1
|
Network Pharmacology of Adaptogens in the Assessment of Their Pleiotropic Therapeutic Activity. Pharmaceuticals (Basel) 2022; 15:ph15091051. [PMID: 36145272 PMCID: PMC9504187 DOI: 10.3390/ph15091051] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Revised: 08/11/2022] [Accepted: 08/19/2022] [Indexed: 02/07/2023] Open
Abstract
The reductionist concept, based on the ligand–receptor interaction, is not a suitable model for adaptogens, and herbal preparations affect multiple physiological functions, revealing polyvalent pharmacological activities, and are traditionally used in many conditions. This review, for the first time, provides a rationale for the pleiotropic therapeutic efficacy of adaptogens based on evidence from recent gene expression studies in target cells and where the network pharmacology and systems biology approaches were applied. The specific molecular targets and adaptive stress response signaling mechanisms involved in nonspecific modes of action of adaptogens are identified.
Collapse
|
2
|
Jörg DJ, Fuertinger DH, Cherif A, Bushinsky DA, Mermelstein A, Raimann JG, Kotanko P. Modeling osteoporosis to design and optimize pharmacological therapies comprising multiple drug types. eLife 2022; 11:76228. [PMID: 35942681 PMCID: PMC9363122 DOI: 10.7554/elife.76228] [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: 12/08/2021] [Accepted: 06/26/2022] [Indexed: 11/13/2022] Open
Abstract
For the treatment of postmenopausal osteoporosis, several drug classes with different mechanisms of action are available. Since only a limited set of dosing regimens and drug combinations can be tested in clinical trials, it is currently unclear whether common medication strategies achieve optimal bone mineral density gains or are outperformed by alternative dosing schemes and combination therapies that have not been explored so far. Here, we develop a mathematical framework of drug interventions for postmenopausal osteoporosis that unifies fundamental mechanisms of bone remodeling and the mechanisms of action of four drug classes: bisphosphonates, parathyroid hormone analogs, sclerostin inhibitors, and receptor activator of NF-κB ligand inhibitors. Using data from several clinical trials, we calibrate and validate the model, demonstrating its predictive capacity for complex medication scenarios, including sequential and parallel drug combinations. Via simulations, we reveal that there is a large potential to improve gains in bone mineral density by exploiting synergistic interactions between different drug classes, without increasing the total amount of drug administered.
Collapse
Affiliation(s)
- David J Jörg
- Biomedical Modeling and Simulation Group, Global Research and Development, Fresenius Medical Care Germany, Bad Homburg, Germany
| | - Doris H Fuertinger
- Biomedical Modeling and Simulation Group, Global Research and Development, Fresenius Medical Care Germany, Bad Homburg, Germany
| | | | - David A Bushinsky
- Department of Medicine, University of Rochester School of Medicine and Dentistry, Rochester, United States
| | | | | | - Peter Kotanko
- Renal Research Institute, New York, United States.,Icahn School of Medicine at Mount Sinai, New York, United States
| |
Collapse
|
3
|
Lien YTK, Madrasi K, Samant S, Kim MJ, Li F, Li L, Wang Y, Schmidt S. Establishment of a Disease-Drug Trial Model for Postmenopausal Osteoporosis: A Zoledronic Acid Case Study. J Clin Pharmacol 2020; 60 Suppl 2:S86-S102. [PMID: 33274518 DOI: 10.1002/jcph.1748] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2020] [Accepted: 08/31/2020] [Indexed: 11/09/2022]
Abstract
Costly and lengthy clinical trials hinder the development of safe and effective treatments for postmenopausal osteoporosis. To reduce the expense associated with these trials, we established a mechanistic disease-drug trial model for postmenopausal osteoporosis that can predict phase 3 trial outcome based on short-term bone turnover marker data. To this end, we applied a previously developed model for tibolone to bisphosphonates using zoledronic acid as paradigm compound by (1) linking the mechanistic bone cell interaction model to bone turnover markers as well as bone mineral density in lumbar spine and total hip, (2) employing a mechanistic disease progression function, and (3) accounting for zoledronic acid's mechanism of action. Once developed, we fitted the model to clinical trial data of 581 postmenopausal women receiving (1) 5-mg zoledronic acid in year 1 and saline in year 2, (2) 5-mg zoledronic acid in year 1 and year 2, or (3) placebo (saline), calcium (500 mg), and vitamin D (400 IU). All biomarker data was fitted reasonably well, with no apparent bias or model misspecification. Age, years since menopause, and body mass index at baseline were identified as significant covariates. In the future, the model can be modified to explore the link between short-term biomarkers and fracture risk.
Collapse
Affiliation(s)
- Yi Ting Kayla Lien
- Center for Pharmacometrics and Systems Pharmacology, Department of Pharmaceutics, College of Pharmacy, University of Florida, Orlando, Florida, USA.,Office of Clinical Pharmacology, Office of Translational Sciences (OTS), Center for Drug Evaluation and Research (CDER), U.S. Food and Drug Administration (FDA), Silver Spring, Maryland, USA
| | - Kumpal Madrasi
- Center for Pharmacometrics and Systems Pharmacology, Department of Pharmaceutics, College of Pharmacy, University of Florida, Orlando, Florida, USA.,Office of Clinical Pharmacology, Office of Translational Sciences (OTS), Center for Drug Evaluation and Research (CDER), U.S. Food and Drug Administration (FDA), Silver Spring, Maryland, USA
| | - Snehal Samant
- Center for Pharmacometrics and Systems Pharmacology, Department of Pharmaceutics, College of Pharmacy, University of Florida, Orlando, Florida, USA.,Office of Clinical Pharmacology, Office of Translational Sciences (OTS), Center for Drug Evaluation and Research (CDER), U.S. Food and Drug Administration (FDA), Silver Spring, Maryland, USA
| | - Myong-Jin Kim
- Office of Clinical Pharmacology, Office of Translational Sciences (OTS), Center for Drug Evaluation and Research (CDER), U.S. Food and Drug Administration (FDA), Silver Spring, Maryland, USA
| | - Fang Li
- Office of Clinical Pharmacology, Office of Translational Sciences (OTS), Center for Drug Evaluation and Research (CDER), U.S. Food and Drug Administration (FDA), Silver Spring, Maryland, USA
| | - Li Li
- Office of Clinical Pharmacology, Office of Translational Sciences (OTS), Center for Drug Evaluation and Research (CDER), U.S. Food and Drug Administration (FDA), Silver Spring, Maryland, USA
| | - Yaning Wang
- Office of Clinical Pharmacology, Office of Translational Sciences (OTS), Center for Drug Evaluation and Research (CDER), U.S. Food and Drug Administration (FDA), Silver Spring, Maryland, USA
| | - Stephan Schmidt
- Center for Pharmacometrics and Systems Pharmacology, Department of Pharmaceutics, College of Pharmacy, University of Florida, Orlando, Florida, USA
| |
Collapse
|
4
|
Bai JPF, Earp JC, Pillai VC. Translational Quantitative Systems Pharmacology in Drug Development: from Current Landscape to Good Practices. AAPS JOURNAL 2019; 21:72. [PMID: 31161268 DOI: 10.1208/s12248-019-0339-5] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/02/2019] [Accepted: 05/07/2019] [Indexed: 12/12/2022]
Abstract
Systems pharmacology approaches have the capability of quantitatively linking the key biological molecules relevant to a drug candidate's mechanism of action (drug-induced signaling pathways) to the clinical biomarkers associated with the proposed target disease, thereby quantitatively facilitating its development and life cycle management. In this review, the model attributes of published quantitative systems pharmacology (QSP) modeling for lowering cholesterol, treating salt-sensitive hypertension, and treating rare diseases as well as describing bone homeostasis and related pharmacological effects are critically reviewed with respect to model quality, calibration, validation, and performance. We further reviewed the common practices in optimizing QSP modeling and prediction. Notably, leveraging genetics and genomic studies for model calibration and validation is common. Statistical and quantitative assessment of QSP prediction and handling of model uncertainty are, however, mostly lacking as are the quantitative and statistical criteria for assessing QSP predictions and the covariance matrix of coefficients between the parameters in a validated virtual population. To accelerate advances and application of QSP with consistent quality, a list of key questions is proposed to be addressed when assessing the quality of a QSP model in hopes of stimulating the scientific community to set common expectations. The common expectations as to what constitutes the best QSP modeling practices, which the scientific community supports, will advance QSP modeling in the realm of informed drug development. In the long run, good practices will extend the life cycles of QSP models beyond the life cycles of individual drugs.
Collapse
Affiliation(s)
- Jane P F Bai
- Office of Clinical Pharmacology, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, 20903, USA.
| | - Justin C Earp
- Office of Clinical Pharmacology, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, 20903, USA
| | - Venkateswaran C Pillai
- Office of Clinical Pharmacology, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, 20903, USA
| |
Collapse
|
5
|
Cremers S, Drake MT, Ebetino FH, Bilezikian JP, Russell RGG. Pharmacology of bisphosphonates. Br J Clin Pharmacol 2019; 85:1052-1062. [PMID: 30650219 DOI: 10.1111/bcp.13867] [Citation(s) in RCA: 113] [Impact Index Per Article: 22.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2018] [Revised: 01/07/2019] [Accepted: 01/07/2019] [Indexed: 12/27/2022] Open
Abstract
The biological effects of the bisphosphonates (BPs) as inhibitors of calcification and bone resorption were first described in the late 1960s. In the 50 years that have elapsed since then, the BPs have become the leading drugs for the treatment of skeletal disorders characterized by increased bone resorption, including Paget's disease of bone, bone metastases, multiple myeloma, osteoporosis and several childhood inherited disorders. The discovery and development of the BPs as a major class of drugs for the treatment of bone diseases is a paradigm for the successful journey from "bench to bedside and back again". Several of the leading BPs achieved "blockbuster" status as branded drugs. However, these BPs have now come to the end of their patent life, making them highly affordable. The opportunity for new clinical applications for BPs also exists in other areas of medicine such as ageing, cardiovascular disease and radiation protection. Their use as inexpensive generic medicines is therefore likely to continue for many years to come. Fifty years of research into the pharmacology of bisphosphonates have led to a fairly good understanding about how these drugs work and how they can be used safely in patients with metabolic bone diseases. However, while we seemingly know much about these drugs, a number of key aspects related to BP distribution and action remain incompletely understood. This review summarizes the existing knowledge of the (pre)clinical and translational pharmacology of BPs, and highlights areas in which understanding is lacking.
Collapse
Affiliation(s)
- Serge Cremers
- Division of Laboratory Medicine, Department of Pathology and Cell Biology, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, NY, USA.,Division of Endocrinology, Department of Medicine, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, NY, USA
| | - Matthew T Drake
- Department of Endocrinology and Kogod Center of Aging, Mayo Clinic College of Medicine, Rochester, MN, USA
| | - F Hal Ebetino
- Department of Chemistry, University of Rochester, Rochester, NY, USA.,Mellanby Centre for Bone Research, Medical School, University of Sheffield, UK
| | - John P Bilezikian
- Division of Endocrinology, Department of Medicine, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, NY, USA
| | - R Graham G Russell
- Mellanby Centre for Bone Research, Medical School, University of Sheffield, UK.,Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, The Oxford University Institute of Musculoskeletal Sciences, The Botnar Research Centre, Nuffield Orthopaedic Centre, Oxford, UK
| |
Collapse
|
6
|
Lemaire V, Cox DR. Dynamics of Bone Cell Interactions and Differential Responses to PTH and Antibody-Based Therapies. Bull Math Biol 2018; 81:3575-3622. [PMID: 30460589 DOI: 10.1007/s11538-018-0533-0] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2017] [Accepted: 11/01/2018] [Indexed: 01/04/2023]
Abstract
We propose a mathematical model describing the dynamics of osteoblasts and osteoclasts in bone remodeling. The goal of this work is to develop an integrated modeling framework for bone remodeling and bone cell signaling dynamics that could be used to explore qualitatively combination treatments for osteoporosis in humans. The model has been calibrated using 57 checks from the literature. Specific global optimization methods based on qualitative objectives have been developed to perform the model calibration. We also added pharmacokinetics representations of three drugs to the model, which are teriparatide (PTH(1-34)), denosumab (a RANKL antibody) and romosozumab (a sclerostin antibody), achieving excellent goodness-of-fit of human clinical data. The model reproduces the paradoxical effects of PTH on the bone mass, where continuous administration of PTH results in bone loss but intermittent administration of PTH leads to bone gain, thus proposing an explanation of this phenomenon. We used the model to simulate different categories of osteoporosis. The main attributes of each disease are qualitatively well captured by the model, for example changes in bone turnover in the disease states. We explored dosing regimens for each disease based on the combination of denosumab and romosozumab, identifying adequate ratios and doses of both drugs for subpopulations of patients in function of categories of osteoporosis and the degree of severity of the disease.
Collapse
Affiliation(s)
- Vincent Lemaire
- Rinat (Pfizer Inc.), 230 East Grand Avenue, South San Francisco, CA, 94080, USA. .,Genentech, 1 DNA Way, MS 463A, South San Francisco, CA, 94080, USA.
| | - David R Cox
- Rinat (Pfizer Inc.), 230 East Grand Avenue, South San Francisco, CA, 94080, USA
| |
Collapse
|
7
|
Madrasi K, Li F, Kim MJ, Samant S, Voss S, Kehoe T, Bashaw ED, Ahn HY, Wang Y, Florian J, Schmidt S, Lesko LJ, Li L. Regulatory Perspectives in Pharmacometric Models of Osteoporosis. J Clin Pharmacol 2018; 58:572-585. [PMID: 29485684 DOI: 10.1002/jcph.1071] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2017] [Accepted: 11/24/2017] [Indexed: 11/12/2022]
Abstract
Osteoporosis is a disorder of the bones in which they are weakened to the extent that they become more prone to fracture. There are various forms of osteoporosis: some of them are induced by drugs, and others occur as a chronic progressive disorder as an individual gets older. As the median age of the population rises across the world, the chronic form of the bone disease is drawing attention as an important worldwide health issue. Developing new treatments for osteoporosis and comparing them with existing treatments are complicated processes due to current acceptance by regulatory authorities of bone mineral density (BMD) and fracture risk as clinical end points, which require clinical trials to be large, prolonged, and expensive to determine clinically significant impacts in BMD and fracture risk. Moreover, changes in BMD and fracture risk are not always correlated, with some clinical trials showing BMD improvement without a reduction in fractures. More recently, bone turnover markers specific to bone formation and resorption have been recognized that reflect bone physiology at a cellular level. These bone turnover markers change faster than BMD and fracture risk, and mathematically linking the biomarkers via a computational model to BMD and/or fracture risk may help in predicting BMD and fracture risk changes over time during the progression of a disease or when under treatment. Here, we discuss important concepts of bone physiology, osteoporosis, treatment options, mathematical modeling of osteoporosis, and the use of these models by the pharmaceutical industry and the Food and Drug Administration.
Collapse
Affiliation(s)
- Kumpal Madrasi
- Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, USA
| | - Fang Li
- Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, USA
| | - Myong-Jin Kim
- Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, USA
| | - Snehal Samant
- Center for Pharmacometrics and Systems Pharmacology, Department of Pharmaceutics, University of Florida, Gainesville, FL, USA
| | - Stephen Voss
- Office of New Drugs, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, USA
| | - Theresa Kehoe
- Office of New Drugs, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, USA
| | - E Dennis Bashaw
- Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, USA
| | - Hae Young Ahn
- Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, USA
| | - Yaning Wang
- Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, USA
| | - Jeffy Florian
- Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, USA
| | - Stephan Schmidt
- Center for Pharmacometrics and Systems Pharmacology, Department of Pharmaceutics, University of Florida, Gainesville, FL, USA
| | - Lawrence J Lesko
- Center for Pharmacometrics and Systems Pharmacology, Department of Pharmaceutics, University of Florida, Gainesville, FL, USA
| | - Li Li
- Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, USA
| |
Collapse
|
8
|
Gaitonde P, Hurtado FK, Garhyan P, Chien JY, Schmidt S. Development and Qualification of a Drug-Disease Modeling Platform to Characterize Clinically Relevant Endpoints in Type 2 Diabetes Trials. Clin Pharmacol Ther 2018; 104:699-708. [DOI: 10.1002/cpt.998] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2017] [Revised: 12/14/2017] [Accepted: 12/17/2017] [Indexed: 02/02/2023]
Affiliation(s)
- Puneet Gaitonde
- Center for Pharmacometrics & Systems Pharmacology, Department of Pharmaceutics; University of Florida; Lake Nona (Orlando) Florida USA
- Clinical Pharmacology, Global Product Development, Pfizer; Groton Connecticut USA
| | - Felipe K. Hurtado
- Center for Pharmacometrics & Systems Pharmacology, Department of Pharmaceutics; University of Florida; Lake Nona (Orlando) Florida USA
| | - Parag Garhyan
- Global PK/PD & Pharmacometrics, Eli Lilly and Company, Lilly Corporate Center; Indianapolis Indiana USA
| | - Jenny Y. Chien
- Global PK/PD & Pharmacometrics, Eli Lilly and Company, Lilly Corporate Center; Indianapolis Indiana USA
| | - Stephan Schmidt
- Center for Pharmacometrics & Systems Pharmacology, Department of Pharmaceutics; University of Florida; Lake Nona (Orlando) Florida USA
| |
Collapse
|
9
|
Zhang XY, Trame MN, Lesko LJ, Schmidt S. Sobol Sensitivity Analysis: A Tool to Guide the Development and Evaluation of Systems Pharmacology Models. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2018; 4:69-79. [PMID: 27548289 PMCID: PMC5006244 DOI: 10.1002/psp4.6] [Citation(s) in RCA: 139] [Impact Index Per Article: 23.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/09/2014] [Accepted: 10/18/2014] [Indexed: 02/06/2023]
Abstract
A systems pharmacology model typically integrates pharmacokinetic, biochemical network, and systems biology concepts into a unifying approach. It typically consists of a large number of parameters and reaction species that are interlinked based upon the underlying (patho)physiology and the mechanism of drug action. The more complex these models are, the greater the challenge of reliably identifying and estimating respective model parameters. Global sensitivity analysis provides an innovative tool that can meet this challenge. CPT Pharmacometrics Syst. Pharmacol. (2015) 4, 69-79; doi:10.1002/psp4.6; published online 25 February 2015.
Collapse
Affiliation(s)
- X-Y Zhang
- Department of Pharmaceutics, Center for Pharmacometrics and Systems Pharmacology, College of Pharmacy, University of Florida, Orlando, Florida, USA
| | - M N Trame
- Department of Pharmaceutics, Center for Pharmacometrics and Systems Pharmacology, College of Pharmacy, University of Florida, Orlando, Florida, USA
| | - L J Lesko
- Department of Pharmaceutics, Center for Pharmacometrics and Systems Pharmacology, College of Pharmacy, University of Florida, Orlando, Florida, USA
| | - S Schmidt
- Department of Pharmaceutics, Center for Pharmacometrics and Systems Pharmacology, College of Pharmacy, University of Florida, Orlando, Florida, USA
| |
Collapse
|
10
|
Cheng Y, Thalhauser CJ, Smithline S, Pagidala J, Miladinov M, Vezina HE, Gupta M, Leil TA, Schmidt BJ. QSP Toolbox: Computational Implementation of Integrated Workflow Components for Deploying Multi-Scale Mechanistic Models. AAPS JOURNAL 2017; 19:1002-1016. [PMID: 28540623 DOI: 10.1208/s12248-017-0100-x] [Citation(s) in RCA: 39] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/05/2017] [Accepted: 05/08/2017] [Indexed: 01/09/2023]
Abstract
Quantitative systems pharmacology (QSP) modeling has become increasingly important in pharmaceutical research and development, and is a powerful tool to gain mechanistic insights into the complex dynamics of biological systems in response to drug treatment. However, even once a suitable mathematical framework to describe the pathophysiology and mechanisms of interest is established, final model calibration and the exploration of variability can be challenging and time consuming. QSP models are often formulated as multi-scale, multi-compartment nonlinear systems of ordinary differential equations. Commonly accepted modeling strategies, workflows, and tools have promise to greatly improve the efficiency of QSP methods and improve productivity. In this paper, we present the QSP Toolbox, a set of functions, structure array conventions, and class definitions that computationally implement critical elements of QSP workflows including data integration, model calibration, and variability exploration. We present the application of the toolbox to an ordinary differential equations-based model for antibody drug conjugates. As opposed to a single stepwise reference model calibration, the toolbox also facilitates simultaneous parameter optimization and variation across multiple in vitro, in vivo, and clinical assays to more comprehensively generate alternate mechanistic hypotheses that are in quantitative agreement with available data. The toolbox also includes scripts for developing and applying virtual populations to mechanistic exploration of biomarkers and efficacy. We anticipate that the QSP Toolbox will be a useful resource that will facilitate implementation, evaluation, and sharing of new methodologies in a common framework that will greatly benefit the community.
Collapse
Affiliation(s)
- Yougan Cheng
- Bristol-Myers Squibb, PO Box 4000, Princeton, New Jersey, 08543-4000, USA
| | - Craig J Thalhauser
- Bristol-Myers Squibb, PO Box 4000, Princeton, New Jersey, 08543-4000, USA
| | - Shepard Smithline
- Bristol-Myers Squibb, PO Box 4000, Princeton, New Jersey, 08543-4000, USA
| | - Jyotsna Pagidala
- Bristol-Myers Squibb, PO Box 4000, Princeton, New Jersey, 08543-4000, USA
| | - Marko Miladinov
- Bristol-Myers Squibb, PO Box 4000, Princeton, New Jersey, 08543-4000, USA
| | - Heather E Vezina
- Bristol-Myers Squibb, PO Box 4000, Princeton, New Jersey, 08543-4000, USA
| | - Manish Gupta
- Bristol-Myers Squibb, PO Box 4000, Princeton, New Jersey, 08543-4000, USA
| | - Tarek A Leil
- Bristol-Myers Squibb, PO Box 4000, Princeton, New Jersey, 08543-4000, USA
| | - Brian J Schmidt
- Bristol-Myers Squibb, PO Box 4000, Princeton, New Jersey, 08543-4000, USA.
| |
Collapse
|
11
|
Berkhout J, Stone JA, Verhamme KM, Danhof M, Post TM. Disease Systems Analysis of Bone Mineral Density and Bone Turnover Markers in Response to Alendronate, Placebo, and Washout in Postmenopausal Women. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2016; 5:656-664. [PMID: 27869358 PMCID: PMC5193000 DOI: 10.1002/psp4.12135] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/01/2016] [Accepted: 09/08/2016] [Indexed: 01/23/2023]
Abstract
A previously established mechanism-based disease systems model for osteoporosis that is based on a mathematically reduced version of a model describing the interactions between osteoclast (bone removing) and osteoblast (bone forming) cells in bone remodeling has been applied to clinical data from women (n = 1,379) receiving different doses and treatment regimens of alendronate, placebo, and washout. The changes in the biomarkers, plasma bone-specific alkaline phosphatase activity (BSAP), urinary N-telopeptide (NTX), lumbar spine bone mineral density (BMD), and total hip BMD, were linked to the underlying mechanistic core of the model. The final model gave an accurate description of all four biomarkers for the different treatments. Simulations were used to visualize the dynamics of the underlying network and the natural disease progression upon alendronate treatment and discontinuation. These results complement the previous applications of this mechanism-based disease systems model to data from various treatments for osteoporosis.
Collapse
Affiliation(s)
- J Berkhout
- Department of Medical Informatics, Erasmus Medical Centre, Rotterdam, The Netherlands.,Leiden Academic Centre for Drug Research, Division of Pharmacology, Leiden, The Netherlands.,Leiden Experts on Advanced Pharmacokinetics and Pharmacodynamics (LAP&P), Leiden, The Netherlands
| | - J A Stone
- Merck Sharp & Dohme Corp., Kenilworth, New Jersey, USA
| | - K M Verhamme
- Department of Medical Informatics, Erasmus Medical Centre, Rotterdam, The Netherlands
| | - M Danhof
- Leiden Academic Centre for Drug Research, Division of Pharmacology, Leiden, The Netherlands.,Leiden Experts on Advanced Pharmacokinetics and Pharmacodynamics (LAP&P), Leiden, The Netherlands
| | - T M Post
- Leiden Academic Centre for Drug Research, Division of Pharmacology, Leiden, The Netherlands.,Leiden Experts on Advanced Pharmacokinetics and Pharmacodynamics (LAP&P), Leiden, The Netherlands
| |
Collapse
|
12
|
Bakshi S, de Lange EC, van der Graaf PH, Danhof M, Peletier LA. Understanding the Behavior of Systems Pharmacology Models Using Mathematical Analysis of Differential Equations: Prolactin Modeling as a Case Study. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2016; 5:339-51. [PMID: 27405001 PMCID: PMC4961077 DOI: 10.1002/psp4.12098] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/11/2016] [Revised: 04/21/2016] [Accepted: 05/19/2016] [Indexed: 01/20/2023]
Abstract
In this tutorial, we introduce basic concepts in dynamical systems analysis, such as phase‐planes, stability, and bifurcation theory, useful for dissecting the behavior of complex and nonlinear models. A precursor‐pool model with positive feedback is used to demonstrate the power of mathematical analysis. This model is nonlinear and exhibits multiple steady states, the stability of which is analyzed. The analysis offers insight into model behavior and suggests useful parameter regions, which simulations alone could not.
Collapse
Affiliation(s)
- S Bakshi
- Systems Pharmacology, Division of Pharmacology, LACDR, Leiden University, Leiden, The Netherlands
| | - E C de Lange
- Systems Pharmacology, Division of Pharmacology, LACDR, Leiden University, Leiden, The Netherlands
| | - P H van der Graaf
- Systems Pharmacology, Division of Pharmacology, LACDR, Leiden University, Leiden, The Netherlands.,Certara QSP, Canterbury Innovation House, Canterbury, United Kingdom
| | - M Danhof
- Systems Pharmacology, Division of Pharmacology, LACDR, Leiden University, Leiden, The Netherlands
| | - L A Peletier
- Mathematical Institute, Leiden University, Leiden, The Netherlands
| |
Collapse
|
13
|
Danhof M. Systems pharmacology - Towards the modeling of network interactions. Eur J Pharm Sci 2016; 94:4-14. [PMID: 27131606 DOI: 10.1016/j.ejps.2016.04.027] [Citation(s) in RCA: 93] [Impact Index Per Article: 11.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2016] [Revised: 04/21/2016] [Accepted: 04/24/2016] [Indexed: 12/13/2022]
Abstract
Mechanism-based pharmacokinetic and pharmacodynamics (PKPD) and disease system (DS) models have been introduced in drug discovery and development research, to predict in a quantitative manner the effect of drug treatment in vivo in health and disease. This requires consideration of several fundamental properties of biological systems behavior including: hysteresis, non-linearity, variability, interdependency, convergence, resilience, and multi-stationarity. Classical physiology-based PKPD models consider linear transduction pathways, connecting processes on the causal path between drug administration and effect, as the basis of drug action. Depending on the drug and its biological target, such models may contain expressions to characterize i) the disposition and the target site distribution kinetics of the drug under investigation, ii) the kinetics of target binding and activation and iii) the kinetics of transduction. When connected to physiology-based DS models, PKPD models can characterize the effect on disease progression in a mechanistic manner. These models have been found useful to characterize hysteresis and non-linearity, yet they fail to explain the effects of the other fundamental properties of biological systems behavior. Recently systems pharmacology has been introduced as novel approach to predict in vivo drug effects, in which biological networks rather than single transduction pathways are considered as the basis of drug action and disease progression. These models contain expressions to characterize the functional interactions within a biological network. Such interactions are relevant when drugs act at multiple targets in the network or when homeostatic feedback mechanisms are operative. As a result systems pharmacology models are particularly useful to describe complex patterns of drug action (i.e. synergy, oscillatory behavior) and disease progression (i.e. episodic disorders). In this contribution it is shown how physiology-based PKPD and disease models can be extended to account for internal systems interactions. It is demonstrated how SP models can be used to predict the effects of multi-target interactions and of homeostatic feedback on the pharmacological response. In addition it is shown how DS models may be used to distinguish symptomatic from disease modifying effects and to predict the long term effects on disease progression, from short term biomarker responses. It is concluded that incorporation of expressions to describe the interactions in biological network analysis opens new avenues to the understanding of the effects of drug treatment on the fundamental aspects of biological systems behavior.
Collapse
Affiliation(s)
- Meindert Danhof
- Systems Pharmacology, Leiden Academic Centre for Drug Research, Leiden University, Einsteinweg 55, P.O. Box 9502, 2300 RA Leiden, The Netherlands.
| |
Collapse
|
14
|
Gadkar K, Kirouac DC, Mager DE, van der Graaf PH, Ramanujan S. A Six-Stage Workflow for Robust Application of Systems Pharmacology. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2016; 5:235-49. [PMID: 27299936 PMCID: PMC4879472 DOI: 10.1002/psp4.12071] [Citation(s) in RCA: 51] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/08/2015] [Accepted: 02/18/2016] [Indexed: 12/30/2022]
Abstract
Quantitative and systems pharmacology (QSP) is increasingly being applied in pharmaceutical research and development. One factor critical to the ultimate success of QSP is the establishment of commonly accepted language, technical criteria, and workflows. We propose an integrated workflow that bridges conceptual objectives with underlying technical detail to support the execution, communication, and evaluation of QSP projects.
Collapse
Affiliation(s)
- K Gadkar
- Translational & Systems Pharmacology, PKPD, Genentech, South San Francisco, California, USA
| | - D C Kirouac
- Translational & Systems Pharmacology, PKPD, Genentech, South San Francisco, California, USA
| | - D E Mager
- Department of Pharmaceutical Sciences, University at Buffalo, SUNY, Buffalo, New York
| | - P H van der Graaf
- Division of Pharmacology, Leiden Academic Centre for Drug Research, Leiden University, Leiden, The Netherlands.,Certara QSP, Canterbury, UK
| | - S Ramanujan
- Translational & Systems Pharmacology, PKPD, Genentech, South San Francisco, California, USA
| |
Collapse
|
15
|
Top-down and bottom-up modeling in system pharmacology to understand clinical efficacy: An example with NRTIs of HIV-1. Eur J Pharm Sci 2016; 94:72-83. [PMID: 26796142 DOI: 10.1016/j.ejps.2016.01.016] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2015] [Revised: 01/07/2016] [Accepted: 01/14/2016] [Indexed: 11/22/2022]
Abstract
A major aim of Systems Pharmacology is to understand clinically relevant mechanisms of action (MOA) of drugs and to use this knowledge in order to optimize therapy. To enable this mission it is necessary to obtain knowledge on how in vitro testable insights translate into clinical efficacy. Mathematical modeling and data integration are essential components to achieve this goal. Two modeling philosophies are prevalent, each of which in isolation is not sufficient to achieve the above described: In a 'top-down' approach, a minimal pharmacokinetic-pharmacodynamic (PK-PD) model is derived from- and fitted to available clinical data. This model may lack interpretability in terms of mechanisms and may only be predictive for scenarios already covered by the data used to derive it. A 'bottom-up' approach builds on mechanistic insights derived from in vitro/ex vivo experiments, which can be conducted under controlled conditions, but may not be fully representative for the in vivo/clinical situation. In this work, we employ both approaches side-by-side to predict the clinical potency (IC50 values) of the nucleoside reverse transcriptase inhibitors (NRTIs) lamivudine, emtricitabine and tenofovir. In the 'top-down' approach, this requires to establish the dynamic link between the intracellularly active NRTI-triphosphates (which exert the effect) and plasma prodrug PK and to subsequently link this composite PK model to viral kinetics. The 'bottom-up' approach assesses inhibition of reverse transcriptase-mediated viral DNA polymerization by the intracellular, active NRTI-triphosphates, which has to be brought into the context of target cell infection. By using entirely disparate sets of data to derive and parameterize the respective models, our approach serves as a means to assess the clinical relevance of the 'bottom-up' approach. We obtain very good qualitative and quantitative agreement between 'top-down' vs. 'bottom-up' predicted IC50 values, arguing for the validity of the 'bottom-up' approach. We noted, however, that the 'top-down' approach is strongly dependent on the sparse and noisy intracellular pharmacokinetic data. All in all, our work provides confidence that we can translate in vitro parameters into measures of clinical efficacy using the 'bottom-up' approach. This may allow to infer the potency of various NRTIs in inhibiting e.g. mutant viruses, to distinguish sources of interaction of NRTI combinations and to assess the efficacy of different NRTIs for repurposing, e.g. for pre-exposure prophylaxis.
Collapse
|
16
|
Danhof M. Kinetics of drug action in disease states: towards physiology-based pharmacodynamic (PBPD) models. J Pharmacokinet Pharmacodyn 2015; 42:447-62. [PMID: 26319673 PMCID: PMC4582079 DOI: 10.1007/s10928-015-9437-x] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2015] [Accepted: 08/17/2015] [Indexed: 11/26/2022]
Abstract
Gerhard Levy started his investigations on the "Kinetics of Drug Action in Disease States" in the fall of 1980. The objective of his research was to study inter-individual variation in pharmacodynamics. To this end, theoretical concepts and experimental approaches were introduced, which enabled assessment of the changes in pharmacodynamics per se, while excluding or accounting for the cofounding effects of concomitant changes in pharmacokinetics. These concepts were applied in several studies. The results, which were published in 45 papers in the years 1984-1994, showed considerable variation in pharmacodynamics. These initial studies on kinetics of drug action in disease states triggered further experimental research on the relations between pharmacokinetics and pharmacodynamics. Together with the concepts in Levy's earlier publications "Kinetics of Pharmacologic Effects" (Clin Pharmacol Ther 7(3): 362-372, 1966) and "Kinetics of pharmacologic effects in man: the anticoagulant action of warfarin" (Clin Pharmacol Ther 10(1): 22-35, 1969), they form a significant impulse to the development of physiology-based pharmacodynamic (PBPD) modeling as novel discipline in the pharmaceutical sciences. This paper reviews Levy's research on the "Kinetics of Drug Action in Disease States". Next it addresses the significance of his research for the evolution of PBPD modeling as a scientific discipline. PBPD models contain specific expressions to characterize in a strictly quantitative manner processes on the causal path between exposure (in terms of concentration at the target site) and the drug effect (in terms of the change in biological function). Pertinent processes on the causal path are: (1) target site distribution, (2) target binding and activation and (3) transduction and homeostatic feedback.
Collapse
Affiliation(s)
- Meindert Danhof
- Leiden Academic Centre for Drug Research, Leiden University, P.O. Box 9502, 2300 RA, Leiden, The Netherlands.
| |
Collapse
|
17
|
Berkhout J, Stone JA, Verhamme KM, Stricker BH, Sturkenboom MC, Danhof M, Post TM. Application of a Systems Pharmacology-Based Placebo Population Model to Analyze Long-Term Data of Postmenopausal Osteoporosis. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2015; 4:516-26. [PMID: 26451331 PMCID: PMC4592531 DOI: 10.1002/psp4.12006] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/25/2015] [Accepted: 06/07/2015] [Indexed: 12/16/2022]
Abstract
Osteoporosis is a progressive bone disease characterized by decreased bone mass resulting in increased fracture risk. The objective of this investigation was to test whether a recently developed disease systems analysis model for osteoporosis could describe disease progression in a placebo-treated population from the Early Postmenopausal Intervention Cohort (EPIC) study. First, we qualified the model using a subset from the placebo arm of the EPIC study of 222 women who had similar demographic characteristics as the 149 women from the placebo arm of the original population. Second, we applied the model to all 470 women. Bone mineral density (BMD) dynamics were changed to an indirect response model to describe lumbar spine and total hip BMD in this second population. This updated disease systems analysis placebo model describes the dynamics of all biomarkers in the corresponding datasets to a very good approximation; a good description of an individual placebo response will be valuable for evaluating treatments for osteoporosis.
Collapse
Affiliation(s)
- J Berkhout
- Department of Medical Informatics, Erasmus Medical Centre Rotterdam, The Netherlands ; Leiden Academic Centre for Drug Research, Division of Pharmacology Leiden, The Netherlands
| | - J A Stone
- Merck Sharp & Dohme Corp. Whitehouse Station, New Jersey, USA
| | - K M Verhamme
- Department of Medical Informatics, Erasmus Medical Centre Rotterdam, The Netherlands
| | - B H Stricker
- Department of Epidemiology, Erasmus Medical Centre Rotterdam, The Netherlands ; Drug Safety Unit, The Health Care Inspectorate The Hague, The Netherlands
| | - M C Sturkenboom
- Department of Medical Informatics, Erasmus Medical Centre Rotterdam, The Netherlands
| | - M Danhof
- Leiden Academic Centre for Drug Research, Division of Pharmacology Leiden, The Netherlands
| | - T M Post
- Leiden Experts on Advanced Pharmacokinetics and Pharmacodynamics (LAP&P) Leiden, The Netherlands
| |
Collapse
|
18
|
Lon HK, DuBois DC, Earp JC, Almon RR, Jusko WJ. Modeling effects of dexamethasone on disease progression of bone mineral density in collagen-induced arthritic rats. Pharmacol Res Perspect 2015; 3:e00169. [PMID: 26516581 PMCID: PMC4618640 DOI: 10.1002/prp2.169] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2015] [Accepted: 06/23/2015] [Indexed: 11/30/2022] Open
Abstract
A mechanism-based model was developed to characterize the crosstalk between proinflammatory cytokines, bone remodeling biomarkers, and bone mineral density (BMD) in collagen-induced arthritic (CIA) rats. Male Lewis rats were divided into five groups: healthy control, CIA control, CIA receiving single 0.225 mg kg−1 subcutaneous (SC) dexamethasone (DEX), CIA receiving single 2.25 mg kg−1 SC DEX, and CIA receiving chronic 0.225 mg kg−1 SC DEX. The CIA rats underwent collagen induction at day 0 and DEX was injected at day 21 post-induction. Disease activity was monitored throughout the study and rats were sacrificed at different time points for blood and paw collection. Protein concentrations of interleukin (IL)-1β, IL-6, receptor activator of nuclear factor kappa-B ligand (RANKL), osteoprotegerin (OPG), and tartrate-resistant acid phosphatase 5b (TRACP-5b) in paws were measured by enzyme-linked immunosorbent assays (ELISA). Disease progression and DEX pharmacodynamic profiles of IL-1β, IL-6, RANKL, and OPG were fitted simultaneously and parameters were sequentially applied to fit the TRACP-5b and BMD data. The model was built according to the mechanisms reported in the literature and modeling was performed using ADAPT 5 software with naïve pooling. Time profiles of IL-1β and IL-6 protein concentrations correlated with their mRNAs. The RANKL and OPG profiles matched previous findings in CIA rats. DEX inhibited the expressions of IL-1β, IL-6, and RANKL, but did not alter OPG. TRACP-5b was also inhibited by DEX. Model predictions suggested that anti-IL-1β therapy and anti-RANKL therapy would result in similar efficacy for prevention of bone loss among the cytokine antagonists.
Collapse
Affiliation(s)
- Hoi-Kei Lon
- Department of Pharmaceutical Sciences, School of Pharmacy and Pharmaceutical Sciences, University at Buffalo Buffalo, New York, 14214
| | - Debra C DuBois
- Department of Biological Sciences, University at Buffalo Buffalo, New York, 14260
| | - Justin C Earp
- Center for Drug Evaluation and Research, U.S. Food and Drug Administration Silver Spring, Maryland, 20993
| | - Richard R Almon
- Department of Biological Sciences, University at Buffalo Buffalo, New York, 14260
| | - William J Jusko
- Department of Pharmaceutical Sciences, School of Pharmacy and Pharmaceutical Sciences, University at Buffalo Buffalo, New York, 14214
| |
Collapse
|
19
|
van Schaick E, Zheng J, Perez Ruixo JJ, Gieschke R, Jacqmin P. A semi-mechanistic model of bone mineral density and bone turnover based on a circular model of bone remodeling. J Pharmacokinet Pharmacodyn 2015; 42:315-32. [PMID: 26123920 DOI: 10.1007/s10928-015-9423-3] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2014] [Accepted: 06/09/2015] [Indexed: 11/24/2022]
Abstract
Development of novel therapies for bone diseases can benefit from mathematical models that predict drug effect on bone remodeling biomarkers. Therefore, a bone cycle model (BCM) was developed that takes into consideration the concept of the basic multicellular unit and the dynamic equilibrium of bone remodeling. The model is a closed form cyclical model with four compartments representing resorption, formation, primary mineralization, and secondary mineralization. Equations describing the time course of bone turnover biomarkers were developed using the flow rate of bone cycle units (BCU) between the compartments or the amount of BCU in each compartment. A disease progression model representing bone loss in osteoporosis, a vitamin D and calcium supplementation (placebo) model, and a drug model for antiresorptive treatments were added to the model. Initial model parameter values were derived from published bone turnover data. The BCM accurately described biomarker-time profiles in postmenopausal women receiving either placebo or bisphosphonate treatment. The slow continual increase in bone mineral density (BMD) observed after 1 year of treatment was accurately described when changes in bone turnover were combined with increases in mineralization. For this purpose, the secondary mineralization compartment was replaced by three catenary chain compartments representing increasing mineral content. The refined BCM satisfactorily predicted biomarker profiles after long-term (10-year) bisphosphonate treatment. Furthermore, the model successfully described individual bone turnover markers and BMD results following treatment with denosumab in postmenopausal women. Analyses with this model could be used to optimize dosing regimens and to predict effects of novel osteoporotic treatments.
Collapse
Affiliation(s)
- Erno van Schaick
- SGS Exprimo NV, Generaal de Wittelaan 19A b5, 2800, Mechelen, Belgium,
| | | | | | | | | |
Collapse
|
20
|
Abstract
Adverse drug events (ADEs) remain a universal problem in drug development, regulatory review, and clinical practice with a substantial financial burden on the global health-care system. Recent advances in molecular and "omics" technologies, along with online databases and bioinformatics, have enabled a more integrative approach to understanding drug-target (protein) interactions, both desirable and undesirable, within a biological system. This has led to the development of systems approaches to risk assessment in an attempt to complement and improve on contemporary observational and predictive strategies for assessing risk. Although still in an evolutionary phase, systems approaches have the potential to markedly advance our understanding of ADEs and ability to predict them. Systems approaches will also move personalized medicine forward by enabling better identification of individual and subgroup risk factors.
Collapse
|
21
|
Post TM, Schmidt S, Peletier LA, de Greef R, Kerbusch T, Danhof M. Application of a mechanism-based disease systems model for osteoporosis to clinical data. J Pharmacokinet Pharmacodyn 2013; 40:143-56. [DOI: 10.1007/s10928-012-9294-9] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2012] [Accepted: 12/21/2012] [Indexed: 01/08/2023]
|
22
|
Riggs MM, Bennetts M, van der Graaf PH, Martin SW. Integrated pharmacometrics and systems pharmacology model-based analyses to guide GnRH receptor modulator development for management of endometriosis. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2012; 1:e11. [PMID: 23887363 PMCID: PMC3606940 DOI: 10.1038/psp.2012.10] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/23/2012] [Accepted: 08/18/2012] [Indexed: 01/21/2023]
Abstract
Endometriosis is a gynecological condition resulting from proliferation of endometrial-like tissue outside the endometrial cavity. Estrogen suppression therapies, mediated through gonadotropin-releasing hormone (GnRH) modulation, decrease endometriotic implants and diminish associated pain albeit at the expense of bone mineral density (BMD) loss. Our goal was to provide model-based guidance for GnRH-modulating clinical programs intended for endometriosis management. This included developing an estrogen suppression target expected to provide symptomatic relief with minimal BMD loss and to evaluate end points and study durations supportive of efficient development decisions. An existing multiscale model of calcium and bone was adapted to include systematic estrogen pharmacologic effects to describe estrogen concentration-related effects on BMD. A logistic regression fit to patient-level data from three clinical GnRH agonist (nafarelin) studies described the relationship of estrogen with endometrial-related pain. Targeting estradiol between 20 and 40 pg/ml was predicted to provide efficacious endometrial pain response while minimizing BMD effects.
Collapse
Affiliation(s)
- M M Riggs
- Metrum Research Group LLC, Tariffville, Connecticut, USA
| | | | | | | |
Collapse
|
23
|
Iyengar R, Zhao S, Chung SW, Mager DE, Gallo JM. Merging systems biology with pharmacodynamics. Sci Transl Med 2012; 4:126ps7. [PMID: 22440734 DOI: 10.1126/scitranslmed.3003563] [Citation(s) in RCA: 127] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
The emerging discipline of systems pharmacology aims to combine analysis and computational modeling of cellular regulatory networks with quantitative pharmacology approaches to drive the drug discovery processes, predict rare adverse events, and catalyze the practice of personalized precision medicine. Here, we introduce the concept of enhanced pharmacodynamic (ePD) models, which synergistically combine the desirable features of systems biology and current PD models within the framework of ordinary or partial differential equations. ePD models that analyze regulatory networks involved in drug action can account for a drug's multiple targets and for the effects of genomic, epigenomic, and posttranslational changes on the drug efficacy. This new knowledge can drive drug discovery and shape precision medicine.
Collapse
Affiliation(s)
- Ravi Iyengar
- Department of Pharmacology and Systems Therapeutics, Systems Biology Center New York, Mount Sinai School of Medicine, New York, NY 10029, USA.
| | | | | | | | | |
Collapse
|
24
|
LIANG WENNA, LIN MUNAN, LI XIHAI, LI CANDONG, GAO BIZHENG, GAN HUIJUAN, YANG ZHAOYANG, LIN XUEJUAN, LIAO LINGHONG, YANG MIN. Icariin promotes bone formation via the BMP-2/Smad4 signal transduction pathway in the hFOB 1.19 human osteoblastic cell line. Int J Mol Med 2012; 30:889-95. [DOI: 10.3892/ijmm.2012.1079] [Citation(s) in RCA: 68] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2012] [Accepted: 06/23/2012] [Indexed: 11/05/2022] Open
|
25
|
Abstract
Inflammation is an array of immune responses to infection and injury. It results from a complex immune cascade and is the basis of many chronic diseases such as arthritis, diabetes, and cancer. Numerous mathematical models have been developed to describe the disease progression and effects of anti-inflammatory drugs. This review illustrates the state of the art in modeling the effects of diverse drugs for treating inflammation, describes relevant biomarkers amenable to modeling, and summarizes major advantages and limitations of the published pharmacokinetic/ pharmacodynamic (PK/PD) models. Simple direct inhibitory models are often used to describe in vitro effects of anti-inflammatory drugs. Indirect response models are more mechanism based and have been widely applied to the turnover of symptoms and biomarkers. These, along with target-mediated and transduction models, have been successfully applied to capture the PK/PD of many anti-inflammatory drugs and describe disease progression of inflammation. Biologics have offered opportunities to address specific mechanisms of action, and evolve small systems models to quantitatively capture the underlying physiological processes. More advanced mechanistic models should allow evaluation of the roles of some key mediators in disease progression, assess drug interactions, and better translate drug properties from in vitro and animal data to patients.
Collapse
Affiliation(s)
- Hoi-Kei Lon
- Department of Pharmaceutical Sciences, School of Pharmacy and Pharmaceutical Sciences, University at Buffalo, Buffalo, NY 14214, USA
| | | | | |
Collapse
|