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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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Putnins M, Campagne O, Mager DE, Androulakis IP. From data to QSP models: a pipeline for using Boolean networks for hypothesis inference and dynamic model building. J Pharmacokinet Pharmacodyn 2022; 49:101-115. [PMID: 34988912 PMCID: PMC9876619 DOI: 10.1007/s10928-021-09797-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Accepted: 10/27/2021] [Indexed: 01/27/2023]
Abstract
Quantitative Systems Pharmacology (QSP) models capture the physiological underpinnings driving the response to a drug and express those in a semi-mechanistic way, often involving ordinary differential equations (ODEs). The process of developing a QSP model generally starts with the definition of a set of reasonable hypotheses that would support a mechanistic interpretation of the expected response which are used to form a network of interacting elements. This is a hypothesis-driven and knowledge-driven approach, relying on prior information about the structure of the network. However, with recent advances in our ability to generate large datasets rapidly, often in a hypothesis-neutral manner, the opportunity emerges to explore data-driven approaches to establish the network topologies and models in a robust, repeatable manner. In this paper, we explore the possibility of developing complex network representations of physiological responses to pharmaceuticals using a logic-based analysis of available data and then convert the logic relations to dynamic ODE-based models. We discuss an integrated pipeline for converting data to QSP models. This pipeline includes using k-means clustering to binarize continuous data, inferring likely network relationships using a Best-Fit Extension method to create a Boolean network, and finally converting the Boolean network to a continuous ODE model. We utilized an existing QSP model for the dual-affinity re-targeting antibody flotetuzumab to demonstrate the robustness of the process. Key output variables from the QSP model were used to generate a continuous data set for use in the pipeline. This dataset was used to reconstruct a possible model. This reconstruction had no false-positive relationships, and the output of each of the species was similar to that of the original QSP model. This demonstrates the ability to accurately infer relationships in a hypothesis-neutral manner without prior knowledge of a system using this pipeline.
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Affiliation(s)
- M. Putnins
- Biomedical Engineering Department, Rutgers University, Piscataway, USA
| | - O. Campagne
- Department of Pharmaceutical Sciences, University at Buffalo, State University of New York, Buffalo, USA
| | - D. E. Mager
- Department of Pharmaceutical Sciences, University at Buffalo, State University of New York, Buffalo, USA
| | - I. P. Androulakis
- Biomedical Engineering Department, Rutgers University, Piscataway, USA,Chemical & Biochemical Engineering Department, Rutgers University, Piscataway, USA
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Androulakis IP. Teaching computational systems biology with an eye on quantitative systems pharmacology at the undergraduate level: Why do it, who would take it, and what should we teach? FRONTIERS IN SYSTEMS BIOLOGY 2022; 2:1044281. [PMID: 36866242 PMCID: PMC9977321 DOI: 10.3389/fsysb.2022.1044281] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Abstract
Computational systems biology (CSB) is a field that emerged primarily as the product of research activities. As such, it grew in several directions in a distributed and uncoordinated manner making the area appealing and fascinating. The idea of not having to follow a specific path but instead creating one fueled innovation. As the field matured, several interdisciplinary graduate programs emerged attempting to educate future generations of computational systems biologists. These educational initiatives coordinated the dissemination of information across student populations that had already decided to specialize in this field. However, we are now entering an era where CSB, having established itself as a valuable research discipline, is attempting the next major step: Entering undergraduate curricula. As interesting as this endeavor may sound, it has several difficulties, mainly because the field is not uniformly defined. In this manuscript, we argue that this diversity is a significant advantage and that several incarnations of an undergraduate-level CSB biology course could, and should, be developed tailored to programmatic needs. In this manuscript, we share our experiences creating a course as part of a Biomedical Engineering program.
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Affiliation(s)
- Ioannis P Androulakis
- Biomedical Engineering Department, New Brunswick, NJ, United States.,Chemical and Biochemical Engineering Department, Rutgers University, New Brunswick, NJ, United States
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Alfonso S, Jenner AL, Craig M. Translational approaches to treating dynamical diseases through in silico clinical trials. CHAOS (WOODBURY, N.Y.) 2020; 30:123128. [PMID: 33380031 DOI: 10.1063/5.0019556] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/24/2020] [Accepted: 11/20/2020] [Indexed: 06/12/2023]
Abstract
The primary goal of drug developers is to establish efficient and effective therapeutic protocols. Multifactorial pathologies, including dynamical diseases and complex disorders, can be difficult to treat, given the high degree of inter- and intra-patient variability and nonlinear physiological relationships. Quantitative approaches combining mechanistic disease modeling and computational strategies are increasingly leveraged to rationalize pre-clinical and clinical studies and to establish effective treatment strategies. The development of clinical trials has led to new computational methods that allow for large clinical data sets to be combined with pharmacokinetic and pharmacodynamic models of diseases. Here, we discuss recent progress using in silico clinical trials to explore treatments for a variety of complex diseases, ultimately demonstrating the immense utility of quantitative methods in drug development and medicine.
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Affiliation(s)
- Sofia Alfonso
- Department of Physiology, McGill University, Montreal, Quebec H3A 0G4, Canada
| | - Adrianne L Jenner
- Department of Mathematics and Statistics, Université de Montréal, Montreal, Quebec H3C 3J7, Canada
| | - Morgan Craig
- Department of Physiology, McGill University, Montreal, Quebec H3A 0G4, Canada
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Selen A, Müllertz A, Kesisoglou F, Ho RJY, Cook JA, Dickinson PA, Flanagan T. Integrated Multi-stakeholder Systems Thinking Strategy: Decision-making with Biopharmaceutics Risk Assessment Roadmap (BioRAM) to Optimize Clinical Performance of Drug Products. AAPS JOURNAL 2020; 22:97. [PMID: 32719954 DOI: 10.1208/s12248-020-00470-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/22/2020] [Accepted: 06/04/2020] [Indexed: 12/20/2022]
Abstract
Decision-making in drug development benefits from an integrated systems approach, where the stakeholders identify and address the critical questions for the system through carefully designed and performed studies. Biopharmaceutics Risk Assessment Roadmap (BioRAM) is such a systems approach for application of systems thinking to patient focused and timely decision-making, suitable for all stages of drug discovery and development. We described the BioRAM therapy-driven drug delivery framework, strategic roadmap, and integrated risk assessment instrument (BioRAM Scoring Grid) in previous publications (J Pharm Sci 103:3377-97, 2014; J Pharm Sci 105:3243-55, 2016). Integration of systems thinking with pharmaceutical development, manufacturing, and clinical sciences and health care is unique to BioRAM where the developed strategy identifies the system and enables risk characterization and balancing for the entire system. Successful decision-making process in BioRAM starts with the Blueprint (BP) meetings. Through shared understanding of the system, the program strategy is developed and captured in the program BP. Here, we provide three semi-hypothetical examples for illustrating risk-based decision-making in high and moderate risk settings. In the high-risk setting, which is a rare disease area, two completely alternate development approaches are considered (gene therapy and small molecule). The two moderate-risk examples represent varied knowledge levels and drivers for the programs. In one moderate-risk example, knowledge leveraging opportunities are drawn from the manufacturing knowledge and clinical performance of a similar drug substance. In the other example, knowledge on acute tolerance patterns for a similar mechanistic pathway is utilized for identifying markers to inform the drug release profile from the dosage form with the necessary "flexibility" for dosing. All examples illustrate implementation of the BioRAM strategy for leveraging knowledge and decision-making to optimize the clinical performance of drug products for patient benefit.
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Affiliation(s)
- Arzu Selen
- US Food and Drug Administration, Center for Drug Evaluation and Research, Office of Pharmaceutical Quality, Office of Testing and Research, 10903 New Hampshire Ave., Silver Spring, Maryland, 20993, USA.
| | - Anette Müllertz
- Bioneer: FARMA, Department of Pharmacy, University of Copenhagen, 2100, Copenhagen, Denmark
| | - Filippos Kesisoglou
- Biopharmaceutics, Pharmaceutical Sciences and Clinical Supply, Merck & Co, Inc., West Point, Pennsylvania, 19486, USA
| | - Rodney J Y Ho
- University of Washington, Seattle, Washington, 98195, USA
| | - Jack A Cook
- Clinical Pharmacology Department, Global Product Development, Pfizer, Inc., Groton, Connecticut, 06340, USA
| | - Paul A Dickinson
- Seda Pharmaceutical Development Services, Alderley Park, Alderley Edge, Cheshire, SK10 4TG, UK
| | - Talia Flanagan
- UCB Pharma S.A., Avenue de l'Industrie, 1420, Braine - l'Alleud, Belgium
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van Esdonk MJ, Burggraaf J, van der Graaf PH, Stevens J. Model informed quantification of the feed-forward stimulation of growth hormone by growth hormone-releasing hormone. Br J Clin Pharmacol 2020; 86:1575-1584. [PMID: 32087619 PMCID: PMC7373696 DOI: 10.1111/bcp.14265] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2019] [Revised: 09/27/2019] [Accepted: 11/05/2019] [Indexed: 12/21/2022] Open
Abstract
Aims Growth hormone (GH) secretion is pulsatile and secretion varies highly between individuals. To understand and ultimately predict GH secretion, it is important to first delineate and quantify the interaction and variability in the biological processes underlying stimulated GH secretion. This study reports on the development of a population nonlinear mixed effects model for GH stimulation, incorporating individual GH kinetics and the stimulation of GH by GH‐releasing hormone (GHRH). Methods Literature data on the systemic circulation, the median eminence, and the anterior pituitary were included as system parameters in the model. Population parameters were estimated on data from 8 healthy normal weight and 16 obese women who received a 33 μg recombinant human GH dose. The next day, a bolus injection of 100 μg GHRH was given to stimulate GH secretion. Results The GH kinetics were best described with the addition of 2 distribution compartments with a bodyweight dependent clearance (increasing linearly from 24.7 L/h for a 60‐kg subject to 32.1 L/h for a 100‐kg subject). The model described the data adequately with high parameter precision and significant interindividual variability on the GH clearance and distribution volume. Additionally, high variability in the amount of secreted GH, driven by GHRH receptor activation, was identified (coefficient of variation = 90%). Conclusion The stimulation of GH by GHRH was quantified and significant interindividual variability was identified on multiple parameters. This model sets the stage for further development of by inclusion of additional physiological components to quantify GH secretion in humans.
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Affiliation(s)
- Michiel J van Esdonk
- Division of Systems Biomedicine and Pharmacology, Leiden Academic Centre for Drug Research, Leiden University, Leiden, The Netherlands.,Centre for Human Drug Research, Leiden, The Netherlands
| | - Jacobus Burggraaf
- Division of Systems Biomedicine and Pharmacology, Leiden Academic Centre for Drug Research, Leiden University, Leiden, The Netherlands.,Centre for Human Drug Research, Leiden, The Netherlands
| | - Piet H van der Graaf
- Division of Systems Biomedicine and Pharmacology, Leiden Academic Centre for Drug Research, Leiden University, Leiden, The Netherlands.,Certara QSP, Canterbury, UK
| | - Jasper Stevens
- Department of Clinical Pharmacy and Pharmacology, University of Groningen, University Medical Center Groningen, Groningen, Netherlands
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Androulakis IP. The quest for digital health: From diseases to patients. Comput Chem Eng 2019. [DOI: 10.1016/j.compchemeng.2019.05.030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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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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Caccavo D, Vietri A, Lamberti G, Barba AA, Larsson A. Modeling the mechanics and the transport phenomena in hydrogels. COMPUTER AIDED CHEMICAL ENGINEERING 2018. [DOI: 10.1016/b978-0-444-63964-6.00012-x] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
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Rao RT, Scherholz ML, Hartmanshenn C, Bae SA, Androulakis IP. On the analysis of complex biological supply chains: From Process Systems Engineering to Quantitative Systems Pharmacology. Comput Chem Eng 2017; 107:100-110. [PMID: 29353945 DOI: 10.1016/j.compchemeng.2017.06.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
The use of models in biology has become particularly relevant as it enables investigators to develop a mechanistic framework for understanding the operating principles of living systems as well as in quantitatively predicting their response to both pathological perturbations and pharmacological interventions. This application has resulted in a synergistic convergence of systems biology and pharmacokinetic-pharmacodynamic modeling techniques that has led to the emergence of quantitative systems pharmacology (QSP). In this review, we discuss how the foundational principles of chemical process systems engineering inform the progressive development of more physiologically-based systems biology models.
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Affiliation(s)
- Rohit T Rao
- Department of Chemical and Biochemical Engineering, Rutgers, The State University of New Jersey, 98 Brett Road, Piscataway, NJ 08854
| | - Megerle L Scherholz
- Department of Chemical and Biochemical Engineering, Rutgers, The State University of New Jersey, 98 Brett Road, Piscataway, NJ 08854
| | - Clara Hartmanshenn
- Department of Chemical and Biochemical Engineering, Rutgers, The State University of New Jersey, 98 Brett Road, Piscataway, NJ 08854
| | - Seul-A Bae
- Department of Chemical and Biochemical Engineering, Rutgers, The State University of New Jersey, 98 Brett Road, Piscataway, NJ 08854
| | - Ioannis P Androulakis
- Department of Chemical and Biochemical Engineering, Rutgers, The State University of New Jersey, 98 Brett Road, Piscataway, NJ 08854.,Department of Biomedical Engineering, Rutgers The State University of New Jersey, 599 Taylor Road, Piscataway, NJ 08854
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Kamisoglu K, Acevedo A, Almon RR, Coyle S, Corbett S, Dubois DC, Nguyen TT, Jusko WJ, Androulakis IP. Understanding Physiology in the Continuum: Integration of Information from Multiple - Omics Levels. Front Pharmacol 2017; 8:91. [PMID: 28289389 PMCID: PMC5327699 DOI: 10.3389/fphar.2017.00091] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2016] [Accepted: 02/13/2017] [Indexed: 01/18/2023] Open
Abstract
In this paper, we discuss approaches for integrating biological information reflecting diverse physiologic levels. In particular, we explore statistical and model-based methods for integrating transcriptomic, proteomic and metabolomics data. Our case studies reflect responses to a systemic inflammatory stimulus and in response to an anti-inflammatory treatment. Our paper serves partly as a review of existing methods and partly as a means to demonstrate, using case studies related to human endotoxemia and response to methylprednisolone (MPL) treatment, how specific questions may require specific methods, thus emphasizing the non-uniqueness of the approaches. Finally, we explore novel ways for integrating -omics information with PKPD models, toward the development of more integrated pharmacology models.
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Affiliation(s)
- Kubra Kamisoglu
- Department of Pharmaceutical Sciences, School of Pharmacy and Pharmaceutical Sciences, University at Buffalo, Buffalo NY, USA
| | - Alison Acevedo
- Department of Biomedical Engineering, Rutgers University, Piscataway NJ, USA
| | - Richard R Almon
- Department of Biological Sciences, University at Buffalo, Buffalo NY, USA
| | - Susette Coyle
- Department of Surgery, Rutgers Robert Wood Johnson Medical School, New Brunswick NJ, USA
| | - Siobhan Corbett
- Department of Surgery, Rutgers Robert Wood Johnson Medical School, New Brunswick NJ, USA
| | - Debra C Dubois
- Department of Biological Sciences, University at Buffalo, Buffalo NY, USA
| | - Tung T Nguyen
- BioMaPS Institute for Quantitative Biology, Rutgers University, Piscataway NJ, USA
| | - William J Jusko
- Department of Pharmaceutical Sciences, School of Pharmacy and Pharmaceutical Sciences, University at Buffalo, Buffalo NY, USA
| | - Ioannis P Androulakis
- Department of Biomedical Engineering, Rutgers University, PiscatawayNJ, USA; Department of Chemical Engineering, Rutgers University, PiscatawayNJ, USA
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