Dataset Information

A model-based approach leveraging in vitro data to support dose selection from the outset: A framework for bispecific antibodies in immuno-oncology.

ABSTRACT: FAP-4-1BBL is a bispecific antibody exerting 4-1BB-associated T-cell activation only while simultaneously bound to the fibroblast activation protein (FAP) receptor, expressed on the surface of cancer-associated fibroblasts. The trimeric complex formed when FAP-4-1BBL is simultaneously bound to FAP and 4-1BB represents a promising mechanism to achieve tumor-specific 4-1BB stimulation. We integrated in vitro data with mathematical modeling to characterize the pharmacology of FAP-4-1BBL as a function of trimeric complex formation when combined with the T-cell engager cibisatamab. This relationship was used to prospectively predict a range of clinical doses where trimeric complex formation is expected to be at its maximum. Depending on the dosing schedule and FAP-4-1BBL plasma: tumor distribution, doses between 2 and 145 mg could lead to maximum trimeric complex formation in the clinic. Due to the expected variability in both pharmacokinetic and FAP expression in the patient population, we predict that detecting a clear dose-response relationship would remain difficult without a large number of patients per dose level, highlighting that mathematical modeling techniques based on in vitro data could aid dose selection.

SUBMITTER: Sanchez J

PROVIDER: S-EPMC10681425 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A model-based approach leveraging in vitro data to support dose selection from the outset: A framework for bispecific antibodies in immuno-oncology.

Sánchez Javier J Claus Christina C Albrecht Rosmarie R Gaillard Brenda C BC Marinho Joana J McIntyre Christine C Tanos Tamara T Boehnke Axel A Friberg Lena E LE Jönsson Siv S Frances Nicolas N

CPT: pharmacometrics & systems pharmacology 20231115 11

FAP-4-1BBL is a bispecific antibody exerting 4-1BB-associated T-cell activation only while simultaneously bound to the fibroblast activation protein (FAP) receptor, expressed on the surface of cancer-associated fibroblasts. The trimeric complex formed when FAP-4-1BBL is simultaneously bound to FAP and 4-1BB represents a promising mechanism to achieve tumor-specific 4-1BB stimulation. We integrated in vitro data with mathematical modeling to characterize the pharmacology of FAP-4-1BBL as a functi ...[more]

PMID: 37964753

Similar Datasets

Project description:Immune checkpoint inhibitors (ICI) revolutionized the field of immuno-oncology and opened new avenues towards the development of novel assets to achieve durable immune control of cancer. Yet, the presence of tumor immune evasion mechanisms represents a challenge for the development of efficient treatment options. Therefore, combination therapies are taking the center of the stage in immuno-oncology. Such combination therapies should boost anti-tumor immune responses and/or target tumor immune escape mechanisms, especially those created by major players in the tumor microenvironment (TME) such as tumor-associated macrophages (TAM). Natural killer (NK) cells were recently positioned at the forefront of many immunotherapy strategies, and several new approaches are being designed to fully exploit NK cell antitumor potential. One of the most relevant NK cell-activating receptors is NKG2D, a receptor that recognizes 8 different NKG2D ligands (NKG2DL), including MICA and MICB. MICA and MICB are poorly expressed on normal cells but become upregulated on the surface of damaged, transformed or infected cells as a result of post-transcriptional or post-translational mechanisms and intracellular pathways. Their engagement of NKG2D triggers NK cell effector functions. Also, MICA/B are polymorphic and such polymorphism affects functional responses through regulation of their cell-surface expression, intracellular trafficking, shedding of soluble immunosuppressive isoforms, or the affinity of NKG2D interaction. Although immunotherapeutic approaches that target the NKG2D-NKG2DL axis are under investigation, several tumor immune escape mechanisms account for reduced cell surface expression of NKG2DL and contribute to tumor immune escape. Also, NKG2DL polymorphism determines functional NKG2D-dependent responses, thus representing an additional challenge for leveraging NKG2DL in immuno-oncology. In this review, we discuss strategies to boost MICA/B expression and/or inhibit their shedding and propose that combination strategies that target MICA/B with antibodies and strategies aimed at promoting their upregulation on tumor cells or at reprograming TAM into pro-inflammatory macrophages and remodeling of the TME, emerge as frontrunners in immuno-oncology because they may unleash the antitumor effector functions of NK cells and cytotoxic CD8 T cells (CTL). Pursuing several of these pipelines might lead to innovative modalities of immunotherapy for the treatment of a wide range of cancer patients.

Project description:The application of messenger RNA (mRNA) technology in antigen-based immuno-oncology therapies represents a significant advancement in cancer treatment. Cancer vaccines are an effective combinatorial partner to sensitize the host immune system to the tumor and boost the efficacy of immune therapies. Selecting suitable tumor antigens is the key step to devising effective vaccinations and amplifying the immune response. Tumor neoantigens are de novo epitopes derived from somatic mutations, avoiding T-cell central tolerance of self-epitopes and inducing immune responses to tumors. The identification and prioritization of patient-specific tumor neoantigens are based on advanced computational algorithms taking advantage of the profiling with next-generation sequencing considering factors involved in human leukocyte antigen (HLA)-peptide-T-cell receptor (TCR) complex formation, including peptide presentation, HLA-peptide affinity, and TCR recognition. This review discusses the development and clinical application of mRNA vaccines in oncology, with a particular focus on recent clinical trials and the computational workflows and methodologies for identifying both shared and individual antigens. While this review centers on therapeutic mRNA vaccines targeting existing tumors, it does not cover preventative vaccines. Preclinical experimental validations are crucial in cancer vaccine development, but we emphasize the computational approaches that facilitate neoantigen selection and design, highlighting their role in advancing mRNA vaccine development. The versatility and rapid development potential of mRNA make it an ideal platform for personalized neoantigen immunotherapy. We explore various strategies for antigen target identification, including tumor-associated and tumor-specific antigens and the computational tools used to predict epitopes capable of eliciting strong immune responses. We address key design considerations for enhancing the immunogenicity and stability of mRNA vaccines, as well as emerging trends and challenges in the field. This comprehensive overview highlights the therapeutic potential of mRNA-based cancer vaccines and underscores ongoing research efforts aimed at optimizing these therapies for improved clinical outcomes.

Project description:ImportanceClinical interpretation of complex biomarkers for precision oncology currently requires manual investigations of previous studies and databases. Conversational large language models (LLMs) might be beneficial as automated tools for assisting clinical decision-making.ObjectiveTo assess performance and define their role using 4 recent LLMs as support tools for precision oncology.Design, setting, and participantsThis diagnostic study examined 10 fictional cases of patients with advanced cancer with genetic alterations. Each case was submitted to 4 different LLMs (ChatGPT, Galactica, Perplexity, and BioMedLM) and 1 expert physician to identify personalized treatment options in 2023. Treatment options were masked and presented to a molecular tumor board (MTB), whose members rated the likelihood of a treatment option coming from an LLM on a scale from 0 to 10 (0, extremely unlikely; 10, extremely likely) and decided whether the treatment option was clinically useful.Main outcomes and measuresNumber of treatment options, precision, recall, F1 score of LLMs compared with human experts, recognizability, and usefulness of recommendations.ResultsFor 10 fictional cancer patients (4 with lung cancer, 6 with other; median [IQR] 3.5 [3.0-4.8] molecular alterations per patient), a median (IQR) number of 4.0 (4.0-4.0) compared with 3.0 (3.0-5.0), 7.5 (4.3-9.8), 11.5 (7.8-13.0), and 13.0 (11.3-21.5) treatment options each was identified by the human expert and 4 LLMs, respectively. When considering the expert as a criterion standard, LLM-proposed treatment options reached F1 scores of 0.04, 0.17, 0.14, and 0.19 across all patients combined. Combining treatment options from different LLMs allowed a precision of 0.29 and a recall of 0.29 for an F1 score of 0.29. LLM-generated treatment options were recognized as AI-generated with a median (IQR) 7.5 (5.3-9.0) points in contrast to 2.0 (1.0-3.0) points for manually annotated cases. A crucial reason for identifying AI-generated treatment options was insufficient accompanying evidence. For each patient, at least 1 LLM generated a treatment option that was considered helpful by MTB members. Two unique useful treatment options (including 1 unique treatment strategy) were identified only by LLM.Conclusions and relevanceIn this diagnostic study, treatment options of LLMs in precision oncology did not reach the quality and credibility of human experts; however, they generated helpful ideas that might have complemented established procedures. Considering technological progress, LLMs could play an increasingly important role in assisting with screening and selecting relevant biomedical literature to support evidence-based, personalized treatment decisions.

Dataset Information

A model-based approach leveraging in vitro data to support dose selection from the outset: A framework for bispecific antibodies in immuno-oncology.

Publications

A model-based approach leveraging in vitro data to support dose selection from the outset: A framework for bispecific antibodies in immuno-oncology.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets