A Modeling Framework to Characterize Cytokine Release upon T‐Cell–Engaging Bispecific Antibody Treatment: Methodology and Opportunities

Chen, Xiaoying; Kamperschroer, Cris; Wong, Gilbert Y.; Xuan, Dawei

doi:10.1111/cts.12662

Cited by 38 publications

(56 citation statements)

References 26 publications

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“…Case study 2 uses the same modeling framework to predict clinical starting dose and demonstrates in vitro to clinical translation 28 . Case study 3 demonstrates a QSP approach to predict and therefore minimize CRS toxicities upon bsAb dosing 73 . In each case, the focus is on the strategic applications of the mechanistic modeling and its impact.…”

Section: Case Studiesmentioning

confidence: 99%

“…In each case, the focus is on the strategic applications of the mechanistic modeling and its impact. Technical details, including specific models structures, equations, and parameter values, are not included, and can be found in the published manuscripts 13,28,73 . The case studies all describe a generalized CD3 bsAb model based on CD3 engaged through trimer formation, as the important variable driving efficacy and on‐target/off‐tumor toxicity.…”

Section: Case Studiesmentioning

confidence: 99%

“…In the work by Chen et al ., 2019, 73 a quantitative modeling framework was developed for characterizing cytokine profiles upon CD3 bsAb treatment, with the goal to facilitate the design of priming dose strategies to minimize CRS toxicities ( Figure ). The model describes cytokine release stimulated by CD3 bsAbs forming trimers by binding to CD3 on T cells and TAA on tumor cells.…”

Section: Case Studiesmentioning

confidence: 99%

“…Reproduced with permissions from ref. 73. Briefly, an appropriate PK model accounts for the drug exposure.…”

Section: Case Studiesmentioning

confidence: 99%

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Mechanistic Quantitative Pharmacology Strategies for the Early Clinical Development of Bispecific Antibodies in Oncology

Betts

Graaf

2020

Clin Pharma and Therapeutics

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Bispecific antibodies (bsAbs) have become an integral component of the therapeutic research strategy to treat cancer. In addition to clinically validated immune cell re‐targeting, bsAbs are being designed for tumor targeting and as dual immune modulators. Explorative preclinical and emerging clinical data indicate potential for enhanced efficacy and reduced systemic toxicity. However, bsAbs are a complex modality with challenges to overcome in early clinical trials, including selection of relevant starting doses using a minimal anticipated biological effect level approach, and predicting efficacious dose despite nonintuitive dose response relationships. Multiple factors can contribute to variability in the clinic, including differences in functional affinity due to avidity, receptor expression, effector to target cell ratio, and presence of soluble target. Mechanistic modeling approaches are a powerful integrative tool to understand the complexities and aid in clinical translation, trial design, and prediction of regimens and strategies to reduce dose limiting toxicities of bsAbs. In this tutorial, the use of mechanistic modeling to impact decision making for bsAbs is presented and illustrated using case study examples.

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Section: Case Studiesmentioning

confidence: 99%

Section: Case Studiesmentioning

confidence: 99%

Section: Case Studiesmentioning

confidence: 99%

“…Reproduced with permissions from ref. 73. Briefly, an appropriate PK model accounts for the drug exposure.…”

Section: Case Studiesmentioning

confidence: 99%

See 2 more Smart Citations

Mechanistic Quantitative Pharmacology Strategies for the Early Clinical Development of Bispecific Antibodies in Oncology

Betts

Graaf

2020

Clin Pharma and Therapeutics

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“…This may include slowing or suspending the infusion, or using a lower, "priming dose," when a high "first dose effect" is anticipated. The latter has been used extensively for T-cell engaging bispecific antibodies, which are particularly susceptible to this effect [88].…”

Section: Dosing Regimenmentioning

confidence: 99%

Regulatory and strategic considerations for addressing immunogenicity and related responses in biopharmaceutical development programs

Vandivort

Horton

Johnson

2020

J. Clin. Trans. Sci.

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The last three decades have seen the biotherapeutic drug market evolve from promising concept to market dominance in a range of clinical indications. This growth has been spurred by the success of established drug classes like monoclonal antibodies, but also by the introduction of biosimilars, and more recently, multiple novel cell and gene therapies. Biotherapeutic drug development presents many unique challenges, but unintended immune responses are among the most common reasons for program attrition. Anti-drug antibodies can impact the safety and efficacy of drug products, and related immune responses, like the cytokine release syndrome that occurred in the infamous TGN-1412 clinical trial, can be challenging to predict with nonclinical models. For this reason, it is important that development programs proceed with a scientifically grounded and measured approach to these responses. This process begins at the discovery stage with the application of “quality by design,” continues into the clinic with the development of quality assays and management strategies, and culminates in the effective presentation of this information in regulatory documents. This review provides an overview of some of the key strategic and regulatory considerations for biotherapeutics as they pertain to immunogenicity and related responses.

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A Computational Model of Cytokine Release Syndrome during CAR T‐Cell Therapy

Zhang

Liu

et al. 2022

Advanced Therapeutics

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Cytokine release syndrome (CRS) is a lethal adverse event in chimeric antigen receptor (CAR) T-cell therapy, hindering this promising therapy for cancers, such as B-cell acute lymphoblastic leukemia (B-ALL). Clinical management of CRS requires a better understanding of its underlying mechanisms. In this study, a computational model of CRS during CAR T-cell therapy is built to depict how the cellular interactions among CAR T-cells, B-ALL cells, and bystander monocytes, as well as the accompanying molecular interactions among various inflammatory cytokines, influence the severity of CRS. The model successfully defines the factors related to severe CRS and studies the effects of immunomodulatory therapy on CRS. The use of the model is also demonstrated as a precision medicine tool to optimize the treatment scheme, including personalized choice of CAR T-cell products and control of switchable CAR T-cell activity, for a more efficient and safer immunotherapy. This new computational oncology model can serve as a precision medicine tool to guide the clinical management of CRS during CAR T cell therapy.

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A Modeling Framework to Characterize Cytokine Release upon T‐Cell–Engaging Bispecific Antibody Treatment: Methodology and Opportunities

Cited by 38 publications

References 26 publications

Mechanistic Quantitative Pharmacology Strategies for the Early Clinical Development of Bispecific Antibodies in Oncology

Mechanistic Quantitative Pharmacology Strategies for the Early Clinical Development of Bispecific Antibodies in Oncology

Regulatory and strategic considerations for addressing immunogenicity and related responses in biopharmaceutical development programs

A Computational Model of Cytokine Release Syndrome during CAR T‐Cell Therapy

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