Managing Immunogenicity Using Quantitative Systems Pharmacology

Managing Immunogenicity Using Quantitative Systems Pharmacology

The Challenge of Immunogenicity in Biologics Drug Development

Biologic drug development is a rapidly evolving sector in the biopharmaceutical industry. Biologically-based therapeutic drugs comprise monoclonal antibodies (MAbs), vaccines, recombinant hormones and proteins, antibody-drug conjugates, RNAi, antisense, blood factors, and other large molecules. Although the success of biologics has been demonstrated, there are inherent operational and technological challenges associated with this complex class of drugs. One of these challenges— immunogenicity—has become a key area of regulatory interaction. Immunogenicity (IG) is defined by the FDA as the propensity of the therapeutic protein to generate immune responses to itself and to related proteins or to induce immunologically-related adverse clinical events. In a recent FDA review of 121 approved biological products, 89% of the products had reported immunogenicity, and in 49% of the cases, IG impacted the drug’s efficacy.

Despite being “biological,” most therapeutic proteins are synthetic. Even fully humanized biologicals exhibit properties that can potentially be recognized as “non-self” and therefore have an increased risk of promoting an antigenic response. Although IG is clearly an important issue, the understanding of the phenomenon is limited. A big gap in understanding IG has been trying to determine how therapeutic proteins interact with the body’s immune system

The IG response typically takes place in the form of the production of anti-drug antibodies (ADAs). ADAs may be an inevitable consequence of using biological drugs. But a given ADA level with respect to its binding may be manageable provided certain parameters are correctly optimized (e.g., dose, frequency, route of administration, target patient population, tolerability strategy, co-medications). Finding the optimum parameters for each drug will require a quantitative approach, hence the interest in Quantitative Systems Pharmacology (QSP) modeling.

Quantitative Systems Pharmacology—Bridging Pharmacokinetics and Systems Biology

QSP is a relatively new discipline with enormous potential to improve pharma R&D productivity and inform decision-making across the drug development process from early discovery to Phase 3. QSP combines computational modeling and experimental data to examine the relationships between a drug, the biological system, and the disease process.

QSP provides an in silico framework for constructing mechanistic, mathematical models of drug action. QSP focuses on the area between PK/PD and systems biology; it translates PK or exposure into pharmacological effect and builds on gaining insights from pharmacometric, PK/PD, and PBPK approaches with systems biology models of biological and biochemical processes. QSP models can be used to design first-in-human clinical trials, inform the mechanisms of drug efficacy and safety, confirm drug target binding and modulation, and as an approach that can impact preclinical development.

Using a Quantitative Modeling Approach to Better Understand Immunogenicity

Managing IG is a challenge not just in drug development but also in manufacturing and, in particular, patient care.  In part, any immune response to a biological is, in part, related to the properties of the molecule itself and can be controlled by design to some extent. However, the data show that IG is complex and heterogeneous, depending, for example, upon the initial state of the immune system. Many factors contribute to the complexity of immunogenicity including (1) limited understanding on the impact of ADAs on drug pharmacology, (2) route and frequency of drug administration, (3) duration of drug treatment, (4) formation of aggregates, and (5) co-administration of immunosuppressive agents.

A QSP-based approach can be used to predict and better manage immunogenicity and guide clinical and regulatory decision-making in biologics drug development.

Using QSP Models to Predict and Manage Immunogenicity of Therapeutic Proteins

The development of IG to treatment with a biologic range from mild transient antibody response (with no apparent clinical manifestation) to life-threatening reactions can have a profound effect on clinical outcome with reduced efficacy. The high prevalence of IG not only impacts the clinical utility of existing treatments for patients but also the development of novel biologicals. Therefore, IG will be associated with an increasingly large proportion of the global pharmaceutical development portfolio and will feature as a significant and recurring topic in interactions between pharmaceutical industry sponsors and regulatory agencies. A mechanistic QSP approach is required to understand the issue and to manage it in the context of drug development and decision-making.

Creating a Consortium: Tackling Immunogenicity through Expertise and Cooperation

Certara formed a QSP IG Consortium in 2017 that brings together leading biopharmaceutical companies in a pre-competitive environment to cooperatively develop an Immunogenicity Simulator based on state-of-the art QSP science and methods. The IG Simulator will predict IG and its impact on compound PK, efficacy, and safety in diverse patient populations in drug discovery and development. This new tool will enable sponsors to manage immunogenicity by adjusting the biologic dose, route of administration, patient population and/or co-medications.

To learn more on how QSP can be leveraged by pharma organizations to improve R&D productivity, watch this webinar by my colleague Dr. Cesar Pichardo.

Piet van der Graaf

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Piet van der Graaf

Piet van der Graaf

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Piet has 15 years’ experience working in the pharmaceutical industry at Synthélabo (Sanofi) and Pfizer, where he held leadership positions in Discovery Biology, Pharmacokinetics & Drug Metabolism (PDM) and Clinical Pharmacology/Pharmacometrics. He obtained his PharmD from the University of Groningen, The Netherlands, and a PhD in quantitative pharmacology from King’s College London, UK, and was elected as a Fellow of the British Pharmacological Society. He is also Editor-in-Chief of CPT: Pharmacometrics & Systems Pharmacology. He brings his considerable skill and experience to our projects and contributes to the strategic development of Certara.