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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: 46] [Impact Index Per Article: 15.3] [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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Abrams R, Kaddi CD, Tao M, Leiser RJ, Simoni G, Reali F, Tolsma J, Jasper P, van Rijn Z, Li J, Niesner B, Barrett JS, Marchetti L, Peterschmitt MJ, Azer K, Neves-Zaph S. A Quantitative Systems Pharmacology Model of Gaucher Disease Type 1 Provides Mechanistic Insight Into the Response to Substrate Reduction Therapy With Eliglustat. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2020; 9:374-383. [PMID: 32558397 PMCID: PMC7376290 DOI: 10.1002/psp4.12506] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/20/2019] [Accepted: 02/17/2020] [Indexed: 12/27/2022]
Abstract
Gaucher’s disease type 1 (GD1) leads to significant morbidity and mortality through clinical manifestations, such as splenomegaly, hematological complications, and bone disease. Two types of therapies are currently approved for GD1: enzyme replacement therapy (ERT), and substrate reduction therapy (SRT). In this study, we have developed a quantitative systems pharmacology (QSP) model, which recapitulates the effects of eliglustat, the only first‐line SRT approved for GD1, on treatment‐naïve or patients with ERT‐stabilized adult GD1. This multiscale model represents the mechanism of action of eliglustat that leads toward reduction of spleen volume. Model capabilities were illustrated through the application of the model to predict ERT and eliglustat responses in virtual populations of adult patients with GD1, representing patients across a spectrum of disease severity as defined by genotype‐phenotype relationships. In summary, the QSP model provides a mechanistic computational platform for predicting treatment response via different modalities within the heterogeneous GD1 patient population.
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Affiliation(s)
- Ruth Abrams
- Translational Disease Modelling, Digital Data Science, Sanofi, Bridgewater, New Jersey, USA
| | - Chanchala D Kaddi
- Translational Disease Modelling, Digital Data Science, Sanofi, Bridgewater, New Jersey, USA
| | - Mengdi Tao
- Translational Disease Modelling, Digital Data Science, Sanofi, Bridgewater, New Jersey, USA
| | - Randolph J Leiser
- Translational Disease Modelling, Digital Data Science, Sanofi, Bridgewater, New Jersey, USA
| | - Giulia Simoni
- Fondazione The Microsoft Research - University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto, Italy
| | - Federico Reali
- Fondazione The Microsoft Research - University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto, Italy
| | | | | | - Zachary van Rijn
- Translational Disease Modelling, Digital Data Science, Sanofi, Bridgewater, New Jersey, USA
| | - Jing Li
- Translational Disease Modelling, Digital Data Science, Sanofi, Bridgewater, New Jersey, USA
| | - Bradley Niesner
- Translational Disease Modelling, Digital Data Science, Sanofi, Bridgewater, New Jersey, USA
| | - Jeffrey S Barrett
- Translational Disease Modelling, Digital Data Science, Sanofi, Bridgewater, New Jersey, USA
| | - Luca Marchetti
- Fondazione The Microsoft Research - University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto, Italy
| | | | - Karim Azer
- Translational Disease Modelling, Digital Data Science, Sanofi, Bridgewater, New Jersey, USA
| | - Susana Neves-Zaph
- Translational Disease Modelling, Digital Data Science, Sanofi, Bridgewater, New Jersey, USA
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Kaddi CD, Niesner B, Baek R, Jasper P, Pappas J, Tolsma J, Li J, van Rijn Z, Tao M, Ortemann‐Renon C, Easton R, Tan S, Puga AC, Schuchman EH, Barrett JS, Azer K. Quantitative Systems Pharmacology Modeling of Acid Sphingomyelinase Deficiency and the Enzyme Replacement Therapy Olipudase Alfa Is an Innovative Tool for Linking Pathophysiology and Pharmacology. CPT Pharmacometrics Syst Pharmacol 2018; 7:442-452. [PMID: 29920993 PMCID: PMC6063739 DOI: 10.1002/psp4.12304] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2018] [Revised: 03/27/2018] [Accepted: 04/10/2018] [Indexed: 12/12/2022] Open
Abstract
Acid sphingomyelinase deficiency (ASMD) is a rare lysosomal storage disorder with heterogeneous clinical manifestations, including hepatosplenomegaly and infiltrative pulmonary disease, and is associated with significant morbidity and mortality. Olipudase alfa (recombinant human acid sphingomyelinase) is an enzyme replacement therapy under development for the non-neurological manifestations of ASMD. We present a quantitative systems pharmacology (QSP) model supporting the clinical development of olipudase alfa. The model is multiscale and mechanistic, linking the enzymatic deficiency driving the disease to molecular-level, cellular-level, and organ-level effects. Model development was informed by natural history, and preclinical and clinical studies. By considering patient-specific pharmacokinetic (PK) profiles and indicators of disease severity, the model describes pharmacodynamic (PD) and clinical end points for individual patients. The ASMD QSP model provides a platform for quantitatively assessing systemic pharmacological effects in adult and pediatric patients, and explaining variability within and across these patient populations, thereby supporting the extrapolation of treatment response from adults to pediatrics.
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Affiliation(s)
| | - Bradley Niesner
- Translational Informatics, TMED, Sanofi, BridgewaterNew JerseyUSA
| | - Rena Baek
- Sanofi Genzyme, CambridgeMassachusettsUSA
| | | | | | | | - Jing Li
- Translational Informatics, TMED, Sanofi, BridgewaterNew JerseyUSA
| | - Zachary van Rijn
- Translational Informatics, TMED, Sanofi, BridgewaterNew JerseyUSA
| | - Mengdi Tao
- Translational Informatics, TMED, Sanofi, BridgewaterNew JerseyUSA
| | | | - Rachael Easton
- Translational Informatics, TMED, Sanofi, BridgewaterNew JerseyUSA
| | - Sharon Tan
- Sanofi Genzyme, CambridgeMassachusettsUSA
| | | | - Edward H. Schuchman
- Genetics & Genomic Sciences, Icahn School of Medicine at Mount SinaiNew YorkNYUSA
| | | | - Karim Azer
- Translational Informatics, TMED, Sanofi, BridgewaterNew JerseyUSA
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Ming JE, Abrams RE, Bartlett DW, Tao M, Nguyen T, Surks H, Kudrycki K, Kadambi A, Friedrich CM, Djebli N, Goebel B, Koszycki A, Varshnaya M, Elassal J, Banerjee P, Sasiela WJ, Reed MJ, Barrett JS, Azer K. A Quantitative Systems Pharmacology Platform to Investigate the Impact of Alirocumab and Cholesterol-Lowering Therapies on Lipid Profiles and Plaque Characteristics. GENE REGULATION AND SYSTEMS BIOLOGY 2017; 11:1177625017710941. [PMID: 28804243 PMCID: PMC5484552 DOI: 10.1177/1177625017710941] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/21/2016] [Accepted: 04/17/2017] [Indexed: 12/20/2022]
Abstract
Reduction in low-density lipoprotein cholesterol (LDL-C) is associated with decreased risk for cardiovascular disease. Alirocumab, an antibody to proprotein convertase subtilisin/kexin type 9 (PCSK9), significantly reduces LDL-C. Here, we report development of a quantitative systems pharmacology (QSP) model integrating peripheral and liver cholesterol metabolism, as well as PCSK9 function, to examine the mechanisms of action of alirocumab and other lipid-lowering therapies, including statins. The model predicts changes in LDL-C and other lipids that are consistent with effects observed in clinical trials of single or combined treatments of alirocumab and other treatments. An exploratory model to examine the effects of lipid levels on plaque dynamics was also developed. The QSP platform, on further development and qualification, may support dose optimization and clinical trial design for PCSK9 inhibitors and lipid-modulating drugs. It may also improve our understanding of factors affecting therapeutic responses in different phenotypes of dyslipidemia and cardiovascular disease.
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Affiliation(s)
- Jeffrey E Ming
- Sanofi, Bridgewater, NJ, USA; Frankfurt Am Main, Germany, and Montpellier, France
| | - Ruth E Abrams
- Sanofi, Bridgewater, NJ, USA; Frankfurt Am Main, Germany, and Montpellier, France
| | | | - Mengdi Tao
- Sanofi, Bridgewater, NJ, USA; Frankfurt Am Main, Germany, and Montpellier, France
| | - Tu Nguyen
- Sanofi, Bridgewater, NJ, USA; Frankfurt Am Main, Germany, and Montpellier, France
| | - Howard Surks
- Sanofi, Bridgewater, NJ, USA; Frankfurt Am Main, Germany, and Montpellier, France
| | | | | | | | - Nassim Djebli
- Sanofi, Bridgewater, NJ, USA; Frankfurt Am Main, Germany, and Montpellier, France
| | - Britta Goebel
- Sanofi, Bridgewater, NJ, USA; Frankfurt Am Main, Germany, and Montpellier, France
| | - Alex Koszycki
- Sanofi, Bridgewater, NJ, USA; Frankfurt Am Main, Germany, and Montpellier, France
| | - Meera Varshnaya
- Sanofi, Bridgewater, NJ, USA; Frankfurt Am Main, Germany, and Montpellier, France
| | | | | | | | | | - Jeffrey S Barrett
- Sanofi, Bridgewater, NJ, USA; Frankfurt Am Main, Germany, and Montpellier, France
| | - Karim Azer
- Sanofi, Bridgewater, NJ, USA; Frankfurt Am Main, Germany, and Montpellier, France
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