Quantitative Structure-pharmacokinetic Relationship Model Research Articles

How effective are ionization state-based QSPKR models at predicting pharmacokinetic parameters in humans?

Optimizing the pharmacokinetics (PK) of a drug candidate to support oral dosing is a key challenge in drug development. PK parameters are usually estimated from the concentration-time profile following intravenous administration; however, traditional methods are time-consuming and expensive. In recent years, quantitative structure-pharmacokinetic relationship (QSPKR), an in silico tool that aims to develop a mathematical relationship between the structure of a molecule and its PK properties, has emerged as a useful alternative to experimental testing. Due to the complex nature of the various processes involved in dictating the fate of a drug, the development of adequate QSPKR models that can be used in real-world pre-screening situations has proved challenging. Given the crucial role played by a molecule's ionization state in determining its PK properties, this work aims to build predictive QSPKR models for PK parameters in humans using an ionization state-based strategy. We divide a high-quality dataset into clusters based on ionization state at physiological pH and build global and ion subset-based 'local' models for three major PK parameters: plasma clearance (CL), steady-state volume of distribution (VDss), and half-life (t1/2). We use a robust methodology developed in our lab entitled 'EigenValue ANalySis' that accounts for the stereospecificity in drug disposition and use the support vector machine algorithm for model building. Our findings suggest that categorizing compounds in accordance with ionization state does not result in improved QSPKR models. The narrow ranges in the endpoints along with redundancies in the data adversely affect the ion subset-based QSPKR models. We suggest alternative approaches such as elimination route-based models that account for drug-transporter interactions for CL and chemotype-specific QSPKR for VDss.

Presence of the ketamine analog of 2-fluorodeschloroketamine residues in wastewater.

Ketamine (KET) analogs are increasingly emerging as new psychoactive substances (NPS). The present report describes the first detection of the KET analog, 2-fluorodeschloroketamine (2F-DCK), in influent samples collected from nine wastewater treatment plants in seven major Chinese cities from 2018 to 2020 by wastewater-based epidemiology (WBE). An analytical method based on solid-phase extraction and subsequent gas chromatography-mass spectrometry was developed for the detection of 2F-DCK and KET. The stability experiments showed that 2F-DCK and KET remained stable in wastewater for 15 days at room and frozen temperatures, and at two pH values (pH = 7 and pH = 2), with residue amounts between 90% and 110%. KET was detected in all samples, whereas 2F-DCK was detected in only four samples: from Guangzhou in 2018, Shenzhen in 2019, and Quanzhou and Nanning in 2020, indicating that 2F-DCK has been used as early as 2018 in China. The renal clearance of 2F-DCK was predicted based on the quantitative structure-pharmacokinetic relationship model, which was used to calculate an excretion factor of 3.7. The 2F-DCK consumption in four cities ranged from 3.71 ± 0.05 to 55 ± 0.09 mg/day/1000 inh, and KET ranged from 1.3 ± 0.04 to 76.5 ± 4.63 mg/day/1000 inh. This is the first study to investigate 2F-DCK by WBE, which provides relevant real-time data on the growth of NPS use, as well as useful information for the government to develop new policies.

QUANTITATIVE STRUCTURE–PHARMACOKINETICS MODELING OF THE UNBOUND CLEARANCE FOR NEUTRAL DRUGS

Objective: Prediction of pharmacokinetic behaviour of new candidate drugs is an important step in drug design. Clearance is a key pharmacokinetic parameter, controlling drug exposure in the body. It depends on numerous factors and is frequently restricted by plasma protein binding. The study is focused on the development of quantitative structure-pharmacokinetic relationship (QSPkR) for the unbound clearance (CLu) of neutral drugs.Methods: The dataset consisted of 117 neutral drugs, divided into training set (n = 94) and external test set (n = 23). Chemical structures were encoded by 113 theoretical descriptors. Genetic algorithm and step-wise multiple linear regression were applied for model development. The model was evaluated by cross-validation in the training set and external test set.Results: Significant, predictive and interpretable QSPkR model was developed with explained variance r2 = 0.617, cross-validated correlation coefficient q2LOO-CV = 0.554, external test set predictive coefficient r2pred = 0.656, and root mean square error in prediction RMSEP = 1.89. The model was able to predict CLu for 56% of the drugs in the external test set within the 2-fold error of experimental values.Conclusion: The model reveals the main molecular features governing CLu of neutral drugs. CLu is favoured by lipophilicity, the presence of fused aromatic rings, ester groups, dihydropyridine moieties and nine-member ring systems, while polarity, molecular size and strong electron withdrawing atoms and groups as substituents in aromatic rings affect negatively CL

Quantitative structure-pharmacokinetic relationships for the prediction of renal clearance in humans.

Renal clearance (CLR), a major route of elimination for many drugs and drug metabolites, represents the net result of glomerular filtration, active secretion and reabsorption, and passive reabsorption. The aim of this study was to develop quantitative structure-pharmacokinetic relationships (QSPKR) to predict CLR of drugs or drug-like compounds in humans. Human CLR data for 382 compounds were obtained from the literature. Step-wise multiple linear regression was used to construct QSPKR models for training sets and their predictive performance was evaluated using internal validation (leave-one-out method). All qualified models were validated externally using test sets. QSPKR models were also constructed for compounds in accordance with their 1) net elimination pathways (net secretion, extensive net secretion, net reabsorption, and extensive net reabsorption), 2) net elimination clearances (net secretion clearance, CLSEC; or net reabsorption clearance, CLREAB), 3) ion status, and 4) substrate/inhibitor specificity for renal transporters. We were able to predict 1) CLREAB (Q(2) = 0.77) of all compounds undergoing net reabsorption; 2) CLREAB (Q(2) = 0.81) of all compounds undergoing extensive net reabsorption; and 3) CLR for substrates and/or inhibitors of OAT1/3 (Q(2) = 0.81), OCT2 (Q(2) = 0.85), MRP2/4 (Q(2) = 0.78), P-gp (Q(2) = 0.71), and MATE1/2K (Q(2) = 0.81). Moreover, compounds undergoing net reabsorption/extensive net reabsorption predominantly belonged to Biopharmaceutics Drug Disposition Classification System classes 1 and 2. In conclusion, constructed parsimonious QSPKR models can be used to predict CLR of compounds that 1) undergo net reabsorption/extensive net reabsorption and 2) are substrates and/or inhibitors of human renal transporters.

Prediction of human volume of distribution values for drugs using linear and nonlinear quantitative structure pharmacokinetic relationship models

In the present study the volume of distribution values in humans of 121 drugs was estimated using quantitative structure pharmacokinetic relationship analysis. The multiple linear regression (MLR) method and nonlinear artificial neural network (ANN) and support vector machines (SVM) were employed for modeling. The theoretically calculated molecular descriptors were used for modeling and best set of descriptors selected by correlation based feature selection (CFS) method. The performance and predictive capability of linear method was investigated and compared with nonlinear method. The ANN gave better model with an average fold error of 1.66. The test set prediction accuracy shows human volume of distribution values could be predicted, on average, within 2-fold of the actual value.

Prediction of Pharmacokinetic Parameters Using a Genetic Algorithm Combined with an Artificial Neural Network for a Series of Alkaloid Drugs

An important goal for drug development within the pharmaceutical industry is the application of simple methods to determine human pharmacokinetic parameters. Effective computing tools are able to increase scientists’ ability to make precise selections of chemical compounds in accordance with desired pharmacokinetic and safety profiles. This work presents a method for making predictions of the clearance, plasma protein binding, and volume of distribution for alkaloid drugs. The tools used in this method were genetic algorithms (GAs) combined with artificial neural networks (ANNs) and these were applied to select the most relevant molecular descriptors and to develop quantitative structure-pharmacokinetic relationship (QSPkR) models. Results showed that three-dimensional structural descriptors had more influence on QSPkR models. The models developed in this study were able to predict systemic clearance, volume of distribution, and plasma protein binding with normalized root mean square error (NRMSE) values of 0.151, 0.263, and 0.423, respectively. These results demonstrate an acceptable level of efficiency of the developed models for the prediction of pharmacokinetic parameters.

Reevaluation of a Quantitative Structure Pharmacokinetic Model for Biliary Excretion in Rats

Quantitative structure pharmacokinetic relationship (QSPKR) modeling can be used to predict the biliary clearance and percentage of dose eliminated in bile (PD(b)) in humans before clinical studies. Recently, a QSPKR model based on in-house compounds was derived using simple physicochemical descriptors to predict the PD(b) in rats (Drug Metab Dispos 38:422-430, 2010). Our objective was to evaluate the QSPKR model derived for the prediction of PD(b) for our larger dataset of 164 compounds in the rat and for the 97 compounds in our human dataset (AAPS J 11:511-525, 2009). Re-analysis of the published QSPKR model revealed the model to be statistically insignificant (Drug Metab Dispos 38:422-430, 2010). Thus, a new statistically significant QSPKR model, consisting of one less descriptor than the published model, was derived from the published data. The newly derived model performed as well as the published model in predicting the PD(b) for the training and test sets (Drug Metab Dispos 38:422-430, 2010). In contrast, the new model performed poorly in predicting the PD(b) for our larger rat (r(2) = 0.253) and human dataset (r(2) = 0.013). The poor predictions for our datasets may be due, in part, to the quality and diversity of the data used to derive and test the model. Our reevaluation suggests that hepatobiliary excretion is a process that cannot truly be captured by simple physicochemical descriptors when examining chemically dissimilar compounds. A simple approach involving a limited number of physicochemical predictors may be useful when examining a structurally similar series of compounds.

Quantitative structure–pharmacokinetic relationships

Importance of the field: Quantitative structure–pharmacokinetic relationships (QSPKR) modeling is a valuable tool in drug discovery and development. It can provide insights into the molecular determinants of processes governing the time course of drug exposure and response.Areas covered in this review: Empirical and mechanism-based QSPKR models are discussed, including specific examples for oral absorption, nonspecific protein binding, volume of distribution, total metabolic stability and specific interactions with drug metabolizing enzymes. Emphasis is placed on state-of-the-art techniques, including new approaches for the direct simulation of concentration–time profiles from molecular descriptors (temporal QSPKR).What the reader will gain: Reviewing the application of current QSPKR modeling techniques will place these methods in context and highlight their respective advantages and limitations, as well as opportunities for further refinement.Take home message: The expansion of readily available molecular descriptors and advanced algorithms has improved empirical models and enabled the development of robust models for non-congeneric series. Empirical models focus on point estimates of global PK processes and physiologically-based models may be more desirable than data-driven methods. Further integration of relevant biological and pharmacological mechanisms will improve the ability to predict the full time course of drug concentration and effect profiles for diverse compounds and experimental conditions.

Pharmacokinetics and Biliary Excretion of Mitoxantrone in Rats

The objective of this investigation was to compare the observed biliary clearance (CLb) and % of dose excreted in the bile (PDb) of mitoxantrone with the predicted values obtained from quantitative structure pharmacokinetic relationship (QSPKR) models. Blood and bile samples were collected from bile duct cannulated rats after an intravenous bolus dose of 0.5 or 2 mg/kg mitoxantrone, and the concentrations were measured by HPLC. Mitoxantrone plasma concentrations exhibited a tri‐exponential profile with systemic clearance of 118 ± 6.8 mL/min/kg. After dosing, 6.08 ± 2.32% and 5.69 ± 0.59% of the dose were excreted into bile in unchanged form after a 3‐h collection. CLb was 7.20 ± 4.54 and 7.46 ± 0.62 mL/min/kg after the two doses. With the co‐administration of 10 mg/kg GF‐120918, a P‐glycoprotein and BCRP inhibitor, PDb was reduced to 0.69 ± 0.07%, suggesting that BCRP or P‐glycoprotein may play an important role in the biliary elimination of mitoxantrone. Using QSPKR models developed for the biliary excretion of cations/neutral compounds in rats, CLb and PDb of mitoxantrone were predicted as 5.18 mL/min/kg and 7.21%, respectively, suggesting that the models could be used to predict the biliary excretion of mitoxantrone. © 2009 Wiley‐Liss, Inc. and the American Pharmacists Association J Pharm Sci 99: 2502–2510, 2010

Prediction of Biliary Excretion in Rats and Humans Using Molecular Weight and Quantitative Structure–Pharmacokinetic Relationships

The aims were (1) to evaluate the molecular weight (MW) dependence of biliary excretion and (2) to develop quantitative structure-pharmacokinetic relationships (QSPKR) to predict biliary clearance (CL(b)) and percentage of administered dose excreted in bile as parent drug (PD(b)) in rats and humans. CL(b) and PD(b) data were collected from the literature for rats and humans. Receiver operating characteristic curve analysis was utilized to determine whether a MW threshold exists for PD(b). Stepwise multiple linear regression (MLR) was used to derive QSPKR models. The predictive performance of the models was evaluated by internal validation using the leave-one-out method and external test groups. A MW threshold of 400 Da was determined for PD(b) for anions in rats, while 475 Da was the cutoff for anions in humans. MW thresholds were not present for cations or cations/neutral compounds in either rats or humans. The QSPKR model for human CL(b) showed a significant correlation (R (2) = 0.819) with good prediction performance (Q (2) = 0.722). The model was further assessed using a test group, yielding a geometric mean fold-error of 2.68. QSPKR models with significant correlation and good predictability were also developed for CL(b) in rats and PD(b) data for anions or cation/neutral compounds in rats and humans. Both CL(b) and PD(b) data were further evaluated for subsets of MRP2 or P-glycoprotein substrates, and significant relationships were derived. QSPKR models were successfully developed for biliary excretion of non-congeneric compounds in rats and humans, providing a quantitative prediction of biliary clearance of compounds.

Prediction of Vitreal Half-Life Based on Drug Physicochemical Properties: Quantitative Structure–Pharmacokinetic Relationships (QSPKR)

The aim of this study was to develop quantitative structure pharmacokinetic relationships (QSPKR) to correlate drug physicochemical properties (molecular weight, lipophilicity, and drug solubility), dose, salt form factor, and eye pigmentation factor to intravitreal half-life in the rabbit model. Dataset derived from prior literature reports, which included molecules with complete structural diversity, was used to develop the QSPKR models. Entire dataset as well as subsets limited to albino rabbit data, pigmented rabbit data, acids, bases, zwitterions, neutral compounds, suspensions, and macromolecules were analyzed. Multiple linear regression analysis was carried out with noncollinear independent variables and the best-fit models were selected based on correlation coefficients and goodness of fit statistics. The analysis indicated that logarithm of MW (Log MW), lipophilicity (Log P or Log D) and dose number (dose/solubility at pH 7.4), are the most critical determinants of intravitreal half-life of the compounds analyzed. The best-fit models obtained from the entire dataset (Log t (1/2) = -0.178 + 0.267 Log MW - 0.093 Log D + 0.003 dose/solubility at pH 7.4 + 0.153 Pigmentation Factor and Log t (1/2) = -0.32 + 0.432 Log MW - 0.157 Log P + 0.003 dose/solubility at pH 7.4) predicted the various subsets well. Pigmented dataset and zwitterions were better predicted by Log P rather than Log D. The present study confirmed that intravitreal half-life could be better predicted by a group of variables (Log MW, Log P or Log D, dose number) rather than a single variable. In general, increasing Log MW and dose number, while reducing Log D or Log P would be beneficial for prolonging intravitreal half-life of drugs.

QSPR models for the prediction of apparent volume of distribution

An estimate of volume of distribution ( V d) is of paramount importance both in drug choice as well as maintenance and loading dose calculations in therapeutics. It can also be used in the prediction of drug biological half life. This study employs quantitative structure–pharmacokinetic relationship (QSPR) techniques for the prediction of volume of distribution. Values of V d for 129 drugs were collated from the literature. Structural descriptors consisted of partitioning, quantum mechanical, molecular mechanical, and connectivity parameters calculated by specialized software and p K a values obtained from ACD labs/log D database. Genetic algorithm and stepwise regression analyses were used for variable selection and model development. Models were validated using a leave-many-out procedure. QSPR analyses resulted in a number of significant models for acidic and basic drugs separately, and for all the drugs. Validation studies showed that mean fold error of predictions for the selected models were between 1.79 and 2.17. Although separate QSPR models for acids and bases resulted in lower prediction errors than models for all the drugs, the external validation study showed a limited applicability for the equation obtained for acids. Therefore, the universal model that requires only calculated structural descriptors was recommended. The QSPR model is able to predict the volume of distribution of drugs belonging to different chemical classes with a prediction error similar to that of the other more complicated prediction methods including the commonly practiced interspecies scaling. The structural descriptors in the model can be interpreted based on the known mechanisms of distribution and the molecular structures of the drugs.

Quantitative structure–pharmacokinetic relationships for drug clearance by using statistical learning methods

Quantitative structure–pharmacokinetic relationships (QSPkR) have increasingly been used for the prediction of the pharmacokinetic properties of drug leads. Several QSPkR models have been developed to predict the total clearance (CL tot) of a compound. These models give good prediction accuracy but they are primarily based on a limited number of related compounds which are significantly lesser in number and diversity than the 503 compounds with known CL tot described in the literature. It is desirable to examine whether these and other statistical learning methods can be used for predicting the CL tot of a more diverse set of compounds. In this work, three statistical learning methods, general regression neural network (GRNN), support vector regression (SVR) and k-nearest neighbour (KNN) were explored for modeling the CL tot of all of the 503 known compounds. Six different sets of molecular descriptors, DS-MIXED, DS-3DMoRSE, DS-ATS, DS-GETAWAY, DS-RDF and DS-WHIM, were evaluated for their usefulness in the prediction of CL tot. GRNN-, SVR- and KNN-developed models have average-fold errors in the range of 1.63 to 1.96, 1.66–1.95 and 1.90–2.23, respectively. For the best GRNN-, SVR- and KNN-developed models, the percentage of compounds with predicted CL tot within two-fold error of actual values are in the range of 61.9–74.3% and are comparable or slightly better than those of earlier studies. QSPkR models developed by using DS-MIXED, which is a collection of constitutional, geometrical, topological and electrotopological descriptors, generally give better prediction accuracies than those developed by using other descriptor sets. These results suggest that GRNN, SVR, and their consensus model are potentially useful for predicting QSPkR properties of drug leads.

Quantitative Structure-Pharmacokinetic Relationships for Drug Distribution Properties by Using General Regression Neural Network

Quantitative Structure-Pharmacokinetic Relationships (QSPkR) have increasingly been used for developing models for the prediction of the pharmacokinetic properties of drug leads. QSPkR models are primarily developed by means of statistical methods such as multiple linear regression (MLR). These methods often explore a linear relationship between the pharmacokinetic property of interest and the structural and physicochemical properties of the studied compounds, which are not applicable to those agents with nonlinear relationships. Hence, statistical methods capable of modeling nonlinear relationships need to be developed. In this work, a relatively new kind of nonlinear method, general regression neural network (GRNN), was explored for modeling three drug distribution properties based on diverse sets of drugs. The three properties are blood-brain barrier penetration, binding to human serum albumin, and milk-plasma distribution. The prediction capability of GRNN‐developed models was compared to those developed using MLR and a nonlinear multilayer feedforward neural network (MLFN) method. For blood-brain barrier penetration, the computed r2 and MSE values of the GRNN‐, MLR‐, and MLFN‐developed models are 0.701 and 0.130, 0.649 and 0.154, and 0.662 and 0.147, respectively, by using an independent validation set. The corresponding values for human serum albumin binding are 0.851 and 0.041, 0.770 and 0.079, and 0.749 and 0.089, respectively, and that for milk-plasma distribution are 0.677 and 0.206, 0.224 and 0.647, and 0.201 and 0.587, respectively. These suggest that GRNN is potentially useful for predicting QSPkR properties of chemical agents.

Cassette Dosing Approach and Quantitative Structure—Pharmacokinetic Relationship Study of Antifungal N‐Myristoyltransferase Inhibitors.

ChemInformVolume 33, Issue 42 p. 248-248 Other Subjects Cassette Dosing Approach and Quantitative Structure—Pharmacokinetic Relationship Study of Antifungal N-Myristoyltransferase Inhibitors. Kiyoshi Hasegawa, Kiyoshi Hasegawa Nippon Roche Res. Cent., Kamakura, Kanagawa 247, JapanSearch for more papers by this authorHidetoshi Shindoh, Hidetoshi Shindoh Nippon Roche Res. Cent., Kamakura, Kanagawa 247, JapanSearch for more papers by this authorYasuhiko Shiratori, Yasuhiko Shiratori Nippon Roche Res. Cent., Kamakura, Kanagawa 247, JapanSearch for more papers by this authorTatsuo Ohtsuka, Tatsuo Ohtsuka Nippon Roche Res. Cent., Kamakura, Kanagawa 247, JapanSearch for more papers by this authorYuko Aoki, Yuko Aoki Nippon Roche Res. Cent., Kamakura, Kanagawa 247, JapanSearch for more papers by this authorShigeyasu Ichihara, Shigeyasu Ichihara Nippon Roche Res. Cent., Kamakura, Kanagawa 247, JapanSearch for more papers by this authorIkuo Horii, Ikuo Horii Nippon Roche Res. Cent., Kamakura, Kanagawa 247, JapanSearch for more papers by this authorNobuo Shimma, Nobuo Shimma Nippon Roche Res. Cent., Kamakura, Kanagawa 247, JapanSearch for more papers by this author Kiyoshi Hasegawa, Kiyoshi Hasegawa Nippon Roche Res. Cent., Kamakura, Kanagawa 247, JapanSearch for more papers by this authorHidetoshi Shindoh, Hidetoshi Shindoh Nippon Roche Res. Cent., Kamakura, Kanagawa 247, JapanSearch for more papers by this authorYasuhiko Shiratori, Yasuhiko Shiratori Nippon Roche Res. Cent., Kamakura, Kanagawa 247, JapanSearch for more papers by this authorTatsuo Ohtsuka, Tatsuo Ohtsuka Nippon Roche Res. Cent., Kamakura, Kanagawa 247, JapanSearch for more papers by this authorYuko Aoki, Yuko Aoki Nippon Roche Res. Cent., Kamakura, Kanagawa 247, JapanSearch for more papers by this authorShigeyasu Ichihara, Shigeyasu Ichihara Nippon Roche Res. Cent., Kamakura, Kanagawa 247, JapanSearch for more papers by this authorIkuo Horii, Ikuo Horii Nippon Roche Res. Cent., Kamakura, Kanagawa 247, JapanSearch for more papers by this authorNobuo Shimma, Nobuo Shimma Nippon Roche Res. Cent., Kamakura, Kanagawa 247, JapanSearch for more papers by this author First published: 19 May 2010 https://doi.org/10.1002/chin.200242248AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume33, Issue42October 22, 2002Pages 248-248 RelatedInformation

Cassette dosing approach and quantitative structure-pharmacokinetic relationship study of antifungal N-myristoyltransferase inhibitors.

Pharmacokinetic (PK) parameters of N-myristoyltransferase (Nmt) inhibitors were measured, and a multivariate quantitative structure-pharmacokinetic relationship (QSPKR) model for predicting rat elimination half-life (t(1/2)) values was constructed. One hundred seven benzofuran derivatives have been selected as the data set for QSPKR analysis. The correlation between the t(1/2) values and 30 physicochemical descriptors was examined by a stepwise multiple linear regression method. The statistical analysis gives a significant QSPKR model (r = 0.843) with the following three variables: partial negative surface area (PNSA), atomic-based octanol/water partition coefficient (AlogP), and the number of rotational bonds (Rotlbonds). The QSPKR model obtained is predictive and simple, and would give a direction for designing new Nmt inhibitors having good PK profiles.

Multivariate Quantitative Structure–Pharmacokinetic Relationships (QSPKR) Analysis of Adenosine A1 Receptor Agonists in rat

The aim of this study was to investigate the feasibility of a quantitative structure–pharmacokinetic relationships (QSPKR) method based on contemporary three-dimensional (3D) molecular characterization and multivariate statistical analysis. For this purpose, the programs SYBYL/CoMFA, GRID, and Pallas, in combination with the multivariate statistical technique principal component analysis were employed to generate a total of 16 descriptor variables for a series of 12 structurally related adenosine A1 receptor agonists. Subsequently, the multivariate regression method, partial least squares, was used to predict clearance (CL), volume of distribution (VdSS) and protein binding (fraction unbound, fU). The QSPKR models obtained could account for most of the variation in CL, VdSS, and fU (R2 = 0.82, 0.61 and 0.78, respectively). Cross-validation confirmed the predictive ability of the models (Q2 = 0.59, 0.41 and 0.62 for CL, VdSS, and fU, respectively). In conclusion, we have developed a multivariate 3D QSPKR model that could adequately predict overall pharmacokinetic behavior of adenosine A1 receptor agonists in rat. This methodology can also be used for other classes of compounds and may facilitate the further integration of QSPKR in drug discovery and preclinical development.