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Bisaso KR, Mukonzo JK, Ette EI. A mechanistic assessment of the nature of pharmacodynamic drug-drug interaction in vivo and in vitro. In Silico Pharmacol 2023; 11:31. [PMID: 37899968 PMCID: PMC10611690 DOI: 10.1007/s40203-023-00168-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: 05/29/2023] [Accepted: 10/04/2023] [Indexed: 10/31/2023] Open
Abstract
Combination pharmacotherapy is becoming increasingly necessary because most diseases are pathophysiologically controlled at the subcellular level by target proteins in a combinatorial manner. We demonstrate the application of the stimulus-response mechanistic model in characterising the drug and physiological properties of pharmacodynamic drug-drug interactions (PDDI) using previously published in vitro and in vivo drug combination experiments. The in vitro experiment tested the effect of a combination of SCH66336 and 4-HPR on the survival of in squamous cell carcinoma cell lines, while the in vivo experiment tested the effect of a combination of cetuximab and cisplatin on tumour growth inhibition in female xenograft mice. The model adequately described both experiments, quantified both system and drug properties and predicted the nature of the PDDI mechanism. Strong baseline signals of 7.35 and 610 units existed in the in vitro and in vivo experiments respectively. An overall synergistic relationship (interaction index = 1.03E-8) was detected in the in vitro experiment. In the in vivo model, the overall interaction index was 70,139.45 implying an antagonistic interaction between the cisplatin and the cetuximab signals.
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Affiliation(s)
| | - Jackson K. Mukonzo
- Deparment of Pharmacology, Makerere University College of Health Sciences, Kampala, Uganda
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Helmlinger G, Sokolov V, Peskov K, Hallow KM, Kosinsky Y, Voronova V, Chu L, Yakovleva T, Azarov I, Kaschek D, Dolgun A, Schmidt H, Boulton DW, Penland RC. Quantitative Systems Pharmacology: An Exemplar Model-Building Workflow With Applications in Cardiovascular, Metabolic, and Oncology Drug Development. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2019; 8:380-395. [PMID: 31087533 PMCID: PMC6617832 DOI: 10.1002/psp4.12426] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/01/2019] [Accepted: 05/03/2019] [Indexed: 12/13/2022]
Abstract
Quantitative systems pharmacology (QSP), a mechanistically oriented form of drug and disease modeling, seeks to address a diverse set of problems in the discovery and development of therapies. These problems bring a considerable amount of variability and uncertainty inherent in the nonclinical and clinical data. Likewise, the available modeling techniques and related software tools are manifold. Appropriately, the development, qualification, application, and impact of QSP models have been similarly varied. In this review, we describe the progressive maturation of a QSP modeling workflow: a necessary step for the efficient, reproducible development and qualification of QSP models, which themselves are highly iterative and evolutive. Furthermore, we describe three applications of QSP to impact drug development; one supporting new indications for an approved antidiabetic clinical asset through mechanistic hypothesis generation, one highlighting efficacy and safety differentiation within the sodium‐glucose cotransporter‐2 inhibitor drug class, and one enabling rational selection of immuno‐oncology drug combinations.
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Affiliation(s)
- Gabriel Helmlinger
- Quantitative Clinical Pharmacology, Early Clinical Development, IMED Biotech Unit, AstraZeneca Pharmaceuticals, Boston, Massachusetts, USA
| | | | - Kirill Peskov
- M&S Decisions LLC, Moscow, Russia.,Computational Oncology Group, I.M. Sechenov First Moscow State Medical University of the Russian Ministry of Health, Moscow, Russia
| | - Karen M Hallow
- School of Chemical, Materials, and Biomedical Engineering, University of Georgia, Athens, Georgia, USA.,Department of Epidemiology and Biostatistics, University of Georgia, Athens, Georgia, USA
| | | | | | - Lulu Chu
- Quantitative Clinical Pharmacology, Early Clinical Development, IMED Biotech Unit, AstraZeneca Pharmaceuticals, Boston, Massachusetts, USA
| | | | | | | | | | | | - David W Boulton
- Quantitative Clinical Pharmacology, Early Clinical Development, IMED Biotech Unit, AstraZeneca Pharmaceuticals, Gaithersburg, Maryland, USA
| | - Robert C Penland
- Quantitative Clinical Pharmacology, Early Clinical Development, IMED Biotech Unit, AstraZeneca Pharmaceuticals, Boston, Massachusetts, USA
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Taylor DL, Gough A, Schurdak ME, Vernetti L, Chennubhotla CS, Lefever D, Pei F, Faeder JR, Lezon TR, Stern AM, Bahar I. Harnessing Human Microphysiology Systems as Key Experimental Models for Quantitative Systems Pharmacology. Handb Exp Pharmacol 2019; 260:327-367. [PMID: 31201557 PMCID: PMC6911651 DOI: 10.1007/164_2019_239] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Two technologies that have emerged in the last decade offer a new paradigm for modern pharmacology, as well as drug discovery and development. Quantitative systems pharmacology (QSP) is a complementary approach to traditional, target-centric pharmacology and drug discovery and is based on an iterative application of computational and systems biology methods with multiscale experimental methods, both of which include models of ADME-Tox and disease. QSP has emerged as a new approach due to the low efficiency of success in developing therapeutics based on the existing target-centric paradigm. Likewise, human microphysiology systems (MPS) are experimental models complementary to existing animal models and are based on the use of human primary cells, adult stem cells, and/or induced pluripotent stem cells (iPSCs) to mimic human tissues and organ functions/structures involved in disease and ADME-Tox. Human MPS experimental models have been developed to address the relatively low concordance of human disease and ADME-Tox with engineered, experimental animal models of disease. The integration of the QSP paradigm with the use of human MPS has the potential to enhance the process of drug discovery and development.
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Affiliation(s)
- D Lansing Taylor
- University of Pittsburgh Drug Discovery Institute, Pittsburgh, PA, USA.
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA.
| | - Albert Gough
- University of Pittsburgh Drug Discovery Institute, Pittsburgh, PA, USA
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Mark E Schurdak
- University of Pittsburgh Drug Discovery Institute, Pittsburgh, PA, USA
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Lawrence Vernetti
- University of Pittsburgh Drug Discovery Institute, Pittsburgh, PA, USA
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Chakra S Chennubhotla
- University of Pittsburgh Drug Discovery Institute, Pittsburgh, PA, USA
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Daniel Lefever
- University of Pittsburgh Drug Discovery Institute, Pittsburgh, PA, USA
| | - Fen Pei
- University of Pittsburgh Drug Discovery Institute, Pittsburgh, PA, USA
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA
| | - James R Faeder
- University of Pittsburgh Drug Discovery Institute, Pittsburgh, PA, USA
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Timothy R Lezon
- University of Pittsburgh Drug Discovery Institute, Pittsburgh, PA, USA
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Andrew M Stern
- University of Pittsburgh Drug Discovery Institute, Pittsburgh, PA, USA
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Ivet Bahar
- University of Pittsburgh Drug Discovery Institute, Pittsburgh, PA, USA
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA
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van Hasselt JGC, Iyengar R. Systems Pharmacology: Defining the Interactions of Drug Combinations. Annu Rev Pharmacol Toxicol 2018; 59:21-40. [PMID: 30260737 DOI: 10.1146/annurev-pharmtox-010818-021511] [Citation(s) in RCA: 38] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The majority of diseases are associated with alterations in multiple molecular pathways and complex interactions at the cellular and organ levels. Single-target monotherapies therefore have intrinsic limitations with respect to their maximum therapeutic benefits. The potential of combination drug therapies has received interest for the treatment of many diseases and is well established in some areas, such as oncology. Combination drug treatments may allow us to identify synergistic drug effects, reduce adverse drug reactions, and address variability in disease characteristics between patients. Identification of combination therapies remains challenging. We discuss current state-of-the-art systems pharmacology approaches to enable rational identification of combination therapies. These approaches, which include characterization of mechanisms of disease and drug action at a systems level, can enable understanding of drug interactions at the molecular, cellular, physiological, and organismal levels. Such multiscale understanding can enable precision medicine by promoting the rational development of combination therapy at the level of individual patients for many diseases.
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Affiliation(s)
- J G Coen van Hasselt
- Department of Pharmacological Sciences, Systems Biology Center, Mount Sinai Institute for Systems Biomedicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; .,Division of Systems Biomedicine and Pharmacology, Leiden Academic Center for Drug Research, Leiden University, 2333 Leiden, Netherlands;
| | - Ravi Iyengar
- Department of Pharmacological Sciences, Systems Biology Center, Mount Sinai Institute for Systems Biomedicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA;
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Yin A, Yamada A, Stam WB, van Hasselt JGC, van der Graaf PH. Quantitative systems pharmacology analysis of drug combination and scaling to humans: the interaction between noradrenaline and vasopressin in vasoconstriction. Br J Pharmacol 2018; 175:3394-3406. [PMID: 29859008 DOI: 10.1111/bph.14385] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2018] [Accepted: 05/27/2018] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND AND PURPOSE Development of combination therapies has received significant interest in recent years. Previously, a two-receptor one-transducer (2R-1T) model was proposed to characterize drug interactions with two receptors that lead to the same phenotypic response through a common transducer pathway. We applied, for the first time, the 2R-1T model to characterize the interaction of noradrenaline and arginine-vasopressin on vasoconstriction and performed inter-species scaling to humans using this mechanism-based model. EXPERIMENTAL APPROACH Contractile data were obtained from in vitro rat small mesenteric arteries after exposure to single or combined challenges of noradrenaline and arginine-vasopressin with or without pretreatment with the irreversible α-adrenoceptor antagonist, phenoxybenzamine. Data were analysed using the 2R-1T model to characterize the observed exposure-response relationships and drug-drug interaction. The model was then scaled to humans by accounting for differences in receptor density. KEY RESULTS With receptor affinities set to published values, the 2R-1T model satisfactorily characterized the interaction between noradrenaline and arginine-vasopressin in rat small mesenteric arteries (relative standard error ≤20%), as well as the effect of phenoxybenzamine. Furthermore, after scaling the model to human vascular tissue, the model also adequately predicted the interaction between both agents on human renal arteries. CONCLUSIONS AND IMPLICATIONS The 2R-1T model can be of relevance to quantitatively characterize the interaction between two drugs that interact via different receptors and a common transducer pathway. Its mechanistic properties are valuable for scaling the model across species. This approach is therefore of significant value to rationally optimize novel combination treatments.
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Affiliation(s)
- Anyue Yin
- Division of Systems Biomedicine and Pharmacology, Leiden Academic Centre for Drug Research (LACDR), Leiden University, Leiden, The Netherlands.,Department of Clinical Pharmacy and Toxicology, Leiden University Medical Centre, Leiden, The Netherlands
| | - Akihiro Yamada
- Division of Systems Biomedicine and Pharmacology, Leiden Academic Centre for Drug Research (LACDR), Leiden University, Leiden, The Netherlands.,Clinical Pharmacology PKMS Group, Astellas Pharma Inc., Tokyo, Japan
| | - Wiro B Stam
- Dutch Ministry of Health and Sports, Den Haag, The Netherlands
| | - Johan G C van Hasselt
- Division of Systems Biomedicine and Pharmacology, Leiden Academic Centre for Drug Research (LACDR), Leiden University, Leiden, The Netherlands
| | - Piet H van der Graaf
- Division of Systems Biomedicine and Pharmacology, Leiden Academic Centre for Drug Research (LACDR), Leiden University, Leiden, The Netherlands.,Certara QSP, Canterbury, UK
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