Pharmacokinetic parameter prediction from drug structure using artificial neural networks

Turner, JV, Maddalena, DJ and Cutler, DJ (2004) Pharmacokinetic parameter prediction from drug structure using artificial neural networks. International Journal of Pharmaceutics, 270 1-2: 209-219. doi:10.1016/j.ijpharm.2003.10.011


Author Turner, JV
Maddalena, DJ
Cutler, DJ
Title Pharmacokinetic parameter prediction from drug structure using artificial neural networks
Journal name International Journal of Pharmaceutics   Check publisher's open access policy
ISSN 0378-5173
Publication date 2004-02-01
Sub-type Article (original research)
DOI 10.1016/j.ijpharm.2003.10.011
Open Access Status Not Open Access
Volume 270
Issue 1-2
Start page 209
End page 219
Total pages 11
Publisher Wiley-Blackwell
Language eng
Abstract Artificial neural networks (ANNs) were used for voxel-wise parameter estimation with the combined intravoxel incoherent motion (IVIM) and kurtosis model facilitating robust diffusion parameter mapping in the human brain. The proposed ANN approach was compared with conventional least-squares regression (LSR) and state-of-the-art multi-step fitting (LSR-MS) in Monte-Carlo simulations and in vivo in terms of estimation accuracy and precision, number of outliers and sensitivity in the distinction between grey (GM) and white (WM) matter. Both the proposed ANN approach and LSR-MS yielded visually increased parameter map quality. Estimations of all parameters (perfusion fraction f, diffusion coefficient D, pseudo-diffusion coefficient D*, kurtosis K) were in good agreement with the literature using ANN, whereas LSR-MS resulted in D* overestimation and LSR yielded increased values for f and D*, as well as decreased values for K. Using ANN, outliers were reduced for the parameters f (ANN, 1%; LSR-MS, 19%; LSR, 8%), D* (ANN, 21%; LSR-MS, 25%; LSR, 23%) and K (ANN, 0%; LSR-MS, 0%; LSR, 15%). Moreover, ANN enabled significant distinction between GM and WM based on all parameters, whereas LSR facilitated this distinction only based on D and LSR-MS on f, D and K. Overall, the proposed ANN approach was found to be superior to conventional LSR, posing a powerful alternative to the state-of-the-art method LSR-MS with several advantages in the estimation of IVIM-kurtosis parameters, which might facilitate increased applicability of enhanced diffusion models at clinical scan times.
Keyword QSPkR
Qspr
neural networks
Ann
theoretical descriptors
Clinical Pharmacokinetics
Quantitative Structure
Therapeutic Efficacy
Topological Approach
Disposition
Pharmacodynamics
Cefaclor
Binding
Cephalosporins
Ampicillin
Q-Index Code C1
Q-Index Status Provisional Code
Grant ID
FT140100865
Institutional Status Unknown

Document type: Journal Article
Sub-type: Article (original research)
Collection: ResearcherID Downloads - Archived
 
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