scholarly journals Computational Models for Diagnosing and Treating Endometriosis

2021 ◽  
Vol 3 ◽  
Author(s):  
Wangui Mbuguiro ◽  
Adriana Noemi Gonzalez ◽  
Feilim Mac Gabhann
Keyword(s):  
Epithelial Cells ◽  
Computational Modeling ◽  
Clinical Data ◽  
Immune Cells ◽  
Computational Models ◽  
Systems Pharmacology ◽  
Biological Mechanisms ◽  
Stromal Fibroblasts ◽  
Disease Symptoms ◽  
Quantitative Systems Pharmacology

Endometriosis is a common but poorly understood disease. Symptoms can begin early in adolescence, with menarche, and can be debilitating. Despite this, people often suffer several years before being correctly diagnosed and adequately treated. Endometriosis involves the inappropriate growth of endometrial-like tissue (including epithelial cells, stromal fibroblasts, vascular cells, and immune cells) outside of the uterus. Computational models can aid in understanding the mechanisms by which immune, hormone, and vascular disruptions manifest in endometriosis and complicate treatment. In this review, we illustrate how three computational modeling approaches (regression, pharmacokinetics/pharmacodynamics, and quantitative systems pharmacology) have been used to improve the diagnosis and treatment of endometriosis. As we explore these approaches and their differing detail of biological mechanisms, we consider how each approach can answer different questions about endometriosis. We summarize the mathematics involved, and we use published examples of each approach to compare how researchers: (1) shape the scope of each model, (2) incorporate experimental and clinical data, and (3) generate clinically useful predictions and insight. Lastly, we discuss the benefits and limitations of each modeling approach and how we can combine these approaches to further understand, diagnose, and treat endometriosis.

Computational exploration of mechanistic determinants of antibody drug-conjugate pharmacokinetics using quantitative systems pharmacology modeling strategies.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. e14000-e14000
Author(s):  
John M Burke ◽  
Anna Katharina Wilkins ◽  
Andrew Matteson ◽  
Lore Gruenbaum ◽  
Josh F Apgar
Keyword(s):  
Half Life ◽  
Computational Models ◽  
Systems Pharmacology ◽  
Antibody Drug Conjugate ◽  
Quantitative Systems Pharmacology ◽  
Drug Conjugate ◽  
Drug Antibody Ratio ◽  
The Impact ◽  
Antibody Drug

e14000 Background: The pharmacokinetics of antibody drug conjugate (ADC) therapeutics typically show a discrepancy between the PK of total antibody (conjugated and unconjugated antibody) and that of conjugated antibody, carrying one or more payload molecules This discrepancy is often attributed to deconjugation (Kamath, 2014), however recent evidence suggests that the underlying mechanisms may be more complex. Methods: This work employs a computational quantitative systems pharmacology (QSP) approach to understand the impact of drug antibody ratio (DAR) and the resulting changes in molecular properties on overall PK and relative payload disposition as observed in preclinical and clinical studies. Results: Using QSP approaches, the model (1) describes the kinetics of individual DAR species and agrees well with typical ADC PK, individual DAR PK, and average DAR measurements in vivo; (2), quantitatively describes the trade-off between higher DAR and lower exposure; consequently, we predict that ADC2 is half as potent as ADC4 and ADC8, which are equipotent; (3) longer mAb half-life reduces payload delivery after multiple doses; and (4) ADC half-life affects the percent of payload delivered through different mechanisms. Conclusions: A QSP model describing mechanism is a useful tool to translate and understand PK from preclinical species to human, by acting as a central repository of data, knowledge, and hypotheses. It provided a rational basis to generate testable hypotheses and provide early insights into complex ADC PK data and established the benefit of using computational models to design novel ADCs and to optimize the discovery and development of existing ADCs.

scholarly journals Virtual Populations for Quantitative Systems Pharmacology Models

2021 ◽  
Author(s):  
Yougan Cheng ◽  
Ronny Straube ◽  
Abed Alnaif ◽  
Lu Huang ◽  
Tarek Leil ◽  
...  
Keyword(s):  
Clinical Data ◽  
Model Calibration ◽  
Algorithm Design ◽  
Computational Time ◽  
Systems Pharmacology ◽  
Drug Induced ◽  
Quantitative Systems Pharmacology ◽  
Clinical Biomarkers ◽  
Calibration Algorithm ◽  
Methodological Considerations

Quantitative systems pharmacology (QSP) places an emphasis on dynamic systems modeling, incorporating considerations from systems biology modeling and pharmacodynamics. The goal of QSP is often to quantitatively predict the effects of clinical therapeutics, their combinations, and their doses on clinical biomarkers and endpoints. In order to achieve this goal, strategies for incorporating clinical data into model calibration are critical. Virtual population (VPop) approaches facilitate model calibration while faced with challenges encountered in QSP model application, including modeling a breadth of clinical therapies, biomarkers, endpoints, utilizing data of varying structure and source, capturing observed clinical variability, and simulating with models that may require more substantial computational time and resources than often found in pharmacometrics applications. VPops are frequently developed in a process that may involve parameterization of isolated pathway models, integration into a larger QSP model, incorporation of clinical data, calibration, and quantitative validation that the model with the accompanying, calibrated VPop is suitable to address the intended question or help with the intended decision. Here, we introduce previous strategies for developing VPops in the context of a variety of therapeutic and safety areas: metabolic disorders, drug-induced liver injury, auto-immune diseases, and cancer. We introduce methodological considerations, prior work for sensitivity analysis and VPop algorithm design, and potential areas for future advancement. Finally, we give a more detailed application example of a VPop calibration algorithm that illustrates recent progress and many of the methodological considerations. In conclusion, although methodologies have varied, VPop strategies have been successfully applied to give valid clinical insights and predictions with the assistance of carefully defined and designed calibration and validation strategies. While a uniform VPop approach for all potential QSP applications may be challenging given the heterogeneity in use considerations, we anticipate continued innovation will help to drive VPop application for more challenging cases of greater scale while developing new rigorous methodologies and metrics.

JAK3-inhibition: Suppressing immune cells while preserving epithelial cells

2012 ◽  
Vol 60 (S 01) ◽  
Author(s):  
T Gossler ◽  
F Ricklefs ◽  
T Deuse ◽  
E Masuda ◽  
V Taylor ◽  
...  
Keyword(s):  
Epithelial Cells ◽  
Immune Cells

COMPUTATIONAL MODELING OF ALZHEIMER'S DISEASE SYMPTOMS USING VENN'S NETWORK

2016 ◽  
Author(s):  
Anderson Tenorio Sérgio ◽  
Diego de Siqueira Braga ◽  
Fernando Buarque de L. Neto
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Computational Modeling ◽  
Disease Symptoms

Computing Possible Futures

2019 ◽  
Author(s):  
William B. Rouse
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Computational Modeling ◽  
Mental Model ◽  
Data Analytics ◽  
Computational Models ◽  
Senior Managers ◽  
Interactive Visualizations ◽  
Use Of Models ◽  
Better Than

This book discusses the use of models and interactive visualizations to explore designs of systems and policies in determining whether such designs would be effective. Executives and senior managers are very interested in what “data analytics” can do for them and, quite recently, what the prospects are for artificial intelligence and machine learning. They want to understand and then invest wisely. They are reasonably skeptical, having experienced overselling and under-delivery. They ask about reasonable and realistic expectations. Their concern is with the futurity of decisions they are currently entertaining. They cannot fully address this concern empirically. Thus, they need some way to make predictions. The problem is that one rarely can predict exactly what will happen, only what might happen. To overcome this limitation, executives can be provided predictions of possible futures and the conditions under which each scenario is likely to emerge. Models can help them to understand these possible futures. Most executives find such candor refreshing, perhaps even liberating. Their job becomes one of imagining and designing a portfolio of possible futures, assisted by interactive computational models. Understanding and managing uncertainty is central to their job. Indeed, doing this better than competitors is a hallmark of success. This book is intended to help them understand what fundamentally needs to be done, why it needs to be done, and how to do it. The hope is that readers will discuss this book and develop a “shared mental model” of computational modeling in the process, which will greatly enhance their chances of success.

Prevention of Dextran Sulfate Sodium-induced Mouse Colitis by a Fungal Protein Ling Zhi-8 via Promoting the Barrier Function of Intestinal Epithelial Cells

2021 ◽  
Author(s):  
Yu-Huan Chen ◽  
Jenn-Yeu Shin ◽  
Hsiu-Mei Wei ◽  
Chi-Chen Lin ◽  
Linda Chia-Hui Yu ◽  
...  
Keyword(s):  
Epithelial Cells ◽  
Immune Cells ◽  
Ganoderma Lucidum ◽  
Dextran Sulfate ◽  
Dextran Sulfate Sodium ◽  
Intestinal Epithelial Cells ◽  
Intestinal Epithelial ◽  
Fungal Protein ◽  
Fungal Immunomodulatory Protein

A fungal immunomodulatory protein Ling Zhi-8 (LZ-8) isolated from Ganoderma lucidum (GL) regulates immune cells and inhibits tumor growth; however, the role of LZ-8 in intestinal epithelial cells (IECs) is...

scholarly journals Quantitative systems pharmacology in neuroscience: Novel methodologies and technologies

2021 ◽  
Author(s):  
Peter Bloomingdale ◽  
Tatiana Karelina ◽  
Murat Cirit ◽  
Sarah F. Muldoon ◽  
Justin Baker ◽  
...  
Keyword(s):  
Systems Pharmacology ◽  
Quantitative Systems Pharmacology
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