J Chem Inf Model - Quantitative structure-activity relationship models of clinical pharmacokinetics: clearance and volume of distribution.


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Reliable prediction of two fundamental human pharmacokinetic (PK) parameters, systemic clearance (CL) and apparent volume of distribution (Vd), determine the size and frequency of drug dosing and are at the heart of drug discovery and development. Traditionally, estimated CL and Vd are derived from preclinical in vitro and in vivo absorption, distribution, metabolism, and excretion (ADME) measurements. In this paper, we report quantitative structure-activity relationship (QSAR) models for prediction of systemic CL and steady-state Vd (Vdss) from intravenous (iv) dosing in humans. These QSAR models avoid uncertainty associated with preclinical-to-clinical extrapolation and require two-dimensional structure drawing as the sole input. The clean, uniform training sets for these models were derived from the compilation published by Obach et al. (Drug Metab. Disp. 2008, 36, 1385-1405). Models for CL and Vdss were developed using both a support vector regression (SVR) method and a multiple linear regression (MLR) method. The SVR models employ a minimum of 2048-bit fingerprints developed in-house as structure quantifiers. The MLR models, on the other hand, are based on information-rich electro-topological states of two-atom fragments as descriptors and afford reverse QSAR (RQSAR) analysis to help model-guided, in silico modulation of structures for desired CL and Vdss. The capability of the models to predict iv CL and Vdss with acceptable accuracy was established by randomly splitting data into training and test sets. On average, for both CL and Vdss, 75% of test compounds were predicted within 2.5-fold of the value observed and 90% of test compounds were within 5.0-fold of the value observed. The performance of the final models developed from 525 compounds for CL and 569 compounds for Vdss was evaluated on an external set of 56 compounds. The predictions were either better or comparable to those predicted by other in silico models reported in the literature. To demonstrate the practical application of the RQSAR approach, the structure of vildagliptin, a high-CL and a high-Vdss compound, is modified based on the atomic contributions to its predicted CL and Vdss to propose compounds with lower CL and lower Vdss.

Resumo Limpo

reliabl predict two fundament human pharmacokinet pk paramet system clearanc cl appar volum distribut vd determin size frequenc drug dose heart drug discoveri develop tradit estim cl vd deriv preclin vitro vivo absorpt distribut metabol excret adm measur paper report quantit structureact relationship qsar model predict system cl steadyst vd vdss intraven iv dose human qsar model avoid uncertainti associ preclinicaltoclin extrapol requir twodimension structur draw sole input clean uniform train set model deriv compil publish obach et al drug metab disp model cl vdss develop use support vector regress svr method multipl linear regress mlr method svr model employ minimum bit fingerprint develop inhous structur quantifi mlr model hand base informationrich electrotopolog state twoatom fragment descriptor afford revers qsar rqsar analysi help modelguid silico modul structur desir cl vdss capabl model predict iv cl vdss accept accuraci establish random split data train test set averag cl vdss test compound predict within fold valu observ test compound within fold valu observ perform final model develop compound cl compound vdss evalu extern set compound predict either better compar predict silico model report literatur demonstr practic applic rqsar approach structur vildagliptin highcl highvdss compound modifi base atom contribut predict cl vdss propos compound lower cl lower vdss

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