{"id":3407,"date":"2014-03-10T17:58:07","date_gmt":"2014-03-10T17:58:07","guid":{"rendered":"http:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/?p=3407"},"modified":"2014-03-10T17:58:07","modified_gmt":"2014-03-10T17:58:07","slug":"applications-predictive-modeling-drug-development","status":"publish","type":"post","link":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/applications-predictive-modeling-drug-development\/3407\/","title":{"rendered":"Applications of Predictive Modeling in Drug Development"},"content":{"rendered":"Recently, various empirical and semi-empirical models embedded in different modeling tools have been developed and recognized for their role in predicting pharmacokinetics of drugs in humans. These models have also been used to evaluate the effects of intrinsic (e.g., organ dysfunction, age, genetics) and extrinsic (e.g., drug-drug interactions) factors, alone or in combinations, on drug exposure at all stages of the drug development process. Apart from shortening the time for bringing the drug to market, and minimizing the risk of failure, various physiologically-based pharmacokinetic (PBPK) modeling and simulation tools have gained popularity in the pharmaceutical industry and the <a href=\"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/applications-predictive-modeling-drug-development\/3407\/\" class=\"more-link\">(more&hellip;)<\/a>","protected":false},"excerpt":{"rendered":"<p>Recently, various empirical and semi-empirical models embedded in different modeling tools have been developed and recognized for their role in predicting pharmacokinetics of drugs in humans. These models have also been used to evaluate the effects of intrinsic (e.g., organ dysfunction, age, genetics) and extrinsic (e.g., drug-drug interactions) factors, alone or in combinations, on drug [&hellip;]<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":"","_links_to":"","_links_to_target":""},"categories":[11],"tags":[313,314,315,316,317],"class_list":["post-3407","post","type-post","status-publish","format-standard","hentry","category-industry-news","tag-aaps-newsmagazine","tag-biopharmaceutics","tag-drug-development","tag-pbpk-modeling","tag-physical-pharmacy"],"_links":{"self":[{"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/posts\/3407","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/comments?post=3407"}],"version-history":[{"count":3,"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/posts\/3407\/revisions"}],"predecessor-version":[{"id":3410,"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/posts\/3407\/revisions\/3410"}],"wp:attachment":[{"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/media?parent=3407"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/categories?post=3407"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/tags?post=3407"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}