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Wu YE, Zheng YY, Li QY, Yao BF, Cao J, Liu HX, Hao GX, van den Anker J, Zheng Y, Zhao W. Model-informed drug development in pediatric, pregnancy and geriatric drug development: States of the art and future. Adv Drug Deliv Rev 2024; 211:115364. [PMID: 38936664 DOI: 10.1016/j.addr.2024.115364] [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: 09/25/2023] [Revised: 06/09/2024] [Accepted: 06/19/2024] [Indexed: 06/29/2024]
Abstract
The challenges of drug development in pediatric, pregnant and geriatric populations are a worldwide concern shared by regulatory authorities, pharmaceutical companies, and healthcare professionals. Model-informed drug development (MIDD) can integrate and quantify real-world data of physiology, pharmacology, and disease processes by using modeling and simulation techniques to facilitate decision-making in drug development. In this article, we reviewed current MIDD policy updates, reflected on the integrity of physiological data used for MIDD and the effects of physiological changes on the drug PK, as well as summarized current MIDD strategies and applications, so as to present the state of the art of MIDD in pediatric, pregnant and geriatric populations. Some considerations are put forth for the future improvements of MIDD including refining regulatory considerations, improving the integrity of physiological data, applying the emerging technologies, and exploring the application of MIDD in new therapies like gene therapies for special populations.
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Affiliation(s)
- Yue-E Wu
- Department of Clinical Pharmacy, Institute of Clinical Pharmacology, Key Laboratory of Chemical Biology (Ministry of Education), NMPA Key Laboratory for Clinical Research and Evaluation of Innovative Drug, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Yuan-Yuan Zheng
- Department of Clinical Pharmacy, Institute of Clinical Pharmacology, Key Laboratory of Chemical Biology (Ministry of Education), NMPA Key Laboratory for Clinical Research and Evaluation of Innovative Drug, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Qiu-Yue Li
- Department of Clinical Pharmacy, Institute of Clinical Pharmacology, Key Laboratory of Chemical Biology (Ministry of Education), NMPA Key Laboratory for Clinical Research and Evaluation of Innovative Drug, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Bu-Fan Yao
- Department of Clinical Pharmacy, Institute of Clinical Pharmacology, Key Laboratory of Chemical Biology (Ministry of Education), NMPA Key Laboratory for Clinical Research and Evaluation of Innovative Drug, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Jing Cao
- Department of Clinical Pharmacy, Institute of Clinical Pharmacology, Key Laboratory of Chemical Biology (Ministry of Education), NMPA Key Laboratory for Clinical Research and Evaluation of Innovative Drug, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Hui-Xin Liu
- Department of Clinical Pharmacy, Institute of Clinical Pharmacology, Key Laboratory of Chemical Biology (Ministry of Education), NMPA Key Laboratory for Clinical Research and Evaluation of Innovative Drug, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Guo-Xiang Hao
- Department of Clinical Pharmacy, Institute of Clinical Pharmacology, Key Laboratory of Chemical Biology (Ministry of Education), NMPA Key Laboratory for Clinical Research and Evaluation of Innovative Drug, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - John van den Anker
- Division of Clinical Pharmacology, Children's National Medical Center, Washington, DC, USA; Departments of Pediatrics, Pharmacology & Physiology, George Washington University, School of Medicine and Health Sciences, Washington, DC, USA; Department of Paediatric Pharmacology and Pharmacometrics, University Children's Hospital Basel, Basel, Switzerland
| | - Yi Zheng
- Department of Clinical Pharmacy, Institute of Clinical Pharmacology, Key Laboratory of Chemical Biology (Ministry of Education), NMPA Key Laboratory for Clinical Research and Evaluation of Innovative Drug, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Wei Zhao
- Department of Clinical Pharmacy, Institute of Clinical Pharmacology, Key Laboratory of Chemical Biology (Ministry of Education), NMPA Key Laboratory for Clinical Research and Evaluation of Innovative Drug, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, China.
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Wang H, Arulraj T, Ippolito A, Popel AS. From virtual patients to digital twins in immuno-oncology: lessons learned from mechanistic quantitative systems pharmacology modeling. NPJ Digit Med 2024; 7:189. [PMID: 39014005 PMCID: PMC11252162 DOI: 10.1038/s41746-024-01188-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Accepted: 07/03/2024] [Indexed: 07/18/2024] Open
Abstract
Virtual patients and digital patients/twins are two similar concepts gaining increasing attention in health care with goals to accelerate drug development and improve patients' survival, but with their own limitations. Although methods have been proposed to generate virtual patient populations using mechanistic models, there are limited number of applications in immuno-oncology research. Furthermore, due to the stricter requirements of digital twins, they are often generated in a study-specific manner with models customized to particular clinical settings (e.g., treatment, cancer, and data types). Here, we discuss the challenges for virtual patient generation in immuno-oncology with our most recent experiences, initiatives to develop digital twins, and how research on these two concepts can inform each other.
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Affiliation(s)
- Hanwen Wang
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
| | - Theinmozhi Arulraj
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Alberto Ippolito
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Aleksander S Popel
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Departments of Medicine and Oncology, and the Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA
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3
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Mitra A, Tania N, Ahmed MA, Rayad N, Krishna R, Albusaysi S, Bakhaidar R, Shang E, Burian M, Martin-Pozo M, Younis IR. New Horizons of Model Informed Drug Development in Rare Diseases Drug Development. Clin Pharmacol Ther 2024. [PMID: 38989644 DOI: 10.1002/cpt.3366] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2024] [Accepted: 06/23/2024] [Indexed: 07/12/2024]
Abstract
Model-informed approaches provide a quantitative framework to integrate all available nonclinical and clinical data, thus furnishing a totality of evidence approach to drug development and regulatory evaluation. Maximizing the use of all available data and information about the drug enables a more robust characterization of the risk-benefit profile and reduces uncertainty in both technical and regulatory success. This offers the potential to transform rare diseases drug development, where conducting large well-controlled clinical trials is impractical and/or unethical due to a small patient population, a significant portion of which could be children. Additionally, the totality of evidence generated by model-informed approaches can provide confirmatory evidence for regulatory approval without the need for additional clinical data. In the article, applications of novel quantitative approaches such as quantitative systems pharmacology, disease progression modeling, artificial intelligence, machine learning, modeling of real-world data using model-based meta-analysis and strategies such as external control and patient-reported outcomes as well as clinical trial simulations to optimize trials and sample collection are discussed. Specific case studies of these modeling approaches in rare diseases are provided to showcase applications in drug development and regulatory review. Finally, perspectives are shared on the future state of these modeling approaches in rare diseases drug development along with challenges and opportunities for incorporating such tools in the rational development of drug products.
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Affiliation(s)
- Amitava Mitra
- Clinical Pharmacology, Kura Oncology Inc., Boston, Massachusetts, USA
| | - Nessy Tania
- Translational Clinical Sciences, Pfizer Research and Development, Cambridge, Massachusetts, USA
| | - Mariam A Ahmed
- Quantitative Clinical Pharmacology, Takeda Development Center, Cambridge, Massachusetts, USA
| | - Noha Rayad
- Clinical Pharmacology, Modeling and Simulation, Parexel International (Canada) LTD, Mississauga, Ontario, Canada
| | - Rajesh Krishna
- Certara Drug Development Solutions, Certara USA, Inc., Princeton, New Jersey, USA
| | - Salwa Albusaysi
- Department of Pharmaceutics, Faculty of Pharmacy, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Rana Bakhaidar
- Department of Pharmaceutics, Faculty of Pharmacy, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Elizabeth Shang
- Global Regulatory Affairs and Clinical Safety, Merck &Co., Inc., Rahway, New Jersey, USA
| | - Maria Burian
- Clinical Science, UCB Biopharma SRL, Braine-l'Alleud, Belgium
| | - Michelle Martin-Pozo
- Department of Medicine, Division of Infectious Diseases, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Islam R Younis
- Quantitative Pharmacology and Pharmacometrics, Merck &Co., Inc., Rahway, New Jersey, USA
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4
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Kudrycki K, Friedrich C, Reed M, Baillie RA. Life scientists improve QSP model quality and impact. Front Pharmacol 2024; 15:1392747. [PMID: 39015367 PMCID: PMC11250279 DOI: 10.3389/fphar.2024.1392747] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2024] [Accepted: 06/10/2024] [Indexed: 07/18/2024] Open
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Garaci E, Paci M, Matteucci C, Costantini C, Puccetti P, Romani L. Phenotypic drug discovery: a case for thymosin alpha-1. Front Med (Lausanne) 2024; 11:1388959. [PMID: 38903817 PMCID: PMC11187271 DOI: 10.3389/fmed.2024.1388959] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Accepted: 05/28/2024] [Indexed: 06/22/2024] Open
Abstract
Phenotypic drug discovery (PDD) involves screening compounds for their effects on cells, tissues, or whole organisms without necessarily understanding the underlying molecular targets. PDD differs from target-based strategies as it does not require knowledge of a specific drug target or its role in the disease. This approach can lead to the discovery of drugs with unexpected therapeutic effects or applications and allows for the identification of drugs based on their functional effects, rather than through a predefined target-based approach. Ultimately, disease definitions are mostly symptom-based rather than mechanism-based, and the therapeutics should be likewise. In recent years, there has been a renewed interest in PDD due to its potential to address the complexity of human diseases, including the holistic picture of multiple metabolites engaging with multiple targets constituting the central hub of the metabolic host-microbe interactions. Although PDD presents challenges such as hit validation and target deconvolution, significant achievements have been reached in the era of big data. This article explores the experiences of researchers testing the effect of a thymic peptide hormone, thymosin alpha-1, in preclinical and clinical settings and discuss how its therapeutic utility in the precision medicine era can be accommodated within the PDD framework.
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Affiliation(s)
| | - Maurizio Paci
- Department of Chemical Sciences and Technologies, University of Rome “Tor Vergata”, Rome, Italy
| | - Claudia Matteucci
- Department of Experimental Medicine, University of Rome Tor Vergata, Rome, Italy
| | - Claudio Costantini
- Department of Medicine and Surgery, University of Perugia, Perugia, Italy
| | - Paolo Puccetti
- Department of Medicine and Surgery, University of Perugia, Perugia, Italy
| | - Luigina Romani
- San Raffaele Sulmona, L’Aquila, Italy
- Department of Medicine and Surgery, University of Perugia, Perugia, Italy
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Arulraj T, Wang H, Ippolito A, Zhang S, Fertig EJ, Popel AS. Leveraging multi-omics data to empower quantitative systems pharmacology in immuno-oncology. Brief Bioinform 2024; 25:bbae131. [PMID: 38557676 PMCID: PMC10982948 DOI: 10.1093/bib/bbae131] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 02/20/2024] [Accepted: 03/08/2024] [Indexed: 04/04/2024] Open
Abstract
Understanding the intricate interactions of cancer cells with the tumor microenvironment (TME) is a pre-requisite for the optimization of immunotherapy. Mechanistic models such as quantitative systems pharmacology (QSP) provide insights into the TME dynamics and predict the efficacy of immunotherapy in virtual patient populations/digital twins but require vast amounts of multimodal data for parameterization. Large-scale datasets characterizing the TME are available due to recent advances in bioinformatics for multi-omics data. Here, we discuss the perspectives of leveraging omics-derived bioinformatics estimates to inform QSP models and circumvent the challenges of model calibration and validation in immuno-oncology.
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Affiliation(s)
- Theinmozhi Arulraj
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Hanwen Wang
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Alberto Ippolito
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Shuming Zhang
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Elana J Fertig
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
- Department of Oncology, and the Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
- Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD, USA
| | - Aleksander S Popel
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
- Department of Oncology, and the Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
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Wang CY, Dai HR, Tan YP, Yang DH, Niu XM, Han L, Wang W, Ma LL, Julku A, Jiao Z. Development and Evaluation of a Quantitative Systems Pharmacology Model for Mechanism Interpretation and Efficacy Prediction of Atezolizumab in Combination with Carboplatin and Nab-Paclitaxel in Patients with Non-Small-Cell Lung Cancer. Pharmaceuticals (Basel) 2024; 17:238. [PMID: 38399453 PMCID: PMC10893226 DOI: 10.3390/ph17020238] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2024] [Revised: 02/05/2024] [Accepted: 02/09/2024] [Indexed: 02/25/2024] Open
Abstract
Immunotherapy has shown clinical benefit in patients with non-small-cell lung cancer (NSCLC). Due to the limited response of monotherapy, combining immune checkpoint inhibitors (ICIs) and chemotherapy is considered a treatment option for advanced NSCLC. However, the mechanism of combined therapy and the potential patient population that could benefit from combined therapy remain undetermined. Here, we developed an NSCLC model based on the published quantitative systems pharmacology (QSP)-immuno-oncology platform by making necessary adjustments. After calibration and validation, the established QSP model could adequately characterise the biological mechanisms of action of the triple combination of atezolizumab, nab-paclitaxel, and carboplatin in patients with NSCLC, and identify predictive biomarkers for precision dosing. The established model could efficiently characterise the objective response rate and duration of response of the IMpower131 trial, reproducing the efficacy of alternative dosing. Furthermore, CD8+ and CD4+ T cell densities in tumours were found to be significantly related to the response status. This significant extension of the QSP model not only broadens its applicability but also more accurately reflects real-world clinical settings. Importantly, it positions the model as a critical foundation for model-informed drug development and the customisation of treatment plans, especially in the context of combining single-agent ICIs with platinum-doublet chemotherapy.
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Affiliation(s)
- Chen-Yu Wang
- Department of Pharmacy, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China; (C.-Y.W.); (H.-R.D.); (Y.-P.T.); (D.-H.Y.); (L.H.)
| | - Hao-Ran Dai
- Department of Pharmacy, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China; (C.-Y.W.); (H.-R.D.); (Y.-P.T.); (D.-H.Y.); (L.H.)
| | - Yu-Ping Tan
- Department of Pharmacy, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China; (C.-Y.W.); (H.-R.D.); (Y.-P.T.); (D.-H.Y.); (L.H.)
| | - Di-Hong Yang
- Department of Pharmacy, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China; (C.-Y.W.); (H.-R.D.); (Y.-P.T.); (D.-H.Y.); (L.H.)
- Department of Pharmacy, Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou 310022, China
| | - Xiao-Min Niu
- Department of Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China;
| | - Lu Han
- Department of Pharmacy, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China; (C.-Y.W.); (H.-R.D.); (Y.-P.T.); (D.-H.Y.); (L.H.)
| | - Wen Wang
- Puissan Biotech Oy, 00510 Helsinki, Finland; (W.W.); (L.-L.M.)
| | - Ling-Ling Ma
- Puissan Biotech Oy, 00510 Helsinki, Finland; (W.W.); (L.-L.M.)
| | - Aleksi Julku
- Puissan Biotech Oy, 00510 Helsinki, Finland; (W.W.); (L.-L.M.)
| | - Zheng Jiao
- Department of Pharmacy, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China; (C.-Y.W.); (H.-R.D.); (Y.-P.T.); (D.-H.Y.); (L.H.)
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Arsène S, Parès Y, Tixier E, Granjeon-Noriot S, Martin B, Bruezière L, Couty C, Courcelles E, Kahoul R, Pitrat J, Go N, Monteiro C, Kleine-Schultjann J, Jemai S, Pham E, Boissel JP, Kulesza A. In Silico Clinical Trials: Is It Possible? Methods Mol Biol 2024; 2716:51-99. [PMID: 37702936 DOI: 10.1007/978-1-0716-3449-3_4] [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] [Indexed: 09/14/2023]
Abstract
Modeling and simulation (M&S), including in silico (clinical) trials, helps accelerate drug research and development and reduce costs and have coined the term "model-informed drug development (MIDD)." Data-driven, inferential approaches are now becoming increasingly complemented by emerging complex physiologically and knowledge-based disease (and drug) models, but differ in setup, bottlenecks, data requirements, and applications (also reminiscent of the different scientific communities they arose from). At the same time, and within the MIDD landscape, regulators and drug developers start to embrace in silico trials as a potential tool to refine, reduce, and ultimately replace clinical trials. Effectively, silos between the historically distinct modeling approaches start to break down. Widespread adoption of in silico trials still needs more collaboration between different stakeholders and established precedence use cases in key applications, which is currently impeded by a shattered collection of tools and practices. In order to address these key challenges, efforts to establish best practice workflows need to be undertaken and new collaborative M&S tools devised, and an attempt to provide a coherent set of solutions is provided in this chapter. First, a dedicated workflow for in silico clinical trial (development) life cycle is provided, which takes up general ideas from the systems biology and quantitative systems pharmacology space and which implements specific steps toward regulatory qualification. Then, key characteristics of an in silico trial software platform implementation are given on the example of jinkō.ai (nova's end-to-end in silico clinical trial platform). Considering these enabling scientific and technological advances, future applications of in silico trials to refine, reduce, and replace clinical research are indicated, ranging from synthetic control strategies and digital twins, which overall shows promise to begin a new era of more efficient drug development.
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Ahmed MA, Burnham J, Dwivedi G, AbuAsal B. Achieving big with small: quantitative clinical pharmacology tools for drug development in pediatric rare diseases. J Pharmacokinet Pharmacodyn 2023; 50:429-444. [PMID: 37140724 DOI: 10.1007/s10928-023-09863-x] [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: 03/04/2023] [Accepted: 04/26/2023] [Indexed: 05/05/2023]
Abstract
Pediatric populations represent a major fraction of rare diseases and compound the intrinsic challenges of pediatric drug development and drug development for rare diseases. The intertwined complexities of pediatric and rare disease populations impose unique challenges to clinical pharmacologists and require integration of novel clinical pharmacology and quantitative tools to overcome multiple hurdles during the discovery and development of new therapies. Drug development strategies for pediatric rare diseases continue to evolve to meet the inherent challenges and produce new medicines. Advances in quantitative clinical pharmacology research have been a key component in advancing pediatric rare disease research to accelerate drug development and inform regulatory decisions. This article will discuss the evolution of the regulatory landscape in pediatric rare diseases, the challenges encountered during the design of rare disease drug development programs and will highlight the use of innovative tools and potential solutions for future development programs.
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Affiliation(s)
- Mariam A Ahmed
- Takeda Development Center Americas Inc, 125 Binney St, Cambridge, MA, 02142-1123, USA.
| | | | - Gaurav Dwivedi
- Takeda Development Center Americas Inc, 125 Binney St, Cambridge, MA, 02142-1123, USA
| | - Bilal AbuAsal
- US Food and Drug Administration, 10903, New Hampshire Ave, Silver Spring, MD, 20993, USA
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Qi T, Liao X, Cao Y. Development of bispecific T cell engagers: harnessing quantitative systems pharmacology. Trends Pharmacol Sci 2023; 44:880-890. [PMID: 37852906 PMCID: PMC10843027 DOI: 10.1016/j.tips.2023.09.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2023] [Revised: 09/22/2023] [Accepted: 09/25/2023] [Indexed: 10/20/2023]
Abstract
Bispecific T cell engagers (bsTCEs) have emerged as a promising class of cancer immunotherapy. Several bsTCEs have achieved marketing approval; dozens more are under clinical investigation. However, the clinical development of bsTCEs remains rife with challenges, including nuanced pharmacology, limited translatability of preclinical findings, frequent on-target toxicity, and convoluted dosing regimens. In this opinion article we present a distinct perspective on how quantitative systems pharmacology (QSP) can serve as a powerful tool for overcoming these obstacles. Recent advances in QSP modeling have empowered developers of bsTCEs to gain a deeper understanding of their context-dependent pharmacology, bridge gaps in experimental data, guide first-in-human (FIH) dose selection, design dosing regimens with expanded therapeutic windows, and improve long-term treatment outcomes. We use recent case studies to exemplify the potential of QSP techniques to support future bsTCE development.
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Affiliation(s)
- Timothy Qi
- Division of Pharmacotherapy and Experimental Therapeutics, Eshelman School of Pharmacy, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Xiaozhi Liao
- Division of Pharmacotherapy and Experimental Therapeutics, Eshelman School of Pharmacy, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Yanguang Cao
- Division of Pharmacotherapy and Experimental Therapeutics, Eshelman School of Pharmacy, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; Lineberger Comprehensive Cancer Center, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
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Stankevičiūtė K, Woillard JB, Peck RW, Marquet P, van der Schaar M. Bridging the Worlds of Pharmacometrics and Machine Learning. Clin Pharmacokinet 2023; 62:1551-1565. [PMID: 37803104 DOI: 10.1007/s40262-023-01310-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/19/2023] [Indexed: 10/08/2023]
Abstract
Precision medicine requires individualized modeling of disease and drug dynamics, with machine learning-based computational techniques gaining increasing popularity. The complexity of either field, however, makes current pharmacological problems opaque to machine learning practitioners, and state-of-the-art machine learning methods inaccessible to pharmacometricians. To help bridge the two worlds, we provide an introduction to current problems and techniques in pharmacometrics that ranges from pharmacokinetic and pharmacodynamic modeling to pharmacometric simulations, model-informed precision dosing, and systems pharmacology, and review some of the machine learning approaches to address them. We hope this would facilitate collaboration between experts, with complementary strengths of principled pharmacometric modeling and flexibility of machine learning leading to synergistic effects in pharmacological applications.
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Affiliation(s)
- Kamilė Stankevičiūtė
- Department of Computer Science and Technology, University of Cambridge, 15 JJ Thomson Avenue, Cambridge, CB3 0FD, UK
| | - Jean-Baptiste Woillard
- INSERM U1248 P&T, University of Limoges, 2 rue du Pr Descottes, 87000, Limoges, France.
- Department of Pharmacology and Toxicology, CHU Limoges, Limoges, France.
| | - Richard W Peck
- Department of Pharmacology and Therapeutics, University of Liverpool, Liverpool, UK
- Pharma Research and Development, Roche Innovation Center, Basel, Switzerland
| | - Pierre Marquet
- INSERM U1248 P&T, University of Limoges, 2 rue du Pr Descottes, 87000, Limoges, France
- Department of Pharmacology and Toxicology, CHU Limoges, Limoges, France
| | - Mihaela van der Schaar
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK
- The Alan Turing Institute, London, UK
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Augustin D, Lambert B, Robinson M, Wang K, Gavaghan D. Simulating clinical trials for model-informed precision dosing: using warfarin treatment as a use case. Front Pharmacol 2023; 14:1270443. [PMID: 37927586 PMCID: PMC10621790 DOI: 10.3389/fphar.2023.1270443] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Accepted: 10/05/2023] [Indexed: 11/07/2023] Open
Abstract
Treatment response variability across patients is a common phenomenon in clinical practice. For many drugs this inter-individual variability does not require much (if any) individualisation of dosing strategies. However, for some drugs, including chemotherapies and some monoclonal antibody treatments, individualisation of dosages are needed to avoid harmful adverse events. Model-informed precision dosing (MIPD) is an emerging approach to guide the individualisation of dosing regimens of otherwise difficult-to-administer drugs. Several MIPD approaches have been suggested to predict dosing strategies, including regression, reinforcement learning (RL) and pharmacokinetic and pharmacodynamic (PKPD) modelling. A unified framework to study the strengths and limitations of these approaches is missing. We develop a framework to simulate clinical MIPD trials, providing a cost and time efficient way to test different MIPD approaches. Central for our framework is a clinical trial model that emulates the complexities in clinical practice that challenge successful treatment individualisation. We demonstrate this framework using warfarin treatment as a use case and investigate three popular MIPD methods: 1. Neural network regression; 2. Deep RL; and 3. PKPD modelling. We find that the PKPD model individualises warfarin dosing regimens with the highest success rate and the highest efficiency: 75.1% of the individuals display INRs inside the therapeutic range at the end of the simulated trial; and the median time in the therapeutic range (TTR) is 74%. In comparison, the regression model and the deep RL model have success rates of 47.0% and 65.8%, and median TTRs of 45% and 68%. We also find that the MIPD models can attain different degrees of individualisation: the Regression model individualises dosing regimens up to variability explained by covariates; the Deep RL model and the PKPD model individualise dosing regimens accounting also for additional variation using monitoring data. However, the Deep RL model focusses on control of the treatment response, while the PKPD model uses the data also to further the individualisation of predictions.
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Affiliation(s)
- David Augustin
- Department of Computer Science, University of Oxford, Oxford, United Kingdom
| | - Ben Lambert
- College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, United Kingdom
| | - Martin Robinson
- Department of Computer Science, University of Oxford, Oxford, United Kingdom
| | - Ken Wang
- Research and Early Development, F. Hoffmann-La Roche AG, Basel, Switzerland
| | - David Gavaghan
- Department of Computer Science, University of Oxford, Oxford, United Kingdom
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13
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Ballesta A, Gallo JM. Quantitative Systems Pharmacology: A Foundation To Establish Precision Medicine-Editorial. J Pharmacol Exp Ther 2023; 387:27-30. [PMID: 37714689 DOI: 10.1124/jpet.123.001842] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Accepted: 07/31/2023] [Indexed: 09/17/2023] Open
Affiliation(s)
- Annabelle Ballesta
- INSERM U900, Institut Curie, Mines ParisTech CBIO, Université PSL, Paris, France (A.B.) and Department of Pharmaceutical Sciences, School of Pharmacy and Pharmaceutical Sciences, University at Buffalo, Buffalo, New York (J.M.G.)
| | - James M Gallo
- INSERM U900, Institut Curie, Mines ParisTech CBIO, Université PSL, Paris, France (A.B.) and Department of Pharmaceutical Sciences, School of Pharmacy and Pharmaceutical Sciences, University at Buffalo, Buffalo, New York (J.M.G.)
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14
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Sadri A. Is Target-Based Drug Discovery Efficient? Discovery and "Off-Target" Mechanisms of All Drugs. J Med Chem 2023; 66:12651-12677. [PMID: 37672650 DOI: 10.1021/acs.jmedchem.2c01737] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/08/2023]
Abstract
Target-based drug discovery is the dominant paradigm of drug discovery; however, a comprehensive evaluation of its real-world efficiency is lacking. Here, a manual systematic review of about 32000 articles and patents dating back to 150 years ago demonstrates its apparent inefficiency. Analyzing the origins of all approved drugs reveals that, despite several decades of dominance, only 9.4% of small-molecule drugs have been discovered through "target-based" assays. Moreover, the therapeutic effects of even this minimal share cannot be solely attributed and reduced to their purported targets, as they depend on numerous off-target mechanisms unconsciously incorporated by phenotypic observations. The data suggest that reductionist target-based drug discovery may be a cause of the productivity crisis in drug discovery. An evidence-based approach to enhance efficiency seems to be prioritizing, in selecting and optimizing molecules, higher-level phenotypic observations that are closer to the sought-after therapeutic effects using tools like artificial intelligence and machine learning.
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Affiliation(s)
- Arash Sadri
- Lyceum Scientific Charity, Tehran, Iran, 1415893697
- Interdisciplinary Neuroscience Research Program (INRP), Students' Scientific Research Center, Tehran University of Medical Sciences, Tehran, Iran, 1417755331
- Faculty of Pharmacy, Tehran University of Medical Sciences, Tehran, Iran, 1417614411
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15
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Zhang S, Deshpande A, Verma BK, Wang H, Mi H, Yuan L, Ho WJ, Jaffee EM, Zhu Q, Anders RA, Yarchoan M, Kagohara LT, Fertig EJ, Popel AS. Informing virtual clinical trials of hepatocellular carcinoma with spatial multi-omics analysis of a human neoadjuvant immunotherapy clinical trial. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.11.553000. [PMID: 37645761 PMCID: PMC10462044 DOI: 10.1101/2023.08.11.553000] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/31/2023]
Abstract
Human clinical trials are important tools to advance novel systemic therapies improve treatment outcomes for cancer patients. The few durable treatment options have led to a critical need to advance new therapeutics in hepatocellular carcinoma (HCC). Recent human clinical trials have shown that new combination immunotherapeutic regimens provide unprecedented clinical response in a subset of patients. Computational methods that can simulate tumors from mathematical equations describing cellular and molecular interactions are emerging as promising tools to simulate the impact of therapy entirely in silico. To facilitate designing dosing regimen and identifying potential biomarkers, we developed a new computational model to track tumor progression at organ scale while reflecting the spatial heterogeneity in the tumor at tissue scale in HCC. This computational model is called a spatial quantitative systems pharmacology (spQSP) platform and it is also designed to simulate the effects of combination immunotherapy. We then validate the results from the spQSP system by leveraging real-world spatial multi-omics data from a neoadjuvant HCC clinical trial combining anti-PD-1 immunotherapy and a multitargeted tyrosine kinase inhibitor (TKI) cabozantinib. The model output is compared with spatial data from Imaging Mass Cytometry (IMC). Both IMC data and simulation results suggest closer proximity between CD8 T cell and macrophages among non-responders while the reverse trend was observed for responders. The analyses also imply wider dispersion of immune cells and less scattered cancer cells in responders' samples. We also compared the model output with Visium spatial transcriptomics analyses of samples from post-treatment tumor resections in the original clinical trial. Both spatial transcriptomic data and simulation results identify the role of spatial patterns of tumor vasculature and TGFβ in tumor and immune cell interactions. To our knowledge, this is the first spatial tumor model for virtual clinical trials at a molecular scale that is grounded in high-throughput spatial multi-omics data from a human clinical trial.
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Affiliation(s)
- Shuming Zhang
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Atul Deshpande
- Bloomberg-Kimmel Immunotherapy Institute for Cancer Immunotherapy, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Convergence Institute, Johns Hopkins University, Baltimore, MD, USA
| | - Babita K. Verma
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Hanwen Wang
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Haoyang Mi
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Long Yuan
- Bloomberg-Kimmel Immunotherapy Institute for Cancer Immunotherapy, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Immunology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Won Jin Ho
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Convergence Institute, Johns Hopkins University, Baltimore, MD, USA
| | - Elizabeth M. Jaffee
- Bloomberg-Kimmel Immunotherapy Institute for Cancer Immunotherapy, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Convergence Institute, Johns Hopkins University, Baltimore, MD, USA
| | - Qingfeng Zhu
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Robert A. Anders
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Convergence Institute, Johns Hopkins University, Baltimore, MD, USA
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Mark Yarchoan
- Bloomberg-Kimmel Immunotherapy Institute for Cancer Immunotherapy, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Convergence Institute, Johns Hopkins University, Baltimore, MD, USA
| | - Luciane T. Kagohara
- Bloomberg-Kimmel Immunotherapy Institute for Cancer Immunotherapy, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Convergence Institute, Johns Hopkins University, Baltimore, MD, USA
| | - Elana J. Fertig
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Bloomberg-Kimmel Immunotherapy Institute for Cancer Immunotherapy, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Convergence Institute, Johns Hopkins University, Baltimore, MD, USA
- Department of Applied Mathematics and Statistics, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Jointly supervised research
| | - Aleksander S. Popel
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Jointly supervised research
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16
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Arulraj T, Wang H, Emens LA, Santa-Maria CA, Popel AS. A transcriptome-informed QSP model of metastatic triple-negative breast cancer identifies predictive biomarkers for PD-1 inhibition. SCIENCE ADVANCES 2023; 9:eadg0289. [PMID: 37390206 PMCID: PMC10313177 DOI: 10.1126/sciadv.adg0289] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Accepted: 05/26/2023] [Indexed: 07/02/2023]
Abstract
Triple-negative breast cancer (TNBC), a highly metastatic breast cancer subtype, has limited treatment options. While a small number of patients attain clinical benefit with single-agent checkpoint inhibitors, identifying these patients before the therapy remains challenging. Here, we developed a transcriptome-informed quantitative systems pharmacology model of metastatic TNBC by integrating heterogenous metastatic tumors. In silico clinical trial with an anti-PD-1 drug, pembrolizumab, predicted that several features, such as the density of antigen-presenting cells, the fraction of cytotoxic T cells in lymph nodes, and the richness of cancer clones in tumors, could serve individually as biomarkers but had a higher predictive power as combinations of two biomarkers. We showed that PD-1 inhibition neither consistently enhanced all antitumorigenic factors nor suppressed all protumorigenic factors but ultimately reduced the tumor carrying capacity. Collectively, our predictions suggest several candidate biomarkers that might effectively predict the response to pembrolizumab monotherapy and potential therapeutic targets to develop treatment strategies for metastatic TNBC.
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Affiliation(s)
- Theinmozhi Arulraj
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Hanwen Wang
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Leisha A. Emens
- University of Pittsburgh Medical Center, Hillman Cancer Center, Pittsburgh, PA, 15213, USA
| | - Cesar A. Santa-Maria
- Department of Oncology, and the Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Aleksander S. Popel
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
- Department of Oncology, and the Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
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17
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Hariri A, Mirian M, Zarrabi A, Kohandel M, Amini-Pozveh M, Aref AR, Tabatabaee A, Prabhakar PK, Sivakumar PM. The circadian rhythm: an influential soundtrack in the diabetes story. Front Endocrinol (Lausanne) 2023; 14:1156757. [PMID: 37441501 PMCID: PMC10333930 DOI: 10.3389/fendo.2023.1156757] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Accepted: 05/03/2023] [Indexed: 07/15/2023] Open
Abstract
Type 2 Diabetes Mellitus (T2DM) has been the main category of metabolic diseases in recent years due to changes in lifestyle and environmental conditions such as diet and physical activity. On the other hand, the circadian rhythm is one of the most significant biological pathways in humans and other mammals, which is affected by light, sleep, and human activity. However, this cycle is controlled via complicated cellular pathways with feedback loops. It is widely known that changes in the circadian rhythm can alter some metabolic pathways of body cells and could affect the treatment process, particularly for metabolic diseases like T2DM. The aim of this study is to explore the importance of the circadian rhythm in the occurrence of T2DM via reviewing the metabolic pathways involved, their relationship with the circadian rhythm from two perspectives, lifestyle and molecular pathways, and their effect on T2DM pathophysiology. These impacts have been demonstrated in a variety of studies and led to the development of approaches such as time-restricted feeding, chronotherapy (time-specific therapies), and circadian molecule stabilizers.
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Affiliation(s)
- Amirali Hariri
- Department of Pharmaceutical Biotechnology, School of Pharmacy and Pharmaceutical Sciences, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Mina Mirian
- Department of Pharmaceutical Biotechnology, School of Pharmacy and Pharmaceutical Sciences, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Ali Zarrabi
- Department of Biomedical Engineering, Faculty of Engineering and Natural Sciences, Istinye University, Istanbul, Türkiye
| | - Mohammad Kohandel
- Department of Applied Mathematics, Faculty of Mathematics, University of Waterloo, Waterloo, ON, Canada
| | - Maryam Amini-Pozveh
- Department of Prosthodontics Dentistry, Dental Materials Research Center, Dental Research Institute, School of Dentistry, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Amir Reza Aref
- Belfer Center for Applied Cancer Science, Dana Farber Cancer Institute, Boston, MA, United States
- Translational Sciences, Xsphera Biosciences Inc., Boston, MA, United States
| | - Aliye Tabatabaee
- School of Pharmacy and Pharmaceutical Sciences, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Pranav Kumar Prabhakar
- Department of Medical Laboratory Sciences, School of Allied Medical Sciences, Lovely Professional University, Phagwara, Punjab, India
- Division of Research and Development, Lovely Professional University, Phagwara Punjab, India
| | - Ponnurengam Malliappan Sivakumar
- Institute of Research and Development, Duy Tan University, Da Nang, Vietnam
- School of Medicine and Pharmacy, Duy Tan University, Da Nang, Vietnam
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18
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Anbari S, Wang H, Zhang Y, Wang J, Pilvankar M, Nickaeen M, Hansel S, Popel AS. Using quantitative systems pharmacology modeling to optimize combination therapy of anti-PD-L1 checkpoint inhibitor and T cell engager. Front Pharmacol 2023; 14:1163432. [PMID: 37408756 PMCID: PMC10318535 DOI: 10.3389/fphar.2023.1163432] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Accepted: 06/06/2023] [Indexed: 07/07/2023] Open
Abstract
Although immune checkpoint blockade therapies have shown evidence of clinical effectiveness in many types of cancer, the outcome of clinical trials shows that very few patients with colorectal cancer benefit from treatments with checkpoint inhibitors. Bispecific T cell engagers (TCEs) are gaining popularity because they can improve patients' immunological responses by promoting T cell activation. The possibility of combining TCEs with checkpoint inhibitors to increase tumor response and patient survival has been highlighted by preclinical and clinical outcomes. However, identifying predictive biomarkers and optimal dose regimens for individual patients to benefit from combination therapy remains one of the main challenges. In this article, we describe a modular quantitative systems pharmacology (QSP) platform for immuno-oncology that includes specific processes of immune-cancer cell interactions and was created based on published data on colorectal cancer. We generated a virtual patient cohort with the model to conduct in silico virtual clinical trials for combination therapy of a PD-L1 checkpoint inhibitor (atezolizumab) and a bispecific T cell engager (cibisatamab). Using the model calibrated against the clinical trials, we conducted several virtual clinical trials to compare various doses and schedules of administration for two drugs with the goal of therapy optimization. Moreover, we quantified the score of drug synergy for these two drugs to further study the role of the combination therapy.
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Affiliation(s)
- Samira Anbari
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Hanwen Wang
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Yu Zhang
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Jun Wang
- Biotherapeutics Discovery Research, Boehringer Ingelheim Pharmaceuticals Inc., Ridgefield, CT, United States
| | - Minu Pilvankar
- Biotherapeutics Discovery Research, Boehringer Ingelheim Pharmaceuticals Inc., Ridgefield, CT, United States
| | - Masoud Nickaeen
- Biotherapeutics Discovery Research, Boehringer Ingelheim Pharmaceuticals Inc., Ridgefield, CT, United States
| | - Steven Hansel
- Biotherapeutics Discovery Research, Boehringer Ingelheim Pharmaceuticals Inc., Ridgefield, CT, United States
| | - Aleksander S. Popel
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- Department of Oncology, Sidney Kimmel Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, United States
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19
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Nikfar M, Mi H, Gong C, Kimko H, Popel AS. Quantifying Intratumoral Heterogeneity and Immunoarchitecture Generated In-Silico by a Spatial Quantitative Systems Pharmacology Model. Cancers (Basel) 2023; 15:2750. [PMID: 37345087 DOI: 10.3390/cancers15102750] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Revised: 05/05/2023] [Accepted: 05/11/2023] [Indexed: 06/23/2023] Open
Abstract
Spatial heterogeneity is a hallmark of cancer. Tumor heterogeneity can vary with time and location. The tumor microenvironment (TME) encompasses various cell types and their interactions that impart response to therapies. Therefore, a quantitative evaluation of tumor heterogeneity is crucial for the development of effective treatments. Different approaches, such as multiregional sequencing, spatial transcriptomics, analysis of autopsy samples, and longitudinal analysis of biopsy samples, can be used to analyze the intratumoral heterogeneity (ITH) and temporal evolution and to reveal the mechanisms of therapeutic response. However, because of the limitations of these data and the uncertainty associated with the time points of sample collection, having a complete understanding of intratumoral heterogeneity role is challenging. Here, we used a hybrid model that integrates a whole-patient compartmental quantitative-systems-pharmacology (QSP) model with a spatial agent-based model (ABM) describing the TME; we applied four spatial metrics to quantify model-simulated intratumoral heterogeneity and classified the TME immunoarchitecture for representative cases of effective and ineffective anti-PD-1 therapy. The four metrics, adopted from computational digital pathology, included mixing score, average neighbor frequency, Shannon's entropy and area under the curve (AUC) of the G-cross function. A fifth non-spatial metric was used to supplement the analysis, which was the ratio of the number of cancer cells to immune cells. These metrics were utilized to classify the TME as "cold", "compartmentalized" and "mixed", which were related to treatment efficacy. The trends in these metrics for effective and ineffective treatments are in qualitative agreement with the clinical literature, indicating that compartmentalized immunoarchitecture is likely to result in more efficacious treatment outcomes.
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Affiliation(s)
- Mehdi Nikfar
- Department of Biomedical Engineering, School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA
| | - Haoyang Mi
- Department of Biomedical Engineering, School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA
| | - Chang Gong
- Clinical Pharmacology & Quantitative Pharmacology, AstraZeneca, Waltham, MA 02451, USA
| | - Holly Kimko
- Clinical Pharmacology & Quantitative Pharmacology, AstraZeneca, Gaithersburg, MD 20878, USA
| | - Aleksander S Popel
- Department of Biomedical Engineering, School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA
- Sidney Kimmel Comprehensive Cancer Center, Department of Oncology, Johns Hopkins University, Baltimore, MD 21231, USA
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20
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Zhang Y, Popel AS, Bazzazi H. Combining Multikinase Tyrosine Kinase Inhibitors Targeting the Vascular Endothelial Growth Factor and Cluster of Differentiation 47 Signaling Pathways Is Predicted to Increase the Efficacy of Antiangiogenic Combination Therapies. ACS Pharmacol Transl Sci 2023; 6:710-726. [PMID: 37200806 PMCID: PMC10186363 DOI: 10.1021/acsptsci.3c00008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Indexed: 05/20/2023]
Abstract
Angiogenesis is a critical step in tumor growth, development, and invasion. Nascent tumor cells secrete vascular endothelial growth factor (VEGF) that significantly remodels the tumor microenvironment through interaction with multiple receptors on vascular endothelial cells, including type 2 VEGF receptor (VEGFR2). The complex pathways initiated by VEGF binding to VEGFR2 lead to enhanced proliferation, survival, and motility of vascular endothelial cells and formation of a new vascular network, enabling tumor growth. Antiangiogenic therapies that inhibit VEGF signaling pathways were among the first drugs that targeted stroma rather than tumor cells. Despite improvements in progression-free survival and higher response rates relative to chemotherapy in some types of solid tumors, the impact on overall survival (OS) has been limited, with the majority of tumors eventually relapsing due to resistance or activation of alternate angiogenic pathways. Here, we developed a molecularly detailed computational model of endothelial cell signaling and angiogenesis-driven tumor growth to investigate combination therapies targeting different nodes of the endothelial VEGF/VEGFR2 signaling pathway. Simulations predicted a strong threshold-like behavior in extracellular signal-regulated kinases 1/2 (ERK1/2) activation relative to phosphorylated VEGFR2 levels, as continuous inhibition of at least 95% of receptors was necessary to abrogate phosphorylated ERK1/2 (pERK1/2). Combinations with mitogen-activated protein kinase/ERK kinase (MEK) and spingosine-1-phosphate inhibitors were found to be effective in overcoming the ERK1/2 activation threshold and abolishing activation of the pathway. Modeling results also identified a mechanism of resistance whereby tumor cells could reduce pERK1/2 sensitivity to inhibitors of VEGFR2 by upregulation of Raf, MEK, and sphingosine kinase 1 (SphK1), thus highlighting the need for deeper investigation of the dynamics of the crosstalk between VEGFR2 and SphK1 pathways. Inhibition of VEGFR2 phosphorylation was found to be more effective at blocking protein kinase B, also known as AKT, activation; however, to effectively abolish AKT activation, simulations identified Axl autophosphorylation or the Src kinase domain as potent targets. Simulations also supported activating cluster of differentiation 47 (CD47) on endothelial cells as an effective combination partner with tyrosine kinase inhibitors to inhibit angiogenesis signaling and tumor growth. Virtual patient simulations supported the effectiveness of CD47 agonism in combination with inhibitors of VEGFR2 and SphK1 pathways. Overall, the rule-based system model developed here provides new insights, generates novel hypothesis, and makes predictions regarding combinations that may enhance the OS with currently approved antiangiogenic therapies.
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Affiliation(s)
- Yu Zhang
- Department of Biomedical Engineering, The Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, United States
| | - Aleksander S. Popel
- Department of Biomedical Engineering, The Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, United States
| | - Hojjat Bazzazi
- Department of Biomedical Engineering, The Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, United States
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21
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Denaro C, Merrill NJ, McQuade ST, Reed L, Kaddi C, Azer K, Piccoli B. A pipeline for testing drug mechanism of action and combination therapies: From microarray data to simulations via Linear-In-Flux-Expressions: Testing four-drug combinations for tuberculosis treatment. Math Biosci 2023; 360:108983. [PMID: 36931620 DOI: 10.1016/j.mbs.2023.108983] [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: 12/20/2022] [Revised: 02/13/2023] [Accepted: 02/14/2023] [Indexed: 03/17/2023]
Abstract
Computational methods are becoming commonly used in many areas of medical research. Recently, the modeling of biological mechanisms associated with disease pathophysiology have benefited from approaches such as Quantitative Systems Pharmacology (briefly QSP) and Physiologically Based Pharmacokinetics (briefly PBPK). These methodologies show the potential to enhance, if not substitute animal models. The main reasons for this success are the high accuracy and low cost. Solid mathematical foundations of such methods, such as compartmental systems and flux balance analysis, provide a good base on which to build computational tools. However, there are many choices to be made in model design, that will have a large impact on how these methods perform as we scale up the network or perturb the system to uncover the mechanisms of action of new compounds or therapy combinations. A computational pipeline is presented here that starts with available -omic data and utilizes advanced mathematical simulations to inform the modeling of a biochemical system. Specific attention is devoted to creating a modular workflow, including the mathematical rigorous tools to represent complex chemical reactions, and modeling drug action in terms of its impact on multiple pathways. An application to optimizing combination therapy for tuberculosis shows the potential of the approach.
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Affiliation(s)
- Christopher Denaro
- Center for Computational and Integrative Biology, Rutgers Camden, 201 S. Broadway, Camden, 08102, NJ, USA.
| | - Nathaniel J Merrill
- Pacific Northwest National Laboratory, 902 Battelle Blvd, Richland, 99254, WA, USA
| | - Sean T McQuade
- Center for Computational and Integrative Biology, Rutgers Camden, 201 S. Broadway, Camden, 08102, NJ, USA
| | - Logan Reed
- Department of Mathematical Sciences, Rutgers Camden, 311 N. Fifth Street, Camden, 08102, NJ, USA
| | | | - Karim Azer
- Axcella, 840 Memorial Drive, Cambridge, 02139, MA, USA
| | - Benedetto Piccoli
- Center for Computational and Integrative Biology, Rutgers Camden, 201 S. Broadway, Camden, 08102, NJ, USA; Department of Mathematical Sciences, Rutgers Camden, 311 N. Fifth Street, Camden, 08102, NJ, USA
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22
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Joshi A, Ramanujan S, Jin JY. The convergence of pharmacometrics and quantitative systems pharmacology in pharmaceutical research and development. Eur J Pharm Sci 2023; 182:106380. [PMID: 36638898 DOI: 10.1016/j.ejps.2023.106380] [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: 12/01/2022] [Accepted: 01/09/2023] [Indexed: 01/12/2023]
Abstract
Quantitative systems pharmacology (QSP) models are an important facet of pharmaceutical and clinical research as they combine mechanistic models of physiology in health and disease with pharmacokinetics/pharmacodynamics to predict systems-level effects. The quantitative clinical pharmacology toolbox has traditionally included both mechanistic modeling and population approaches, collectively called pharmacometrics, but the current landscape requires the optimization and use of multiple models together. Here, we explore several case studies in drug development that exemplify three approaches for using QSP and pharmacometrics models together - parallel synchronization, cross-informative use, and sequential integration. While these approaches are increasingly applied in drug development, achieving a true convergence of QSP and pharmacometrics that fully exploits their synergy will require new tools and methods that enable greater technical integration, in addition to nurturing scientists with diverse modeling expertise that enable cross-discipline strategy. Extensions of existing methods used in each approach as well as additional resources including machine learning models, data-at-scale, end-to-end computation platforms, and real-time analytics will enable this convergence.
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Affiliation(s)
- Amita Joshi
- Clinical Pharmacology, Genentech Inc., South San Francisco, CA 94080, USA.
| | - Saroja Ramanujan
- Preclinical and Translational Pharmacokinetics, Genentech Inc., South San Francisco, CA 94080, USA
| | - Jin Y Jin
- Clinical Pharmacology, Genentech Inc., South San Francisco, CA 94080, USA
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23
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O'Brien Laramy MN, Luthra S, Brown MF, Bartlett DW. Delivering on the promise of protein degraders. Nat Rev Drug Discov 2023; 22:410-427. [PMID: 36810917 DOI: 10.1038/s41573-023-00652-2] [Citation(s) in RCA: 22] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/17/2023] [Indexed: 02/23/2023]
Abstract
Over the past 3 years, the first bivalent protein degraders intentionally designed for targeted protein degradation (TPD) have advanced to clinical trials, with an initial focus on established targets. Most of these clinical candidates are designed for oral administration, and many discovery efforts appear to be similarly focused. As we look towards the future, we propose that an oral-centric discovery paradigm will overly constrain the chemical designs that are considered and limit the potential to drug novel targets. In this Perspective, we summarize the current state of the bivalent degrader modality and propose three categories of degrader designs, based on their likely route of administration and requirement for drug delivery technologies. We then describe a vision for how parenteral drug delivery, implemented early in research and supported by pharmacokinetic-pharmacodynamic modelling, can enable exploration of a broader drug design space, expand the scope of accessible targets and deliver on the promise of protein degraders as a therapeutic modality.
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Affiliation(s)
| | - Suman Luthra
- Discovery Pharmaceutical Sciences, Merck & Co., Inc., Boston, MA, USA
| | - Matthew F Brown
- Discovery Sciences, Worldwide Research, Development, and Medical, Pfizer Inc., Groton, CT, USA
| | - Derek W Bartlett
- Pharmacokinetics, Dynamics, & Metabolism, Worldwide Research, Development, and Medical, Pfizer Inc., San Diego, CA, USA
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24
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Nayak SS, Sundararajan V. Robust anti-inflammatory activity of genistein against neutrophil elastase: a microsecond molecular dynamics simulation study. J Biomol Struct Dyn 2023; 41:11612-11628. [PMID: 36705087 DOI: 10.1080/07391102.2023.2170919] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Accepted: 12/26/2022] [Indexed: 01/28/2023]
Abstract
Human Neutrophil Elastase (HNE) is one of the major causes of tissue destruction in numerous chronic and inflammatory disorders and has been reported as a therapeutic target for inflammatory diseases. Overexpression of this enzyme plays a critical role in the pathogenesis of rheumatoid arthritis (RA). The focus of this study is to identify potent natural inhibitors that could target the active site of the HNE through the use of computational methods. The molecular structure of small molecules was retrieved from several natural compound databases. This was followed by structure-based virtual screening, molecular docking, ADMET property predictions and molecular dynamic simulation studies to screen potential HNE inhibitors. In total, 1881 natural compounds were extracted and subjected to molecular docking studies, and 10 compounds were found to have good interactions, exhibiting the best docking scores. Genistein showed higher binding efficacy (-10.28 Kcal/mol) to HNE in comparison to other natural compounds. The conformational stability of the docked complex of the ELANE gene (HNE) with genistein was assessed using 1-microsecond molecular dynamic simulation (MDs), which reliably revealed the unique stereochemical alteration of the complex, indicating its conformational stability and flexibility. Alterations in the enzyme structure upon complex formation were further characterized through clustering analysis and linear interaction energy (LIE) calculation. The outcomes of this research propose novel potential candidates against target HNE.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Smruti Sudha Nayak
- Department of Bio-Sciences, School of Bio Sciences and Technology, Vellore Institute of Technology, Vellore, Tamilnadu, India
| | - Vino Sundararajan
- Department of Bio-Sciences, School of Bio Sciences and Technology, Vellore Institute of Technology, Vellore, Tamilnadu, India
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Zhao J, Zhu X, Tan S, Chen C, Kaddoumi A, Guo XL, Lin YW, Cheung SYA. Editorial: Model-informed drug development and evidence-based translational pharmacology. Front Pharmacol 2022; 13:1086551. [PMID: 36578539 PMCID: PMC9791580 DOI: 10.3389/fphar.2022.1086551] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Accepted: 12/05/2022] [Indexed: 12/14/2022] Open
Affiliation(s)
- Jinxin Zhao
- Biomedicine Discovery Institute, Infection and Immunity Program and Department of Microbiology, Monash University, Melbourne, VIC, Australia
| | - Xiao Zhu
- Department of Clinical Pharmacy and Pharmacy Administration, School of Pharmacy, Fudan University, Shanghai, China
| | - Songwen Tan
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, China,*Correspondence: Songwen Tan, ; Chuanpin Chen, ; Amal Kaddoumi, ; Xiu-Li Guo, ; Yu-Wei Lin, ; S. Y. Amy Cheung,
| | - Chuanpin Chen
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, China,*Correspondence: Songwen Tan, ; Chuanpin Chen, ; Amal Kaddoumi, ; Xiu-Li Guo, ; Yu-Wei Lin, ; S. Y. Amy Cheung,
| | - Amal Kaddoumi
- Department of Drug Discovery and Development, Harrison College of Pharmacy, Auburn University, Auburn, AL, United States,*Correspondence: Songwen Tan, ; Chuanpin Chen, ; Amal Kaddoumi, ; Xiu-Li Guo, ; Yu-Wei Lin, ; S. Y. Amy Cheung,
| | - Xiu-Li Guo
- Department of Pharmacology, School of Pharmaceutical Science, Shandong University, Jinan, China,*Correspondence: Songwen Tan, ; Chuanpin Chen, ; Amal Kaddoumi, ; Xiu-Li Guo, ; Yu-Wei Lin, ; S. Y. Amy Cheung,
| | - Yu-Wei Lin
- Biomedicine Discovery Institute, Infection and Immunity Program and Department of Microbiology, Monash University, Melbourne, VIC, Australia,Malaya Translational and Clinical Pharmacometrics Group, Faculty of Pharmacy, University of Malaya, Kuala Lumpur, Malaysia,Department of Clinical Pharmacy and Pharmacy Practice, Faculty of Pharmacy, University of Malaya, Kuala Lumpur, Malaysia,Integrated Drug Development, Certara, NJ, United States,*Correspondence: Songwen Tan, ; Chuanpin Chen, ; Amal Kaddoumi, ; Xiu-Li Guo, ; Yu-Wei Lin, ; S. Y. Amy Cheung,
| | - S. Y. Amy Cheung
- Integrated Drug Development, Certara, NJ, United States,*Correspondence: Songwen Tan, ; Chuanpin Chen, ; Amal Kaddoumi, ; Xiu-Li Guo, ; Yu-Wei Lin, ; S. Y. Amy Cheung,
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26
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New Drug Design Avenues Targeting Alzheimer's Disease by Pharmacoinformatics-Aided Tools. Pharmaceutics 2022; 14:pharmaceutics14091914. [PMID: 36145662 PMCID: PMC9503559 DOI: 10.3390/pharmaceutics14091914] [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: 08/05/2022] [Revised: 09/03/2022] [Accepted: 09/06/2022] [Indexed: 11/30/2022] Open
Abstract
Neurodegenerative diseases (NDD) have been of great interest to scientists for a long time due to their multifactorial character. Among these pathologies, Alzheimer’s disease (AD) is of special relevance, and despite the existence of approved drugs for its treatment, there is still no efficient pharmacological therapy to stop, slow, or repair neurodegeneration. Existing drugs have certain disadvantages, such as lack of efficacy and side effects. Therefore, there is a real need to discover new drugs that can deal with this problem. However, as AD is multifactorial in nature with so many physiological pathways involved, the most effective approach to modulate more than one of them in a relevant manner and without undesirable consequences is through polypharmacology. In this field, there has been significant progress in recent years in terms of pharmacoinformatics tools that allow the discovery of bioactive molecules with polypharmacological profiles without the need to spend a long time and excessive resources on complex experimental designs, making the drug design and development pipeline more efficient. In this review, we present from different perspectives how pharmacoinformatics tools can be useful when drug design programs are designed to tackle complex diseases such as AD, highlighting essential concepts, showing the relevance of artificial intelligence and new trends, as well as different databases and software with their main results, emphasizing the importance of coupling wet and dry approaches in drug design and development processes.
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27
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Gong W, Wang K, Wang X, Chen Y, Qin X, Lu A, Guan D. Mathematical algorithm–based identification of the functional components and mechanisms in depression treatment: An example of Danggui-Shaoyao-San. Front Cell Dev Biol 2022; 10:937621. [PMID: 36072347 PMCID: PMC9441958 DOI: 10.3389/fcell.2022.937621] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Accepted: 07/14/2022] [Indexed: 12/02/2022] Open
Abstract
Depression, a complex epidemiological mental disorder, affects around 350 million people worldwide. Despite the availability of antidepressants based on monoamine hypothesis of depression, most patients suffer side effects from these drugs, including psychomotor impairment and dependence liability. Traditional Chinese medicine (TCM) is receiving more and more attention due to the advantages of high therapeutic performance and few side effects in depression treatment. However, complex multicomponents and multi-targets in TCM hinder our ability to identify the functional components and molecular mechanisms of its efficacy. In this study, we designed a novel strategy to capture the functional components and mechanisms of TCM based on a mathematical algorithm. To establish proof of principle, the TCM formula Danggui-Shaoyao-San (DSS), which possesses remarkable antidepressant effect but its functional components and mechanisms are unclear, is used as an example. According to the network motif detection algorithm, key core function motifs (CIM) of DSS in treating depression were captured, followed by a functional analysis and verification. The results demonstrated that 198 pathways were enriched by the target genes of the CIM, and 179 coincided with the enriched pathways of pathogenic genes, accounting for 90.40% of the gene enrichment pathway of the C-T network. Then the functional components group (FCG) comprising 40 components was traced from CIM based on the target coverage accumulation algorithm, after which the pathways enriched by the target genes of FCG were selected to elucidate the potential mechanisms of DSS in treating depression. Finally, the pivotal components in FCG of DSS and the related pathways were selected for experimental validation in vitro and in vivo. Our results indicated good accuracy of the proposed mathematical algorithm in sifting the FCG from the TCM formula, which provided a methodological reference for discovering functional components and interpreting molecular mechanisms of the TCM formula in treating complex diseases.
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Affiliation(s)
- Wenxia Gong
- Modern Research Center for Traditional Chinese Medicine of Shanxi University, Taiyuan, Shanxi, China
- Key Laboratory of Chemical Biology and Molecular Engineering of Ministry of Education, Taiyuan, Shanxi, China
- Key Laboratory of Effective Substances Research and Utilization in TCM of Shanxi Province, Taiyuan, Shanxi, China
| | - Kexin Wang
- National Key Clinical Specialty/Engineering Technology Research Center of Education Ministry of China, Guangdong Provincial Key Laboratory on Brain Function Repair and Regeneration, Department of Neurosurgery, Neurosurgery Institute, Guangzhou, China
- Institute of Integrated Bioinformedicine and Translational Science, Hong Kong Baptist University, Hong Kong, China
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Xueyuan Wang
- Modern Research Center for Traditional Chinese Medicine of Shanxi University, Taiyuan, Shanxi, China
| | - Yupeng Chen
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Xuemei Qin
- Modern Research Center for Traditional Chinese Medicine of Shanxi University, Taiyuan, Shanxi, China
- Key Laboratory of Chemical Biology and Molecular Engineering of Ministry of Education, Taiyuan, Shanxi, China
- Key Laboratory of Effective Substances Research and Utilization in TCM of Shanxi Province, Taiyuan, Shanxi, China
- *Correspondence: Xuemei Qin, ; Aiping Lu, ; Daogang Guan,
| | - Aiping Lu
- Institute of Integrated Bioinformedicine and Translational Science, Hong Kong Baptist University, Hong Kong, China
- *Correspondence: Xuemei Qin, ; Aiping Lu, ; Daogang Guan,
| | - Daogang Guan
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
- Guangdong Key Laboratory of Single Cell Technology and Application, Southern Medical University, Guangzhou, China
- *Correspondence: Xuemei Qin, ; Aiping Lu, ; Daogang Guan,
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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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29
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Siler SQ. Applications of Quantitative Systems Pharmacology (QSP) in Drug Development for NAFLD and NASH and Its Regulatory Application. Pharm Res 2022; 39:1789-1802. [PMID: 35610402 PMCID: PMC9314276 DOI: 10.1007/s11095-022-03295-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Accepted: 05/17/2022] [Indexed: 02/08/2023]
Abstract
Nonalcoholic steatohepatitis (NASH) is a widely prevalent disease, but approved pharmaceutical treatments are not available. As such, there is great activity within the pharmaceutical industry to accelerate drug development in this area and improve the quality of life and reduce mortality for NASH patients. The use of quantitative systems pharmacology (QSP) can help make this overall process more efficient. This mechanism-based mathematical modeling approach describes both the pathophysiology of a disease and how pharmacological interventions can modify pathophysiologic mechanisms. Multiple capabilities are provided by QSP modeling, including the use of model predictions to optimize clinical studies. The use of this approach has grown over the last 20 years, motivating discussions between modelers and regulators to agree upon methodologic standards. These include model transparency, documentation, and inclusion of clinical pharmacodynamic biomarkers. Several QSP models have been developed that describe NASH pathophysiology to varying extents. One specific application of NAFLDsym, a QSP model of NASH, is described in this manuscript. Simulations were performed to help understand if patient behaviors could help explain the relatively high rate of fibrosis stage reductions in placebo cohorts. Simulated food intake and body weight fluctuated periodically over time. The relatively slow turnover of liver collagen allowed persistent reductions in predicted fibrosis stage despite return to baseline for liver fat, plasma ALT, and the NAFLD activity score. Mechanistic insights such as this that have been derived from QSP models can help expedite the development of safe and effective treatments for NASH patients.
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Affiliation(s)
- Scott Q Siler
- DILIsym Services, a Division of Simulations Plus, 510-862-6027, 6 Davis Drive, PO Box 12317, Research Triangle Park, North Carolina, 27709, USA.
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30
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Zhang T, Cho CR, Bonate PL. Perspectives on training quantitative systems pharmacologists. CPT Pharmacometrics Syst Pharmacol 2022; 11:669-672. [PMID: 35388628 PMCID: PMC9197534 DOI: 10.1002/psp4.12783] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Revised: 01/24/2022] [Accepted: 02/28/2022] [Indexed: 11/08/2022] Open
Affiliation(s)
- Tongli Zhang
- Department of Pharmacology & Systems Physiology, College of Medicine University of Cincinnati Cincinnati Ohio USA
| | - Carolyn R. Cho
- Quantitative Pharmacology and Pharmacometrics‐Immuno‐onocology Merck & Co., Inc. Kenilworth New Jersey USA
| | - Peter L. Bonate
- Clinical Pharmacology and Exploratory Development, New Technologies Astellas Northbrook Illinois USA
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31
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Quantitative Systems Pharmacology and Biased Agonism at Opioid Receptors: A Potential Avenue for Improved Analgesics. Int J Mol Sci 2022; 23:ijms23095114. [PMID: 35563502 PMCID: PMC9104178 DOI: 10.3390/ijms23095114] [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: 03/31/2022] [Revised: 05/01/2022] [Accepted: 05/02/2022] [Indexed: 11/25/2022] Open
Abstract
Chronic pain is debilitating and represents a significant burden in terms of personal and socio-economic costs. Although opioid analgesics are widely used in chronic pain treatment, many patients report inadequate pain relief or relevant adverse effects, highlighting the need to develop analgesics with improved efficacy/safety. Multiple evidence suggests that G protein-dependent signaling triggers opioid-induced antinociception, whereas arrestin-mediated pathways are credited with modulating different opioid adverse effects, thus spurring extensive research for G protein-biased opioid agonists as analgesic candidates with improved pharmacology. Despite the increasing expectations of functional selectivity, translating G protein-biased opioid agonists into improved therapeutics is far from being fully achieved, due to the complex, multidimensional pharmacology of opioid receptors. The multifaceted network of signaling events and molecular processes underlying therapeutic and adverse effects induced by opioids is more complex than the mere dichotomy between G protein and arrestin and requires more comprehensive, integrated, network-centric approaches to be fully dissected. Quantitative Systems Pharmacology (QSP) models employing multidimensional assays associated with computational tools able to analyze large datasets may provide an intriguing approach to go beyond the greater complexity of opioid receptor pharmacology and the current limitations entailing the development of biased opioid agonists as improved analgesics.
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Karatza E, Yakovleva T, Adams K, Rao GG, Ait-Oudhia S. Knowledge dissemination and central indexing of resources in pharmacometrics: an ISOP education working group initiative. J Pharmacokinet Pharmacodyn 2022; 49:397-400. [PMID: 35474412 DOI: 10.1007/s10928-022-09809-9] [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: 04/03/2022] [Accepted: 04/11/2022] [Indexed: 10/18/2022]
Abstract
Pharmacometrics is a constantly evolving field that plays a major role in decision making in drug development and clinical monitoring. Scientists in Pharmacometrics, especially in their early phases of career, are often faced with the challenge of identifying adequate resources for self-training and education. Hence, the ISoP Education Committee through its working group dedicated to Central Indexing and knowledge Dissemination has built a database of worldwide educational programs and most common references in Pharmacometrics.
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Affiliation(s)
- Eleni Karatza
- Division of Pharmacotherapy and Experimental Therapeutics, UNC Eshelman School of Pharmacy, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | | | - Kimberly Adams
- University at Buffalo, State University of New York at Buffalo, Buffalo, NY, USA
| | - Gauri G Rao
- Division of Pharmacotherapy and Experimental Therapeutics, UNC Eshelman School of Pharmacy, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Sihem Ait-Oudhia
- Quantitative Pharmacology and Pharmacometrics (QP2), Merck & Co., Inc, 2000 Galloping Hill Rd., Kenilworth, NJ, 07033, USA.
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Bartlett DW, Gilbert AM. Translational PK-PD for targeted protein degradation. Chem Soc Rev 2022; 51:3477-3486. [PMID: 35438107 DOI: 10.1039/d2cs00114d] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Targeted protein degradation has emerged from the chemical biology toolbox as one of the most exciting areas for novel therapeutic development across the pharmaceutical industry. The ability to induce the degradation, and not just inhibition, of target proteins of interest (POIs) with high potency and selectivity is a particularly attractive property for a protein degrader therapeutic. However, the physicochemical properties and mechanism of action for protein degraders can lead to unique pharmacokinetic (PK) and pharmacodynamic (PD) properties relative to traditional small molecule drugs, requiring a shift in perspective for translational pharmacology. In this review, we provide practical insights for building the PK-PD understanding of protein degraders in the context of translational drug development through the use of quantitative mathematical frameworks and standard experimental assays. Published datasets describing protein degrader pharmacology are used to illustrate the applicability of these insights. The learnings are consolidated into a translational PK-PD roadmap for targeted protein degradation that can enable a systematic, rational design workflow for protein degrader therapeutics.
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Affiliation(s)
- Derek W Bartlett
- Pharmacokinetics, Dynamics, & Metabolism, Pfizer Worldwide Research, Development and Medical, Pfizer Inc, San Diego, CA, USA.
| | - Adam M Gilbert
- Discovery Sciences, Pfizer Worldwide Research, Development and Medical, Pfizer Inc, Groton, CT, USA
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34
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Xavier JB, Monk JM, Poudel S, Norsigian CJ, Sastry AV, Liao C, Bento J, Suchard MA, Arrieta-Ortiz ML, Peterson EJ, Baliga NS, Stoeger T, Ruffin F, Richardson RA, Gao CA, Horvath TD, Haag AM, Wu Q, Savidge T, Yeaman MR. Mathematical models to study the biology of pathogens and the infectious diseases they cause. iScience 2022; 25:104079. [PMID: 35359802 PMCID: PMC8961237 DOI: 10.1016/j.isci.2022.104079] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
Mathematical models have many applications in infectious diseases: epidemiologists use them to forecast outbreaks and design containment strategies; systems biologists use them to study complex processes sustaining pathogens, from the metabolic networks empowering microbial cells to ecological networks in the microbiome that protects its host. Here, we (1) review important models relevant to infectious diseases, (2) draw parallels among models ranging widely in scale. We end by discussing a minimal set of information for a model to promote its use by others and to enable predictions that help us better fight pathogens and the diseases they cause.
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Affiliation(s)
- Joao B. Xavier
- Program for Computational and Systems Biology, Memorial Sloan-Kettering Cancer Center, New York, NY, USA
| | | | - Saugat Poudel
- Department of Bioengineering, UC San Diego, San Diego, CA, USA
| | | | - Anand V. Sastry
- Department of Bioengineering, UC San Diego, San Diego, CA, USA
| | - Chen Liao
- Program for Computational and Systems Biology, Memorial Sloan-Kettering Cancer Center, New York, NY, USA
| | - Jose Bento
- Computer Science Department, Boston College, Chestnut Hill, MA, USA
| | - Marc A. Suchard
- Department of Computational Medicine, David Geffen School of Medicine at UCLA, University of California, Los Angeles, CA, USA
| | | | | | | | - Thomas Stoeger
- Department of Chemical and Biological Engineering; Northwestern University, Evanston, IL 60208, USA
- Successful Clinical Response in Pneumonia Therapy (SCRIPT) Systems Biology Center, Northwestern University, Chicago, IL, USA
| | - Felicia Ruffin
- Division of Infectious Diseases, Department of Medicine, Duke University School of Medicine, Durham, NC, USA
| | - Reese A.K. Richardson
- Department of Chemical and Biological Engineering; Northwestern University, Evanston, IL 60208, USA
- Successful Clinical Response in Pneumonia Therapy (SCRIPT) Systems Biology Center, Northwestern University, Chicago, IL, USA
| | - Catherine A. Gao
- Successful Clinical Response in Pneumonia Therapy (SCRIPT) Systems Biology Center, Northwestern University, Chicago, IL, USA
- Division of Pulmonary and Critical Care, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Thomas D. Horvath
- Department of Pathology and Immunology, Baylor College of Medicine, Houston, TX 77030, USA
- Department of Pathology, Texas Children’s Microbiome Center, Texas Children’s Hospital, Houston, TX 77030, USA
| | - Anthony M. Haag
- Department of Pathology and Immunology, Baylor College of Medicine, Houston, TX 77030, USA
- Department of Pathology, Texas Children’s Microbiome Center, Texas Children’s Hospital, Houston, TX 77030, USA
| | - Qinglong Wu
- Department of Pathology and Immunology, Baylor College of Medicine, Houston, TX 77030, USA
- Department of Pathology, Texas Children’s Microbiome Center, Texas Children’s Hospital, Houston, TX 77030, USA
| | - Tor Savidge
- Department of Pathology and Immunology, Baylor College of Medicine, Houston, TX 77030, USA
- Department of Pathology, Texas Children’s Microbiome Center, Texas Children’s Hospital, Houston, TX 77030, USA
| | - Michael R. Yeaman
- David Geffen School of Medicine at UCLA & Lundquist Institute for Infection & Immunity at Harbor UCLA Medical Center, Los Angeles, CA, USA
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Zhang T, Androulakis IP, Bonate P, Cheng L, Helikar T, Parikh J, Rackauckas C, Subramanian K, Cho CR. Two heads are better than one: current landscape of integrating QSP and machine learning : An ISoP QSP SIG white paper by the working group on the integration of quantitative systems pharmacology and machine learning. J Pharmacokinet Pharmacodyn 2022; 49:5-18. [PMID: 35103884 PMCID: PMC8837505 DOI: 10.1007/s10928-022-09805-z] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Accepted: 01/10/2022] [Indexed: 12/02/2022]
Abstract
Quantitative systems pharmacology (QSP) modeling is applied to address essential questions in drug development, such as the mechanism of action of a therapeutic agent and the progression of disease. Meanwhile, machine learning (ML) approaches also contribute to answering these questions via the analysis of multi-layer ‘omics’ data such as gene expression, proteomics, metabolomics, and high-throughput imaging. Furthermore, ML approaches can also be applied to aspects of QSP modeling. Both approaches are powerful tools and there is considerable interest in integrating QSP modeling and ML. So far, a few successful implementations have been carried out from which we have learned about how each approach can overcome unique limitations of the other. The QSP + ML working group of the International Society of Pharmacometrics QSP Special Interest Group was convened in September, 2019 to identify and begin realizing new opportunities in QSP and ML integration. The working group, which comprises 21 members representing 18 academic and industry organizations, has identified four categories of current research activity which will be described herein together with case studies of applications to drug development decision making. The working group also concluded that the integration of QSP and ML is still in its early stages of moving from evaluating available technical tools to building case studies. This paper reports on this fast-moving field and serves as a foundation for future codification of best practices.
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Affiliation(s)
- Tongli Zhang
- University of Cincinnati, Cincinnati, OH, 45267, USA.
| | | | | | | | - Tomáš Helikar
- Department of Biochemistry, University of Nebraska-Lincoln, Lincoln, NE, USA
| | | | - Christopher Rackauckas
- Pumas-AI, Baltimore, MD, USA.,Department of Mathematics, Massachusetts Institute of Technology, Boston, MA, USA
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36
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Wen HN, Wang CY, Li JM, Jiao Z. Precision Cardio-Oncology: Use of Mechanistic Pharmacokinetic and Pharmacodynamic Modeling to Predict Cardiotoxicities of Anti-Cancer Drugs. Front Oncol 2022; 11:814699. [PMID: 35083161 PMCID: PMC8784755 DOI: 10.3389/fonc.2021.814699] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2021] [Accepted: 12/15/2021] [Indexed: 12/18/2022] Open
Abstract
The cardiotoxicity of anti-cancer drugs presents as a challenge to both clinicians and patients. Significant advances in cancer treatments have improved patient survival rates, but have also led to the chronic effects of anti-cancer therapies becoming more prominent. Additionally, it is difficult to clinically predict the occurrence of cardiovascular toxicities given that they can be transient or irreversible, with large between-subject variabilities. Further, cardiotoxicities present a range of different symptoms and pathophysiological mechanisms. These notwithstanding, mechanistic pharmacokinetic (PK) and pharmacodynamic (PD) modeling offers an important approach to predict cardiotoxicities and offering precise cardio-oncological care. Efforts have been made to integrate the structures of physiological and pharmacological networks into PK-PD modeling to the end of predicting cardiotoxicities based on clinical evaluation as well as individual variabilities, such as protein expression, and physiological changes under different disease states. Thus, this review aims to report recent progress in the use of PK-PD modeling to predict cardiovascular toxicities, as well as its application in anti-cancer therapies.
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Affiliation(s)
- Hai-Ni Wen
- Department of Pharmacy, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Chen-Yu Wang
- Department of Pharmacy, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Jin-Meng Li
- Department of Pharmacy, Affiliated Hangzhou Chest Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Zheng Jiao
- Department of Pharmacy, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China
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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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Mehta K, Spaink HP, Ottenhoff THM, van der Graaf PH, van Hasselt JGC. Host-directed therapies for tuberculosis: quantitative systems pharmacology approaches. Trends Pharmacol Sci 2021; 43:293-304. [PMID: 34916092 DOI: 10.1016/j.tips.2021.11.016] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2021] [Revised: 10/26/2021] [Accepted: 11/18/2021] [Indexed: 12/26/2022]
Abstract
Host-directed therapies (HDTs) that modulate host-pathogen interactions offer an innovative strategy to combat Mycobacterium tuberculosis (Mtb) infections. When combined with tuberculosis (TB) antibiotics, HDTs could contribute to improving treatment outcomes, reducing treatment duration, and preventing resistance development. Translation of the interplay of host-pathogen interactions leveraged by HDTs towards therapeutic outcomes in patients is challenging. Quantitative understanding of the multifaceted nature of the host-pathogen interactions is vital to rationally design HDT strategies. Here, we (i) provide an overview of key Mtb host-pathogen interactions as basis for HDT strategies; and (ii) discuss the components and utility of quantitative systems pharmacology (QSP) models to inform HDT strategies. QSP models can be used to identify and optimize treatment targets, to facilitate preclinical to human translation, and to design combination treatment strategies.
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Affiliation(s)
| | | | - Tom H M Ottenhoff
- Department of Infectious Diseases, Leiden University Medical Center, Leiden, The Netherlands
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Selvaggio G, Leonardelli L, Lofano G, Fresnay S, Parolo S, Medini D, Siena E, Marchetti L. A quantitative systems pharmacology approach to support mRNA vaccine development and optimization. CPT Pharmacometrics Syst Pharmacol 2021; 10:1448-1451. [PMID: 34672423 PMCID: PMC8674002 DOI: 10.1002/psp4.12721] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Revised: 09/13/2021] [Accepted: 09/20/2021] [Indexed: 12/27/2022] Open
Affiliation(s)
- Gianluca Selvaggio
- Fondazione The Microsoft Research – University of Trento Centre for Computational and Systems Biology (COSBI)RoveretoItaly
| | - Lorena Leonardelli
- Fondazione The Microsoft Research – University of Trento Centre for Computational and Systems Biology (COSBI)RoveretoItaly
| | | | | | - Silivia Parolo
- Fondazione The Microsoft Research – University of Trento Centre for Computational and Systems Biology (COSBI)RoveretoItaly
| | | | | | - Luca Marchetti
- Fondazione The Microsoft Research – University of Trento Centre for Computational and Systems Biology (COSBI)RoveretoItaly
- Department of Cellular, Computational and Integrative Biology (CIBIO)University of TrentoTrentoItaly
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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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Aghamiri SS, Amin R, Helikar T. Recent applications of quantitative systems pharmacology and machine learning models across diseases. J Pharmacokinet Pharmacodyn 2021; 49:19-37. [PMID: 34671863 PMCID: PMC8528185 DOI: 10.1007/s10928-021-09790-9] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Accepted: 10/07/2021] [Indexed: 12/29/2022]
Abstract
Quantitative systems pharmacology (QSP) is a quantitative and mechanistic platform describing the phenotypic interaction between drugs, biological networks, and disease conditions to predict optimal therapeutic response. In this meta-analysis study, we review the utility of the QSP platform in drug development and therapeutic strategies based on recent publications (2019-2021). We gathered recent original QSP models and described the diversity of their applications based on therapeutic areas, methodologies, software platforms, and functionalities. The collection and investigation of these publications can assist in providing a repository of recent QSP studies to facilitate the discovery and further reusability of QSP models. Our review shows that the largest number of QSP efforts in recent years is in Immuno-Oncology. We also addressed the benefits of integrative approaches in this field by presenting the applications of Machine Learning methods for drug discovery and QSP models. Based on this meta-analysis, we discuss the advantages and limitations of QSP models and propose fields where the QSP approach constitutes a valuable interface for more investigations to tackle complex diseases and improve drug development.
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Affiliation(s)
- Sara Sadat Aghamiri
- Department of Biochemistry, University of Nebraska-Lincoln, Lincoln, NE, USA
| | - Rada Amin
- Department of Biochemistry, University of Nebraska-Lincoln, Lincoln, NE, USA.
| | - Tomáš Helikar
- Department of Biochemistry, University of Nebraska-Lincoln, Lincoln, NE, USA.
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Parolo S, Tomasoni D, Bora P, Ramponi A, Kaddi C, Azer K, Domenici E, Neves-Zaph S, Lombardo R. Reconstruction of the Cytokine Signaling in Lysosomal Storage Diseases by Literature Mining and Network Analysis. Front Cell Dev Biol 2021; 9:703489. [PMID: 34490253 PMCID: PMC8417786 DOI: 10.3389/fcell.2021.703489] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Accepted: 07/30/2021] [Indexed: 11/13/2022] Open
Abstract
Lysosomal storage diseases (LSDs) are characterized by the abnormal accumulation of substrates in tissues due to the deficiency of lysosomal proteins. Among the numerous clinical manifestations, chronic inflammation has been consistently reported for several LSDs. However, the molecular mechanisms involved in the inflammatory response are still not completely understood. In this study, we performed text-mining and systems biology analyses to investigate the inflammatory signals in three LSDs characterized by sphingolipid accumulation: Gaucher disease, Acid Sphingomyelinase Deficiency (ASMD), and Fabry Disease. We first identified the cytokines linked to the LSDs, and then built on the extracted knowledge to investigate the inflammatory signals. We found numerous transcription factors that are putative regulators of cytokine expression in a cell-specific context, such as the signaling axes controlled by STAT2, JUN, and NR4A2 as candidate regulators of the monocyte Gaucher disease cytokine network. Overall, our results suggest the presence of a complex inflammatory signaling in LSDs involving many cellular and molecular players that could be further investigated as putative targets of anti-inflammatory therapies.
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Affiliation(s)
- Silvia Parolo
- Fondazione the Microsoft Research-University of Trento Centre for Computational and Systems Biology, Rovereto, Italy
| | - Danilo Tomasoni
- Fondazione the Microsoft Research-University of Trento Centre for Computational and Systems Biology, Rovereto, Italy
| | - Pranami Bora
- Fondazione the Microsoft Research-University of Trento Centre for Computational and Systems Biology, Rovereto, Italy
| | - Alan Ramponi
- Fondazione the Microsoft Research-University of Trento Centre for Computational and Systems Biology, Rovereto, Italy
| | - Chanchala Kaddi
- Data and Data Science - Translational Disease Modeling, Sanofi, Bridgewater, NJ, United States
| | - Karim Azer
- Data and Data Science - Translational Disease Modeling, Sanofi, Bridgewater, NJ, United States
| | - Enrico Domenici
- Fondazione the Microsoft Research-University of Trento Centre for Computational and Systems Biology, Rovereto, Italy.,Department of Cellular, Computational and Integrative Biology (CIBIO), University of Trento, Trento, Italy
| | - Susana Neves-Zaph
- Data and Data Science - Translational Disease Modeling, Sanofi, Bridgewater, NJ, United States
| | - Rosario Lombardo
- Fondazione the Microsoft Research-University of Trento Centre for Computational and Systems Biology, Rovereto, Italy
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Tomasoni D, Paris A, Giampiccolo S, Reali F, Simoni G, Marchetti L, Kaddi C, Neves-Zaph S, Priami C, Azer K, Lombardo R. QSPcc reduces bottlenecks in computational model simulations. Commun Biol 2021; 4:1022. [PMID: 34471226 PMCID: PMC8410852 DOI: 10.1038/s42003-021-02553-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Accepted: 08/09/2021] [Indexed: 01/13/2023] Open
Abstract
Mathematical models have grown in size and complexity becoming often computationally intractable. In sensitivity analysis and optimization phases, critical for tuning, validation and qualification, these models may be run thousands of times. Scientific programming languages popular for prototyping, such as MATLAB and R, can be a bottleneck in terms of performance. Here we show a compiler-based approach, designed to be universal at handling engineering and life sciences modeling styles, that automatically translates models into fast C code. At first QSPcc is demonstrated to be crucial in enabling the research on otherwise intractable Quantitative Systems Pharmacology models, such as in rare Lysosomal Storage Disorders. To demonstrate the full value in seamlessly accelerating, or enabling, the R&D efforts in natural sciences, we then benchmark QSPcc against 8 solutions on 24 real-world projects from different scientific fields. With speed-ups of 22000x peak, and 1605x arithmetic mean, our results show consistent superior performances. Lombardo and colleagues present QSPcc, a computational code compiler designed to convert code from popular scientific programming languages, such as MATLAB or R, into fast-running C code. This reduces the computational load required for complex modelling approaches and reduces user investment learning additional complex languages.
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Affiliation(s)
- Danilo Tomasoni
- Fondazione the Microsoft Research, University of Trento Centre for Computational and Systems Biology, Rovereto, Italy
| | - Alessio Paris
- Fondazione the Microsoft Research, University of Trento Centre for Computational and Systems Biology, Rovereto, Italy
| | - Stefano Giampiccolo
- Fondazione the Microsoft Research, University of Trento Centre for Computational and Systems Biology, Rovereto, Italy
| | - Federico Reali
- Fondazione the Microsoft Research, University of Trento Centre for Computational and Systems Biology, Rovereto, Italy
| | - Giulia Simoni
- Fondazione the Microsoft Research, University of Trento Centre for Computational and Systems Biology, Rovereto, Italy
| | - Luca Marchetti
- Fondazione the Microsoft Research, University of Trento Centre for Computational and Systems Biology, Rovereto, Italy
| | - Chanchala Kaddi
- Data and Data Science - Translational Disease Modeling, Sanofi, Bridgewater, NJ, USA
| | - Susana Neves-Zaph
- Data and Data Science - Translational Disease Modeling, Sanofi, Bridgewater, NJ, USA
| | - Corrado Priami
- Fondazione the Microsoft Research, University of Trento Centre for Computational and Systems Biology, Rovereto, Italy.,Department of Computer Science, University of Pisa, Pisa, Italy
| | - Karim Azer
- Data and Data Science - Translational Disease Modeling, Sanofi, Bridgewater, NJ, USA.,Axcella Health, Cambridge, MA, USA
| | - Rosario Lombardo
- Fondazione the Microsoft Research, University of Trento Centre for Computational and Systems Biology, Rovereto, Italy.
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