title: Generalized in vitro-in vivo relationship (IVIVR) model based on artificial neural networks.
abstract: BACKGROUND
  The aim of this study was to develop a generalized in vitro-in vivo relationship (IVIVR) model based on in vitro dissolution profiles together with quantitative and qualitative composition of dosage formulations as covariates. Such a model would be of substantial aid in the early stages of development of a pharmaceutical formulation, when no in vivo results are yet available and it is impossible to create a classical in vitro-in vivo correlation (IVIVC)/IVIVR.
  
  
  METHODS
  Chemoinformatics software was used to compute the molecular descriptors of drug substances (ie, active pharmaceutical ingredients) and excipients. The data were collected from the literature. Artificial neural networks were used as the modeling tool. The training process was carried out using the 10-fold cross-validation technique.
  
  
  RESULTS
  The database contained 93 formulations with 307 inputs initially, and was later limited to 28 in a course of sensitivity analysis. The four best models were introduced into the artificial neural network ensemble. Complete in vivo profiles were predicted accurately for 37.6% of the formulations.
  
  
  CONCLUSIONS
  It has been shown that artificial neural networks can be an effective predictive tool for constructing IVIVR in an integrated generalized model for various formulations. Because IVIVC/IVIVR is classically conducted for 2-4 formulations and with a single active pharmaceutical ingredient, the approach described here is unique in that it incorporates various active pharmaceutical ingredients and dosage forms into a single model. Thus, preliminary IVIVC/IVIVR can be available without in vivo data, which is impossible using current IVIVC/IVIVR procedures.
authors.affiliation: Department of Pharmaceutical Technology and Biopharmaceutics, Faculty of Pharmacy, Jagiellonian University Medical College, Kraków, Poland. mfmendyk@cyf-kr.edu.pl
authors.forename: Aleksander
authors.initials: A
authors.lastname: Mendyk
authors.forename: Paweł K
authors.initials: PK
authors.lastname: Tuszyński
authors.forename: Sebastian
authors.initials: S
authors.lastname: Polak
authors.forename: Renata
authors.initials: R
authors.lastname: Jachowicz
mesh_terms: D001682:Biological Availability; D003198:Computer Simulation; D016208:Databases, Factual; D015195:Drug Design; D005079:Excipients / Q000737:chemistry; D006801:Humans; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D004364:Pharmaceutical Preparations / Q000008:administration & dosage* / Q000737:chemistry / Q000378:metabolism; D012995:Solubility
chemical_list: D005079:Excipients; D004364:Pharmaceutical Preparations
keywords: artificial neural networks; bioavailability; correlation; in vitro-in vivo; relationship; soft computing