TY - JOUR
T1 - Computational Models for Diagnosing and Treating Endometriosis
AU - Mbuguiro, Wangui
AU - Gonzalez, Adriana Noemi
AU - Mac Gabhann, Feilim
N1 - Funding Information:
This study was supported by the National Science Foundation Graduate Research Fellowship Program under Grant No. DGE1746891 (to WM) and the National Institutes of Health Medical Scientist Training Program under Grant No. T32GM136577 (to AG).
Publisher Copyright:
Copyright © 2021 Mbuguiro, Gonzalez and Mac Gabhann.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - biomarker
KW - computational
KW - endometriosis
KW - hormone therapy
KW - machine learning
KW - mechanism
KW - pharmacokinetics
KW - systems biology
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U2 - 10.3389/frph.2021.699133
DO - 10.3389/frph.2021.699133
M3 - Review article
AN - SCOPUS:85149950343
SN - 2673-3153
VL - 3
JO - Frontiers in Reproductive Health
JF - Frontiers in Reproductive Health
M1 - 699133
ER -