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Akki AJ, Patil SA, Hungund S, Sahana R, Patil MM, Kulkarni RV, Raghava Reddy K, Zameer F, Raghu AV. Advances in Parkinson's disease research - A computational network pharmacological approach. Int Immunopharmacol 2024; 139:112758. [PMID: 39067399 DOI: 10.1016/j.intimp.2024.112758] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2024] [Revised: 07/22/2024] [Accepted: 07/22/2024] [Indexed: 07/30/2024]
Abstract
Parkinson's disease (PD), the second most prevalent neurodegenerative disorder, is projected to see a significant rise in incidence over the next three decades. The precise treatment of PD remains a formidable challenge, prompting ongoing research into early diagnostic methodologies. Network pharmacology, a burgeoning field grounded in systems biology, examines the intricate networks of biological systems to identify critical signal nodes, facilitating the development of multi-target therapeutic molecules. This approach systematically maps the components of Parkinson's disease, thereby reducing its complexity. In this review, we explore the application of network pharmacology workflows in PD, discuss the techniques employed in this field, and evaluate the current advancements and status of network pharmacology in the context of Parkinson's disease. The comprehensive insights will pave newer paths to explore early disease biomarkers and to develop diagnosis with a holistic in silico, in vitro, in vivo and clinical studies.
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Affiliation(s)
- Ali Jawad Akki
- Faculty of Science and Technology, BLDE (Deemed-to-be University), Vijayapura 586 103, India
| | - Shruti A Patil
- Faculty of Science and Technology, BLDE (Deemed-to-be University), Vijayapura 586 103, India
| | - Sphoorty Hungund
- Faculty of Science and Technology, BLDE (Deemed-to-be University), Vijayapura 586 103, India
| | - R Sahana
- Department of Computer Science and Engineering, RV Institute of Technology and Management, 560 076 Bengaluru, India
| | - Malini M Patil
- Department of Computer Science and Engineering, RV Institute of Technology and Management, 560 076 Bengaluru, India.
| | - Raghavendra V Kulkarni
- Faculty of Science and Technology, BLDE (Deemed-to-be University), Vijayapura 586 103, India
| | - K Raghava Reddy
- School of Chemical and Biomolecular Engineering, The University of Sydney, Sydney, NSW 12 2006, Australia
| | - Farhan Zameer
- Department of Dravyaguna (Ayurveda Pharmacology), Alva's Ayurveda Medical College, and PathoGutOmics Laboratory, ATMA Research Centre, Dakshina Kannada 574 227, India.
| | - Anjanapura V Raghu
- Department of Basic Sciences, Faculty of Engineering and Technology, CMR University, 562149 Bangalore, India.
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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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Bloomingdale P, Karelina T, Ramakrishnan V, Bakshi S, Véronneau‐Veilleux F, Moye M, Sekiguchi K, Meno‐Tetang G, Mohan A, Maithreye R, Thomas VA, Gibbons F, Cabal A, Bouteiller J, Geerts H. Hallmarks of neurodegenerative disease: A systems pharmacology perspective. CPT Pharmacometrics Syst Pharmacol 2022; 11:1399-1429. [PMID: 35894182 PMCID: PMC9662204 DOI: 10.1002/psp4.12852] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Revised: 07/17/2022] [Accepted: 07/19/2022] [Indexed: 11/09/2022] Open
Abstract
Age-related central neurodegenerative diseases, such as Alzheimer's and Parkinson's disease, are a rising public health concern and have been plagued by repeated drug development failures. The complex nature and poor mechanistic understanding of the etiology of neurodegenerative diseases has hindered the discovery and development of effective disease-modifying therapeutics. Quantitative systems pharmacology models of neurodegeneration diseases may be useful tools to enhance the understanding of pharmacological intervention strategies and to reduce drug attrition rates. Due to the similarities in pathophysiological mechanisms across neurodegenerative diseases, especially at the cellular and molecular levels, we envision the possibility of structural components that are conserved across models of neurodegenerative diseases. Conserved structural submodels can be viewed as building blocks that are pieced together alongside unique disease components to construct quantitative systems pharmacology (QSP) models of neurodegenerative diseases. Model parameterization would likely be different between the different types of neurodegenerative diseases as well as individual patients. Formulating our mechanistic understanding of neurodegenerative pathophysiology as a mathematical model could aid in the identification and prioritization of drug targets and combinatorial treatment strategies, evaluate the role of patient characteristics on disease progression and therapeutic response, and serve as a central repository of knowledge. Here, we provide a background on neurodegenerative diseases, highlight hallmarks of neurodegeneration, and summarize previous QSP models of neurodegenerative diseases.
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Affiliation(s)
- Peter Bloomingdale
- Quantitative Pharmacology and PharmacometricsMerck & Co., Inc.BostonMassachusettsUSA
| | | | | | - Suruchi Bakshi
- Certara QSPOssThe Netherlands,Certara QSPPrincetonNew JerseyUSA
| | | | - Matthew Moye
- Quantitative Pharmacology and PharmacometricsMerck & Co., Inc.BostonMassachusettsUSA
| | - Kazutaka Sekiguchi
- Shionogi & Co., Ltd.OsakaJapan,SUNY Downstate Medical CenterNew YorkNew YorkUSA
| | | | | | | | | | - Frank Gibbons
- Clinical Pharmacology and PharmacometricsBiogenCambridgeMassachusettsUSA
| | | | - Jean‐Marie Bouteiller
- Center for Neural EngineeringDepartment of Biomedical Engineering at the Viterbi School of EngineeringLos AngelesCaliforniaUSA,Institute for Technology and Medical Systems Innovation, Keck School of MedicineUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
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Leonov V, Mogilevskaya E, Gerasimuk E, Gizzatkulov N, Demin O. CYTOCON: The manually curated database of human in vivo cell and molecule concentrations. CPT Pharmacometrics Syst Pharmacol 2022; 12:41-49. [PMID: 36128761 PMCID: PMC9835118 DOI: 10.1002/psp4.12867] [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: 05/15/2022] [Revised: 08/08/2022] [Accepted: 09/05/2022] [Indexed: 01/19/2023] Open
Abstract
Cell and cYTOkine CONcentrations DataBase (CYTOCON DB) is a project undertaken by the InSysBio team and aimed at the development of a database that allows collecting, processing, and visualizing publicly available in vivo human data on baseline concentrations of cells, cytokines, chemokines, and other molecules. Besides manual curation, an important feature of CYTOCON is that most values found in papers are converted from a huge variety of original units (i.e., mg/ml and number of cells per mm2 of biopsy surface section) to unified units: "pM" for cytokine and "kcell/L" for cell concentration. These features of the database can help researchers facilitate the creation and calibration of quantitative systems pharmacology (QSP) models. Possible applications can be: estimation of the average value or variability of the cell, cytokine or chemokine concentrations for patient groups with different characteristics, such as age, gender, disease severity, disease subtype, etc.; and analysis of correlations among the cell, cytokine, or chemokine concentrations in patients with different characteristics, such as severity of the disease.
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Affiliation(s)
| | | | | | | | - Oleg Demin
- INSYSBIO UK LTD.EdinburghUK,INSYSBIO LLCMoscowRussia
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Giacomini KM, Yee SW, Koleske ML, Zou L, Matsson P, Chen EC, Kroetz DL, Miller MA, Gozalpour E, Chu X. New and Emerging Research on Solute Carrier and ATP Binding Cassette Transporters in Drug Discovery and Development: Outlook From the International Transporter Consortium. Clin Pharmacol Ther 2022; 112:540-561. [PMID: 35488474 PMCID: PMC9398938 DOI: 10.1002/cpt.2627] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Accepted: 03/16/2022] [Indexed: 02/06/2023]
Abstract
Enabled by a plethora of new technologies, research in membrane transporters has exploded in the past decade. The goal of this state-of-the-art article is to describe recent advances in research on membrane transporters that are particularly relevant to drug discovery and development. This review covers advances in basic, translational, and clinical research that has led to an increased understanding of membrane transporters at all levels. At the basic level, we describe the available crystal structures of membrane transporters in both the solute carrier (SLC) and ATP binding cassette superfamilies, which has been enabled by the development of cryogenic electron microscopy methods. Next, we describe new research on lysosomal and mitochondrial transporters as well as recently deorphaned transporters in the SLC superfamily. The translational section includes a summary of proteomic research, which has led to a quantitative understanding of transporter levels in various cell types and tissues and new methods to modulate transporter function, such as allosteric modulators and targeted protein degraders of transporters. The section ends with a review of the effect of the gut microbiome on modulation of transporter function followed by a presentation of 3D cell cultures, which may enable in vivo predictions of transporter function. In the clinical section, we describe new genomic and pharmacogenomic research, highlighting important polymorphisms in transporters that are clinically relevant to many drugs. Finally, we describe new clinical tools, which are becoming increasingly available to enable precision medicine, with the application of tissue-derived small extracellular vesicles and real-world biomarkers.
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Affiliation(s)
- Kathleen M. Giacomini
- Department of Bioengineering and Therapeutic SciencesUniversity of California, San FranciscoSan FranciscoCaliforniaUSA
| | - Sook W. Yee
- Department of Bioengineering and Therapeutic SciencesUniversity of California, San FranciscoSan FranciscoCaliforniaUSA
| | - Megan L. Koleske
- Department of Bioengineering and Therapeutic SciencesUniversity of California, San FranciscoSan FranciscoCaliforniaUSA
| | - Ling Zou
- Pharmacokinetics and Drug MetabolismAmgen Inc.South San FranciscoCaliforniaUSA
| | - Pär Matsson
- Department of PharmacologySahlgrenska AcademyUniversity of GothenburgGothenburgSweden
| | - Eugene C. Chen
- Department of Drug Metabolism and PharmacokineticsGenentech, Inc.South San FranciscoCaliforniaUSA
| | - Deanna L. Kroetz
- Department of Bioengineering and Therapeutic SciencesUniversity of California, San FranciscoSan FranciscoCaliforniaUSA
| | - Miles A. Miller
- Center for Systems BiologyMassachusetts General HospitalBostonMassachusettsUSA
| | - Elnaz Gozalpour
- Drug Safety and MetabolismIMED Biotech UnitSafety and ADME Translational Sciences DepartmentAstraZeneca R&DCambridgeUK
| | - Xiaoyan Chu
- Department of ADME and Discovery ToxicologyMerck & Co. IncKenilworthNew JerseyUSA
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Kolesova G, Stepanov A, Lebedeva G, Demin O. Application of different approaches to generate virtual patient populations for the quantitative systems pharmacology model of erythropoiesis. J Pharmacokinet Pharmacodyn 2022; 49:511-524. [PMID: 35798926 DOI: 10.1007/s10928-022-09814-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2022] [Accepted: 06/19/2022] [Indexed: 12/01/2022]
Abstract
In a standard situation, a quantitative systems pharmacology model describes a "reference patient," and the model parameters are fixed values allowing only the mean values to be described. However, the results of clinical trials include a description of variability in patients' responses to a drug, which is typically expressed in terms of conventional statistical parameters, such as standard deviations (SDs) from mean values. Therefore, in this study, we propose and compare four different approaches: (1) Monte Carlo Markov Chain (MCMC); (2) model fitting to Monte Carlo sample; (3) population of clones; (4) stochastically bounded selection to generate virtual patient populations based on experimentally measured mean data and SDs. We applied these approaches to generate virtual patient populations in the QSP model of erythropoiesis. According to the results of our research, stochastically bounded selection showed slightly better results than the other three methods as it allowed the description of any number of patients from clinical trials and could be applied in the case of complex models with a large number of variable parameters.
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Affiliation(s)
| | | | - Galina Lebedeva
- InSysBio UK Limited, 17-19 East London Street, Edinburgh, EH7 4ZD, UK
| | - Oleg Demin
- InSysBio LLC, Nauchny proezd, 19, Moscow, Russia, 117246.,InSysBio UK Limited, 17-19 East London Street, Edinburgh, EH7 4ZD, UK
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Ribba B. Reinforcement learning as an innovative model-based approach: Examples from precision dosing, digital health and computational psychiatry. Front Pharmacol 2022; 13:1094281. [PMID: 36873047 PMCID: PMC9981647 DOI: 10.3389/fphar.2022.1094281] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Accepted: 12/12/2022] [Indexed: 02/19/2023] Open
Abstract
Model-based approaches are instrumental for successful drug development and use. Anchored within pharmacological principles, through mathematical modeling they contribute to the quantification of drug response variability and enables precision dosing. Reinforcement learning (RL)-a set of computational methods addressing optimization problems as a continuous learning process-shows relevance for precision dosing with high flexibility for dosing rule adaptation and for coping with high dimensional efficacy and/or safety markers, constituting a relevant approach to take advantage of data from digital health technologies. RL can also support contributions to the successful development of digital health applications, recognized as key players of the future healthcare systems, in particular for reducing the burden of non-communicable diseases to society. RL is also pivotal in computational psychiatry-a way to characterize mental dysfunctions in terms of aberrant brain computations-and represents an innovative modeling approach forpsychiatric indications such as depression or substance abuse disorders for which digital therapeutics are foreseen as promising modalities.
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Affiliation(s)
- Benjamin Ribba
- Roche Pharma Research and Early Development (pRED), F. Hoffmann-La Roche Ltd, Basel, Switzerland
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Bloomingdale P, Bakshi S, Maass C, van Maanen E, Pichardo-Almarza C, Yadav DB, van der Graaf P, Mehrotra N. Minimal brain PBPK model to support the preclinical and clinical development of antibody therapeutics for CNS diseases. J Pharmacokinet Pharmacodyn 2021; 48:861-871. [PMID: 34378151 PMCID: PMC8604880 DOI: 10.1007/s10928-021-09776-7] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2021] [Accepted: 07/29/2021] [Indexed: 11/01/2022]
Abstract
There are several antibody therapeutics in preclinical and clinical development, industry-wide, for the treatment of central nervous system (CNS) disorders. Due to the limited permeability of antibodies across brain barriers, the quantitative understanding of antibody exposure in the CNS is important for the design of antibody drug characteristics and determining appropriate dosing regimens. We have developed a minimal physiologically-based pharmacokinetic (mPBPK) model of the brain for antibody therapeutics, which was reduced from an existing multi-species platform brain PBPK model. All non-brain compartments were combined into a single tissue compartment and cerebral spinal fluid (CSF) compartments were combined into a single CSF compartment. The mPBPK model contains 16 differential equations, compared to 100 in the original PBPK model, and improved simulation speed approximately 11-fold. Area under the curve ratios for minimal versus full PBPK models were close to 1 across species for both brain and plasma compartments, which indicates the reduced model simulations are similar to those of the original model. The minimal model retained detailed physiological processes of the brain while not significantly affecting model predictability, which supports the law of parsimony in the context of balancing model complexity with added predictive power. The minimal model has a variety of applications for supporting the preclinical development of antibody therapeutics and can be expanded to include target information for evaluating target engagement to inform clinical dose selection.
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Affiliation(s)
- Peter Bloomingdale
- Pharmacokinetics, Pharmacodynamics, and Drug Metabolism, Merck & Co. Inc., Boston, MA, USA.
| | | | | | | | | | - Daniela Bumbaca Yadav
- Pharmacokinetics, Pharmacodynamics, and Drug Metabolism, Merck & Co. Inc., Boston, MA, USA
| | | | - Nitin Mehrotra
- Pharmacokinetics, Pharmacodynamics, and Drug Metabolism, Merck & Co. Inc., Boston, MA, USA
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