Evaluating a Multiscale Mechanistic Model of the Immune System to Predict Human Immunogenicity for a Biotherapeutic in Phase 1

An overview of immunotoxicity in drug discovery and development

The immune system is one of the common targets of drugs' toxicity (Immunotoxicity) and/or efficacy (Immunotherapy). Immunotoxicity leads to adverse effects on human health, which raises serious concerns for the regulatory agencies. Currently, immunotoxicity assessment is conducted using different in vitro and in vivo assays. In silico and in vitro human cell-based immunotoxicity assays should also be explored for screening purposes as these are time and cost effective as well as for ethical reasons. For in vivo studies, tier 1-3 assessments (Tier 1: hematology, serum globulin levels, lymphoid organ's weight and histopathology; Tier 2: immunophenotyping, TDAR and cell mediated immunity; and Tier 3: host resistance) should be used. These non-clinical in vivo assessments are useful to select immunological endpoints for clinical trials as well as for precautionary labeling. As per regulatory guidelines, adverse immunogenicity information of drug should be included in product's labeling to make health care practitioner aware of safety concerns before prescribing medicines and patient management (USFDA, 2022a, 2022b). This review mainly focuses on the importance of immunotoxicity assessment during drug discovery and development.

Assessing the performance of QSP models: biology as the driver for validation

Validation of a quantitative model is a critical step in establishing confidence in the model's suitability for whatever analysis it was designed. While processes for validation are well-established in the statistical sciences, the field of quantitative systems pharmacology (QSP) has taken a more piecemeal approach to defining and demonstrating validation. Although classical statistical methods can be used in a QSP context, proper validation of a mechanistic systems model requires a more nuanced approach to what precisely is being validated, and what role said validation plays in the larger context of the analysis. In this review, we summarize current thoughts of QSP validation in the scientific community, contrast the aims of statistical validation from several contexts (including inference, pharmacometrics analysis, and machine learning) with the challenges faced in QSP analysis, and use examples from published QSP models to define different stages or levels of validation, any of which may be sufficient depending on the context at hand.

Characterization and root cause analysis of immunogenicity to pasotuxizumab (AMG 212), a prostate-specific membrane antigen-targeting bispecific T-cell engager therapy

Introduction

In oncology, anti-drug antibody (ADA) development that significantly curtails response durability has not historically risen to a level of concern. The relevance and attention ascribed to ADAs in oncology clinical studies have therefore been limited, and the extant literature on this subject scarce. In recent years, T cell engagers have gained preeminence within the prolific field of cancer immunotherapy. These drugs whose mode of action is expected to potently stimulate anti-tumor immunity, may potentially induce ADAs as an unintended corollary due to an overall augmentation of the immune response. ADA formation is therefore emerging as an important determinant in the successful clinical development of such biologics.

Methods

Here we describe the immunogenicity and its impact observed to pasotuxizumab (AMG 212), a prostate-specific membrane antigen (PSMA)-targeting bispecific T cell engager (BiTE®) molecule in NCT01723475, a first-in-human (FIH), multicenter, dose-escalation study in patients with metastatic castration-resistant prostate cancer (mCRPC). To explain the disparity in ADA incidence observed between the SC and CIV arms of the study, we interrogated other patient and product-specific factors that may have explained the difference beyond the route of administration.

Results

Treatment-emergent ADAs (TE-ADA) developed in all subjects treated with at least 1 cycle of AMG 212 in the subcutaneous (SC) arm. These ADAs were neutralizing and resulted in profound exposure loss that was associated with contemporaneous reversal of initial Prostate Surface Antigen (PSA) responses, curtailing durability of PSA response in patients. Pivoting from SC to a continuous intravenous (CIV) administration route remarkably yielded no subjects developing ADA to AMG 212. Through a series of stepwise functional assays, our investigation revealed that alongside a more historically immunogenic route of administration, non-tolerant T cell epitopes within the AMG 212 amino acid sequence were likely driving the high-titer, sustained ADA response observed in the SC arm.

Discussion

These mechanistic insights into the AMG 212 ADA response underscore the importance of performing preclinical immunogenicity risk evaluation as well as advocate for continuous iteration to better our biologics.

Immunoinformatic Risk Assessment of Host Cell Proteins During Process Development for Biologic Therapeutics

The identification and removal of host cell proteins (HCPs) from biologic products is a critical step in drug development. Despite recent improvements to purification processes, biologics such as monoclonal antibodies, enzyme replacement therapies, and vaccines that are manufactured in a range of cell lines and purified using diverse processes may contain HCP impurities, making it necessary for developers to identify and quantify impurities during process development for each drug product. HCPs that contain sequences that are less conserved with human homologs may be more immunogenic than those that are more conserved. We have developed a computational tool, ISPRI-HCP, that estimates the immunogenic potential of HCP sequences by evaluating and quantifying T cell epitope density and relative conservation with similar T cell epitopes in the human proteome. Here we describe several case studies that support the use of this method for classifying candidate HCP impurities according to their immunogenicity risk.

Engineering therapeutic antibodies for patient safety: tackling the immunogenicity problem

Established monoclonal antibodies (mAbs) allow treatment of cancers, autoimmune diseases and other severe illnesses. Side effects either arise due to interaction with the target protein and its biology or result from of the patient's immune system reacting to the foreign protein. This immunogenic reaction against therapeutic antibodies is dependent on various factors. The presence of non-human sequences can trigger immune responses as well as chemical and post-translational modifications of the antibody. However, even fully human antibodies can induce immune response through T cell epitopes or aggregates. In this review, we briefly describe, how therapeutic antibodies can interact with the patient's immune system and summarize recent advancements in protein engineering and in silico methods to reduce immunogenicity of therapeutic monoclonal antibodies.

Immunogenicity risk assessment for biotherapeutics through in vitro detection of CD134 and CD137 on T helper cells

Biotherapeutics, which are biologic medications that are natural or bioengineered products of living cells, have revolutionized the treatment of many diseases. However, unwanted immune responses still present a major challenge to their widespread adoption. Many patients treated with biotherapeutics develop antigen-specific anti-drug antibodies (ADAs) that may reduce the efficacy of the therapy or cross-react with the endogenous counterpart of a protein therapeutic, or both. Here, we describe an in vitro method for assessing the immunogenic risk of a biotherapeutic. We found a correlation between clinical immunogenicity and the frequency with which a biotherapeutic stimulated an increase in CD134, CD137, or both cell surface markers on CD4+ T cells. Using high-throughput flow cytometry, we examined the effects of 14 biotherapeutics with diverse rates of clinical immunogenicity on peripheral blood mononuclear cells from 120 donors with diverse human leukocyte antigen class II-encoding alleles. Biotherapeutics with high rates of ADA development in the clinic had higher proportions of CD4+ T cells positive for CD134 or CD137 than biotherapeutics with low clinical immunogenicity. This method provides a rapid and simple preclinical test of the immunogenic potential of a new candidate biotherapeutic or biosimilar. Implementation of this approach during biotherapeutic research and development enables rapid elimination of candidates that are likely to cause ADA-related adverse events and detrimental consequences.

Integration of Omics Data Sources to Inform Mechanistic Modeling of Immune-Oncology Therapies: A Tutorial for Clinical Pharmacologists

Application of contemporary molecular biology techniques to clinical samples in oncology resulted in the accumulation of unprecedented experimental data. These "omics" data are mined for discovery of therapeutic target combinations and diagnostic biomarkers. It is less appreciated that omics resources could also revolutionize development of the mechanistic models informing clinical pharmacology quantitative decisions about dose amount, timing, and sequence. We discuss the integration of omics data to inform mechanistic models supporting drug development in immuno-oncology. To illustrate our arguments, we present a minimal clinical model of the Cancer Immunity Cycle (CIC), calibrated for non-small cell lung carcinoma using tumor microenvironment composition inferred from transcriptomics of clinical samples. We review omics data resources, which can be integrated to parameterize mechanistic models of the CIC. We propose that virtual trial simulations with clinical Quantitative Systems Pharmacology platforms informed by omics data will be making increasing impact in the development of cancer immunotherapies.

Validation of a de-immunization strategy for monoclonal antibodies using cynomolgus macaque as a surrogate for human

The immunogenicity of biotherapeutics presents a major challenge during the clinical development of new protein drugs including monoclonal antibodies. To address this, multiple humanization and de-immunization techniques that employ in silico algorithms and in vitro test systems have been proposed and implemented. However, the success of these approaches has been variable and to date, the ability of these techniques to predict immunogenicity has not been systematically tested in humans or other primates. This study tested whether antibody humanization and de-immunization strategies reduce the risk of anti-drug antibody (ADA) development using cynomolgus macaque as a surrogate for human. First human-cyno chimeric antibodies were constructed by grafting the variable domains of the adalimumab and golimumab monoclonal antibodies onto cynomolgus macaque IgG1 and Igκ constant domains followed by framework germlining to cyno to reduce the xenogenic content. Next, B and T cell epitopes and aggregation-prone regions were identified using common in silico methods to select domains with an ADA risk for additional modification. The resultant engineered antibodies had a comparable affinity for TNFα, demonstrated similar biophysical properties, and exhibited significantly reduced ADA levels in cynomolgus macaque compared with the parental antibodies, with a corresponding improvement in the pharmacokinetic profile. Notably, plasma concentrations of the engineered antibodies were quantifiable through 504 hours (chimeric) and 840 hours (germlined/de-immunized), compared with only 336 hours (adalimumab) or 336-672 hours (golimumab). The results point to the significant value in the investment in these engineering strategies as an important guide for monoclonal antibody optimization that can contribute to improved clinical outcomes.