Quantitative Systems Pharmacology Model Research Articles

Optimizing Clinical Translation of Bispecific T-cell Engagers through Context Unification with a Quantitative Systems Pharmacology Model.

Bispecific T-cell engagers (bsTCEs) have emerged as a promising class of cancer immunotherapy. BsTCEs enable physical connections between T cells and tumor cells to enhance T-cell activity against cancer. Despite several marketing approvals, the development of bsTCEs remains challenging, especially at early clinical translational stages. The intricate design of bsTCEs makes their pharmacologic effects and safety profiles highly dependent on patient's immunological and tumor conditions. Such context-dependent pharmacology introduces considerable uncertainty into translational efforts. In this study, we developed a Quantitative Systems Pharmacology (QSP) model, through context unification, that can facilitate the translation of bsTCEs preclinical data into clinical activity. Through characterizing the formation dynamics of immunological synapse (IS) induced by bsTCEs, this model unifies a broad range of contexts related to target affinity, tumor characteristics, and immunological conditions. After rigorous calibration using both experimental and clinical data, the model enables consistent translation of drug potency observed under diverse experimental conditions into predictable exposure-response relationships in patients. Moreover, the model can help identify optimal target-binding affinities and minimum efficacious concentrations across different clinical contexts. This QSP approach holds significant promise for the future development of bsTCEs.

Quantitative systems pharmacology modeling of tumor heterogeneity in response to BH3-mimetics using virtual tumors calibrated with cell viability assays.

Both primary and acquired resistance mechanisms that involve intra-tumoral cell heterogeneity limit the use of BH3-mimetics to trigger tumor cell apoptosis. This article proposes a new quantitative systems pharmacology (QSP)-based methodology in which cell viability assays are used to calibrate virtual tumors (VTs) made of virtual cells whose fate is determined by simulations from an apoptosis QSP model. VTs representing SU-DHL-4 and KARPAS-422 cell lines were calibrated using invitro data involving venetoclax (anti-BCL2), A-1155463 (anti-BCLXL), and/or A-1210477 (anti-MCL1). The calibrated VTs provide insights into the combination of several BH3-mimetics, such as the distinction between cells eliminated by at least one of the drugs (monotherapies) from the cells eliminated by a pharmacological combination only. Calibrated VTs can also be used as initial conditions in an agent-based model (ABM) framework, and a minimal ABM was developed to bridge invitro SU-DHL-4 cell viability results to tumor growth inhibition experiments in mice.

A quantitative systems pharmacology model of plasma kallikrein-kinin system dysregulation in hereditary angioedema.

Hereditary angioedema (HAE) due to C1-inhibitor deficiency is a rare, debilitating, genetic disorder characterized by recurrent, unpredictable, attacks of edema. The clinical symptoms of HAE arise from excess bradykinin generation due to dysregulation of the plasma kallikrein-kinin system (KKS). A quantitative systems pharmacology (QSP) model that mechanistically describes the KKS and its role in HAE pathophysiology was developed based on HAE attacks being triggered by autoactivation of factor XII (FXII) to activated FXII (FXIIa), resulting in kallikrein production from prekallikrein. A base pharmacodynamic model was constructed and parameterized from literature data and ex vivo assays measuring inhibition of kallikrein activity in plasma of HAE patients or healthy volunteers who received lanadelumab. HAE attacks were simulated using a virtual patient population, with attacks recorded when systemic bradykinin levels exceeded 20pM. The model was validated by comparing the simulations to observations from lanadelumab and plasma-derived C1-inhibitor clinical trials. The model was then applied to analyze the impact of nonadherence to a daily oral preventive therapy; simulations showed a correlation between the number of missed doses per month and reduced drug effectiveness. The impact of reducing lanadelumab dosing frequency from 300mg every 2weeks (Q2W) to every 4weeks (Q4W) was also examined and showed that while attack rates with Q4W dosing were substantially reduced, the extent of reduction was greater with Q2W dosing. Overall, the QSP model showed good agreement with clinical data and could be used for hypothesis testing and outcome predictions.

Viral Kinetics Model of SARS-CoV-2 Infection Informs Drug Discovery, Clinical Dose, and Regimen Selection.

Quantitative systems pharmacology (QSP) has been an important tool to project safety and efficacy of novel or repurposed therapies for the SARS-CoV-2 virus. Here, we present a QSP modeling framework to predict response to antiviral therapeutics with three mechanisms of action (MoA): cell entry inhibitors, anti-replicatives, and neutralizing biologics. We parameterized three distinct model structures describing virus-host interaction by fitting to published viral kinetics data of untreated COVID-19 patients. The models were used to test theoretical behaviors and map therapeutic design criteria of the different MoAs, identifying the most rapid and robust antiviral activity from neutralizing biologic and anti-replicative MoAs. We found good agreement between model predictions and clinical viral load reduction observed with anti-replicative nirmatrelvir/ritonavir (Paxlovid®) and neutralizing biologics bamlanivimab and casirivimab/imdevimab (REGEN-COV®), building confidence in the modeling framework to inform a dose selection. Finally, the model was applied to predict antiviral response with ensovibep, a novel DARPin therapeutic designed as a neutralizing biologic. We developed a new in silico measure of antiviral activity, area under the curve (AUC) of free spike protein concentration, as a metric with larger dynamic range than viral load reduction. By benchmarking to bamlanivimab predictions, we justified dose levels of 75, 225, and 600 mg ensovibep to be administered intravenously in a Phase 2 clinical investigation. Upon trial completion, we found model predictions to be in good agreement with the observed patient data. These results demonstrate the utility of this modeling framework to guide the development of novel antiviral therapeutics.

Using quantitative systems pharmacology (QSP) models to characterize the PK of novel drug modalities

Using quantitative systems pharmacology (QSP) models to characterize the PK of novel drug modalities

Leveraging multi-omics data to empower quantitative systems pharmacology in immuno-oncology.

Understanding the intricate interactions of cancer cells with the tumor microenvironment (TME) is a pre-requisite for the optimization of immunotherapy. Mechanistic models such as quantitative systems pharmacology (QSP) provide insights into the TME dynamics and predict the efficacy of immunotherapy in virtual patient populations/digital twins but require vast amounts of multimodal data for parameterization. Large-scale datasets characterizing the TME are available due to recent advances in bioinformatics for multi-omics data. Here, we discuss the perspectives of leveraging omics-derived bioinformatics estimates to inform QSP models and circumvent the challenges of model calibration and validation in immuno-oncology.

Abstract 875: Quantitative systems pharmacology modeling of loncastuximab tesirine combined with mosunetuzumab and glofitamab helps guide dosing for patients with DLBCL

Abstract Introduction: CD19 and CD20, B-lymphocyte surface antigens, are clinically validated therapeutic targets for the treatment of B-cell malignancies. Loncastuximab tesirine (loncastuximab tesirine-lpyl [Lonca]) is an antibody-drug conjugate comprising an anti-CD19 antibody conjugated to a pyrrolobenzodiazepine dimer cytotoxin. Mosunetuzumab (Mosun) and glofitamab (Glofit) are CD20 × CD3 T-cell-engaging bispecific antibodies (bsAbs) that redirect T cells to eliminate malignant B cells. These agents target a different B-cell surface antigen than Lonca; hence, combining Lonca with either one of these bsAbs is expected to have additive or even synergistic efficacy. Previously, a novel physiologically-based pharmacokinetic (PBPK)-quantitative systems pharmacology (QSP) model was developed (Utsey K, et al. Clin Pharmacol Drug Dev. 2023;12:123-125) and validated with Lonca monotherapy clinical observations in R/R DLBCL (Caimi P, et al. Blood. 2022;140:9548-9550). Modeling results of Lonca in combination with Mosun or Glofit in R/R DLBCL are described herein. Methods: The model was based on the previously validated and published Lonca PBPK-QSP model. This model was reduced in physiological complexity to be compatible with a published minimal PBPK model of Mosun (Hosseini I, et al. NPJ Syst Biol Appl. 2020;6:28) while maintaining the core functionality of predicting tumor dynamics during Lonca monotherapy. The following assumptions were made: (1) Lonca or Mosun/Glofit induce healthy and malignant B-cell killing; (2) the tumor comprises malignant B cells and T cells; (3) tumor volume is calculated using malignant B-cell number; and (4) T cells can enter or leave the tumor as they enter or leave other tissues. Results: Improved tumor growth inhibition (TGI) should result from Lonca + bsAb combination therapy compared with any of the agents as monotherapy. While TGI was predicted to plateau by Cycle (C) 2 with Mosun and Glofit monotherapies, maximum activity of Lonca + bsAb combination therapies may not be observed until C4 or later. Additionally, predicted TGI increased with additional treatment cycles. Conclusions: Results indicate that treatment by the end of C3 for Lonca + Mosun and by the end of C4 for Lonca + Glofit would promote substantially more TGI compared with any of these agents alone. Increased doses of Lonca in combinations were predicted to have limited additional therapeutic benefit; however, additional cycles of combination treatment were predicted to increase TGI. This model suggests that Lonca doses could be reduced to allow for improved tolerability for longer periods of time, up to the point of maximal benefit, although clinical testing is needed to explore these results. The ongoing LOTIS-7 clinical trial (NCT04970901) evaluates the safety and anticancer activity of Lonca in combination with both Glofit and Mosun. Citation Format: Yuezhe Li, A Katharina Wilkins, Tim Knab, Joseph P. Boni. Quantitative systems pharmacology modeling of loncastuximab tesirine combined with mosunetuzumab and glofitamab helps guide dosing for patients with DLBCL [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 875.

Abstract 1238: ISB 2001, a BCMA and CD38 dual targeting T cell engager, demonstrates superior cytotoxicity relative to teclistamab in the samples of patient relapsing from CD38 and BCMA targeted immunotherapies

Abstract Immune therapies targeting single tumor associated antigens (TAA) demonstrated efficacy against multiple myeloma (MM). However, durable responses are still limited1 potentially due to expansion of clones with low target expression2 We have previously demonstrated that simultaneous targeting of BCMA and CD38 on MM tumor cells with heterogenous expression of these antigens using dual targeting ISB 2001, enables superior killing relative to mono-targeting T cell engagers (TCE) 3. Here we present a further characterization of a dual targeting TCE utilizing bone marrow aspirates from multiple myeloma patients. In relapsed/refractory (r/r) patients, which received CD38 targeted therapy, daratumumab cytotoxicity was substantially reduced due to low CD38 expression. Teclistamab is considered the next line of treatment in such patients. However, ISB 2001 consistently demonstrated increased cytotoxicity compared to teclistamab in both newly diagnosed patient and r/r patient samples. Remarkably, ISB 2001 also induced stronger cytotoxic response in one patient relapsing after BCMA targeted therapy, suggesting that the dual targeting ISB 2001 TCE can overcome the escape mechanisms. ISB 2001 was also compared to the combination of teclistamab and daratumumab in cytotoxic assay using healthy donors. ISB 2001 shows superiority to both teclistamab and daratumumab as single agents, as well as when they were used in combination. Additionally, ISB 2001 was compared against a combination of daratumumab and teclistamab in a humanized mouse model of multiple myeloma with a low expression of both CD38 and BCMA, mimicking potential tumor escaping clones. ISB 2001 induced complete eradication of the tumors in almost all animals, whereas the combination only showed partial tumor protection, underscoring that dual targeting by a TCE is superior to two therapeutic agents individually targeting the same antigens. Based on the promising preclinical in vitro, ex vivo and in vivo data, we have advanced ISB 2001 into clinical studies. For calculating the first-in-human (FIH) dose, we have developed a quantitative systems pharmacology (QSP) model. The calculated FIH dose was 50-100 fold higher than that generated by a traditional minimum anticipated biological effect level calculation. Therefore, using this approach will substantially reduce patient exposure to sub-efficacious doses of ISB 2001. This plan was accepted by the HREC in Australia and the FDA in the US to initiate a Phase 1 FIH study of ISB 2001 for the treatment of relapsed/refractory multiple myeloma (NCT05862012)4. 1. Munshi, N. C. et al. 384, 705-716 (2021). 2. Nijhof, I. S. et al. Blood 128, 12 (2016). 3. (https://doi.org/10.1182/blood-2022-159353) 4. Abstract# 3396, ASH 2023, Hanlon Sia at al. Citation Format: Laura Carretero-Iglesia, Maria Pihlgren, Jeremy Berret, Adam Drake, Daniela Pais, Cyrille Dreyfus, Vinu Menon, Thomas Matthes, Claire Edwards, James Edwards, Catherine Pellat-Deceunynck, Philippe Moreau, Cyrille Touzeau, Tomomi Matsuura, Piet van der Graaf, Lida Pacaud, Cyril Konto, Eugene Zhukovsky, Mario Perro. ISB 2001, a BCMA and CD38 dual targeting T cell engager, demonstrates superior cytotoxicity relative to teclistamab in the samples of patient relapsing from CD38 and BCMA targeted immunotherapies [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1238.

Advancing precision medicine therapeutics for Parkinson’s utilizing a shared quantitative systems pharmacology model and framework

A rich pipeline of therapeutic candidates is advancing for Parkinson’s disease, many of which are targeting the underlying pathophysiology of disease. Emerging evidence grounded in novel genetics and biomarker discoveries is illuminating the true promise of precision medicine-based therapeutic strategies for PD. There has been a growing effort to investigate disease-modifying therapies by designing clinical trials for genetic forms of PD - providing a clearer link to underlying pathophysiology. Leading candidate genes based on human genetic findings that are under active investigation in an array of basic and translational models include SNCA, LRRK2, and GBA. Broad investigations across mechanistic models show that these genes signal through common molecular pathways, namely, autosomal lysosomal pathways, inflammation and mitochondrial function. Therapeutic clinical trials to date based on genetically defined targets have not yet achieved approvals; however, much is to be learned from such pioneering trials. Fundamental principles of drug development that include proof of pharmacology in target tissue are critical to have confidence in advancing such precision-based therapies. There is a clear need for downstream biomarkers of leading candidate therapies to demonstrate proof of mechanism. The current regulatory landscape is poised and primed to support translational modeling strategies for the effective advancement of PD disease-modifying therapeutic candidates. A convergence of rich complex data that is available, the regulatory framework of model informed drug development (MIDD), and the new biological integrated staging frameworks when combined are collectively setting the stage for advancing new approaches in PD to accelerate progress. This perspective review highlights the potential of quantitative systems pharmacology (QSP) modeling in contributing to the field and hastening the pace of progress in advancing collaborative approaches for urgently needed PD disease-modifying treatments.

Biomarkers and computational models for predicting efficacy to tumor ICI immunotherapy.

Numerous studies have shown that immune checkpoint inhibitor (ICI) immunotherapy has great potential as a cancer treatment, leading to significant clinical improvements in numerous cases. However, it benefits a minority of patients, underscoring the importance of discovering reliable biomarkers that can be used to screen for potential beneficiaries and ultimately reduce the risk of overtreatment. Our comprehensive review focuses on the latest advancements in predictive biomarkers for ICI therapy, particularly emphasizing those that enhance the efficacy of programmed cell death protein 1 (PD-1)/programmed cell death-ligand 1 (PD-L1) inhibitors and cytotoxic T-lymphocyte antigen-4 (CTLA-4) inhibitors immunotherapies. We explore biomarkers derived from various sources, including tumor cells, the tumor immune microenvironment (TIME), body fluids, gut microbes, and metabolites. Among them, tumor cells-derived biomarkers include tumor mutational burden (TMB) biomarker, tumor neoantigen burden (TNB) biomarker, microsatellite instability (MSI) biomarker, PD-L1 expression biomarker, mutated gene biomarkers in pathways, and epigenetic biomarkers. TIME-derived biomarkers include immune landscape of TIME biomarkers, inhibitory checkpoints biomarkers, and immune repertoire biomarkers. We also discuss various techniques used to detect and assess these biomarkers, detailing their respective datasets, strengths, weaknesses, and evaluative metrics. Furthermore, we present a comprehensive review of computer models for predicting the response to ICI therapy. The computer models include knowledge-based mechanistic models and data-based machine learning (ML)models. Among the knowledge-based mechanistic models are pharmacokinetic/pharmacodynamic (PK/PD) models, partial differential equation (PDE) models, signal networks-based models, quantitative systems pharmacology (QSP) models, and agent-based models (ABMs). ML models include linear regression models, logistic regression models, support vector machine (SVM)/random forest/extra trees/k-nearest neighbors (KNN) models, artificial neural network (ANN) and deep learning models. Additionally, there are hybrid models of systems biology and ML. We summarized the details of these models, outlining the datasets they utilize, their evaluation methods/metrics, and their respective strengths and limitations. By summarizing the major advances in the research on predictive biomarkers and computer models for the therapeutic effect and clinical utility of tumor ICI, we aim to assist researchers in choosing appropriate biomarkers or computer models for research exploration and help clinicians conduct precision medicine by selecting the best biomarkers.

Characterisation of cotadutide's dual GLP-1/glucagon receptor agonistic effects on glycaemic control using an in vivo human glucose regulation quantitative systems pharmacology model.

Cotadutide is a dual GLP-1 and glucagon receptor agonist with balanced agonistic activity at each receptor designed to harness the advantages on promoting liver health, weight loss and glycaemic control. We characterised the effects of cotadutide on glucose, insulin, GLP-1, GIP, and glucagon over time in a quantitative manner using our glucose dynamics systems model (4GI systems model), in combination with clinical data from a multiple ascending dose/Phase 2a (MAD/Ph2a) study in overweight and obese subjects with a history of Type 2 diabetes mellitus (NCT02548585). The cotadutide PK-4GI systems model was calibrated to clinical data by re-estimating only food related parameters. In vivo cotadutide efficacy was scaled based on in vitro potency. The model was used to explore the effect of weight loss on insulin sensitivity and predict the relative contribution of the GLP-1 and glucagon receptor agonistic effects on glucose. Cotadutide MAD/Ph2a clinical endpoints were successfully predicted. The 4GI model captured a positive effect of weight loss on insulin sensitivity and showed that the stimulating effect of glucagon on glucose production counteracts the GLP-1 receptor-mediated decrease in glucose, resulting in a plateau for glucose decrease around a 200-μg cotadutide dose. The 4GI quantitative systems pharmacology model was able to predict the clinical effects of cotadutide on glucose, insulin, GLP-1, glucagon and GIP given known in vitro potency. The analyses demonstrated that the quantitative systems pharmacology model, and its successive refinements, will be a valuable tool to support the clinical development of cotadutide and related compounds.

A quantitative systems pharmacology workflow toward optimal design and biomarker stratification of atopic dermatitis clinical trials

BackgroundThe development of atopic dermatitis (AD) drugs is confronted by many disease phenotypes and trial design options, which are hard to explore experimentally. ObjectiveOptimize AD trial design using simulations. MethodsWe constructed a quantitative systems pharmacology (QSP) model of AD and standard of care (SoC) treatments and generated a phenotypically diverse virtual population whose parameter distribution is a) derived from known relationships between AD biomarkers and disease severity and b) calibrated using disease severity evolution under SoC regimens. ResultsWe applied this workflow to the immunomodulator OM-85, currently being investigated for its potential use in AD, and calibrated investigational treatment model with the efficacy profile of an existing trial (thereby enriching it with plausible marker levels and dynamics). We assessed the sensitivity of trial outcomes to trial protocol and found that for this particular example, a) the choice of endpoint is more important than the choice of dosing-regimen and b) patient selection by model-based responder enrichment could increase the expected effect size. A global sensitivity analysis revealed that only a limited subset of baseline biomarkers is needed to predict the drug response of the full virtual population. ConclusionThis AD QSP workflow built around knowledge of marker-severity relationships as well as SoC efficacy can be tailored to specific development cases so as to optimize several trial protocol parameters and biomarker-stratification and therefore holds promise to become a powerful model-informed AD drug development and personalized medicine tool.

Development and Evaluation of a Quantitative Systems Pharmacology Model for Mechanism Interpretation and Efficacy Prediction of Atezolizumab in Combination with Carboplatin and Nab-Paclitaxel in Patients with Non-Small-Cell Lung Cancer.

Immunotherapy has shown clinical benefit in patients with non-small-cell lung cancer (NSCLC). Due to the limited response of monotherapy, combining immune checkpoint inhibitors (ICIs) and chemotherapy is considered a treatment option for advanced NSCLC. However, the mechanism of combined therapy and the potential patient population that could benefit from combined therapy remain undetermined. Here, we developed an NSCLC model based on the published quantitative systems pharmacology (QSP)-immuno-oncology platform by making necessary adjustments. After calibration and validation, the established QSP model could adequately characterise the biological mechanisms of action of the triple combination of atezolizumab, nab-paclitaxel, and carboplatin in patients with NSCLC, and identify predictive biomarkers for precision dosing. The established model could efficiently characterise the objective response rate and duration of response of the IMpower131 trial, reproducing the efficacy of alternative dosing. Furthermore, CD8+ and CD4+ T cell densities in tumours were found to be significantly related to the response status. This significant extension of the QSP model not only broadens its applicability but also more accurately reflects real-world clinical settings. Importantly, it positions the model as a critical foundation for model-informed drug development and the customisation of treatment plans, especially in the context of combining single-agent ICIs with platinum-doublet chemotherapy.

Digital twins elucidate critical role of Tscm in clinical persistence of TCR-engineered cell therapy

Despite recent progress in adoptive T cell therapy for cancer, understanding and predicting the kinetics of infused T cells remains a challenge. Multiple factors can impact the distribution, expansion, and decay or persistence of infused T cells in patients. We have developed a novel quantitative systems pharmacology (QSP) model of TCR-transgenic T cell therapy in patients with solid tumors to describe the kinetics of endogenous T cells and multiple memory subsets of engineered T cells after infusion. These T cells undergo lymphodepletion, proliferation, trafficking, differentiation, and apoptosis in blood, lymph nodes, tumor site, and other peripheral tissues. Using the model, we generated patient-matched digital twins that recapitulate the circulating T cell kinetics reported from a clinical trial of TCR-engineered T cells targeting E7 in patients with metastatic HPV-associated epithelial cancers. Analyses of key parameters influencing cell kinetics and differences among digital twins identify stem cell-like memory T cells (Tscm) cells as an important determinant of both expansion and persistence and suggest that Tscm-related differences contribute significantly to the observed variability in cellular kinetics among patients. We simulated in silico clinical trials using digital twins and predict that Tscm enrichment in the infused product improves persistence of the engineered T cells and could enable administration of a lower dose. Finally, we verified the broader relevance of the QSP model, the digital twins, and findings on the importance of Tscm enrichment by predicting kinetics for two patients with pancreatic cancer treated with KRAS G12D targeting T cell therapy. This work offers insight into the key role of Tscm biology on T cell kinetics and provides a quantitative framework to evaluate cellular kinetics for future efforts in the development and clinical application of TCR-engineered T cell therapies.

Emulation of Quantitative Systems Pharmacology models to accelerate virtual population inference in immuno-oncology

Quantitative Systems Pharmacology (QSP) models are increasingly being applied for target discovery and dose selection in immuno-oncology (IO). Typical application involves virtual trial, a simulation of a virtual population of hundreds of model instances with model inputs reflecting individual variability. While the structure of the model and initial parameterisation are based on literature describing the underlying biology, calibration of the virtual population by existing clinical data is frequently required to create tumour and patient population specific model instances. Since comparison of a virtual trial with clinical output requires hundreds of large-scale, non-linear model evaluations, the inference of a virtual population is computationally expensive, frequently becoming a bottleneck. Here, we present novel approach to virtual population inference in IO using emulation of the QSP model and an objective function based on Kolmogorov-Smirnov statistics to maximise congruence of simulated and observed clinical tumour size distributions. We sample the parameter space of a QSP IO model to collect a set of tumour growth time profiles. We evaluate performance of several machine learning approaches in interpolating these time profiles and create a surrogate model, which computes tumor growth profiles faster than the original model and allows examination of tens of millions of virtual patients. We use the surrogate model to infer a virtual population maximising congruence with the waterfall plot of a pembrolizumab clinical trial. We believe that our approach is applicable not only in QSP IO, but also in other applications where virtual populations need to be inferred for computationally expensive mechanistic models.

A Comprehensive Review of the Clinical Pharmacokinetics, Pharmacodynamics, and Drug Interactions of Nirmatrelvir/Ritonavir.

Nirmatrelvir is a potent and selective inhibitor of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) main protease that is used as an oral antiviral coronavirus disease 2019 (COVID-19) treatment. To sustain unbound systemic trough concentrations above the antiviral in vitro 90% effective concentration value (EC90), nirmatrelvir is coadministered with 100 mg of ritonavir, a pharmacokinetic enhancer. Ritonavir inhibits nirmatrelvir's cytochrome P450 (CYP) 3A4-mediated metabolism which results in renal elimination becoming the primary route of nirmatrelvir elimination when dosed concomitantly. Nirmatrelvir exhibits absorption-limited nonlinear pharmacokinetics. When coadministered with ritonavir in patients with mild-to-moderate COVID-19, nirmatrelvir reaches a maximum concentration of 3.43 µg/mL (11.7× EC90) in approximately 3 h on day 5 of dosing, with a geometric mean day 5 trough concentration of 1.57 µg/mL (5.4× EC90). Drug interactions with nirmatrelvir/ritonavir (PAXLOVIDTM) are primarily attributed to ritonavir-mediated CYP3A4 inhibition, and to a lesser extent CYP2D6 and P-glycoprotein inhibition. Population pharmacokinetics and quantitative systems pharmacology modeling support twice daily dosing of 300 mg/100 mg nirmatrelvir/ritonavir for 5 days, with a reduced 150 mg/100 mg dose for patients with moderate renal impairment. Rapid clinical development of nirmatrelvir/ritonavir in response to the emerging COVID-19 pandemic was enabled by innovations in clinical pharmacology research, including an adaptive phase 1 trial design allowing direct to pivotal phase 3 development, fluorine nuclear magnetic resonance spectroscopy to delineate absorption, distribution, metabolism, and excretion profiles, and innovative applications of model-informed drug development to accelerate development.

Dynamical behavior-based approach for the evaluation of treatment efficacy: The case of immuno-oncology.

Sophistication of mathematical models in the pharmacological context reflects the progress being made in understanding physiological, pharmacological, and disease relationships. This progress has illustrated once more the need for advanced quantitative tools able to efficiently extract information from these models. While dynamical systems theory has a long history in the analysis of systems biology models, as emphasized under the dynamical disease concept by Mackey and Glass [Science 197, 287-289 (1977)], its adoption in pharmacometrics is only at the beginning [Chae, Transl. Clin. Pharmacol. 28, 109 (2020)]. Using a quantitative systems pharmacology model of tumor immune dynamics as a case study [Kosinsky et al., J. Immunother. Cancer 6, 17 (2018)], we here adopt a dynamical systems analysis to describe, in an exhaustive way, six different statuses that refer to the response of the system to therapy, in the presence or absence of a tumor-free attractor. To evaluate the therapy success, we introduce the concept of TBA, related to the Time to enter the tumor-free Basin of Attraction, and corresponding to the earliest time at which the therapy can be stopped without jeopardizing its efficacy. TBA can determine the optimal time to stop drug administration and consequently quantify the reduction in drug exposure.

Eliciting the antitumor immune response with a conditionally activated PD-L1 targeting antibody analyzed with a quantitative systems pharmacology model.

Conditionally activated molecules, such as Probody therapeutics (PbTx), have recently been investigated to improve antitumoral response while reducing systemic toxicity. PbTx are engineered to be proteolytically activated by proteases that are preferentially active locally in the tumor microenvironment (TME). Here, we perform an exploratory study using our recently published quantitative systems pharmacology model, previously validated for other drugs, to evaluate the effectiveness and targeting specificity of an anti-PD-L1 PbTx compared to the non-modified antibody. We have informed the model using the PbTx dynamics and pharmacokinetics published in the literature for anti-PD-L1 in patients with triple-negative breast cancer (TNBC). Our results suggest masking of the antibody slightly decreases its efficacy, while increasing the localization of active therapeutic component in the TME. We also perform a parameter optimization for the PbTx design and drug dosing regimens to maximize the response rate. Although our results are specific to the case of TNBC, our findings are generalizable to any conditionally activated PbTx molecule in solid tumors and suggest that design of a highly effective and selective PbTx is feasible.

Mechanistic modeling of tau biomarkers for amyloid load. A Quantitative Systems Pharmacology study of amyloid‐tau interaction.

AbstractBackgroundRecent clinical trials with amyloid antibodies have suggested a beneficial effect on cognitive scales after clearing most of the amyloid load. Interestingly, substantial reduction in plasma or CSF tau biomarkers were documented providing interesting insights into the amyloid‐tau interactions in the human AD patient.MethodWe developed a Quantitative Systems Pharmacology (QSP) platform for late MCI‐prodromal AD subjects that combined an earlier published Physiology‐Based Pharmacokinetic (PBPK)‐ QSP model of amyloid aggregation with a novel QSP model describing the impact of amyloid monomers on glutamatergic and nicotinic neurotransmission and neuronal firing, the neuronal activity‐dependent tau release, subsequent tau uptake in the afferent neuron and dynamics of tau biomarkers in CSF and plasma.ResultThe combined model quantitatively reproduced (1) the cortical hyperactivity and higher tau biomarker levels associated with increasing amyloid deposition in observational studies, (2) the differential decrease in CSF and plasma tau biomarkers in clinical trials with various amyloid antibodies. Dynamics of monomeric brain Ab40 and Ab42 and their effect on neuronal firing, rather than reduction in SUVR can better explain the clinically observed changes in CSF or plasma tau between aducanumab, lecanemab, donanemab and gantenerumab. The range of commonly used comedications and common dopaminergic and serotonergic genotype variants affecting neuronal activity can partially explain the variability of tau biomarkers response as downstream effects of amyloid removal.ConclusionThis study suggests a direct link between amyloid dynamics and plasma / CSF tau levels through modulation of neuronal activity. The calibrated QSP model sets the stage for modeling long‐term effects of amyloid therapy on tau pathology progression and identification of optimal trial design for combination therapies.

Exploring treatment guidelines for amyloid therapies in Alzheimer’s Disease Using Predictive Quantitative System Pharmacology Modeling

AbstractThe recent approvals of amyloid antibodies for treatment of Alzheimer’s disease have heralded a new era in the physician’s treatment toolbox and the availability of individualized treatment guidelines for patients will greatly benefit an ever greater patient population. However, generating these guidelines using clinical experiments is not feasible.We used a mechanism‐based Quantitative Systems Pharmacology model, well validated with published natural history and group average clinical trial data of six amyloid antibodies and applied it to individual scenarios, including APOE genotype. For baseline SUVR between 70 and 180 centiloids, the time to reach amyloid negativity was predicted to range on average from 25 to 64 months for aducanumab, 18 to 47 months for lecanemab and 10 to 25 months for donanemab. Interestingly, we derived a relationship between the baseline SUVR load and the changes in plasma Ab42/Ab40 ratio or CSF Ab42 level associated with reaching amyloid negativity. This relationship is different for each antibody with lecanemab more sensitive than donanemab and aducanemab and provides an alternative to PET imaging to determine the time of reaching amyloid negativity. ARIA‐E liability for an APOE4 carrier ranges between 20 and 31% for aducanumab, 10‐18% for lecanamab and 17‐27% for donanemab during the time to reach Abeta negativity.After reaching amyloid negativity, stabilization over 10 years follow‐up can be achieved with a 32‐fold reduction in exposure for all three antibodies, either with a lower dose, lower frequency or a combination of both.For patients with a three‐month dose holiday after an ARIA‐E incident, the delay in reaching amyloid negativity is predicted to range from 3 to 6 months for aducanumab, 2 to 4 months for lecanemab and 1.7 to 3 months for donanemab, depending on the titration schedule after reinstatement of the treatment.Finally, after implementation of the trisomy 21 effect, we explored optimal trial designs for autosomal dominant Down syndrome patients for each of the three antibodies.This simulation can be applied to any newly approved drug given the pharmacology (affinities against different Abeta peptides) and PK profile and many individual case scenarios.