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Androulakis IP. Towards a comprehensive assessment of QSP models: what would it take? J Pharmacokinet Pharmacodyn 2022:10.1007/s10928-022-09820-0. [PMID: 35962928 PMCID: PMC9922790 DOI: 10.1007/s10928-022-09820-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Accepted: 07/15/2022] [Indexed: 10/15/2022]
Abstract
Quantitative Systems Pharmacology (QSP) has emerged as a powerful ensemble of approaches aiming at developing integrated mathematical and computational models elucidating the complex interactions between pharmacology, physiology, and disease. As the field grows and matures its applications expand beyond the boundaries of research and development and slowly enter the decision making and regulatory arenas. However, widespread acceptance and eventual adoption of a new modeling approach requires assessment criteria and quantifiable metrics that establish credibility and increase confidence in model predictions. QSP aims to provide an integrated understanding of pathology in the context of therapeutic interventions. Because of its ambitious nature and the fact that QSP emerged in an uncoordinated manner as a result of activities distributed across organizations and academic institutions, high entropy characterizes the tools, methods, and computational methodologies and approaches used. The eventual acceptance of QSP model predictions as supporting material for an application to a regulatory agency will require that two key aspects are considered: (1) increase confidence in the QSP framework, which drives standardization and assessment; and (2) careful articulation of the expectations. Both rely heavily on our ability to rigorously and consistently assess QSP models. In this manuscript, we wish to discuss the meaning and purpose of such an assessment in the context of QSP model development and elaborate on the differentiating features of QSP that render such an endeavor challenging. We argue that QSP establishes a conceptual, integrative framework rather than a specific and well-defined computational methodology. QSP elicits the use of a wide variety of modeling and computational methodologies optimized with respect to specific applications and available data modalities, which exceed the data structures employed by chemometrics and PK/PD models. While the range of options fosters creativity and promises to substantially advance our ability to design pharmaceutical interventions rationally and optimally, our expectations of QSP models need to be clearly articulated and agreed on, with assessment emphasizing the scope of QSP studies rather than the methods used. Nevertheless, QSP should not be considered an independent approach, rather one of many in the broader continuum of computational models.
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Affiliation(s)
- Ioannis P Androulakis
- Biomedical Engineering Department and Chemical & Biochemical Engineering Department, Rutgers, The State University of New Jersey, New Brunswick, USA.
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2
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Cucurull-Sanchez L, Chappell MJ, Chelliah V, Amy Cheung SY, Derks G, Penney M, Phipps A, Malik-Sheriff RS, Timmis J, Tindall MJ, van der Graaf PH, Vicini P, Yates JWT. Best Practices to Maximize the Use and Reuse of Quantitative and Systems Pharmacology Models: Recommendations From the United Kingdom Quantitative and Systems Pharmacology Network. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2019; 8:259-272. [PMID: 30667172 PMCID: PMC6533407 DOI: 10.1002/psp4.12381] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/20/2018] [Revised: 12/04/2018] [Accepted: 12/17/2018] [Indexed: 12/13/2022]
Abstract
The lack of standardization in the way that quantitative and systems pharmacology (QSP) models are developed, tested, and documented hinders their reproducibility, reusability, and expansion or reduction to alternative contexts. This in turn undermines the potential impact of QSP in academic, industrial, and regulatory frameworks. This article presents a minimum set of recommendations from the UK Quantitative and Systems Pharmacology Network (UK QSP Network) to guide QSP practitioners seeking to maximize their impact, and stakeholders considering the use of QSP models in their environment.
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Affiliation(s)
| | | | | | - S Y Amy Cheung
- Quantitative Clinical Pharmacology, Early Clinical Development, Innovative Medicines and Early Development Biotech Unit, AstraZeneca, Cambridge, UK.,Certara, Princeton, New Jersey, USA
| | - Gianne Derks
- Department of Mathematics, University of Surrey, Guildford, UK
| | - Mark Penney
- Union Chimique Belge-Celltech, Slough, Berkshire, UK
| | - Alex Phipps
- Pharmaceutical Sciences, Roche Pharmaceutical Research & Early Development, Roche Innovation Center, Welwyn Garden City, UK
| | - Rahuman S Malik-Sheriff
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Cambridge, UK
| | - Jon Timmis
- Department of Electronic Engineering, University of York, York, UK
| | - Marcus J Tindall
- Department of Mathematics and Statistics, University of Reading, Reading, UK.,The Institute for Cardiovascular and Metabolic Research, University of Reading, Reading, UK
| | - Piet H van der Graaf
- Certara QSP, Canterbury, UK.,Leiden Academic Centre for Drug Research, Leiden, The Netherlands
| | - Paolo Vicini
- Clinical Pharmacology, Pharmacometrics and Drug Metabolism and Pharmaco-Kinetics, MedImmune, Cambridge, UK.,Development Sciences, Kymab Ltd, Cambridge, UK
| | - James W T Yates
- Drug Metabolism and Pharmaco-Kinetics, Oncology, Innovative Medicines and Early Development, AstraZeneca, Chesterford Research Park, Cambridge, UK
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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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