Which factors matter the most? Revisiting and dissecting antibody therapeutic doses

Rat as a Predictive Model for Human Clearance and Bioavailability of Monoclonal Antibodies

BACKGROUND

The prediction of human clearance (CL) and subcutaneous (SC) bioavailability is a critical aspect of monoclonal antibody (mAb) selection for clinical development. While monkeys are a well-accepted model for predicting human CL, other preclinical species have been less-thoroughly explored. Unlike CL, predicting the bioavailability of SC administered mAbs in humans remains challenging as contributing factors are not well understood, and preclinical models have not been systematically evaluated.

METHODS

Non-clinical and clinical pharmacokinetic (PK) parameters were mined from public and internal sources for rats, cynomolgus monkeys, and humans. Intravenous (IV) and SC PK was determined in Sprague Dawley rats for fourteen mAbs without existing PK data. Together, we obtained cross-species data for 25 mAbs to evaluate CL and SC bioavailability relationships among rats, monkeys, and humans.

RESULTS

Rat and monkey CL significantly correlated with human CL and supported the use of species-specific exponents for body-weight-based allometric scaling. Notably, rat SC bioavailability significantly correlated with human SC bioavailability, while monkey SC bioavailability did not. Bioavailability also correlated with clearance.

CONCLUSIONS

The rat model enables an early assessment of mAb PK properties, allowing discrimination among molecules in the discovery pipeline and prediction of human PK. Importantly, rat SC bioavailability significantly correlated with human SC bioavailability, which has not been observed with other species. Rats are cost-effective and efficient relative to monkeys and provide a valuable tool for pharmacokinetic predictions in therapeutic antibody discovery.

Advancing cancer drug development with mechanistic mathematical modeling: bridging the gap between theory and practice

Quantitative predictive modeling of cancer growth, progression, and individual response to therapy is a rapidly growing field. Researchers from mathematical modeling, systems biology, pharmaceutical industry, and regulatory bodies, are collaboratively working on predictive models that could be applied for drug development and, ultimately, the clinical management of cancer patients. A plethora of modeling paradigms and approaches have emerged, making it challenging to compile a comprehensive review across all subdisciplines. It is therefore critical to gauge fundamental design aspects against requirements, and weigh opportunities and limitations of the different model types. In this review, we discuss three fundamental types of cancer models: space-structured models, ecological models, and immune system focused models. For each type, it is our goal to illustrate which mechanisms contribute to variability and heterogeneity in cancer growth and response, so that the appropriate architecture and complexity of a new model becomes clearer. We present the main features addressed by each of the three exemplary modeling types through a subjective collection of literature and illustrative exercises to facilitate inspiration and exchange, with a focus on providing a didactic rather than exhaustive overview. We close by imagining a future multi-scale model design to impact critical decisions in oncology drug development.

Comparison of monoclonal antibody disposition predictions using different physiologically based pharmacokinetic modelling platforms

Physiologically based pharmacokinetic (PBPK) models can be used to leverage physiological and in vitro data to predict monoclonal antibody (mAb) concentrations in serum and tissues. However, it is currently not known how consistent predictions of mAb disposition are across PBPK modelling platforms. In this work PBPK simulations of IgG, adalimumab and infliximab were compared between three platforms (Simcyp, PK-Sim, and GastroPlus). Accuracy of predicted serum and tissue concentrations was assessed using observed data collected from the literature. Physiological and mAb related input parameters were also compared and sensitivity analyses were carried out to evaluate model behavior when input values were altered. Differences in serum kinetics of IgG between platforms were minimal for a dose of 1 mg/kg, but became more noticeable at higher dosages (> 100 mg/kg) and when reference (healthy) physiological input values were altered. Predicted serum concentrations of both adalimumab and infliximab were comparable across platforms, but were noticeably higher than observed values. Tissue concentrations differed remarkably between the platforms, both for total- and interstitial fluid (ISF) concentrations. The accuracy of total tissue concentrations was within a three-fold of observed values for all tissues, except for brain tissue concentrations, which were overpredicted. Predictions of tissue ISF concentrations were less accurate and were best captured by GastroPlus. Overall, these simulations show that the different PBPK platforms generally predict similar mAb serum concentrations, but variable tissue concentrations. Caution is therefore warranted when PBPK models are used to simulate effect site tissue concentrations of mAbs without data to verify the predictions.

Navigating large-volume subcutaneous injections of biopharmaceuticals: a systematic review of clinical pipelines and approved products

Subcutaneous (SC) administration is transforming the delivery of biopharmaceuticals, facilitating care in a variety of healthcare settings, including home self-treatment. Large-volume single SC doses have gained attention for their potential to expand therapeutic applications and improve long-term, patient-centric dosing regimens, often at a reduced SC injection frequency. However, a systematic understanding of dose volumes and frequencies for large-volume (>2.0 mL) SC biopharmaceuticals (LVSCs) is lacking. Accordingly, this study systematically reviewed clinical-stage and approved intravenous (IV) and SC biopharmaceuticals, identifying 182 LVSCs - predominantly monoclonal or bispecific antibodies - which correspond to approximately 15% of all IV and SC biopharmaceuticals. These LVSCs are designed to target cancer and a range of non-cancer chronic disease states, including autoimmune, neurological, and cardiovascular diseases. Results show that anti-cancer LVSCs (n = 75) typically require 5.0 to 20.0 mL doses every three weeks and are administered by healthcare professionals. In contrast, non-cancer LVSCs (n = 107), which are typically self-administered monthly, show more significant dosing variability, with < 5.0 mL being the predominant volume range. Furthermore, the study identified a substantial clinical pipeline of potential LVSCs, many of which are being injected at increasingly lower dosing frequencies, suggesting significant future growth in this area. Most non-cancer LVSCs are currently undergoing clinical trials via the SC route, whereas the majority of the cancer LVSCs are being administered IV and require transition to the SC route. These findings highlight the importance of developing large-volume drug delivery systems and novel formulations to reduce injection volumes. The analysis provides valuable guidance for new product development, as well as for marketing and commercialization strategies in the rapidly evolving LVSC landscape.

Clearance as an Early Indicator of Efficacy for Therapeutic Monoclonal Antibodies: Circumventing Dose Selection Challenges in Oncology

BACKGROUND AND OBJECTIVE

The designs of first-in-human (FIH) studies in oncology (e.g., 3 + 3 dose escalation design) usually do not provide a sufficient sample size to determine the dose-response relationship for efficacy. This study aimed to assess the feasibility of using monoclonal antibody (mAb) clearance as a biomarker for efficacy to facilitate the identification of potentially efficacious doses across cancer types and drug targets.

METHODS

We performed electronic searches of the Drugs@FDA website, the European Medicines Agency website, and PubMed to identify reports of FIH trials of approved mAbs in oncology. The clearance, half-life, and overall response rate (ORR) data for the mAbs at different dose levels were extracted.

RESULTS

Twenty-five approved mAbs were included in this study. As expected, due to the small sample sizes in FIH studies, there was no clear dose-response for ORR. However, we found a clear negative association between mAb clearance and ORR across tumors/drug targets, and a clear negative dose-clearance relationship, with clearance decreasing and saturated at high dose levels. The approved mAb doses (1-25 mg/kg) are approximately 2-fold the saturation doses (1-10 mg/kg). The associated clearance values at the approved doses vary across different cancers and drug targets (0.17-1.56 L/day), while tend to be similar within a disease/drug target. Anti-CD20 mAbs for B-cell lymphomas show a higher clearance (~ 1 L/day) than other cancers and targets (e.g., ~ 0.3 L/day for anti-PD-1).

CONCLUSIONS

Clearance of mAbs can be a tumor/drug target-agnostic biomarker for potential anti-tumor activity as clearance decreases with increasing ORR. Our findings shed important insights into target clearance values that may lead to desired efficacy for different cancers and drug targets, which can be used to guide dose selection for the future development of mAbs during FIH oncology studies.

Review of the Existing Translational Pharmacokinetics Modeling Approaches Specific to Monoclonal Antibodies (mAbs) to Support the First-In-Human (FIH) Dose Selection

The interest in therapeutic monoclonal antibodies (mAbs) has continuously growing in several diseases. However, their pharmacokinetics (PK) is complex due to their target-mediated drug disposition (TMDD) profiles which can induce a non-linear PK. This point is particularly challenging during the pre-clinical and translational development of a new mAb. This article reviews and describes the existing PK modeling approaches used to translate the mAbs PK from animal to human for intravenous (IV) and subcutaneous (SC) administration routes. Several approaches are presented, from the most empirical models to full physiologically based pharmacokinetic (PBPK) models, with a focus on the population PK methods (compartmental and minimal PBPK models). They include the translational approaches for the linear part of the PK and the TMDD mechanism of mAbs. The objective of this article is to provide an up-to-date overview and future perspectives of the translational PK approaches for mAbs during a model-informed drug development (MIDD), since the field of PK modeling has gained recently significant interest for guiding mAbs drug development.