J Chem Inf Model - In silico prediction of total human plasma clearance.

Tópicos

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Resumo

The prediction of the total human plasma clearance of novel chemical entities continues to be of paramount importance in drug design and optimization, because it impacts both dose size and dose regimen. Although many in vivo and in vitro methods have been proposed, a well-constructed, well-validated, and less resource-intensive computational tool would still be very useful in an iterative compound design cycle. A new completely in silico linear PLS (partial least-squares) model to predict the human plasma clearance was built on the basis of a large data set of 754 compounds using physicochemical descriptors and structural fragments, the latter able to better represent biotransformation processes. The model has been validated using the "ELASTICO" approach (Enhanced Leave Analog-Structural, Therapeutic, Ionization Class Out) based on ten therapeutic/structural analog classes. The model yields a geometric mean fold error (GMFE) of 2.1 and a percentage of compounds predicted within 2- and 3-fold error of 59% and 80%, respectively, showing an improved performance when compared with previous published works in predicting clearance of neutral compounds, and a very good performance with ionized molecules at pH 7.5, able to compare favorably with fairly accurate in vivo methods.

Resumo Limpo

predict total human plasma clearanc novel chemic entiti continu paramount import drug design optim impact dose size dose regimen although mani vivo vitro method propos wellconstruct wellvalid less resourceintens comput tool still use iter compound design cycl new complet silico linear pls partial leastsquar model predict human plasma clearanc built basi larg data set compound use physicochem descriptor structur fragment latter abl better repres biotransform process model valid use elastico approach enhanc leav analogstructur therapeut ioniz class base ten therapeuticstructur analog class model yield geometr mean fold error gmfe percentag compound predict within fold error respect show improv perform compar previous publish work predict clearanc neutral compound good perform ioniz molecul ph abl compar favor fair accur vivo method

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