Morris Muliaditan

Successful development of monoclonal antibodies (mAbs) for the treatment of central nervous system disorders has been challenging due to their minimal ability to cross the blood-brain barrier (BBB), resulting in poor brain exposure. Bispecific antibodies (bsAb) that bind to transmembrane protein expressed at the BBB, such as the transferrin receptor (TfR), have shown enhanced brain exposure in rodents and non-human primate (NHP) due to receptor-mediated transcytosis. However, it remains unclear how preclinical findings translate to humans. Moreover, optimal TfR binding affinity remains a subject of debate. Model-informed drug discovery and development is a powerful approach that has been successfully used to support research and development. The goal of this analysis was to expand a published brain minimal physiologically based pharmacokinetic (mPBPK) model to investigate the optimal TfR binding affinity for maximal brain delivery in NHP and to facilitate prediction of the PK of anti-TfR bsAbs in humans from NHP data. Literature data for plasma, cerebrospinal fluid (CSF), and brain exposure after administration of non-TfR mAbs and monovalent bsAbs with respect to TfR in NHP were used to develop the TfR mPBPK model. Clinical validation using human PK data from plasma and CSF for the monovalent anti-TfR bsAb trontinemab demonstrated good predictive performance without major model recalibration. The availability of the TfR mPBPK model is envisaged to provide better understanding of the relationship between TfR binding affinity, dose, and brain exposure, which would lead to more robust selection of lead candidates and efficacious dosing regimens.

The gold standard for regulatory approval in oncology is overall survival (OS). Because OS data are initially limited, early drug development decisions are often based on early efficacy endpoints, such as objective response rate and progression‐free survival. Tumor size (TS)‐OS models provide a framework to support decision‐making on potential late‐stage success based on early readouts, through leveraging TS data with limited follow‐up and treatment‐agnostic TS‐OS link functions, to predict longer‐term OS. Conditional simulations (also known as Bayesian forecasting) with TS‐OS models can be used to simulate long‐term OS outcomes for an ongoing study, conditional on the available TS and OS data at interim data cuts of the same study. This tutorial provides a comprehensive overview of the steps involved in using such conditional simulations to support better informed drug development decisions in oncology. The tutorial covers the selection of the TS‐OS framework model; applying the TS‐OS model to the interim data; performing conditional simulations; generating relevant output; as well as correct interpretation and communication of the output for decision making.

Tumor size–overall survival (TS‐OS) models can support decision‐making in oncology drug development by predicting long‐term OS based on TS data from early data cuts and baseline patient factors. The current work describes the development of a TS‐OS framework capable of predicting OS across a variety of treatment modalities and mechanisms of action in patients with non‐small cell lung cancer from seven clinical studies. The presented framework jointly models TS with a bi‐exponential Stein model and OS with an accelerated failure time log‐normal survival model. In the corresponding link function between TS and OS, the most significant predictor of OS was the tumor growth rate (k_g), applied via an Emax function. Time to tumor growth and baseline TS were additional TS predictors informing OS. Albumin, total protein, and neutrophil‐to‐lymphocyte ratio were selected from the tested baseline factors as the most significant predictors of OS. Significant baseline covariates for the TS model included number of target lesions on baseline TS, tumor PD‐L1 expression on tumor shrinkage rate, and lactate dehydrogenase levels on k_g. The TS‐OS framework model adequately describes the OS distributions within this specific set of treatment modalities—chemotherapies, immuno‐oncology treatments, and combinations thereof—using a single treatment‐independent link function, supporting the use of the framework to support evaluation and design of future studies. Our findings contribute to a body of literature exploring and qualifying TS‐OS modeling as a methodology capable of supporting and accelerating oncology drug development.

Model-informed drug discovery advocates the use of mathematical modeling and simulation for improved efficacy in drug discovery. In the case of monoclonal antibodies (mAbs) against cell membrane antigens, this requires quantitative insight into the target tissue concentration levels. Protein mass spectrometry data are often available but the values are expressed in relative, rather than in molar concentration units that are easier to incorporate into pharmacokinetic models. Here, we present an empirical correlation that converts the parts per million (ppm) concentrations in the PaxDb database to their molar equivalents that are more suitable for pharmacokinetic modeling. We evaluate the insight afforded to target tissue distribution by analyzing the likely tumor-targeting accuracy of mAbs recognizing either epidermal growth factor receptor or its homolog HER2. Surprisingly, the predicted tissue concentrations of both these targets exceed the Kd values of their respective therapeutic mAbs. Physiologically based pharmacokinetic (PBPK) modeling indicates that in these conditions only about 0.05% of the dosed mAb is likely to reach the solid tumor target cells. The rest of the dose is eliminated in healthy tissues via both nonspecific and target-mediated processes. The presented approach allows evaluation of the interplay between the target expression level in different tissues that determines the overall pharmacokinetic properties of the drug and the fraction that reaches the cells of interest. This methodology can help to evaluate the efficacy and safety properties of novel drugs, especially if the off-target cell degradation has cytotoxic outcomes, as in the case of antibody-drug conjugates.