1
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Cucurull-Sanchez L. An industry perspective on current QSP trends in drug development. J Pharmacokinet Pharmacodyn 2024:10.1007/s10928-024-09905-y. [PMID: 38443663 DOI: 10.1007/s10928-024-09905-y] [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: 06/29/2023] [Accepted: 02/07/2024] [Indexed: 03/07/2024]
Abstract
2023 marks the 10th anniversary of Natpara's submission to the US FDA, which led to the first recorded regulatory interaction where a decision was supported by Quantitative and Systems Pharmacology (QSP) simulations. It had taken about 5 years for the timid QSP discipline to emerge as an effective Model-Informed Drug Development (MIDD) tool with visible impact in the pharmaceutical industry. Since then, the presence of QSP in the regulatory environment has continued to increase, to the point that the Agency reported 60 QSP submissions in 2020 alone, representing ~ 4% of their annual IND submissions [1]. What sort of industry mindset has enabled QSP to reach this level of success? How does QSP fit within the MIDD paradigm? Does QSP mean the same to Discovery and to Clinical Development projects? How do 'platforms' compare to 'fit-for-purpose' QSP models in an industrial setting? Can QSP and empirical Pharmacokinetic-Pharmacodynamic (PKPD) modelling be complementary? What level of validation is required to inform drug development decisions? This article reflects on all these questions, in particular addressing those audiences with limited line-of-sight into the drug industry decision-making machinery.
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2
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Singh FA, Afzal N, Smithline SJ, Thalhauser CJ. Assessing the performance of QSP models: biology as the driver for validation. J Pharmacokinet Pharmacodyn 2023:10.1007/s10928-023-09871-x. [PMID: 37386340 DOI: 10.1007/s10928-023-09871-x] [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: 11/28/2022] [Accepted: 06/15/2023] [Indexed: 07/01/2023]
Abstract
Validation of a quantitative model is a critical step in establishing confidence in the model's suitability for whatever analysis it was designed. While processes for validation are well-established in the statistical sciences, the field of quantitative systems pharmacology (QSP) has taken a more piecemeal approach to defining and demonstrating validation. Although classical statistical methods can be used in a QSP context, proper validation of a mechanistic systems model requires a more nuanced approach to what precisely is being validated, and what role said validation plays in the larger context of the analysis. In this review, we summarize current thoughts of QSP validation in the scientific community, contrast the aims of statistical validation from several contexts (including inference, pharmacometrics analysis, and machine learning) with the challenges faced in QSP analysis, and use examples from published QSP models to define different stages or levels of validation, any of which may be sufficient depending on the context at hand.
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Affiliation(s)
- Fulya Akpinar Singh
- Genmab US, Inc., 777 Scudders Mill Rd Bldg 2 4th Floor, Plainsboro, NJ, 08536, USA
| | - Nasrin Afzal
- Genmab US, Inc., 777 Scudders Mill Rd Bldg 2 4th Floor, Plainsboro, NJ, 08536, USA
| | - Shepard J Smithline
- Genmab US, Inc., 777 Scudders Mill Rd Bldg 2 4th Floor, Plainsboro, NJ, 08536, USA
| | - Craig J Thalhauser
- Genmab US, Inc., 777 Scudders Mill Rd Bldg 2 4th Floor, Plainsboro, NJ, 08536, USA.
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3
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Goteti K, Hanan N, Magee M, Wojciechowski J, Mensing S, Lalovic B, Hang Y, Solms A, Singh I, Singh R, Rieger TR, Jin JY. Opportunities and Challenges of Disease Progression Modeling in Drug Development - An IQ Perspective. Clin Pharmacol Ther 2023. [PMID: 36802040 DOI: 10.1002/cpt.2873] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Accepted: 02/06/2023] [Indexed: 02/20/2023]
Abstract
Disease progression modeling (DPM) represents an important model-informed drug development framework. The scientific communities support the use of DPM to accelerate and increase efficiency in drug development. This article summarizes International Consortium for Innovation & Quality (IQ) in Pharmaceutical Development mediated survey conducted across multiple biopharmaceutical companies on challenges and opportunities for DPM. Additionally, this summary highlights the viewpoints of IQ from the 2021 workshop hosted by the US Food and Drug Administration (FDA). Sixteen pharmaceutical companies participated in the IQ survey with 36 main questions. The types of questions included single/multiple choice, dichotomous, rank questions, and open-ended or free text. The key results show that DPM has different representation, it encompasses natural disease history, placebo response, standard of care as background therapy, and can even be interpreted as pharmacokinetic/pharmacodynamic modeling. The most common reasons for not implementing DPM as frequently seem to be difficulties in internal cross-functional alignment, lack of knowledge of disease/data, and time constraints. If successfully implemented, DPM can have an impact on dose selection, reduction of sample size, trial read-out support, patient selection/stratification, and supportive evidence for regulatory interactions. The key success factors and key challenges of disease progression models were highlighted in the survey and about 24 case studies across different therapeutic areas were submitted from various survey sponsors. Although DPM is still evolving, its current impact is limited but promising. The success of such models in the future will depend on collaboration, advanced analytics, availability of and access to relevant and adequate-quality data, collaborative regulatory guidance, and published examples of impact.
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Affiliation(s)
- Kosalaram Goteti
- Quantitative Pharmacology, EMD Serono Research and Development Institute, Inc., Billerica, Massachusetts, USA
| | - Nathan Hanan
- Clinical Pharmacology Modeling and Simulation, GlaxoSmithKline, Collegeville, Pennsylvania, USA
| | - Mindy Magee
- Clinical Pharmacology Modeling and Simulation, GlaxoSmithKline, Collegeville, Pennsylvania, USA
| | | | - Sven Mensing
- Clinical Pharmacology, AbbVie Deutschland GmbH & Co. KG, Ludwigshafen, Germany
| | - Bojan Lalovic
- Clinical Pharmacology Modeling and Simulation, Eisai Inc, Nutley, New Jersey, USA
| | - Yaming Hang
- Quantitative Clinical Pharmacology, Takeda, Cambridge, Massachusetts, USA
| | - Alexander Solms
- Clinical Pharmacometrics/Modeling & Simulation, Bayer AG, Berlin, Germany
| | - Indrajeet Singh
- Clinical Pharmacology, Gilead Sciences, Foster City, California, USA
| | | | | | - Jin Y Jin
- Clinical Pharmacology, Genentech, Inc., South San Francisco, California, USA
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4
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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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5
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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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6
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Cheng Y, Straube R, Alnaif AE, Huang L, Leil TA, Schmidt BJ. Virtual Populations for Quantitative Systems Pharmacology Models. METHODS IN MOLECULAR BIOLOGY (CLIFTON, N.J.) 2022; 2486:129-179. [PMID: 35437722 DOI: 10.1007/978-1-0716-2265-0_8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
Quantitative systems pharmacology (QSP) places an emphasis on dynamic systems modeling, incorporating considerations from systems biology modeling and pharmacodynamics. The goal of QSP is often to quantitatively predict the effects of clinical therapeutics, their combinations, and their doses on clinical biomarkers and endpoints. In order to achieve this goal, strategies for incorporating clinical data into model calibration are critical. Virtual population (VPop) approaches facilitate model calibration while faced with challenges encountered in QSP model application, including modeling a breadth of clinical therapies, biomarkers, endpoints, utilizing data of varying structure and source, capturing observed clinical variability, and simulating with models that may require more substantial computational time and resources than often found in pharmacometrics applications. VPops are frequently developed in a process that may involve parameterization of isolated pathway models, integration into a larger QSP model, incorporation of clinical data, calibration, and quantitative validation that the model with the accompanying, calibrated VPop is suitable to address the intended question or help with the intended decision. Here, we introduce previous strategies for developing VPops in the context of a variety of therapeutic and safety areas: metabolic disorders, drug-induced liver injury, autoimmune diseases, and cancer. We introduce methodological considerations, prior work for sensitivity analysis and VPop algorithm design, and potential areas for future advancement. Finally, we give a more detailed application example of a VPop calibration algorithm that illustrates recent progress and many of the methodological considerations. In conclusion, although methodologies have varied, VPop strategies have been successfully applied to give valid clinical insights and predictions with the assistance of carefully defined and designed calibration and validation strategies. While a uniform VPop approach for all potential QSP applications may be challenging given the heterogeneity in use considerations, we anticipate continued innovation will help to drive VPop application for more challenging cases of greater scale while developing new rigorous methodologies and metrics.
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Affiliation(s)
- Yougan Cheng
- QSP and PBPK, Bristol Myers Squibb, Princeton, NJ, USA.,Daiichi Sankyo, Inc., Pennington, NJ, USA
| | - Ronny Straube
- QSP and PBPK, Bristol Myers Squibb, Princeton, NJ, USA
| | - Abed E Alnaif
- QSP and PBPK, Bristol Myers Squibb, Princeton, NJ, USA.,EMD Serono, Billerica, MA, USA
| | - Lu Huang
- QSP and PBPK, Bristol Myers Squibb, Princeton, NJ, USA
| | - Tarek A Leil
- QSP and PBPK, Bristol Myers Squibb, Princeton, NJ, USA.,Daiichi Sankyo, Inc., Pennington, NJ, USA
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7
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Data-driven learning how oncogenic gene expression locally alters heterocellular networks. Nat Commun 2022; 13:1986. [PMID: 35418177 PMCID: PMC9007999 DOI: 10.1038/s41467-022-29636-3] [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: 02/25/2021] [Accepted: 03/22/2022] [Indexed: 11/21/2022] Open
Abstract
Developing drugs increasingly relies on mechanistic modeling and simulation. Models that capture causal relations among genetic drivers of oncogenesis, functional plasticity, and host immunity complement wet experiments. Unfortunately, formulating such mechanistic cell-level models currently relies on hand curation, which can bias how data is interpreted or the priority of drug targets. In modeling molecular-level networks, rules and algorithms are employed to limit a priori biases in formulating mechanistic models. Here we combine digital cytometry with Bayesian network inference to generate causal models of cell-level networks linking an increase in gene expression associated with oncogenesis with alterations in stromal and immune cell subsets from bulk transcriptomic datasets. We predict how increased Cell Communication Network factor 4, a secreted matricellular protein, alters the tumor microenvironment using data from patients diagnosed with breast cancer and melanoma. Predictions are then tested using two immunocompetent mouse models for melanoma, which provide consistent experimental results. While mechanistic models play increasing roles in immuno-oncology, hand network curation is current practice. Here the authors use a Bayesian data-driven approach to infer how expression of a secreted oncogene alters the cellular landscape within the tumor.
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8
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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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9
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Chaudhury A, Zhu X, Chu L, Goliaei A, June CH, Kearns JD, Stein AM. Chimeric Antigen Receptor T Cell Therapies: A Review of Cellular Kinetic-Pharmacodynamic Modeling Approaches. J Clin Pharmacol 2021; 60 Suppl 1:S147-S159. [PMID: 33205434 DOI: 10.1002/jcph.1691] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2020] [Accepted: 06/13/2020] [Indexed: 12/16/2022]
Abstract
Chimeric antigen receptor T cell (CAR-T cell) therapies have shown significant efficacy in CD19+ leukemias and lymphomas. There remain many challenges and questions for improving next-generation CAR-T cell therapies, and mathematical modeling of CAR-T cells may play a role in supporting further development. In this review, we introduce a mathematical modeling taxonomy for a set of relatively simple cellular kinetic-pharmacodynamic models that describe the in vivo dynamics of CAR-T cell and their interactions with cancer cells. We then discuss potential extensions of this model to include target binding, tumor distribution, cytokine-release syndrome, immunophenotype differentiation, and genotypic heterogeneity.
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Affiliation(s)
- Anwesha Chaudhury
- Pharmacometrics, Novartis Institutes of BioMedical Research, Cambridge, Massachusetts, USA
| | - Xu Zhu
- PK Sciences Oncology, Novartis Institutes of BioMedical Research, Cambridge, Massachusetts, USA
| | - Lulu Chu
- PK Sciences Modeling & Simulation, Novartis Institutes of BioMedical Research, Cambridge, Massachusetts, USA
| | - Ardeshir Goliaei
- PK Sciences Modeling & Simulation, Novartis Institutes of BioMedical Research, Cambridge, Massachusetts, USA
| | - Carl H June
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Jeffrey D Kearns
- PK Sciences Modeling & Simulation, Novartis Institutes of BioMedical Research, Cambridge, Massachusetts, USA
| | - Andrew M Stein
- Pharmacometrics, Novartis Institutes of BioMedical Research, Cambridge, Massachusetts, USA
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10
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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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11
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Chelliah V, Lazarou G, Bhatnagar S, Gibbs JP, Nijsen M, Ray A, Stoll B, Thompson RA, Gulati A, Soukharev S, Yamada A, Weddell J, Sayama H, Oishi M, Wittemer-Rump S, Patel C, Niederalt C, Burghaus R, Scheerans C, Lippert J, Kabilan S, Kareva I, Belousova N, Rolfe A, Zutshi A, Chenel M, Venezia F, Fouliard S, Oberwittler H, Scholer-Dahirel A, Lelievre H, Bottino D, Collins SC, Nguyen HQ, Wang H, Yoneyama T, Zhu AZX, van der Graaf PH, Kierzek AM. Quantitative Systems Pharmacology Approaches for Immuno-Oncology: Adding Virtual Patients to the Development Paradigm. Clin Pharmacol Ther 2020; 109:605-618. [PMID: 32686076 PMCID: PMC7983940 DOI: 10.1002/cpt.1987] [Citation(s) in RCA: 43] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2020] [Accepted: 07/06/2020] [Indexed: 12/12/2022]
Abstract
Drug development in oncology commonly exploits the tools of molecular biology to gain therapeutic benefit through reprograming of cellular responses. In immuno‐oncology (IO) the aim is to direct the patient’s own immune system to fight cancer. After remarkable successes of antibodies targeting PD1/PD‐L1 and CTLA4 receptors in targeted patient populations, the focus of further development has shifted toward combination therapies. However, the current drug‐development approach of exploiting a vast number of possible combination targets and dosing regimens has proven to be challenging and is arguably inefficient. In particular, the unprecedented number of clinical trials testing different combinations may no longer be sustainable by the population of available patients. Further development in IO requires a step change in selection and validation of candidate therapies to decrease development attrition rate and limit the number of clinical trials. Quantitative systems pharmacology (QSP) proposes to tackle this challenge through mechanistic modeling and simulation. Compounds’ pharmacokinetics, target binding, and mechanisms of action as well as existing knowledge on the underlying tumor and immune system biology are described by quantitative, dynamic models aiming to predict clinical results for novel combinations. Here, we review the current QSP approaches, the legacy of mathematical models available to quantitative clinical pharmacologists describing interaction between tumor and immune system, and the recent development of IO QSP platform models. We argue that QSP and virtual patients can be integrated as a new tool in existing IO drug development approaches to increase the efficiency and effectiveness of the search for novel combination therapies.
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Affiliation(s)
| | | | | | | | | | - Avijit Ray
- Abbvie Inc., North Chicago, Illinois, USA
| | | | | | - Abhishek Gulati
- Astellas Pharma Global Development Inc./Astellas Pharma Inc., Northbrook, Illinois, USA.,Astellas Pharma Global Development Inc./Astellas Pharma Inc., Tokyo or Tsukuba-shi, Japan
| | - Serguei Soukharev
- Astellas Pharma Global Development Inc./Astellas Pharma Inc., Northbrook, Illinois, USA.,Astellas Pharma Global Development Inc./Astellas Pharma Inc., Tokyo or Tsukuba-shi, Japan
| | - Akihiro Yamada
- Astellas Pharma Global Development Inc./Astellas Pharma Inc., Northbrook, Illinois, USA.,Astellas Pharma Global Development Inc./Astellas Pharma Inc., Tokyo or Tsukuba-shi, Japan
| | - Jared Weddell
- Astellas Pharma Global Development Inc./Astellas Pharma Inc., Northbrook, Illinois, USA.,Astellas Pharma Global Development Inc./Astellas Pharma Inc., Tokyo or Tsukuba-shi, Japan
| | - Hiroyuki Sayama
- Astellas Pharma Global Development Inc./Astellas Pharma Inc., Northbrook, Illinois, USA.,Astellas Pharma Global Development Inc./Astellas Pharma Inc., Tokyo or Tsukuba-shi, Japan
| | - Masayo Oishi
- Astellas Pharma Global Development Inc./Astellas Pharma Inc., Northbrook, Illinois, USA.,Astellas Pharma Global Development Inc./Astellas Pharma Inc., Tokyo or Tsukuba-shi, Japan
| | | | | | | | | | | | | | | | - Irina Kareva
- EMD Serono, Merck KGaA, Billerica, Massachusetts, USA
| | | | - Alex Rolfe
- EMD Serono, Merck KGaA, Billerica, Massachusetts, USA
| | - Anup Zutshi
- EMD Serono, Merck KGaA, Billerica, Massachusetts, USA
| | | | | | | | | | | | | | - Dean Bottino
- Millennium Pharmaceuticals Inc., a wholly owned subsidiary of Takeda Pharmaceutical Company Ltd., Cambridge, Massachusetts, USA
| | - Sabrina C Collins
- Millennium Pharmaceuticals Inc., a wholly owned subsidiary of Takeda Pharmaceutical Company Ltd., Cambridge, Massachusetts, USA
| | - Hoa Q Nguyen
- Millennium Pharmaceuticals Inc., a wholly owned subsidiary of Takeda Pharmaceutical Company Ltd., Cambridge, Massachusetts, USA
| | - Haiqing Wang
- Millennium Pharmaceuticals Inc., a wholly owned subsidiary of Takeda Pharmaceutical Company Ltd., Cambridge, Massachusetts, USA
| | - Tomoki Yoneyama
- Millennium Pharmaceuticals Inc., a wholly owned subsidiary of Takeda Pharmaceutical Company Ltd., Cambridge, Massachusetts, USA
| | - Andy Z X Zhu
- Millennium Pharmaceuticals Inc., a wholly owned subsidiary of Takeda Pharmaceutical Company Ltd., Cambridge, Massachusetts, USA
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12
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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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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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