Biology subjects

Popel, A. S.

Publications and source records attributed to Popel, A. S..

14 recordsLinked to original sources

bioRxiv2026

Quantitative Systems Pharmacology Model for Trop-2 Targeting Antibody-Drug Conjugate in Triple-Negative Breast Cancer

TROP2-targeted antibody-drug conjugates (ADCs) have demonstrated promising clinical activity in triple-negative breast cancer (TNBC) as monotherapies; however, therapeutic benefit varies among patients. Combination strategies pairing TROP2-targeted ADCs with immune checkpoint inhibitors are also being investigated. Elucidating the mechanistic drivers of ADC monotherapy variability and enabling the rational development of combination regimens require computational frameworks that integrate ADC pharmacology with tumor-immune interactions. A quantitative systems pharmacology (QSP) model is presented that incorporates an ADC module into our established immuno-oncology model for TNBC. The module captures ADC and payload pharmacokinetics and pharmacodynamics. TNBC heterogeneity is represented by two tumor cell clones with high and low TROP2 expression, informed by prior characterizations, and differential sensitivity to the ADC payload is incorporated as an intrinsic property of each clone. Although generalizable, the model was applied to the TROP2-targeted ADC sacituzumab govitecan (SG, TRODELVY). A virtual patient cohort was generated using Latin hypercube sampling and calibrated against objective response rate (ORR) data from SG Phase I/II TNBC basket trial. The model predicted an ORR of 33.2% consistent with ASCENT study (NCT02574455). Simulations suggest TROP2-mediated delivery contributes modestly to SG efficacy with tumor exposure driven largely by systemically released SN-38 payload being sufficient to induce cytotoxicity. Tumor heterogeneity emerged as a key determinant of response with ORR increasing as the fraction of payload-sensitive clones increased. Overall, this QSP framework for TROP2-targeted ADCs accounts for TNBC heterogeneity and is extendable to other ADCs and targets enabling interrogation of ADC mechanisms of action in conjunction with tumor-immune interactions.

pharmacology and toxicology↗

bioRxiv2026

Suprachoroidal Delivery of Anti-Angiogenic Peptide Microparticles Enables Sustained Activity with Favorable Ocular Safety

Retinal and choroidal vascular diseases are major causes of vision loss that require frequent intravitreal anti-VEGF therapy. Anti-angiogenic peptide AXT107 demonstrated efficacy in preclinical studies and was advanced to the clinical stage. To provide for sustained delivery of the peptide and avoid complications with intravitreal injection, we evaluated suprachoroidal delivery of AXT107 microparticles (MP-AXT107). The original, soluble AXT107 formulation was ineffective at inhibiting laser-induced choroidal neovascularization (CNV) in our rat model and was consequently reformulated as microparticles. MP-AXT107 demonstrated high peptide incorporation efficiency, reproducible morphology, and physical and chemical stability for at least 9 months under refrigerated storage. In the rat CNV model, suprachoroidal MP-AXT107 significantly reduced neovascular area by approximately 60% relative to vehicle controls. Safety and durability were evaluated in a 9-month GLP toxicology study in Gottingen minipigs following a single suprachoroidal injection of vehicle or MP-AXT107 (0.125-1.25 mg/eye). Transient increases in IOP and mild ocular inflammatory findings were observed immediately following administration but resolved rapidly without lasting effects. No treatment-related adverse ocular findings were observed during the remainder of the study, and the highest tested dose (1.25 mg/eye) was established as the no-observed-adverse-effect level. Bioanalysis at study completion demonstrated persistent AXT107 localization primarily within choroid/RPE and scleral tissues, with no signs of systemic exposure. Collectively, these findings demonstrate that suprachoroidal delivery of MP-AXT107 enables sustained anti-angiogenic activity with favorable ocular safety and prolonged tissue retention, supporting further clinical development as a durable therapy for retinal and choroidal vascular diseases.

pharmacology and toxicology↗

bioRxiv2026

Structured Schemas for LLM-Modeler Collaboration in Quantitative Systems Pharmacology Model Calibration

Quantitative systems pharmacology (QSP) models require calibration data from literature, yet manual curation is inconsistently documented and large language model (LLM) extraction can hallucinate values and fabricate citations. We present MAPLE (Model-Aware Parameterization from Literature Evidence), which uses structured validation schemas as a collaboration interface between LLMs and modelers. Two schemas span two scales: the SubmodelTarget schema for isolated experiments constraining individual parameters, and the CalibrationTarget schema for clinical and in vivo endpoints constraining the full model. Both separate data extraction from modeling decisions, recording every value with full provenance. Targeted validators catch characteristic LLM errors by matching values to source snippets, resolving DOIs, and executing code. For a pancreatic ductal adenocarcinoma QSP model, we used MAPLE to extract and curate 37 SubmodelTargets and 45 CalibrationTargets. Before any human review, the validators triggered 50 automated retries; every value carries a direct quote from its source and a verified citation; and 11 of 19 parameters are supported by more than one independent source.The LLM drafted usable forward models and code from context, while the modeler supplied the context and scientific judgment it cannot infer, revising forward-model choices in 65% of SubmodelTargets, priors in 46%, and source relevance in all files. This evaluation covers one model in one disease area, by a single group, so it characterizes the framework rather than establishing how broadly it generalizes. MAPLE records the modelers reasoning in a form that can be re-run and independently checked, so it is not lost when the modeling team changes.

systems biology↗

bioRxiv2025

Quantitative Calibration of a Spatial QSP Model Identifies Fibroblast Impact on HCC Immunotherapy

AbstractComputational models are increasingly used to predict treatment response and optimize cancer therapy strategies. Among these, quantitative systems pharmacology (QSP) models mechanistically simulate tumor progression and pharmacological interventions, enabling virtual clinical trials, model-informed drug development, and biomarker identification. Coupling QSP with an agent-based model yields a spatial QSP (spQSP) platform that captures tissue-level spatial organization of the tumor microenvironment (TME). However, parameterizing such models to represent tumor biology remains an open problem. In this study, we developed a calibration framework using the Approximate Bayesian Computation - Sequential Monte Carlo (ABC-SMC) approach to calibrate the spQSP model with a combination of clinical and spatial molecular data, reflecting the TME characteristics of human tumors. This calibration framework matches tumor architectures between spQSP model predictions and patient spatial molecular data by fitting statistical summaries of cellular neighborhoods. We demonstrate that model calibration using CODEX data from untreated HCC patients enables prediction of TME spatial molecular states in an independent cohort receiving immune-checkpoint inhibitor (ICI) and tyrosine kinase inhibitor (TKI) combination therapy. Finally, we identify spatial and non-spatial pretreatment biomarkers and assess their predictive power for therapeutic response. This workflow demonstrates how integrating spatial-omics with multiscale mechanistic models enables quantitative calibration, biological insight, and in silico biomarker discovery, providing a framework for personalized cancer therapy across tumor types. SignificanceDigital twins and computational models are increasingly used to simulate disease and guide therapy, but they often struggle to capture the immense complexity driving the spatiotemporal evolution of the TME. The challenge is compounded by clinical sample limitations, which typically provide measurements from only static snapshots of the TME for parameter estimation. We demonstrate how mechanistic modeling frameworks can overcome this limitation by enabling inference of spatiotemporal model parameters from static spatial data - an intractable task for purely data-driven approaches. Ultimately, our work presents a workflow that integrates spatial-omics with multiscale mechanistic models, enabling quantitative calibration, deeper biological insight, and in silico biomarker discovery.

bioinformatics↗

bioRxiv2025

Oncolytic peptide NF27 effectively inhibits tumor growth and eradicates tumors in multiple cancer types

Oncolytic peptides are amphipathic peptides that specifically induce cell death in cancer cells by rupturing the cell membrane. Despite their therapeutic potential, few have advanced to clinical trials, and none have been approved for cancer treatment, highlighting the need for more potent and safe candidates. Moreover, the structure-activity relationship (SAR) of oncolytic peptides remains poorly understood. To address these challenges, we designed a series of peptides based on the previously reported oncolytic peptide CKS1 and evaluated their activity to induce cancer cell death. By comparing the structures and the activities of these peptides, we discovered novel insights in the SAR of oncolytic peptides. Among the peptides, we identified NF27 as the most potent peptide. NF27 showed broad cytotoxicity across multiple cancer types but displayed minimal toxicity against healthy cells and low hemolysis. Cell death induced by NF27 was immunogenic and promoted infiltration of immune cells in murine tumors. In murine tumor models, NF27 effectively suppressed tumor growth and achieved complete eradication in some cases, with no observable side effects. These findings highlight NF27 as a promising lead peptide for the development of safe and effective oncolytic therapies. One Sentence SummaryWe identified NF27, a novel oncolytic peptide that induces immunogenic cancer cell death and eradicates tumors with low toxicity via SAR study.

pharmacology and toxicology↗

bioRxiv2024

Virtual patient analysis identifies strategies to improve the performance of predictive biomarkers for PD-1 blockade

Patients with metastatic triple-negative breast cancer (TNBC) show variable responses to PD-1 inhibition. Efficient patient selection by predictive biomarkers would be desirable, but is hindered by the limited performance of existing biomarkers. Here, we leveraged in-silico patient cohorts generated using a quantitative systems pharmacology model of metastatic TNBC, informed by transcriptomic and clinical data, to explore potential ways to improve patient selection. We tested 90 biomarker candidates, including various cellular and molecular species, by a cutoff-based biomarker testing algorithm combined with machine learning-based feature selection. Combinations of pre-treatment biomarkers improved the specificity compared to single biomarkers at the cost of reduced sensitivity. On the other hand, early on-treatment biomarkers, such as the relative change in tumor diameter from baseline measured at two weeks after treatment initiation, achieved remarkably higher sensitivity and specificity. Further, blood-based biomarkers had a comparable ability to tumor- or lymph node-based biomarkers in identifying a subset of responders, potentially suggesting a less invasive way for patient selection.

systems biology↗

bioRxiv2024

Bi-level Graph Learning Unveils Prognosis-Relevant Tumor Microenvironment Patterns from Breast Multiplexed Digital Pathology

The tumor microenvironment is widely recognized for its central role in driving cancer progression and influencing prognostic outcomes. There have been increasing efforts dedicated to characterizing this complex and heterogeneous environment, including developing potential prognostic tools by leveraging modern deep learning methods. However, the identification of generalizable data-driven biomarkers has been limited, in part due to the inability to interpret the complex, black-box predictions made by these models. In this study, we introduce a data-driven yet interpretable approach for identifying patterns of cell organizations in the tumor microenvironment that are associated with patient prognoses. Our methodology relies on the construction of a bi-level graph model: (i) a cellular graph, which models the intricate tumor microenvironment, and (ii) a population graph that captures inter-patient similarities, given their respective cellular graphs, by means of a soft Weisfeiler-Lehman subtree kernel. This systematic integration of information across different scales enables us to identify patient subgroups exhibiting unique prognoses while unveiling tumor microenvironment patterns that characterize them. We demonstrate our approach in a cohort of breast cancer patients and show that the identified tumor microenvironment patterns result in a risk stratification system that provides new complementary information with respect to standard stratification systems. Our results, which are validated in two independent cohorts, allow for new insights into the prognostic implications of the breast tumor microenvironment. This methodology could be applied to other cancer types more generally, providing insights into the cellular patterns of organization associated with different outcomes.

bioinformatics↗

bioRxiv2023

Spatial and Compositional Biomarkers in Tumor Microenvironment Predicts Clinical Outcomes in Triple-Negative Breast Cancer

Triple-negative breast cancer (TNBC) is an aggressive subtype of breast cancer with limited treatment options, which warrants identification of novel therapeutic targets. Deciphering nuances in the tumor microenvironment (TME) may unveil insightful links between anti-tumor immunity and clinical outcomes, yet such connections remain underexplored. Here we employed a dataset derived from imaging mass cytometry of 58 TNBC patient specimens at single-cell resolution and performed in-depth quantifications with a suite of multi-scale computational algorithms. We detected distinct cell distribution patterns among clinical subgroups, potentially stemming from different infiltration related to tumor vasculature and fibroblast heterogeneity. Spatial analysis also identified ten recurrent cellular neighborhoods (CNs) - a collection of local TME characteristics with unique cell components. Coupling of the prevalence of pan-immune and perivasculature immune hotspot CNs, enrichment of inter-CN interactions was associated with improved survival. Using a deep learning model trained on engineered spatial data, we can with high accuracy (mean AUC of 5-fold cross-validation = 0.71) how a separate cohort of patients in the NeoTRIP clinical trial will respond to treatment based on baseline TME features. These data reinforce that the TME architecture is structured in cellular compositions, spatial organizations, vasculature biology, and molecular profiles, and suggest novel imaging-based biomarkers for treatment development in the context of TNBC.

bioinformatics↗

bioRxiv2023

Informing virtual clinical trials of hepatocellular carcinoma with spatial multi-omics analysis of a human neoadjuvant immunotherapy clinical trial

Human clinical trials are important tools to advance novel systemic therapies improve treatment outcomes for cancer patients. The few durable treatment options have led to a critical need to advance new therapeutics in hepatocellular carcinoma (HCC). Recent human clinical trials have shown that new combination immunotherapeutic regimens provide unprecedented clinical response in a subset of patients. Computational methods that can simulate tumors from mathematical equations describing cellular and molecular interactions are emerging as promising tools to simulate the impact of therapy entirely in silico. To facilitate designing dosing regimen and identifying potential biomarkers, we developed a new computational model to track tumor progression at organ scale while reflecting the spatial heterogeneity in the tumor at tissue scale in HCC. This computational model is called a spatial quantitative systems pharmacology (spQSP) platform and it is also designed to simulate the effects of combination immunotherapy. We then validate the results from the spQSP system by leveraging real-world spatial multi-omics data from a neoadjuvant HCC clinical trial combining anti-PD-1 immunotherapy and a multitargeted tyrosine kinase inhibitor (TKI) cabozantinib. The model output is compared with spatial data from Imaging Mass Cytometry (IMC). Both IMC data and simulation results suggest closer proximity between CD8 T cell and macrophages among non-responders while the reverse trend was observed for responders. The analyses also imply wider dispersion of immune cells and less scattered cancer cells in responders samples. We also compared the model output with Visium spatial transcriptomics analyses of samples from post-treatment tumor resections in the original clinical trial. Both spatial transcriptomic data and simulation results identify the role of spatial patterns of tumor vasculature and TGF{beta} in tumor and immune cell interactions. To our knowledge, this is the first spatial tumor model for virtual clinical trials at a molecular scale that is grounded in high-throughput spatial multi-omics data from a human clinical trial.

bioengineering↗

bioRxiv2023

Endothelial cells signaling and patterning under hypoxia: a mechanistic computational model

IntroductionSeveral signaling pathways are activated during hypoxia to promote angiogenesis, leading to endothelial cell patterning, interaction, and downstream signaling. Understanding the mechanistic signaling differences between endothelial cells under normoxia and hypoxia and their response to different stimuli can guide therapies to modulate angiogenesis. We present a novel mechanistic model of interacting endothelial cells, including the main pathways involved in angiogenesis. MethodsWe calibrate and fit the model parameters based on well-established modeling techniques that include structural and practical parameter identifiability, uncertainty quantification, and global sensitivity. ResultsOur results indicate that the main pathways involved in patterning tip and stalk endothelial cells under hypoxia differ, and the time under hypoxia interferes with how different stimuli affect patterning. Additionally, our simulations indicate that Notch signaling might regulate vascular permeability and establish different Nitric Oxide release patterns for tip/stalk cells. Following simulations with various stimuli, our model suggests that factors such as time under hypoxia and oxygen availability must be considered for EC pattern control. DiscussionThis project provides insights into the signaling and patterning of endothelial cells under various oxygen levels and stimulation by VEGFA and is our first integrative approach toward achieving EC control as a method for improving angiogenesis. Overall, our model provides a computational framework that can be built on to test angiogenesis-related therapies by modulation of different pathways, such as the Notch pathway.

systems biology↗

bioRxiv2023

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.

systems biology↗

bioRxiv2023

Experiment-based Computational Model Predicts that IL-6 Trans-Signaling Plays a Dominant Role in IL-6 mediated signaling in Endothelial Cells

Inflammatory cytokine mediated responses are important in the development of many diseases that are associated with angiogenesis. Targeting angiogenesis as a prominent strategy has shown limited effects in many contexts such as peripheral arterial disease (PAD) and cancer. One potential reason for the unsuccessful outcome is the mutual dependent role between inflammation and angiogenesis. Inflammation-based therapies primarily target inflammatory cytokines such as interleukin-6 (IL-6) in T cells, macrophages, cancer cells, muscle cells, and there is a limited understanding of how these cytokines act on endothelial cells. Thus, we focus on one of the major inflammatory cytokines, IL-6, mediated intracellular signaling in endothelial cells by developing a detailed computational model. Our model quantitatively characterized the effects of IL-6 classic and trans-signaling in activating the signal transducer and activator of transcription 3 (STAT3), phosphatidylinositol 3-kinase/protein kinase B (PI3K/Akt), and mitogen-activated protein kinase (MAPK) signaling to phosphorylate STAT3, extracellular regulated kinase (ERK) and Akt, respectively. We applied the trained and validated experiment-based computational model to characterize the dynamics of phosphorylated STAT3 (pSTAT3), Akt (pAkt), and extracellular regulated kinase (pERK) in response to IL-6 classic and/or trans-signaling. The model predicts that IL-6 classic and trans-signaling induced responses are IL-6 and soluble IL-6 receptor (sIL-6R) dose-dependent. Also, IL-6 trans-signaling induces stronger downstream signaling and plays a dominant role in the overall effects from IL-6. In addition, both IL-6 and sIL-6R levels regulate signaling strength. Moreover, our model identifies the influential species and kinetic parameters that specifically modulate the pSTAT3, pAkt, and pERK responses, which represent potential targets for inflammatory cytokine mediated signaling and angiogenesis-based therapies. Overall, the model predicts the effects of IL-6 classic and/or trans-signaling stimulation quantitatively and provides a framework for analyzing and integrating experimental data. More broadly, this model can be utilized to identify targets that influence inflammatory cytokine mediated signaling in endothelial cells and to study the effects of angiogenesis- and inflammation-based therapies.

systems biology↗

bioRxiv2023

Spatial transcriptomics analysis of neoadjuvant cabozantinib and nivolumab in advanced hepatocellular carcinoma identifies independent mechanisms of resistance and recurrence

Novel immunotherapy combination therapies have improved outcomes for patients with hepatocellular carcinoma (HCC), but responses are limited to a subset of patients and recurrence can also occur. Little is known about the inter- and intra-tumor heterogeneity in cellular signaling networks within the HCC tumor microenvironment (TME) that underlie responses to modern systemic therapy. We applied spatial transcriptomics (ST) profiling to characterize the tumor microenvironment in HCC resection specimens from a clinical trial of neoadjuvant cabozantinib, a multi-tyrosine kinase inhibitor that primarily blocks VEGF, and nivolumab, a PD-1 inhibitor in which 5 out of 15 patients were found to have a pathologic response. ST profiling demonstrated that the TME of responding tumors was enriched for immune cells and cancer associated fibroblasts (CAF) with pro-inflammatory signaling relative to the non-responders. The enriched cancer-immune interactions in responding tumors are characterized by activation of the PAX5 module, a known regulator of B cell maturation, which colocalized with spots with increased B cell markers expression suggesting strong activity of these cells. Cancer-CAF interactions were also enriched in the responding tumors and were associated with extracellular matrix (ECM) remodeling as there was high activation of FOS and JUN in CAFs adjacent to tumor. The ECM remodeling is consistent with proliferative fibrosis in association with immune-mediated tumor regression. Among the patients with major pathologic response, a single patient experienced early HCC recurrence. ST analysis of this clinical outlier demonstrated marked tumor heterogeneity, with a distinctive immune-poor tumor region that resembles the non-responding TME across patients and was characterized by cancer-CAF interactions and expression of cancer stem cell markers, potentially mediating early tumor immune escape and recurrence in this patient. These data show that responses to modern systemic therapy in HCC are associated with distinctive molecular and cellular landscapes and provide new targets to enhance and prolong responses to systemic therapy in HCC.

cancer biology↗

bioRxiv2022

Uncovering the spatial landscape of molecular interactions within the tumor microenvironment through latent spaces

Recent advances in spatial transcriptomics (ST) enable gene expression measurements from a tissue sample while retaining its spatial context. This technology enables unprecedented in situ resolution of the regulatory pathways that underlie the heterogeneity in the tumor and its microenvironment (TME). The direct characterization of cellular co-localization with spatial technologies facilities quantification of the molecular changes resulting from direct cell-cell interaction, as occurs in tumor-immune interactions. We present SpaceMarkers, a novel bioinformatics algorithm to infer molecular changes from cell-cell interaction from latent space analysis of ST data. We apply this approach to infer molecular changes from tumor-immune interactions in Visium spatial transcriptomics data of metastasis, invasive and precursor lesions, and immunotherapy treatment. Further transfer learning in matched scRNA-seq data enabled further quantification of the specific cell types in which SpaceMarkers are enriched. Altogether, SpaceMarkers can identify the location and context-specific molecular interactions within the TME from ST data.

bioinformatics↗