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Zhao D, Huang P, Yu L, He Y. Pharmacokinetics-Pharmacodynamics Modeling for Evaluating Drug-Drug Interactions in Polypharmacy: Development and Challenges. Clin Pharmacokinet 2024; 63:919-944. [PMID: 38888813 DOI: 10.1007/s40262-024-01391-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/03/2024] [Indexed: 06/20/2024]
Abstract
Polypharmacy is commonly employed in clinical settings. The potential risks of drug-drug interactions (DDIs) can compromise efficacy and pose serious health hazards. Integrating pharmacokinetics (PK) and pharmacodynamics (PD) models into DDIs research provides a reliable method for evaluating and optimizing drug regimens. With advancements in our comprehension of both individual drug mechanisms and DDIs, conventional models have begun to evolve towards more detailed and precise directions, especially in terms of the simulation and analysis of physiological mechanisms. Selecting appropriate models is crucial for an accurate assessment of DDIs. This review details the theoretical frameworks and quantitative benchmarks of PK and PD modeling in DDI evaluation, highlighting the establishment of PK/PD modeling against a backdrop of complex DDIs and physiological conditions, and further showcases the potential of quantitative systems pharmacology (QSP) in this field. Furthermore, it explores the current advancements and challenges in DDI evaluation based on models, emphasizing the role of emerging in vitro detection systems, high-throughput screening technologies, and advanced computational resources in improving prediction accuracy.
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Affiliation(s)
- Di Zhao
- School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou, 310000, China
- Henan University of Chinese Medicine, Zhengzhou, China
| | - Ping Huang
- School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou, 310000, China
| | - Li Yu
- School of Basic Medical Sciences, Zhejiang Chinese Medical University, Hangzhou, China
| | - Yu He
- School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou, 310000, China.
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2
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Oishi M, Sayama H, Toshimoto K, Nakayama T, Nagasaka Y. Practical QSP application from the preclinical phase to enhance the probability of clinical success: Insights from case studies in oncology. Drug Metab Pharmacokinet 2024; 56:101020. [PMID: 38797089 DOI: 10.1016/j.dmpk.2024.101020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Revised: 02/02/2024] [Accepted: 05/06/2024] [Indexed: 05/29/2024]
Abstract
Quantitative Systems Pharmacology (QSP) has emerged as a promising modeling and simulation (M&S) approach in drug development, with potential to improve clinical success rates. While conventional M&S has significantly contributed to quantitative understanding in late preclinical and clinical phases, it falls short in explaining unexpected phenomena and testing hypotheses in the early research phase. QSP presents a solution to these limitations. To harness the full potential of QSP in early preclinical stages, preclinical modelers who are familiar with conventional M&S need to update their understanding of the differences between conventional M&S and QSP. This review focuses on QSP applications during the preclinical stage, citing case examples and sharing our experiences in oncology. We emphasize the critical role of QSP in increasing the probability of success for clinical proof of concept (PoC) when applied from the early preclinical stage. Enhancing the quality of both hypotheses and QSP models from early preclinical stage is of critical importance. Once a QSP model achieves credibility, it facilitates predictions of clinical responses and potential biomarkers. We propose that sequential QSP applications from preclinical stages can improve success rates of clinical PoC, and emphasize the importance of refining both hypotheses and QSP models throughout the process.
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Affiliation(s)
- Masayo Oishi
- Systems Pharmacology, Non-Clinical Biomedical Science, Applied Research & Operations, Astellas Pharma Inc., Tsukuba, Ibaraki, 305-8585, Japan.
| | - Hiroyuki Sayama
- Systems Pharmacology, Non-Clinical Biomedical Science, Applied Research & Operations, Astellas Pharma Inc., Tsukuba, Ibaraki, 305-8585, Japan
| | - Kota Toshimoto
- Systems Pharmacology, Non-Clinical Biomedical Science, Applied Research & Operations, Astellas Pharma Inc., Tsukuba, Ibaraki, 305-8585, Japan
| | - Takeshi Nakayama
- Systems Pharmacology, Non-Clinical Biomedical Science, Applied Research & Operations, Astellas Pharma Inc., Tsukuba, Ibaraki, 305-8585, Japan
| | - Yasuhisa Nagasaka
- Non-Clinical Biomedical Science, Applied Research & Operations, Astellas Pharma Inc., Tsukuba, Ibaraki, 305-8585, Japan
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3
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Anzabi Zadeh S, Street WN, Thomas BW. Optimizing warfarin dosing using deep reinforcement learning. J Biomed Inform 2023; 137:104267. [PMID: 36494060 DOI: 10.1016/j.jbi.2022.104267] [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: 02/07/2022] [Revised: 10/30/2022] [Accepted: 12/03/2022] [Indexed: 12/12/2022]
Abstract
Warfarin is a widely used anticoagulant, and has a narrow therapeutic range. Dosing of warfarin should be individualized, since slight overdosing or underdosing can have catastrophic or even fatal consequences. Despite much research on warfarin dosing, current dosing protocols do not live up to expectations, especially for patients sensitive to warfarin. We propose a deep reinforcement learning-based dosing model for warfarin. To overcome the issue of relatively small sample sizes in dosing trials, we use a Pharmacokinetic/ Pharmacodynamic (PK/PD) model of warfarin to simulate dose-responses of virtual patients. Applying the proposed algorithm on virtual test patients shows that this model outperforms a set of clinically accepted dosing protocols by a wide margin. We tested the robustness of our dosing protocol on a second PK/PD model and showed that its performance is comparable to the set of baseline protocols.
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Affiliation(s)
- Sadjad Anzabi Zadeh
- Department of Business Analytics, Tippie College of Business, University of Iowa, Iowa City, IA 52242, USA.
| | - W Nick Street
- Department of Business Analytics, Tippie College of Business, University of Iowa, Iowa City, IA 52242, USA
| | - Barrett W Thomas
- Department of Business Analytics, Tippie College of Business, University of Iowa, Iowa City, IA 52242, USA
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4
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Wang H, Zhao C, Santa-Maria CA, Emens LA, Popel AS. Dynamics of tumor-associated macrophages in a quantitative systems pharmacology model of immunotherapy in triple-negative breast cancer. iScience 2022; 25:104702. [PMID: 35856032 PMCID: PMC9287616 DOI: 10.1016/j.isci.2022.104702] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Revised: 06/05/2022] [Accepted: 06/27/2022] [Indexed: 11/07/2022] Open
Abstract
Quantitative systems pharmacology (QSP) modeling is an emerging mechanistic computational approach that couples drug pharmacokinetics/pharmacodynamics and the course of disease progression. It has begun to play important roles in drug development for complex diseases such as cancer, including triple-negative breast cancer (TNBC). The combination of the anti-PD-L1 antibody atezolizumab and nab-paclitaxel has shown clinical activity in advanced TNBC with PD-L1-positive tumor-infiltrating immune cells. As tumor-associated macrophages (TAMs) serve as major contributors to the immuno-suppressive tumor microenvironment, we incorporated the dynamics of TAMs into our previously published QSP model to investigate their impact on cancer treatment. We show that through proper calibration, the model captures the macrophage heterogeneity in the tumor microenvironment while maintaining its predictive power of the trial results at the population level. Despite its high mechanistic complexity, the modularized QSP platform can be readily reproduced, expanded for new species of interest, and applied in clinical trial simulation. A mechanistic model of quantitative systems pharmacology in immuno-oncology Dynamics of tumor-associated macrophages are integrated into our previous work Conducting in silico clinical trials to predict clinical response to cancer therapy
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Affiliation(s)
- Hanwen Wang
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Chen Zhao
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA.,School of Pharmacy, Nanjing Medical University, Nanjing, Jiangsu211166, China
| | - Cesar A Santa-Maria
- Department of Oncology, the Sidney Kimmel Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD21205, USA
| | - Leisha A Emens
- University of Pittsburgh Medical Center, Hillman Cancer Center, Pittsburgh, PA, USA
| | - Aleksander S Popel
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA.,Department of Oncology, the Sidney Kimmel Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD21205, USA
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5
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Yow HY, Govindaraju K, Lim AH, Abdul Rahim N. Optimizing Antimicrobial Therapy by Integrating Multi-Omics With Pharmacokinetic/Pharmacodynamic Models and Precision Dosing. Front Pharmacol 2022; 13:915355. [PMID: 35814236 PMCID: PMC9260690 DOI: 10.3389/fphar.2022.915355] [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: 04/07/2022] [Accepted: 06/01/2022] [Indexed: 12/02/2022] Open
Abstract
In the era of “Bad Bugs, No Drugs,” optimizing antibiotic therapy against multi-drug resistant (MDR) pathogens is crucial. Mathematical modelling has been employed to further optimize dosing regimens. These models include mechanism-based PK/PD models, systems-based models, quantitative systems pharmacology (QSP) and population PK models. Quantitative systems pharmacology has significant potential in precision antimicrobial chemotherapy in the clinic. Population PK models have been employed in model-informed precision dosing (MIPD). Several antibiotics require close monitoring and dose adjustments in order to ensure optimal outcomes in patients with infectious diseases. Success or failure of antibiotic therapy is dependent on the patient, antibiotic and bacterium. For some drugs, treatment responses vary greatly between individuals due to genotype and disease characteristics. Thus, for these drugs, tailored dosing is required for successful therapy. With antibiotics, inappropriate dosing such as insufficient dosing may put patients at risk of therapeutic failure which could lead to mortality. Conversely, doses that are too high could lead to toxicities. Hence, precision dosing which customizes doses to individual patients is crucial for antibiotics especially those with a narrow therapeutic index. In this review, we discuss the various strategies in optimizing antimicrobial therapy to address the challenges in the management of infectious diseases and delivering personalized therapy.
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Affiliation(s)
- Hui-Yin Yow
- Faculty of Health and Medical Sciences, School of Pharmacy, Taylor’s University, Subang Jaya, Malaysia
- Centre for Drug Discovery and Molecular Pharmacology, Faculty of Health and Medical Sciences, Taylor’s University, Subang Jaya, Malaysia
| | - Kayatri Govindaraju
- Department of Pharmaceutical Life Sciences, Faculty of Pharmacy, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Audrey Huili Lim
- Centre for Clinical Outcome Research (CCORE), Institute for Clinical Research, National Institutes of Health, Shah Alam, Malaysia
| | - Nusaibah Abdul Rahim
- Department of Clinical Pharmacy and Pharmacy Practice, Faculty of Pharmacy, Universiti Malaya, Kuala Lumpur, Malaysia
- *Correspondence: Nusaibah Abdul Rahim,
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The role of DMPK science in improving pharmaceutical research and development efficiency. Drug Discov Today 2021; 27:705-729. [PMID: 34774767 DOI: 10.1016/j.drudis.2021.11.005] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Revised: 10/09/2021] [Accepted: 11/03/2021] [Indexed: 12/14/2022]
Abstract
The successful regulatory authority approval rate of drug candidates in the drug development pipeline is crucial for determining pharmaceutical research and development (R&D) efficiency. Regulatory authorities include the US Food and Drug Administration (FDA), European Medicines Agency (EMA), and Pharmaceutical and Food Safety Bureau Japan (PFSB), among others. Optimal drug metabolism and pharmacokinetics (DMPK) properties influence the progression of a drug candidate from the preclinical to the clinical phase. In this review, we provide a comprehensive assessment of essential concepts, methods, improvements, and challenges in DMPK science and its significance in drug development. This information provides insights into the association of DMPK science with pharmaceutical R&D efficiency.
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Ayaz Z, Zainab B, Khan S, Abbasi AM, Elshikh MS, Munir A, Al-Ghamdi AA, Alajmi AH, Alsubaie QD, Mustafa AEZMA. In silico authentication of amygdalin as a potent anticancer compound in the bitter kernels of family Rosaceae. Saudi J Biol Sci 2020; 27:2444-2451. [PMID: 32884428 PMCID: PMC7451698 DOI: 10.1016/j.sjbs.2020.06.041] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2020] [Revised: 06/15/2020] [Accepted: 06/23/2020] [Indexed: 02/07/2023] Open
Abstract
Amygdalin a naturally occurring compound, predominantly in the bitter kernels of apricot, almond, apple and other members of Rosaceae family. Though, amygdalin is used as an alternative therapy to treat various types of cancer but its role in cancer pathways has rarely been explored yet. Therefore, present study was intended with the aim to investigate the alleged anti-cancerous effects of amygdalin specifically on PI3K-AKT-mTOR and Ras pathways of cancer in human body. Computational modelling and simulation techniques were used to assess the effect of amygdalin on PI3K-AKT-mTOR and Ras pathways using different level of dosage. It was observed that amygdalin had direct and substantial contribution to regulate PI3K-mTOR activities on threshold levels while the other caner pathways were effected indirectly. Consequently, amygdalin is a down-regulator of a cancer within a specified amount and contribute considerably to reduce various types of cancer in human. Furthermore, in-vitro and in-vivo analyses of amygdalin could be of helpful to authenticate its pharmacological effects.
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Affiliation(s)
- Zainab Ayaz
- Department of Bioinformatics, Govt. Post Graduate College Mandian Abbottabad, Pakistan.,Department of Environmental Sciences, COMSATS University Islamabad, Abbottabad Campus, 22060, Pakistan
| | - Bibi Zainab
- Department of Bioinformatics, Govt. Post Graduate College Mandian Abbottabad, Pakistan.,Department of Environmental Sciences, COMSATS University Islamabad, Abbottabad Campus, 22060, Pakistan
| | - Sajid Khan
- Department of Bioinformatics, Govt. Post Graduate College Mandian Abbottabad, Pakistan
| | - Arshad Mehmood Abbasi
- Department of Environmental Sciences, COMSATS University Islamabad, Abbottabad Campus, 22060, Pakistan
| | - Mohamed S Elshikh
- Department of Botany and Microbiology, College of Science, King Saud University, Riyadh, Saudi Arabia
| | - Anum Munir
- Department of Bioinformatics, Govt. Post Graduate College Mandian Abbottabad, Pakistan.,Department of Bioinformatics and Biosciences, Capital University of Science and Technology Islamabad, Pakistan
| | - Abdullah Ahmed Al-Ghamdi
- Department of Botany and Microbiology, College of Science, King Saud University, Riyadh, Saudi Arabia
| | - Amal H Alajmi
- Department of Botany and Microbiology, College of Science, King Saud University, Riyadh, Saudi Arabia
| | - Qasi D Alsubaie
- Department of Botany and Microbiology, College of Science, King Saud University, Riyadh, Saudi Arabia
| | - Abd El-Zaher M A Mustafa
- Department of Botany and Microbiology, College of Science, King Saud University, Riyadh, Saudi Arabia.,Botany Department, Faculty of Science, Tanta University, Tanta, Egypt
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Huang W, Isoherranen N. Sampling Site Has a Critical Impact on Physiologically Based Pharmacokinetic Modeling. J Pharmacol Exp Ther 2019; 372:30-45. [PMID: 31604807 DOI: 10.1124/jpet.119.262154] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2019] [Accepted: 10/09/2019] [Indexed: 12/11/2022] Open
Abstract
It has been shown that arterial (central) and venous (peripheral) plasma drug concentrations can be very different. While pharmacokinetic studies typically measure drug concentrations from the peripheral vein such as the arm vein, physiologically based pharmacokinetic (PBPK) models generally output simulated concentrations from the central venous compartment that physiologically represents the right atrium, a merge of the superior and inferior vena cava. In this study, a physiologically based peripheral forearm sampling site model was developed and verified using nicotine, ketamine, lidocaine, and fentanyl as model drugs. This verified model allows output of simulated peripheral venous concentrations that can be meaningfully compared with observed pharmacokinetic data from the arm vein. The generalized effect of PBPK model sampling site on simulation output was investigated. Drugs and metabolites with large volumes of distribution showed considerable concentration discrepancy between the simulated central venous compartment and the peripheral arm vein after intravenous or oral administration, resulting in significant differences in values for C max and time taken to reach C max (t max ) In addition, the simulated central venous metabolite profile showed an unexpected profile that was not observed in the peripheral arm vein. Using fentanyl as a model compound, we show that using the wrong sampling site in PBPK models can lead to biased model evaluation and subsequent erroneous model parameter optimization. Such an error in model parameters along with the discrepant sampling site could dramatically mislead the pharmacokinetic prediction in unstudied clinical scenarios, affecting the assessment of drug safety and efficacy. Overall, this study shows that PBPK model publications should specify the model sampling sites and match them with those employed in clinical studies. SIGNIFICANCE STATEMENT: Our study shows that sampling from the central venous compartment (right atrium) during physiologically based pharmacokinetic model development gives rise to biased model evaluation and erroneous model parameterization when observed data are collected from the peripheral arm vein. This can lead to a clinically significant error in predictions of plasma concentration-time profiles in unstudied scenarios. To address this error, we developed and verified a novel peripheral sampling site model to simulate arm vein drug concentrations that can be applied to different drug dosing scenarios.
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Affiliation(s)
- Weize Huang
- Department of Pharmaceutics, School of Pharmacy, University of Washington, Seattle, Washington
| | - Nina Isoherranen
- Department of Pharmaceutics, School of Pharmacy, University of Washington, Seattle, Washington
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Polak S, Tylutki Z, Holbrook M, Wiśniowska B. Better prediction of the local concentration-effect relationship: the role of physiologically based pharmacokinetics and quantitative systems pharmacology and toxicology in the evolution of model-informed drug discovery and development. Drug Discov Today 2019; 24:1344-1354. [PMID: 31132414 DOI: 10.1016/j.drudis.2019.05.016] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2018] [Revised: 03/04/2019] [Accepted: 05/21/2019] [Indexed: 12/15/2022]
Abstract
Model-informed drug discovery and development (MID3) is an umbrella term under which sit several computational approaches: quantitative systems pharmacology (QSP), quantitative systems toxicology (QST) and physiologically based pharmacokinetics (PBPK). QSP models are built using mechanistic knowledge of the pharmacological pathway focusing on the putative mechanism of drug efficacy; whereas QST models focus on safety and toxicity issues and the molecular pathways and networks that drive these adverse effects. These can be mediated through exaggerated on-target or off-target pharmacology, immunogenicity or the physiochemical nature of the compound. PBPK models provide a mechanistic description of individual organs and tissues to allow the prediction of the intra- and extra-cellular concentration of the parent drug and metabolites under different conditions. Information on biophase concentration enables the prediction of a drug effect in different organs and assessment of the potential for drug-drug interactions. Together, these modelling approaches can inform the exposure-response relationship and hence support hypothesis generation and testing, compound selection, hazard identification and risk assessment through to clinical proof of concept (POC) and beyond to the market.
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Affiliation(s)
- Sebastian Polak
- Faculty of Pharmacy, Jagiellonian University Medical College, Medyczna 9 Street, 30-688 Kraków, Poland; Certara-Simcyp, Level 2-Acero, 1 Concourse Way, Sheffield, S1 2BJ, UK.
| | - Zofia Tylutki
- Faculty of Pharmacy, Jagiellonian University Medical College, Medyczna 9 Street, 30-688 Kraków, Poland; Certara-Simcyp, Level 2-Acero, 1 Concourse Way, Sheffield, S1 2BJ, UK
| | - Mark Holbrook
- Certara-Simcyp, Level 2-Acero, 1 Concourse Way, Sheffield, S1 2BJ, UK
| | - Barbara Wiśniowska
- Faculty of Pharmacy, Jagiellonian University Medical College, Medyczna 9 Street, 30-688 Kraków, Poland
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11
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Thiel C, Smit I, Baier V, Cordes H, Fabry B, Blank LM, Kuepfer L. Using quantitative systems pharmacology to evaluate the drug efficacy of COX-2 and 5-LOX inhibitors in therapeutic situations. NPJ Syst Biol Appl 2018; 4:28. [PMID: 30083389 PMCID: PMC6072773 DOI: 10.1038/s41540-018-0062-3] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2017] [Revised: 05/07/2018] [Accepted: 05/18/2018] [Indexed: 02/07/2023] Open
Abstract
A quantitative analysis of dose-response relationships is essential in preclinical and clinical drug development in order to optimize drug efficacy and safety, respectively. However, there is a lack of quantitative understanding about the dynamics of pharmacological drug-target interactions in biological systems. In this study, a quantitative systems pharmacology (QSP) approach is applied to quantify the drug efficacy of cyclooxygenase-2 (COX-2) and 5-lipoxygenase (5-LOX) inhibitors by coupling physiologically based pharmacokinetic models, at the whole-body level, with affected biological networks, at the cellular scale. Both COX-2 and 5-LOX are key enzymes in the production of inflammatory mediators and are known targets in the design of anti-inflammatory drugs. Drug efficacy is here evaluated for single and appropriate co-treatment of diclofenac, celecoxib, zileuton, and licofelone by quantitatively studying the reduction of prostaglandins and leukotrienes. The impact of rifampicin pre-treatment on prostaglandin formation is also investigated by considering pharmacokinetic drug interactions with diclofenac and celecoxib, finally suggesting optimized dose levels to compensate for the reduced drug action. Furthermore, a strong correlation was found between pain relief observed in patients as well as celecoxib- and diclofenac-induced decrease in prostaglandins after 6 h. The findings presented reveal insights about drug-induced modulation of cellular networks in a whole-body context, thereby describing complex pharmacokinetic/pharmacodynamic behavior of COX-2 and 5-LOX inhibitors in therapeutic situations. The results demonstrate the clinical benefit of using QSP to predict drug efficacy and, hence, encourage its use in future drug discovery and development programs.
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Affiliation(s)
- Christoph Thiel
- Institute of Applied Microbiology (iAMB), Aachen Biology and Biotechnology (ABBt), RWTH Aachen University, Worringerweg 1, 52074 Aachen, Germany
| | - Ines Smit
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD UK
| | - Vanessa Baier
- Institute of Applied Microbiology (iAMB), Aachen Biology and Biotechnology (ABBt), RWTH Aachen University, Worringerweg 1, 52074 Aachen, Germany
| | - Henrik Cordes
- Institute of Applied Microbiology (iAMB), Aachen Biology and Biotechnology (ABBt), RWTH Aachen University, Worringerweg 1, 52074 Aachen, Germany
| | - Brigida Fabry
- Institute of Applied Microbiology (iAMB), Aachen Biology and Biotechnology (ABBt), RWTH Aachen University, Worringerweg 1, 52074 Aachen, Germany
| | - Lars Mathias Blank
- Institute of Applied Microbiology (iAMB), Aachen Biology and Biotechnology (ABBt), RWTH Aachen University, Worringerweg 1, 52074 Aachen, Germany
| | - Lars Kuepfer
- Institute of Applied Microbiology (iAMB), Aachen Biology and Biotechnology (ABBt), RWTH Aachen University, Worringerweg 1, 52074 Aachen, Germany
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Pichardo-Almarza C, Diaz-Zuccarini V. Understanding the Effect of Statins and Patient Adherence in Atherosclerosis via a Quantitative Systems Pharmacology Model Using a Novel, Hybrid, and Multi-Scale Approach. Front Pharmacol 2017; 8:635. [PMID: 28955237 PMCID: PMC5601395 DOI: 10.3389/fphar.2017.00635] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2017] [Accepted: 08/29/2017] [Indexed: 11/13/2022] Open
Abstract
Background and Objective: Statins are one of the most prescribed drugs to treat atherosclerosis. They inhibit the hepatic HMG-CoA reductase, causing a reduction of circulating cholesterol and LDL levels. Statins have had undeniable success; however, the benefits of statin therapy crystallize only if patients adhere to the prescribed treatment, which is far away from reality since adherence decreases with time with around half of patients discontinue statin therapy within the first year. The objective of this work is to; firstly, demonstrate a formal in-silico methodology based on a hybrid, multiscale mathematical model used to study the effect of statin treatment on atherosclerosis under different patient scenarios, including cases where the influence of medication adherence is examined and secondly, to propose a flexible simulation framework that allows extensions or simplifications, allowing the possibility to design other complex simulation strategies, both interesting features for software development. Methods: Different mathematical modeling paradigms are used to present the relevant dynamic behavior observed in biological/physiological data and clinical trials. A combination of continuous and discrete event models are coupled to simulate the pharmacokinetics (PK) of statins, their pharmacodynamic (PD) effect on lipoproteins levels (e.g., LDL) and relevant inflammatory pathways whilst simultaneously studying the dynamic effect of flow-related variables on atherosclerosis progression. Results: Different scenarios were tested showing the impact of: (1) patient variability: a virtual population shows differences in plaque growth for different individuals could be as high as 100%; (2) statin effect on atherosclerosis: it is shown how a patient with a 1-year statin treatment will reduce his plaque growth by 2-3% in a 2-year period; (3) medical adherence: we show that a patient missing 10% of the total number of doses could increase the plaque growth by ~1% (after 2 years) compared to the same "regular" patient under a 1-year treatment with statins. Conclusions: The results in this paper describe the effect of pharmacological intervention combined with biological/physiological or behavioral factors in atherosclerosis progression and treatment in specific patients. It also provides an exemplar of basic research that can be practically developed into an application software.
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Affiliation(s)
- Cesar Pichardo-Almarza
- Multiscale Cardiovascular Engineering Group (MUSE), Department of Mechanical Engineering, University College LondonLondon, United Kingdom.,Institute of Healthcare Engineering, University College LondonLondon, United Kingdom
| | - Vanessa Diaz-Zuccarini
- Multiscale Cardiovascular Engineering Group (MUSE), Department of Mechanical Engineering, University College LondonLondon, United Kingdom.,Institute of Healthcare Engineering, University College LondonLondon, United Kingdom
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