N-octanol Water Partition Coefficients Research Articles

Prediction of n-octanol–water partition coefficient of platinum (IV) complexes using correlation weights of fragments of local symmetry

Prediction of n-octanol–water partition coefficient of platinum (IV) complexes using correlation weights of fragments of local symmetry

Prediction of the n-Octanol/Water Partition Coefficients of Basic Compounds Using Multi-Parameter QSRR Models Based on IS-RPLC Retention Behavior in a Wide pH Range.

The n-octanol-water partition coefficient (logP) is an important physicochemical parameter which describes the behavior of organic compounds. In this work, the apparent n-octanol/water partition coefficients (logD) of basic compounds were determined using ion-suppression reversed-phase liquid chromatography (IS-RPLC) on a silica-based C18 column. The quantitative structure-retention relationship (QSRR) models between logD and logkw (logarithm of retention factor corresponding to 100% aqueous fraction of mobile phase) were established at pH 7.0-10.0. It was found that logD had a poor linear correlation with logkw at pH 7.0 and pH 8.0 when strongly ionized compounds were included in the model compounds. However, the linearity of the QSRR model was significantly improved, especially at pH 7.0, when molecular structure parameters such as electrostatic charge ne and hydrogen bonding parameters A and B were introduced. External validation experiments further confirmed that the multi-parameter models could accurately predict the logD value of basic compounds not only under strong alkaline conditions, but also under weak alkaline and even neutral conditions. The logD values of basic sample compounds were predicted based on the multi-parameter QSRR models. Compared with previous work, the findings of this study extended the pH range for the determination of the logD values of basic compounds, providing an optional mild pH for IS-RPLC experiments.

Accumulation characteristics of polychlorinated dibenzo-p-dioxins and dibenzofurans and polychlorinated biphenyls in human breast milk from a seaside city of North China

Breast milk samples were collected from 51 mothers in a seaside city Dalian, where the residents usually have higher dietary exposure to polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs) and polychlorinated biphenyls (PCBs) due to the larger consumption of seafood. The lipid-basis concentrations of ∑Cl2–8DD/Fs, ∑Cl2–10Bs, and total toxicity equivalent (WHO-TEQ) were measured to be in the ranges of 35.7–2727.8 pg/g, 4.91–52.64 ng/g, and 2.27–36.30 pg/g, respectively. The average proportion of ∑Cl2–3DD/Fs was higher than that of ∑Cl4–7DD/Fs in the collected human breast milk samples, suggesting that the health risk of Cl2–3DD/Fs should be especially concerned. The concentration data of PCDD/Fs and PCBs in human breast milk essentially followed a positive skew probability distribution. Women in high-level exposure scenarios exhibited a higher potential to accumulate homologues Cl4DFs, Cl7DFs, Cl8DF, and Cl6Bs in breast milk. Three PCDD/F congeners (1,2,3,6,7,8-Cl6DF, 1,2,3,4,7,8-Cl6DF, and 1,2,3,4,6,7,8-Cl7DD) and three PCB congeners (PCB 126, PCB 138, and PCB 169) were identified as good indicators for the accumulation of PCDD/Fs and PCBs in human breast milk, respectively. The food-to-milk accumulation factors (FMAF) were calculated to evaluate the accumulation potentials of different PCDD/F and PCB congeners in human breast milk via dietary exposure. The calculated FMAF value presented a non-monotonic variation with the logarithm of n-octanol–water partition coefficient (log KOW) with a peak at a log KOW value of about 7.3 and a valley at a log KOW value of about 8. The mean value of the estimated daily intake (EDI) of total WHO-TEQ for breast-fed infants in Dalian, predicted by Monte Carlo simulation, was 10 folds higher than the upper range of the tolerable daily intake (TDI) value (4 pg WHO-TEQ/kg bw/d), suggesting continued and enhanced efforts should be made to reduce the exposure risk of infants to PCDD/Fs and PCBs.

Electrodialysis as a sample processing tool for bulk organic matter and target pollutant analysis of seawater

Electrodialysis (ED) is an advancing seawater sample processing tool that enables the separation of analytes from the often interfering salt matrix. In this study, we present the evaluation of a laboratory scale ED system for both dissolved organic matter (DOM) and target pollutant analysis of seawater.The developed sample processing protocol yields reproducible data and was found to be robust towards moderate changes in sample composition. At the final salinity of 0.1, the average recovery of DOM in the form of dissolved organic carbon, nitrogen and phosphorus (DOC, DON and DOP) was 44, 53 and 89%, respectively. DOM loss occurred mainly in the late stage of the ED process.When investigating specific ED processing parameters, it was discovered that the initial sample salinity does not influence DOM recovery. The final salinity, by contrast, is a dominant influence factor on DOM recovery. Furthermore, DOC and DOP recoveries could be improved by 8% by refining the electrical current in the ED cell. Surprisingly, adjustments of the sample pH did not lead to any improvements in DOM recovery.The experiments with target analytes showed that the recovery of individual molecules is determined by their n-octanol water partition coefficients logKow. High recoveries > 80% were achieved for compounds with medium logKow of −1 to 3. Hydrophobic compounds with logKow > 3 were lost through surface adsorption to the system walls and tubing. Small, polar and charged compounds with logKow < −1 are prone to loss via ED membrane passage, which occurred predominantly in the late stage of the ED process. Consequently, sample processing with ED was deemed beneficial for the LC-MS or GC–MS analysis of polar target compounds, because they are often difficult to enrich from seawater. Furthermore, during LC-MS or GC–MS analyses, matrix-dependent ion suppression was reduced in ED isolates, giving rise to increased signal responses of 25 to 620%, which resulted in improved instrumental sensitivity.

ILPC: simple chemometric tool supporting the design of ionic liquids.

BackgroundIonic liquids (ILs) found a variety of applications in today’s chemistry. Since their properties depend on the ions constituting particular ionic liquid, it is possible to synthetize IL with desired specification, dependently on its further function. However, this task is not trivial, since knowledge regarding the influence of particular ion on the property of concern is crucial. Therefore, there is a strong need for new, fast and inexpensive methods supporting the process of ionic liquids’ design, making it possible to predefine IL’s properties even before the synthesis.ResultsWe have developed a simple tool (called Ionic Liquid PhysicoChemical predictor: ILPC) that allows for the simultaneous qualitative prediction of four physicochemical properties of ionic liquids: viscosity, n-octanol–water partition coefficient, solubility and enthalpy of fusion. By the means of Principal Component Analysis, we studied 172 ILs and defined distribution trends of those four properties, dependently on the ILs structures. We proved that the qualitative prediction of mentioned properties could be performed on the basis of most simple information we can deliver about ILs, which are their molecular formulas.ConclusionsCreated tool presented in this paper allows fast, pre-synthesis screening of ILs, with the omission of any experimental steps. It can be helpful in the process of designing ILs with preferred properties. We proved that the information encrypted in molecular formula of ionic liquid could be a valuable source of knowledge regarding the IL’s viscosity, n-octanol–water partition coefficient, solubility and enthalpy of fusion. Moreover, we proved that the influence of both ions, constituting the IL, on each of those four properties indicates same, additive trend.Graphical Schematic representation of ILPC performance - the exact position of the ionic liquid on the linear map is determined by its chemical structureElectronic supplementary materialThe online version of this article (doi:10.1186/s13321-016-0152-4) contains supplementary material, which is available to authorized users.

Estimation of Octanol-Water Partition Coefficient Using Cationic Gemini Surfactants by Micellar Electrokinetic Chromatography

Micellar electrokinetic chromatography (MEKC) provides a simple and rapid approach for determining n-octanolwater partition coefficients (log Pow). A set of non-hydrogen bonding (NHB), hydrogen bond accepting (HBA) and hydrogen bond donating (HBD) benzene derivatives with known log Pow values was used as sample solutes. Two novel cationic gemini surfactants with different head groups were used as pseudostationary phases. Sodium dodecyl sulfate (SDS) was also used for comparison. Two approaches were applied for the determination of log Pow values: calibration curve and phase ratio. In calibration curve approach, the MEKC retention factors (log k) of six alkyl phenyl ketones were plotted against their literature log Pow values for constructing the calibration curve. Log Pow values of benzene derivatives were then determined from the slope and the y-intercept of the linear calibration line. In the phase ratio approach, total surfactant concentration, critical micelle concentration, partial specific molar volume and experimental log k values were utilized for estimation of the log Pow values. Both approaches provided comparable results for HBA solutes; however, the calibration curve approach and phase ratio approach were found to be more successful for NHB and HBD solutes, respectively. In general, gemini surfactants provided better estimated log Pow values for NHB and HBA solutes while SDS gave better values for HBD solutes.

In vitro and in silico determination of oral, jejunum and Caco-2 human absorption of fatty acids and polyphenols. Micellar liquid chromatography

In this investigation chosen saturated, mono- and polyunsaturated fatty acids as well as polyphenols have been analyzed.The main aim of this study was to determine oral, jejunum and Caco-2 human absorption of chosen fatty acids and polyphenols using in vitro and in silico methods. For in vitro determination of human drug absorption, the usefulness of Micellar Liquid Chromatography (MLC) with mobile phases containing different surfactants (including Brij35–Biopartitioning Micellar Chromatography (BMC)) has been confirmed.On the basis of Foley׳s equation, 1/k vs. CM correlations for the tested compounds have been done. Satisfactory linearity of the relationships was found over the whole eluents composition range studied with R2 approximately 0.99 in each case. Moreover, the analyte–micelle association constants (Kma) from Foley׳s equation have been compared for different micellar environments, containing Brij35, SDS and CTAB as a main component of micellar mobile phases. Completely new models describing human oral as well as Caco-2 and jejunum absorption have been constructed and compared with the cited models. These models are based on the Abraham descriptors and lipophilicity parameters as well as steric descriptors. Furthermore, many different correlations between physicochemical parameters and human intestinal absorption have been done, e.g. the correlation between human jejunum permeability estimated in silico and received using LSER parameters was excellent (R2 nearly 0.99).Chromatographic parameters have been collated with steric, electronic and physicochemical ones using QRAR (Quantitative Retention – Activity Relationships) and QSAR (Quantitative Structure – Activity Relationships) models. Moreover, retention BMC data have been compared with lipophilicity parameter logPo/w (n-octanol–water partition coefficient). The influence of lipophilicity on oral absorption (%) has been checked. The correlation between predicted oral absorption (%) and logPo/w has been done. Obtained R2 was 0.82.On the basis of chromatographic, lipophilicity, steric and different physicochemical parameters, the principal components analysis (PCA) has been done.

Analysis of organic contaminant desorption kinetic data for sediments and soils: Implications for the Tenax extraction time for the determination of bioavailable concentrations

Solid-phase extractions with adsorbents like Tenax have been widely used to assess bioaccessible or bioavailable concentrations and non-extractable residues (NER) of organic contaminants in soils or sediments. This paper presents an analysis of literature rate constants and fractions for rapid, slow and very slow contaminant desorption from soils and sediments. Contaminant fractions desorbed from sediment to Tenax in 6 or 24h were evaluated as to their adequacy as a proxy for rapidly desorbing fractions, which have been shown to correlate with bioavailable concentrations. Desorption rate constants appear to decrease with increasing contaminant n-octanol–water partition coefficient. The ratio of the fraction of contaminant desorbed from sediment to Tenax in 6h and the rapidly desorbing fraction appeared to slightly decrease on increasing contaminant hydrophobicity. This was not the case for the extraction for 24h. Rapidly desorbing fractions or bioavailable fractions can be estimated, within a factor of 1.4, by multiplying the fraction desorbed in 24h by a factor of 0.7.

Diffusion of phenolic compounds through polyethylene films

ABSTRACT: This paper presents experimental results of the diffusion of phenolic compounds through virgin and aged polyethylene (PE) films. The study considers five chlorophenols contaminants (4-chlorophenol, 2,4-dichlorophenol, 2,4,6-tricholophenol, 2,3,5,6-tetrachlorophenol, and pentachlorophenol) that are known to be toxic even at very low concentrations. The concentration dynamics in the source and receptor chambers of the diffusion cells is interpreted using the numerical code Pollute. For virgin PE film, partition coefficients ranged from 0.68 to 278, which corresponded to 4-chlorophenol and pentachlorophenol, respectively. Diffusion coefficients ranged from 1.0 × 10−14 to 2.7 × 10−13 m2/s, which corresponded to pentachlorophenol and 2,4-dichlorophenol, respectively. The study also looked at how the partition and diffusion coefficients were affected by the aqueous solubility, n-octanol–water partition coefficient, molecular diameter, and molecular weight. The trends observed in this study are similar to those found for diffusion of phenolic compounds through PE geomembranes. The results also revealed trends that differ from those previously reported for diffusion of volatile organic compounds (VOCs) through geomembranes. The existing empirical relationships for VOCs are not applicable except for calculating how the diffusion coefficient evolves as a function of solubility in water. Increasing the number of chlorine atoms on the phenolic nucleus causes an increase in the partition coefficient, which is tentatively attributed to the polarity of the molecules under study.

QSPR prediction of physico-chemical properties for REACH

For registration of a chemical, European Union REACH legislation requires information on the relevant physico-chemical properties of the chemical. Predicted property values can be used when the predictions can be shown to be valid and adequate. The relevant physico-chemical properties that are amenable to prediction are: melting/freezing point, boiling point, relative density, vapour pressure, surface tension, water solubility, n-octanol–water partition coefficient, flash point, flammability, explosive properties, self-ignition temperature, adsorption/desorption, dissociation constant, viscosity, and air–water partition coefficient (Henry’s law constant). Published quantitative structure–property relationship (QSPR) methods for all of these properties are discussed, together with relevant property prediction software, as an aid for those wishing to use predicted property values in submissions to the European Chemicals Agency (ECHA).

逐次抽出による農耕地土壌における有機塩素系農薬のエージング過程評価の試み

Long time aging of organochlorine pesticides (OCPs) in agricultural soil was characterized by the sequential solvent extraction using series of shaking with 50% methanol-aq and n-buthanol, and finally soxhlet extracted with dichlorormethane. First, the extractability and the organic carbon normalized adsorption coefficient (Koc) of OCPs by these solvents were calculated to characterize the sequential extraction using 3 type soils. The 50% methanol-aq extractability of OCPs with low n-octanol water partition coefficient (Kow) values in soil with low organic carbon content tended to be high. The Koc of OCPs extracted by 50% methanol-aq tended to be related with Kow.The exhaustive and the sequential extraction were applied to the paddy soil preserved since 1960s to characterize the long time aging of OCPs in soils. Although OCPs concentrations in paddy soil obtained by the exhaustive extraction have decreased since 1960s, recent decreasing rate is very slow. The half-lives of OCPs for disappearance from the paddy soils were estimated to be ∼5 years from 1960s to 1970s, around 10 years from 1980s to 1990s, respectively. The extractability of α-HCH with the 50% methanol-aq by shaking has been decreased. Additionally, the extractability of α-HCH with the dichlorormethane by soxhlet has been increased gradually. On the other hand, the changes in temporal extractabilities of dieldrin and p,p′-DDT was not found. These results can be indicated that long time aging of α-HCH among OCPs in agricultural soil for a half century can be found by the sequential solvent extraction.

Advancing Prediction of Tissue Distribution and Volume of Distribution of Highly Lipophilic Compounds from a Simplified Tissue-Composition-Based Model as a Mechanistic Animal Alternative Method

It has been reported that values of tissue-plasma ratios (K(p)) and resulting volume of distribution at steady state (V(ss)) are substantially overpredicted for several highly lipophilic drugs. This effect was observed particularly with the published version of the tissue-composition-based model, which used experimentally determined unbound fraction in plasma (fu(p)) as input for drugs. The reasons for the unreasonably high V(ss) predictions were investigated in this study for 14 highly lipophilic compounds with a log n-octanol-water partition coefficient (log P(ow)) of at least 5.8. Here, we argue that the experimentally determined fu(p) is inaccurate for these compounds, which affected the prediction of K(p) and V(ss). Alternatively, the tissue-plasma ratio of neutral lipids (nl) equivalent was used as the main factor governing K(p), and hence V(ss), in addition to log P(ow). The average fold error of deviation between the predicted and observed human V(ss) is 124 for the published model, whereas it significantly decreased to 1.5 for the proposed model. The sensitivity analysis confirmed the importance of nl content and drug lipophilicity. Overall, this study proposes a generic and simplified tissue-composition-based model for highly lipophilic drugs and chemicals, which is a step forward toward improving prediction of V(ss) into physiologically based pharmacokinetic (PBPK) models.

Determination of Multiresidues in Rapeseed, Rapeseed Oil, and Rapeseed Meal by Acetonitrile Extraction, Low-Temperature Cleanup, and Detection by Liquid Chromatography with Tandem Mass Spectrometry

A multiresidue method for determining pesticides in rapeseed, rapeseed oil, and rapeseed meal by use of liquid chromatography-tandem mass spectrometry is developed. Samples were extracted with acetonitrile or acidified acetonitrile and cleaned up by a 12 h freezing step. The recovery data were obtained by spiking blank samples at three concentration levels. The recoveries of 27 selected pesticides in rapeseed, rapeseed oil, and rapeseed meal were in the range of 70-118%, at the concentration level of 10 μg kg(-1), with intraday and interday precisions of lower than 22 and 27%, respectively. Linearity was studied between 2 and 500 μg L(-1) with determination coefficients (R(2)) of higher than 0.98 for all compounds in the three matrices. The limits of quantitation (LOQs) of pesticides in rapeseed, rapeseed oil, and rapeseed meal ranged from 0.3 to 18 μg kg(-1). The n-octanol-water partition coefficient showed more influence than water solubility in extracting pesticides by acetonitrile from matrices of high fat content. This method was successfully applied for routine analysis in commercial products.

Sorption and desorption of 17β-estradiol to natural sediment

The sorption and desorption of 17β-estradiol (E2) to various natural sediment were investigated. First, the quantitative solvent-water partition indices were measured. Significant differences were found between the n-octanol-water partition coefficient (K(OW)) and the n-hexane-water partition coefficient (K(HW)) of E2. The value of K(HW) (Log K(HW) = 0.07) is lower than those of two to four ring polyaromatic hydrocarbons (PAHs), while the value of K(OW) (Log K(OW) = 3.99) and that of organic matter-water partition coefficient (K(OC)) onto humic acid (Log K(OC) = 4.30) were similar to those of the PAHs. Five natural sediments of various characteristics and origins were selected for sorption and desorption experiments. Linear isotherms were obtained for sorption and desorption. The equilibrium partitioning coefficients of E2 were well-correlated with their values of weight fraction of organic carbon in sediments (f(OC)). Results suggest that E2 is sorbed mainly onto the organic portion of sediments and that its sorption coefficient can be estimated from K(OW) and f(OC), as in the case of non-polar PAHs. However, because of its polarity, the sorption mechanism of E2 onto sediments cannot be explained solely by the hydrophobic interaction.

On some novel extended topochemical atom (ETA) parameters for effective encoding of chemical information and modelling of fundamental physicochemical properties

Extended topochemical atom (ETA) indices developed by our group have been extensively applied in our previous reports for toxicity and ecotoxicity modelling in the field of quantitative structure–activity relationships (QSARs). In the present study these indices have been further explored by defining additional novel parameters to model n-octanol–water partition coefficient (two data sets; n = 168 and 139), water solubility (n = 193), molar refractivity (n = 166), and aromatic substituent constants π, MR, σ m, and σ p (n = 99). All the models developed in the present study have undergone rigorous internal and external validation tests and the models have high statistical significance and prediction potential. In terms of Q 2 and r 2 values the models developed for the datasets of whole molecules are better than those previously reported, with topochemically arrived unique (TAU) indices on the same datasets of chemicals. An attempt has also been made to develop models using non-ETA topological and information indices. Interestingly, ETA and non-ETA models have been found to have similar predictive capacity.

A Quantitative Structure-Property Relationship (QSPR) Study of Aliphatic Alcohols by the Method of Dividing the Molecular Structure into Substructure

A quantitative structure–property relationship (QSPR) analysis of aliphatic alcohols is presented. Four physicochemical properties were studied: boiling point (BP), n-octanol–water partition coefficient (lg POW), water solubility (lg W) and the chromatographic retention indices (RI) on different polar stationary phases. In order to investigate the quantitative structure–property relationship of aliphatic alcohols, the molecular structure ROH is divided into two parts, R and OH to generate structural parameter. It was proposed that the property is affected by three main factors for aliphatic alcohols, alkyl group R, substituted group OH, and interaction between R and OH. On the basis of the polarizability effect index (PEI), previously developed by Cao, the novel molecular polarizability effect index (MPEI) combined with odd-even index (OEI), the sum eigenvalues of bond-connecting matrix (SX1CH) previously developed in our team, were used to predict the property of aliphatic alcohols. The sets of molecular descriptors were derived directly from the structure of the compounds based on graph theory. QSPR models were generated using only calculated descriptors and multiple linear regression techniques. These QSPR models showed high values of multiple correlation coefficient (R > 0.99) and Fisher-ratio statistics. The leave-one-out cross-validation demonstrated the final models to be statistically significant and reliable.

A quantitative structure–activity approach for lipophilicity estimation of antitumor complexes of different metals using microemulsion electrokinetic chromatography

Microemulsion electrokinetic chromatography (MEEKC) offers a valuable tool for the rapid and highly productive determination of lipophilicity for metal-based anticancer agents. In this investigation, the MEEKC technique was applied for estimation of n-octanol–water partition coefficient (log P oct) of a series of antiproliferative complexes of gallium(III) and iron(III) with 4 N-substituted α- N-heterocyclic thiosemicarbazones. Analysis of relationships between the experimental log P oct and the retention factors of compounds showed their satisfactory consistency in the case of single metal sets, as well as for both metals. Since none of available calculation programs allows for evaluating the contribution of central metal ion into log P oct (i.e. Δlog P oct) of complexes of different metals, this parameter was measured experimentally, by the standard ‘shake-flask’ method. Extension of the log P oct programs by adding Δlog P oct data resulted in good lipophilicity predictions for the complexes of gallium(III) and iron(III) included in one regression set. Comparison of metal-thiosemicarbazonates under examination in terms of log P oct vs. antiproliferative activities (i.e. 50% inhibitory concentration in cancer cells) provided evidence that their cytotoxic potency is associated with the ability to cross the lipid bilayer of the cell-membrane via passive diffusion.

Strategy for reduced calibration sets to develop quantitative structure–retention relationships in high-performance liquid chromatography

In high-performance liquid chromatography, quantitative structure–retention relationships (QSRRs) are applied to model the relation between chromatographic retention and quantities derived from molecular structure of analytes. Classically a substantial number of test analytes is used to build QSRR models. This makes their application laborious and time consuming. In this work a strategy is presented to build QSRR models based on selected reduced calibration sets. The analytes in the reduced calibration sets are selected from larger sets of analytes by applying the algorithm of Kennard and Stone on the molecular descriptors used in the QSRR concerned. The strategy was applied on three QSRR models of different complexity, relating log k w or log k with either: ( i) log P, the n-octanol–water partition coefficient, ( ii) calculated quantum chemical indices (QCI), or ( iii) descriptors from the linear solvation energy relationship (LSER). Models were developed and validated for 76 reversed-phase high-performance liquid chromatography systems. From the results we can conclude that it is possible to develop log P models suitable for the future prediction of retentions with as few as seven analytes. For the QCI and LSER models we derived the rule that three selected analytes per descriptor are sufficient. Both the dependent variable space, formed by the retention values, and the independent variable space, formed by the descriptors, are covered well by the reduced calibration sets. Finally guidelines to construct small calibration sets are formulated.

Artificial neural networks analysis used to evaluate the molecular interactions between selected drugs and human α 1-acid glycoprotein

Quantitative structure-retention relationships (QSRR) were proposed for α 1-acid glycoprotein (AGP) column using physicochemical molecular descriptors of the selected drugs and interacting with that column. The set of 52 structurally diverse drug compounds, with experimentally derived logarithms of retention factors (log k) values was considered. Thirty-six physicochemical property descriptors were calculated by standard molecular modeling and used to establish QSRR and predict the retention data by artificial neural network (ANN). The QSRR indicated that heat of formation (HF), Moriguchi n-octanol–water partition coefficient (M log P) and the energy of the highest occupied molecular orbital (HOMO) are the most important for interactions between drugs and AGP. The proposed ANN model based on selected molecular descriptors showed a high degree of correlation between log k observed and computed. The final model possessed a 36-5-1 architecture and correlation coefficients for learning, validating and testing sets equaled 0.975, 0.950 and 0.972, respectively.

The molecular descriptor log Sum AA and its alternatives in QSRR models to predict the retention of peptides

The use of the experimental molecular descriptor log Sum AA and some possible alternatives were evaluated in the QSRR analysis of peptides. To quantitatively characterize the structure of analytes in a previously proposed QSRR the following three structural descriptors were applied: the logarithm of the sum of gradient retention times of the amino acids composing the individual peptide, log Sum AA; the logarithm of the peptide’s van der Waals volume, log VDW Vol; and the logarithm of its theoretically calculated n-octanol–water partition coefficient, clog P. Taking into consideration that most amino acids were hardly retained in the different RP-HPLC systems on which the peptides retention was measured, the contribution of most amino acids to the log Sum AA descriptor is rather constant. Therefore, to enlarge the variability of the descriptor and the amino acids contributions for a given series of peptides, in a first instance, it was evaluated whether, by changing the chromatographic conditions, the retention differences between the amino acids could be increased, while maintaining their mutual selectivity. It was not evident to find such conditions. Secondly, it was also investigated whether the experimental descriptor log Sum AA can be replaced by a theoretical, either based on a simple or on a weighted counting of the amino acids composing the peptide. The weighting factor for the retained amino acids was determined by their experimental gradient retention times measured on different systems. The predictive abilities of the new QSRR models (applying the alternative descriptors) were assessed using the leave-one-out cross-validation procedure and compared to that of the initial model. Finally, a descriptor was defined for which the retention measurement of only a limited number of amino acids is required. It resulted in QSRR models with similar predictive properties as those with log Sum AA, but with a reduced workload.