1
|
Vallianatou T, Tsopelas F, Tsantili-Kakoulidou A. Prediction Models for Brain Distribution of Drugs Based on Biomimetic Chromatographic Data. Molecules 2022; 27:molecules27123668. [PMID: 35744794 PMCID: PMC9227077 DOI: 10.3390/molecules27123668] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Revised: 05/27/2022] [Accepted: 06/03/2022] [Indexed: 11/16/2022] Open
Abstract
The development of high-throughput approaches for the valid estimation of brain disposition is of great importance in the early drug screening of drug candidates. However, the complexity of brain tissue, which is protected by a unique vasculature formation called the blood−brain barrier (BBB), complicates the development of robust in silico models. In addition, most computational approaches focus only on brain permeability data without considering the crucial factors of plasma and tissue binding. In the present study, we combined experimental data obtained by HPLC using three biomimetic columns, i.e., immobilized artificial membranes, human serum albumin, and α1-acid glycoprotein, with molecular descriptors to model brain disposition of drugs. Kp,uu,brain, as the ratio between the unbound drug concentration in the brain interstitial fluid to the corresponding plasma concentration, brain permeability, the unbound fraction in the brain, and the brain unbound volume of distribution, was collected from literature. Given the complexity of the investigated biological processes, the extracted models displayed high statistical quality (R2 > 0.6), while in the case of the brain fraction unbound, the models showed excellent performance (R2 > 0.9). All models were thoroughly validated, and their applicability domain was estimated. Our approach highlighted the importance of phospholipid, as well as tissue and protein, binding in balance with BBB permeability in brain disposition and suggests biomimetic chromatography as a rapid and simple technique to construct models with experimental evidence for the early evaluation of CNS drug candidates.
Collapse
Affiliation(s)
- Theodosia Vallianatou
- Medical Mass Spectrometry Imaging, Department of Pharmaceutical Biosciences, Uppsala University, 751 24 Uppsala, Sweden
- Correspondence: (T.V.); (A.T.-K.)
| | - Fotios Tsopelas
- Laboratory of Inorganic and Analytical Chemistry, School of Chemical Engineering, National Technical University of Athens, 157 80 Athens, Greece;
| | - Anna Tsantili-Kakoulidou
- Faculty of Pharmacy, National and Kapodistrian University of Athens, 157 71 Athens, Greece
- Correspondence: (T.V.); (A.T.-K.)
| |
Collapse
|
2
|
Kosugi Y, Hosea N. Direct Comparison of Total Clearance Prediction: Computational Machine Learning Model versus Bottom-Up Approach Using In Vitro Assay. Mol Pharm 2020; 17:2299-2309. [PMID: 32478525 DOI: 10.1021/acs.molpharmaceut.9b01294] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
The in vitro-in vivo extrapolation (IVIVE) approach for predicting total plasma clearance (CLtot) has been widely used to rank order compounds early in discovery. More recently, a computational machine learning approach utilizing physicochemical descriptors and fingerprints calculated from chemical structure information has emerged, enabling virtual predictions even earlier in discovery. Previously, this approach focused more on in vitro intrinsic clearance (CLint) prediction. Herein, we directly compare these two approaches for predicting CLtot in rats. A structurally diverse set of 1114 compounds with known in vivo CLtot, in vitro CLint, and plasma protein binding was used as the basis for this evaluation. The machine learning models were assessed by validation approaches using the time- and cluster-split training and test sets, and five-fold cross validation. Assessed by five-fold validation, the random forest regression (RF) and radial basis function (RBF) models demonstrated better prediction performance in eight attempted machine learning models. The CLtot values predicted by the RF and RBF models were within two-fold of the observed values for 67.7 and 71.9% of cluster-split test set compounds, respectively, while the predictivity was worse in the time-split dataset. The predictivity of both models tended to be improved by incorporating in vitro parameters, unbound fraction in plasma (fu,p), and CLint. CLtot prediction utilizing in vitro CLint and the well-stirred model, correcting for the fraction unbound in blood, was substantially worse compared to machine learning approaches for the same cluster-split test set. The reason that CLtot is underestimated by IVIVE is not fully explained by considering the calculated microsomal unbound fraction (cfu,mic), extended clearance classification system (ECCS), and omitting high clearance compounds in excess of hepatic blood flow. The analysis suggests that in silico machine learning models may have the power to reduce reliance on or replace in vitro and in vivo studies for chemical structure optimization in early drug discovery.
Collapse
Affiliation(s)
- Yohei Kosugi
- Global DMPK, Takeda California Inc., San Diego, California 92121, United States
| | - Natalie Hosea
- Global DMPK, Takeda California Inc., San Diego, California 92121, United States
| |
Collapse
|
3
|
Advani P, Joseph B, Ambre P, Pissurlenkar R, Khedkar V, Iyer K, Gabhe S, Iyer RP, Coutinho E. In silico optimization of pharmacokinetic properties and receptor binding affinity simultaneously: a 'parallel progression approach to drug design' applied to β-blockers. J Biomol Struct Dyn 2015; 34:384-98. [PMID: 25854164 DOI: 10.1080/07391102.2015.1033646] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Abstract
The present work exploits the potential of in silico approaches for minimizing attrition of leads in the later stages of drug development. We propose a theoretical approach, wherein 'parallel' information is generated to simultaneously optimize the pharmacokinetics (PK) and pharmacodynamics (PD) of lead candidates. β-blockers, though in use for many years, have suboptimal PKs; hence are an ideal test series for the 'parallel progression approach'. This approach utilizes molecular modeling tools viz. hologram quantitative structure activity relationships, homology modeling, docking, predictive metabolism, and toxicity models. Validated models have been developed for PK parameters such as volume of distribution (log Vd) and clearance (log Cl), which together influence the half-life (t1/2) of a drug. Simultaneously, models for PD in terms of inhibition constant pKi have been developed. Thus, PK and PD properties of β-blockers were concurrently analyzed and after iterative cycling, modifications were proposed that lead to compounds with optimized PK and PD. We report some of the resultant re-engineered β-blockers with improved half-lives and pKi values comparable with marketed β-blockers. These were further analyzed by the docking studies to evaluate their binding poses. Finally, metabolic and toxicological assessment of these molecules was done through in silico methods. The strategy proposed herein has potential universal applicability, and can be used in any drug discovery scenario; provided that the data used is consistent in terms of experimental conditions, endpoints, and methods employed. Thus the 'parallel progression approach' helps to simultaneously fine-tune various properties of the drug and would be an invaluable tool during the drug development process.
Collapse
Affiliation(s)
- Poonam Advani
- a Department of Pharmaceutical Chemistry , C.U. Shah College of Pharmacy, S.N.D.T. Women's University , Mumbai , Maharashtra , India.,e Mumbai Educational Trust , Institute of Pharmacy , Bandra Reclamation, Bandra (W), Mumbai , India
| | - Blessy Joseph
- b Department of Pharmaceutical Chemistry , Bombay College of Pharmacy , Mumbai , Maharashtra , India
| | - Premlata Ambre
- b Department of Pharmaceutical Chemistry , Bombay College of Pharmacy , Mumbai , Maharashtra , India
| | - Raghuvir Pissurlenkar
- b Department of Pharmaceutical Chemistry , Bombay College of Pharmacy , Mumbai , Maharashtra , India
| | - Vijay Khedkar
- b Department of Pharmaceutical Chemistry , Bombay College of Pharmacy , Mumbai , Maharashtra , India
| | - Krishna Iyer
- b Department of Pharmaceutical Chemistry , Bombay College of Pharmacy , Mumbai , Maharashtra , India
| | - Satish Gabhe
- c Department of Pharmaceutical Chemistry , Poona College of Pharmacy, Bharati Vidyapeeth Deemed University , Pune , India
| | | | - Evans Coutinho
- b Department of Pharmaceutical Chemistry , Bombay College of Pharmacy , Mumbai , Maharashtra , India
| |
Collapse
|
4
|
Huang W, Geng L, Deng R, Lu S, Ma G, Yu J, Zhang J, Liu W, Hou T, Lu X. Prediction of human clearance based on animal data and molecular properties. Chem Biol Drug Des 2015; 86:990-7. [PMID: 25845625 DOI: 10.1111/cbdd.12567] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2015] [Revised: 03/15/2015] [Accepted: 03/30/2015] [Indexed: 11/29/2022]
Abstract
Human clearance is often predicted prior to clinical study from in vivo preclinical data by virtue of interspecies allometric scaling methods. The aims of this study were to determine the important molecular descriptors for the extrapolation of animal data to human clearance and further to build a model to predict human clearance by combination of animal data and the selected molecular descriptors. These important molecular descriptors selected by genetic algorithm (GA) were from five classes: quantum mechanical, shadow indices, E-state keys, molecular properties, and molecular property counts. Although the data set contained many outliers determined by the conventional Mahmood method, the variation of most outliers was reduced significantly by our final support vector machine (SVM) model. The values of cross-validated correlation coefficient and root-mean-squared error (RMSE) for leave-one-out cross-validation (LOOCV) of the final SVM model were 0.783 and 0.305, respectively. Meanwhile, the reliability and consistency of the final model were also validated by an external test set. In conclusion, the SVM model based on the molecular descriptors selected by GA and animal data achieved better prediction performance than the Mahmood method. This approach can be applied as an improved interspecies allometric scaling method in drug research and development.
Collapse
Affiliation(s)
- Wenkang Huang
- Department of Pathophysiology, Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Lv Geng
- Department of Pathophysiology, Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Rong Deng
- Department of Biochemistry and Molecular Cell Biology & Shanghai Key Laboratory of Tumor Microenvironment and Inflammation, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Shaoyong Lu
- Department of Pathophysiology, Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.,Department of Obstetrics and Gynecology, Institute of Obstetrics and Gynecology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200001, China
| | - Guangli Ma
- Pfizer (China) Research and Development Co., Ltd., Shanghai, 201203, China
| | - Jianxiu Yu
- Department of Biochemistry and Molecular Cell Biology & Shanghai Key Laboratory of Tumor Microenvironment and Inflammation, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Jian Zhang
- Department of Pathophysiology, Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Wei Liu
- Department of Pathophysiology, Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Tingjun Hou
- Functional Nano & Soft Materials Laboratory (FUNSOM) and Jiangsu Key Laboratory for Carbon-Based Functional Materials & Devices, Soochow University, Suzhou, Jiangsu, 215123, China
| | - Xuefeng Lu
- Department of Obstetrics and Gynecology, Institute of Obstetrics and Gynecology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200001, China
| |
Collapse
|
5
|
Louis B, Agrawal VK. Prediction of human volume of distribution values for drugs using linear and nonlinear quantitative structure pharmacokinetic relationship models. Interdiscip Sci 2014; 6:71-83. [DOI: 10.1007/s12539-014-0166-4] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2012] [Revised: 10/29/2012] [Accepted: 11/21/2012] [Indexed: 11/30/2022]
|
6
|
Applying linear and non-linear methods for parallel prediction of volume of distribution and fraction of unbound drug. PLoS One 2013; 8:e74758. [PMID: 24116008 PMCID: PMC3792104 DOI: 10.1371/journal.pone.0074758] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2013] [Accepted: 08/07/2013] [Indexed: 01/26/2023] Open
Abstract
Volume of distribution and fraction unbound are two key parameters in pharmacokinetics. The fraction unbound describes the portion of free drug in plasma that may extravasate, while volume of distribution describes the tissue access and binding of a drug. Reliable in silico predictions of these pharmacokinetic parameters would benefit the early stages of drug discovery, as experimental measuring is not feasible for screening purposes. We have applied linear and nonlinear multivariate approaches to predict these parameters: linear partial least square regression and non-linear recursive partitioning classification. The volume of distribution and fraction of unbound drug in plasma are predicted in parallel within the model, since the two are expected to be affected by similar physicochemical drug properties. Predictive models for both parameters were built and the performance of the linear models compared to models included in the commercial software Volsurf+. Our models performed better in predicting the unbound fraction (Q2 0.54 for test set compared to 0.38 with Volsurf+ model), but prediction accuracy of the volume of distribution was comparable to the Volsurf+ model (Q2 of 0.70 for test set compared to 0.71 with Volsurf+ model). The nonlinear classification models were able to identify compounds with a high or low volume of distribution (sensitivity 0.81 and 0.71, respectively, for test set), while classification of fraction unbound was less successful. The interrelationship between the volume of distribution and fraction unbound is investigated and described in terms of physicochemical descriptors. Lipophilicity and solubility descriptors were found to have a high influence on both volume of distribution and fraction unbound, but with an inverse relationship.
Collapse
|
7
|
Abstract
In silico tools specifically developed for prediction of pharmacokinetic parameters are of particular interest to pharmaceutical industry because of the high potential of discarding inappropriate molecules during an early stage of drug development itself with consequent saving of vital resources and valuable time. The ultimate goal of the in silico models of absorption, distribution, metabolism, and excretion (ADME) properties is the accurate prediction of the in vivo pharmacokinetics of a potential drug molecule in man, whilst it exists only as a virtual structure. Various types of in silico models developed for successful prediction of the ADME parameters like oral absorption, bioavailability, plasma protein binding, tissue distribution, clearance, half-life, etc. have been briefly described in this chapter.
Collapse
Affiliation(s)
- A K Madan
- Pt. BD Sharma University of Health Sciences, Rohtak, India.
| | | |
Collapse
|
8
|
Zhivkova Z, Doytchinova I. Prediction of steady-state volume of distribution of acidic drugs by quantitative structure-pharmacokinetics relationships. J Pharm Sci 2011; 101:1253-66. [PMID: 22170307 DOI: 10.1002/jps.22819] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2011] [Revised: 10/17/2011] [Accepted: 10/28/2011] [Indexed: 11/06/2022]
Abstract
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.
Collapse
Affiliation(s)
- Zvetanka Zhivkova
- Faculty of Pharmacy, Medical University of Sofia, 1000 Sofia, Bulgaria.
| | | |
Collapse
|
9
|
Vrakas D, Hadjipavlou-Litina D, Tsantili-Kakoulidou A. Analysis of the interaction of substituted coumarins with the DPPH free radical by means of multivariate statistics. J Pharm Pharmacol 2010; 56:1191-4. [PMID: 15324489 DOI: 10.1211/0022357044157] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/31/2022]
Abstract
Abstract
The interaction of some substituted coumarin derivatives with the stable 1,1-diphenyl-2-picrylhydrazyl (DPPH) radical was analysed by means of multivariate statistics using a variety of molecular descriptors. The compounds contain a conjugated double bond system, which was considered to be an essential structural characteristic for the free-radical scavenging activity. Partial least-square analysis led to an adequate two-component model based on bulk descriptors and the electronic properties concerning atoms involved or next to the double-bond system.
Collapse
Affiliation(s)
- Demetris Vrakas
- School of Pharmacy, Department of Pharmaceutical Chemistry, University of Athens, Panepistimiopolis, Zografou, Athens 157 71, Greece
| | | | | |
Collapse
|
10
|
Pang KS, Weiss M, Macheras P. Advanced pharmacokinetic models based on organ clearance, circulatory, and fractal concepts. AAPS J 2007; 9:E268-83. [PMID: 17907768 PMCID: PMC2751417 DOI: 10.1208/aapsj0902030] [Citation(s) in RCA: 43] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2007] [Accepted: 05/14/2007] [Indexed: 12/22/2022] Open
Abstract
Three advanced models of pharmacokinetics are described. In the first class are physiologically based pharmacokinetic models based on in vitro data on transport and metabolism. The information is translated as transporter and enzyme activities and their attendant heterogeneities into liver and intestine models. Second are circulatory models based on transit time distribution and plasma concentration time curves. The third are fractal models for nonhomogeneous systems and non-Fickian processes are presented. The usefulness of these pharmacokinetic models, with examples, is compared.
Collapse
Affiliation(s)
- K Sandy Pang
- Leslie Dan Faculty of Pharmacy, University of Toronto, 144 College Street, Toronto, Ontario, Canada M5S 3M2.
| | | | | |
Collapse
|
11
|
Millan MJ. Multi-target strategies for the improved treatment of depressive states: Conceptual foundations and neuronal substrates, drug discovery and therapeutic application. Pharmacol Ther 2006; 110:135-370. [PMID: 16522330 DOI: 10.1016/j.pharmthera.2005.11.006] [Citation(s) in RCA: 419] [Impact Index Per Article: 23.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2005] [Accepted: 11/28/2005] [Indexed: 12/20/2022]
Abstract
Major depression is a debilitating and recurrent disorder with a substantial lifetime risk and a high social cost. Depressed patients generally display co-morbid symptoms, and depression frequently accompanies other serious disorders. Currently available drugs display limited efficacy and a pronounced delay to onset of action, and all provoke distressing side effects. Cloning of the human genome has fuelled expectations that symptomatic treatment may soon become more rapid and effective, and that depressive states may ultimately be "prevented" or "cured". In pursuing these objectives, in particular for genome-derived, non-monoaminergic targets, "specificity" of drug actions is often emphasized. That is, priority is afforded to agents that interact exclusively with a single site hypothesized as critically involved in the pathogenesis and/or control of depression. Certain highly selective drugs may prove effective, and they remain indispensable in the experimental (and clinical) evaluation of the significance of novel mechanisms. However, by analogy to other multifactorial disorders, "multi-target" agents may be better adapted to the improved treatment of depressive states. Support for this contention is garnered from a broad palette of observations, ranging from mechanisms of action of adjunctive drug combinations and electroconvulsive therapy to "network theory" analysis of the etiology and management of depressive states. The review also outlines opportunities to be exploited, and challenges to be addressed, in the discovery and characterization of drugs recognizing multiple targets. Finally, a diversity of multi-target strategies is proposed for the more efficacious and rapid control of core and co-morbid symptoms of depression, together with improved tolerance relative to currently available agents.
Collapse
Affiliation(s)
- Mark J Millan
- Institut de Recherches Servier, Centre de Recherches de Croissy, Psychopharmacology Department, 125, Chemin de Ronde, 78290-Croissy/Seine, France.
| |
Collapse
|
12
|
Yap CW, Li ZR, Chen YZ. Quantitative structure-pharmacokinetic relationships for drug clearance by using statistical learning methods. J Mol Graph Model 2005; 24:383-95. [PMID: 16290201 DOI: 10.1016/j.jmgm.2005.10.004] [Citation(s) in RCA: 44] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2005] [Revised: 10/04/2005] [Accepted: 10/04/2005] [Indexed: 10/25/2022]
Abstract
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.
Collapse
Affiliation(s)
- C W Yap
- Department of Computational Science, National University of Singapore, Blk SOC1, Level 7, 3 Science Drive 2, Singapore 117543, Singapore
| | | | | |
Collapse
|
13
|
Ghafourian T, Barzegar-Jalali M, Hakimiha N, Cronin MTD. Quantitative structure-pharmacokinetic relationship modelling: apparent volume of distribution. J Pharm Pharmacol 2004; 56:339-50. [PMID: 15025859 DOI: 10.1211/0022357022890] [Citation(s) in RCA: 25] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
The purpose of this study was to develop a quantitative structure-activity relationship (QSAR) for the prediction of the apparent volume of distribution (Vd) in man for a heterogeneous series of drugs. The relationship of many computed, and some experimental, structural descriptors with Vd, and the Vd corrected for protein binding (unbound Vd), was investigated. Models were constructed using stepwise regression analysis for all the 70 drugs in the dataset, as well as for acidic drugs and basic drugs separately. The predictive power of the models was assessed using half the chemicals as a test set, and revealed that the models for Vd yielded lower prediction errors than those constructed for the unbound Vd (mean fold error of 2.01 for Vd compared with 2.28 for unbound Vd). Moreover, the separation of the compounds into acids and bases did not reduce the prediction error significantly.
Collapse
|
14
|
Dokoumetzidis A, Karalis V, Iliadis A, Macheras P. The heterogeneous course of drug transit through the body. Trends Pharmacol Sci 2004; 25:140-6. [PMID: 15019269 DOI: 10.1016/j.tips.2004.01.008] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
|