A next generation mathematical model for the in vitro to clinical translation of T-cell engagers.

T-cell engager (TCE) molecules activate the immune system and direct it to kill tumor cells. The key mechanism of action of TCEs is to crosslink CD3 on T cells and tumor associated antigens (TAAs) on tumor cells. The formation of this trimolecular complex (i.e. trimer) mimics the immune synapse, leading to therapeutic-dependent T-cell activation and killing of tumor cells. Computational models supporting TCE development must predict trimer formation accurately. Here, we present a next-generation two-step binding mathematical model for TCEs to describe trimer formation. Specifically, we propose to model the second binding step with trans-avidity and as a two-dimensional (2D) process where the reactants are modeled as the cell-surface density. Compared to the 3D binding model where the reactants are described in terms of concentration, the 2D model predicts less sensitivity of trimer formation to varying cell densities, which better matches changes in EC50 from in vitro cytotoxicity assay data with varying E:T ratios. In addition, when translating in vitro cytotoxicity data to predict in vivo active clinical dose for blinatumomab, the choice of model leads to a notable difference in dose prediction. The dose predicted by the 2D model aligns better with the approved clinical dose and the prediction is robust under variations in the in vitro to in vivo translation assumptions. In conclusion, the 2D model with trans-avidity to describe trimer formation is an improved approach for TCEs and is likely to produce more accurate predictions to support TCE development.

Systematic in silico analysis of clinically tested drugs for reducing amyloid-beta plaque accumulation in Alzheimer's disease.

INTRODUCTION: Despite strong evidence linking amyloid beta (Aß) to Alzheimer's disease, most clinical trials have shown no clinical efficacy for reasons that remain unclear. To understand why, we developed a quantitative systems pharmacology (QSP) model for seven therapeutics: aducanumab, crenezumab, solanezumab, bapineuzumab, elenbecestat, verubecestat, and semagacestat. METHODS: Ordinary differential equations were used to model the production, transport, and aggregation of Aß; pharmacology of the drugs; and their impact on plaque. RESULTS: The calibrated model predicts that endogenous plaque turnover is slow, with an estimated half-life of 2.75 years. This is likely why beta-secretase inhibitors have a smaller effect on plaque reduction. Of the mechanisms tested, the model predicts binding to plaque and inducing antibody-dependent cellular phagocytosis is the best approach for plaque reduction. DISCUSSION: A QSP model can provide novel insights to clinical results. Our model explains the results of clinical trials and provides guidance for future therapeutic development.

A quantitative systems pharmacology model of colonic motility with applications in drug development.

We developed a mathematical model of colon physiology driven by serotonin signaling in the enteric nervous system. No such models are currently available to assist drug discovery and development for GI motility disorders. Model parameterization was informed by published preclinical and clinical data. Our simulations provide clinically relevant readouts of bowel movement frequency and stool consistency. The model recapitulates healthy and slow transit constipation phenotypes, and the effect of a 5-HT4 receptor agonist in healthy volunteers. Using the calibrated model, we predicted the agonist dose to normalize defecation frequency in slow transit constipation while avoiding the onset of diarrhea. Model sensitivity analysis predicted that changes in HAPC frequency and liquid secretion have the greatest impact on colonic motility. However, exclusively increasing the liquid secretion can lead to diarrhea. In contrast, increasing HAPC frequency alone can enhance bowel frequency without leading to diarrhea. The quantitative systems pharmacology approach used here demonstrates how mechanistic modeling of disease pathophysiology expands our understanding of biology and supports judicious hypothesis generation for therapeutic intervention.

An Introduction to the Regulatory and Nonclinical Aspects of the Nonclinical Development of Antibody Drug Conjugates.

Antibody drug conjugates (ADCs) are promising therapies currently in development for oncology with unique and challenging regulatory and scientific considerations. While there are currently no regulatory guidelines specific for the nonclinical development of ADCs, there are harmonized international guidelines (e.g., ICHS6(R1), ICHM3(R2), ICHS9) that apply to ADCs and provide a framework for their complex development with issues that apply to both small and large molecules. The regulatory and scientific perspectives on ADCs are evolving due to both the advances in ADC technology and a better understanding of the safety and efficacy of ADCs in clinical development. This paper introduces the key scientific and regulatory aspects of the nonclinical development of ADCs, discusses important regulatory and scientific issues in the nonclinical to clinical dose translation of ADCs, and introduces new concepts in the areas of pharmacokinetic/pharmacodynamic (PK/PD) modeling and simulation.

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.

Mechanistic PK/PD modeling to address early-stage biotherapeutic dosing feasibility questions.

Early assessment of dosing requirements should be an integral part of developability assessments for a discovery program. If a very high dose is required to achieve the desired pharmacological effect, it may not be clinically feasible or commercially desirable to develop the biotherapeutic for the selected target unless extra measures are taken to develop a high concentration formulation or maximize yield during manufacturing. A quantitative understanding of the impact of target selection, biotherapeutic format, and optimal drug properties on potential dosing requirements to achieve efficacy can affect many early decisions. Early prediction of dosing requirements for biotherapeutics, as opposed to small molecules, is possible due to a strong influence of target biology on pharmacokinetics and dosing. Mechanistic pharmacokinetic/pharmacodynamic (PK/PD) models leverage knowledge and competitor data available at an early stage of drug development, including biophysics of the target(s) and disease physiology, to rationally inform drug design criteria. Here we review how mathematical mechanistic PK/PD modeling can and has been applied to guide early drug development decisions.

Quantitative systems pharmacology model of GITR-mediated T cell dynamics in tumor microenvironment.

T cell interaction in the tumor microenvironment is a key component of immuno-oncology therapy. Glucocorticoid-induced tumor necrosis factor receptor (TNFR)-related protein (GITR) is expressed on immune cells including regulatory T cells (Tregs) and effector T cells (Teffs). Preclinical data suggest that agonism of GITR in combination with Fc-γ receptor-mediated depletion of Tregs results in increased intratumoral Teff:Treg ratio and tumor shrinkage. A novel quantitative systems pharmacology (QSP) model was developed for the murine anti-GITR agonist antibody, DTA-1.mIgG2a, to describe the kinetics of intratumoral Tregs and Teffs in Colon26 and A20 syngeneic mouse tumor models. It adequately captured the time profiles of intratumoral Treg and Teff and serum DTA-1.mIgG2a and soluble GITR concentrations in both mouse models, and described the response differences between the two models. The QSP model provides a quantitative understanding of the trade-off between maximizing Treg depletion versus Teff agonism, and offers insights to optimize drug design and dose regimen.

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 model for Alzheimer's disease to predict the effect of aducanumab on brain amyloid.

Alzheimer's disease (AD) is an irreversible, progressive brain disorder that impairs memory and cognitive function. Dysregulation of the amyloid-ß (Aß) pathway and amyloid plaque accumulation in the brain are hallmarks of AD. Aducanumab is a human, immunoglobulin gamma 1 monoclonal antibody targeting aggregated forms of Aß. In phase Ib and phase III studies, aducanumab reduced Aß plaques in a dose dependent manner, as measured by standard uptake value ratio of amyloid positron emission tomography imaging. The goal of this work was to develop a quantitative systems pharmacology model describing the production, aggregation, clearance, and transport of Aß as well as the mechanism of action for the drug to understand the relationship between aducanumab dosing regimens and changes of different Aß species, particularly plaques in the brain. The model was used to better understand the pharmacodynamic effects observed in the clinical trials of aducanumab and assist in the clinical development of future Aß therapies.

Model-Informed Drug Development of the Masked Anti-PD-L1 Antibody CX-072.

CX-072 is an anti-PD-L1 (programmed death ligand 1) Probody therapeutic (Pb-Tx) designed to be preferentially activated by proteases in the tumor microenvironment and not in healthy tissue. Here, we report the model-informed drug development of CX-072. A quantitative systems pharmacology (QSP) model that captured known mechanisms of Pb-Tx activation, biodistribution, elimination, and target engagement was used to inform clinical translation. The QSP model predicted that a trough level of masked CX-072 (intact CX-072) of 13-99 nM would correspond to a targeted, 95% receptor occupancy in the tumor. The QSP model predictions appeared consistent with preliminary human single-dose pharmacokinetic (PK) data following CX-072 0.03-30.0 mg/kg as monotherapy: CX-072 circulated predominantly as intact CX-072 with minimal evidence of target-mediated drug disposition. A preliminary population PK (POPPK) analysis based upon 130 subjects receiving 0.03-30.0 mg/kg as monotherapy included a provision for a putative time-dependent and dose-dependent antidrug antibody (ADA) effect on clearance (CL) with a mixture model. Preliminary POPPK estimates for intact CX-072 time-invariant CL and volume of distribution were 0.306 L/day and 4.84 L, respectively. Exposure-response analyses did not identify statistically significant relationships with best change from baseline sum of measurements and either adverse events of grade ≥ 3 or of special interest. Simulations suggested that > 95% of patients receiving CX-072 10 mg/kg every two weeks would exceed the targeted trough level regardless of ADA, and that dose adjustment by body weight was not necessary, supporting a fixed 800 mg dose for evaluation in phase II.

Virtual clinical trial simulations for a novel KRASG12C inhibitor (ASP2453) in non-small cell lung cancer.

KRAS is a small GTPase family protein that relays extracellular growth signals to cell nucleus. KRASG12C mutations lead to constitutive proliferation signaling and are prevalent across human cancers. ASP2453 is a novel, highly potent, and selective inhibitor of KRASG12C . Although preclinical data suggested impressive efficacy, it remains unclear whether ASP2453 will show more favorable clinical response compared to more advanced competitors, such as AMG 510. Here, we developed a quantitative systems pharmacology (QSP) model linking KRAS signaling to tumor growth in patients with non-small cell lung cancer. The model was parameterized using in vitro ERK1/2 phosphorylation and in vivo xenograft data for ASP2453. Publicly disclosed clinical data for AMG 510 were used to generate a virtual population, and tumor size changes in response to ASP2453 and AMG 510 were simulated. The QSP model predicted ASP2453 exhibits greater clinical response than AMG 510, supporting potential differentiation and critical thinking for clinical trials.

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.

Stimulus design for model selection and validation in cell signaling.

Mechanism-based chemical kinetic models are increasingly being used to describe biological signaling. Such models serve to encapsulate current understanding of pathways and to enable insight into complex biological processes. One challenge in model development is that, with limited experimental data, multiple models can be consistent with known mechanisms and existing data. Here, we address the problem of model ambiguity by providing a method for designing dynamic stimuli that, in stimulus-response experiments, distinguish among parameterized models with different topologies, i.e., reaction mechanisms, in which only some of the species can be measured. We develop the approach by presenting two formulations of a model-based controller that is used to design the dynamic stimulus. In both formulations, an input signal is designed for each candidate model and parameterization so as to drive the model outputs through a target trajectory. The quality of a model is then assessed by the ability of the corresponding controller, informed by that model, to drive the experimental system. We evaluated our method on models of antibody-ligand binding, mitogen-activated protein kinase (MAPK) phosphorylation and de-phosphorylation, and larger models of the epidermal growth factor receptor (EGFR) pathway. For each of these systems, the controller informed by the correct model is the most successful at designing a stimulus to produce the desired behavior. Using these stimuli we were able to distinguish between models with subtle mechanistic differences or where input and outputs were multiple reactions removed from the model differences. An advantage of this method of model discrimination is that it does not require novel reagents, or altered measurement techniques; the only change to the experiment is the time course of stimulation. Taken together, these results provide a strong basis for using designed input stimuli as a tool for the development of cell signaling models.

Quantitative Systems Pharmacology Model of a Masked, Tumor-Activated Antibody.

PROBODY therapeutics (Pb-Tx) are protease-activatable prodrugs of monoclonal antibodies (mAbs) designed to target tumors where protease activity is elevated while avoiding normal tissue. They are composed of a parental mAb, a mask that inhibits antibody binding to target, and a protease-cleavable substrate between the mask and the mAb. We report a quantitative systems pharmacology model for the rational design and clinical translation of Pb-Tx. The model adequately described monkey pharmacokinetic data following the administration of six anti-CD166 Pb-Tx of varying mask strength and substrate cleavability and captured the trend of decreasing Pb-Tx systemic clearance with increasing mask strength. Projections to humans suggested both higher levels of Pb-Tx in tumor relative to parental mAb and an optimal mask strength for maximizing tumor receptor-mediated uptake. Simulations further suggested the majority of circulating species in humans would be intact/masked Pb-Tx, with no significant flux of cleaved/activated species from tumor to the systemic compartment.

Quantitative Systems Pharmacology Model of hUGT1A1-modRNA Encoding for the UGT1A1 Enzyme to Treat Crigler-Najjar Syndrome Type 1.

Crigler-Najjar syndrome type 1 (CN1) is an autosomal recessive disease caused by a marked decrease in uridine-diphosphate-glucuronosyltransferase (UGT1A1) enzyme activity. Delivery of hUGT1A1-modRNA (a modified messenger RNA encoding for UGT1A1) as a lipid nanoparticle is anticipated to restore hepatic expression of UGT1A1, allowing normal glucuronidation and clearance of bilirubin in patients. To support translation from preclinical to clinical studies, and first-in-human studies, a quantitative systems pharmacology (QSP) model was developed. The QSP model was calibrated to plasma and liver mRNA, and total serum bilirubin in Gunn rats, an animal model of CN1. This QSP model adequately captured the observed plasma and liver biomarker behavior across a range of doses and dose regimens in Gunn rats. First-in-human dose projections made using the translated model indicated that 0.5 mg/kg Q4W dose should provide a clinically meaningful and sustained reduction of >5 mg/dL in total bilirubin levels.

Quantitative systems toxicology.

The overarching goal of modern drug development is to optimize therapeutic benefits while minimizing adverse effects. However, inadequate efficacy and safety concerns remain to be the major causes of drug attrition in clinical development. For the past 80 years, toxicity testing has consisted of evaluating the adverse effects of drugs in animals to predict human health risks. The U.S. Environmental Protection Agency recognized the need to develop innovative toxicity testing strategies and asked the National Research Council to develop a long-range vision and strategy for toxicity testing in the 21st century. The vision aims to reduce the use of animals and drug development costs through the integration of computational modeling and in vitro experimental methods that evaluates the perturbation of toxicity-related pathways. Towards this vision, collaborative quantitative systems pharmacology and toxicology modeling endeavors (QSP/QST) have been initiated amongst numerous organizations worldwide. In this article, we discuss how quantitative structure-activity relationship (QSAR), network-based, and pharmacokinetic/pharmacodynamic modeling approaches can be integrated into the framework of QST models. Additionally, we review the application of QST models to predict cardiotoxicity and hepatotoxicity of drugs throughout their development. Cell and organ specific QST models are likely to become an essential component of modern toxicity testing, and provides a solid foundation towards determining individualized therapeutic windows to improve patient safety.

Sloppy models, parameter uncertainty, and the role of experimental design.

Computational models are increasingly used to understand and predict complex biological phenomena. These models contain many unknown parameters, at least some of which are difficult to measure directly, and instead are estimated by fitting to time-course data. Previous work has suggested that even with precise data sets, many parameters are unknowable by trajectory measurements. We examined this question in the context of a pathway model of epidermal growth factor (EGF) and neuronal growth factor (NGF) signaling. Computationally, we examined a palette of experimental perturbations that included different doses of EGF and NGF as well as single and multiple gene knockdowns and overexpressions. While no single experiment could accurately estimate all of the parameters, experimental design methodology identified a set of five complementary experiments that could. These results suggest optimism for the prospects for calibrating even large models, that the success of parameter estimation is intimately linked to the experimental perturbations used, and that experimental design methodology is important for parameter fitting of biological models and likely for the accuracy that can be expected from them.