Comparison of dose selection based on target engagement versus inhibition of receptor-ligand interaction for checkpoint inhibitors.

Immune checkpoint inhibitors block the interaction between a receptor on one cell and its ligand on another cell, thus preventing the transduction of an immunosuppressive signal. While inhibition of the receptor-ligand interaction is key to the pharmacological activity of these drugs, it can be technically challenging to measure these intercellular interactions directly. Instead, target engagement (or receptor occupancy) is commonly measured, but may not always be an accurate predictor of receptor-ligand inhibition, and can be misleading when used to inform clinical dose projections for this class of drugs. In this study, a mathematical model explicitly representing the intercellular receptor-ligand interaction is used to compare dose prediction based on target engagement or receptor-ligand inhibition for two checkpoint inhibitors, atezolizumab and magrolimab. For atezolizumab, there is little difference between target engagement and receptor-ligand inhibition, but for magrolimab, the model predicts that receptor-ligand inhibition is significantly less than target engagement. The key variables explaining the difference between these two drugs are the relative concentrations of the target receptors and their ligands. Drug-target affinity and receptor-ligand affinity can also have divergent effects on target engagement and inhibition. These results suggest that it is important to consider ligand-receptor inhibition in addition to target engagement and demonstrate the impact of using modeling for efficacious dose estimation.

Early Feasibility Assessment: A Method for Accurately Predicting Biotherapeutic Dosing to Inform Early Drug Discovery Decisions.

The application of model-informed drug discovery and development (MID3) approaches in the early stages of drug discovery can help determine feasibility of drugging a target, prioritize between targets, or define optimal drug properties for a target product profile (TPP). However, applying MID3 in early discovery can be challenging due to the lack of pharmacokinetic (PK) and pharmacodynamic (PD) data at this stage. Early Feasibility Assessment (EFA) is the application of mechanistic PKPD models, built from first principles, and parameterized by data that is readily available early in drug discovery to make effective dose predictions. This manuscript demonstrates the ability of EFA to make accurate predictions of clinical effective doses for nine approved biotherapeutics and outlines the potential of extending this approach to novel therapeutics to impact early drug discovery decisions.

Quantitative systems pharmacology modeling provides insight into inter-mouse variability of Anti-CTLA4 response.

Clinical responses of immuno-oncology therapies are highly variable among patients. Similar response variability has been observed in syngeneic mouse models. Understanding of the variability in the mouse models may shed light on patient variability. Using a murine anti-CTLA4 antibody as a case study, we developed a quantitative systems pharmacology model to capture the molecular interactions of the antibody and relevant cellular interactions that lead to tumor cell killing. Nonlinear mixed effect modeling was incorporated to capture the inter-animal variability of tumor growth profiles in response to anti-CTLA4 treatment. The results suggested that intratumoral CD8+ T cell kinetics and tumor proliferation rate were the main drivers of the variability. In addition, simulations indicated that nonresponsive mice to anti-CTLA4 treatment could be converted to responders by increasing the number of intratumoral CD8+ T cells. The model provides a mechanistic starting point for translation of CTLA4 inhibitors from syngeneic mice to the clinic.

A systems pharmacology model for gene therapy in sickle cell disease.

We developed a mathematical model for autologous stem cell therapy to cure sickle cell disease (SCD). Experimental therapies using this approach seek to engraft stem cells containing a curative gene. These stem cells are expected to produce a lifelong supply of red blood cells (RBCs) containing an anti-sickling hemoglobin. This complex, multistep treatment is expensive, and there is limited patient data available from early clinical trials. Our objective was to quantify the impact of treatment parameters, such as initial stem cell dose, efficiency of lentiviral transduction, and degree of bone marrow preconditioning on engraftment efficiency, peripheral RBC numbers, and anti-sickling hemoglobin levels over time. We used ordinary differential equations to model RBC production from progenitor cells in the bone marrow, and hemoglobin assembly from its constituent globin monomers. The model recapitulates observed RBC and hemoglobin levels in healthy and SCD phenotypes. Treatment simulations predict dynamics of stem cell engraftment and RBC containing the therapeutic gene product. Post-treatment dynamics show an early phase of reconstitution due to short lived stem cells, followed by a sustained RBC production from stable engraftment of long-term stem cells. This biphasic behavior was previously reported in the literature. Sensitivity analysis of the model quantified relationships between treatment parameters and efficacy. The initial dose of transduced stem cells, and the intensity of myeloablative bone marrow preconditioning are predicted to most positively impact long-term outcomes. The quantitative systems pharmacology approach used here demonstrates the value of model-assisted therapeutic design for gene therapies in SCD.

Lessons learned from quantitative dynamical modeling in systems biology.

Due to the high complexity of biological data it is difficult to disentangle cellular processes relying only on intuitive interpretation of measurements. A Systems Biology approach that combines quantitative experimental data with dynamic mathematical modeling promises to yield deeper insights into these processes. Nevertheless, with growing complexity and increasing amount of quantitative experimental data, building realistic and reliable mathematical models can become a challenging task: the quality of experimental data has to be assessed objectively, unknown model parameters need to be estimated from the experimental data, and numerical calculations need to be precise and efficient. Here, we discuss, compare and characterize the performance of computational methods throughout the process of quantitative dynamic modeling using two previously established examples, for which quantitative, dose- and time-resolved experimental data are available. In particular, we present an approach that allows to determine the quality of experimental data in an efficient, objective and automated manner. Using this approach data generated by different measurement techniques and even in single replicates can be reliably used for mathematical modeling. For the estimation of unknown model parameters, the performance of different optimization algorithms was compared systematically. Our results show that deterministic derivative-based optimization employing the sensitivity equations in combination with a multi-start strategy based on latin hypercube sampling outperforms the other methods by orders of magnitude in accuracy and speed. Finally, we investigated transformations that yield a more efficient parameterization of the model and therefore lead to a further enhancement in optimization performance. We provide a freely available open source software package that implements the algorithms and examples compared here.

An enzyme kinetic model of blood island formation.

Blood islands are conglomerations of prevascular stem cells that form during vasculogenesis, a function critical to early vascular and hematopoietic development. In this paper, a model of blood island formation is proposed employing the approach of classical enzyme kinetics. Deterministic simulations of the model show the formation of blood island-like structures. These are compared to murine blood islands.