Investigating brain uptake of a non-targeting monoclonal antibody after intravenous and intracerebroventricular administration.

Monoclonal antibodies play an important role in the treatment of various diseases. However, the development of these drugs against neurological disorders where the drug target is located in the brain is challenging and requires a good understanding of the local drug concentration in the brain. In this original research, we investigated the systemic and local pharmacokinetics in the brain of healthy rats after either intravenous (IV) or intracerebroventricular (ICV) administration of EGFRvIII-T-Cell bispecific (TCB), a bispecific monoclonal antibody. We established an experimental protocol that allows serial sampling in serum, cerebrospinal fluid (CSF) and interstitial fluid (ISF) of the prefrontal cortex in freely moving rats. For detection of drug concentration in ISF, a push-pull microdialysis technique with large pore membranes was applied. Brain uptake into CSF and ISF was characterized and quantified with a reduced brain physiologically-based pharmacokinetic model. The model allowed us to interpret the pharmacokinetic processes of brain uptake after different routes of administration. The proposed model capturing the pharmacokinetics in serum, CSF and ISF of the prefrontal cortex suggests a barrier function between the CSF and ISF that impedes free antibody transfer. This finding suggests that ICV administration may not be better suited to reach higher local drug exposure as compared to IV administration. The model enabled us to quantify the relative contribution of the blood-brain barrier (BBB) and Blood-CSF-Barrier to the uptake into the interstitial fluid of the brain. In addition, we compared the brain uptake of three monoclonal antibodies after IV dosing. In summary, the presented approach can be applied to profile compounds based on their relative uptake in the brain and provides quantitative insights into which pathways are contributing to the net exposure in the brain.

Translating pharmacology models effectively to predict therapeutic benefit.

Many in vitro and in vivo models are used in pharmacological research to evaluate the role of targeted proteins in a disease. Understanding the translational relevance and limitation of these models for analyzing a drug's disposition, pharmacokinetic/pharmacodynamic (PK/PD) profile, mechanism, and efficacy, is essential when selecting the most appropriate model of the disease of interest and predicting clinically efficacious doses of the investigational drug. Selected animal models used in ophthalmology, infectious diseases, oncology, autoimmune diseases, and neuroscience are reviewed here. Each area has specific challenges around translatability and determination of an efficacious dose: new patient-specific dosing methods may help overcome these limitations.

Leveraging modeling and simulation to optimize the therapeutic window for epigenetic modifier drugs.

Dysregulated epigenetic processes can lead to altered gene expression and give rise to malignant transformation and tumorigenesis. Epigenetic drugs aim to revert the phenotype of cancer cells to normally functioning cells, and are developed and applied to treat both hematological and solid cancers. Despite this promising therapeutic avenue, the successful development of epigenetic modulators has been challenging. We argue that besides identifying the right responder patient population, the selection of an optimized dosing regimen is equally important. For the majority of epigenetic modulators, hematological adverse effects such as thrombocytopenia, anemia or neutropenia are frequently observed and may limit their therapeutic potential. Therefore, one of the key challenges is to identify a dosing regimen that maximizes drug efficacy and minimizes toxicity. This requires a good understanding of the quantitative relationship between the administered dose, the drug exposure and the magnitude and duration of drug response related to safety and efficacy. With case examples, we highlight how modeling and simulation has been successfully applied to address those questions. As an outlook, we suggest the combination of efficacy and safety prediction models that capture the quantitative, mechanistic relationships governing the balance between their safety and efficacy dynamics. A stepwise approach for its implementation is presented. Utilizing in silico explorations, the impact of dosing regimen on the therapeutic window can be explored. This will serve as a basis to select the most promising dosing regimen that maximizes efficacy while minimizing adverse effects and to increase the probability of success for the given epigenetic drug.

A Novel Approach for Quantifying the Pharmacological Activity of T-Cell Engagers Utilizing In Vitro Time Course Experiments and Streamlined Data Analysis.

CD3-bispecific antibodies are a new class of immunotherapeutic drugs against cancer. The pharmacological activity of CD3-bispecifics is typically assessed through in vitro assays of cancer cell lines co-cultured with human peripheral blood mononuclear cells (PBMCs). Assay results depend on experimental conditions such as incubation time and the effector-to-target cell ratio, which can hinder robust quantification of pharmacological activity. In order to overcome these limitations, we developed a new, holistic approach for quantification of the in vitro dose-response relationship. Our experimental design integrates a time-independent analysis of the dose-response across different time points as an alternative to the static, "snap-shot" analysis based on a single time point commonly used in dose-response assays. We show that the potency values derived from static in vitro experiments depend on the incubation time, which leads to inconsistent results across multiple assays and compounds. We compared the potency values from the time-independent analysis with a model-based approach. We find comparably accurate potency estimates from the model-based and time-independent analyses and that the time-independent analysis provides a robust quantification of pharmacological activity. This approach may allow for an improved head-to-head comparison of different compounds and test systems and may prove useful for supporting first-in-human dose selection.

Cytokine Release Syndrome By T-cell-Redirecting Therapies: Can We Predict and Modulate Patient Risk?

T-cell-redirecting therapies are promising new therapeutic options in the field of cancer immunotherapy, but the development of these modalities is challenging. A commonly observed adverse event in patients treated with T-cell-redirecting therapies is cytokine release syndrome (CRS). Its clinical manifestation is a burden on patients, and continues to be a big hurdle in the clinical development of this class of therapeutics. We review different T-cell-redirecting therapies, discuss key factors related to cytokine release and potentially leading to CRS, and present clinical mitigation strategies applied for those modalities. We propose to dissect those risk factors into drug-target-disease-related factors and individual patient risk factors. Aiming to optimize the therapeutic intervention of these modalities, we illustrate how the knowledge on drug-target-disease-related factors, such as target expression, binding affinity, and target accessibility, can be leveraged in a model-based framework and highlight with case examples how modeling and simulation is applied to guide drug discovery and development. We draw attention to the current gaps in predicting the individual patient's risk towards a high-grade CRS, which requires further considerations of risk factors related, but not limited to, the patient's demographics, genetics, underlying pathologies, treatment history, and environmental exposures. The drug-target-disease-related factors together with the individual patient's risk factors can be regarded as the patient's propensity for developing CRS in response to therapy. As an outlook, we suggest implementing a risk scoring system combined with mechanistic modeling to enable the prediction of an individual patient's risk of CRS for a given therapeutic intervention.

Predicting Tumor Killing and T-Cell Activation by T-Cell Bispecific Antibodies as a Function of Target Expression: Combining In Vitro Experiments with Systems Modeling.

Targeted T-cell redirection is a promising field in cancer immunotherapy. T-cell bispecific antibodies (TCB) are novel antibody constructs capable of binding simultaneously to T cells and tumor cells, allowing cross-linking and the formation of immunologic synapses. This in turn results in T-cell activation, expansion, and tumor killing. TCB activity depends on system-related properties such as tumor target antigen expression as well as antibody properties such as binding affinities to target and T cells. Here, we developed a systems model integrating in vitro data to elucidate further the mechanism of action and to quantify the cytotoxic effects as the relationship between targeted antigen expression and corresponding TCB activity. In the proposed model, we capture relevant processes, linking immune synapse formation to T-cell activation, expansion, and tumor killing for TCBs in vitro to differentiate the effect between tumor cells expressing high or low levels of the tumor antigen. We used cibisatamab, a TCB binding to carcinoembryonic antigen (CEA), to target different tumor cell lines with high and low CEA expression in vitro We developed a model to capture and predict our observations, as a learn-and-confirm cycle. Although full tumor killing and substantial T-cell activation was observed in high expressing tumor cells, the model correctly predicted partial tumor killing and minimal T-cell activation in low expressing tumor cells when exposed to cibisatamab. Furthermore, the model successfully predicted cytotoxicity across a wide range of tumor cell lines, spanning from very low to high CEA expression.