The investigational antigen-specific immunotherapy mRNA-4359 is a lipid nanoparticle-encapsulated mRNA-based immunotherapy that encodes for the immunogenic indoleamine 2,3-dioxygenase (IDO) and programmed death-ligand 1 (PD-L1) antigens. An ongoing first-in-human (FIH) phase 1/2 clinical trial (NCT05533697) will evaluate the safety and antitumor activity of mRNA-4359 when administered alone and in combination with the anti-programmed death-1 agent pembrolizumab in participants with advanced solid tumors. The current analysis applied a novel immunostimulatory/immunodynamic (IS/ID) modeling approach to determine a plausible starting dose of mRNA-4359 for the FIH trial. The model used for the FIH dose prediction was calibrated to previously published clinical trial data obtained for an immunomodulatory peptide-based vaccine activating IDO- and PD-L1-specific T cells in patients with metastatic melanoma. The analysis found that a 180 μg dose of mRNA-4359 would possibly elicit a T-cell response similar to a 200 μg dose of the peptide-based vaccine with a range of 45-360 μg, assuming a potential 4-fold higher to 2-fold lower efficiency (the ability to elicit IFN-γ secreting T cells, indicative of cytotoxic potential). Model simulations further predicted that a 15-cycle every 3 weeks regimen of mRNA-4359 could be expected to provide longer responses than other feasible simulated regimens. Finally, the IS/ID modeling analysis determined that a 100 μg dose of mRNA-4359 would be the most appropriate starting dose for FIH trials. The described approach represents a unique application of IS/ID modeling to determine a therapeutically relevant FIH starting dose in the absence of supporting preclinical animal data.

Objectives: Antibody-drug conjugates (ADCs) signify a major leap forward in cancer treatment, providing a precise method to deliver potent cytotoxic drugs directly to cancer cells. Understanding and analyzing various aspects of ADCs are crucial for making well-informed decisions during the development of these groundbreaking medications. Although ADCs are generally well tolerated by patients, certain treatment-related adverse events are commonly reported and lead to patient discontinuation. Dose-limiting toxicities (DLTs) are frequently observed across various antibody-drug conjugates (ADCs) that administer the same cytotoxic payload, regardless of the targeted antigen or the specific type of cancer being treated. Here we outline a quantitative systems pharmacology (QSP) model framework developed to predict both clinical efficacy and hematological toxicity for antibody drug conjugates. Methods: In the current work we have utilized a previously reported (1), QSP ADC model as the base model. This model comprehensively represents both the cellular and systemic dynamics of drug disposition, taking into account both drug and target antigen related properties such as expression of the target antigen, formation of drug-antigen complexes and their internalization. Additionally, the model integrates modules to simulate drug distribution within a tumor, inhibition of tumor growth and the phenomenon of bystander effect induced by the drug. For capturing ADC payload induced hematological toxicity (namely thrombocytopenia), a pharmacodynamics (PD) model of chemotherapy induced myelosuppression was repurposed (2,3). The model was informed and calibrated with in-vivo preclinical and clinical data for Trastuzumab emtansine, and used for clinical therapeutic index and alternate dosing strategy predictions. Results: The developed model effectively captured in-vivo preclinical PK and efficacy data for Trastuzumab emtansine (4). The model output for translation to humans accurately described clinical response in terms of tumor growth inhibition (5). In addition, the model predicted platelet dynamics in response to payload concentration mirrored published data (6). Conclusions: The QSP model framework described here can be utilized for effective prediction of efficacy and toxicity for ADCs. The model can be used to determine dosing regimen and therapeutic window. Given availability of data, the model can be used for informing translation and early clinical development of novel ADCs.Citations: [1] Channavazzala M, et al. Development of a mechanistic mPBPK/QSP Antibody drug conjugates (ADC) platform model with applications across preclinical, translational and clinical stages of drug development. ACoP13 (2022) QSP-427[2] Friberg LE, et al. Model of chemotherapy-induced myelosuppression with parameter consistency across drugs. J Clin Oncol. 2002 Dec 15;20(24):4713-21. doi: 10.1200/JCO.2002.02.140. [3] Ait-Oudhia S, et al. A Mechanism-Based PK/PD Model for Hematological Toxicities Induced by Antibody-Drug Conjugates. AAPS J. 2017 Sep;19(5):1436-1448. doi: 10.1208/s12248-017-0113-5[4] Haddish-Berhane N, et al. On translation of antibody drug conjugates efficacy from mouse experimental tumors to the clinic: a PK/PD approach. J Pharmacokinet Pharmacodyn. 2013 Oct;40(5):557-71. doi: 10.1007/s10928-013-9329-x[5] Burris HA 3rd, et al. Phase II study of the antibody drug conjugate trastuzumab-DM1 for the treatment of human epidermal growth factor receptor 2 (HER2)-positive breast cancer after prior HER2-directed therapy. J Clin Oncol. 2011 Feb 1;29(4):398-405. doi: 10.1200/JCO.2010.29.5865[6] Bender BC,et al. A population pharmacokinetic/pharmacodynamic model of thrombocytopenia characterizing the effect of trastuzumab emtansine (T-DM1) on platelet counts in patients with HER2-positive metastatic breast cancer. Cancer Chemother Pharmacol. 2012 Oct;70(4):591-601. doi: 10.1007/s00280-012-1934-7

ABSTRACTThe pharmaceutical industry constantly strives to improve drug development processes to reduce costs, increase efficiencies, and enhance therapeutic outcomes for patients. Model‐Informed Drug Development (MIDD) uses mathematical models to simulate intricate processes involved in drug absorption, distribution, metabolism, and excretion, as well as pharmacokinetics and pharmacodynamics. Artificial intelligence (AI), encompassing techniques such as machine learning, deep learning, and Generative AI, offers powerful tools and algorithms to efficiently identify meaningful patterns, correlations, and drug–target interactions from big data, enabling more accurate predictions and novel hypothesis generation. The union of MIDD with AI enables pharmaceutical researchers to optimize drug candidate selection, dosage regimens, and treatment strategies through virtual trials to help derisk drug candidates. However, several challenges, including the availability of relevant, labeled, high‐quality datasets, data privacy concerns, model interpretability, and algorithmic bias, must be carefully managed. Standardization of model architectures, data formats, and validation processes is imperative to ensure reliable and reproducible results. Moreover, regulatory agencies have recognized the need to adapt their guidelines to evaluate recommendations from AI‐enhanced MIDD methods. In conclusion, integrating model‐driven drug development with AI offers a transformative paradigm for pharmaceutical innovation. By integrating the predictive power of computational models and the data‐driven insights of AI, the synergy between these approaches has the potential to accelerate drug discovery, optimize treatment strategies, and usher in a new era of personalized medicine, benefiting patients, researchers, and the pharmaceutical industry as a whole.

ABSTRACT:Quantitative and systems pharmacology (QSP) is an innovative and integrative approach combining physiology and pharmacology to accelerate medical research. This review focuses on QSP's pivotal role in drug development and its broader applications, introducing clinical pharmacologists/researchers to QSP's quantitative approach and the potential to enhance their practice and decision-making. The history of QSP adoption reveals its impact in diverse areas, including glucose regulation, oncology, autoimmune disease, and HIV treatment. By considering receptor-ligand interactions of various cell types, metabolic pathways, signaling networks, and disease biomarkers simultaneously, QSP provides a holistic understanding of interactions between the human body, diseases, and drugs. Integrating knowledge across multiple time and space scales enhances versatility, enabling insights into personalized responses and general trends. QSP consolidates vast data into robust mathematical models, predicting clinical trial outcomes and optimizing dosing based on preclinical data. QSP operates under a "learn and confirm paradigm," integrating experimental findings to generate testable hypotheses and refine them through precise experimental designs. An interdisciplinary collaboration involving expertise in pharmacology, biochemistry, genetics, mathematics, and medicine is vital. QSP's utility in drug development is demonstrated through integration in various stages, predicting drug responses, optimizing dosing, and evaluating combination therapies. Challenges exist in model complexity, communication, and peer review. Standardized workflows and evaluation methods ensure reliability and transparency.

Rheumatoid Arthritis (RA) is a chronic autoimmune inflammatory disease that affects about 0.1% to 2% of the population worldwide. Despite the development of several novel therapies, there is only limited benefit for many patients. Thus, there is room for new approaches to improve response to therapy, including designing better trials e.g., by identifying subpopulations that can benefit from specific classes of therapy and enabling reverse translation by analyzing completed clinical trials. We have developed an open-source, mechanistic multi-scale model of RA, which captures the interactions of key immune cells and mediators in an inflamed joint. The model consists of a treatment-naive Virtual Population (Vpop) that responds appropriately (i.e. as reported in clinical trials) to standard-of-care treatment options-Methotrexate (MTX) and Adalimumab (ADA, anti-TNF-α) and an MTX inadequate responder sub-population that responds appropriately to Tocilizumab (TCZ, anti-IL-6R) therapy. The clinical read-outs of interest are the American College of Rheumatology score (ACR score) and Disease Activity Score (DAS28-CRP), which is modeled to be dependent on the physiological variables in the model. Further, we have validated the Vpop by predicting the therapy response of TCZ on ADA Non-responders. This paper aims to share our approach, equations, and code to enable community evaluation and greater adoption of mechanistic models in drug development for autoimmune diseases.

In drug development, quantitative systems pharmacology (QSP) models are becoming an increasingly important mathematical tool for understanding response variability and for generating predictions to inform development decisions. Virtual populations are essential for sampling uncertainty and potential variability in QSP model predictions, but many clinical efficacy endpoints can be difficult to capture with QSP models that typically rely on mechanistic biomarkers. In oncology, challenges are particularly significant when connecting tumor size with time-to-event endpoints like progression-free survival while also accounting for censoring due to consent withdrawal, loss in follow-up, or safety criteria. Here, we expand on our prior work and propose an extended virtual population selection algorithm that can jointly match tumor burden dynamics and progression-free survival times in the presence of censoring. We illustrate the core components of our algorithm through simulation and calibration of a signaling pathway model that was fitted to clinical data for a small molecule targeted inhibitor. This methodology provides an approach that can be tailored to other virtual population simulations aiming to match survival endpoints for solid-tumor clinical datasets.

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.

As a result of the escalating number of new cancer treatments being developed and competition among pharmaceutical companies, decisions regarding how to proceed with phase III trials are frequently based on findings from either single-arm phase I expansion cohorts or phase II studies that compare the efficacy of the study drug to a standard-of-care benchmark derived from historical data. However, even when eligibility criteria are matched, differences in the distribution of baseline patient features may influence the outcome of single-arm trials in real-world scenarios. Therefore, novel methods are needed to enhance the accuracy of efficacy prediction from current cohorts relative to historical data. In this study, we demonstrated the feasibility of using the propensity score matching (PSM) method to improve decision making by matching relevant baseline features between current and historical cohorts. According to our findings, utilizing the PSM method may provide a less biased means of comparing outcomes between current and historical cohorts relative to a naïve approach, which relies solely on differences in average outcomes between the cohorts.

Anemia induced by chronic kidney disease (CKD) has multiple underlying mechanistic causes and generally worsens as CKD progresses. Erythropoietin (EPO) is a key endogenous protein which increases the number of erythrocyte progenitors that mature into red blood cells that carry hemoglobin (Hb). Recombinant human erythropoietin (rHuEPO) in its native and re-engineered forms is used as a therapeutic to alleviate CKD-induced anemia by stimulating erythropoiesis. However, due to safety risks associated with erythropoiesis-stimulating agents (ESAs), a new class of drugs, prolyl hydroxylase inhibitors (PHIs), has been developed. Instead of administering exogenous EPO, PHIs facilitate the accumulation of HIF-α, which results in the increased production of endogenous EPO. Clinical trials for ESAs and PHIs generally involve balancing decisions related to safety and efficacy by carefully evaluating the criteria for patient selection and adaptive trial design. To enable such decisions, we developed a quantitative systems pharmacology (QSP) model of erythropoiesis which captures key aspects of physiology and its disruption in CKD. Furthermore, CKD virtual populations of varying severities were developed, calibrated, and validated against public data. Such a model can be used to simulate alternative trial protocols while designing phase 3 clinical trials, as well as an asset for reverse translation in understanding emerging clinical data.

A quantitative systems pharmacology model for metastatic melanoma was developed for immuno-oncology with the goal of predicting efficacy of combination checkpoint therapy with pembrolizumab and ipilimumab. This literature-based model is developed at multiple scales: (i) tumor and immune cell interactions at a lesion level; (ii) multiple heterogeneous target lesions, nontarget lesion growth, and appearance of new metastatic lesion at a patient level; and (iii) interpatient differences at a population level. The model was calibrated to pembrolizumab and ipilimumab monotherapy in patients with melanoma from Robert et al., specifically, waterfall plot showing target lesion response and overall response rate (Response Evaluation Criteria in Solid Tumors [RECIST] version 1.1), which additionally considers nontarget lesion growth and appearance of new metastatic lesions. We then used the model to predict waterfall and RECIST version 1.1 for combination treatment reported in Long et al. A key insight from this work was that nontarget lesions growth and appearance of new metastatic lesion contributed significantly to disease progression, despite reduction in target lesions. Further, the lesion level simulations of combination therapy show substantial efficacy in warm lesions (intermediary immunogenicity) but limited advantage of combination in both cold and hot lesions (low and high immunogenicity). Because many patients with metastatic disease are expected to have a mixture of these lesions, disease progression in such patients may be driven by a subset of cold lesions that are unresponsive to checkpoint inhibitors. These patients may benefit more from the combinations which include therapies to target cold lesions than double checkpoint inhibitors.

Abstract Introduction: In clinical trials of patients treated with pembrolizumab, RECIST-defined progressive disease (PD) is often assumed to reflect generalized lack or loss of efficacy. However, mixed lesion-level responses within a given patient may reflect more clinically and immunobiologically-relevant heterogeneity than has been previously appreciated. To examine the heterogeneity and clinical impact of progressive disease, we characterized the patient-level and lesion-level responses in 511 patients with melanoma treated with pembrolizumab. Methods: Of 655 patients with melanoma treated with pembrolizumab in KEYNOTE-001 (NCT01295827), 511 patients had at least two scans and qualified for analysis. Patients were categorized as primary progression (RECIST-defined PD at first scan) or secondary progression (RECIST-defined PD as second scan or beyond). The percent change in diameter of overall and individual target lesions were examined, as well as patterns and timing of progression in non-target and new lesions. Results: Of 511 patients, 308 (60%) developed progression at some point during the study (primary progression 211/308, 69%; secondary progression 97/308, 31%). Of patients with primary progression, 105 (50%) had increased tumor burden (>20% sum growth), while 90 (43%) had stable tumor burden (-30-+20% sum change) and 16 (8%) had decreased tumor burden (>-30% sum decrease). Lesion-level heterogeneity was substantial, with 167 (79%) of patients having at least one shrinking metastasis despite primary progression. In patients with secondary progression, timing of onset was variable (median 56 weeks, +/- 35 weeks). Only 15% of patients with acquired resistance, (5% of patients with PD), displayed rebound of all lesions. The remaining patients displayed rebound of a subset of lesions (38%) and/or appearance of new metastatic lesions (46%). Lesion-level responses that have been achieved often persisted at/after onset of secondary progression. Conclusions: Patterns of progression to pembrolizumab are heterogeneous. In primary progression, there is evidence of some lesion-level benefit in the majority of patients. In secondary progression, many lesions demonstrate continued benefit despite isolated/oligo sites of progression. Consideration of these patterns may inform optimal treatment strategies. Lesion-specific analysis of biomarkers may be necessary to improve correlative analyses and response prediction. Citation Format: Brian Topp, Rukmini Kumar, Kapil Mayawala, Dinesh de Alwis, Matt D. Hellmann, Alexandra Snyder. Inter-tumoral heterogeneity of progressive disease in melanoma patients treated with pembrolizumab [abstract]. In: Proceedings of the AACR Virtual Special Conference on Tumor Heterogeneity: From Single Cells to Clinical Impact; 2020 Sep 17-18. Philadelphia (PA): AACR; Cancer Res 2020;80(21 Suppl):Abstract nr PO-131.