Rational Use of Plasma Protein and Tissue Binding Data in Drug Design

2014 ◽  
Vol 57 (20) ◽  
pp. 8238-8248 ◽  
Author(s):  
Xingrong Liu ◽  
Matthew Wright ◽  
Cornelis E. C. A. Hop
Keyword(s):  
Drug Design ◽  
Plasma Protein ◽  
Tissue Binding ◽  
Rational Use ◽  
Binding Data

How Well Do Lipophilicity Parameters, MEEKC Microemulsion Capacity Factor, and Plasma Protein Binding Predict CNS Tissue Binding?

2012 ◽  
Vol 101 (5) ◽  
pp. 1932-1940 ◽  
Author(s):  
Maciej J. Zamek-Gliszczynski ◽  
Karen E. Sprague ◽  
Alfonso Espada ◽  
Thomas J. Raub ◽  
Stuart M. Morton ◽  
...  
Keyword(s):  
Protein Binding ◽  
Plasma Protein Binding ◽  
Plasma Protein ◽  
Capacity Factor ◽  
Tissue Binding ◽  
Lipophilicity Parameters

Do We Need to Optimize Plasma Protein and Tissue Binding in Drug Discovery?

2011 ◽  
Vol 11 (4) ◽  
pp. 450-466 ◽  
Author(s):  
Xingrong Liu ◽  
Cuiping Chen ◽  
Cornelis E.C.A. Hop
Keyword(s):  
Drug Discovery ◽  
Plasma Protein ◽  
Tissue Binding

Effect of plasma protein and tissue binding on the biologic half-life of drugs

1978 ◽  
Vol 24 (1) ◽  
pp. 1-4 ◽  
Author(s):  
Milo Gibaldi ◽  
Gerhard Levy ◽  
Patrick J. McNamara
Keyword(s):  
Plasma Protein ◽  
Half Life ◽  
Tissue Binding

Species differences in drug plasma protein binding

MedChemComm ◽  
2014 ◽  
Vol 5 (7) ◽  
pp. 963-967 ◽  
Author(s):  
Nicola Colclough ◽  
Linette Ruston ◽  
J. Matthew Wood ◽  
Philip A. MacFaul
Keyword(s):  
Drug Discovery ◽  
Protein Binding ◽  
Human Plasma ◽  
Plasma Protein Binding ◽  
Plasma Protein ◽  
Plasma Proteins ◽  
Species Differences ◽  
Human Plasma Protein ◽  
Binding Data ◽  
Drug Plasma

Comparison of the human plasma protein binding data for a variety of drug discovery compounds indicates that compounds tend to be slightly more bound to human plasma proteins, than compared to plasma proteins from rats, dogs or mice.

Prediction of Drug-Plasma Protein Binding Using Artificial Intelligence Based Algorithms

2018 ◽  
Vol 21 (1) ◽  
pp. 57-64 ◽  
Author(s):  
Rajnish Kumar ◽  
Anju Sharma ◽  
Mohammed Haris Siddiqui ◽  
Rajesh Kumar Tiwari
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Drug Design ◽  
Protein Binding ◽  
Plasma Protein Binding ◽  
Plasma Protein ◽  
Drug Distribution ◽  
Support Vector ◽  
Design And Development ◽  
Drug Candidates

Aim and Objective: Plasma protein binding (PPB) has vital importance in the characterization of drug distribution in the systemic circulation. Unfavorable PPB can pose a negative effect on clinical development of promising drug candidates. The drug distribution properties should be considered at the initial phases of the drug design and development. Therefore, PPB prediction models are receiving an increased attention. Materials and Methods: In the current study, we present a systematic approach using Support vector machine, Artificial neural network, k- nearest neighbor, Probabilistic neural network, Partial least square and Linear discriminant analysis to relate various in vitro and in silico molecular descriptors to a diverse dataset of 736 drugs/drug-like compounds. Results: The overall accuracy of Support vector machine with Radial basis function kernel came out to be comparatively better than the rest of the applied algorithms. The training set accuracy, validation set accuracy, precision, sensitivity, specificity and F1 score for the Suprort vector machine was found to be 89.73%, 89.97%, 92.56%, 87.26%, 91.97% and 0.898, respectively. Conclusion: This model can potentially be useful in screening of relevant drug candidates at the preliminary stages of drug design and development.

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