CPT: Pharmacometrics & Systems Pharmacology - Inception, Maturation, and Future Vision.

[Introduction] Quantitative Systems Pharmacology (QSP) modeling is a promising technique for model-informed drug discovery and development, and various QSP models for immuno-oncology (IO) have been published. Syngeneic tumor mice are often used for in vivo pharmacology study, and many kinds of IO QSP models have been reported to understand in vivo data and make prediction. However, published QSP models have varying structures across tumor types that makes it difficult to analyze data across different syngeneic tumor models. In addition, there are few QSP models calibrated by actual data of tumor infiltrating lymphocyte (TIL) dynamics. In this study, we present platform IO QSP modeling for syngeneic tumor mice (MC38, B16F10, CT26, 4T1 and LLC1) with a unified structure based on observed data of TIL dynamics and antitumor efficacy of anti-programmed cell death-1 (anti-PD-1) treatment. [Methods] (Mouse study for TIL dynamics) Five mouse tumors were inoculated into C57BL/6 (MC38, B16F10, LLC1) or BALB/c (CT26, 4T1). Tumors were sampled at three time points (mean tumor volume was about 50 mm3, 300-700 mm3 and 500-2000 mm3) and immune cells in the tumors were analyzed by flow cytometry. (IO QSP platform development) The structure of IO QSP platform was based on a published QSP model for CT26-bearing mice [1] and modified with reference to a comprehensive IO QSP model for breast cancer in human [2] to improve physiological interpretability of model components. The TIL dynamics data and published anti-mouse PD-1 (anti-mPD-1) antibody efficacy data for syngeneic tumor mice [3] were used for model calibration. The platform model was validated by confirming predictability of combination therapy of anti-mPD-1 antibody with a multiple kinase inhibitor (lenvatinib) for syngeneic tumor mice. [Results] The IO QSP platform model contains 12 tumor-specific parameters for each tumor type of syngeneic mice and successfully captured the observed TIL dynamics and antitumor effect of anti-mPD-1 antibody treatment. Mechanism of action of lenvatinib was incorporated into the IO QSP platform and calibrated with published data. The final model was successfully validated by comparing simulation and observation of combination therapy of anti-mPD-1 antibody with lenvatinib. [Conclusions] The IO QSP platform was established for several types of syngeneic tumor mice, which captured TIL dynamics and antitumor efficacy of anti-mPD-1 antibody. This platform model enables us to test a hypothesis by incorporating candidate compounds, to support study design with a translational biomarker and to investigate combination strategies, thus having the potential to facilitate new drug development. [References] [1] Kosinsky Y, et al. J Immunother Cancer. 2018;6(1):17. [2] Wang H, et al. Front Bioeng Biotechnol. 2020;8:141. [3] Georgiev P, et al. Mol Cancer Ther. 2022;21(3):427-439. Citation Format: Takeshi Nakayama, Aya Kikuchi, Kota Toshimoto, Hiroyuki Sayama, Taisuke Nakazawa, Masayo Oishi. Establishment of a quantitative systems pharmacology platform for syngeneic tumor mouse models: Application in immuno-oncology drug development [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 6274.

The conventional strategy of prescribing the same dosage to all patients can result in suboptimal efficacy and safety. This is particularly true when considering drug-gene interactions (DGIs), drug-drug interactions (DDIs), or in individuals with compromised organ function. Precision medicine, which aims to tailor drug regimens based on individual patient characteristics, offers a promising alternative by focusing on drug disposition, efficacy, and safety. However, clinical trials face ethical and practical challenges and cannot cover all real-world patient scenarios. Thus, physiological based pharmacokinetic (PBPK) modeling offers a unique framework for enhancing model-informed drug development (MIDD) and precision dosing (MIPD). Despite this, most PBPK applications primarily assess drug pharmacokinetics without evaluating efficacy or safety outcomes. This limits the full potential of mechanistic models. In this study we used integrated PBPK, Quantitative Systems Pharmacology (QSP), and toxicology models to predict risks in scenarios like DGIs, DDIs, and varied renal impairment by simultaneously assessing drug PK, pharmacological effect, and toxicity. The findings underscore the importance of considering pharmacological effects and myotoxicity risks, which differed from changes seen in plasma exposure. This study demonstrates the value of PBPK-QSP models in guiding dose adjustments to optimize the efficacy and safety balance in target patient populations, showcasing their strength in MIDD and MIPD strategies.

Systems pharmacology approaches have the capability of quantitatively linking the key biological molecules relevant to a drug candidate's mechanism of action (drug-induced signaling pathways) to the clinical biomarkers associated with the proposed target disease, thereby quantitatively facilitating its development and life cycle management. In this review, the model attributes of published quantitative systems pharmacology (QSP) modeling for lowering cholesterol, treating salt-sensitive hypertension, and treating rare diseases as well as describing bone homeostasis and related pharmacological effects are critically reviewed with respect to model quality, calibration, validation, and performance. We further reviewed the common practices in optimizing QSP modeling and prediction. Notably, leveraging genetics and genomic studies for model calibration and validation is common. Statistical and quantitative assessment of QSP prediction and handling of model uncertainty are, however, mostly lacking as are the quantitative and statistical criteria for assessing QSP predictions and the covariance matrix of coefficients between the parameters in a validated virtual population. To accelerate advances and application of QSP with consistent quality, a list of key questions is proposed to be addressed when assessing the quality of a QSP model in hopes of stimulating the scientific community to set common expectations. The common expectations as to what constitutes the best QSP modeling practices, which the scientific community supports, will advance QSP modeling in the realm of informed drug development. In the long run, good practices will extend the life cycles of QSP models beyond the life cycles of individual drugs.

Technology in bioanalysis, -omics, and computation have evolved over the past half century to allow for comprehensive assessments of the molecular to whole body pharmacology of diverse corticosteroids. Such studies have advanced pharmacokinetic and pharmacodynamic (PK/PD) concepts and models that often generalize across various classes of drugs. These models encompass the "pillars" of pharmacology, namely PK and target drug exposure, the mass-law interactions of drugs with receptors/targets, and the consequent turnover and homeostatic control of genes, biomarkers, physiologic responses, and disease symptoms. Pharmacokinetic methodology utilizes noncompartmental, compartmental, reversible, physiologic [full physiologically based pharmacokinetic (PBPK) and minimal PBPK], and target-mediated drug disposition models using a growing array of pharmacometric considerations and software. Basic PK/PD models have emerged (simple direct, biophase, slow receptor binding, indirect response, irreversible, turnover with inactivation, and transduction models) that place emphasis on parsimony, are mechanistic in nature, and serve as highly useful "top-down" methods of quantitating the actions of diverse drugs. These are often components of more complex quantitative systems pharmacology (QSP) models that explain the array of responses to various drugs, including corticosteroids. Progressively deeper mechanistic appreciation of PBPK, drug-target interactions, and systems physiology from the molecular (genomic, proteomic, metabolomic) to cellular to whole body levels provides the foundation for enhanced PK/PD to comprehensive QSP models. Our research based on cell, animal, clinical, and theoretical studies with corticosteroids have provided ideas and quantitative methods that have broadly advanced the fields of PK/PD and QSP modeling and illustrates the transition toward a global, systems understanding of actions of diverse drugs. SIGNIFICANCE STATEMENT: Over the past half century, pharmacokinetics (PK) and pharmacokinetics/pharmacodynamics (PK/PD) have evolved to provide an array of mechanism-based models that help quantitate the disposition and actions of most drugs. We describe how many basic PK and PK/PD model components were identified and often applied to the diverse properties of corticosteroids (CS). The CS have complications in disposition and a wide array of simple receptor-to complex gene-mediated actions in multiple organs. Continued assessments of such complexities have offered opportunities to develop models ranging from simple PK to enhanced PK/PD to quantitative systems pharmacology (QSP) that help explain therapeutic and adverse CS effects. Concurrent development of state-of-the-art PK, PK/PD, and QSP models are described alongside experimental studies that revealed diverse CS actions.

Better prediction of the local concentration–effect relationship: the role of physiologically based pharmacokinetics and quantitative systems pharmacology and toxicology in the evolution of model-informed drug discovery and development

Model-informed drug development (MIDD) approaches have become indispensable for new drug development and to address regulatory challenges. Dynamic tools, such as population pharmacokinetics (popPK), physiologically-based pharmacokinetics (PBPK), and quantitative systems pharmacology (QSP) models, are routinely employed to enhance the efficiency of drug development. Recently, the Fit-for-Purpose (FFP) initiative and the Model Master File (MMF) framework have emerged to support model reusability and sharing in regulatory settings. In this manuscript we share key insights from the Session "Pathways for Regulatory Acceptance of Dynamic Tools in the New Drug Space" of Workshop “Considerations and Potential Regulatory Applications for a Model Master File”, hosted by the U.S. Food and Drug Administration (FDA) and the Center for Research on Complex Generics (CRCG) and discuss the considerations for regulatory acceptance of dynamic modeling tools. Presentations at the workshop explored current practices in PBPK model evaluation, the potential for popPK models in bioequivalence (BE) assessments, and the implications of reusing models. Challenges such as context-specific validation, version control, and the impact of scientific and technological advancements on model reuse were emphasized. The workshop underscored the importance of clear regulatory pathways and structured frameworks for the consistent application of reusable models. The MMF's potential to streamline reviews and reduce redundancies was noted, although operational details require further elaboration. Continued collaboration among stakeholders is essential to refine model-sharing practices, enhance model validation processes, and promote transparency, ensuring that MIDD approaches remain robust and adaptable to evolving regulatory needs.

The Changing Face of Oncology Research, Drug Development, and Clinical Practice: Toward Patient-Focused Precision Therapeutics.

Beyond the basics: A deep dive into parameter estimation for advanced PBPK and QSP models

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

The COVID-19 pandemic has halted many ongoing CNS clinical trials, especially in Alzheimer's disease. These long-duration trials involve many stakeholders, especially the patients and their family members, who have demonstrated their commitment to developing new therapeutic interventions for this devastating disease. We certainly do not want to lose all the knowledge we have gained from these ongoing trials because of the pandemic. While some of these trials will need to restart, others can re-start at different points along the trial protocol with substantial protocol amendments. However, there is an urgent need to combine useful information from the completers with those subjects undergoing complex protocols deviations and amendments after re-start. We propose the concept of mechanistic modeling-based virtual twin patients as a possible solution to harmonize the readouts from these complex and fragmented clinical datasets in a biologically relevant way.

Quantitative systems pharmacology (QSP) is a quantitative and mechanistic platform describing the phenotypic interaction between drugs, biological networks, and disease conditions to predict optimal therapeutic response. In this meta-analysis study, we review the utility of the QSP platform in drug development and therapeutic strategies based on recent publications (2019–2021). We gathered recent original QSP models and described the diversity of their applications based on therapeutic areas, methodologies, software platforms, and functionalities. The collection and investigation of these publications can assist in providing a repository of recent QSP studies to facilitate the discovery and further reusability of QSP models. Our review shows that the largest number of QSP efforts in recent years is in Immuno-Oncology. We also addressed the benefits of integrative approaches in this field by presenting the applications of Machine Learning methods for drug discovery and QSP models. Based on this meta-analysis, we discuss the advantages and limitations of QSP models and propose fields where the QSP approach constitutes a valuable interface for more investigations to tackle complex diseases and improve drug development.

Rare diseases, affecting millions globally, present significant drug development challenges. This is due to the limited patient populations and the unique pathophysiology of these diseases, which can make traditional clinical trial designs unfeasible. Quantitative Systems Pharmacology (QSP) models offer a promising approach to expedite drug development, particularly in rare diseases. QSP models provide a mechanistic representation of the disease and drug response in virtual patients that can complement routinely applied empirical modeling and simulation approaches. QSP models can generate digital twins of actual patients and mechanistically simulate the disease progression of rare diseases, accounting for phenotypic heterogeneity. QSP models can also support drug development in various drug modalities, such as gene therapy. Impactful QSP models case studies are presented here to illustrate their value in supporting various aspects of drug development in rare indications. As these QSP model applications continue to mature, there is a growing possibility that they could be more widely integrated into routine drug development steps. This integration could provide a robust framework for addressing some of the inherent challenges in rare disease drug development.

Quantitative systems toxicology (QST) is emerging as an independent field of model-informed drug development (MIDD) with a focus on predicting toxicity endpoints. To enable toxicological predictions, QST models require incorporation of mechanistic details specific to safety applications including the ability to accurately model supratherapeutic doses and appropriately represent safety endpoints. Unique to the field of toxicology, mechanistic knowledge is often described through the use of adverse outcome pathways (AOPs), which formally represent existing knowledge about mechanisms of toxicity. The toxicities represented by QST models can arise from exaggerated or adverse pharmacological effects of engaging the drug's intended target (on-target toxicity) or from adverse events due to modulation of additional targets beyond the primary target (off-target toxicity). In cases of on-target toxicity, QST models can be considered as a type of Quantitative Systems Pharmacology (QSP) model that incorporates safety biomarkers and often includes simulations performed outside the therapeutic dose range to explore potential adverse consequences of exaggerated pharmacology in a pre-clinical or clinical setting. QST models assessing off-target toxicities can be considered distinct from QSP models in that they are typically applicable across molecules of a given modality which can (and often do) have different primary therapeutic targets. Off-target QST models commonly focus on the interrogation of general (e.g. pan-compound) toxicity mechanisms, often within a specific organ system. It can be difficult to categorize a model as purely QSP or QST (given that some models can be considered as both a QSP and a QST model), and therefore, we encourage readers to refer to a model based on its context of use and application. Thus, throughout this chapter, we refer to models as QST models when the context of use is to understand safety-related questions. To illustrate QST modeling approaches, examples of QST model applications for on-target and off-target toxicities at different stages of the drug discovery and development pipeline are presented and discussed. Additionally, contexts of use, triggers, key objectives, and potential impacts of QST models including the types of decisions QST applications can inform across drug discovery and development are reviewed. The chapter concludes with an overview of key challenges and future perspectives in the field of QST.

Quantitative systems pharmacology (QSP) modeling is applied to address essential questions in drug development, such as the mechanism of action of a therapeutic agent and the progression of disease. Meanwhile, machine learning (ML) approaches also contribute to answering these questions via the analysis of multi-layer ‘omics’ data such as gene expression, proteomics, metabolomics, and high-throughput imaging. Furthermore, ML approaches can also be applied to aspects of QSP modeling. Both approaches are powerful tools and there is considerable interest in integrating QSP modeling and ML. So far, a few successful implementations have been carried out from which we have learned about how each approach can overcome unique limitations of the other. The QSP + ML working group of the International Society of Pharmacometrics QSP Special Interest Group was convened in September, 2019 to identify and begin realizing new opportunities in QSP and ML integration. The working group, which comprises 21 members representing 18 academic and industry organizations, has identified four categories of current research activity which will be described herein together with case studies of applications to drug development decision making. The working group also concluded that the integration of QSP and ML is still in its early stages of moving from evaluating available technical tools to building case studies. This paper reports on this fast-moving field and serves as a foundation for future codification of best practices.

A Quantitative Systems Pharmacology Model for the Coagulation Network Describes Biomarker Changes Observed in a Clinical Study with FXa Variant and Predicts Age-Associated Biomarker Variations