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Disease activity scores are efficacy endpoints in clinical trials of inflammatory bowel disease (IBD) therapies. Crohn’s disease activity index (CDAI), Mayo endoscopic score (MES) and Mayo score are frequently used in clinical trials. They rely on either the physician’s observation of the inflammatory state of the patient’s gastrointestinal tissue alone or combined with the patient’s subjective evaluation of general wellbeing. Given the importance of these scores in evaluating the efficacy of drug treatment and disease severity, there has been interest in developing a computational approach to reliably predict these scores. A promising approach is using mechanistic models such as quantitative systems pharmacology (QSP) which simulate the mechanisms of the disease and its modulation by the drug pharmacology. However, extending QSP model simulations to clinical score predictions has been challenging due to the limited availability of gut biopsy measurements and the subjective nature of some of the evaluation criteria for these scores that cannot be described using mechanistic relationships. In this perspective, we examine details of IBD disease activity scores and current progress in building predictive models for these scores (such as biomarkers for disease activity). Then, we propose a method to leverage simulated markers of inflammation from a QSP model to predict IBD clinical scores using a machine learning algorithm. We will demonstrate how this combined approach can be used to (1) explore mechanistic insights underlying clinical observations; and (2) simulate novel therapeutic strategies that could potentially improve clinical outcomes.

B cell-mediated immunity plays a crucial role in long-term humoral protection. However, dysregulation of B cell development and differentiation may lead to the persistence of autoreactive clones, contributing to autoimmune diseases. Despite a number of therapeutic agents in preclinical and clinical development targeting B cell biology, several challenges still limit their successful translation into clinical use. To better understand B cell-targeting mechanisms of action quantitatively and mechanistically, we developed an integrative systems pharmacology model that describes T cell-dependent B cell response to antigen exposure in mouse. The model includes 20 ordinary differential equations representing key biological processes involved in the B lymphocyte response: B cell activation in secondary lymphoid organs; antibody-secreting cell (ASC) generation; migration to the bone marrow; and redistribution to peripheral tissues. The model adequately described ASC dynamics across tissues and IgG time profiles in plasma. Local and global sensitivity analyses identified the ASC production rate as the main contributor to cell counts in the spleen and in lymph nodes, while ASC levels in the bone marrow were primarily controlled by their influx rate, reflecting survival niche availability. Moderate variations of this influx rate parameter allowed the model to capture high inter-study variability in bone marrow ASC levels, explaining the observed heterogeneity in the functional immune response. The model can be further used as a quantitative tool to study B cell responses and their dysregulation in autoimmunity. It can be extended by integrating plasma-cell biology and autoantibody production, ultimately supporting the development of new therapeutic strategies for autoimmune diseases.

RECISTv1.1 (Response Evaluation Criteria In Solid Tumors) is the most commonly used response grading criteria in early oncology trials. In this perspective, we argue that RECISTv1.1 is ambiguous regarding lesion-to-lesion variation that can introduce bias in decision making. We show theoretical examples of how lesion-to-lesion variability causes bias in RECISTv1.1, leading to misclassification of patient response. Next, we review immune checkpoint inhibitor (ICI) clinical trial data and find that lesion-to-lesion heterogeneity is widespread in ICI-treated patients. We illustrate the implications of ignoring lesion-to-lesion heterogeneity in interpreting biomarker data, selecting treatments for patients with progressive disease, and go/no-go decisions in drug development. Further, we propose that Quantitative Systems Pharmacology (QSP) models can aid in developing better metrics of patient response and treatment efficacy by capturing patient responses robustly by considering lesion-to-lesion heterogeneity. Overall, we believe patient response evaluation with an appreciation of lesion-to-lesion heterogeneity can potentially improve decision-making at the early stage of oncology drug development and benefit patient care.

The failure of immunotherapies in cancer patients is being widely studied due to the complexities present in the tumor microenvironment (TME), where regulatory T cells (Treg) appear to actively participate in providing an immune escape mechanism for tumors. Therefore, therapies to specifically inhibit tumor-infiltrating Treg represent a challenge, because Treg are distributed throughout the body and provide physiological immune homeostasis to prevent autoimmune diseases. Characterization of immunological and functional profiles could help to identify the mechanisms that need to be inhibited or activated to ensure Treg modulation in the tumor. To address this, quantitative in silico approaches based on mechanistic mathematical models integrating multi-scale information from immune and tumor cells and the effect of different therapies have allowed the building of computational frameworks to simulate different hypotheses, some of which have subsequently been experimentally validated. Therefore, this review presents a list of diverse computational mathematical models that examine the role of Treg as a crucial immune resistance mechanism contributing to the failure of immunotherapy. In addition, this review highlights the relevance of certain molecules expressed in Treg that are associated with the TME immunosuppression, which could be incorporated into the mathematical model for a better understanding of the contribution of Treg modulation. Finally, different preclinical and clinical combinations of molecules are also included to show the trend of new therapies targeting Treg.

CAR‐T cell therapy, renowned for its success in oncology, is now venturing into the realm of B cell‐mediated autoimmune diseases. Recent observations have revealed significant pharmacological effects of CD19 CAR‐T cells in patients with systemic lupus erythematosus (SLE), suggesting promising applications in other autoimmune conditions. Consequently, as of December 2024, there are 116 different clinical trials evaluating CAR‐T cells against autoimmune conditions. While the field is starting to understand the overall pharmacological actions of CAR‐T cells in autoimmune diseases, the dose‐exposure‐response relationship remains inadequately characterized due to limited clinical data. To address these uncertainties, we have developed a Quantitative Systems Pharmacology (QSP) model using short‐term limited clinical data of anti‐CD19 CAR‐Ts in autoimmune disease patients (n = 5), followed by a model qualification step utilizing an external dataset (n = 13). The developed QSP model integrated and effectively characterized the (1) cellular kinetics of different immunophenotypic population of CAR‐T cells, (2) impact of lymphodepletion chemotherapy on host immune cells, (3) CAR‐mediated elimination of CD19+ B‐cells and (4) dynamic changes in disease surrogate biomarkers and its relationship with clinical score. The key pharmacological biomarkers which were incorporated within the QSP model included anti double stranded DNA (anti‐dsDNA) antibodies, proteinuria, C3 protein and IFN‐alpha. Later, a linear regression analysis‐based relationship was developed between continuous disease biomarkers and the categorical SLE disease activity index (SLE‐DAI) determined by the investigators offering a predictive framework for disease progression in SLE patients. This proposed QSP model holds potential to elucidate quantitative pharmacology and expedite clinical advancement of autologous and allogeneic cell therapies in autoimmune diseases.

Tyrosine kinase inhibitors (TKIs) are targeted therapies rapidly becoming favored over conventional cytotoxic chemotherapeutics. Our study investigates two FDA approved TKIs, Dasatinib; indicated for Imatinib-refractory chronic myeloid leukemia, and Sorafenib; indicated for hepatocellular carcinoma and advanced renal cell carcinoma. Limited but crucial evidence suggests that these agents can have cardiotoxic side effects ranging from hypertension to heart failure. A greater understanding of the underlying mechanisms of this cardiotoxicity are needed as concerns grow and the capacity to anticipate them is lacking. The objective of this study was to explore the mitochondrial-mediated cardiotoxic mechanisms of the two selected TKIs. This was achieved experimentally using immortalized human cardiomyocytes, AC16 cells, to investigate dose- and time-dependent cell killing, along with measurements of temporal changes in key signaling proteins involved in the intrinsic apoptotic and autophagy pathways upon exposure to these agents. Quantitative systems pharmacology (QSP) models were developed to capture the toxicological response in AC16 cells using protein dynamic data. The developed QSP models captured well all the various trends in protein signaling and cellular responses with good precision on the parameter estimates, and were successfully qualified using external data sets. An interplay between the apoptotic and autophagic pathways was identified to play a major role in determining toxicity associated with the investigated TKIs. The established modeling platform showed utility in elucidating the mechanisms of cardiotoxicity of Sorafenib and Dasatinib. It may be useful for other small molecule targeted therapies demonstrating cardiac toxicities, and may aid in informing alternate dosing strategies to alleviate cardiotoxicity associated with these therapies.

Objectives: There is growing concern over tyrosine kinase inhibitor (TKI)-induced cardiotoxicity, particularly regarding left ventricular dysfunction and heart failure in clinical treatment. These adverse effects often lead to treatment discontinuation, severely impacting patient outcomes. Therefore, there is an urgent need for more precise risk assessment methods. This study aimed to assess the cardiotoxicity of TKIs, refine in vitro to in vivo extrapolation (IVIVE) methodologies to improve predictive accuracy, and identify critical in vitro parameters for assessment. Methods: By leveraging high-throughput cardiotoxicity screening with human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs), a mechanism-based toxicodynamic (TD) model for TKIs was constructed. A QSP-PK-TD model was developed by integrating pharmacokinetic (PK) and quantitative systems pharmacology (QSP) models. This model incorporates critical drug exposure factors, such as plasma protein binding, tissue–plasma partitioning, and drug distribution heterogeneity to enhance extrapolation accuracy. Results: The QSP-PK-TD model validated the reliability of IVIVE and identified the area under the curve of drug effects on mitochondrial membrane potential (AEMMP) and cardiomyocyte contractility (AEAAC) as key in vitro parameters for assessing TKI-induced cardiotoxicity. Incorporating critical drug exposure factors obviously improved qualitative and quantitative extrapolation accuracy. Conclusions: This study established a framework for predicting in vivo cardiotoxicity from in vitro parameters, enabling efficient translation of preclinical data into clinical risk assessment. These findings provide valuable insights for drug development and regulatory decision-making, offering a powerful tool for evaluating TKI-induced cardiotoxicity.

Quantitative Systems Pharmacology (QSP) is a powerful approach to provide decision‐making support throughout the drug development process. QSP comes with many challenges in model development, validation, and applications. Traditional QSP workflows are limited by slow knowledge integration, labor‐intensive model construction, inconsistent validation practices, and restricted scalability. In this work, we introduce QSP‐Copilot, the first end‐to‐end AI‐augmented solution designed to improve QSP modeling workflows by integrating a multi‐agent system utilizing large language models (LLMs). QSP‐Copilot provides modular support from project scoping and model structuring to model evaluation and reporting. Through the automation of routine tasks, QSP‐Copilot reduces model development time by approximately 40% and improves methodological transparency through systematic documentation of literature sources and modeling assumptions. We demonstrate QSP‐Copilot's application for two rare diseases of blood coagulation and Gaucher disease. In the blood coagulation case, automated extraction from ten peer‐reviewed articles yielded 179 biological entity interaction pairs; out of these, only 105 unique mechanisms were retained after standardization. For Gaucher disease, screening nine articles produced 151 pairs, which were consolidated into 68 distinct biological interactions following the same post‐processing workflow. The extraction precision for blood coagulation and Gaucher disease is 99.1% and 100.0%, respectively. QSP‐Copilot extractions can be incorporated into effect diagrams with minimal expert filtering, significantly reducing the manual curation burden. The integration of AI‐augmented workflows like QSP‐Copilot represents a pivotal shift toward enhanced scalability and impact for QSP across the drug development pipelines, especially in disease areas where biological knowledge is sparse, such as rare diseases.

Multiscale systems biology and systems pharmacology are powerful methodologies that are playing increasingly important roles in understanding the fundamental mechanisms of biological phenomena and in clinical applications. In this review, we summarize the state of the art in the applications of agent-based models (ABM) and hybrid modeling to the tumor immune microenvironment and cancer immune response, including immunotherapy. Heterogeneity is a hallmark of cancer; tumor heterogeneity at the molecular, cellular, and tissue scales is a major determinant of metastasis, drug resistance, and low response rate to molecular targeted therapies and immunotherapies. Agent-based modeling is an effective methodology to obtain and understand quantitative characteristics of these processes and to propose clinical solutions aimed at overcoming the current obstacles in cancer treatment. We review models focusing on intra-tumor heterogeneity, particularly on interactions between cancer cells and stromal cells, including immune cells, the role of tumor-associated vasculature in the immune response, immune-related tumor mechanobiology, and cancer immunotherapy. We discuss the role of digital pathology in parameterizing and validating spatial computational models and potential applications to therapeutics.

2023 marks the 10th anniversary of Natpara’s submission to the US FDA, which led to the first recorded regulatory interaction where a decision was supported by Quantitative and Systems Pharmacology (QSP) simulations. It had taken about 5 years for the timid QSP discipline to emerge as an effective Model-Informed Drug Development (MIDD) tool with visible impact in the pharmaceutical industry. Since then, the presence of QSP in the regulatory environment has continued to increase, to the point that the Agency reported 60 QSP submissions in 2020 alone, representing ~ 4% of their annual IND submissions [1]. What sort of industry mindset has enabled QSP to reach this level of success? How does QSP fit within the MIDD paradigm? Does QSP mean the same to Discovery and to Clinical Development projects? How do ‘platforms’ compare to ‘fit-for-purpose’ QSP models in an industrial setting? Can QSP and empirical Pharmacokinetic-Pharmacodynamic (PKPD) modelling be complementary? What level of validation is required to inform drug development decisions? This article reflects on all these questions, in particular addressing those audiences with limited line-of-sight into the drug industry decision-making machinery.