Quantitative Systems Pharmacology Research Articles

Optimizing Clinical Translation of Bispecific T-cell Engagers through Context Unification with a Quantitative Systems Pharmacology Model.

Bispecific T-cell engagers (bsTCEs) have emerged as a promising class of cancer immunotherapy. BsTCEs enable physical connections between T cells and tumor cells to enhance T-cell activity against cancer. Despite several marketing approvals, the development of bsTCEs remains challenging, especially at early clinical translational stages. The intricate design of bsTCEs makes their pharmacologic effects and safety profiles highly dependent on patient's immunological and tumor conditions. Such context-dependent pharmacology introduces considerable uncertainty into translational efforts. In this study, we developed a Quantitative Systems Pharmacology (QSP) model, through context unification, that can facilitate the translation of bsTCEs preclinical data into clinical activity. Through characterizing the formation dynamics of immunological synapse (IS) induced by bsTCEs, this model unifies a broad range of contexts related to target affinity, tumor characteristics, and immunological conditions. After rigorous calibration using both experimental and clinical data, the model enables consistent translation of drug potency observed under diverse experimental conditions into predictable exposure-response relationships in patients. Moreover, the model can help identify optimal target-binding affinities and minimum efficacious concentrations across different clinical contexts. This QSP approach holds significant promise for the future development of bsTCEs.

Quantitative systems pharmacology modeling of tumor heterogeneity in response to BH3-mimetics using virtual tumors calibrated with cell viability assays.

Both primary and acquired resistance mechanisms that involve intra-tumoral cell heterogeneity limit the use of BH3-mimetics to trigger tumor cell apoptosis. This article proposes a new quantitative systems pharmacology (QSP)-based methodology in which cell viability assays are used to calibrate virtual tumors (VTs) made of virtual cells whose fate is determined by simulations from an apoptosis QSP model. VTs representing SU-DHL-4 and KARPAS-422 cell lines were calibrated using invitro data involving venetoclax (anti-BCL2), A-1155463 (anti-BCLXL), and/or A-1210477 (anti-MCL1). The calibrated VTs provide insights into the combination of several BH3-mimetics, such as the distinction between cells eliminated by at least one of the drugs (monotherapies) from the cells eliminated by a pharmacological combination only. Calibrated VTs can also be used as initial conditions in an agent-based model (ABM) framework, and a minimal ABM was developed to bridge invitro SU-DHL-4 cell viability results to tumor growth inhibition experiments in mice.

A quantitative systems pharmacology model of plasma kallikrein-kinin system dysregulation in hereditary angioedema.

Hereditary angioedema (HAE) due to C1-inhibitor deficiency is a rare, debilitating, genetic disorder characterized by recurrent, unpredictable, attacks of edema. The clinical symptoms of HAE arise from excess bradykinin generation due to dysregulation of the plasma kallikrein-kinin system (KKS). A quantitative systems pharmacology (QSP) model that mechanistically describes the KKS and its role in HAE pathophysiology was developed based on HAE attacks being triggered by autoactivation of factor XII (FXII) to activated FXII (FXIIa), resulting in kallikrein production from prekallikrein. A base pharmacodynamic model was constructed and parameterized from literature data and ex vivo assays measuring inhibition of kallikrein activity in plasma of HAE patients or healthy volunteers who received lanadelumab. HAE attacks were simulated using a virtual patient population, with attacks recorded when systemic bradykinin levels exceeded 20pM. The model was validated by comparing the simulations to observations from lanadelumab and plasma-derived C1-inhibitor clinical trials. The model was then applied to analyze the impact of nonadherence to a daily oral preventive therapy; simulations showed a correlation between the number of missed doses per month and reduced drug effectiveness. The impact of reducing lanadelumab dosing frequency from 300mg every 2weeks (Q2W) to every 4weeks (Q4W) was also examined and showed that while attack rates with Q4W dosing were substantially reduced, the extent of reduction was greater with Q2W dosing. Overall, the QSP model showed good agreement with clinical data and could be used for hypothesis testing and outcome predictions.

Insights into drug development with quantitative systems pharmacology: A prospective case study of uncovering hyperkalemia risk in diabetic nephropathy with virtual clinical trials

The quantitative systems pharmacology (QSP) approach is widely applied to address various essential questions in drug discovery and development, such as identification of the mechanism of action of a therapeutic agent, patient stratification, and the mechanistic understanding of the progression of disease. In this review article, we show the current landscape of the application of QSP modeling using a survey of QSP publications over 10 years from 2013 to 2022. We also present a use case for the risk assessment of hyperkalemia in patients with diabetic nephropathy treated with mineralocorticoid receptor antagonists (MRAs, renin-angiotensin-aldosterone system inhibitors), as a prospective simulation of late clinical development. A QSP model for generating virtual patients with diabetic nephropathy was used to quantitatively assess that the nonsteroidal MRAs, finerenone and apararenone, have a lower risk of hyperkalemia than the steroidal MRA, eplerenone. Prospective simulation studies using a QSP model are useful to prioritize pharmaceutical candidates in clinical development and validate mechanism-based pharmacological concepts related to the risk-benefit, before conducting large-scale clinical trials.

Viral Kinetics Model of SARS-CoV-2 Infection Informs Drug Discovery, Clinical Dose, and Regimen Selection.

Quantitative systems pharmacology (QSP) has been an important tool to project safety and efficacy of novel or repurposed therapies for the SARS-CoV-2 virus. Here, we present a QSP modeling framework to predict response to antiviral therapeutics with three mechanisms of action (MoA): cell entry inhibitors, anti-replicatives, and neutralizing biologics. We parameterized three distinct model structures describing virus-host interaction by fitting to published viral kinetics data of untreated COVID-19 patients. The models were used to test theoretical behaviors and map therapeutic design criteria of the different MoAs, identifying the most rapid and robust antiviral activity from neutralizing biologic and anti-replicative MoAs. We found good agreement between model predictions and clinical viral load reduction observed with anti-replicative nirmatrelvir/ritonavir (Paxlovid®) and neutralizing biologics bamlanivimab and casirivimab/imdevimab (REGEN-COV®), building confidence in the modeling framework to inform a dose selection. Finally, the model was applied to predict antiviral response with ensovibep, a novel DARPin therapeutic designed as a neutralizing biologic. We developed a new in silico measure of antiviral activity, area under the curve (AUC) of free spike protein concentration, as a metric with larger dynamic range than viral load reduction. By benchmarking to bamlanivimab predictions, we justified dose levels of 75, 225, and 600 mg ensovibep to be administered intravenously in a Phase 2 clinical investigation. Upon trial completion, we found model predictions to be in good agreement with the observed patient data. These results demonstrate the utility of this modeling framework to guide the development of novel antiviral therapeutics.

Leveraging QSP Models for MIPD: A Case Study for Warfarin/INR.

Warfarin dosing remains challenging due to substantial inter-individual variability, which can lead to unsafe or ineffective therapy with standard dosing. Model-informed precision dosing (MIPD) can help individualize warfarin dosing, requiring the selection of a suitable model. For models developed from clinical data, the dependence on the study design and population raises questions about generalizability. Quantitative system pharmacology (QSP) models promise better extrapolation abilities; however, their complexity and lack of validation on clinical data raise questions about applicability in MIPD. We have previously derived a mechanistic warfarin/international normalized ratio (INR) model from a blood coagulation QSP model. In this article, we evaluated the predictive performance of the warfarin/INR model in the context of MIPD using an external dataset with INR data from patients starting warfarin treatment. We assessed the accuracy and precision of model predictions, benchmarked against an empirically based reference model. Additionally, we evaluated covariate contributions and assessed the predictive performance separately in the more challenging outpatient data. The warfarin/INR model performed comparably to the reference model across various measures despite not being calibrated with warfarin initiation data. Including CYP2C9 and/or VKORC1 genotypes as covariates improved the prediction quality of the warfarin/INR model, even after assimilating 4 days of INR data. The outpatient INR exhibited higher unexplained variability, and predictions slightly exceeded observed values, suggesting that model adjustments might be necessary when transitioning from an inpatient to an outpatient setting. Overall, this research underscores the potential of QSP-derived models for MIPD, offering a complementary approach to empirical model development.

Neural network emulation of the human ventricular cardiomyocyte action potential for more efficient computations in pharmacological studies.

Computer models of the human ventricular cardiomyocyte action potential (AP) have reached a level of detail and maturity that has led to an increasing number of applications in the pharmaceutical sector. However, interfacing the models with experimental data can become a significant computational burden. To mitigate the computational burden, the present study introduces a neural network (NN) that emulates the AP for given maximum conductances of selected ion channels, pumps, and exchangers. Its applicability in pharmacological studies was tested on synthetic and experimental data. The NN emulator potentially enables massive speed-ups compared to regular simulations and the forward problem (find drugged AP for pharmacological parameters defined as scaling factors of control maximum conductances) on synthetic data could be solved with average root-mean-square errors (RMSE) of 0.47 mV in normal APs and of 14.5 mV in abnormal APs exhibiting early afterdepolarizations (72.5% of the emulated APs were alining with the abnormality, and the substantial majority of the remaining APs demonstrated pronounced proximity). This demonstrates not only very fast and mostly very accurate AP emulations but also the capability of accounting for discontinuities, a major advantage over existing emulation strategies. Furthermore, the inverse problem (find pharmacological parameters for control and drugged APs through optimization) on synthetic data could be solved with high accuracy shown by a maximum RMSE of 0.22 in the estimated pharmacological parameters. However, notable mismatches were observed between pharmacological parameters estimated from experimental data and distributions obtained from the Comprehensive in vitro Proarrhythmia Assay initiative. This reveals larger inaccuracies which can be attributed particularly to the fact that small tissue preparations were studied while the emulator was trained on single cardiomyocyte data. Overall, our study highlights the potential of NN emulators as powerful tool for an increased efficiency in future quantitative systems pharmacology studies.

Neural network emulation of the human ventricular cardiomyocyte action potential for more efficient computations in pharmacological studies

Computer models of the human ventricular cardiomyocyte action potential (AP) have reached a level of detail and maturity that has led to an increasing number of applications in the pharmaceutical sector. However, interfacing the models with experimental data can become a significant computational burden. To mitigate the computational burden, the present study introduces a neural network (NN) that emulates the AP for given maximum conductances of selected ion channels, pumps, and exchangers. Its applicability in pharmacological studies was tested on synthetic and experimental data. The NN emulator potentially enables massive speed-ups compared to regular simulations and the forward problem (find drugged AP for pharmacological parameters defined as scaling factors of control maximum conductances) on synthetic data could be solved with average root-mean-square errors (RMSE) of 0.47 mV in normal APs and of 14.5 mV in abnormal APs exhibiting early afterdepolarizations (72.5% of the emulated APs were alining with the abnormality, and the substantial majority of the remaining APs demonstrated pronounced proximity). This demonstrates not only very fast and mostly very accurate AP emulations but also the capability of accounting for discontinuities, a major advantage over existing emulation strategies. Furthermore, the inverse problem (find pharmacological parameters for control and drugged APs through optimization) on synthetic data could be solved with high accuracy shown by a maximum RMSE of 0.22 in the estimated pharmacological parameters. However, notable mismatches were observed between pharmacological parameters estimated from experimental data and distributions obtained from the Comprehensive in vitro Proarrhythmia Assay initiative. This reveals larger inaccuracies which can be attributed particularly to the fact that small tissue preparations were studied while the emulator was trained on single cardiomyocyte data. Overall, our study highlights the potential of NN emulators as powerful tool for an increased efficiency in future quantitative systems pharmacology studies.

Using quantitative systems pharmacology (QSP) models to characterize the PK of novel drug modalities

Using quantitative systems pharmacology (QSP) models to characterize the PK of novel drug modalities

Quantitative systems pharmacology-based exploration of relevant anti-amyloid therapy challenges in clinical practice.

Addressing practical challenges in clinical practice after the recent approvals of amyloid antibodies in Alzheimer's disease (AD) will benefit more patients. However, generating these answers using clinical trials or real-world evidence is not practical, nor feasible. Here we use a Quantitative Systems Pharmacology (QSP) computational model of amyloid aggregation dynamics, well validated with clinical data on biomarkers and amyloid-related imaging abnormality-edema (ARIA-E) liability of six amyloid antibodies in clinical trials to explore various clinical practice challenges. Treatment duration to reach amyloid negativity ranges from 12 to 44, 16 to 40, and 6 to 20 months for lecanemab, aducanumab, and donanemab, respectively, for baseline central amyloid values between 50 and 200 Centiloids (CL). Changes in plasma cerebrospinal fluid Aβ42 and the plasma Aβ42/ Aβ40 ratio-fluid biomarkers to detect central amyloid negativity-is greater for lecanemab than for aducanumab and donanemab, indicating that these fluid amyloid biomarkers are only suitable for lecanemab. After reaching amyloid negativity an optimal maintenance schedule consists of a 24-month, 48-month and 64-month interval for 10mg/kg (mpk) lecanemab, 10mpk aducanumab, and 20mpk donanemab, respectively, to keep central amyloid negative for 10 years. Cumulative ARIA-E liability could be reduced to almost half by introducing a drug holiday in the first months. For patients experiencing ARIA-E, restarting treatment with a conservative titration strategy resulted in an additional delay ranging between 3 and 4 months (donanemab), 5 months (lecanemab), and up to 7 months (aducanumab) for reaching amyloid negativity, depending upon the timing of the incident. Clinical trial designs for Down syndrome patients suggested the same rank order for central amyloid reduction, but higher ARIA-E liability especially for donanemab, which can be significantly mitigated by adopting a longer titration period. This QSP platform could support clinical practice challenges to optimize real-world treatment paradigms for new and existing amyloid drugs.

Leveraging multi-omics data to empower quantitative systems pharmacology in immuno-oncology.

Understanding the intricate interactions of cancer cells with the tumor microenvironment (TME) is a pre-requisite for the optimization of immunotherapy. Mechanistic models such as quantitative systems pharmacology (QSP) provide insights into the TME dynamics and predict the efficacy of immunotherapy in virtual patient populations/digital twins but require vast amounts of multimodal data for parameterization. Large-scale datasets characterizing the TME are available due to recent advances in bioinformatics for multi-omics data. Here, we discuss the perspectives of leveraging omics-derived bioinformatics estimates to inform QSP models and circumvent the challenges of model calibration and validation in immuno-oncology.

Abstract 875: Quantitative systems pharmacology modeling of loncastuximab tesirine combined with mosunetuzumab and glofitamab helps guide dosing for patients with DLBCL

Abstract Introduction: CD19 and CD20, B-lymphocyte surface antigens, are clinically validated therapeutic targets for the treatment of B-cell malignancies. Loncastuximab tesirine (loncastuximab tesirine-lpyl [Lonca]) is an antibody-drug conjugate comprising an anti-CD19 antibody conjugated to a pyrrolobenzodiazepine dimer cytotoxin. Mosunetuzumab (Mosun) and glofitamab (Glofit) are CD20 × CD3 T-cell-engaging bispecific antibodies (bsAbs) that redirect T cells to eliminate malignant B cells. These agents target a different B-cell surface antigen than Lonca; hence, combining Lonca with either one of these bsAbs is expected to have additive or even synergistic efficacy. Previously, a novel physiologically-based pharmacokinetic (PBPK)-quantitative systems pharmacology (QSP) model was developed (Utsey K, et al. Clin Pharmacol Drug Dev. 2023;12:123-125) and validated with Lonca monotherapy clinical observations in R/R DLBCL (Caimi P, et al. Blood. 2022;140:9548-9550). Modeling results of Lonca in combination with Mosun or Glofit in R/R DLBCL are described herein. Methods: The model was based on the previously validated and published Lonca PBPK-QSP model. This model was reduced in physiological complexity to be compatible with a published minimal PBPK model of Mosun (Hosseini I, et al. NPJ Syst Biol Appl. 2020;6:28) while maintaining the core functionality of predicting tumor dynamics during Lonca monotherapy. The following assumptions were made: (1) Lonca or Mosun/Glofit induce healthy and malignant B-cell killing; (2) the tumor comprises malignant B cells and T cells; (3) tumor volume is calculated using malignant B-cell number; and (4) T cells can enter or leave the tumor as they enter or leave other tissues. Results: Improved tumor growth inhibition (TGI) should result from Lonca + bsAb combination therapy compared with any of the agents as monotherapy. While TGI was predicted to plateau by Cycle (C) 2 with Mosun and Glofit monotherapies, maximum activity of Lonca + bsAb combination therapies may not be observed until C4 or later. Additionally, predicted TGI increased with additional treatment cycles. Conclusions: Results indicate that treatment by the end of C3 for Lonca + Mosun and by the end of C4 for Lonca + Glofit would promote substantially more TGI compared with any of these agents alone. Increased doses of Lonca in combinations were predicted to have limited additional therapeutic benefit; however, additional cycles of combination treatment were predicted to increase TGI. This model suggests that Lonca doses could be reduced to allow for improved tolerability for longer periods of time, up to the point of maximal benefit, although clinical testing is needed to explore these results. The ongoing LOTIS-7 clinical trial (NCT04970901) evaluates the safety and anticancer activity of Lonca in combination with both Glofit and Mosun. Citation Format: Yuezhe Li, A Katharina Wilkins, Tim Knab, Joseph P. Boni. Quantitative systems pharmacology modeling of loncastuximab tesirine combined with mosunetuzumab and glofitamab helps guide dosing for patients with DLBCL [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 875.

Abstract 5293: Preclinical development of NI-3201, a PD-L1xCD28 bispecific antibody mediating CD28 costimulation upon PD-L1 blockade

Abstract NI-3201 is a PD-L1xCD28 bispecific antibody generated on the κλ body platform that blocks the PD-L1/PD-1 immune checkpoint pathway and conditionally provides T cell activation “signal 2” when engaging PD-L1+ tumor or immune cells (e.g., antigen-presenting cells). By combining two distinct mechanisms of action into a single molecule, NI-3201 overcomes two major immune suppression mechanisms commonly observed in a variety of solid tumors, namely: [1] suppression of T cell response (induced by PD-L1+ cells engaging PD-1 on T cells) and [2] restricted/limited T cell activation (due to the lack of costimulatory signal associated to the scarcity of CD28 ligands in the TME). Importantly, NI-3201 was designed to require primary T cell stimulation (“signal 1”) to afford its activity: in the absence of TCR engagement, NI-3201 is unable to activate T cells, thus preventing unintended systemic inflammation. The single agent pharmacological activity of NI-3201 was assessed in vitro through a Cytomegalovirus (CMV) recall assay in which CMV+ PBMCs were cocultured with PD-L1+ cancer cell lines ectopically loaded with CMV-specific MHC-I peptides, thus providing “signal 1”. NI-3201 induced the activation and proliferation of CMV-specific T cells which resulted in killing of the target tumor cells. In vivo, the single agent activity of NI-3201 was shown in immunocompetent huCD28-transgenic mice engrafted with huPD-L1-expressing murine colon adenocarcinoma MC38 cells, a model in which NI-3201 induced durable anti-tumor immunological memory response. In humanized mouse models where T cell engagers (TCEs) were ineffective, NI-3201 was able to synergize with TCEs to control tumor growth, even inducing tumor regression in one model. The pharmacokinetics (PK) and tolerability of NI-3201 were investigated in non-human primates (NHP) after single and repeated-dosing. NI-3201 demonstrated favorable PK with close to dose-proportional concentrations being achieved. Upon repeated NI-3201 administration, no hypercytokinemia was observed; only a mild and transient IL-6 release and associated increase in C-reactive protein (CRP) levels were seen, with no clinical signs reported. The favorable profile seen in NHP was supported by data generated in vitro further de-risking the cytokine release syndrome (CRS) potential. Quantitative systems pharmacology (QSP) modelling integrating both in vitro and NHP data predicted a drug concentration required for optimal NI-3201 activity in the TME, and preliminary data suggests optimal patient activity at Q2W-Q3W dosing schedule. The anti-tumor activity as a single agent or as universal combination partner for TCEs, shown in vitro and in vivo, together with the favorable safety profile of NI-3201, support its clinical development planned to start in Q1 2025. Citation Format: Sara Majocchi, Nadia Anceriz, Alizée Viandier, Adeline Lesnier, Laura Cons, Lise Nouveau, Emily A. Pace, Bjorn L. Millard, Bruno Daubeuf, Valéry Moine, Susana Salgado-Pires, José Saro, Limin Shang, Krzysztof Masternak, Walter G. Ferlin. Preclinical development of NI-3201, a PD-L1xCD28 bispecific antibody mediating CD28 costimulation upon PD-L1 blockade [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5293.

Abstract 1238: ISB 2001, a BCMA and CD38 dual targeting T cell engager, demonstrates superior cytotoxicity relative to teclistamab in the samples of patient relapsing from CD38 and BCMA targeted immunotherapies

Abstract Immune therapies targeting single tumor associated antigens (TAA) demonstrated efficacy against multiple myeloma (MM). However, durable responses are still limited1 potentially due to expansion of clones with low target expression2 We have previously demonstrated that simultaneous targeting of BCMA and CD38 on MM tumor cells with heterogenous expression of these antigens using dual targeting ISB 2001, enables superior killing relative to mono-targeting T cell engagers (TCE) 3. Here we present a further characterization of a dual targeting TCE utilizing bone marrow aspirates from multiple myeloma patients. In relapsed/refractory (r/r) patients, which received CD38 targeted therapy, daratumumab cytotoxicity was substantially reduced due to low CD38 expression. Teclistamab is considered the next line of treatment in such patients. However, ISB 2001 consistently demonstrated increased cytotoxicity compared to teclistamab in both newly diagnosed patient and r/r patient samples. Remarkably, ISB 2001 also induced stronger cytotoxic response in one patient relapsing after BCMA targeted therapy, suggesting that the dual targeting ISB 2001 TCE can overcome the escape mechanisms. ISB 2001 was also compared to the combination of teclistamab and daratumumab in cytotoxic assay using healthy donors. ISB 2001 shows superiority to both teclistamab and daratumumab as single agents, as well as when they were used in combination. Additionally, ISB 2001 was compared against a combination of daratumumab and teclistamab in a humanized mouse model of multiple myeloma with a low expression of both CD38 and BCMA, mimicking potential tumor escaping clones. ISB 2001 induced complete eradication of the tumors in almost all animals, whereas the combination only showed partial tumor protection, underscoring that dual targeting by a TCE is superior to two therapeutic agents individually targeting the same antigens. Based on the promising preclinical in vitro, ex vivo and in vivo data, we have advanced ISB 2001 into clinical studies. For calculating the first-in-human (FIH) dose, we have developed a quantitative systems pharmacology (QSP) model. The calculated FIH dose was 50-100 fold higher than that generated by a traditional minimum anticipated biological effect level calculation. Therefore, using this approach will substantially reduce patient exposure to sub-efficacious doses of ISB 2001. This plan was accepted by the HREC in Australia and the FDA in the US to initiate a Phase 1 FIH study of ISB 2001 for the treatment of relapsed/refractory multiple myeloma (NCT05862012)4. 1. Munshi, N. C. et al. 384, 705-716 (2021). 2. Nijhof, I. S. et al. Blood 128, 12 (2016). 3. (https://doi.org/10.1182/blood-2022-159353) 4. Abstract# 3396, ASH 2023, Hanlon Sia at al. Citation Format: Laura Carretero-Iglesia, Maria Pihlgren, Jeremy Berret, Adam Drake, Daniela Pais, Cyrille Dreyfus, Vinu Menon, Thomas Matthes, Claire Edwards, James Edwards, Catherine Pellat-Deceunynck, Philippe Moreau, Cyrille Touzeau, Tomomi Matsuura, Piet van der Graaf, Lida Pacaud, Cyril Konto, Eugene Zhukovsky, Mario Perro. ISB 2001, a BCMA and CD38 dual targeting T cell engager, demonstrates superior cytotoxicity relative to teclistamab in the samples of patient relapsing from CD38 and BCMA targeted immunotherapies [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1238.

Abstract 2312: Integrating spatial multi-omics data with spatial quantitative pharmacology (spQSP) model to simulate human neoadjuvant immunotherapy clinical trial of hepatocellular carcinoma

Abstract Human clinical trials provided tremendous insights to advance novel systemic therapies and to 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 demonstrated that new combination of immunotherapeutic regimens provide augmented 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 therapies entirely in silico. To facilitate designing dosing regimens and selecting potential predictive biomarkers, we developed a new computational model to track tumor progression at organ scale while reflecting the spatial heterogeneity in the tumor at cellular and molecular 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. The model is first calibrated using clinical outcomes and measured immune cell densities based on recent neoadjuvant HCC clinical trial combining anti-PD-1 immunotherapy and a multitargeted tyrosine kinase inhibitor (TKI) cabozantinib (NCT03299946). Then, we validate the results from the spQSP model by leveraging real-world spatial multi-omics data from the same clinical trial. 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 Arginase 1 (Arg1) positive macrophages among non-responders while the reverse trend was observed for responders. 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 TGFβ in tumor and immune cell interactions. To our knowledge, this is the first spatial tumor model for virtual clinical trials at a molecular and cellular scale that is grounded in high-throughput spatial multi-omics data from a human clinical trial. Citation Format: Shuming Zhang, Atul Deshpande, Babita K. Verma, Hanwen Wang, Haoyang Mi, Long Yuan, Won Jin Ho, Elizabeth M. Jaffee, Qingfeng Zhu, Robert A. Anders, Mark Yarchoan, Luciane T. Kagohara, Elana J. Fertig, Aleksander S. Popel. Integrating spatial multi-omics data with spatial quantitative pharmacology (spQSP) model to simulate human neoadjuvant immunotherapy clinical trial of hepatocellular carcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2312.

Predicting efficacy assessment of combined treatment of radiotherapy and nivolumab for NSCLC patients through virtual clinical trials using QSP modeling.

Non-Small Cell Lung Cancer (NSCLC) remains one of the main causes of cancer death worldwide. In the urge of finding an effective approach to treat cancer, enormous therapeutic targets and treatment combinations are explored in clinical studies, which are not only costly, suffer from a shortage of participants, but also unable to explore all prospective therapeutic solutions. Within the evolving therapeutic landscape, the combined use of radiotherapy (RT) and checkpoint inhibitors (ICIs) emerged as a promising avenue. Exploiting the power of quantitative system pharmacology (QSP), we undertook a study to anticipate the therapeutic outcomes of these interventions, aiming to address the limitations of clinical trials. After enhancing a pre-existing QSP platform and accurately replicating clinical data outcomes, we conducted an in-depth study, examining different treatment protocols with nivolumab and RT, both as monotherapy and in combination, by assessing their efficacy through clinical endpoints, namely time to progression (TTP) and duration of response (DOR). As result, the synergy of combined protocols showcased enhanced TTP and extended DOR, suggesting dual advantages of extended response and slowed disease progression with certain combined regimens. Through the lens of QSP modeling, our findings highlight the potential to fine-tune combination therapies for NSCLC, thereby providing pivotal insights for tailoring patient-centric therapeutic interventions.

Biomarkers and computational models for predicting efficacy to tumor ICI immunotherapy.

Numerous studies have shown that immune checkpoint inhibitor (ICI) immunotherapy has great potential as a cancer treatment, leading to significant clinical improvements in numerous cases. However, it benefits a minority of patients, underscoring the importance of discovering reliable biomarkers that can be used to screen for potential beneficiaries and ultimately reduce the risk of overtreatment. Our comprehensive review focuses on the latest advancements in predictive biomarkers for ICI therapy, particularly emphasizing those that enhance the efficacy of programmed cell death protein 1 (PD-1)/programmed cell death-ligand 1 (PD-L1) inhibitors and cytotoxic T-lymphocyte antigen-4 (CTLA-4) inhibitors immunotherapies. We explore biomarkers derived from various sources, including tumor cells, the tumor immune microenvironment (TIME), body fluids, gut microbes, and metabolites. Among them, tumor cells-derived biomarkers include tumor mutational burden (TMB) biomarker, tumor neoantigen burden (TNB) biomarker, microsatellite instability (MSI) biomarker, PD-L1 expression biomarker, mutated gene biomarkers in pathways, and epigenetic biomarkers. TIME-derived biomarkers include immune landscape of TIME biomarkers, inhibitory checkpoints biomarkers, and immune repertoire biomarkers. We also discuss various techniques used to detect and assess these biomarkers, detailing their respective datasets, strengths, weaknesses, and evaluative metrics. Furthermore, we present a comprehensive review of computer models for predicting the response to ICI therapy. The computer models include knowledge-based mechanistic models and data-based machine learning (ML)models. Among the knowledge-based mechanistic models are pharmacokinetic/pharmacodynamic (PK/PD) models, partial differential equation (PDE) models, signal networks-based models, quantitative systems pharmacology (QSP) models, and agent-based models (ABMs). ML models include linear regression models, logistic regression models, support vector machine (SVM)/random forest/extra trees/k-nearest neighbors (KNN) models, artificial neural network (ANN) and deep learning models. Additionally, there are hybrid models of systems biology and ML. We summarized the details of these models, outlining the datasets they utilize, their evaluation methods/metrics, and their respective strengths and limitations. By summarizing the major advances in the research on predictive biomarkers and computer models for the therapeutic effect and clinical utility of tumor ICI, we aim to assist researchers in choosing appropriate biomarkers or computer models for research exploration and help clinicians conduct precision medicine by selecting the best biomarkers.

Advancing precision medicine therapeutics for Parkinson’s utilizing a shared quantitative systems pharmacology model and framework

A rich pipeline of therapeutic candidates is advancing for Parkinson’s disease, many of which are targeting the underlying pathophysiology of disease. Emerging evidence grounded in novel genetics and biomarker discoveries is illuminating the true promise of precision medicine-based therapeutic strategies for PD. There has been a growing effort to investigate disease-modifying therapies by designing clinical trials for genetic forms of PD - providing a clearer link to underlying pathophysiology. Leading candidate genes based on human genetic findings that are under active investigation in an array of basic and translational models include SNCA, LRRK2, and GBA. Broad investigations across mechanistic models show that these genes signal through common molecular pathways, namely, autosomal lysosomal pathways, inflammation and mitochondrial function. Therapeutic clinical trials to date based on genetically defined targets have not yet achieved approvals; however, much is to be learned from such pioneering trials. Fundamental principles of drug development that include proof of pharmacology in target tissue are critical to have confidence in advancing such precision-based therapies. There is a clear need for downstream biomarkers of leading candidate therapies to demonstrate proof of mechanism. The current regulatory landscape is poised and primed to support translational modeling strategies for the effective advancement of PD disease-modifying therapeutic candidates. A convergence of rich complex data that is available, the regulatory framework of model informed drug development (MIDD), and the new biological integrated staging frameworks when combined are collectively setting the stage for advancing new approaches in PD to accelerate progress. This perspective review highlights the potential of quantitative systems pharmacology (QSP) modeling in contributing to the field and hastening the pace of progress in advancing collaborative approaches for urgently needed PD disease-modifying treatments.

Characterisation of cotadutide's dual GLP-1/glucagon receptor agonistic effects on glycaemic control using an in vivo human glucose regulation quantitative systems pharmacology model.

Cotadutide is a dual GLP-1 and glucagon receptor agonist with balanced agonistic activity at each receptor designed to harness the advantages on promoting liver health, weight loss and glycaemic control. We characterised the effects of cotadutide on glucose, insulin, GLP-1, GIP, and glucagon over time in a quantitative manner using our glucose dynamics systems model (4GI systems model), in combination with clinical data from a multiple ascending dose/Phase 2a (MAD/Ph2a) study in overweight and obese subjects with a history of Type 2 diabetes mellitus (NCT02548585). The cotadutide PK-4GI systems model was calibrated to clinical data by re-estimating only food related parameters. In vivo cotadutide efficacy was scaled based on in vitro potency. The model was used to explore the effect of weight loss on insulin sensitivity and predict the relative contribution of the GLP-1 and glucagon receptor agonistic effects on glucose. Cotadutide MAD/Ph2a clinical endpoints were successfully predicted. The 4GI model captured a positive effect of weight loss on insulin sensitivity and showed that the stimulating effect of glucagon on glucose production counteracts the GLP-1 receptor-mediated decrease in glucose, resulting in a plateau for glucose decrease around a 200-μg cotadutide dose. The 4GI quantitative systems pharmacology model was able to predict the clinical effects of cotadutide on glucose, insulin, GLP-1, glucagon and GIP given known in vitro potency. The analyses demonstrated that the quantitative systems pharmacology model, and its successive refinements, will be a valuable tool to support the clinical development of cotadutide and related compounds.

Warfarin–A natural anticoagulant: A review of research trends for precision medication

BackgroundWarfarin is a widely prescribed anticoagulant in the clinic. It has a more considerable individual variability, and many factors affect its variability. Mathematical models can quantify the quantitative impact of these factors on individual variability. PurposeThe aim is to comprehensively analyze the advanced warfarin dosing algorithm based on pharmacometrics and machine learning models of personalized warfarin dosage. MethodsA bibliometric analysis of the literature retrieved from PubMed and Scopus was performed using VOSviewer. The relevant literature that reported the precise dosage of warfarin calculation was retrieved from the database. The multiple linear regression (MLR) algorithm was excluded because a recent systematic review that mainly reviewed this algorithm has been reported. The following terms of quantitative systems pharmacology, mechanistic model, physiologically based pharmacokinetic model, artificial intelligence, machine learning, pharmacokinetic, pharmacodynamic, pharmacokinetics, pharmacodynamics, and warfarin were added as MeSH Terms or appearing in Title/Abstract into query box of PubMed, then humans and English as filter were added to retrieve the literature. ResultsBibliometric analysis revealed important co-occuring MeShH and index keywords. Further, the United States, China, and the United Kingdom were among the top countries contributing in this domain. Some studies have established personalized warfarin dosage models using pharmacometrics and machine learning-based algorithms. There were 54 related studies, including 14 pharmacometric models, 31 artificial intelligence models, and 9 model evaluations. Each model has its advantages and disadvantages. The pharmacometric model contains biological or pharmacological mechanisms in structure. The process of pharmacometric model development is very time- and labor-intensive. Machine learning is a purely data-driven approach; its parameters are more mathematical and have less biological interpretation. However, it is faster, more efficient, and less time-consuming. Most published models of machine learning algorithms were established based on cross-sectional data sourced from the database. ConclusionFuture research on personalized warfarin medication should focus on combining the advantages of machine learning and pharmacometrics algorithms to establish a more robust warfarin dosage algorithm. Randomized controlled trials should be performed to evaluate the established algorithm of warfarin dosage. Moreover, a more user-friendly and accessible warfarin precision medicine platform should be developed.