Physiological Indirect Response Model to Omics-Powered Quantitative Systems Pharmacology Model.

Over the past several decades, mathematical modeling has been applied to increasingly wider scopes of questions in drug development. Accordingly, the range of modeling tools has also been evolving, as showcased by contributions of Jusko and colleagues: from basic pharmacokinetics/pharmacodynamics (PK/PD) modeling to today's platform-based approach of quantitative systems pharmacology (QSP) modeling. Aimed at understanding the mechanism of action of investigational drugs, QSP models characterize systemic effects by incorporating information about cellular signaling networks, which is often represented by omics data. In this perspective, we share a few examples illustrating approaches for the integration of omics into mechanistic QSP modeling. We briefly overview how the evolution of PK/PD modeling into QSP has been accompanied by an increase in available data and the complexity of mathematical methods that integrate it. We discuss current gaps and challenges of integrating omics data into QSP models and propose several potential areas where integrated QSP and omics modeling may benefit drug development.

A tutorial for model-based evaluation and translation of cardiovascular safety in preclinical trials.

Assessment of drug-induced effects on the cardiovascular (CV) system remains a critical component of the drug discovery process enabling refinement of the therapeutic index. Predicting potential drug-related unintended CV effects in the preclinical stage is necessary for first-in-human dose selection and preclusion of adverse CV effects in the clinical stage. According to the current guidelines for small molecules, nonclinical CV safety assessment conducted via telemetry analyses should be included in the safety pharmacology core battery studies. However, the manual for quantitative evaluation of the CV safety signals in animals is available only for electrocardiogram parameters (i.e., QT interval assessment), not for hemodynamic parameters (i.e., heart rate, blood pressure, etc.). Various model-based approaches, including empirical pharmacokinetic-toxicodynamic analyses and systems pharmacology modeling could be used in the framework of telemetry data evaluation. In this tutorial, we provide a comprehensive workflow for the analysis of nonclinical CV safety on hemodynamic parameters with a sequential approach, highlight the challenges associated with the data, and propose respective solutions, complemented with a reproducible example. The work is aimed at helping researchers conduct model-based analyses of the CV safety in animals with subsequent translation of the effect to humans seamlessly and efficiently.

Homeostasis, injury, and recovery dynamics at multiple scales in a self-organizing mouse intestinal crypt.

The maintenance of the functional integrity of the intestinal epithelium requires a tight coordination between cell production, migration, and shedding along the crypt-villus axis. Dysregulation of these processes may result in loss of the intestinal barrier and disease. With the aim of generating a more complete and integrated understanding of how the epithelium maintains homeostasis and recovers after injury, we have built a multi-scale agent-based model (ABM) of the mouse intestinal epithelium. We demonstrate that stable, self-organizing behaviour in the crypt emerges from the dynamic interaction of multiple signalling pathways, such as Wnt, Notch, BMP, ZNRF3/RNF43, and YAP-Hippo pathways, which regulate proliferation and differentiation, respond to environmental mechanical cues, form feedback mechanisms, and modulate the dynamics of the cell cycle protein network. The model recapitulates the crypt phenotype reported after persistent stem cell ablation and after the inhibition of the CDK1 cycle protein. Moreover, we simulated 5-fluorouracil (5-FU)-induced toxicity at multiple scales starting from DNA and RNA damage, which disrupts the cell cycle, cell signalling, proliferation, differentiation, and migration and leads to loss of barrier integrity. During recovery, our in silico crypt regenerates its structure in a self-organizing, dynamic fashion driven by dedifferentiation and enhanced by negative feedback loops. Thus, the model enables the simulation of xenobiotic-, in particular chemotherapy-, induced mechanisms of intestinal toxicity and epithelial recovery. Overall, we present a systems model able to simulate the disruption of molecular events and its impact across multiple levels of epithelial organization and demonstrate its application to epithelial research and drug development.

Quantifying Intratumoral Heterogeneity and Immunoarchitecture Generated In-Silico by a Spatial Quantitative Systems Pharmacology Model.

Spatial heterogeneity is a hallmark of cancer. Tumor heterogeneity can vary with time and location. The tumor microenvironment (TME) encompasses various cell types and their interactions that impart response to therapies. Therefore, a quantitative evaluation of tumor heterogeneity is crucial for the development of effective treatments. Different approaches, such as multiregional sequencing, spatial transcriptomics, analysis of autopsy samples, and longitudinal analysis of biopsy samples, can be used to analyze the intratumoral heterogeneity (ITH) and temporal evolution and to reveal the mechanisms of therapeutic response. However, because of the limitations of these data and the uncertainty associated with the time points of sample collection, having a complete understanding of intratumoral heterogeneity role is challenging. Here, we used a hybrid model that integrates a whole-patient compartmental quantitative-systems-pharmacology (QSP) model with a spatial agent-based model (ABM) describing the TME; we applied four spatial metrics to quantify model-simulated intratumoral heterogeneity and classified the TME immunoarchitecture for representative cases of effective and ineffective anti-PD-1 therapy. The four metrics, adopted from computational digital pathology, included mixing score, average neighbor frequency, Shannon's entropy and area under the curve (AUC) of the G-cross function. A fifth non-spatial metric was used to supplement the analysis, which was the ratio of the number of cancer cells to immune cells. These metrics were utilized to classify the TME as "cold", "compartmentalized" and "mixed", which were related to treatment efficacy. The trends in these metrics for effective and ineffective treatments are in qualitative agreement with the clinical literature, indicating that compartmentalized immunoarchitecture is likely to result in more efficacious treatment outcomes.

Generating immunogenomic data-guided virtual patients using a QSP model to predict response of advanced NSCLC to PD-L1 inhibition.

Generating realistic virtual patients from a limited amount of patient data is one of the major challenges for quantitative systems pharmacology modeling in immuno-oncology. Quantitative systems pharmacology (QSP) is a mathematical modeling methodology that integrates mechanistic knowledge of biological systems to investigate dynamics in a whole system during disease progression and drug treatment. In the present analysis, we parameterized our previously published QSP model of the cancer-immunity cycle to non-small cell lung cancer (NSCLC) and generated a virtual patient cohort to predict clinical response to PD-L1 inhibition in NSCLC. The virtual patient generation was guided by immunogenomic data from iAtlas portal and population pharmacokinetic data of durvalumab, a PD-L1 inhibitor. With virtual patients generated following the immunogenomic data distribution, our model predicted a response rate of 18.6% (95% bootstrap confidence interval: 13.3-24.2%) and identified CD8/Treg ratio as a potential predictive biomarker in addition to PD-L1 expression and tumor mutational burden. We demonstrated that omics data served as a reliable resource for virtual patient generation techniques in immuno-oncology using QSP models.

Generating immunogenomic data-guided virtual patients using a QSP model to predict response of advanced NSCLC to PD-L1 inhibition.

Generating realistic virtual patients from a limited amount of patient data is one of the major challenges for quantitative systems pharmacology modeling in immuno-oncology. Quantitative systems pharmacology (QSP) is a mathematical modeling methodology that integrates mechanistic knowledge of biological systems to investigate dynamics in a whole system during disease progression and drug treatment. In the present analysis, we parameterized our previously published QSP model of the cancer-immunity cycle to non-small cell lung cancer (NSCLC) and generated a virtual patient cohort to predict clinical response to PD-L1 inhibition in NSCLC. The virtual patient generation was guided by immunogenomic data from iAtlas portal and population pharmacokinetic data of durvalumab, a PD-L1 inhibitor. With virtual patients generated following the immunogenomic data distribution, our model predicted a response rate of 18.6% (95% bootstrap confidence interval: 13.3-24.2%) and identified CD8/Treg ratio as a potential predictive biomarker in addition to PD-L1 expression and tumor mutational burden. We demonstrated that omics data served as a reliable resource for virtual patient generation techniques in immuno-oncology using QSP models.

Simulations of tumor growth and response to immunotherapy by coupling a spatial agent-based model with a whole-patient quantitative systems pharmacology model.

Quantitative systems pharmacology (QSP) models and spatial agent-based models (ABM) are powerful and efficient approaches for the analysis of biological systems and for clinical applications. Although QSP models are becoming essential in discovering predictive biomarkers and developing combination therapies through in silico virtual trials, they are inadequate to capture the spatial heterogeneity and randomness that characterize complex biological systems, and specifically the tumor microenvironment. Here, we extend our recently developed spatial QSP (spQSP) model to analyze tumor growth dynamics and its response to immunotherapy at different spatio-temporal scales. In the model, the tumor spatial dynamics is governed by the ABM, coupled to the QSP model, which includes the following compartments: central (blood system), tumor, tumor-draining lymph node, and peripheral (the rest of the organs and tissues). A dynamic recruitment of T cells and myeloid-derived suppressor cells (MDSC) from the QSP central compartment has been implemented as a function of the spatial distribution of cancer cells. The proposed QSP-ABM coupling methodology enables the spQSP model to perform as a coarse-grained model at the whole-tumor scale and as an agent-based model at the regions of interest (ROIs) scale. Thus, we exploit the spQSP model potential to characterize tumor growth, identify T cell hotspots, and perform qualitative and quantitative descriptions of cell density profiles at the invasive front of the tumor. Additionally, we analyze the effects of immunotherapy at both whole-tumor and ROI scales under different tumor growth and immune response conditions. A digital pathology computational analysis of triple-negative breast cancer specimens is used as a guide for modeling the immuno-architecture of the invasive front.

Population Pharmacokinetic Modeling of Inebilizumab in Subjects with Neuromyelitis Optica Spectrum Disorders, Systemic Sclerosis, or Relapsing Multiple Sclerosis.

BACKGROUND AND OBJECTIVE: Inebilizumab is a humanized, affinity-optimized, afucosylated immunoglobulin (Ig)-G1κ monoclonal antibody that binds to CD19, resulting in effective depletion of peripheral B cells. It is being developed to treat various autoimmune diseases, including neuromyelitis optica spectrum disorders (NMOSD), systemic sclerosis (SSc), and relapsing multiple sclerosis (MS). METHODS: Pharmacokinetic data from a pivotal study in adult subjects with NMOSD and two early-stage studies in subjects with SSc or relapsing MS were pooled and simultaneously analyzed using a population approach. RESULTS: Upon intravenous administration, the pharmacokinetics of inebilizumab were adequately described by a two-compartment model with parallel first-order and time-dependent nonlinear elimination pathways. An asymptotic nonlinear elimination suggests that inebilizumab undergoes receptor (CD19)-mediated clearance. The estimated systemic clearance (CL) of the first-order elimination pathway (0.188 L/day) and the volume of distribution (Vd) (5.52 L) were typical for therapeutic immunoglobulins. The elimination half-life was approximately 18 days. The maximum velocity (Vmax) of the nonlinear elimination pathway decreased with time, presumably due to the depletion of B cells upon inebilizumab administration. As for other therapeutic monoclonal antibodies, the CL and Vd of inebilizumab increased with body weight. CONCLUSIONS: The presence of antidrug antibodies, status of hepatic or renal function, and use of small-molecule drugs commonly used by subjects with NMOSD had no clinically relevant impact on the pharmacokinetics of inebilizumab. The nonlinear elimination pathway at the 300 mg therapeutic dose level is not considered clinically relevant.

A Spatial Quantitative Systems Pharmacology Platform spQSP-IO for Simulations of Tumor-Immune Interactions and Effects of Checkpoint Inhibitor Immunotherapy.

Quantitative systems pharmacology (QSP) models have become increasingly common in fundamental mechanistic studies and drug discovery in both academic and industrial environments. With imaging techniques widely adopted and other spatial quantification of tumor such as spatial transcriptomics gaining traction, it is crucial that these data reflecting tumor spatial heterogeneity be utilized to inform the QSP models to enhance their predictive power. We developed a hybrid computational model platform, spQSP-IO, to extend QSP models of immuno-oncology with spatially resolved agent-based models (ABM), combining their powers to track whole patient-scale dynamics and recapitulate the emergent spatial heterogeneity in the tumor. Using a model of non-small-cell lung cancer developed based on this platform, we studied the role of the tumor microenvironment and cancer-immune cell interactions in tumor development and applied anti-PD-1 treatment to virtual patients and studied how the spatial distribution of cells changes during tumor growth in response to the immune checkpoint inhibition treatment. Using parameter sensitivity analysis and biomarker analysis, we are able to identify mechanisms and pretreatment measurements correlated with treatment efficacy. By incorporating spatial data that highlight both heterogeneity in tumors and variability among individual patients, spQSP-IO models can extend the QSP framework and further advance virtual clinical trials.

Systems Modeling to Quantify Safety Risks in Early Drug Development: Using Bifurcation Analysis and Agent-Based Modeling as Examples.

Quantitative Systems Toxicology (QST) models, recapitulating pharmacokinetics and mechanism of action together with the organic response at multiple levels of biological organization, can provide predictions on the magnitude of injury and recovery dynamics to support study design and decision-making during drug development. Here, we highlight the application of QST models to predict toxicities of cancer treatments, such as cytopenia(s) and gastrointestinal adverse effects, where narrow therapeutic indexes need to be actively managed. The importance of bifurcation analysis is demonstrated in QST models of hematologic toxicity to understand how different regions of the parameter space generate different behaviors following cancer treatment, which results in asymptotically stable predictions, yet highly irregular for specific schedules, or oscillating predictions of blood cell levels. In addition, an agent-based model of the intestinal crypt was used to simulate how the spatial location of the injury within the crypt affects the villus disruption severity. We discuss the value of QST modeling approaches to support drug development and how they align with technological advances impacting trial design including patient selection, dose/regimen selection, and ultimately patient safety.

Blocking endothelial lipase with monoclonal antibody MEDI5884 durably increases high density lipoprotein in nonhuman primates and in a phase 1 trial.

Cardiovascular disease (CVD) is the leading global cause of death, and treatments that further reduce CV risk remain an unmet medical need. Epidemiological studies have consistently identified low high-density lipoprotein cholesterol (HDL-C) as an independent risk factor for CVD, making HDL elevation a potential clinical target for improved CVD resolution. Endothelial lipase (EL) is a circulating enzyme that regulates HDL turnover by hydrolyzing HDL phospholipids and driving HDL particle clearance. Using MEDI5884, a first-in-class, EL-neutralizing, monoclonal antibody, we tested the hypothesis that pharmacological inhibition of EL would increase HDL-C by enhancing HDL stability. In nonhuman primates, MEDI5884 treatment resulted in lasting, dose-dependent elevations in HDL-C and circulating phospholipids, confirming the mechanism of EL action. We then showed that a favorable lipoprotein profile of elevated HDL-C and reduced low-density lipoprotein cholesterol (LDL-C) could be achieved by combining MEDI5884 with a PCSK9 inhibitor. Last, when tested in healthy human volunteers, MEDI5884 not only raised HDL-C but also increased HDL particle numbers and average HDL size while enhancing HDL functionality, reinforcing EL neutralization as a viable clinical approach aimed at reducing CV risk.

Evaluation of Combination Strategies for the A2AR Inhibitor AZD4635 Across Tumor Microenvironment Conditions via a Systems Pharmacology Model.

Background: Adenosine receptor type 2 (A2AR) inhibitor, AZD4635, has been shown to reduce immunosuppressive adenosine effects within the tumor microenvironment (TME) and to enhance the efficacy of checkpoint inhibitors across various syngeneic models. This study aims at investigating anti-tumor activity of AZD4635 alone and in combination with an anti-PD-L1-specific antibody (anti-PD-L1 mAb) across various TME conditions and at identifying, via mathematical quantitative modeling, a therapeutic combination strategy to further improve treatment efficacy. Methods: The model is represented by a set of ordinary differential equations capturing: 1) antigen-dependent T cell migration into the tumor, with subsequent proliferation and differentiation into effector T cells (Teff), leading to tumor cell lysis; 2) downregulation of processes mediated by A2AR or PD-L1, as well as other immunosuppressive mechanisms; 3) A2AR and PD-L1 inhibition by, respectively, AZD4635 and anti-PD-L1 mAb. Tumor size dynamics data from CT26, MC38, and MCA205 syngeneic mice treated with vehicle, anti-PD-L1 mAb, AZD4635, or their combination were used to inform model parameters. Between-animal and between-study variabilities (BAV, BSV) in treatment efficacy were quantified using a non-linear mixed-effects methodology. Results: The model reproduced individual and cohort trends in tumor size dynamics for all considered treatment regimens and experiments. BSV and BAV were explained by variability in T cell-to-immunosuppressive cell (ISC) ratio; BSV was additionally driven by differences in intratumoral adenosine content across the syngeneic models. Model sensitivity analysis and model-based preclinical study simulations revealed therapeutic options enabling a potential increase in AZD4635-driven efficacy; e.g., adoptive cell transfer or treatments affecting adenosine-independent immunosuppressive pathways. Conclusions: The proposed integrative modeling framework quantitatively characterized the mechanistic activity of AZD4635 and its potential added efficacy in therapy combinations, across various immune conditions prevailing in the TME. Such a model may enable further investigations, via simulations, of mechanisms of tumor resistance to treatment and of AZD4635 combination optimization strategies.

What Does it Take to Make Model-Informed Precision Dosing Common Practice? Report from the 1st Asian Symposium on Precision Dosing.

Model-informed precision dosing (MIPD) is modeling and simulation in healthcare to predict the drug dose for a given patient based on their individual characteristics that is most likely to improve efficacy and/or lower toxicity in comparison to traditional dosing. This paper describes the background and status of MIPD and the activities at the 1st Asian Symposium of Precision Dosing. The theme of the meeting was the question, "What does it take to make MIPD common practice?" Formal presentations highlighted the distinction between genetic and non-genetic sources of variability in drug exposure and response, the use of modeling and simulation as decision support tools, and the facilitators to MIPD implementation. A panel discussion addressed the types of models used for MIPD, how the pharmaceutical industry views MIPD, ways to upscale MIPD beyond academic hospital centers, and the essential role of healthcare professional education as a way to progress. The meeting concluded with an ongoing commitment to use MIPD to improve patient care.

Toward Dynamic Prescribing Information: Codevelopment of Companion Model-Informed Precision Dosing Tools in Drug Development.

Model-informed precision dosing (MIPD) is biosimulation in healthcare to predict the drug dose for a given patient based on their individual characteristics that is most likely to improve efficacy and/or lower toxicity compared with traditional dosing. Despite widespread use of biosimulation in drug development, MIPD has not been adopted beyond academic-hospital centers. A reason for this is that MIPD requires more supporting evidence in the language that everyday doctors understand-evidence-based medicine. In this commentary, codevelopment of companion MIPD tools during drug development is advocated as a way to accelerate the generation of the evidence required for broader clinical implementation of MIPD. Such tools have the potential to evolve into "dynamic" prescribing information that could guide dose selection for complex patients.

Improving Realism in Clinical Trial Simulations via Real-World Data.

Simulation validity depends on how well sampling distributions used reflect real-patient characteristics, such as drug adherence, disease progression, and pharmacologic handling in the body. We challenge the current use of growth charts from nondisease-specific pediatrics in simulations for drug development. Complementary use of data from clinical trials and the real-world is expected to achieve a more realistic representation of clinical outcomes for decisions in drug development, regulatory approval, and health technology assessment.

Use of statistical and pharmacokinetic-pharmacodynamic modeling and simulation to improve decision-making: A section summary report of the trends and innovations in clinical trial statistics conference.

The application of modeling and simulation (M&S) methods to improve decision-making was discussed during the Trends & Innovations in Clinical Trial Statistics Conference held in Durham, North Carolina, USA on May 1-4, 2016. Uses of both pharmacometric and statistical M&S were presented during the conference, highlighting the diversity of the methods employed by pharmacometricians and statisticians to address a broad range of quantitative issues in drug development. Five presentations are summarized herein, which cover the development strategy of employing M&S to drive decision-making; European initiatives on best practice in M&S; case studies of pharmacokinetic/pharmacodynamics modeling in regulatory decisions; estimation of exposure-response relationships in the presence of confounding; and the utility of estimating the probability of a correct decision for dose selection when prior information is limited. While M&S has been widely used during the last few decades, it is expected to play an essential role as more quantitative assessments are employed in the decision-making process. By integrating M&S as a tool to compile the totality of evidence collected throughout the drug development program, more informed decisions will be made.

Efficacy of telaprevir-based therapy in stable liver transplant patients with chronic genotype 1 hepatitis C.

UNLABELLED:  Background and rationale. The REPLACE study (NCT01571583) investigated telaprevir-based triple therapy in patients who have recurrent genotype 1 hepatitis C virus (HCV) infection following liver transplantation and are on a stable immunosuppressant regimen of tacrolimus or cyclosporin A. Patients received telaprevir 750 mg 8-hourly with pegylated interferon 180 ?g weekly and ribavirin 600 mg daily, followed by a further 36 weeks of pegylated interferon and ribavirin alone and 24 weeks of follow-up. Efficacy (sustained virological response [SVR] 12 weeks after last planned study dose), safety and tolerability of telaprevir throughout the study were assessed. Pharmacokinetics of telaprevir, tacrolimus and cyclosporin A were also examined. RESULTS: In total, 74 patients were recruited. Overall, 72% (53/74; 95% CI: 59.9 to 81.5) of patients achieved SVR at 12 weeks following completion of treatment. Anticipated increases in plasma concentrations of tacrolimus and cyclosporin A occurred during telaprevir treatment and were successfully managed through immunosuppressant dose reduction and, for tacrolimus, reduced dosing frequency. Safety and tolerability of telaprevir-based triple therapy were generally comparable with previous data in non-transplant patients, although rates of reported anemia (55% [41/74]) were higher. Elevated plasma creatinine (46% [34/74]) was observed during REPLACE - consistent with the post-liver transplant population and the co-administered immunosuppressants. CONCLUSION: Telaprevir-based triple therapy in patients with recurrent genotype 1 HCV infection following liver transplantation produced high rates of SVR. Therapeutic concentrations of immunosuppressants were maintained successfully through dose modification during telaprevir treatment.

Modeling population heterogeneity in viral dynamics for chronic hepatitis C infection: Insights from Phase 3 telaprevir clinical studies.

Viral dynamic modelling has proven useful for designing clinical studies and predicting treatment outcomes for patients infected with the hepatitis C virus. Generally these models aim to capture and predict the on-treatment viral load dynamics from a small study of individual patients. Here, we explored extending these models (1) to clinical studies with numerous patients and (2) by incorporating additional data types, including sequence data and prior response to interferon. Data from Phase 3 clinical studies of the direct-acting antiviral telaprevir (T; total daily dose of 2250 mg) combined with pegylated-interferon alfa and ribavirin (PR) were used for the analysis. The following data in the treatment-naïve population were reserved to verify the model: (1) a T/PR regimen where T was dosed every 8 h for 8 weeks (T8(q8h)/PR) and (2) a T/PR regimen where T was dosed twice daily for 12 weeks (T12(b.i.d.)/PR). The resulting model accurately predicted (1) sustained virologic response rates for both of these dosing regimens and (2) viral breakthrough characteristics of the T8(q8h)/PR regimen. Since the observed viral variants depend on the T exposure, the second verification suggested that the model was correctly sensitive to the different T regimen even though the model was developed using data from another T regimen. Furthermore, the model predicted that b.i.d. T dosing was comparable to q8h T dosing in the PR-experienced population, a comparison that has not been made in a controlled clinical study. The methods developed in this work to estimate the variability occurring below the limit of detection for the viral load were critical for making accurate predictions.

Helicobacter pylori treatment in children: defining a dose for rabeprazole as a part of a triple therapy regimen.

The proton pump inhibitor, rabeprazole, has been studied in children for the treatment of gastroesophageal reflux disease (GERD). In adults, rabeprazole is indicated for Helicobacter pylori eradication in combination with amoxicillin and clarithromycin. Nonlinear mixed effects modeling was conducted to estimate pharmacokinetic (PK) parameters for rabeprazole and its thioether metabolite from 336 subjects, 35% of whom were children 1-11 years with GERD from phase I and III studies. A 2-compartment disposition model with a transit absorption model provided the best fit for rabeprazole PK. The steady-state area under the concentration-time curves given several candidate doses were simulated to identify a dose per each body weight group that is comparable to a 20 mg twice-daily dose in adults, which is the recommended dose for treatment of H. pylori in adults. Simulations provided the following recommended twice-daily weight-based doses for children ≥1 year and <16 years: 10 mg for 6-10 kg, 15 mg for 10-30 kg, and 20 mg for ≥30 kg.