Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Translational Systems Pharmacology Studies in Pregnant Women.

ABSTRACT: Pregnancy involves rapid physiological adaptation and complex interplay between mother and fetus. New analytic technologies provide large amounts of genomic, proteomic, and metabolomics data. The integration of these data through bioinformatics, statistical, and systems pharmacology techniques can improve our understanding of the mechanisms of normal maternal physiologic changes and fetal development. New insights into the mechanisms of pregnancy-related disorders, such as preterm birth (PTB), may lead to the development of new therapeutic interventions and novel biomarkers.

SUBMITTER: Quinney SK

PROVIDER: S-EPMC5824114 | biostudies-literature | 2018 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Translational Systems Pharmacology Studies in Pregnant Women.

Quinney Sara K SK Gullapelli Rakesh R Haas David M DM

CPT: pharmacometrics & systems pharmacology 20171214 2

Pregnancy involves rapid physiological adaptation and complex interplay between mother and fetus. New analytic technologies provide large amounts of genomic, proteomic, and metabolomics data. The integration of these data through bioinformatics, statistical, and systems pharmacology techniques can improve our understanding of the mechanisms of normal maternal physiologic changes and fetal development. New insights into the mechanisms of pregnancy-related disorders, such as preterm birth (PTB), m ...[more]

PMID: 29239132

Similar Datasets

Optimizing Pharmacology Studies in Pregnant and Lactating Women Using Lessons From HIV: A Consensus Statement.

Project description:Information on the extent of drug exposure to mothers and infants during pregnancy and lactation normally becomes available years after regulatory approval of a drug. Clinicians face knowledge gaps on drug selection and dosing in pregnancy and infant exposure during breastfeeding. Physiological changes during pregnancy often result in lower drug exposures of antiretrovirals, and in some cases a risk of reduced virologic efficacy. The International Maternal Pediatric Adolescent AIDS Clinical Trials (IMPAACT) network and the World Health Organization (WHO)-convened Pediatric Antiretrovirals Working Group collaboratively organized a workshop of key stakeholders in June 2019 to define key standards to generate pharmacology data for antiretrovirals to be used among pregnant and lactating women; review the antiretroviral product pipeline; describe key gaps for use in low-income and middle-income countries; and identify opportunities to undertake optimal studies allowing for rapid implementation in the clinical field. We discussed ethical and regulatory principles, systemic approaches to obtaining data for pregnancy pharmacokinetic/pharmacodynamic (PK/PD) studies, control groups, optimal sampling times during pregnancy, and pharmacokinetic parameters to be considered as primary end points in pregnancy PK/PD studies. For lactation studies, the type of milk to collect, ascertainment of maternal adherence, and optimal PK methods to estimate exposure were discussed. Participants strongly recommended completion of preclinical reproductive toxicology studies prior to phase III, to allow study protocols to include pregnant women or to allow women who become pregnant after enrolment to continue in the trial. The meeting concluded by developing an algorithm for design and interpretation of results and noted that recruitment of pregnant and lactating women into clinical trials is critical.

| S-EPMC8167886 | biostudies-literature

Translational systems pharmacology-based predictive assessment of drug-induced cardiomyopathy.

Project description:Drug-induced cardiomyopathy contributes to drug attrition. We compared two pipelines of predictive modeling: (1) applying elastic net (EN) to differentially expressed genes (DEGs) of drugs; (2) applying integer linear programming (ILP) to construct each drug's signaling pathway starting from its targets to downstream proteins, to transcription factors, and to its DEGs in human cardiomyocytes, and then subjecting the genes/proteins in the drugs' signaling networks to EN regression. We classified 31 drugs with availability of DEGs into 13 toxic and 18 nontoxic drugs based on a clinical cardiomyopathy incidence cutoff of 0.1%. The ILP-augmented modeling increased prediction accuracy from 79% to 88% (sensitivity: 88%; specificity: 89%) under leave-one-out cross validation. The ILP-constructed signaling networks of drugs were better predictors than DEGs. Per literature, the microRNAs that reportedly regulate expression of our six top predictors are of diagnostic value for natural heart failure or doxorubicin-induced cardiomyopathy. This translational predictive modeling might uncover potential biomarkers.

| S-EPMC5869547 | biostudies-literature

Applied Concepts in PBPK Modeling: How to Extend an Open Systems Pharmacology Model to the Special Population of Pregnant Women.

Project description:This tutorial presents the workflow of adapting an adult physiologically based pharmacokinetic (PBPK) model to the pregnant populations using the Open Systems Pharmacology (OSP) software suite (www.open-systems-pharmacology.org). This workflow is illustrated using a previously published PBPK model for metronidazole that is extrapolated to pregnancy by parameterizing and extending the model structure in terms of pregnancy-induced physiological changes. Importantly, this workflow can be applied to other scenarios where PBPK models need to be re-parameterized or structurally modified.

| S-EPMC6063743 | biostudies-literature

Interconnected Microphysiological Systems for Quantitative Biology and Pharmacology Studies.

Project description:Microphysiological systems (MPSs) are in vitro models that capture facets of in vivo organ function through use of specialized culture microenvironments, including 3D matrices and microperfusion. Here, we report an approach to co-culture multiple different MPSs linked together physiologically on re-useable, open-system microfluidic platforms that are compatible with the quantitative study of a range of compounds, including lipophilic drugs. We describe three different platform designs - "4-way", "7-way", and "10-way" - each accommodating a mixing chamber and up to 4, 7, or 10 MPSs. Platforms accommodate multiple different MPS flow configurations, each with internal re-circulation to enhance molecular exchange, and feature on-board pneumatically-driven pumps with independently programmable flow rates to provide precise control over both intra- and inter-MPS flow partitioning and drug distribution. We first developed a 4-MPS system, showing accurate prediction of secreted liver protein distribution and 2-week maintenance of phenotypic markers. We then developed 7-MPS and 10-MPS platforms, demonstrating reliable, robust operation and maintenance of MPS phenotypic function for 3 weeks (7-way) and 4 weeks (10-way) of continuous interaction, as well as PK analysis of diclofenac metabolism. This study illustrates several generalizable design and operational principles for implementing multi-MPS "physiome-on-a-chip" approaches in drug discovery.

| S-EPMC5852083 | biostudies-other

A Translational Systems Pharmacology Model for A? Kinetics in Mouse, Monkey, and Human.

Project description:A mechanistic model of amyloid beta production, degradation, and distribution was constructed for mouse, monkey, and human, calibrated and externally verified across multiple datasets. Simulations of single-dose avagacestat treatment demonstrate that the A?42 brain inhibition may exceed that in cerebrospinal fluid (CSF). The dose that achieves 50% CSF A?40 inhibition for humans (both healthy and with Alzheimer's disease (AD)) is about 1 mpk, one order of magnitude lower than for mouse (10 mpk), mainly because of differences in pharmacokinetics. The predicted maximal percent of brain A?42 inhibition after single-dose avagacestat is higher for AD subjects (about 60%) than for healthy individuals (about 45%). The probability of achieving a normal physiological level for A?42 in brain (1 nM) during multiple avagacestat dosing can be increased by using a dosing regimen that achieves higher exposure. The proposed model allows prediction of brain pharmacodynamics for different species given differing dosing regimens.

| S-EPMC5658289 | biostudies-literature

Application of a systems pharmacology model for translational prediction of hERG-mediated QTc prolongation.

Project description:Drug-induced QTc interval prolongation (? QTc) is a main surrogate for proarrhythmic risk assessment. A higher in vivo than in vitro potency for hERG-mediated QTc prolongation has been suggested. Also, in vivo between-species and patient populations' sensitivity to drug-induced QTc prolongation seems to differ. Here, a systems pharmacology model integrating preclinical in vitro (hERG binding) and in vivo (conscious dog ? QTc) data of three hERG blockers (dofetilide, sotalol, moxifloxacin) was applied (1) to compare the operational efficacy of the three drugs in vivo and (2) to quantify dog-human differences in sensitivity to drug-induced QTc prolongation (for dofetilide only). Scaling parameters for translational in vivo extrapolation of drug effects were derived based on the assumption of system-specific myocardial ion channel densities and transduction of ion channel block: the operational efficacy (transduction of hERG block) in dogs was drug specific (1-19% hERG block corresponded to ?10 msec ? QTc). System-specific maximal achievable ? QTc was estimated to 28% from baseline in both dog and human, while %hERG block leading to half-maximal effects was 58% lower in human, suggesting a higher contribution of hERG-mediated potassium current to cardiac repolarization. These results suggest that differences in sensitivity to drug-induced QTc prolongation may be well explained by drug- and system-specific differences in operational efficacy (transduction of hERG block), consistent with experimental reports. The proposed scaling approach may thus assist the translational risk assessment of QTc prolongation in different species and patient populations, if mediated by the hERG channel.

| S-EPMC5226282 | biostudies-literature

Translational studies of phenotypic probes for the mononuclear phagocyte system and liposomal pharmacology.

Project description:As nanoparticles (NPs) are cleared via phagocytes of the mononuclear phagocyte system (MPS), we hypothesized that the function of circulating monocytes and dendritic cells (MO/DC) in blood can predict NP clearance (CL). We measured MO/DC phagocytosis and reactive oxygen species (ROS) production in mice, rats, dogs, and patients with refractory solid tumors. Pharmacokinetic studies of polyethylene glycol (PEG)-encapsulated liposomal doxorubicin (PEGylated liposomal doxirubicin [PLD]), CKD-602 (S-CKD602), and cisplatin (SPI-077) were performed at the maximum tolerated dose. MO/DC function was also evaluated in patients with recurrent epithelial ovarian cancer (EOC) administered PLD. Across species, a positive association was observed between cell function and CL of PEGylated liposomes. In patients with EOC, associations were observed between PLD CL and phagocytosis (R(2) = 0.43, P = 0.04) and ROS production (R(2) = 0.61, P = 0.008) in blood MO/DC. These findings suggest that probes of MPS function may help predict PEGylated liposome CL across species and PLD CL in patients with EOC.

| S-EPMC3836305 | biostudies-literature

Vaginal microbiota, pregnant women

Project description:Parity and gestational age associates with vaginal microbiota composition in term and late term pregnancies

| PRJEB47492 | ENA

Cerebrospinal fluid of preeclamptic and normotensive pregnant women compared to non-pregnant women analyzed with mass spectrometry.

Project description:The objective of the present study was to study whether pregnancy-related proteins are associated with preeclampsia.

2021-09-09 | PXD017862 | Pride

Systems pharmacology of arrhythmias.

Project description:Long QT syndrome (LQTS) is a congenital or drug-induced change in electrical activity of the heart that can lead to fatal arrhythmias. Mutations in 12 genes encoding ion channels and associated proteins are linked with congenital LQTS. With a computational systems biology approach, we found that gene products involved in LQTS formed a distinct functional neighborhood within the human interactome. Other diseases form similarly selective neighborhoods, and comparison of the LQTS neighborhood with other disease-centered neighborhoods suggested a molecular basis for associations between seemingly unrelated diseases that have increased risk of cardiac complications. By combining the LQTS neighborhood with published genome-wide association study data, we identified previously unknown single-nucleotide polymorphisms likely to affect the QT interval. We found that targets of U.S. Food and Drug Administration (FDA)-approved drugs that cause LQTS as an adverse event were enriched in the LQTS neighborhood. With the LQTS neighborhood as a classifier, we predicted drugs likely to have risks for QT effects and we validated these predictions with the FDA's Adverse Events Reporting System, illustrating how network analysis can enhance the detection of adverse drug effects associated with drugs in clinical use. Thus, the identification of disease-selective neighborhoods within the human interactome can be useful for predicting new gene variants involved in disease, explaining the complexity underlying adverse drug side effects, and predicting adverse event susceptibility for new drugs.

| S-EPMC3068558 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data