Quantitative Structure–Activity Relationship Models of Clinical Pharmacokinetics: Clearance and Volume of Distribution

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.