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Xue L, Singla RK, He S, Arrasate S, González-Díaz H, Miao L, Shen B. Warfarin-A natural anticoagulant: A review of research trends for precision medication. PHYTOMEDICINE : INTERNATIONAL JOURNAL OF PHYTOTHERAPY AND PHYTOPHARMACOLOGY 2024; 128:155479. [PMID: 38493714 DOI: 10.1016/j.phymed.2024.155479] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Revised: 01/29/2024] [Accepted: 02/22/2024] [Indexed: 03/19/2024]
Abstract
BACKGROUND Warfarin is a widely prescribed anticoagulant in the clinic. It has a more considerable individual variability, and many factors affect its variability. Mathematical models can quantify the quantitative impact of these factors on individual variability. PURPOSE The aim is to comprehensively analyze the advanced warfarin dosing algorithm based on pharmacometrics and machine learning models of personalized warfarin dosage. METHODS A bibliometric analysis of the literature retrieved from PubMed and Scopus was performed using VOSviewer. The relevant literature that reported the precise dosage of warfarin calculation was retrieved from the database. The multiple linear regression (MLR) algorithm was excluded because a recent systematic review that mainly reviewed this algorithm has been reported. The following terms of quantitative systems pharmacology, mechanistic model, physiologically based pharmacokinetic model, artificial intelligence, machine learning, pharmacokinetic, pharmacodynamic, pharmacokinetics, pharmacodynamics, and warfarin were added as MeSH Terms or appearing in Title/Abstract into query box of PubMed, then humans and English as filter were added to retrieve the literature. RESULTS Bibliometric analysis revealed important co-occuring MeShH and index keywords. Further, the United States, China, and the United Kingdom were among the top countries contributing in this domain. Some studies have established personalized warfarin dosage models using pharmacometrics and machine learning-based algorithms. There were 54 related studies, including 14 pharmacometric models, 31 artificial intelligence models, and 9 model evaluations. Each model has its advantages and disadvantages. The pharmacometric model contains biological or pharmacological mechanisms in structure. The process of pharmacometric model development is very time- and labor-intensive. Machine learning is a purely data-driven approach; its parameters are more mathematical and have less biological interpretation. However, it is faster, more efficient, and less time-consuming. Most published models of machine learning algorithms were established based on cross-sectional data sourced from the database. CONCLUSION Future research on personalized warfarin medication should focus on combining the advantages of machine learning and pharmacometrics algorithms to establish a more robust warfarin dosage algorithm. Randomized controlled trials should be performed to evaluate the established algorithm of warfarin dosage. Moreover, a more user-friendly and accessible warfarin precision medicine platform should be developed.
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Affiliation(s)
- Ling Xue
- Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, China; Department of Pharmacy, The First Affiliated Hospital of Soochow University, Suzhou, China; Department of Pharmacology, Faculty of Medicine, University of The Basque Country (UPV/EHU), Bilbao, Basque Country, Spain
| | - Rajeev K Singla
- Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, China; School of Pharmaceutical Sciences, Lovely Professional University, Phagwara, Punjab-144411, India
| | - Shan He
- IKERDATA S.l., ZITEK, University of The Basque Country (UPVEHU), Rectorate Building, 48940, Bilbao, Basque Country, Spain; Department of Organic and Inorganic Chemistry, Faculty of Science and Technology, University of The Basque Country (UPV/EHU), P.O. Box 644, 48080, Bilbao, Basque Country, Spain
| | - Sonia Arrasate
- Department of Organic and Inorganic Chemistry, Faculty of Science and Technology, University of The Basque Country (UPV/EHU), P.O. Box 644, 48080, Bilbao, Basque Country, Spain
| | - Humberto González-Díaz
- Department of Organic and Inorganic Chemistry, Faculty of Science and Technology, University of The Basque Country (UPV/EHU), P.O. Box 644, 48080, Bilbao, Basque Country, Spain; BIOFISIKA: Basque Center for Biophysics CSIC, University of The Basque Country (UPV/EHU), Barrio Sarriena s/n, Leioa, Bizkaia 48940, Basque Country, Spain; IKERBASQUE, Basque Foundation for Science, 48011, Bilbao, Basque Country, Spain
| | - Liyan Miao
- Department of Pharmacy, The First Affiliated Hospital of Soochow University, Suzhou, China; Institute for Interdisciplinary Drug Research and Translational Sciences, Soochow University, Suzhou, China; College of Pharmaceutical Sciences, Soochow University, Suzhou, China.
| | - Bairong Shen
- Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, China.
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Augustin D, Lambert B, Robinson M, Wang K, Gavaghan D. Simulating clinical trials for model-informed precision dosing: using warfarin treatment as a use case. Front Pharmacol 2023; 14:1270443. [PMID: 37927586 PMCID: PMC10621790 DOI: 10.3389/fphar.2023.1270443] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Accepted: 10/05/2023] [Indexed: 11/07/2023] Open
Abstract
Treatment response variability across patients is a common phenomenon in clinical practice. For many drugs this inter-individual variability does not require much (if any) individualisation of dosing strategies. However, for some drugs, including chemotherapies and some monoclonal antibody treatments, individualisation of dosages are needed to avoid harmful adverse events. Model-informed precision dosing (MIPD) is an emerging approach to guide the individualisation of dosing regimens of otherwise difficult-to-administer drugs. Several MIPD approaches have been suggested to predict dosing strategies, including regression, reinforcement learning (RL) and pharmacokinetic and pharmacodynamic (PKPD) modelling. A unified framework to study the strengths and limitations of these approaches is missing. We develop a framework to simulate clinical MIPD trials, providing a cost and time efficient way to test different MIPD approaches. Central for our framework is a clinical trial model that emulates the complexities in clinical practice that challenge successful treatment individualisation. We demonstrate this framework using warfarin treatment as a use case and investigate three popular MIPD methods: 1. Neural network regression; 2. Deep RL; and 3. PKPD modelling. We find that the PKPD model individualises warfarin dosing regimens with the highest success rate and the highest efficiency: 75.1% of the individuals display INRs inside the therapeutic range at the end of the simulated trial; and the median time in the therapeutic range (TTR) is 74%. In comparison, the regression model and the deep RL model have success rates of 47.0% and 65.8%, and median TTRs of 45% and 68%. We also find that the MIPD models can attain different degrees of individualisation: the Regression model individualises dosing regimens up to variability explained by covariates; the Deep RL model and the PKPD model individualise dosing regimens accounting also for additional variation using monitoring data. However, the Deep RL model focusses on control of the treatment response, while the PKPD model uses the data also to further the individualisation of predictions.
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Affiliation(s)
- David Augustin
- Department of Computer Science, University of Oxford, Oxford, United Kingdom
| | - Ben Lambert
- College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, United Kingdom
| | - Martin Robinson
- Department of Computer Science, University of Oxford, Oxford, United Kingdom
| | - Ken Wang
- Research and Early Development, F. Hoffmann-La Roche AG, Basel, Switzerland
| | - David Gavaghan
- Department of Computer Science, University of Oxford, Oxford, United Kingdom
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Shen G, Moua KTY, Perkins K, Johnson D, Li A, Curtin P, Gao W, McCune JS. Precision sirolimus dosing in children: The potential for model-informed dosing and novel drug monitoring. Front Pharmacol 2023; 14:1126981. [PMID: 37021042 PMCID: PMC10069443 DOI: 10.3389/fphar.2023.1126981] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2022] [Accepted: 02/14/2023] [Indexed: 04/07/2023] Open
Abstract
The mTOR inhibitor sirolimus is prescribed to treat children with varying diseases, ranging from vascular anomalies to sporadic lymphangioleiomyomatosis to transplantation (solid organ or hematopoietic cell). Precision dosing of sirolimus using therapeutic drug monitoring (TDM) of sirolimus concentrations in whole blood drawn at the trough (before the next dose) time-point is the current standard of care. For sirolimus, trough concentrations are only modestly correlated with the area under the curve, with R 2 values ranging from 0.52 to 0.84. Thus, it should not be surprising, even with the use of sirolimus TDM, that patients treated with sirolimus have variable pharmacokinetics, toxicity, and effectiveness. Model-informed precision dosing (MIPD) will be beneficial and should be implemented. The data do not suggest dried blood spots point-of-care sampling of sirolimus concentrations for precision dosing of sirolimus. Future research on precision dosing of sirolimus should focus on pharmacogenomic and pharmacometabolomic tools to predict sirolimus pharmacokinetics and wearables for point-of-care quantitation and MIPD.
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Affiliation(s)
- Guofang Shen
- Department of Hematologic Malignancies Translational Sciences, City of Hope, and Department of Hematopoietic Cell Transplantation, City of Hope Medical Center, Duarte, CA, United States
| | - Kao Tang Ying Moua
- Alfred E. Mann School of Pharmacy and Pharmaceutical Sciences, University of Southern California, Los Angeles, CA, United States
| | - Kathryn Perkins
- Alfred E. Mann School of Pharmacy and Pharmaceutical Sciences, University of Southern California, Los Angeles, CA, United States
| | - Deron Johnson
- Clinical Informatics, City of Hope Medical Center, Duarte, CA, United States
| | - Arthur Li
- Division of Biostatistics, City of Hope, Duarte, CA, United States
| | - Peter Curtin
- Department of Hematologic Malignancies Translational Sciences, City of Hope, and Department of Hematopoietic Cell Transplantation, City of Hope Medical Center, Duarte, CA, United States
| | - Wei Gao
- Division of Engineering and Applied Science, Andrew and Peggy Cherng Department of Medical Engineering, California Institute of Technology, Pasadena, CA, United States
| | - Jeannine S. McCune
- Department of Hematologic Malignancies Translational Sciences, City of Hope, and Department of Hematopoietic Cell Transplantation, City of Hope Medical Center, Duarte, CA, United States
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A Review of Quantitative Systems Pharmacology Models of the Coagulation Cascade: Opportunities for Improved Usability. Pharmaceutics 2023; 15:pharmaceutics15030918. [PMID: 36986779 PMCID: PMC10054658 DOI: 10.3390/pharmaceutics15030918] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Revised: 03/02/2023] [Accepted: 03/06/2023] [Indexed: 03/16/2023] Open
Abstract
Despite the numerous therapeutic options to treat bleeding or thrombosis, a comprehensive quantitative mechanistic understanding of the effects of these and potential novel therapies is lacking. Recently, the quality of quantitative systems pharmacology (QSP) models of the coagulation cascade has improved, simulating the interactions between proteases, cofactors, regulators, fibrin, and therapeutic responses under different clinical scenarios. We aim to review the literature on QSP models to assess the unique capabilities and reusability of these models. We systematically searched the literature and BioModels database reviewing systems biology (SB) and QSP models. The purpose and scope of most of these models are redundant with only two SB models serving as the basis for QSP models. Primarily three QSP models have a comprehensive scope and are systematically linked between SB and more recent QSP models. The biological scope of recent QSP models has expanded to enable simulations of previously unexplainable clotting events and the drug effects for treating bleeding or thrombosis. Overall, the field of coagulation appears to suffer from unclear connections between models and irreproducible code as previously reported. The reusability of future QSP models can improve by adopting model equations from validated QSP models, clearly documenting the purpose and modifications, and sharing reproducible code. The capabilities of future QSP models can improve from more rigorous validation by capturing a broader range of responses to therapies from individual patient measurements and integrating blood flow and platelet dynamics to closely represent in vivo bleeding or thrombosis risk.
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Braakman S, Pathmanathan P, Moore H. Evaluation framework for systems models. CPT Pharmacometrics Syst Pharmacol 2021; 11:264-289. [PMID: 34921743 PMCID: PMC8923730 DOI: 10.1002/psp4.12755] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2019] [Revised: 11/30/2021] [Accepted: 12/06/2021] [Indexed: 12/16/2022] Open
Abstract
As decisions in drug development increasingly rely on predictions from mechanistic systems models, assessing the predictive capability of such models is becoming more important. Several frameworks for the development of quantitative systems pharmacology (QSP) models have been proposed. In this paper, we add to this body of work with a framework that focuses on the appropriate use of qualitative and quantitative model evaluation methods. We provide details and references for those wishing to apply these methods, which include sensitivity and identifiability analyses, as well as concepts such as validation and uncertainty quantification. Many of these methods have been used successfully in other fields, but are not as common in QSP modeling. We illustrate how to apply these methods to evaluate QSP models, and propose methods to use in two case studies. We also share examples of misleading results when inappropriate analyses are used.
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Affiliation(s)
- Sietse Braakman
- Application Engineering, MathWorks Inc, Natick, Massachusetts, USA
| | - Pras Pathmanathan
- Office of Science and Engineering Laboratories (OSEL), Center for Devices and Radiological Health (CDRH), US Food and Drug Administration (FDA), Silver Spring, Maryland, USA
| | - Helen Moore
- Laboratory for Systems Medicine, Division of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, University of Florida, Gainesville, Florida, USA
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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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Irurzun-Arana I, Rackauckas C, McDonald TO, Trocóniz IF. Beyond Deterministic Models in Drug Discovery and Development. Trends Pharmacol Sci 2020; 41:882-895. [PMID: 33032836 PMCID: PMC7534664 DOI: 10.1016/j.tips.2020.09.005] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2020] [Revised: 07/28/2020] [Accepted: 09/10/2020] [Indexed: 02/06/2023]
Abstract
The model-informed drug discovery and development paradigm is now well established among the pharmaceutical industry and regulatory agencies. This success has been mainly due to the ability of pharmacometrics to bring together different modeling strategies, such as population pharmacokinetics/pharmacodynamics (PK/PD) and systems biology/pharmacology. However, there are promising quantitative approaches that are still seldom used by pharmacometricians and that deserve consideration. One such case is the stochastic modeling approach, which can be important when modeling small populations because random events can have a huge impact on these systems. In this review, we aim to raise awareness of stochastic models and how to combine them with existing modeling techniques, with the ultimate goal of making future drug-disease models more versatile and realistic.
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Affiliation(s)
- Itziar Irurzun-Arana
- Pharmacometrics and Systems Pharmacology, Department of Pharmaceutical Technology and Chemistry, School of Pharmacy and Nutrition, University of Navarra, Pamplona, 31008, Spain; Navarra Institute for Health Research (IdisNA), University of Navarra, 31080, Pamplona, Spain.
| | - Christopher Rackauckas
- Department of Mathematics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Thomas O McDonald
- Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA 02115, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA; Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA 02138, USA; Center for Cancer Evolution, Dana-Farber Cancer Institute, Boston, MA 02115, USA
| | - Iñaki F Trocóniz
- Pharmacometrics and Systems Pharmacology, Department of Pharmaceutical Technology and Chemistry, School of Pharmacy and Nutrition, University of Navarra, Pamplona, 31008, Spain; Navarra Institute for Health Research (IdisNA), University of Navarra, 31080, Pamplona, Spain; Institute of Data Science and Artificial Intelligence, DATAI, University of Navarra, Pamplona, 31080, Spain.
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Hartmann S, Biliouris K, Lesko LJ, Nowak-Göttl U, Trame MN. Quantitative Systems Pharmacology Model-Based Predictions of Clinical Endpoints to Optimize Warfarin and Rivaroxaban Anti-Thrombosis Therapy. Front Pharmacol 2020; 11:1041. [PMID: 32765265 PMCID: PMC7381140 DOI: 10.3389/fphar.2020.01041] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2019] [Accepted: 06/26/2020] [Indexed: 11/25/2022] Open
Abstract
Background Tight monitoring of efficacy and safety of anticoagulants such as warfarin is imperative to optimize the benefit-risk ratio of anticoagulants in patients. The standard tests used are measurements of prothrombin time (PT), usually expressed as international normalized ratio (INR), and activated partial thromboplastin time (aPTT). Objective To leverage a previously developed quantitative systems pharmacology (QSP) model of the human coagulation network to predict INR and aPTT for warfarin and rivaroxaban, respectively. Methods A modeling and simulation approach was used to predict INR and aPTT measurements of patients receiving steady-state anticoagulation therapy. A previously developed QSP model was leveraged for the present analysis. The effect of genetic polymorphisms known to influence dose response of warfarin (CYP2C9, VKORC1) were implemented into the model by modifying warfarin clearance (CYP2C9 *1: 0.2 L/h; *2: 0.14 L/h, *3: 0.04 L/h) and the concentration of available vitamin K (VKORC1 GA: −22% from normal vitamin K concentration; AA: −44% from normal vitamin K concentration). Virtual patient populations were used to assess the ability of the model to accurately predict routine INR and aPTT measurements from patients under long-term anticoagulant therapy. Results The introduced model accurately described the observed INR of patients receiving long-term warfarin treatment. The model was able to demonstrate the influence of genetic polymorphisms of CYP2C9 and VKORC1 on the INR measurements. Additionally, the model was successfully used to predict aPTT measurements for patients receiving long-term rivaroxaban therapy. Conclusion The QSP model accurately predicted INR and aPTT measurements observed during routine therapeutic drug monitoring. This is an exemplar of how a QSP model can be adapted and used as a model-based precision dosing tool during clinical practice and drug development to predict efficacy and safety of anticoagulants to ultimately help optimize anti-thrombotic therapy.
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Affiliation(s)
- Sonja Hartmann
- Center for Pharmacometrics & Systems Pharmacology, Department of Pharmaceutics, University of Florida, Orlando, FL, United States
| | - Konstantinos Biliouris
- Center for Pharmacometrics & Systems Pharmacology, Department of Pharmaceutics, University of Florida, Orlando, FL, United States
| | - Lawrence J Lesko
- Center for Pharmacometrics & Systems Pharmacology, Department of Pharmaceutics, University of Florida, Orlando, FL, United States
| | - Ulrike Nowak-Göttl
- Thrombosis & Hemostasis Treatment Center, Institute of Clinical Chemistry, University of Schleswig-Holstein, Germany
| | - Mirjam N Trame
- Center for Pharmacometrics & Systems Pharmacology, Department of Pharmaceutics, University of Florida, Orlando, FL, United States
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Cheng L, Wei GW, Leil T. Review of quantitative systems pharmacological modeling in thrombosis. COMMUNICATIONS IN INFORMATION AND SYSTEMS 2019; 19:219-240. [PMID: 34045928 DOI: 10.4310/cis.2019.v19.n3.a1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Hemostasis and thrombosis are often thought as two sides of the same clotting mechanism whereas hemostasis is a natural protective mechanism to prevent bleeding and thrombosis is a blood clot abnormally formulated inside a blood vessel, blocking the normal blood flow. The evidence to date suggests that at least arterial thrombosis results from the same critical pathways of hemostasis. Analysis of these complex processes and pathways using quantitative systems pharmacological model-based approach can facilitate the delineation of the causal pathways that lead to the emergence of thrombosis. In this paper, we provide an overview of the main molecular and physiological mechanisms associated with hemostasis and thrombosis, and review the models and quantitative system pharmacological modeling approaches that are relevant in characterizing the interplay among the multiple factors and pathways of thrombosis. An emphasis is given to computational models for drug development. Future trends are discussed.
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Affiliation(s)
- Limei Cheng
- Clinical Pharmacology and Pharmacometrics Bristol-Myers Squibb, Princeton, NJ 08540, USA
| | - Guo-Wei Wei
- Department of Mathematics Michigan State University East Lansing, MI 48824 USA
| | - Tarek Leil
- Clinical Pharmacology and Pharmacometrics Bristol-Myers Squibb, Princeton, NJ 08540, USA
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Trame MN, Riggs M, Biliouris K, Marathe D, Mettetal J, Post TM, Rizk ML, Visser SAG, Musante CJ. Perspective on the State of Pharmacometrics and Systems Pharmacology Integration. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2018; 7:617-620. [PMID: 29761892 PMCID: PMC6202472 DOI: 10.1002/psp4.12313] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/06/2018] [Accepted: 05/04/2018] [Indexed: 12/31/2022]
Abstract
Reliance on modeling and simulation in drug discovery and development has dramatically increased over the past decade. Two disciplines at the forefront of this activity, pharmacometrics and systems pharmacology (SP), emerged independently from different fields; consequently, a perception exists that only few examples integrate these approaches. Herein, we review the state of pharmacometrics and SP integration and describe benefits of combining these approaches in a model-informed drug discovery and development framework.
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Affiliation(s)
- Mirjam N Trame
- Novartis Institutes for BioMedical Research, Inc, Cambridge, Massachusetts, USA
| | - Matthew Riggs
- Metrum Research Group LLC, Tariffville, Connecticut, USA
| | | | | | - Jerome Mettetal
- Drug Safety and Metabolism, IMED Biotech Unit, AstraZeneca, Boston, Massachusetts, USA
| | - Teun M Post
- Leiden Experts on Advanced Pharmacokinetics and Pharmacodynamics (LAP&P), Leiden, The Netherlands.,Division of Systems Biomedicine and Pharmacology, Leiden Academic Centre for Drug Research, Leiden University, Leiden, The Netherlands
| | | | | | - Cynthia J Musante
- Pfizer Worldwide Research & Development, Cambridge, Massachusetts, USA
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Mavroudis PD, Hermes HE, Teutonico D, Preuss TG, Schneckener S. Development and validation of a physiology-based model for the prediction of pharmacokinetics/toxicokinetics in rabbits. PLoS One 2018; 13:e0194294. [PMID: 29561908 PMCID: PMC5862475 DOI: 10.1371/journal.pone.0194294] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2017] [Accepted: 02/28/2018] [Indexed: 01/08/2023] Open
Abstract
The environmental fates of pharmaceuticals and the effects of crop protection products on non-target species are subjects that are undergoing intense review. Since measuring the concentrations and effects of xenobiotics on all affected species under all conceivable scenarios is not feasible, standard laboratory animals such as rabbits are tested, and the observed adverse effects are translated to focal species for environmental risk assessments. In that respect, mathematical modelling is becoming increasingly important for evaluating the consequences of pesticides in untested scenarios. In particular, physiologically based pharmacokinetic/toxicokinetic (PBPK/TK) modelling is a well-established methodology used to predict tissue concentrations based on the absorption, distribution, metabolism and excretion of drugs and toxicants. In the present work, a rabbit PBPK/TK model is developed and evaluated with data available from the literature. The model predictions include scenarios of both intravenous (i.v.) and oral (p.o.) administration of small and large compounds. The presented rabbit PBPK/TK model predicts the pharmacokinetics (Cmax, AUC) of the tested compounds with an average 1.7-fold error. This result indicates a good predictive capacity of the model, which enables its use for risk assessment modelling and simulations.
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Affiliation(s)
| | - Helen E. Hermes
- Bayer AG, Engineering & Technology- Systems Pharmacology, Leverkusen, Germany
| | - Donato Teutonico
- Bayer AG, Engineering & Technology- Systems Pharmacology, Leverkusen, Germany
| | | | - Sebastian Schneckener
- Bayer AG, Engineering & Technology- Systems Pharmacology, Leverkusen, Germany
- * E-mail:
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