-guided drug discovery by transferring the knowledge in reverse direction from development to discovery (reverse translational science approach). Using the PK data from several humanized anti-VEGF IgG1 antibodies with a wide range of FcRn binding affinities, we developed a mechanism-based competitive binding model that can describe the effects of modulating FcRn binding affinity at both acidic and physiological pH on the PK of TMAb when both endogenous and exogenous IgG antibodies are competing for the same FcRn receptor. The developed model allowed us to construct detail quantitative FcRn binding affinity and PK relationship of TMAbs for providing important biological insights to better understand the effects of FcRn binding affinity on the important PK parameters of these molecules. The mechanism-based competitive binding model also serves as an important model-based drug discovery platform to guide the discovery of the future generation of anti-VEGF IgG1 antibodies and other TMAbs with optimized PK properties.

Chee M Ng, Pharm.D., Ph.D., FCP, is an associate professor of the Department of Pharmaceutical Science, University of Kentucky at Lexington. Chee has been a fellow of American College of Clinical Pharmacology since 2009.