In Silico Renal Clearance Model Using Classical Volsurf Approach

Abstract

A data set of 130 diverse compounds containing both central nervous system (CNS) and non-CNS drugs was used to generate a renal clearance model using a classical Volsurf approach. Percentage renal clearance data was used as a biological input. The score plots obtained from principal component analysis and partial least-squares (PLS) analysis clearly separated high-clearance compounds from low-clearance compounds. PLS models were used to predict the renal clearance of the data set. Categorical statistical methods such as SIMCA and recursive partitioning techniques were used for classifying the compounds into low- and high-clearance categories. PLS coefficient plots, Volsurf descriptor profiles, 3D Grid maps, and RP decision trees were used to explain the important descriptors separating low and high renal clearance compounds. For comparative purposes, topological descriptors such as Molconn-Z were also examined. All the models were validated by an external test set of 20 compounds. These models can be used as efficient tools in the classification and prediction of the renal clearance of unknown compounds, the knowledge of which is helpful in understanding their bioavailability behavior.

Quantitative Structure-Pharmacokinetic Relationships for the Prediction of Renal Clearance in Humans

“…However, this model was a qualitative assessment of the extent of metabolism. dmd.aspetjournals.org Doddareddy et al (2006) performed partial least squared analysis (Q 2 = 0.76) to predict the percent of administered dose excreted unchanged in urine of 130 central nervous system and noncentral nervous system compounds. A step-wise MLR model to predict the log unbound renal clearance of 47 acidic compounds (R 2 = 0.62, Q 2 = 0.51) was recently reported (Zhivkova and Doytchinova, 2013); however, in our analyses, a statistically significant QSPKR model could not be constructed for all compounds that were acids (N = 98, Q 2 = 0.16) at the physiologic pH (not shown).…”

“…Applications of empirical and mechanism-based QSPKR models for the prediction of various pharmacokinetic processes have been reviewed extensively (Xu and Mager, 2011). In the past, QSPKR models for the prediction of percent of dose excreted unchanged in urine have been moderately successful (Na'ngono Manga et al, 2003;Doddareddy et al, 2006). Varma et al (2009) analyzed 391 compounds to relate their physicochemical properties to renal clearance in humans.…”

Applicability of QSAR analysis to the evaluation of the toxicological relevance of metabolites and degradates of pesticide active substances for dietary risk assessment

“…For the prediction of human renal clearance, Doddareddy et al (2006) developed models on the basis of 150 diverse CNS and non-CNS drugs, divided into training and test sets of 130 and 20 compounds, respectively. The authors utilized the VolSurf approach to explore the effect of VolSurf descriptors on renal clearance.…”

Quantitative Structure–pharmacokinetics Modeling of the Unbound Clearance for Neutral Drugs

“…Several reports have been published recently on QSPkR modeling of total plasma CL [3][4][5][6][7][8][9], as well as for renal CL [10][11][12]. It is difficult to compare their predictive performance because of the incomplete description of the model's algorithms and validation procedures and the different statistical metrics used.…”