Quantitative Structure-pharmacokinetic Relationship 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.

Spatiotemporal imaging and pharmacokinetics of fluorescent compounds in zebrafish eleuthero-embryos after different routes of administration

Zebrafish (Danio rerio) is increasingly used to assess the pharmacological activity and toxicity of compounds. The spatiotemporal distribution of seven fluorescent alkyne compounds was examined during 48 h after immersion (10 µM) or microinjection (2 mg/kg) in the pericardial cavity (PC), intraperitoneally (IP) and yolk sac (IY) of 3 dpf zebrafish eleuthero-embryos. By modelling the fluorescence of whole-body contours present in fluorescence images, the main pharmacokinetic (PK) parameter values of the compounds were determined. It was demonstrated that especially in case of short incubations (1–3 h) immersion can result in limited intrabody exposure to compounds. In this case, PC and IP microinjections represent excellent alternatives. Significantly, IY microinjections did not result in a suitable intrabody distribution of the compounds. Performing a QSPkR (quantitative structure-pharmacokinetic relationship) analysis, LogD was identified as the only molecular descriptor that explains the final uptake of the selected compounds. It was also shown that combined administration of compounds (immersion and microinjection) provides a more stable intrabody exposure, at least in case of a prolonged immersion and compounds with LogD value > 1. These results will help reduce the risk of false negative results and can offer an invaluable input for future translational research and safety assessment applications.

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 Plasma Clearance of Basic Drugs with Consideration of the Major Elimination Pathway.

The success of a new drug candidate is determined not only by its efficacy and safety, but also by proper pharmacokinetic behavior. The early prediction of pharmacokinetic parameters could save time and resources and accelerate drug development process. Plasma clearance (CL) is one of the key determinants of drug dosing regimen. The aim of the study is development of quantitative structure - pharmacokinetics relationships (QSPkRs) for the CL. A dataset consisted of 263 basic drugs, which chemical structures were described by 154 descriptors. Genetic algorithm, stepwise regression and multiple linear regression were used for variable selection and model development. Predictive ability of the models was assessed by internal and external validation. Results. A number of significant QSPkR models for the CL were derived with respect to the primary elimination pathway (renal excretion, metabolism, or CYP3A4 mediated biotransformation), as well for the unbound clearance (CLu). The models were able to predict 52 - 80% of the drugs from external validation sets within the 2-fold error of the experimental values with geometric mean fold error 1.57 - 2.00. Plasma protein binding was the major restrictive factor for the CL of drugs, primarily cleared by metabolism. The clearance was favored by lipophilicity and several structural features like OH-groups, aromatic rings, large hydrophobic centers, aliphatic groups, connected with electro-negative atoms, and non-substituted aromatic C-atoms. The presence of Cl-atoms and abundance of 6-member aromatic rings or fused rings had negative effect. The presence of ether O-atoms contributed negatively to the CL of both metabolism and renally excreted drugs, and urine excretion was favored by the presence of 3-valence N-atoms. These findings give insight on the main structural features governing plasma CL of basic drugs and could serve as a guide for lead optimization in the drug development process.This article is open to POST-PUBLICATION REVIEW. Registered readers (see "For Readers") may comment by clicking on ABSTRACT on the issue's contents page.

Quantitative Structure - Pharmacokinetics Relationships Analysis of Basic Drugs: Volume of Distribution.

The early prediction of pharmacokinetic behavior is of paramount importance for saving time and resources and for increasing the success of new drug candidates. The steady-state volume of distribution (VDss) is one of the key pharmacokinetic parameters required for the design of a suitable dosage regimen. The aim of the study is to propose a quantitative structure - pharmacokinetics relationships (QSPkR) for VDss of basic drugs. The data set consists of 216 basic drugs, divided to a modeling (n = 180) and external validation set (n = 36). 179 structural and physicochemical descriptors are calculated using validated commercial software. Genetic algorithm, stepwise regression and multiple linear regression are applied for variable selection and model development. The models are validated by internal and external test sets. A number of significant QSPkRs are developed. The most frequently emerged descriptors are used to derive the final consensus model for VDss with good explanatory (r2 0.663) and predictive ability (q2LOO-CV 0.606 and r2pred 0.593). The model reveals clear structural features determining VDss of basic drugs which are summarized in a short list of criteria for rapid discrimination between drugs with a large and small VDss. Descriptors like lipophilicity, fraction ionized as a base at pH 7.4, number of cycles and fused aromatic rings, presence of Cl and F atoms contribute positively to VDss, while polarity and presence of strong electrophiles have a negative effect.

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.

Tissue-to-blood distribution coefficients in the rat: Utility for estimation of the volume of distribution in man

A compilation of rat tissue-to-blood partition coefficient data obtained both in vitro and in vivo in thirteen different tissues for a total of 309 different drugs is presented. An evaluation of the relationship between several fundamental physicochemical molecular descriptors and these distribution parameters was made. In addition, the ability to predict the Human Volume of distribution by regression analysis and by a Physiologically-based approach was also tested. Results have shown different trends between the drug classes and tissues, consistent with earlier described relationships between physicochemical properties and pharmacokinetic behavior. It was also possible to conclude for the acceptable ability to predict the volume of distribution in Humans by both regression and mechanistic approaches, which suggests that this type of data represents a convenient tool to describe the drug distribution on a new drug development context. These observations and analyses, along with the large database of rat tissue distribution data, should enable future efforts aimed toward developing a full in silico quantitative structure–pharmacokinetic relationships and improving our understanding of the correlations between fundamental chemical characteristics and drug distribution.

Quantitative structure-pharmacokinetic relationship (QSPkP) analysis of the volume of distribution values of anti-infective agents from j group of the ATC classification in humans

In this study, a quantitative structure-pharmacokinetic relationship (QSPkR) model for the volume of distribution (Vd) values of 126 anti-infective drugs in humans was developed employing multiple linear regression (MLR), artificial neural network (ANN) and support vector regression (SVM) using theoretical molecular structural descriptors. A correlation-based feature selection (CFS) was employed to select the relevant descriptors for modeling. The model results show that the main factors governing Vd of anti-infective drugs are 3D molecular representations of atomic van der Waals volumes and Sanderson electronegativities, number of aliphatic and aromatic amino groups, number of beta-lactam rings and topological 2D shape of the molecule. Model predictivity was evaluated by external validation, using a variety of statistical tests and the SVM model demonstrated better performance compared to other models. The developed models can be used to predict the Vd values of anti-infective drugs.

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.

Self-organizing molecular field analysis of NSAIDs: Assessment of pharmacokinetic and physicochemical properties using 3D-QSPkR approach

Non-steroidal anti-inflammatory drug (NSAID) induced gastrointestinal toxicity has attracted greater attention over the years. The development of NSAIDs having safer therapeutic profile depends on the better understanding of their mechanisms, physicochemical and pharmacokinetic properties. The present investigation is aimed at in silico three dimensional quantitative structure–pharmacokinetic relationship (3D-QSPkR) assessment of a group of NSAIDs using self-organizing molecular field analysis (SOMFA) approach. Two different statistically validated models for dissociation constant (pKa) and volume of distribution (Vd) were obtained from SOMFA studies of 22 clinically active NSAIDs having diversity in structure. 3D-QSPkR models delivered useful information about the contribution of shape and electrostatic potential on pKa and Vd. The study illustrated the significance of structural variables in molecular architecture of NSAIDs especially etodolac for further optimization of ADMET properties with improved therapeutic profile.

Prediction of Steady-State Volume of Distribution of Acidic Drugs by Quantitative Structure–Pharmacokinetics Relationships

The volume of distribution (VD) is one of the most important pharmacokinetic parameters of drugs. The present study employs quantitative structure-pharmacokinetics relationships (QSPkR) to derive models for VD prediction of acidic drugs. The steady-state volume of distribution (VD(ss)) values of 132 acidic drugs were collected, the chemical structures were described by 178 molecular descriptors, and QSPkR models were derived after variable selection by genetic algorithm and stepwise regression. Models were validated by cross-validation procedures and external test set. According to the molecular descriptors selected as the most predictive for VD(ss), the presence of seven- and nine-member cycles, atom type P(5+), SH groups, and large nonionized substituents increase the VD(ss), whereas atom types S(2+) and S(4+) and polar ionized substituents decrease it. Cross-validation and external validation studies on the QSPkR models derived in the present study showed good predictive ability with mean fold error values ranging from 1.58 (cross-validation) to 2.25 (external validation). The model performance is comparable to more complicated methods requiring in vitro or in vivo experiments and superior to the existing QSPkR models concerning acidic drugs. Apart from the prediction of VD in human, present models are also useful as a curator of available pharmacokinetic databases.

Interspecies scaling: prediction of human biliary clearance and comparison with QSPKR

The aim of this study was to evaluate the prediction performance of various allometric scaling methods in predicting human biliary clearance (CL(b)) from data in rats or multiple animal species and to compare the prediction performance with that of quantitative structure pharmacokinetic relationship (QSPKR) models. CL(b) data of parent drugs in rats and humans were collected from the literature for 18 compounds. A simple allometric approach was applied to CL(b) or unbound CL(b) using 0.75 or 0.66 as the allometric exponent. For scaling from rat studies alone, the prediction using 0.66 as the exponent was better than that using 0.75, and a better prediction was obtained for unbound CL(b) than CL(b). For a subset of compounds, six multiple-species scaling methods were compared, with the best prediction achieved with the simple unbound CL(b) approach. However, in the absence of protein binding data, the correction with maximum life-span potential (MLP) or 'Rule of exponent' (ROE) method offered the best prediction. Overall, multiple species had better predictability than scaling with the rat alone. Comparison of predicted human CL(b) values using multiple animal species and QSPKR offered similar prediction performance. In conclusion, the results of the present study, although based on limited data, suggested that the prediction for human CL(b) by allometry was greatly improved by the incorporation of protein binding. Human CL(b) prediction using rat data alone was not satisfactory. Additionally, QSPKR provides an alternative approach to allometry for the prediction of human biliary clearance.

In Silico ADME prediction on calcium channel blockers using QSPkR studies

T use of in silico approaches for successful prediction of pharmacokinetic properties of compounds during new drug discovery has been increasing exponentially. These in silico models, for the prognosis of absorption, distribution, metabolism, and excretion (ADME) are invariably based upon the implementation of quantitative structure pharmacokinetic relationship (QSPkR) techniques. The primary aim of QSPkR studies is to enable drug designer to modify the chemical structure of a pharmacodynamically active drug in such a manner as to alter its pharmacokinetic properties without diminishing its pharmacodynamic potential. Once such relationship is ascertained with adequate statistical degree of confidence, it can be of valuable assistance in the prognosis of behavior of new molecules, even before these are actually synthesized.

Assessment of biological half life using in silico QSPkR approach: A self organizing molecular field analysis (SOMFA) on a series of antimicrobial quinolone drugs

The quinolones belong to a family of synthetic potent broad-spectrum antibiotics and particularly active against gram-negative organisms, especially Pseudomonas aeruginosa. A 3D-QSPkR approach has been used to obtain the quantitative structure pharmacokinetic relationship for a series of quinolone drugs using SOMFA. The series consisting of 28 molecules have been investigated for their pharmacokinetic performance using biological half life ( t 1/2). A statistically validated robust model for a diverse group of quinolone drugs having flexibility in structure and pharmacokinetic profile ( t 1/2) obtained using SOMFA having good cross-validated correlation coefficient r cv 2 (0.6847), non cross-validated correlation coefficient r 2 values (0.7310) and high F-test value (33.9663). Analysis of 3D-QSPkR models through electrostatic and shape grids provide useful information about the shape and electrostatic potential contributions on t 1/2. The analysis of SOMFA results provide an insight for the generation of novel molecular architecture of quinolones with optimal half life and improved biological profile.

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

Structure–pharmacokinetic relationship of in vivo rat biliary excretion

Accurately measuring and predicting biliary excretion would be extremely valuable in evaluating the contribution of biliary excretion to the total systemic clearance, understanding potential mechanisms of hepatobiliary toxicity as well as potentials for drug-drug interactions in drug discovery. In this study, in vivo rat biliary excretion of drug-like molecules was measured using bile duct cannulated rats. Literature biliary excretion data with similar experimental conditions were collected. A predictive quantitative structure-pharmacokinetic relationship (QSPR) model was developed using genetic algorithm guided principal component regression analysis and 2D molecular descriptors. In the derived model, hydrophobicity expressed with calculated distribution coefficients (cLogD) is the most important molecular property correlating biliary excretion. The derived model has been validated using literature data, and should be useful in estimating biliary excretion potentials of molecules in drug discovery.