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Goto A, Moriya Y, Nakayama M, Iwasaki S, Yamamoto S. DMPK perspective on quantitative model analysis for chimeric antigen receptor cell therapy: Advances and challenges. Drug Metab Pharmacokinet 2024; 56:101003. [PMID: 38843652 DOI: 10.1016/j.dmpk.2024.101003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Revised: 01/26/2024] [Accepted: 02/10/2024] [Indexed: 06/24/2024]
Abstract
Chimeric antigen receptor (CAR) cells are genetically engineered immune cells that specifically target tumor-associated antigens and have revolutionized cancer treatment, particularly in hematological malignancies, with ongoing investigations into their potential applications in solid tumors. This review provides a comprehensive overview of the current status and challenges in drug metabolism and pharmacokinetics (DMPK) for CAR cell therapy, specifically emphasizing on quantitative modeling and simulation (M&S). Furthermore, the recent advances in quantitative model analysis have been reviewed, ranging from clinical data characterization to mechanism-based modeling that connects in vitro and in vivo nonclinical and clinical study data. Additionally, the future perspectives and areas for improvement in CAR cell therapy translation have been reviewed. This includes using formulation quality considerations, characterization of appropriate animal models, refinement of in vitro models for bottom-up approaches, and enhancement of quantitative bioanalytical methodology. Addressing these challenges within a DMPK framework is pivotal in facilitating the translation of CAR cell therapy, ultimately enhancing the patients' lives through efficient CAR cell therapies.
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Affiliation(s)
- Akihiko Goto
- Center of Excellence for Drug Metabolism, Pharmacokinetics and Modeling, Preclinical and Translational Sciences, Research, Takeda Pharmaceutical Company Limited, Kanagawa, Japan
| | - Yuu Moriya
- Center of Excellence for Drug Metabolism, Pharmacokinetics and Modeling, Preclinical and Translational Sciences, Research, Takeda Pharmaceutical Company Limited, Kanagawa, Japan
| | - Miyu Nakayama
- Center of Excellence for Drug Metabolism, Pharmacokinetics and Modeling, Preclinical and Translational Sciences, Research, Takeda Pharmaceutical Company Limited, Kanagawa, Japan
| | - Shinji Iwasaki
- Center of Excellence for Drug Metabolism, Pharmacokinetics and Modeling, Preclinical and Translational Sciences, Research, Takeda Pharmaceutical Company Limited, Kanagawa, Japan
| | - Syunsuke Yamamoto
- Center of Excellence for Drug Metabolism, Pharmacokinetics and Modeling, Preclinical and Translational Sciences, Research, Takeda Pharmaceutical Company Limited, Kanagawa, Japan.
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2
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Oishi M, Sayama H, Toshimoto K, Nakayama T, Nagasaka Y. Practical QSP application from the preclinical phase to enhance the probability of clinical success: Insights from case studies in oncology. Drug Metab Pharmacokinet 2024; 56:101020. [PMID: 38797089 DOI: 10.1016/j.dmpk.2024.101020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Revised: 02/02/2024] [Accepted: 05/06/2024] [Indexed: 05/29/2024]
Abstract
Quantitative Systems Pharmacology (QSP) has emerged as a promising modeling and simulation (M&S) approach in drug development, with potential to improve clinical success rates. While conventional M&S has significantly contributed to quantitative understanding in late preclinical and clinical phases, it falls short in explaining unexpected phenomena and testing hypotheses in the early research phase. QSP presents a solution to these limitations. To harness the full potential of QSP in early preclinical stages, preclinical modelers who are familiar with conventional M&S need to update their understanding of the differences between conventional M&S and QSP. This review focuses on QSP applications during the preclinical stage, citing case examples and sharing our experiences in oncology. We emphasize the critical role of QSP in increasing the probability of success for clinical proof of concept (PoC) when applied from the early preclinical stage. Enhancing the quality of both hypotheses and QSP models from early preclinical stage is of critical importance. Once a QSP model achieves credibility, it facilitates predictions of clinical responses and potential biomarkers. We propose that sequential QSP applications from preclinical stages can improve success rates of clinical PoC, and emphasize the importance of refining both hypotheses and QSP models throughout the process.
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Affiliation(s)
- Masayo Oishi
- Systems Pharmacology, Non-Clinical Biomedical Science, Applied Research & Operations, Astellas Pharma Inc., Tsukuba, Ibaraki, 305-8585, Japan.
| | - Hiroyuki Sayama
- Systems Pharmacology, Non-Clinical Biomedical Science, Applied Research & Operations, Astellas Pharma Inc., Tsukuba, Ibaraki, 305-8585, Japan
| | - Kota Toshimoto
- Systems Pharmacology, Non-Clinical Biomedical Science, Applied Research & Operations, Astellas Pharma Inc., Tsukuba, Ibaraki, 305-8585, Japan
| | - Takeshi Nakayama
- Systems Pharmacology, Non-Clinical Biomedical Science, Applied Research & Operations, Astellas Pharma Inc., Tsukuba, Ibaraki, 305-8585, Japan
| | - Yasuhisa Nagasaka
- Non-Clinical Biomedical Science, Applied Research & Operations, Astellas Pharma Inc., Tsukuba, Ibaraki, 305-8585, Japan
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3
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Sexton D, Nguyen HQ, Juethner S, Luo H, Zhang Z, Jasper P, Zhu AZX. A quantitative systems pharmacology model of plasma kallikrein-kinin system dysregulation in hereditary angioedema. J Pharmacokinet Pharmacodyn 2024:10.1007/s10928-024-09919-6. [PMID: 38734778 DOI: 10.1007/s10928-024-09919-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Accepted: 04/09/2024] [Indexed: 05/13/2024]
Abstract
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 20 pM. 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 300 mg every 2 weeks (Q2W) to every 4 weeks (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.
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Affiliation(s)
- Dan Sexton
- Takeda Development Center Americas, Inc., Lexington, MA, USA
| | - Hoa Q Nguyen
- Takeda Development Center Americas, Inc., Lexington, MA, USA
| | - Salomé Juethner
- Takeda Development Center Americas, Inc., Lexington, MA, USA
| | | | | | | | - Andy Z X Zhu
- Takeda Development Center Americas, Inc., Lexington, MA, USA.
- Preclinical and Translational Science Department, Takeda Pharmaceutical Company Limited, 35 Landsdowne Street, Cambridge, MA, 02139, USA.
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Arulraj T, Wang H, Ippolito A, Zhang S, Fertig EJ, Popel AS. Leveraging multi-omics data to empower quantitative systems pharmacology in immuno-oncology. Brief Bioinform 2024; 25:bbae131. [PMID: 38557676 PMCID: PMC10982948 DOI: 10.1093/bib/bbae131] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 02/20/2024] [Accepted: 03/08/2024] [Indexed: 04/04/2024] Open
Abstract
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.
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Affiliation(s)
- Theinmozhi Arulraj
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Hanwen Wang
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Alberto Ippolito
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Shuming Zhang
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Elana J Fertig
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
- Department of Oncology, and the Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
- Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD, USA
| | - Aleksander S Popel
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
- Department of Oncology, and the Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
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Qin Y, Huo M, Liu X, Li SC. Biomarkers and computational models for predicting efficacy to tumor ICI immunotherapy. Front Immunol 2024; 15:1368749. [PMID: 38524135 PMCID: PMC10957591 DOI: 10.3389/fimmu.2024.1368749] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2024] [Accepted: 02/27/2024] [Indexed: 03/26/2024] Open
Abstract
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.
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Affiliation(s)
- Yurong Qin
- Department of Computer Science, City University of Hong Kong, Kowloon, China
- City University of Hong Kong Shenzhen Research Institute, Shenzhen, Guangdong, China
| | - Miaozhe Huo
- Department of Computer Science, City University of Hong Kong, Kowloon, China
- City University of Hong Kong Shenzhen Research Institute, Shenzhen, Guangdong, China
| | - Xingwu Liu
- School of Mathematical Sciences, Dalian University of Technology, Dalian, Liaoning, China
| | - Shuai Cheng Li
- Department of Computer Science, City University of Hong Kong, Kowloon, China
- City University of Hong Kong Shenzhen Research Institute, Shenzhen, Guangdong, China
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Siddiqui F, Aslam D, Tanveer K, Soudy M. The Role of Artificial Intelligence and Machine Learning in Autoimmune Disorders. STUDIES IN COMPUTATIONAL INTELLIGENCE 2024:61-75. [DOI: 10.1007/978-981-99-9029-0_3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/24/2024]
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Bitencourt-Ferreira G, Villarreal MA, Quiroga R, Biziukova N, Poroikov V, Tarasova O, de Azevedo Junior WF. Exploring Scoring Function Space: Developing Computational Models for Drug Discovery. Curr Med Chem 2024; 31:2361-2377. [PMID: 36944627 DOI: 10.2174/0929867330666230321103731] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Revised: 12/15/2022] [Accepted: 12/29/2022] [Indexed: 03/23/2023]
Abstract
BACKGROUND The idea of scoring function space established a systems-level approach to address the development of models to predict the affinity of drug molecules by those interested in drug discovery. OBJECTIVE Our goal here is to review the concept of scoring function space and how to explore it to develop machine learning models to address protein-ligand binding affinity. METHODS We searched the articles available in PubMed related to the scoring function space. We also utilized crystallographic structures found in the protein data bank (PDB) to represent the protein space. RESULTS The application of systems-level approaches to address receptor-drug interactions allows us to have a holistic view of the process of drug discovery. The scoring function space adds flexibility to the process since it makes it possible to see drug discovery as a relationship involving mathematical spaces. CONCLUSION The application of the concept of scoring function space has provided us with an integrated view of drug discovery methods. This concept is useful during drug discovery, where we see the process as a computational search of the scoring function space to find an adequate model to predict receptor-drug binding affinity.
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Affiliation(s)
| | - Marcos A Villarreal
- CONICET-Departamento de Matemática y Física, Instituto de Investigaciones en Fisicoquímica de Córdoba (INFIQC), Facultad de Ciencias Químicas, Universidad Nacional de Córdoba, Ciudad Universitaria, Córdoba, Argentina
| | - Rodrigo Quiroga
- CONICET-Departamento de Matemática y Física, Instituto de Investigaciones en Fisicoquímica de Córdoba (INFIQC), Facultad de Ciencias Químicas, Universidad Nacional de Córdoba, Ciudad Universitaria, Córdoba, Argentina
| | - Nadezhda Biziukova
- Institute of Biomedical Chemistry, Pogodinskaya Str., 10/8, Moscow, 119121, Russia
| | - Vladimir Poroikov
- Institute of Biomedical Chemistry, Pogodinskaya Str., 10/8, Moscow, 119121, Russia
| | - Olga Tarasova
- Institute of Biomedical Chemistry, Pogodinskaya Str., 10/8, Moscow, 119121, Russia
| | - Walter F de Azevedo Junior
- Pontifical Catholic University of Rio Grande do Sul - PUCRS, Porto Alegre-RS, Brazil
- Specialization Program in Bioinformatics, The Pontifical Catholic University of Rio Grande do Sul (PUCRS), Av. Ipiranga, 6681 Porto Alegre / RS 90619-900, Brazil
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Ballesta A, Gallo JM. Quantitative Systems Pharmacology: A Foundation To Establish Precision Medicine-Editorial. J Pharmacol Exp Ther 2023; 387:27-30. [PMID: 37714689 DOI: 10.1124/jpet.123.001842] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Accepted: 07/31/2023] [Indexed: 09/17/2023] Open
Affiliation(s)
- Annabelle Ballesta
- INSERM U900, Institut Curie, Mines ParisTech CBIO, Université PSL, Paris, France (A.B.) and Department of Pharmaceutical Sciences, School of Pharmacy and Pharmaceutical Sciences, University at Buffalo, Buffalo, New York (J.M.G.)
| | - James M Gallo
- INSERM U900, Institut Curie, Mines ParisTech CBIO, Université PSL, Paris, France (A.B.) and Department of Pharmaceutical Sciences, School of Pharmacy and Pharmaceutical Sciences, University at Buffalo, Buffalo, New York (J.M.G.)
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Myers RC, Augustin F, Huard J, Friedrich CM. Using machine learning surrogate modeling for faster QSP VP cohort generation. CPT Pharmacometrics Syst Pharmacol 2023; 12:1047-1059. [PMID: 37328956 PMCID: PMC10431055 DOI: 10.1002/psp4.12999] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 03/27/2023] [Accepted: 05/02/2023] [Indexed: 06/18/2023] Open
Abstract
Virtual patients (VPs) are widely used within quantitative systems pharmacology (QSP) modeling to explore the impact of variability and uncertainty on clinical responses. In one method of generating VPs, parameters are sampled randomly from a distribution, and possible VPs are accepted or rejected based on constraints on model output behavior. This approach works but can be inefficient (i.e., the vast majority of model runs typically do not result in valid VPs). Machine learning surrogate models offer an opportunity to improve the efficiency of VP creation significantly. In this approach, surrogate models are trained using the full QSP model and subsequently used to rapidly pre-screen for parameter combinations that result in feasible VPs. The overwhelming majority of parameter combinations pre-vetted using the surrogate models result in valid VPs when tested in the original QSP model. This tutorial presents this novel workflow and demonstrates how a surrogate model software application can be used to select and optimize the surrogate models in a case study. We then discuss the relative efficiency of the methods and scalability of the proposed method.
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Moingeon P. Artificial intelligence-driven drug development against autoimmune diseases. Trends Pharmacol Sci 2023; 44:411-424. [PMID: 37268540 DOI: 10.1016/j.tips.2023.04.005] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2023] [Revised: 04/22/2023] [Accepted: 04/25/2023] [Indexed: 06/04/2023]
Abstract
Artificial intelligence (AI)-based predictive models are being used to foster a precision medicine approach to treat complex chronic diseases such as autoimmune and autoinflammatory disorders (AIIDs). In the past few years the first models of systemic lupus erythematosus (SLE), primary Sjögren syndrome (pSS), and rheumatoid arthritis (RA) have been produced by molecular profiling of patients using omic technologies and integrating the data with AI. These advances have confirmed a complex pathophysiology involving multiple proinflammatory pathways and also provide evidence for shared molecular dysregulation across different AIIDs. I discuss how models are used to stratify patients, assess causality in pathophysiology, design drug candidates in silico, and predict drug efficacy in virtual patients. By relating individual patient characteristics to the predicted properties of millions of drug candidates, these models can improve the management of AIIDs through more personalized treatments.
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Affiliation(s)
- Philippe Moingeon
- Research and Development, Servier Laboratories, 50 Rue Carnot, 92150 Suresnes, France; French Academy of Pharmacy, 4 Avenue de l'Observatoire, 75006 Paris, France.
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Wang H, Arulraj T, Kimko H, Popel AS. Generating immunogenomic data-guided virtual patients using a QSP model to predict response of advanced NSCLC to PD-L1 inhibition. NPJ Precis Oncol 2023; 7:55. [PMID: 37291190 DOI: 10.1038/s41698-023-00405-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Accepted: 05/25/2023] [Indexed: 06/10/2023] Open
Abstract
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.
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Affiliation(s)
- Hanwen Wang
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA.
| | - Theinmozhi Arulraj
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA
| | - Holly Kimko
- Clinical Pharmacology & Quantitative Pharmacology, AstraZeneca, Gaithersburg, MD, USA
| | - Aleksander S Popel
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA
- Department of Oncology, and the Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA
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Wang H, Arulraj T, Kimko H, Popel AS. Generating immunogenomic data-guided virtual patients using a QSP model to predict response of advanced NSCLC to PD-L1 inhibition. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.25.538191. [PMID: 37162938 PMCID: PMC10168221 DOI: 10.1101/2023.04.25.538191] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
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.
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Zhao J, Zhu X, Tan S, Chen C, Kaddoumi A, Guo XL, Lin YW, Cheung SYA. Editorial: Model-informed drug development and evidence-based translational pharmacology. Front Pharmacol 2022; 13:1086551. [PMID: 36578539 PMCID: PMC9791580 DOI: 10.3389/fphar.2022.1086551] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Accepted: 12/05/2022] [Indexed: 12/14/2022] Open
Affiliation(s)
- Jinxin Zhao
- Biomedicine Discovery Institute, Infection and Immunity Program and Department of Microbiology, Monash University, Melbourne, VIC, Australia
| | - Xiao Zhu
- Department of Clinical Pharmacy and Pharmacy Administration, School of Pharmacy, Fudan University, Shanghai, China
| | - Songwen Tan
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, China,*Correspondence: Songwen Tan, ; Chuanpin Chen, ; Amal Kaddoumi, ; Xiu-Li Guo, ; Yu-Wei Lin, ; S. Y. Amy Cheung,
| | - Chuanpin Chen
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, China,*Correspondence: Songwen Tan, ; Chuanpin Chen, ; Amal Kaddoumi, ; Xiu-Li Guo, ; Yu-Wei Lin, ; S. Y. Amy Cheung,
| | - Amal Kaddoumi
- Department of Drug Discovery and Development, Harrison College of Pharmacy, Auburn University, Auburn, AL, United States,*Correspondence: Songwen Tan, ; Chuanpin Chen, ; Amal Kaddoumi, ; Xiu-Li Guo, ; Yu-Wei Lin, ; S. Y. Amy Cheung,
| | - Xiu-Li Guo
- Department of Pharmacology, School of Pharmaceutical Science, Shandong University, Jinan, China,*Correspondence: Songwen Tan, ; Chuanpin Chen, ; Amal Kaddoumi, ; Xiu-Li Guo, ; Yu-Wei Lin, ; S. Y. Amy Cheung,
| | - Yu-Wei Lin
- Biomedicine Discovery Institute, Infection and Immunity Program and Department of Microbiology, Monash University, Melbourne, VIC, Australia,Malaya Translational and Clinical Pharmacometrics Group, Faculty of Pharmacy, University of Malaya, Kuala Lumpur, Malaysia,Department of Clinical Pharmacy and Pharmacy Practice, Faculty of Pharmacy, University of Malaya, Kuala Lumpur, Malaysia,Integrated Drug Development, Certara, NJ, United States,*Correspondence: Songwen Tan, ; Chuanpin Chen, ; Amal Kaddoumi, ; Xiu-Li Guo, ; Yu-Wei Lin, ; S. Y. Amy Cheung,
| | - S. Y. Amy Cheung
- Integrated Drug Development, Certara, NJ, United States,*Correspondence: Songwen Tan, ; Chuanpin Chen, ; Amal Kaddoumi, ; Xiu-Li Guo, ; Yu-Wei Lin, ; S. Y. Amy Cheung,
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New Drug Design Avenues Targeting Alzheimer's Disease by Pharmacoinformatics-Aided Tools. Pharmaceutics 2022; 14:pharmaceutics14091914. [PMID: 36145662 PMCID: PMC9503559 DOI: 10.3390/pharmaceutics14091914] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Revised: 09/03/2022] [Accepted: 09/06/2022] [Indexed: 11/30/2022] Open
Abstract
Neurodegenerative diseases (NDD) have been of great interest to scientists for a long time due to their multifactorial character. Among these pathologies, Alzheimer’s disease (AD) is of special relevance, and despite the existence of approved drugs for its treatment, there is still no efficient pharmacological therapy to stop, slow, or repair neurodegeneration. Existing drugs have certain disadvantages, such as lack of efficacy and side effects. Therefore, there is a real need to discover new drugs that can deal with this problem. However, as AD is multifactorial in nature with so many physiological pathways involved, the most effective approach to modulate more than one of them in a relevant manner and without undesirable consequences is through polypharmacology. In this field, there has been significant progress in recent years in terms of pharmacoinformatics tools that allow the discovery of bioactive molecules with polypharmacological profiles without the need to spend a long time and excessive resources on complex experimental designs, making the drug design and development pipeline more efficient. In this review, we present from different perspectives how pharmacoinformatics tools can be useful when drug design programs are designed to tackle complex diseases such as AD, highlighting essential concepts, showing the relevance of artificial intelligence and new trends, as well as different databases and software with their main results, emphasizing the importance of coupling wet and dry approaches in drug design and development processes.
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Quantitative Systems Pharmacology and Biased Agonism at Opioid Receptors: A Potential Avenue for Improved Analgesics. Int J Mol Sci 2022; 23:ijms23095114. [PMID: 35563502 PMCID: PMC9104178 DOI: 10.3390/ijms23095114] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 05/01/2022] [Accepted: 05/02/2022] [Indexed: 11/25/2022] Open
Abstract
Chronic pain is debilitating and represents a significant burden in terms of personal and socio-economic costs. Although opioid analgesics are widely used in chronic pain treatment, many patients report inadequate pain relief or relevant adverse effects, highlighting the need to develop analgesics with improved efficacy/safety. Multiple evidence suggests that G protein-dependent signaling triggers opioid-induced antinociception, whereas arrestin-mediated pathways are credited with modulating different opioid adverse effects, thus spurring extensive research for G protein-biased opioid agonists as analgesic candidates with improved pharmacology. Despite the increasing expectations of functional selectivity, translating G protein-biased opioid agonists into improved therapeutics is far from being fully achieved, due to the complex, multidimensional pharmacology of opioid receptors. The multifaceted network of signaling events and molecular processes underlying therapeutic and adverse effects induced by opioids is more complex than the mere dichotomy between G protein and arrestin and requires more comprehensive, integrated, network-centric approaches to be fully dissected. Quantitative Systems Pharmacology (QSP) models employing multidimensional assays associated with computational tools able to analyze large datasets may provide an intriguing approach to go beyond the greater complexity of opioid receptor pharmacology and the current limitations entailing the development of biased opioid agonists as improved analgesics.
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Gaweda AE, Lederer ED, Brier ME. Use of Artificial Intelligence to Identify New Mechanisms and Approaches to Therapy of Bone Disorders Associated With Chronic Kidney Disease. Front Med (Lausanne) 2022; 9:807994. [PMID: 35402468 PMCID: PMC8990896 DOI: 10.3389/fmed.2022.807994] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Accepted: 02/28/2022] [Indexed: 11/25/2022] Open
Abstract
Chronic kidney disease (CKD) leads to clinically severe bone loss, resulting from the deranged mineral metabolism that accompanies CKD. Each individual patient presents a unique combination of risk factors, pathologies, and complications of bone disease. The complexity of the disorder coupled with our incomplete understanding of the pathophysiology has significantly hampered the ability of nephrologists to prevent fractures, a leading comorbidity of CKD. Much has been learned from animal models; however, we propose in this review that application of multiple techniques of mathematical modeling and artificial intelligence can accelerate our ability to develop relevant and impactful clinical trials and can lead to better understanding of the osteoporosis of CKD. We highlight the foundational work that informed our current model development and discuss the potential applications of our approach combining principles of quantitative systems pharmacology, model predictive control, and reinforcement learning to deliver individualized precision medical therapy of this highly complex disorder.
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Affiliation(s)
- Adam E Gaweda
- Division of Nephrology, Department of Medicine, University of Louisville School of Medicine, Louisville, KY, United States
| | - Eleanor D Lederer
- Medical Services, VA North Texas Health Sciences Center, Dallas, TX, United States.,Division of Nephrology, Department of Medicine, University of Texas Southwestern Medical Center, Dallas, TX, United States.,Charles and Jane Pak Center for Mineral Metabolism and Clinical Research, University of Texas Southwestern Medical Center, Dallas, TX, United States
| | - Michael E Brier
- Division of Nephrology, Department of Medicine, University of Louisville School of Medicine, Louisville, KY, United States.,Research Service, Robley Rex VA Medical Center, Louisville, KY, United States
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Zhang T, Androulakis IP, Bonate P, Cheng L, Helikar T, Parikh J, Rackauckas C, Subramanian K, Cho CR. Two heads are better than one: current landscape of integrating QSP and machine learning : An ISoP QSP SIG white paper by the working group on the integration of quantitative systems pharmacology and machine learning. J Pharmacokinet Pharmacodyn 2022; 49:5-18. [PMID: 35103884 PMCID: PMC8837505 DOI: 10.1007/s10928-022-09805-z] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Accepted: 01/10/2022] [Indexed: 12/02/2022]
Abstract
Quantitative systems pharmacology (QSP) modeling is applied to address essential questions in drug development, such as the mechanism of action of a therapeutic agent and the progression of disease. Meanwhile, machine learning (ML) approaches also contribute to answering these questions via the analysis of multi-layer ‘omics’ data such as gene expression, proteomics, metabolomics, and high-throughput imaging. Furthermore, ML approaches can also be applied to aspects of QSP modeling. Both approaches are powerful tools and there is considerable interest in integrating QSP modeling and ML. So far, a few successful implementations have been carried out from which we have learned about how each approach can overcome unique limitations of the other. The QSP + ML working group of the International Society of Pharmacometrics QSP Special Interest Group was convened in September, 2019 to identify and begin realizing new opportunities in QSP and ML integration. The working group, which comprises 21 members representing 18 academic and industry organizations, has identified four categories of current research activity which will be described herein together with case studies of applications to drug development decision making. The working group also concluded that the integration of QSP and ML is still in its early stages of moving from evaluating available technical tools to building case studies. This paper reports on this fast-moving field and serves as a foundation for future codification of best practices.
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Affiliation(s)
- Tongli Zhang
- University of Cincinnati, Cincinnati, OH, 45267, USA.
| | | | | | | | - Tomáš Helikar
- Department of Biochemistry, University of Nebraska-Lincoln, Lincoln, NE, USA
| | | | - Christopher Rackauckas
- Pumas-AI, Baltimore, MD, USA.,Department of Mathematics, Massachusetts Institute of Technology, Boston, MA, USA
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Bonate PL. Editor's note on the themed issue: integration of machine learning and quantitative systems pharmacology. J Pharmacokinet Pharmacodyn 2022; 49:1-3. [PMID: 35041146 DOI: 10.1007/s10928-022-09803-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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