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Reali F, Fochesato A, Kaddi C, Visintainer R, Watson S, Levi M, Dartois V, Azer K, Marchetti L. A minimal PBPK model to accelerate preclinical development of drugs against tuberculosis. Front Pharmacol 2024; 14:1272091. [PMID: 38239195 PMCID: PMC10794428 DOI: 10.3389/fphar.2023.1272091] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Accepted: 12/04/2023] [Indexed: 01/22/2024] Open
Abstract
Introduction: Understanding drug exposure at disease target sites is pivotal to profiling new drug candidates in terms of tolerability and efficacy. Such quantification is particularly tedious for anti-tuberculosis (TB) compounds as the heterogeneous pulmonary microenvironment due to the infection may alter lung permeability and affect drug disposition. Murine models have been a longstanding support in TB research so far and are here used as human surrogates to unveil the distribution of several anti-TB compounds at the site-of-action via a novel and centralized PBPK design framework. Methods: As an intermediate approach between data-driven pharmacokinetic (PK) models and whole-body physiologically based (PB) PK models, we propose a parsimonious framework for PK investigation (minimal PBPK approach) that retains key physiological processes involved in TB disease, while reducing computational costs and prior knowledge requirements. By lumping together pulmonary TB-unessential organs, our minimal PBPK model counts 9 equations compared to the 36 of published full models, accelerating the simulation more than 3-folds in Matlab 2022b. Results: The model has been successfully tested and validated against 11 anti-TB compounds-rifampicin, rifapentine, pyrazinamide, ethambutol, isoniazid, moxifloxacin, delamanid, pretomanid, bedaquiline, OPC-167832, GSK2556286 - showing robust predictability power in recapitulating PK dynamics in mice. Structural inspections on the proposed design have ensured global identifiability and listed free fraction in plasma and blood-to-plasma ratio as top sensitive parameters for PK metrics. The platform-oriented implementation allows fast comparison of the compounds in terms of exposure and target attainment. Discrepancies in plasma and lung levels for the latest BPaMZ and HPMZ regimens have been analyzed in terms of their impact on preclinical experiment design and on PK/PD indices. Conclusion: The framework we developed requires limited drug- and species-specific information to reconstruct accurate PK dynamics, delivering a unified viewpoint on anti-TB drug distribution at the site-of-action and a flexible fit-for-purpose tool to accelerate model-informed drug design pipelines and facilitate translation into the clinic.
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Affiliation(s)
- Federico Reali
- Fondazione The Microsoft Research—University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto, Italy
| | - Anna Fochesato
- Fondazione The Microsoft Research—University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto, Italy
- Department of Mathematics, University of Trento, Povo, Italy
| | - Chanchala Kaddi
- Gates Medical Research Institute, Cambridge, MD, United States
| | - Roberto Visintainer
- Fondazione The Microsoft Research—University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto, Italy
| | - Shayne Watson
- Gates Medical Research Institute, Cambridge, MD, United States
| | - Micha Levi
- Gates Medical Research Institute, Cambridge, MD, United States
| | | | - Karim Azer
- Gates Medical Research Institute, Cambridge, MD, United States
| | - Luca Marchetti
- Fondazione The Microsoft Research—University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto, Italy
- Department of Cellular, Computational and Integrative Biology (CIBIO), University of Trento, Povo, Italy
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2
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Zhang K, Rengel Z, Zhang F, White PJ, Shen J. Rhizosphere engineering for sustainable crop production: entropy-based insights. TRENDS IN PLANT SCIENCE 2023; 28:390-398. [PMID: 36470795 DOI: 10.1016/j.tplants.2022.11.008] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/01/2022] [Revised: 11/12/2022] [Accepted: 11/16/2022] [Indexed: 06/17/2023]
Abstract
There is a growing interest in exploring interactions at root-soil interface in natural and agricultural ecosystems, but an entropy-based understanding of these dynamic rhizosphere processes is lacking. We have developed a new conceptual model of rhizosphere regulation by localized nutrient supply using thermodynamic entropy. Increased nutrient-use efficiency is achieved by rhizosphere management based on self-organization and minimized entropy via equilibrium attractors comprising (i) optimized root strategies for nutrient acquisition and (ii) improved information exchange related to root-soil-microbe interactions. The cascading effects through different hierarchical levels amplify the underlying processes in plant-soil system. We propose a strategy for manipulating rhizosphere dynamics and improving nutrient-use efficiency by localized nutrient supply with minimization of entropy to underpin sustainable food/feed/fiber production.
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Affiliation(s)
- Kai Zhang
- Centre for Resources, Environment and Food Security, Department of Plant Nutrition, Key Laboratory of Plant-Soil Interactions, National Academy of Agriculture Green Development, China Agricultural University, Beijing 100193, China
| | - Zed Rengel
- Soil Science and Plant Nutrition, UWA School of Agriculture and Environment, The University of Western Australia, Perth, WA 6009, Australia; Institute for Adriatic Crops and Karst Reclamation, Split 21000, Croatia
| | - Fusuo Zhang
- Centre for Resources, Environment and Food Security, Department of Plant Nutrition, Key Laboratory of Plant-Soil Interactions, National Academy of Agriculture Green Development, China Agricultural University, Beijing 100193, China
| | - Philip J White
- Ecological Sciences, The James Hutton Institute, Invergowrie, Dundee DD2 5DA, UK
| | - Jianbo Shen
- Centre for Resources, Environment and Food Security, Department of Plant Nutrition, Key Laboratory of Plant-Soil Interactions, National Academy of Agriculture Green Development, China Agricultural University, Beijing 100193, China.
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3
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Paris A, Bora P, Parolo S, Monine M, Tong X, Eraly S, Masson E, Ferguson T, McCampbell A, Graham D, Domenici E, Nestorov I, Marchetti L. An age‐dependent mathematical model of neurofilament trafficking in healthy conditions. CPT Pharmacometrics Syst Pharmacol 2022; 11:447-457. [PMID: 35146969 PMCID: PMC9007607 DOI: 10.1002/psp4.12770] [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: 10/15/2021] [Revised: 01/20/2022] [Accepted: 01/24/2022] [Indexed: 11/12/2022] Open
Abstract
Neurofilaments (Nfs) are the major structural component of neurons. Their role as a potential biomarker of several neurodegenerative diseases has been investigated in past years with promising results. However, even under physiological conditions, little is known about the leaking of Nfs from the neuronal system and their detection in the cerebrospinal fluid (CSF) and blood. This study aimed at developing a mathematical model of Nf transport in healthy subjects in the 20–90 age range. The model was implemented as a set of ordinary differential equations describing the trafficking of Nfs from the nervous system to the periphery. Model parameters were calibrated on typical Nf levels obtained from the literature. An age‐dependent function modeled on CSF data was also included and validated on data measured in serum. We computed a global sensitivity analysis of model rates and volumes to identify the most sensitive parameters affecting the model’s steady state. Age, Nf synthesis, and degradation rates proved to be relevant for all model variables. Nf levels in the CSF and in blood were observed to be sensitive to the Nf leakage rates from neurons and to the blood clearance rate, and CSF levels were also sensitive to rates representing CSF turnover. An additional parameter perturbation analysis was also performed to investigate possible transient effects on the model variables not captured by the sensitivity analysis. The model provides useful insights into Nf transport and constitutes the basis for implementing quantitative system pharmacology extensions to investigate Nf trafficking in neurodegenerative diseases.
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Affiliation(s)
- Alessio Paris
- 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
| | - Silvia Parolo
- Fondazione The Microsoft Research – University of Trento Centre for Computational and Systems Biology Rovereto Italy
| | | | - Xiao Tong
- Biogen, Inc. Cambridge Massachusetts USA
| | | | | | | | | | | | - Enrico Domenici
- Fondazione The Microsoft Research – University of Trento Centre for Computational and Systems Biology Rovereto Italy
- Department of Cellular, Computational and Integrative Biology University of Trento Trento Italy
| | | | - Luca Marchetti
- Fondazione The Microsoft Research – University of Trento Centre for Computational and Systems Biology Rovereto Italy
- Department of Cellular, Computational and Integrative Biology University of Trento Trento Italy
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4
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Rocca A, Kholodenko BN. Can Systems Biology Advance Clinical Precision Oncology? Cancers (Basel) 2021; 13:6312. [PMID: 34944932 PMCID: PMC8699328 DOI: 10.3390/cancers13246312] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2021] [Accepted: 12/10/2021] [Indexed: 12/13/2022] Open
Abstract
Precision oncology is perceived as a way forward to treat individual cancer patients. However, knowing particular cancer mutations is not enough for optimal therapeutic treatment, because cancer genotype-phenotype relationships are nonlinear and dynamic. Systems biology studies the biological processes at the systems' level, using an array of techniques, ranging from statistical methods to network reconstruction and analysis, to mathematical modeling. Its goal is to reconstruct the complex and often counterintuitive dynamic behavior of biological systems and quantitatively predict their responses to environmental perturbations. In this paper, we review the impact of systems biology on precision oncology. We show examples of how the analysis of signal transduction networks allows to dissect resistance to targeted therapies and inform the choice of combinations of targeted drugs based on tumor molecular alterations. Patient-specific biomarkers based on dynamical models of signaling networks can have a greater prognostic value than conventional biomarkers. These examples support systems biology models as valuable tools to advance clinical and translational oncological research.
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Affiliation(s)
- Andrea Rocca
- Hygiene and Public Health, Local Health Unit of Romagna, 47121 Forlì, Italy
| | - Boris N. Kholodenko
- Systems Biology Ireland, School of Medicine, University College Dublin, Belfield, D04 V1W8 Dublin, Ireland
- Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Belfield, D04 V1W8 Dublin, Ireland
- Department of Pharmacology, Yale University School of Medicine, New Haven, CT 06520, USA
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5
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Simoni G, Kaddi C, Tao M, Reali F, Tomasoni D, Priami C, Azer K, Neves-Zaph S, Marchetti L. A robust computational pipeline for model-based and data-driven phenotype clustering. Bioinformatics 2021; 37:1269-1277. [PMID: 33225350 DOI: 10.1093/bioinformatics/btaa948] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2020] [Revised: 10/06/2020] [Accepted: 10/28/2020] [Indexed: 01/23/2023] Open
Abstract
MOTIVATION Precision medicine is a promising field that proposes, in contrast to a one-size-fits-all approach, the tailoring of medical decisions, treatments or products. In this context, it is crucial to introduce innovative methods to stratify a population of patients on the basis of an accurate system-level knowledge of the disease. This is particularly important in very challenging conditions, where the use of standard statistical methods can be prevented by poor data availability or by the need of oversimplifying the processes regulating a complex disease. RESULTS We define an innovative method for phenotype classification that combines experimental data and a mathematical description of the disease biology. The methodology exploits the mathematical model for inferring additional subject features relevant for the classification. Finally, the algorithm identifies the optimal number of clusters and classifies the samples on the basis of a subset of the features estimated during the model fit. We tested the algorithm in two test cases: an in silico case in the context of dyslipidemia, a complex disease for which a large population of patients has been generated, and a clinical test case, in the context of a lysosomal rare disorder, for which the amount of available data was limited. In both the scenarios, our methodology proved to be accurate and robust, and allowed the inference of an additional phenotype division that the experimental data did not show. AVAILABILITY AND IMPLEMENTATION The code to reproduce the in silico results has been implemented in MATLAB v.2017b and it is available in the Supplementary Material. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Giulia Simoni
- Fondazione The Microsoft Research-University of Trento Centre for Computational and Systems Biology (COSBI), 38068 Rovereto, Italy
| | - Chanchala Kaddi
- Digital Data Science - Translational Disease Modeling, Sanofi, Bridgewater, NJ 08807, USA
| | - Mengdi Tao
- Digital Data Science - Translational Disease Modeling, Sanofi, Bridgewater, NJ 08807, USA
| | - Federico Reali
- Fondazione The Microsoft Research-University of Trento Centre for Computational and Systems Biology (COSBI), 38068 Rovereto, Italy
| | - Danilo Tomasoni
- Fondazione The Microsoft Research-University of Trento Centre for Computational and Systems Biology (COSBI), 38068 Rovereto, Italy
| | - Corrado Priami
- Fondazione The Microsoft Research-University of Trento Centre for Computational and Systems Biology (COSBI), 38068 Rovereto, Italy.,Department of Computer Science, University of Pisa, 56127 Pisa, Italy
| | - Karim Azer
- Digital Data Science - Translational Disease Modeling, Sanofi, Bridgewater, NJ 08807, USA
| | - Susana Neves-Zaph
- Digital Data Science - Translational Disease Modeling, Sanofi, Bridgewater, NJ 08807, USA
| | - Luca Marchetti
- Fondazione The Microsoft Research-University of Trento Centre for Computational and Systems Biology (COSBI), 38068 Rovereto, Italy
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Azer K, Kaddi CD, Barrett JS, Bai JPF, McQuade ST, Merrill NJ, Piccoli B, Neves-Zaph S, Marchetti L, Lombardo R, Parolo S, Immanuel SRC, Baliga NS. History and Future Perspectives on the Discipline of Quantitative Systems Pharmacology Modeling and Its Applications. Front Physiol 2021; 12:637999. [PMID: 33841175 PMCID: PMC8027332 DOI: 10.3389/fphys.2021.637999] [Citation(s) in RCA: 51] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2020] [Accepted: 01/25/2021] [Indexed: 12/24/2022] Open
Abstract
Mathematical biology and pharmacology models have a long and rich history in the fields of medicine and physiology, impacting our understanding of disease mechanisms and the development of novel therapeutics. With an increased focus on the pharmacology application of system models and the advances in data science spanning mechanistic and empirical approaches, there is a significant opportunity and promise to leverage these advancements to enhance the development and application of the systems pharmacology field. In this paper, we will review milestones in the evolution of mathematical biology and pharmacology models, highlight some of the gaps and challenges in developing and applying systems pharmacology models, and provide a vision for an integrated strategy that leverages advances in adjacent fields to overcome these challenges.
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Affiliation(s)
- Karim Azer
- Quantitative Sciences, Bill and Melinda Gates Medical Research Institute, Cambridge, MA, United States
| | - Chanchala D. Kaddi
- Quantitative Sciences, Bill and Melinda Gates Medical Research Institute, Cambridge, MA, United States
| | | | - Jane P. F. Bai
- Office of Clinical Pharmacology, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, MD, United States
| | - Sean T. McQuade
- Center for Computational and Integrative Biology, Rutgers University, Camden, NJ, United States
| | - Nathaniel J. Merrill
- Center for Computational and Integrative Biology, Rutgers University, Camden, NJ, United States
| | - Benedetto Piccoli
- Department of Mathematical Sciences and Center for Computational and Integrative Biology, Rutgers University, Camden, NJ, United States
| | - Susana Neves-Zaph
- Translational Disease Modeling, Data and Data Science, Sanofi, Bridgewater, NJ, United States
| | - Luca Marchetti
- Fondazione the Microsoft Research – University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto, Italy
| | - Rosario Lombardo
- Fondazione the Microsoft Research – University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto, Italy
| | - Silvia Parolo
- Fondazione the Microsoft Research – University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto, Italy
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Coletti R, Leonardelli L, Parolo S, Marchetti L. A QSP model of prostate cancer immunotherapy to identify effective combination therapies. Sci Rep 2020; 10:9063. [PMID: 32493951 PMCID: PMC7270132 DOI: 10.1038/s41598-020-65590-0] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2019] [Accepted: 05/06/2020] [Indexed: 12/19/2022] Open
Abstract
Immunotherapy, by enhancing the endogenous anti-tumor immune responses, is showing promising results for the treatment of numerous cancers refractory to conventional therapies. However, its effectiveness for advanced castration-resistant prostate cancer remains unsatisfactory and new therapeutic strategies need to be developed. To this end, systems pharmacology modeling provides a quantitative framework to test in silico the efficacy of new treatments and combination therapies. In this paper we present a new Quantitative Systems Pharmacology (QSP) model of prostate cancer immunotherapy, calibrated using data from pre-clinical experiments in prostate cancer mouse models. We developed the model by using Ordinary Differential Equations (ODEs) describing the tumor, key components of the immune system, and seven treatments. Numerous combination therapies were evaluated considering both the degree of tumor inhibition and the predicted synergistic effects, integrated into a decision tree. Our simulations predicted cancer vaccine combined with immune checkpoint blockade as the most effective dual-drug combination immunotherapy for subjects treated with androgen-deprivation therapy that developed resistance. Overall, the model presented here serves as a computational framework to support drug development, by generating hypotheses that can be tested experimentally in pre-clinical models.
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Affiliation(s)
- Roberta Coletti
- University of Trento, Department of mathematics, Trento, 38123, Italy
- Fondazione The Microsoft Research - University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto, 38068, Italy
| | - Lorena Leonardelli
- Fondazione The Microsoft Research - University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto, 38068, Italy
| | - Silvia Parolo
- Fondazione The Microsoft Research - University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto, 38068, Italy
| | - Luca Marchetti
- Fondazione The Microsoft Research - University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto, 38068, Italy.
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