<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
  <dc:creator>Ehrmann, Stephan</dc:creator>
  <dc:creator>Schmid, Otmar</dc:creator>
  <dc:creator>Darquenne, Chantal</dc:creator>
  <dc:creator>Rothen-Rutishauser, Barbara</dc:creator>
  <dc:creator>Sznitman, Josue</dc:creator>
  <dc:creator>Yang, Lin</dc:creator>
  <dc:creator>Barosova, Hana</dc:creator>
  <dc:creator>Vecellio, Laurent</dc:creator>
  <dc:creator>Mitchell, Jolyon</dc:creator>
  <dc:creator>Heuze-Vourc’h, Nathalie</dc:creator>
  <dc:date>2020-04-02</dc:date>
  <dc:description xmlns:ns0="xml" ns0:lang="en">Introduction: Pulmonary drug delivery is a complex field of research combining  physics which drive aerosol transport and deposition and biology which underpins  efficacy and toxicity of inhaled drugs. A myriad of preclinical methods, ranging from in- silico to in-vitro, ex–vivo and in-vivo, can be implemented.Areas covered: The present  review covers in-silico mathematical and computational fluid dynamics modelization of  aerosol deposition, cascade impactor technology to estimated drug delivery and  deposition, advanced in-vitro cell culture methods and associated aerosol exposure,  lung-on-chip technology, ex–vivo modeling, in-vivo inhaled drug delivery, lung  imaging, and longitudinal pharmacokinetic analysis.Expert opinion: No single  preclinical model can be advocated; all methods are fundamentally complementary  and should be implemented based on benefits and drawbacks to answer specific  scientific questions. The overall best scientific strategy depends, among others, on the  product under investigations, inhalation device design, disease of interest, clinical  patient population, previous knowledge. Preclinical testing is not to be separated from  clinical evaluation, as small proof-of-concept clinical studies or conversely large-scale  clinical big data may inform preclinical testing. The extend of expertise required for  such translational research is unlikely to be found in one single laboratory calling for  the setup of multinational large-scale research consortiums.</dc:description>
  <dc:format>application/pdf</dc:format>
  <dc:identifier>https://folia.unifr.ch/global/documents/309123</dc:identifier>
  <dc:identifier>https://folia.unifr.ch/documents/309123/files/rot_ipm.pdf</dc:identifier>
  <dc:language>eng</dc:language>
  <dc:relation>info:eu-repo/semantics/altIdentifier/doi/10.1080/17425247.2020.1730807</dc:relation>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>License undefined</dc:rights>
  <dc:source>Expert Opinion on Drug Delivery. - 2020, vol. 17, no. 4, p. 463–478</dc:source>
  <dc:subject>info:eu-repo/classification/udc/57</dc:subject>
  <dc:title xmlns:ns1="xml" ns1:lang="en">Innovative preclinical models for pulmonary drug delivery research</dc:title>
  <dc:type>http://purl.org/coar/resource_type/c_6501</dc:type>
</oai_dc:dc>
