An Integrative Mechanistic Model of Type 1 IFN-Mediated Inflammation in Systemic Lupus Erythematosus.

Type I interferon (IFN1) pathway-targeting therapies represent a highly promising class of remedies for the treatment of systemic lupus erythematosus. However, the overall clinical benefit of these compounds is afflicted by marked variability. In this study, we developed a quantitative systems pharmacology model of type I IFN-mediated inflammation and applied it for an indirect comparison of anifrolumab, sifalimumab, daxdilimab, and litifilimab pharmacodynamic response, represented in the model by the change in IFN1 gene signature (IFNGS).

Meta-Analysis of Exposure-Adverse Event Relationships for Antibody-Drug Conjugates.

Antibody-drug conjugates (ADCs) have become a vital class of therapeutics in oncology because of their ability to selectively deliver potent drug molecules to tumor cells. However, ADC-associated toxicities cause high failure rates in the clinic and hinder their full potential. Due to the complex structure and pharmacokinetics of ADCs, it is challenging to identify the drivers of their toxicities.

Modelling hemodynamics regulation in rats and dogs to facilitate drugs safety risk assessment.

Pharmaceutical companies routinely screen compounds for hemodynamics related safety risk. secondary pharmacology is initially used to prioritize compounds while studies are later used to quantify and translate risk to humans. This strategy has shown limitations but could be improved via the incorporation of molecular findings in the animal-based toxicological risk assessment.

T-cell engagers: model interrogation as a tool to quantify the interplay of relative affinity and target expression on trimer formation.

T-cell engagers (TCEs) represent a promising therapeutic strategy for various cancers and autoimmune disorders. These bispecific antibodies act as bridges, connecting T-cell receptors (TCRs) to target cells (either malignant or autoreactive) via interactions with specific tumour-associated antigens (TAAs) or autoantigens to form trimeric synapses, or trimers, that co-localise T-cells with target cells and stimulate their cytotoxic function. Bispecific TCEs are expected to exhibit a bell-shaped dose-response curve, with a defined optimal TCE exposure for maximizing trimer formation.

Predicting progression-free survival from measurable residual disease in chronic lymphocytic leukemia.

Association between measurable residual disease (MRD) and survival outcomes in chronic lymphocytic leukemia (CLL) has often been reported. However, limited quantitative analyses over large datasets have been undertaken to establish the predictive power of MRD. Here, we provide a comprehensive assessment of published MRD data to explore the utility of MRD in the prediction of progression-free survival (PFS).

Coupling quantitative systems pharmacology modelling to machine learning and artificial intelligence for drug development: its and .

Quantitative Systems Pharmacology (QSP) has become a powerful tool in the drug development landscape. To facilitate its continued implementation and to further enhance its applicability, a symbiotic approach in which QSP is combined with artificial intelligence (AI) and machine learning (ML) seems key. This manuscript presents four case examples where the application of a symbiotic approach could unlock new insights from multidimensional data, including real-world data, potentially leading to breakthroughs in drug development.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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

Background: Adenosine receptor type 2 (AR) 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, 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 AR or PD-L1, as well as other immunosuppressive mechanisms; 3) AR and PD-L1 inhibition by, respectively, AZD4635 and anti-PD-L1 mAb.

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.

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.

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.

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.