Characterization of concurrent target suppression by JNJ-61178104, a bispecific antibody against human tumor necrosis factor and interleukin-17A

Investigational bispecific antibodies for the treatment of rheumatoid arthritis

INTRODUCTION

Rheumatoid arthritis (RA) is an autoimmune disorder with a characteristic chronic inflammation of the synovium that may lead to the destruction of the joints in untreated patients. Interestingly, despite the availability of several effective treatments, many patients do not achieve remission or low disease activity or may experience disease relapse.Following the above unmet needs, bispecific antibodies (BsAbs) have emerged as a new approach to improve the disease's treatment. BsAbs are designed to simultaneously target two different proteins involved in RA pathogenesis, leading to enhanced efficacy and reduced side effects compared to traditional monoclonal antibodies (mAbs).

AREAS COVERED

In this review, we discuss the development of BsAbs for RA treatment, including their mechanism of action, efficacy, and safety profile. We also deal with the challenges and future directions in this field.

EXPERT OPINION

BsAbs show promise in preclinical and clinical evaluations for treating RA. Further research is needed to optimize design and dosage and identify ideal patient groups. BsAbs can benefit disease management and improve outcomes of RA patients.

Mechanism-based pharmacokinetic and pharmacodynamic modeling for bispecific antibodies: challenges and opportunities

INTRODUCTION

Unlike conventional antibodies, bispecific antibodies (bsAbs) are engineered antibody- or antibody fragment-based molecules that can simultaneously recognize two different epitopes or antigens. Over the past decade, there has been an explosion of bsAbs being developed across therapeutic areas. Development of bsAbs presents unique challenges and mechanism-based pharmacokinetic/pharmacodynamic (PK/PD) modeling has served as a powerful tool to optimize their development and realize their clinical utility.

AREAS COVERED

In this review, the guiding principles and case examples of how fit-for-purpose, mechanism-based PK/PD models have been applied to answer questions commonly encountered in bsAb development are presented. Such models characterize the key pharmacological elements of bsAbs, and they can be utilized for model-informed drug development. We also include the discussion of challenges, knowledge gaps and future direction for such models.

EXPERT OPINION

Mechanistic PK/PD modeling is a powerful tool to support the development of bsAbs. These models can be extrapolated to predict treatment outcomes based on mechanisms of action (MoA) and clinical observations to form positive learn-and-confirm cycles during drug development, due to their abilities to differentiate system- and drug-specific parameters. Meanwhile, the models should keep being adapted according to novel drug design and MoA, providing continuous opportunities for model-informed drug development.

TRYBE®: an Fc-free antibody format with three monovalent targeting arms engineered for long in vivo half-life

TrYbe® is an Fc-free therapeutic antibody format, capable of engaging up to three targets simultaneously, with long in vivo half-life conferred by albumin binding. This format is shown by small-angle X-ray scattering to be conformationally flexible with favorable 'reach' properties. We demonstrate the format's broad functionality by co-targeting of soluble and cell surface antigens. The benefit of monovalent target binding is illustrated by the lack of formation of large immune complexes when co-targeting multivalent antigens. TrYbes® are manufactured using standard mammalian cell culture and protein A affinity capture processes. TrYbes® have been formulated at high concentrations and have favorable drug-like properties, including stability, solubility, and low viscosity. The unique functionality and inherent developability of the TrYbe® makes it a promising multi-specific antibody fragment format for antibody therapy.

Utility of in silico prediction of target suppression for antibodies against soluble targets: static versus dynamic models

PURPOSE

Antibodies that bind soluble targets such as cytokines belong to an important class of immunotherapies. Target levels can significantly accumulate after antibody administration due to formation of antibody-target complex, accompanied with suppression in free target which is often difficult to measure. Being a surrogate for pharmacodynamic activity, free target suppression is often predicted using in silico tools. The objective of this work is to illustrate the utility of modelling and to compare static versus dynamic models in the prediction of free target suppression.

METHODS

Using binding principles, we have derived a static equation to predict free target suppression at steady state (FTSS). This equation operates with five input parameters and accounts for target accumulation over time. Its predictivity was compared to a dynamic model and to other existing metrics in literature via simulations and assumptions were illustrated.

RESULTS

We demonstrated the utility of in silico tools in prediction of free target suppression using static and dynamic models and clarified the assumptions in key input parameters and their limitations. Predicted values using the FTSS equation correlate very well with those from the dynamic model at level > 20% target suppression, relevant for antagonistic antibodies.

CONCLUSION

In silico tools are needed to predict target suppression by antibody drugs. Static or dynamic models can be used dependant on the scope, available data and undertaken assumptions. These tools can be used to guide discovery and development of antibodies and has the potential to reduce clinical failure.

Minimal Physiologically-Based Pharmacokinetic (mPBPK) Metamodeling of Target Engagement in Skin Informs Anti-IL17A Drug Development in Psoriasis

The pharmacologic effect(s) of biotherapeutics directed against soluble targets are driven by the magnitude and duration of free target suppression at the tissue site(s) of action. Interleukin (IL)-17A is an inflammatory cytokine that plays a key role in the pathogenesis of psoriasis. In this work, clinical trial data from two monoclonal antibodies (mAbs) targeting IL-17A for treatment of psoriasis (secukinumab and ixekizumab) were analyzed simultaneously to quantitatively predict their target engagement (TE) profiles in psoriatic skin. First, a model-based meta-analysis (MBMA) for clinical responses was conducted separately for each drug based on dose. Next, a minimal physiologically-based pharmacokinetic (mPBPK) model was built to assess skin site IL-17A target engagement for ixekizumab and secukinumab simultaneously. The mPBPK model captured the observed drug PK, serum total IL-17A, and skin drug concentration-time profiles reasonably well across the different dosage regimens investigated. The developed mPBPK model was then used to predict the average TE (i.e., free IL-17A suppression) in skin achieved over a 12-weeks treatment period for each drug following their respective regimens and subsequently assess the TE-efficacy response relationship. It was predicted that secukinumab achieved 98.6% average TE in the skin at 300 mg q4w SC while ixekizumab achieved 99.9% average TE under 160 mg (loading) followed by 80 mg q2w SC. While direct quantification of free IL-17A levels at the site of action is technically challenging, integrated mPBPK-MBMA approaches offer quantitative predictions of free IL-17A levels at the site of action to facilitate future drug development via IL-17A suppression in psoriasis.

The second decade of anti-TNF-a therapy in clinical practice: new lessons and future directions in the COVID-19 era

Since the late 1990s, tumor necrosis factor alpha (TNF-α) inhibitors (anti-TNFs) have revolutionized the therapy of immune-mediated inflammatory diseases (IMIDs) affecting the gut, joints, skin and eyes. Although the therapeutic armamentarium in IMIDs is being constantly expanded, anti-TNFs remain the cornerstone of their treatment. During the second decade of their application in clinical practice, a large body of additional knowledge has accumulated regarding various aspects of anti-TNF-α therapy, whereas new indications have been added. Recent experimental studies have shown that anti-TNFs exert their beneficial effects not only by restoring aberrant TNF-mediated immune mechanisms, but also by de-activating pathogenic fibroblast-like mesenchymal cells. Real-world data on millions of patients further confirmed the remarkable efficacy of anti-TNFs. It is now clear that anti-TNFs alter the physical course of inflammatory arthritis and inflammatory bowel disease, leading to inhibition of local and systemic bone loss and to a decline in the number of surgeries for disease-related complications, while anti-TNFs improve morbidity and mortality, acting beneficially also on cardiovascular comorbidities. On the other hand, no new safety signals emerged, whereas anti-TNF-α safety in pregnancy and amid the COVID-19 pandemic was confirmed. The use of biosimilars was associated with cost reductions making anti-TNFs more widely available. Moreover, the current implementation of the “treat-to-target” approach and treatment de-escalation strategies of IMIDs were based on anti-TNFs. An intensive search to discover biomarkers to optimize response to anti-TNF-α treatment is currently ongoing. Finally, selective targeting of TNF-α receptors, new forms of anti-TNFs and combinations with other agents, are being tested in clinical trials and will probably expand the spectrum of TNF-α inhibition as a therapeutic strategy for IMIDs.

Recent Advances in Translational Pharmacokinetics and Pharmacodynamics Prediction of Therapeutic Antibodies Using Modeling and Simulation

Therapeutic monoclonal antibodies (mAbs) have been a promising therapeutic approach for several diseases and a wide variety of mAbs are being evaluated in clinical trials. To accelerate clinical development and improve the probability of success, pharmacokinetics and pharmacodynamics (PKPD) in humans must be predicted before clinical trials can begin. Traditionally, empirical-approach-based PKPD prediction has been applied for a long time. Recently, modeling and simulation (M&S) methods have also become valuable for quantitatively predicting PKPD in humans. Although several models (e.g., the compartment model, Michaelis–Menten model, target-mediated drug disposition model, and physiologically based pharmacokinetic model) have been established and used to predict the PKPD of mAbs in humans, more complex mechanistic models, such as the quantitative systemics pharmacology model, have been recently developed. This review summarizes the recent advances and future direction of M&S-based approaches to the quantitative prediction of human PKPD for mAbs.

Immunogenicity Risk Assessment for Multi-specific Therapeutics

The objective of this manuscript is to provide the reader with a hypothetical case study to present an immunogenicity risk assessment for a multi-specific therapeutic as part of Investigational New Drug (IND) application. In order to provide context for the bioanalytical strategies used to support the multi-specific therapeutic presented herein, the introduction focuses on known immunogenicity risk factors. The subsequent hypothetical case study applies these principles to a specific example HC-12, based loosely on anti-TNFα and anti-IL-17A bispecific molecules previously in development, structured as an example immunogenicity risk assessment for submission to health authorities. The risk of higher incidence and safety impact of anti-drug antibodies (ADA) due to large protein complexes is explored in the context of multi-specificity and multi-valency of the therapeutic in combination with the oligomeric forms of the targets.

Analysis of the Composition and Anti-Rheumatoid Arthritis Mechanism of Qintengtongbi Decoction Based on Network Pharmacology

Qintengtongbi Decoction (QTTBD) is a traditional prescription for rheumatoid arthritis (RA) treatment in Ruikang Hospital Affiliated to Guangxi University of Chinese Medicine, Nanning, southern China's Guangxi Zhuang Autonomous Region. However, there is not yet any analysis on its active compounds or action mechanism for treating RA. Moreover, the prescription has not been investigated from the perspective of network pharmacology. Therefore, this study aimed to analyze the compounds QTTBD and their potential pharmacological effects and the mechanism by which they treat RA via an integrated network pharmacology approach. With the aid of the relevant database tools and research indices, 188 compounds and 272 related drug targets genes/proteins were collected from QTTBD through the compound-target network, and 175 common gene targets between the QTTBD and RA were obtained by Venn 2.1. Finally, the top 10 gene targets and pathways were identified through the protein–protein interaction network, gene ontology, and KEGG pathway analysis: the gene targets include AKT1, IL6, TP53, VEGFA, MAPK3, TNF, CASP3, JUN, EGF, and EGFR; the pathways include oxytocin signaling pathway, amphetamine addiction, graft-versus-host disease, ovarian steroidogenesis, cGMP-PKG signaling pathway, Rap1 signaling pathway, allograft rejection, cytokine–cytokine receptor interaction, regulation of lipolysis in adipocytes and inflammatory mediator regulation of transient receptor potential channels. Therefore, it is concluded that a network pharmacology-based approach can help reveal and clarify the anti-RA role of QTTBD, and provide a scientific basis for further research into the mechanism.