Severity: Warning
Message: file_get_contents(https://...@remsenmedia.com&api_key=81853a771c3a3a2c6b2553a65bc33b056f08&a=1): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
Filename: helpers/my_audit_helper.php
Line Number: 176
Backtrace:
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 176
Function: file_get_contents
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 250
Function: simplexml_load_file_from_url
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 1034
Function: getPubMedXML
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3152
Function: GetPubMedArticleOutput_2016
File: /var/www/html/application/controllers/Detail.php
Line: 575
Function: pubMedSearch_Global
File: /var/www/html/application/controllers/Detail.php
Line: 489
Function: pubMedGetRelatedKeyword
File: /var/www/html/index.php
Line: 316
Function: require_once
Pregnancy is associated with numerous physiological changes that influence absorption, distribution, metabolism and excretion. Moreover, the magnitude of these effects changes as pregnancy matures. For most medications, there is limited information available about changes in drug disposition that can occur in pregnant patients, yet most women are prescribed one or more medications during pregnancy. In this investigation, PBPK modeling was used to assess the impact of pregnancy on the pharmacokinetic profiles of three medications (metformin, tacrolimus, oseltamivir) using the Simcyp® simulator. The Simcyp pregnancy-PBPK model accounts for the known physiological changes that occur during pregnancy. For each medication, plasma concentration-time profiles were simulated using Simcyp® virtual populations of healthy volunteers and pregnant patients. The predicted systemic exposure metrics (C , AUC) were compared with published clinical data, and the fold error (FE, ratio of predicted and observed data) was calculated. The PBPK model was able to capture the observed changes in C and AUC across each trimester of pregnancy compared with post-partum for metformin (FE range 0.86-1.19), tacrolimus (FE range 1.03-1.64) and oseltamivir (FE range 0.54-1.02). Simcyp model outputs were used to correlate these findings with pregnancy-induced alterations in renal blood flow (metformin, oseltamivir), hepatic CYP3A4 activity (tacrolimus) and reduced plasma protein levels and hemodilution (tacrolimus). The results illustrate how PBPK modeling can help to establish appropriate dosing guidelines for pregnant patients and to predict potential changes in systemic exposure during pregnancy for compounds undergoing clinical development.
Download full-text PDF |
Source |
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http://dx.doi.org/10.1002/bdd.2081 | DOI Listing |
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