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Saito R, Nakada T. Insights into drug development with quantitative systems pharmacology: A prospective case study of uncovering hyperkalemia risk in diabetic nephropathy with virtual clinical trials. Drug Metab Pharmacokinet 2024; 56:101019. [PMID: 38797092 DOI: 10.1016/j.dmpk.2024.101019] [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: 10/30/2023] [Revised: 04/25/2024] [Accepted: 05/02/2024] [Indexed: 05/29/2024]
Abstract
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.
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Affiliation(s)
- Ryuta Saito
- Discovery Technology Laboratories, Sohyaku. Innovative Research Division, Mitsubishi Tanabe Pharma Corporation, Yokohama, 227-0033, Japan.
| | - Tomohisa Nakada
- Discovery Technology Laboratories, Sohyaku. Innovative Research Division, Mitsubishi Tanabe Pharma Corporation, Yokohama, 227-0033, 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] [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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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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4
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Galluppi GR, Ahamadi M, Bhattacharya S, Budha N, Gheyas F, Li CC, Chen Y, Dosne AG, Kristensen NR, Magee M, Samtani MN, Sinha V, Taskar K, Upreti VV, Yang J, Cook J. Considerations for Industry-Preparing for the FDA Model-Informed Drug Development (MIDD) Paired Meeting Program. Clin Pharmacol Ther 2024. [PMID: 38519861 DOI: 10.1002/cpt.3245] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Accepted: 03/01/2024] [Indexed: 03/25/2024]
Abstract
A recent industry perspective published in this journal describes the benefits received by drug companies from participation in the MIDD Pilot Program. Along with the primary objectives of supporting good decision-making in drug development, there were substantial savings in time and development costs. Furthermore, many sponsors reported qualitative benefits such as new learnings and clarity on MIDD strategies and methodology that could be applied to other development programs. Based on the success of the Pilot Program, the FDA recently announced the continuation of the MIDD Paired Meeting Program as part of the Prescription Drug User Fee Act (PDUFA VII). In this report, we describe the collective experiences of industry participants in the MIDD Program to date, including all aspects of the process from meeting request submission to follow-up actions. The purpose is to provide future participants with information to optimize the value of the MIDD Program.
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Affiliation(s)
- Gerald R Galluppi
- Sunovion Pharmaceuticals Inc. a U.S. subsidiary of Sumitomo Pharma America, Marlborough, Massachusetts, USA
| | | | | | | | | | - Chi-Chung Li
- Genentech Inc, South San Francisco, California, USA
| | - Yuan Chen
- Genentech Inc, South San Francisco, California, USA
| | | | | | | | | | - Vikram Sinha
- Novartis Pharmaceutical Corporation, Basel, Switzerland
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5
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Ugolkov Y, Nikitich A, Leon C, Helmlinger G, Peskov K, Sokolov V, Volkova A. Mathematical modeling in autoimmune diseases: from theory to clinical application. Front Immunol 2024; 15:1371620. [PMID: 38550585 PMCID: PMC10973044 DOI: 10.3389/fimmu.2024.1371620] [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: 01/16/2024] [Accepted: 02/29/2024] [Indexed: 04/02/2024] Open
Abstract
The research & development (R&D) of novel therapeutic agents for the treatment of autoimmune diseases is challenged by highly complex pathogenesis and multiple etiologies of these conditions. The number of targeted therapies available on the market is limited, whereas the prevalence of autoimmune conditions in the global population continues to rise. Mathematical modeling of biological systems is an essential tool which may be applied in support of decision-making across R&D drug programs to improve the probability of success in the development of novel medicines. Over the past decades, multiple models of autoimmune diseases have been developed. Models differ in the spectra of quantitative data used in their development and mathematical methods, as well as in the level of "mechanistic granularity" chosen to describe the underlying biology. Yet, all models strive towards the same goal: to quantitatively describe various aspects of the immune response. The aim of this review was to conduct a systematic review and analysis of mathematical models of autoimmune diseases focused on the mechanistic description of the immune system, to consolidate existing quantitative knowledge on autoimmune processes, and to outline potential directions of interest for future model-based analyses. Following a systematic literature review, 38 models describing the onset, progression, and/or the effect of treatment in 13 systemic and organ-specific autoimmune conditions were identified, most models developed for inflammatory bowel disease, multiple sclerosis, and lupus (5 models each). ≥70% of the models were developed as nonlinear systems of ordinary differential equations, others - as partial differential equations, integro-differential equations, Boolean networks, or probabilistic models. Despite covering a relatively wide range of diseases, most models described the same components of the immune system, such as T-cell response, cytokine influence, or the involvement of macrophages in autoimmune processes. All models were thoroughly analyzed with an emphasis on assumptions, limitations, and their potential applications in the development of novel medicines.
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Affiliation(s)
- Yaroslav Ugolkov
- Research Center of Model-Informed Drug Development, Ivan Mikhaylovich (I.M.) Sechenov First Moscow State Medical University, Moscow, Russia
- Marchuk Institute of Numerical Mathematics of the Russian Academy of Sciences (RAS), Moscow, Russia
| | - Antonina Nikitich
- Research Center of Model-Informed Drug Development, Ivan Mikhaylovich (I.M.) Sechenov First Moscow State Medical University, Moscow, Russia
- Marchuk Institute of Numerical Mathematics of the Russian Academy of Sciences (RAS), Moscow, Russia
| | - Cristina Leon
- Modeling and Simulation Decisions FZ - LLC, Dubai, United Arab Emirates
| | | | - Kirill Peskov
- Research Center of Model-Informed Drug Development, Ivan Mikhaylovich (I.M.) Sechenov First Moscow State Medical University, Moscow, Russia
- Marchuk Institute of Numerical Mathematics of the Russian Academy of Sciences (RAS), Moscow, Russia
- Modeling and Simulation Decisions FZ - LLC, Dubai, United Arab Emirates
- Sirius University of Science and Technology, Sirius, Russia
| | - Victor Sokolov
- Marchuk Institute of Numerical Mathematics of the Russian Academy of Sciences (RAS), Moscow, Russia
- Modeling and Simulation Decisions FZ - LLC, Dubai, United Arab Emirates
| | - Alina Volkova
- Marchuk Institute of Numerical Mathematics of the Russian Academy of Sciences (RAS), Moscow, Russia
- Modeling and Simulation Decisions FZ - LLC, Dubai, United Arab Emirates
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6
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Fischer RP, Volpert A, Antonino P, Ahrens TD. Digital patient twins for personalized therapeutics and pharmaceutical manufacturing. Front Digit Health 2024; 5:1302338. [PMID: 38250053 PMCID: PMC10796488 DOI: 10.3389/fdgth.2023.1302338] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Accepted: 12/13/2023] [Indexed: 01/23/2024] Open
Abstract
Digital twins are virtual models of physical artefacts that may or may not be synchronously connected, and that can be used to simulate their behavior. They are widely used in several domains such as manufacturing and automotive to enable achieving specific quality goals. In the health domain, so-called digital patient twins have been understood as virtual models of patients generated from population data and/or patient data, including, for example, real-time feedback from wearables. Along with the growing impact of data science technologies like artificial intelligence, novel health data ecosystems centered around digital patient twins could be developed. This paves the way for improved health monitoring and facilitation of personalized therapeutics based on management, analysis, and interpretation of medical data via digital patient twins. The utility and feasibility of digital patient twins in routine medical processes are still limited, despite practical endeavors to create digital twins of physiological functions, single organs, or holistic models. Moreover, reliable simulations for the prediction of individual drug responses are still missing. However, these simulations would be one important milestone for truly personalized therapeutics. Another prerequisite for this would be individualized pharmaceutical manufacturing with subsequent obstacles, such as low automation, scalability, and therefore high costs. Additionally, regulatory challenges must be met thus calling for more digitalization in this area. Therefore, this narrative mini-review provides a discussion on the potentials and limitations of digital patient twins, focusing on their potential bridging function for personalized therapeutics and an individualized pharmaceutical manufacturing while also looking at the regulatory impacts.
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7
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Marković M, Milošević J, Wang W, Cao Y. Passive Immunotherapies Targeting Amyloid- β in Alzheimer's Disease: A Quantitative Systems Pharmacology Perspective. Mol Pharmacol 2023; 105:1-13. [PMID: 37907353 DOI: 10.1124/molpharm.123.000726] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Revised: 10/04/2023] [Accepted: 10/10/2023] [Indexed: 11/02/2023] Open
Abstract
Alzheimer's disease (AD) is a neurodegenerative disorder characterized by amyloid-β (Aβ) protein accumulation in the brain. Passive immunotherapies using monoclonal antibodies for targeting Aβ have shown promise for AD treatment. Indeed, recent US Food and Drug Administration approval of aducanumab and lecanemab, alongside positive donanemab Phase III results demonstrated clinical efficacy after decades of failed clinical trials for AD. However, the pharmacological basis distinguishing clinically effective from ineffective therapies remains unclear, impeding development of potent therapeutics. This study aimed to provide a quantitative perspective for effectively targeting Aβ with antibodies. We first reviewed the contradicting results associated with the amyloid hypothesis and the pharmacological basis of Aβ immunotherapy. Subsequently, we developed a quantitative systems pharmacology (QSP) model that describes the non-linear progression of Aβ pathology and the pharmacologic actions of the Aβ-targeting antibodies. Using the QSP model, we analyzed various scenarios for effective passive immunotherapy for AD. The model revealed that binding exclusively to the Aβ monomer has minimal effect on Aβ aggregation and plaque reduction, making the antibody affinity toward Aβ monomer unwanted, as it could become a distractive mechanism for plaque reduction. Neither early intervention, high brain penetration, nor increased dose could yield significant improvement of clinical efficacy for antibodies targeting solely monomers. Antibodies that bind all Aβ species but lack effector function exhibited moderate effects in plaque reduction. Our model highlights the importance of binding aggregate Aβ species and incorporating effector functions for efficient and early plaque reduction, guiding the development of more effective therapies for this devastating disease. SIGNIFICANCE STATEMENT: Despite previous unsuccessful attempts spanning several decades, passive immunotherapies utilizing monoclonal antibodies for targeting amyloid-beta (Aβ) have demonstrated promise with two recent FDA approvals. However, the pharmacological basis that differentiates clinically effective therapies from ineffective ones remains elusive. Our study offers a quantitative systems pharmacology perspective, emphasizing the significance of selectively targeting specific Aβ species and importance of antibody effector functions. This perspective sheds light on the development of more effective therapies for this devastating disease.
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Affiliation(s)
- Milica Marković
- Division of Pharmacotherapy and Experimental Therapeutics, Eshelman School of Pharmacy (M.M., Y.C.) and Lineberger Comprehensive Cancer Center, School of Medicine (Y.C.), University of North Carolina at Chapel Hill, North Carolina; Department of Biochemistry (J.M.), University of Belgrade, Faculty of Chemistry, Belgrade, Serbia; and Clinical Pharmacology and Pharmacometrics, Janssen Research & Development (W.W.), LLC, Spring House, Pennsylvania
| | - Jelica Milošević
- Division of Pharmacotherapy and Experimental Therapeutics, Eshelman School of Pharmacy (M.M., Y.C.) and Lineberger Comprehensive Cancer Center, School of Medicine (Y.C.), University of North Carolina at Chapel Hill, North Carolina; Department of Biochemistry (J.M.), University of Belgrade, Faculty of Chemistry, Belgrade, Serbia; and Clinical Pharmacology and Pharmacometrics, Janssen Research & Development (W.W.), LLC, Spring House, Pennsylvania
| | - Weirong Wang
- Division of Pharmacotherapy and Experimental Therapeutics, Eshelman School of Pharmacy (M.M., Y.C.) and Lineberger Comprehensive Cancer Center, School of Medicine (Y.C.), University of North Carolina at Chapel Hill, North Carolina; Department of Biochemistry (J.M.), University of Belgrade, Faculty of Chemistry, Belgrade, Serbia; and Clinical Pharmacology and Pharmacometrics, Janssen Research & Development (W.W.), LLC, Spring House, Pennsylvania
| | - Yanguang Cao
- Division of Pharmacotherapy and Experimental Therapeutics, Eshelman School of Pharmacy (M.M., Y.C.) and Lineberger Comprehensive Cancer Center, School of Medicine (Y.C.), University of North Carolina at Chapel Hill, North Carolina; Department of Biochemistry (J.M.), University of Belgrade, Faculty of Chemistry, Belgrade, Serbia; and Clinical Pharmacology and Pharmacometrics, Janssen Research & Development (W.W.), LLC, Spring House, Pennsylvania
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8
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Samuels S, Bai JPF, Green DJ, Abulwerdi G, Burckart GJ. Application of Quantitative Systems Pharmacology to Pediatric Drug Safety Assessment. Clin Pharmacol Ther 2023; 114:748-750. [PMID: 37452513 DOI: 10.1002/cpt.2990] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Accepted: 06/24/2023] [Indexed: 07/18/2023]
Affiliation(s)
- Sherbet Samuels
- Office of Clinical Pharmacology, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland, USA
| | - Jane P F Bai
- Office of Clinical Pharmacology, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland, USA
| | - Dionna J Green
- Office of Pediatric Therapeutics, Office of the Commissioner, US Food and Drug Administration, Silver Spring, Maryland, USA
| | - Gelareh Abulwerdi
- Office of Clinical Pharmacology, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland, USA
| | - Gilbert J Burckart
- Office of Clinical Pharmacology, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland, USA
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9
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Qu H, Liu J, Zhang D, Xie R, Wang L, Hong J. Glycolysis in Chronic Liver Diseases: Mechanistic Insights and Therapeutic Opportunities. Cells 2023; 12:1930. [PMID: 37566009 PMCID: PMC10417805 DOI: 10.3390/cells12151930] [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: 06/01/2023] [Revised: 07/17/2023] [Accepted: 07/21/2023] [Indexed: 08/12/2023] Open
Abstract
Chronic liver diseases (CLDs) cover a spectrum of liver diseases, ranging from nonalcoholic fatty liver disease to liver cancer, representing a growing epidemic worldwide with high unmet medical needs. Glycolysis is a conservative and rigorous process that converts glucose into pyruvate and sustains cells with the energy and intermediate products required for diverse biological activities. However, abnormalities in glycolytic flux during CLD development accelerate the disease progression. Aerobic glycolysis is a hallmark of liver cancer and is responsible for a broad range of oncogenic functions including proliferation, invasion, metastasis, angiogenesis, immune escape, and drug resistance. Recently, the non-neoplastic role of aerobic glycolysis in immune activation and inflammatory disorders, especially CLD, has attracted increasing attention. Several key mediators of aerobic glycolysis, including HIF-1α and pyruvate kinase M2 (PKM2), are upregulated during steatohepatitis and liver fibrosis. The pharmacological inhibition or ablation of PKM2 effectively attenuates hepatic inflammation and CLD progression. In this review, we particularly focused on the glycolytic and non-glycolytic roles of PKM2 in the progression of CLD, highlighting the translational potential of a glycolysis-centric therapeutic approach in combating CLD.
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Affiliation(s)
| | | | | | | | | | - Jian Hong
- Department of Pathophysiology, School of Medicine, Jinan University, Guangzhou 510632, China; (H.Q.)
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10
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Bloomingdale P, Karelina T, Ramakrishnan V, Bakshi S, Véronneau‐Veilleux F, Moye M, Sekiguchi K, Meno‐Tetang G, Mohan A, Maithreye R, Thomas VA, Gibbons F, Cabal A, Bouteiller J, Geerts H. Hallmarks of neurodegenerative disease: A systems pharmacology perspective. CPT Pharmacometrics Syst Pharmacol 2022; 11:1399-1429. [PMID: 35894182 PMCID: PMC9662204 DOI: 10.1002/psp4.12852] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Revised: 07/17/2022] [Accepted: 07/19/2022] [Indexed: 11/09/2022] Open
Abstract
Age-related central neurodegenerative diseases, such as Alzheimer's and Parkinson's disease, are a rising public health concern and have been plagued by repeated drug development failures. The complex nature and poor mechanistic understanding of the etiology of neurodegenerative diseases has hindered the discovery and development of effective disease-modifying therapeutics. Quantitative systems pharmacology models of neurodegeneration diseases may be useful tools to enhance the understanding of pharmacological intervention strategies and to reduce drug attrition rates. Due to the similarities in pathophysiological mechanisms across neurodegenerative diseases, especially at the cellular and molecular levels, we envision the possibility of structural components that are conserved across models of neurodegenerative diseases. Conserved structural submodels can be viewed as building blocks that are pieced together alongside unique disease components to construct quantitative systems pharmacology (QSP) models of neurodegenerative diseases. Model parameterization would likely be different between the different types of neurodegenerative diseases as well as individual patients. Formulating our mechanistic understanding of neurodegenerative pathophysiology as a mathematical model could aid in the identification and prioritization of drug targets and combinatorial treatment strategies, evaluate the role of patient characteristics on disease progression and therapeutic response, and serve as a central repository of knowledge. Here, we provide a background on neurodegenerative diseases, highlight hallmarks of neurodegeneration, and summarize previous QSP models of neurodegenerative diseases.
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Affiliation(s)
- Peter Bloomingdale
- Quantitative Pharmacology and PharmacometricsMerck & Co., Inc.BostonMassachusettsUSA
| | | | | | - Suruchi Bakshi
- Certara QSPOssThe Netherlands,Certara QSPPrincetonNew JerseyUSA
| | | | - Matthew Moye
- Quantitative Pharmacology and PharmacometricsMerck & Co., Inc.BostonMassachusettsUSA
| | - Kazutaka Sekiguchi
- Shionogi & Co., Ltd.OsakaJapan,SUNY Downstate Medical CenterNew YorkNew YorkUSA
| | | | | | | | | | - Frank Gibbons
- Clinical Pharmacology and PharmacometricsBiogenCambridgeMassachusettsUSA
| | | | - Jean‐Marie Bouteiller
- Center for Neural EngineeringDepartment of Biomedical Engineering at the Viterbi School of EngineeringLos AngelesCaliforniaUSA,Institute for Technology and Medical Systems Innovation, Keck School of MedicineUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
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11
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Chan JR, Allen R, Boras B, Cabal A, Damian V, Gibbons FD, Gulati A, Hosseini I, Kearns JD, Saito R, Cucurull-Sanchez L, Selimkhanov J, Stein AM, Umehara K, Wang G, Wang W, Neves-Zaph S. Current practices for QSP model assessment: an IQ consortium survey. J Pharmacokinet Pharmacodyn 2022:10.1007/s10928-022-09811-1. [PMID: 35953664 PMCID: PMC9371373 DOI: 10.1007/s10928-022-09811-1] [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: 04/28/2022] [Accepted: 06/03/2022] [Indexed: 12/02/2022]
Abstract
Quantitative Systems Pharmacology (QSP) modeling is increasingly applied in the pharmaceutical industry to influence decision making across a wide range of stages from early discovery to clinical development to post-marketing activities. Development of standards for how these models are constructed, assessed, and communicated is of active interest to the modeling community and regulators but is complicated by the wide variability in the structures and intended uses of the underlying models and the diverse expertise of QSP modelers. With this in mind, the IQ Consortium conducted a survey across the pharmaceutical/biotech industry to understand current practices for QSP modeling. This article presents the survey results and provides insights into current practices and methods used by QSP practitioners based on model type and the intended use at various stages of drug development. The survey also highlights key areas for future development including better integration with statistical methods, standardization of approaches towards virtual populations, and increased use of QSP models for late-stage clinical development and regulatory submissions.
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Affiliation(s)
- Jason R Chan
- Global PKPD and Pharmacometrics, Eli Lilly and Company, Indianapolis, IN, 46285, USA.
| | - Richard Allen
- Worldwide Research, Development and Medical, Pfizer Inc. Kendall Square, Cambridge, MA, 02139, USA
| | - Britton Boras
- Worldwide Research, Development and Medical, Pfizer Inc.,, La Jolla, CA, 92121, USA
| | | | | | | | | | | | - Jeffrey D Kearns
- Novartis Institutes for BioMedical Research, Cambridge, MA, 02139, USA
| | - Ryuta Saito
- Innovative Research Division, Mitsubishi Tanabe Pharma Corporation, Yokohama, Japan
| | | | | | - Andrew M Stein
- Pharmacometrics, Novartis Institutes of BioMedical Research, Cambridge, MA, USA
| | - Kenichi Umehara
- Roche Pharmaceutical Research and Early Development, Basel, Switzerland
| | - Guanyu Wang
- Drug Metabolism and Pharmacokinetics, Vertex Pharmaceuticals, Abingdon, Oxfordshire, UK
| | - Weirong Wang
- Clinical Pharmacology and Pharmacometrics, Janssen Research and Development, LLC, Spring House, PA, 19477, USA
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12
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Lam I, Pilla Reddy V, Ball K, Arends RH, Mac Gabhann F. Development of and insights from systems pharmacology models of
antibody‐drug
conjugates. CPT Pharmacometrics Syst Pharmacol 2022; 11:967-990. [PMID: 35712824 PMCID: PMC9381915 DOI: 10.1002/psp4.12833] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Revised: 05/26/2022] [Accepted: 06/02/2022] [Indexed: 01/02/2023] Open
Abstract
Antibody‐drug conjugates (ADCs) have gained traction in the oncology space in the past few decades, with significant progress being made in recent years. Although the use of pharmacometric modeling is well‐established in the drug development process, there is an increasing need for a better quantitative biological understanding of the pharmacokinetic and pharmacodynamic relationships of these complex molecules. Quantitative systems pharmacology (QSP) approaches can assist in this endeavor; recent computational QSP models incorporate ADC‐specific mechanisms and use data‐driven simulations to predict experimental outcomes. Various modeling approaches and platforms have been developed at the in vitro, in vivo, and clinical scales, and can be further integrated to facilitate preclinical to clinical translation. These new tools can help researchers better understand the nature and mechanisms of these targeted therapies to help achieve a more favorable therapeutic window. This review delves into the world of systems pharmacology modeling of ADCs, discussing various modeling efforts in the field thus far.
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Affiliation(s)
- Inez Lam
- Institute for Computational Medicine and Department of Biomedical Engineering Johns Hopkins University Baltimore Maryland USA
| | - Venkatesh Pilla Reddy
- Clinical Pharmacology and Quantitative Pharmacology, Clinical Pharmacology and Safety Sciences, BioPharmaceuticals R&D AstraZeneca Cambridge UK
| | - Kathryn Ball
- Clinical Pharmacology and Quantitative Pharmacology, Clinical Pharmacology and Safety Sciences, BioPharmaceuticals R&D AstraZeneca Cambridge UK
| | - Rosalinda H. Arends
- Clinical Pharmacology and Quantitative Pharmacology, Clinical Pharmacology and Safety Sciences, BioPharmaceuticals R&D AstraZeneca Gaithersburg Maryland USA
| | - Feilim Mac Gabhann
- Institute for Computational Medicine and Department of Biomedical Engineering Johns Hopkins University Baltimore Maryland USA
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13
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Azer K, Barrett JS. Quantitative system pharmacology as a legitimate approach to examine extrapolation strategies used to support pediatric drug development. CPT Pharmacometrics Syst Pharmacol 2022; 11:797-804. [PMID: 35411657 PMCID: PMC9286717 DOI: 10.1002/psp4.12801] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Revised: 03/20/2022] [Accepted: 03/25/2022] [Indexed: 11/15/2022] Open
Abstract
Extrapolation strategies from adult data for designing pediatric drug development programs are explored using the quantitative systems pharmacology (QSP) modeling approach, a mechanistic drug and disease modeling framework that can predict clinical response and guide pediatric drug development in general. This innovative model‐informed drug discovery and development approach can leverage adult‐pediatric pharmacology and disease similarity metrics to validate extrapolation assumptions. We describe the QSP model strategy and framework for extrapolation to design pediatric drug development programs by leveraging adult data across a wide range of therapeutic areas and illustrating stage‐gate decisions informed by such an approach.
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Affiliation(s)
- Karim Azer
- Axcella Therapeutics Cambridge Massachusetts USA
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14
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Zhang T, Cho CR, Bonate PL. Perspectives on training quantitative systems pharmacologists. CPT Pharmacometrics Syst Pharmacol 2022; 11:669-672. [PMID: 35388628 PMCID: PMC9197534 DOI: 10.1002/psp4.12783] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Revised: 01/24/2022] [Accepted: 02/28/2022] [Indexed: 11/08/2022] Open
Affiliation(s)
- Tongli Zhang
- Department of Pharmacology & Systems Physiology, College of Medicine University of Cincinnati Cincinnati Ohio USA
| | - Carolyn R. Cho
- Quantitative Pharmacology and Pharmacometrics‐Immuno‐onocology Merck & Co., Inc. Kenilworth New Jersey USA
| | - Peter L. Bonate
- Clinical Pharmacology and Exploratory Development, New Technologies Astellas Northbrook Illinois USA
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15
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Bansal L, Nichols EM, Howsmon DP, Neisen J, Bessant CM, Cunningham F, Petit-Frere S, Ludbrook S, Damian V. Mathematical Modeling of Complement Pathway Dynamics for Target Validation and Selection of Drug Modalities for Complement Therapies. Front Pharmacol 2022; 13:855743. [PMID: 35517827 PMCID: PMC9061988 DOI: 10.3389/fphar.2022.855743] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2022] [Accepted: 03/14/2022] [Indexed: 11/13/2022] Open
Abstract
Motivation: The complement pathway plays a critical role in innate immune defense against infections. Dysregulation between activation and regulation of the complement pathway is widely known to contribute to several diseases. Nevertheless, very few drugs that target complement proteins have made it to the final regulatory approval because of factors such as high concentrations and dosing requirements for complement proteins and serious side effects from complement inhibition. Methods: A quantitative systems pharmacology (QSP) model of the complement pathway has been developed to evaluate potential drug targets to inhibit complement activation in autoimmune diseases. The model describes complement activation via the alternative and terminal pathways as well as the dynamics of several regulatory proteins. The QSP model has been used to evaluate the effect of inhibiting complement targets on reducing pathway activation caused by deficiency in factor H and CD59. The model also informed the feasibility of developing small-molecule or large-molecule antibody drugs by predicting the drug dosing and affinity requirements for potential complement targets. Results: Inhibition of several complement proteins was predicted to lead to a significant reduction in complement activation and cell lysis. The complement proteins that are present in very high concentrations or have high turnover rates (C3, factor B, factor D, and C6) were predicted to be challenging to engage with feasible doses of large-molecule antibody compounds (≤20 mg/kg). Alternatively, complement fragments that have a short half-life (C3b, C3bB, and C3bBb) were predicted to be challenging or infeasible to engage with small-molecule compounds because of high drug affinity requirements (>1 nM) for the inhibition of downstream processes. The drug affinity requirements for disease severity reduction were predicted to differ more than one to two orders of magnitude than affinities needed for the conventional 90% target engagement (TE) for several proteins. Thus, the QSP model analyses indicate the importance for accounting for TE requirements for achieving reduction in disease severity endpoints during the lead optimization stage.
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Affiliation(s)
- Loveleena Bansal
- Systems Modeling and Translational Biology, Computational Sciences, GSK, Upper Providence, Collegeville, PA, United States
| | | | - Daniel P Howsmon
- Department of Chemical and Biological Engineering, Rensselaer Polytechnic Institute, Troy, NY, United States
| | - Jessica Neisen
- Immunology Research Unit, GSK, Stevenage, United Kingdom
| | | | | | | | - Steve Ludbrook
- Immunology Research Unit, GSK, Stevenage, United Kingdom
| | - Valeriu Damian
- Systems Modeling and Translational Biology, Computational Sciences, GSK, Upper Providence, Collegeville, PA, United States
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16
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Haraya K, Tsutsui H, Komori Y, Tachibana T. Recent Advances in Translational Pharmacokinetics and Pharmacodynamics Prediction of Therapeutic Antibodies Using Modeling and Simulation. Pharmaceuticals (Basel) 2022; 15:ph15050508. [PMID: 35631335 PMCID: PMC9145563 DOI: 10.3390/ph15050508] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Revised: 04/18/2022] [Accepted: 04/20/2022] [Indexed: 02/05/2023] Open
Abstract
Therapeutic monoclonal antibodies (mAbs) have been a promising therapeutic approach for several diseases and a wide variety of mAbs are being evaluated in clinical trials. To accelerate clinical development and improve the probability of success, pharmacokinetics and pharmacodynamics (PKPD) in humans must be predicted before clinical trials can begin. Traditionally, empirical-approach-based PKPD prediction has been applied for a long time. Recently, modeling and simulation (M&S) methods have also become valuable for quantitatively predicting PKPD in humans. Although several models (e.g., the compartment model, Michaelis–Menten model, target-mediated drug disposition model, and physiologically based pharmacokinetic model) have been established and used to predict the PKPD of mAbs in humans, more complex mechanistic models, such as the quantitative systemics pharmacology model, have been recently developed. This review summarizes the recent advances and future direction of M&S-based approaches to the quantitative prediction of human PKPD for mAbs.
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Affiliation(s)
- Kenta Haraya
- Discovery Biologics Department, Research Division, Chugai Pharmaceutical Co., Ltd., 1-135 Komakado, Gotemba 412-8513, Japan;
- Correspondence:
| | - Haruka Tsutsui
- Discovery Biologics Department, Research Division, Chugai Pharmaceutical Co., Ltd., 1-135 Komakado, Gotemba 412-8513, Japan;
| | - Yasunori Komori
- Pharmaceutical Science Department, Translational Research Division, Chugai Pharmaceutical Co., Ltd., 1-135 Komakado, Gotemba 412-8513, Japan; (Y.K.); (T.T.)
| | - Tatsuhiko Tachibana
- Pharmaceutical Science Department, Translational Research Division, Chugai Pharmaceutical Co., Ltd., 1-135 Komakado, Gotemba 412-8513, Japan; (Y.K.); (T.T.)
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17
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Zhu AZX, Rogge M. Applications of Quantitative System Pharmacology Modeling to Model-Informed Drug Development. METHODS IN MOLECULAR BIOLOGY (CLIFTON, N.J.) 2022; 2486:71-86. [PMID: 35437719 DOI: 10.1007/978-1-0716-2265-0_5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Significant advances in analytical technologies have dramatically improved our ability to deconvolute disease biology at molecular, cellular, and tissue levels. Quantitative system pharmacology (QSP) modeling is a computational framework to systematically integrate pharmaceutical properties of a drug candidate with scientific understanding of that deeper disease etiology, target expression, genetic variability, and human physiological processes, thus enabling more insightful drug development decisions related to efficacy and safety. In this chapter, we discuss the key attributes of QSP models in comparison to traditional models. We discuss a recommended four-step process to construct a QSP model to support drug development decisions. A number of illustrative QSP examples related to high-value drug development questions and decisions impacting target identification, lead generation and optimization, first in human studies, and clinical dose and schedule optimization are covered in the chapter. The future perspectives of QSP in the context of potential regulatory acceptance are also discussed.
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Affiliation(s)
- Andy Z X Zhu
- Preclinical and Translational Sciences, Takeda Pharmaceuticals International Co, Cambridge, MA, USA.
| | - Mark Rogge
- Center for Pharmacometrics and Systems Pharmacology, University of Florida, Lake Nona, FL, USA
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18
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Miyano T, Irvine AD, Tanaka RJ. A mathematical model to identify optimal combinations of drug targets for dupilumab poor responders in atopic dermatitis. Allergy 2022; 77:582-594. [PMID: 33894014 DOI: 10.1111/all.14870] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2021] [Revised: 03/08/2021] [Accepted: 03/17/2021] [Indexed: 12/11/2022]
Abstract
BACKGROUND Several biologics for atopic dermatitis (AD) have demonstrated good efficacy in clinical trials, but with a substantial proportion of patients being identified as poor responders. This study aims to understand the pathophysiological backgrounds of patient variability in drug response, especially for dupilumab, and to identify promising drug targets in dupilumab poor responders. METHODS We conducted model-based meta-analysis of recent clinical trials of AD biologics and developed a mathematical model that reproduces reported clinical efficacies for nine biological drugs (dupilumab, lebrikizumab, tralokinumab, secukinumab, fezakinumab, nemolizumab, tezepelumab, GBR 830, and recombinant interferon-gamma) by describing system-level AD pathogenesis. Using this model, we simulated the clinical efficacy of hypothetical therapies on virtual patients. RESULTS Our model reproduced reported time courses of %improved EASI and EASI-75 of the nine drugs. The global sensitivity analysis and model simulation indicated the baseline level of IL-13 could stratify dupilumab good responders. Model simulation on the efficacies of hypothetical therapies revealed that simultaneous inhibition of IL-13 and IL-22 was effective, whereas application of the nine biologic drugs was ineffective, for dupilumab poor responders (EASI-75 at 24 weeks: 21.6% vs. max. 1.9%). CONCLUSION Our model identified IL-13 as a potential predictive biomarker to stratify dupilumab good responders, and simultaneous inhibition of IL-13 and IL-22 as a promising drug therapy for dupilumab poor responders. This model will serve as a computational platform for model-informed drug development for precision medicine, as it allows evaluation of the effects of new potential drug targets and the mechanisms behind patient variability in drug response.
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Affiliation(s)
- Takuya Miyano
- Department of Bioengineering Imperial College London London UK
| | - Alan D. Irvine
- Pediatric Dermatology Children’s Health Ireland at Crumlin Dublin Ireland
- Clinical Medicine Trinity College Dublin Dublin Ireland
| | - Reiko J. Tanaka
- Department of Bioengineering Imperial College London London UK
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19
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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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20
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Murata Y, Neuhoff S, Rostami-Hodjegan A, Takita H, Al-Majdoub ZM, Ogungbenro K. In Vitro to In Vivo Extrapolation Linked to Physiologically Based Pharmacokinetic Models for Assessing the Brain Drug Disposition. AAPS J 2022; 24:28. [PMID: 35028763 PMCID: PMC8817058 DOI: 10.1208/s12248-021-00675-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Accepted: 12/09/2021] [Indexed: 11/30/2022] Open
Abstract
Drug development for the central nervous system (CNS) is a complex endeavour with low success rates, as the structural complexity of the brain and specifically the blood-brain barrier (BBB) poses tremendous challenges. Several in vitro brain systems have been evaluated, but the ultimate use of these data in terms of translation to human brain concentration profiles remains to be fully developed. Thus, linking up in vitro-to-in vivo extrapolation (IVIVE) strategies to physiologically based pharmacokinetic (PBPK) models of brain is a useful effort that allows better prediction of drug concentrations in CNS components. Such models may overcome some known aspects of inter-species differences in CNS drug disposition. Required physiological (i.e. systems) parameters in the model are derived from quantitative values in each organ. However, due to the inability to directly measure brain concentrations in humans, compound-specific (drug) parameters are often obtained from in silico or in vitro studies. Such data are translated through IVIVE which could be also applied to preclinical in vivo observations. In such exercises, the limitations of the assays and inter-species differences should be adequately understood in order to verify these predictions with the observed concentration data. This report summarizes the state of IVIVE-PBPK-linked models and discusses shortcomings and areas of further research for better prediction of CNS drug disposition. Graphical abstract ![]()
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Affiliation(s)
- Yukiko Murata
- Centre for Applied Pharmacokinetic Research, Division of Pharmacy and Optometry, University of Manchester, Manchester, M13 9PT, UK.,Sohyaku.Innovative Research Division, Mitsubishi Tanabe Pharma Corporation, 1000, Kamoshida-cho, Aoba-ku, Yokohama, Kanagawa, 227-0033, Japan
| | - Sibylle Neuhoff
- Certara UK Ltd, Simcyp Division, 1 Concourse Way, Level 2-Acero, Sheffield, S1 2BJ, UK
| | - Amin Rostami-Hodjegan
- Centre for Applied Pharmacokinetic Research, Division of Pharmacy and Optometry, University of Manchester, Manchester, M13 9PT, UK.,Certara UK Ltd, Simcyp Division, 1 Concourse Way, Level 2-Acero, Sheffield, S1 2BJ, UK
| | - Hiroyuki Takita
- Centre for Applied Pharmacokinetic Research, Division of Pharmacy and Optometry, University of Manchester, Manchester, M13 9PT, UK.,Development Planning, Clinical Development Center, Asahi Kasei Pharma Corporation, Hibiya Mitsui Tower, 1-1-2 Yurakucho, Chiyoda-ku, Tokyo, 100-0006, Japan
| | - Zubida M Al-Majdoub
- Centre for Applied Pharmacokinetic Research, Division of Pharmacy and Optometry, University of Manchester, Manchester, M13 9PT, UK
| | - Kayode Ogungbenro
- Centre for Applied Pharmacokinetic Research, Division of Pharmacy and Optometry, University of Manchester, Manchester, M13 9PT, UK.
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21
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Zhang Y, Wang H, Oliveira RHM, Zhao C, Popel AS. Systems biology of angiogenesis signaling: Computational models and omics. WIREs Mech Dis 2021; 14:e1550. [PMID: 34970866 PMCID: PMC9243197 DOI: 10.1002/wsbm.1550] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Revised: 12/03/2021] [Accepted: 12/06/2021] [Indexed: 01/10/2023]
Abstract
Angiogenesis is a highly regulated multiscale process that involves a plethora of cells, their cellular signal transduction, activation, proliferation, differentiation, as well as their intercellular communication. The coordinated execution and integration of such complex signaling programs is critical for physiological angiogenesis to take place in normal growth, development, exercise, and wound healing, while its dysregulation is critically linked to many major human diseases such as cancer, cardiovascular diseases, and ocular disorders; it is also crucial in regenerative medicine. Although huge efforts have been devoted to drug development for these diseases by investigation of angiogenesis‐targeted therapies, only a few therapeutics and targets have proved effective in humans due to the innate multiscale complexity and nonlinearity in the process of angiogenic signaling. As a promising approach that can help better address this challenge, systems biology modeling allows the integration of knowledge across studies and scales and provides a powerful means to mechanistically elucidate and connect the individual molecular and cellular signaling components that function in concert to regulate angiogenesis. In this review, we summarize and discuss how systems biology modeling studies, at the pathway‐, cell‐, tissue‐, and whole body‐levels, have advanced our understanding of signaling in angiogenesis and thereby delivered new translational insights for human diseases. This article is categorized under:Cardiovascular Diseases > Computational Models Cancer > Computational Models
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Affiliation(s)
- Yu Zhang
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Hanwen Wang
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Rebeca Hannah M Oliveira
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Chen Zhao
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.,School of Pharmacy, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Aleksander S Popel
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
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22
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Bai JPF, Earp JC, Florian J, Madabushi R, Strauss DG, Wang Y, Zhu H. Quantitative systems pharmacology: Landscape analysis of regulatory submissions to the US Food and Drug Administration. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2021; 10:1479-1484. [PMID: 34734497 PMCID: PMC8673997 DOI: 10.1002/psp4.12709] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Revised: 08/13/2021] [Accepted: 08/15/2021] [Indexed: 11/29/2022]
Abstract
Quantitative systems pharmacology (QSP) has been proposed as a scientific domain that can enable efficient and informative drug development. During the past several years, there has been a notable increase in the number of regulatory submissions that contain QSP, including Investigational New Drug Applications (INDs), New Drug Applications (NDAs), and Biologics License Applications (BLAs) to the US Food and Drug Administration. However, there has been no comprehensive characterization of the nature of these regulatory submissions regarding model details and intended applications. To address this gap, a landscape analysis of all the QSP submissions as of December 2020 was conducted. This report summarizes the (1) yearly trend of submissions, (2) proportion of submissions between INDs and NDAs/BLAs, (3) percentage distribution along the stages of drug development, (4) percentage distribution across various therapeutic areas, and (5) nature of QSP applications. In brief, QSP is increasingly applied to model and simulate both drug effectiveness and safety throughout the drug development process across disease areas.
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Affiliation(s)
- Jane P F Bai
- Office of Clinical Pharmacology, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland, USA
| | - Justin C Earp
- Office of Clinical Pharmacology, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland, USA
| | - Jeffry Florian
- Office of Clinical Pharmacology, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland, USA
| | - Rajanikanth Madabushi
- Office of Clinical Pharmacology, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland, USA
| | - David G Strauss
- Office of Clinical Pharmacology, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland, USA
| | - Yaning Wang
- Office of Clinical Pharmacology, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland, USA
| | - Hao Zhu
- Office of Clinical Pharmacology, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland, USA
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23
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Aghamiri SS, Amin R, Helikar T. Recent applications of quantitative systems pharmacology and machine learning models across diseases. J Pharmacokinet Pharmacodyn 2021; 49:19-37. [PMID: 34671863 PMCID: PMC8528185 DOI: 10.1007/s10928-021-09790-9] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Accepted: 10/07/2021] [Indexed: 12/29/2022]
Abstract
Quantitative systems pharmacology (QSP) is a quantitative and mechanistic platform describing the phenotypic interaction between drugs, biological networks, and disease conditions to predict optimal therapeutic response. In this meta-analysis study, we review the utility of the QSP platform in drug development and therapeutic strategies based on recent publications (2019-2021). We gathered recent original QSP models and described the diversity of their applications based on therapeutic areas, methodologies, software platforms, and functionalities. The collection and investigation of these publications can assist in providing a repository of recent QSP studies to facilitate the discovery and further reusability of QSP models. Our review shows that the largest number of QSP efforts in recent years is in Immuno-Oncology. We also addressed the benefits of integrative approaches in this field by presenting the applications of Machine Learning methods for drug discovery and QSP models. Based on this meta-analysis, we discuss the advantages and limitations of QSP models and propose fields where the QSP approach constitutes a valuable interface for more investigations to tackle complex diseases and improve drug development.
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Affiliation(s)
- Sara Sadat Aghamiri
- Department of Biochemistry, University of Nebraska-Lincoln, Lincoln, NE, USA
| | - Rada Amin
- Department of Biochemistry, University of Nebraska-Lincoln, Lincoln, NE, USA.
| | - Tomáš Helikar
- Department of Biochemistry, University of Nebraska-Lincoln, Lincoln, NE, USA.
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24
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Kapitanov GI, Chabot JR, Narula J, Roy M, Neubert H, Palandra J, Farrokhi V, Johnson JS, Webster R, Jones HM. A Mechanistic Site-Of-Action Model: A Tool for Informing Right Target, Right Compound, And Right Dose for Therapeutic Antagonistic Antibody Programs. FRONTIERS IN BIOINFORMATICS 2021; 1:731340. [DOI: 10.3389/fbinf.2021.731340] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Accepted: 08/23/2021] [Indexed: 11/13/2022] Open
Abstract
Quantitative modeling is increasingly utilized in the drug discovery and development process, from the initial stages of target selection, through clinical studies. The modeling can provide guidance on three major questions–is this the right target, what are the right compound properties, and what is the right dose for moving the best possible candidate forward. In this manuscript, we present a site-of-action modeling framework which we apply to monoclonal antibodies against soluble targets. We give a comprehensive overview of how we construct the model and how we parametrize it and include several examples of how to apply this framework for answering the questions postulated above. The utilities and limitations of this approach are discussed.
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25
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Al-Majdoub ZM, Scotcher D, Achour B, Barber J, Galetin A, Rostami-Hodjegan A. Quantitative Proteomic Map of Enzymes and Transporters in the Human Kidney: Stepping Closer to Mechanistic Kidney Models to Define Local Kinetics. Clin Pharmacol Ther 2021; 110:1389-1400. [PMID: 34390491 DOI: 10.1002/cpt.2396] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2021] [Accepted: 08/03/2021] [Indexed: 12/20/2022]
Abstract
The applications of translational modeling of local drug concentrations in various organs had a sharp increase over the last decade. These are part of the model-informed drug development initiative, adopted by the pharmaceutical industry and promoted by drug regulatory agencies. With respect to the kidney, the models serve as a bridge for understanding animal vs. human observations related to renal drug disposition and any consequential adverse effects. However, quantitative data on key drug-metabolizing enzymes and transporters relevant for predicting renal drug disposition are limited. Using targeted and global quantitative proteomics, we determined the abundance of multiple enzymes and transporters in 20 human kidney cortex samples. Nine enzymes and 22 transporters were quantified (8 for the first time in the kidneys). In addition, > 4,000 proteins were identified and used to form an open database. CYP2B6, CYP3A5, and CYP4F2 showed comparable, but generally low expression, whereas UGT1A9 and UGT2B7 levels were the highest. Significant correlation between abundance and activity (measured by mycophenolic acid clearance) was observed for UGT1A9 (Rs = 0.65, P = 0.004) and UGT2B7 (Rs = 0.70, P = 0.023). Expression of P-gp ≈ MATE-1 and OATP4C1 transporters were high. Strong intercorrelations were observed between several transporters (P-gp/MRP4, MRP2/OAT3, and OAT3/OAT4); no correlation in expression was apparent for functionally related transporters (OCT2/MATEs). This study extends our knowledge of pharmacologically relevant proteins in the kidney cortex, with implications on more prudent use of mechanistic kidney models under the general framework of quantitative systems pharmacology and toxicology.
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Affiliation(s)
- Zubida M Al-Majdoub
- Centre for Applied Pharmacokinetic Research, University of Manchester, Manchester, UK
| | - Daniel Scotcher
- Centre for Applied Pharmacokinetic Research, University of Manchester, Manchester, UK
| | - Brahim Achour
- Centre for Applied Pharmacokinetic Research, University of Manchester, Manchester, UK
| | - Jill Barber
- Centre for Applied Pharmacokinetic Research, University of Manchester, Manchester, UK
| | - Aleksandra Galetin
- Centre for Applied Pharmacokinetic Research, University of Manchester, Manchester, UK
| | - Amin Rostami-Hodjegan
- Centre for Applied Pharmacokinetic Research, University of Manchester, Manchester, UK.,Certara UK (Simcyp Division), Sheffield, UK
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26
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Schrader SM, Vaubourgeix J, Nathan C. Biology of antimicrobial resistance and approaches to combat it. Sci Transl Med 2021; 12:12/549/eaaz6992. [PMID: 32581135 DOI: 10.1126/scitranslmed.aaz6992] [Citation(s) in RCA: 80] [Impact Index Per Article: 26.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2019] [Accepted: 02/12/2020] [Indexed: 12/14/2022]
Abstract
Insufficient development of new antibiotics and the rising resistance of bacteria to those that we have are putting the world at risk of losing the most widely curative class of medicines currently available. Preventing deaths from antimicrobial resistance (AMR) will require exploiting emerging knowledge not only about genetic AMR conferred by horizontal gene transfer or de novo mutations but also about phenotypic AMR, which lacks a stably heritable basis. This Review summarizes recent advances and continuing limitations in our understanding of AMR and suggests approaches for combating its clinical consequences, including identification of previously unexploited bacterial targets, new antimicrobial compounds, and improved combination drug regimens.
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Affiliation(s)
- Sarah M Schrader
- Department of Microbiology and Immunology, Weill Cornell Medicine, New York, NY 10065, USA
| | - Julien Vaubourgeix
- MRC Centre for Molecular Bacteriology and Infection, Imperial College London, London SW7 2AZ, UK
| | - Carl Nathan
- Department of Microbiology and Immunology, Weill Cornell Medicine, New York, NY 10065, USA.
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Madrasi K, Das R, Mohmmadabdul H, Lin L, Hyman BT, Lauffenburger DA, Albers MW, Rissman RA, Burke JM, Apgar JF, Wille L, Gruenbaum L, Hua F. Systematic in silico analysis of clinically tested drugs for reducing amyloid-beta plaque accumulation in Alzheimer's disease. Alzheimers Dement 2021; 17:1487-1498. [PMID: 33938131 PMCID: PMC8478725 DOI: 10.1002/alz.12312] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2020] [Revised: 01/21/2021] [Accepted: 01/21/2021] [Indexed: 01/28/2023]
Abstract
Introduction Despite strong evidence linking amyloid beta (Aβ) to Alzheimer's disease, most clinical trials have shown no clinical efficacy for reasons that remain unclear. To understand why, we developed a quantitative systems pharmacology (QSP) model for seven therapeutics: aducanumab, crenezumab, solanezumab, bapineuzumab, elenbecestat, verubecestat, and semagacestat. Methods Ordinary differential equations were used to model the production, transport, and aggregation of Aβ; pharmacology of the drugs; and their impact on plaque. Results The calibrated model predicts that endogenous plaque turnover is slow, with an estimated half‐life of 2.75 years. This is likely why beta‐secretase inhibitors have a smaller effect on plaque reduction. Of the mechanisms tested, the model predicts binding to plaque and inducing antibody‐dependent cellular phagocytosis is the best approach for plaque reduction. Discussion A QSP model can provide novel insights to clinical results. Our model explains the results of clinical trials and provides guidance for future therapeutic development.
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Affiliation(s)
| | | | | | - Lin Lin
- Applied Biomath, Concord, Massachusetts, USA
| | - Bradley T Hyman
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts, USA
| | - Douglas A Lauffenburger
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Mark W Albers
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts, USA
| | - Robert A Rissman
- Department of Neurosciences, UCSD School of Medicine, La Jolla, California, USA
| | | | | | - Lucia Wille
- Applied Biomath, Concord, Massachusetts, USA
| | | | - Fei Hua
- Applied Biomath, Concord, Massachusetts, USA
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Rostami-Hodjegan A, Bois FY. Opening a debate on open-source modeling tools: Pouring fuel on fire versus extinguishing the flare of a healthy debate. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2021; 10:420-427. [PMID: 33793084 PMCID: PMC8129708 DOI: 10.1002/psp4.12615] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/20/2021] [Revised: 01/21/2021] [Accepted: 03/01/2021] [Indexed: 12/14/2022]
Abstract
As model‐informed drug development becomes an integral part of modern approaches to the discovery of new therapeutic entities and showing their safety and effectiveness, modalities of incorporating the paradigm into widespread practice require a revisit. Traditionally, modeling and simulation (M&S) have been performed by specialized teams who create bespoke models for each case and have reservations about letting modeling be done by the greater mass of scientists engaged in various stages of drug development. An analogy can be drawn between M&S and automobiles: typical drivers of ordinary cars use them for daily tasks, such as going from point A to B whereas specialized Formula 1 drivers using bespoke individually made cars to test the latest technologies. The reliability and robustness of ordinary cars for the first group requires elements related to quality and endurance that are very different from those applicable to any Formula 1 car supported by a large team of engineers. In this commentary, we frame and analyze the problems concerning the structure and setup of various M&S tools, and their pros and cons. We demonstrate that many misconceptions have precluded having an open discussion on what each modality of M&S tools strives to achieve, and we provide data and evidence that support the move of M&S to main stream use by many, as opposed to specialized usage by few. Parallels are drawn in many other areas involving laboratory instrumentation, statistical analyses, and so on.
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Affiliation(s)
- Amin Rostami-Hodjegan
- Centre for Applied Pharmacokinetic Research, University of Manchester, Manchester, UK.,Certara UK Limited (Simcyp Division), Sheffield, UK
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Pilla Reddy V. Special issue on applications of modeling and simulation in pharmaceutical drug development. Biopharm Drug Dispos 2021; 42:105-106. [PMID: 33880803 DOI: 10.1002/bdd.2280] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- Venkatesh Pilla Reddy
- Guest Editor, Biopharmaceutics and Drug Disposition, Clinical Pharmacology and Quantitative Pharmacology, R&D, AstraZeneca, Cambridge, UK
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Azer K, Kaddi CD, Barrett JS, Bai JPF, McQuade ST, Merrill NJ, Piccoli B, Neves-Zaph S, Marchetti L, Lombardo R, Parolo S, Immanuel SRC, Baliga NS. History and Future Perspectives on the Discipline of Quantitative Systems Pharmacology Modeling and Its Applications. Front Physiol 2021; 12:637999. [PMID: 33841175 PMCID: PMC8027332 DOI: 10.3389/fphys.2021.637999] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2020] [Accepted: 01/25/2021] [Indexed: 12/24/2022] Open
Abstract
Mathematical biology and pharmacology models have a long and rich history in the fields of medicine and physiology, impacting our understanding of disease mechanisms and the development of novel therapeutics. With an increased focus on the pharmacology application of system models and the advances in data science spanning mechanistic and empirical approaches, there is a significant opportunity and promise to leverage these advancements to enhance the development and application of the systems pharmacology field. In this paper, we will review milestones in the evolution of mathematical biology and pharmacology models, highlight some of the gaps and challenges in developing and applying systems pharmacology models, and provide a vision for an integrated strategy that leverages advances in adjacent fields to overcome these challenges.
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Affiliation(s)
- Karim Azer
- Quantitative Sciences, Bill and Melinda Gates Medical Research Institute, Cambridge, MA, United States
| | - Chanchala D. Kaddi
- Quantitative Sciences, Bill and Melinda Gates Medical Research Institute, Cambridge, MA, United States
| | | | - Jane P. F. Bai
- Office of Clinical Pharmacology, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, MD, United States
| | - Sean T. McQuade
- Center for Computational and Integrative Biology, Rutgers University, Camden, NJ, United States
| | - Nathaniel J. Merrill
- Center for Computational and Integrative Biology, Rutgers University, Camden, NJ, United States
| | - Benedetto Piccoli
- Department of Mathematical Sciences and Center for Computational and Integrative Biology, Rutgers University, Camden, NJ, United States
| | - Susana Neves-Zaph
- Translational Disease Modeling, Data and Data Science, Sanofi, Bridgewater, NJ, United States
| | - Luca Marchetti
- Fondazione the Microsoft Research – University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto, Italy
| | - Rosario Lombardo
- Fondazione the Microsoft Research – University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto, Italy
| | - Silvia Parolo
- Fondazione the Microsoft Research – University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto, Italy
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Sové RJ, Jafarnejad M, Zhao C, Wang H, Ma H, Popel AS. QSP-IO: A Quantitative Systems Pharmacology Toolbox for Mechanistic Multiscale Modeling for Immuno-Oncology Applications. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2020; 9:484-497. [PMID: 32618119 PMCID: PMC7499194 DOI: 10.1002/psp4.12546] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/02/2020] [Accepted: 07/17/2020] [Indexed: 12/25/2022]
Abstract
Immunotherapy has shown great potential in the treatment of cancer; however, only a fraction of patients respond to treatment, and many experience autoimmune‐related side effects. The pharmaceutical industry has relied on mathematical models to study the behavior of candidate drugs and more recently, complex, whole‐body, quantitative systems pharmacology (QSP) models have become increasingly popular for discovery and development. QSP modeling has the potential to discover novel predictive biomarkers as well as test the efficacy of treatment plans and combination therapies through virtual clinical trials. In this work, we present a QSP modeling platform for immuno‐oncology (IO) that incorporates detailed mechanisms for important immune interactions. This modular platform allows for the construction of QSP models of IO with varying degrees of complexity based on the research questions. Finally, we demonstrate the use of the platform through two example applications of immune checkpoint therapy.
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Affiliation(s)
- Richard J Sové
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Mohammad Jafarnejad
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Chen Zhao
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Hanwen Wang
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Huilin Ma
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Aleksander S Popel
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.,The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
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32
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Nigam SK, Bush KT, Bhatnagar V, Poloyac SM, Momper JD. The Systems Biology of Drug Metabolizing Enzymes and Transporters: Relevance to Quantitative Systems Pharmacology. Clin Pharmacol Ther 2020; 108:40-53. [PMID: 32119114 PMCID: PMC7292762 DOI: 10.1002/cpt.1818] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2019] [Accepted: 02/12/2020] [Indexed: 12/19/2022]
Abstract
Quantitative systems pharmacology (QSP) has emerged as a transformative science in drug discovery and development. It is now time to fully rethink the biological functions of drug metabolizing enzymes (DMEs) and transporters within the framework of QSP models. The large set of DME and transporter genes are generally considered from the perspective of the absorption, distribution, metabolism, and excretion (ADME) of drugs. However, there is a growing amount of data on the endogenous physiology of DMEs and transporters. Recent studies—including systems biology analyses of “omics” data as well as metabolomics studies—indicate that these enzymes and transporters, which are often among the most highly expressed genes in tissues like liver, kidney, and intestine, have coordinated roles in fundamental biological processes. Multispecific DMEs and transporters work together with oligospecific and monospecific ADME proteins in a large multiorgan remote sensing and signaling network. We use the Remote Sensing and Signaling Theory (RSST) to examine the roles of DMEs and transporters in intratissue, interorgan, and interorganismal communication via metabolites and signaling molecules. This RSST‐based view is applicable to bile acids, uric acid, eicosanoids, fatty acids, uremic toxins, and gut microbiome products, among other small organic molecules of physiological interest. Rooting this broader perspective of DMEs and transporters within QSP may facilitate an improved understanding of fundamental biology, physiologically based pharmacokinetics, and the prediction of drug toxicities based upon the interplay of these ADME proteins with key pathways in metabolism and signaling. The RSST‐based view should also enable more tailored pharmacotherapy in the setting of kidney disease, liver disease, metabolic syndrome, and diabetes. We further discuss the pharmaceutical and regulatory implications of this revised view through the lens of systems physiology.
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Affiliation(s)
- Sanjay K Nigam
- Departments of Pediatrics and Medicine, School of Medicine, University of California, San Diego, La Jolla, California, USA
| | - Kevin T Bush
- Departments of Pediatrics and Medicine, School of Medicine, University of California, San Diego, La Jolla, California, USA
| | - Vibha Bhatnagar
- Department of Family Medicine and Public Health, School of Medicine, University of California, San Diego, La Jolla, California, USA
| | - Samuel M Poloyac
- Department of Pharmaceutical Sciences, School of Pharmacy, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Jeremiah D Momper
- Division of Pharmaceutical Sciences, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, California, USA
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33
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Model-Informed Drug Discovery and Development Strategy for the Rapid Development of Anti-Tuberculosis Drug Combinations. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10072376] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
The increasing emergence of drug-resistant tuberculosis requires new effective and safe drug regimens. However, drug discovery and development are challenging, lengthy and costly. The framework of model-informed drug discovery and development (MID3) is proposed to be applied throughout the preclinical to clinical phases to provide an informative prediction of drug exposure and efficacy in humans in order to select novel anti-tuberculosis drug combinations. The MID3 includes pharmacokinetic-pharmacodynamic and quantitative systems pharmacology models, machine learning and artificial intelligence, which integrates all the available knowledge related to disease and the compounds. A translational in vitro-in vivo link throughout modeling and simulation is crucial to optimize the selection of regimens with the highest probability of receiving approval from regulatory authorities. In vitro-in vivo correlation (IVIVC) and physiologically-based pharmacokinetic modeling provide powerful tools to predict pharmacokinetic drug-drug interactions based on preclinical information. Mechanistic or semi-mechanistic pharmacokinetic-pharmacodynamic models have been successfully applied to predict the clinical exposure-response profile for anti-tuberculosis drugs using preclinical data. Potential pharmacodynamic drug-drug interactions can be predicted from in vitro data through IVIVC and pharmacokinetic-pharmacodynamic modeling accounting for translational factors. It is essential for academic and industrial drug developers to collaborate across disciplines to realize the huge potential of MID3.
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Wang H, Sové RJ, Jafarnejad M, Rahmeh S, Jaffee EM, Stearns V, Torres ETR, Connolly RM, Popel AS. Conducting a Virtual Clinical Trial in HER2-Negative Breast Cancer Using a Quantitative Systems Pharmacology Model With an Epigenetic Modulator and Immune Checkpoint Inhibitors. Front Bioeng Biotechnol 2020; 8:141. [PMID: 32158754 PMCID: PMC7051945 DOI: 10.3389/fbioe.2020.00141] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2019] [Accepted: 02/11/2020] [Indexed: 12/16/2022] Open
Abstract
The survival rate of patients with breast cancer has been improved by immune checkpoint blockade therapies, and the efficacy of their combinations with epigenetic modulators has shown promising results in preclinical studies. In this prospective study, we propose an ordinary differential equation (ODE)-based quantitative systems pharmacology (QSP) model to conduct an in silico virtual clinical trial and analyze potential predictive biomarkers to improve the anti-tumor response in HER2-negative breast cancer. The model is comprised of four compartments: central, peripheral, tumor, and tumor-draining lymph node, and describes immune activation, suppression, T cell trafficking, and pharmacokinetics and pharmacodynamics (PK/PD) of the therapeutic agents. We implement theoretical mechanisms of action for checkpoint inhibitors and the epigenetic modulator based on preclinical studies to investigate their effects on anti-tumor response. According to model-based simulations, we confirm the synergistic effect of the epigenetic modulator and that pre-treatment tumor mutational burden, tumor-infiltrating effector T cell (Teff) density, and Teff to regulatory T cell (Treg) ratio are significantly higher in responders, which can be potential biomarkers to be considered in clinical trials. Overall, we present a readily reproducible modular model to conduct in silico virtual clinical trials on patient cohorts of interest, which is a step toward personalized medicine in cancer immunotherapy.
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Affiliation(s)
- Hanwen Wang
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Richard J. Sové
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Mohammad Jafarnejad
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Sondra Rahmeh
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Elizabeth M. Jaffee
- Department of Oncology, The Sidney Kimmel Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- Viragh Center for Pancreatic Clinical Research and Care, Bloomberg Kimmel Institute for Immunotherapy, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Vered Stearns
- Department of Oncology, The Sidney Kimmel Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Evanthia T. Roussos Torres
- Department of Oncology, The Sidney Kimmel Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Roisin M. Connolly
- Department of Oncology, The Sidney Kimmel Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- Viragh Center for Pancreatic Clinical Research and Care, Bloomberg Kimmel Institute for Immunotherapy, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Aleksander S. Popel
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- Department of Oncology, The Sidney Kimmel Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, United States
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Abstract
The efficacious dose of a drug is perhaps the most holistic metric reflecting its therapeutic potential. Dose is predicted at many stages in drug discovery and development. Prior to the 1990s, dose prediction was limited to the drug "working" at a reasonable dose and dose regimen in an animal model. Through the early 2000s, dose predictions were generated at candidate nomination and then refined during clinical development. Currently, dose predictions can be made early in drug discovery to enable drug design. Dose predictions at this stage can identify critical drug properties for a viable dose regimen and provide clinically relevant context to lead optimization. In this paper, we give an overview of the opportunities and challenges associated with dose prediction for drug design. A number of general considerations, approaches, and case examples are discussed.
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Affiliation(s)
- Tristan S Maurer
- Medicine Design, Pfizer Worldwide Research and Development, Cambridge, Massachusetts 02139, United States
| | | | - Kevin Beaumont
- Medicine Design, Pfizer Worldwide Research and Development, Cambridge, Massachusetts 02139, United States
| | - Li Di
- Medicine Design, Pfizer Worldwide Research and Development, Groton, Connecticut 06340, United States
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36
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Geerts H, Wikswo J, van der Graaf PH, Bai JPF, Gaiteri C, Bennett D, Swalley SE, Schuck E, Kaddurah-Daouk R, Tsaioun K, Pelleymounter M. Quantitative Systems Pharmacology for Neuroscience Drug Discovery and Development: Current Status, Opportunities, and Challenges. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2019; 9:5-20. [PMID: 31674729 PMCID: PMC6966183 DOI: 10.1002/psp4.12478] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/11/2019] [Accepted: 10/09/2019] [Indexed: 12/18/2022]
Abstract
The substantial progress made in the basic sciences of the brain has yet to be adequately translated to successful clinical therapeutics to treat central nervous system (CNS) diseases. Possible explanations include the lack of quantitative and validated biomarkers, the subjective nature of many clinical endpoints, and complex pharmacokinetic/pharmacodynamic relationships, but also the possibility that highly selective drugs in the CNS do not reflect the complex interactions of different brain circuits. Although computational systems pharmacology modeling designed to capture essential components of complex biological systems has been increasingly accepted in pharmaceutical research and development for oncology, inflammation, and metabolic disorders, the uptake in the CNS field has been very modest. In this article, a cross-disciplinary group with representatives from academia, pharma, regulatory, and funding agencies make the case that the identification and exploitation of CNS therapeutic targets for drug discovery and development can benefit greatly from a system and network approach that can span the gap between molecular pathways and the neuronal circuits that ultimately regulate brain activity and behavior. The National Institute of Neurological Disorders and Stroke (NINDS), in collaboration with the National Institute on Aging (NIA), National Institute of Mental Health (NIMH), National Institute on Drug Abuse (NIDA), and National Center for Advancing Translational Sciences (NCATS), convened a workshop to explore and evaluate the potential of a quantitative systems pharmacology (QSP) approach to CNS drug discovery and development. The objective of the workshop was to identify the challenges and opportunities of QSP as an approach to accelerate drug discovery and development in the field of CNS disorders. In particular, the workshop examined the potential for computational neuroscience to perform QSP-based interrogation of the mechanism of action for CNS diseases, along with a more accurate and comprehensive method for evaluating drug effects and optimizing the design of clinical trials. Following up on an earlier white paper on the use of QSP in general disease mechanism of action and drug discovery, this report focuses on new applications, opportunities, and the accompanying limitations of QSP as an approach to drug development in the CNS therapeutic area based on the discussions in the workshop with various stakeholders.
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Affiliation(s)
- Hugo Geerts
- In Silico Biosciences, Berwyn, Pennsylvania, USA
| | - John Wikswo
- Vanderbilt Institute for Integrative Biosystems Research and Education, Vanderbilt University, Nashville, Tennessee, USA
| | | | - Jane P F Bai
- Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland, USA
| | - Chris Gaiteri
- Rush Alzheimer's Disease Center, Rush University, Chicago, Illinois, USA
| | - David Bennett
- Rush Alzheimer's Disease Center, Rush University, Chicago, Illinois, USA
| | | | | | - Rima Kaddurah-Daouk
- Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, North Carolina, USA
| | - Katya Tsaioun
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Mary Pelleymounter
- Division of Translational Research, National Institute of Neurological Disorders and Stroke, Bethesda, Maryland, USA
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37
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Bradshaw EL, Spilker ME, Zang R, Bansal L, He H, Jones RDO, Le K, Penney M, Schuck E, Topp B, Tsai A, Xu C, Nijsen MJMA, Chan JR. Applications of Quantitative Systems Pharmacology in Model-Informed Drug Discovery: Perspective on Impact and Opportunities. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2019; 8:777-791. [PMID: 31535440 PMCID: PMC6875708 DOI: 10.1002/psp4.12463] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/15/2019] [Accepted: 07/19/2019] [Indexed: 12/15/2022]
Abstract
Quantitative systems pharmacology (QSP) approaches have been increasingly applied in the pharmaceutical since the landmark white paper published in 2011 by a National Institutes of Health working group brought attention to the discipline. In this perspective, we discuss QSP in the context of other modeling approaches and highlight the impact of QSP across various stages of drug development and therapeutic areas. We discuss challenges to the field as well as future opportunities.
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Affiliation(s)
| | - Mary E Spilker
- Pfizer Worldwide Research and Development, San Diego, California, USA
| | - Richard Zang
- Genentech Inc., South San Francisco, California, USA
| | | | - Handan He
- Novartis Institutes for Biomedical Research, East Hanover, New Jersey, USA
| | | | - Kha Le
- Agios, Cambridge, Massachusetts, USA
| | | | | | - Brian Topp
- Merck & Co., Inc, Kenilworth, New Jersey, USA
| | - Alice Tsai
- Vertex Pharmaceuticals Incorporated, Boston, Massachusetts, USA
| | | | | | - Jason R Chan
- Eli Lilly and Company, Indianapolis, Indiana, USA
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38
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Das R, Wille L, Zhang L, Chen C, Winchester W, Selimkhanov J, Wykosky J, Apgar JF, Burke JM, Rogge M, Hua F, Vakilynejad M. A quantitative systems pharmacology model of colonic motility with applications in drug development. J Pharmacokinet Pharmacodyn 2019; 46:485-498. [DOI: 10.1007/s10928-019-09651-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2019] [Accepted: 08/09/2019] [Indexed: 12/17/2022]
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40
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Helmlinger G, Sokolov V, Peskov K, Hallow KM, Kosinsky Y, Voronova V, Chu L, Yakovleva T, Azarov I, Kaschek D, Dolgun A, Schmidt H, Boulton DW, Penland RC. Quantitative Systems Pharmacology: An Exemplar Model-Building Workflow With Applications in Cardiovascular, Metabolic, and Oncology Drug Development. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2019; 8:380-395. [PMID: 31087533 PMCID: PMC6617832 DOI: 10.1002/psp4.12426] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/01/2019] [Accepted: 05/03/2019] [Indexed: 12/13/2022]
Abstract
Quantitative systems pharmacology (QSP), a mechanistically oriented form of drug and disease modeling, seeks to address a diverse set of problems in the discovery and development of therapies. These problems bring a considerable amount of variability and uncertainty inherent in the nonclinical and clinical data. Likewise, the available modeling techniques and related software tools are manifold. Appropriately, the development, qualification, application, and impact of QSP models have been similarly varied. In this review, we describe the progressive maturation of a QSP modeling workflow: a necessary step for the efficient, reproducible development and qualification of QSP models, which themselves are highly iterative and evolutive. Furthermore, we describe three applications of QSP to impact drug development; one supporting new indications for an approved antidiabetic clinical asset through mechanistic hypothesis generation, one highlighting efficacy and safety differentiation within the sodium‐glucose cotransporter‐2 inhibitor drug class, and one enabling rational selection of immuno‐oncology drug combinations.
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Affiliation(s)
- Gabriel Helmlinger
- Quantitative Clinical Pharmacology, Early Clinical Development, IMED Biotech Unit, AstraZeneca Pharmaceuticals, Boston, Massachusetts, USA
| | | | - Kirill Peskov
- M&S Decisions LLC, Moscow, Russia.,Computational Oncology Group, I.M. Sechenov First Moscow State Medical University of the Russian Ministry of Health, Moscow, Russia
| | - Karen M Hallow
- School of Chemical, Materials, and Biomedical Engineering, University of Georgia, Athens, Georgia, USA.,Department of Epidemiology and Biostatistics, University of Georgia, Athens, Georgia, USA
| | | | | | - Lulu Chu
- Quantitative Clinical Pharmacology, Early Clinical Development, IMED Biotech Unit, AstraZeneca Pharmaceuticals, Boston, Massachusetts, USA
| | | | | | | | | | | | - David W Boulton
- Quantitative Clinical Pharmacology, Early Clinical Development, IMED Biotech Unit, AstraZeneca Pharmaceuticals, Gaithersburg, Maryland, USA
| | - Robert C Penland
- Quantitative Clinical Pharmacology, Early Clinical Development, IMED Biotech Unit, AstraZeneca Pharmaceuticals, Boston, Massachusetts, USA
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41
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Bai JPF, Earp JC, Pillai VC. Translational Quantitative Systems Pharmacology in Drug Development: from Current Landscape to Good Practices. AAPS JOURNAL 2019; 21:72. [PMID: 31161268 DOI: 10.1208/s12248-019-0339-5] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/02/2019] [Accepted: 05/07/2019] [Indexed: 12/12/2022]
Abstract
Systems pharmacology approaches have the capability of quantitatively linking the key biological molecules relevant to a drug candidate's mechanism of action (drug-induced signaling pathways) to the clinical biomarkers associated with the proposed target disease, thereby quantitatively facilitating its development and life cycle management. In this review, the model attributes of published quantitative systems pharmacology (QSP) modeling for lowering cholesterol, treating salt-sensitive hypertension, and treating rare diseases as well as describing bone homeostasis and related pharmacological effects are critically reviewed with respect to model quality, calibration, validation, and performance. We further reviewed the common practices in optimizing QSP modeling and prediction. Notably, leveraging genetics and genomic studies for model calibration and validation is common. Statistical and quantitative assessment of QSP prediction and handling of model uncertainty are, however, mostly lacking as are the quantitative and statistical criteria for assessing QSP predictions and the covariance matrix of coefficients between the parameters in a validated virtual population. To accelerate advances and application of QSP with consistent quality, a list of key questions is proposed to be addressed when assessing the quality of a QSP model in hopes of stimulating the scientific community to set common expectations. The common expectations as to what constitutes the best QSP modeling practices, which the scientific community supports, will advance QSP modeling in the realm of informed drug development. In the long run, good practices will extend the life cycles of QSP models beyond the life cycles of individual drugs.
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Affiliation(s)
- Jane P F Bai
- Office of Clinical Pharmacology, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, 20903, USA.
| | - Justin C Earp
- Office of Clinical Pharmacology, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, 20903, USA
| | - Venkateswaran C Pillai
- Office of Clinical Pharmacology, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, 20903, USA
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Bai JP, Musante CJ, Petanceska S, Zhang L, Zhao L, Zhao P. American Society for Clinical Pharmacology and Therapeutics 2019 Annual Meeting Pre-Conferences. CPT Pharmacometrics Syst Pharmacol 2019; 8:333-335. [PMID: 31087531 PMCID: PMC6617844 DOI: 10.1002/psp4.12424] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2019] [Accepted: 05/01/2019] [Indexed: 12/13/2022] Open
Affiliation(s)
- Jane P.F. Bai
- Office of Clinical PharmacologyOffice of Translational SciencesCenter for Drug Evaluation and ResearchU.S. Food and Drug AdministrationSilver SpringMarylandUSA
| | - Cynthia J. Musante
- Quantitative Systems PharmacologyEarly Clinical Development, Pfizer IncCambridgeMassachusettsUSA
| | - Suzana Petanceska
- Division of NeuroscienceNational Institute on Aging at the National Institutes of HealthBethesdaMarylandUSA
| | - Lei Zhang
- Office of Research and StandardsOffice of Generic DrugsCenter for Drug Evaluation and ResearchU.S. Food and Drug AdministrationSilver SpringMarylandUSA
| | - Liang Zhao
- Office of Research and StandardsOffice of Generic DrugsCenter for Drug Evaluation and ResearchU.S. Food and Drug AdministrationSilver SpringMarylandUSA
| | - Ping Zhao
- Bill & Melinda Gates FoundationSeattleWashingtonUSA
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Topp B, Trujillo ME, Sinha V. Industrialization of Quantitative Systems Pharmacology. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2019; 8:356-358. [PMID: 31087476 PMCID: PMC6617811 DOI: 10.1002/psp4.12427] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/22/2019] [Accepted: 05/04/2019] [Indexed: 01/02/2023]
Affiliation(s)
- Brian Topp
- Quantitative Pharmacology and Pharmacometrics, Pharmacokinetics, Pharmacodynamics and Drug Metabolism, Merck Research Laboratories, Merck & Co., Inc., Kenilworth, New Jersey, USA
| | - Maria E Trujillo
- Quantitative Pharmacology and Pharmacometrics, Pharmacokinetics, Pharmacodynamics and Drug Metabolism, Merck Research Laboratories, Merck & Co., Inc., Kenilworth, New Jersey, USA
| | - Vikram Sinha
- Quantitative Pharmacology and Pharmacometrics, Pharmacokinetics, Pharmacodynamics and Drug Metabolism, Merck Research Laboratories, Merck & Co., Inc., Kenilworth, New Jersey, USA
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44
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Wang EB, Shen L, Heathman M, Chan JR. Incorporating Placebo Response in Quantitative Systems Pharmacology Models. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2019; 8:344-346. [PMID: 30983120 PMCID: PMC6618253 DOI: 10.1002/psp4.12412] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/21/2019] [Accepted: 04/02/2019] [Indexed: 01/01/2023]
Affiliation(s)
- Evan B Wang
- Eli Lilly and Company, Indianapolis, Indiana, USA
| | - Lei Shen
- Eli Lilly and Company, Indianapolis, Indiana, USA
| | | | - Jason R Chan
- Eli Lilly and Company, Indianapolis, Indiana, USA
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Zineh I. Quantitative Systems Pharmacology: A Regulatory Perspective on Translation. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2019; 8:336-339. [PMID: 30924594 PMCID: PMC6618141 DOI: 10.1002/psp4.12403] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/30/2019] [Accepted: 03/07/2019] [Indexed: 12/16/2022]
Affiliation(s)
- Issam Zineh
- Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland, USA
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46
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Goto A, Abe S, Koshiba S, Yamaguchi K, Sato N, Kurahashi Y. Current status and future perspective on preclinical pharmacokinetic and pharmacodynamic (PK/PD) analysis: Survey in Japan pharmaceutical manufacturers association (JPMA). Drug Metab Pharmacokinet 2019; 34:148-154. [PMID: 30827921 DOI: 10.1016/j.dmpk.2019.01.004] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2018] [Revised: 12/24/2018] [Accepted: 01/15/2019] [Indexed: 11/29/2022]
Abstract
Preclinical pharmacokinetic/pharmacodynamic (PK/PD) analysis is an efficient tool for the translational research and proof of mechanism/concept in animals. The questionnaire survey on the practice of preclinical PK/PD analysis was conducted in the member companies of the Japan Pharmaceutical Manufacturers Association (JPMA). According to the survey, 60% of companies conducted preclinical PK/PD analysis and its impact for drug development was different between each of the companies. The frequently analyzed therapeutic areas of preclinical PK/PD analysis were neurology, inflammation and metabolic disease, and those are different from the therapeutic area (infectious disease and oncology) in which PK/PD analysis was considered as effective by the present survey. Many companies which have used preclinical PK/PD analysis for the translation to human PK/PD and for the prediction of dose/regimen had good communication with other research & development (R&D) departments (e.g. pharmacology/clinical pharmacology). The increase in resources for preclinical PK/PD analysis including education was highly demanded. As a future perspective, the closer collaboration between pharmacokinetics scientists, pharmacologists, toxicologists and clinical pharmacologists and the increase in resources including upskilling and the comprehension of preclinical PK/PD analysis by the project team are considered to lead to efficient contributions to improve the success ratio of drug discovery and development.
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Affiliation(s)
- Akihiko Goto
- Non-Clinical Evaluation Expert Committee, Drug Evaluation Committee, Japan Pharmaceutical Manufacturers Association (JPMA), 2-3-11, Nihombashi-honcho, Chuo-ku, Tokyo, 103-0023, Japan; Drug Metabolism and Pharmacokinetics Research Laboratories, Takeda Pharmaceutical Company Limited, 2-26-1, Muraoka-Higashi, Fujisawa, Kanagawa, 251-8555, Japan.
| | - Sadahiro Abe
- Non-Clinical Evaluation Expert Committee, Drug Evaluation Committee, Japan Pharmaceutical Manufacturers Association (JPMA), 2-3-11, Nihombashi-honcho, Chuo-ku, Tokyo, 103-0023, Japan; Clinical Pharmacology, Clinical Research, Pfizer R&D Japan, Inc., Shinjuku Bunka Quint Building, 3-22-7, Yoyogi, Shibuya-ku, Tokyo, 151-8589, Japan
| | - Shoko Koshiba
- Non-Clinical Evaluation Expert Committee, Drug Evaluation Committee, Japan Pharmaceutical Manufacturers Association (JPMA), 2-3-11, Nihombashi-honcho, Chuo-ku, Tokyo, 103-0023, Japan; Pharmacokinetic Research Laboratories, Translational Research Unit, R&D Division, Kyowa Hakko Kirin Co., Ltd., 1188 Shimotogari, Nagaizumi-cho, Sunto-gun, Shizuoka, 411-8731, Japan
| | - Koji Yamaguchi
- Non-Clinical Evaluation Expert Committee, Drug Evaluation Committee, Japan Pharmaceutical Manufacturers Association (JPMA), 2-3-11, Nihombashi-honcho, Chuo-ku, Tokyo, 103-0023, Japan
| | - Nobuo Sato
- Non-Clinical Evaluation Expert Committee, Drug Evaluation Committee, Japan Pharmaceutical Manufacturers Association (JPMA), 2-3-11, Nihombashi-honcho, Chuo-ku, Tokyo, 103-0023, Japan; Pharmacokinetics and Analysis Laboratory, Pharmaceutical Research Center, Meiji Seika Pharma Co., Ltd., 760 Morooka-cho, Kohoku-ku, Yokohama, 222-8567, Japan
| | - Yoshikazu Kurahashi
- Drug Metabolism & Pharmacokinetics Research Laboratories, Central Pharmaceutical Research Institute, Japan Tobacco Inc., 1-1, Murasaki-cho Takatsuki, Osaka, 569-1125, Japan
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47
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Li F, Ulrich ML, Shih VFS, Cochran JH, Hunter JH, Westendorf L, Neale J, Benjamin DR. Mouse Strains Influence Clearance and Efficacy of Antibody and Antibody-Drug Conjugate Via Fc-FcγR Interaction. Mol Cancer Ther 2019; 18:780-787. [PMID: 30824607 DOI: 10.1158/1535-7163.mct-18-0977] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2018] [Revised: 12/29/2018] [Accepted: 02/20/2019] [Indexed: 11/16/2022]
Abstract
To provide a better understanding of the pharmacokinetics-pharmacodynamics relationships of antibody-based drugs, we analyzed several chimeric and humanized monoclonal antibodies or antibody-drug conjugates (ADC) for PK and efficacy among four strains of mice. Notably, antibodies and ADCs displayed a dose-dependent drug disposition profile in the plasma of NSG mice. The increased clearance rate in NSG mice resulted in the reduction of antitumor activity of ADCs. Furthermore, we identified that the abnormal clearance was mediated by Fc-FcγR interaction by comparing antibodies that lack FcγR binding capacity. We also found a high percentage of FcγR-expressing macrophages in the bone marrow, spleen, and liver of NSG mice, which may be responsible for the abnormal distribution of antibodies. Overall, these findings suggest that preclinical evaluation of efficacy and pharmacokinetics of antibodies and ADCs need to consider mouse strain-induced variations.
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Affiliation(s)
- Fu Li
- Research, Seattle Genetics, Inc., Bothell, Washington.
| | | | | | | | | | | | - Jason Neale
- Translational Science, Seattle Genetics, Inc., Bothell, Washington
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48
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Zhao C, Zhang Y, Popel AS. Mechanistic Computational Models of MicroRNA-Mediated Signaling Networks in Human Diseases. Int J Mol Sci 2019; 20:ijms20020421. [PMID: 30669429 PMCID: PMC6358731 DOI: 10.3390/ijms20020421] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2018] [Revised: 01/14/2019] [Accepted: 01/15/2019] [Indexed: 12/17/2022] Open
Abstract
MicroRNAs (miRs) are endogenous non-coding RNA molecules that play important roles in human health and disease by regulating gene expression and cellular processes. In recent years, with the increasing scientific knowledge and new discovery of miRs and their gene targets, as well as the plentiful experimental evidence that shows dysregulation of miRs in a wide variety of human diseases, the computational modeling approach has emerged as an effective tool to help researchers identify novel functional associations between differential miR expression and diseases, dissect the phenotypic expression patterns of miRs in gene regulatory networks, and elucidate the critical roles of miRs in the modulation of disease pathways from mechanistic and quantitative perspectives. Here we will review the recent systems biology studies that employed different kinetic modeling techniques to provide mechanistic insights relating to the regulatory function and therapeutic potential of miRs in human diseases. Some of the key computational aspects to be discussed in detail in this review include (i) models of miR-mediated network motifs in the regulation of gene expression, (ii) models of miR biogenesis and miR–target interactions, and (iii) the incorporation of such models into complex disease pathways in order to generate mechanistic, molecular- and systems-level understanding of pathophysiology. Other related bioinformatics tools such as computational platforms that predict miR-disease associations will also be discussed, and we will provide perspectives on the challenges and opportunities in the future development and translational application of data-driven systems biology models that involve miRs and their regulatory pathways in human diseases.
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Affiliation(s)
- Chen Zhao
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA.
| | - Yu Zhang
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA.
| | - Aleksander S Popel
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA.
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49
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Saito R. [Estimation of mechanism-of-action of pharmaceutical compounds based on quantitative systems pharmacology]. Nihon Yakurigaku Zasshi 2019; 153:124-128. [PMID: 30867381 DOI: 10.1254/fpj.153.124] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
To accelerate drug discovery and development, improve R&D efficiency and facilitate scientific decision making, quantitative systems pharmacology (QSP) is increasingly being utilized in the pharmaceutical and biotech industry. QSP approach has emerged as a central technology in innovative framework based on "learn and confirm" cycle, which is currently called as model-informed drug discovery and development (MID3). QSP approach is supporting the rational decision in drug discovery and development from exploratory research to late-stage clinical development, and enables selection/validation of novel target molecules, identification of mechanism-of-action of pharmacological/toxicological compounds, prioritization of pharmaceutical candidates, facilitating translational research, optimization of clinical trials, and others. In this review paper, I explained summary and effectiveness of QSP approach by showing two our case-studies related to identification of mechanism-of-action of compounds. These are 1: differentiation of thrombin inhibitors and 2: endocrine disrupting compounds, they have been good practices in my experiences of applying QSP at pharmaceutical company.
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Affiliation(s)
- Ryuta Saito
- Discovery Technology Laboratories, Sohyaku. Innovative Research Division, Mitsubishi Tanabe Pharma Corporation
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50
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Ermakov S, Schmidt BJ, Musante CJ, Thalhauser CJ. A Survey of Software Tool Utilization and Capabilities for Quantitative Systems Pharmacology: What We Have and What We Need. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2018; 8:62-76. [PMID: 30417600 PMCID: PMC6389347 DOI: 10.1002/psp4.12373] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/31/2018] [Accepted: 10/29/2018] [Indexed: 12/14/2022]
Abstract
Quantitative systems pharmacology (QSP) is a rapidly emerging discipline with application across a spectrum of challenges facing the pharmaceutical industry, including mechanistically informed prioritization of target pathways and combinations in discovery, target population, and dose expansion decisions early in clinical development, and analyses for regulatory authorities late in clinical development. QSP's development has influences from physiologic modeling, systems biology, physiologically‐based pharmacokinetic modeling, and pharmacometrics. Given a varied scientific heritage, a variety of tools to accomplish the demands of model development, application, and model‐based analysis of available data have been developed. We report the outcome from a community survey and resulting analysis of how modelers view the impact and growth of QSP, how they utilize existing tools, and capabilities they need improved to further accelerate their impact on drug development. These results serve as a benchmark and roadmap for advancements to the QSP tool set.
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