Chromatographic Retention Data Research Articles

Exploring preferred binding domains of IgG1 mAbs to multimodal adsorbents using a combined biophysics and simulation approach.

In this work, we employ a recently developed biophysical technique that uses diethylpyrocarbonate (DEPC) covalent labeling and mass spectrometry for the identification of mAb binding patches to two multimodal cation exchange resins at different pH. This approach compares the labeling results obtained in the bound and unbound states to identify residues that are sterically shielded and thus located in the mAb binding domains. The results at pH 6 for one mAb (mAb B) indicated that while the complementarity determining region (CDR) had minimal interactions with both resins, the FC domain was actively involved in binding. In contrast, DEPC/MS data with another mAb (mAb C) indicated that both the CDR and FC domains were actively involved in binding. These results corroborated chromatographic retention data with these two mAbs and their fragments and helped to explain the significantly stronger retention of both the intact mAb C and its Fab fragment. In contrast, labeling results with mAb C at pH 7, indicated that only the CDR played a significant role in resin binding, again corroborating chromatographic data. The binding domains identified from the DEPC/MS experiments were also examined using protein surface hydrophobicity maps obtained using a recently developed sparse sampling molecular dynamics (MD) approach in concert with electrostatic potential maps. These results demonstrate that the DEPC covalent labeling/mass spectrometry technique can provide important information about the domain contributions of multidomain proteins such as monoclonal antibodies when interacting with multimodal resins over a range of pH conditions.

Study on binding affinity profile, bioactivity, and lipophilicity of selected antipsychotic drugs

Schizophrenia is a complex mental illness that affects almost 1% of the population and has no curative treatment. Currently, available neuroleptic drugs have severe limitations and induce drug-related adverse effects. Receptor binding affinity parameters and lipophilicity are important characteristics that describe the bioactivity of these drugs. Lipophilicity is an important physicochemical property of a drug, which predicts distribution, binding to plasma protein, crossing the blood–brain barrier, and biological activity. Five antipsychotic drugs (Haloperidol, Risperidone, Aripiprazole, Olanzapine, and Quetiapine) were selected and the experimental determination of lipophilicity was carried out by reverse phase thin-layer chromatography (RP-TLC) using four types of stationary phases (RP-18, CN, Diol, and NH2 modified silica gel HPTLC plates) and acetonitrile-water with increasing percentage of acetonitrile and 0.2% formic acid in each case as mobile phase. Lipophilicity parameters (mRM and RM0) calculated from the experimental chromatographic retention data were discussed in terms of the correlation with different receptor binding affinity parameters determined by computational methods.

Immobilized Keratin HPLC Stationary Phase-A Forgotten Model of Transdermal Absorption: To What Molecular and Biological Properties Is It Relevant?

Chromatographic retention data collected on immobilized keratin (KER) or immobilized artificial membrane (IAM) stationary phases were used to predict skin permeability coefficient (log Kp) and bioconcentration factor (log BCF) of structurally unrelated compounds. Models of both properties contained, apart from chromatographic descriptors, calculated physico-chemical parameters. The log Kp model, containing keratin-based retention factor, has slightly better statistical parameters and is in a better agreement with experimental log Kp data than the model derived from IAM chromatography; both models are applicable primarily to non-ionized compounds.Based on the multiple linear regression (MLR) analyses conducted in this study, it was concluded that immobilized keratin chromatographic support is a moderately useful tool for skin permeability assessment.However, chromatography on immobilized keratin may also be of use for a different purpose-in studies of compounds' bioconcentration in aquatic organisms.

Computational Approach to Drug Penetration across the Blood-Brain and Blood-Milk Barrier Using Chromatographic Descriptors.

Drug penetration through biological barriers is an important aspect of pharmacokinetics. Although the structure of the blood-brain and blood-milk barriers is different, a connection can be found in the literature between drugs entering the central nervous system (CNS) and breast milk. This study was created to reveal such a relationship with the use of statistical modelling. The basic physicochemical properties of 37 active pharmaceutical compounds (APIs) and their chromatographic retention data (TLC and HPLC) were incorporated into calculations as molecular descriptors (MDs). Chromatography was performed in a thin layer format (TLC), where the plates were impregnated with bovine serum albumin to mimic plasma protein binding. Two columns were used in high performance liquid chromatography (HPLC): one with immobilized human serum albumin (HSA), and the other containing an immobilized artificial membrane (IAM). Statistical methods including multiple linear regression (MLR), cluster analysis (CA) and random forest regression (RF) were performed with satisfactory results: the MLR model explains 83% of the independent variable variability related to CNS bioavailability; while the RF model explains up to 87%. In both cases, the parameter related to breast milk penetration was included in the created models. A significant share of reversed-phase TLC retention values was also noticed in the RF model.

Toward the General Mechanistic Model of Liquid Chromatographic Retention.

Large datasets of chromatographic retention times are relatively easy to collect. This statement is particularly true when mixtures of compounds are analyzed under a series of gradient conditions using chromatographic techniques coupled with mass spectrometry detection. Such datasets carry much information about chromatographic retention that, if extracted, can provide useful predictive information. In this work, we proposed a mechanistic model that jointly explains the relationship between pH, organic modifier type, temperature, gradient duration, and analyte retention based on liquid chromatography retention data collected for 187 small molecules. The model was built utilizing a Bayesian multilevel framework. The model assumes (i) a deterministic Neue equation that describes the relationship between retention time and analyte-specific and instrument-specific parameters, (ii) the relationship between analyte-specific descriptors (log P, pKa, and functional groups) and analyte-specific chromatographic parameters, and (iii) stochastic components of between-analyte and residual variability. The model utilizes prior knowledge about model parameters to regularize predictions which is important as there is ample information about the retention behavior of analytes in various stationary phases in the literature. The usefulness of the proposed model in providing interpretable summaries of complex data and in decision making is discussed.

Abraham Solvation Parameter Model: Calculation of L Solute Descriptors for Large C11 to C42 Methylated Alkanes from Measured Gas–Liquid Chromatographic Retention Data

Abraham model L solute descriptors have been determined for 149 additional C11 to C42 monomethylated and polymethylated alkanes based on published Kovat’s retention indices based upon gas–liquid chromatographic measurements. The calculated solute descriptors, in combination with previously published Abraham model correlations, can be used to predict a number of very important chemical and thermodynamic properties including partition coefficients, molar solubility ratios, gas–liquid chromatographic and HPLC retention data, infinite dilution activity coefficients, molar enthalpies of solvation, standard molar vaporization and sublimation at 298 K, vapor pressures, and limiting diffusion coefficients. The predictive computations are illustrated by estimating both the standard molar enthalpies of sublimation and the enthalpies of solvation in benzene for the monomethylated and polymethylated alkanes considered in the current study.

Insights into the lipophilicity of four commonly prescribed antidiabetic drugs and their simultaneous analysis using a simple TLC-spectrodensitometric method: Application to fixed-dose combination tablets and human plasma

The retention and lipophilicity characteristics of four oral antidiabetic drugs namely; Metformin (MET), Linagliptin (LIN), Empagliflozin (EMP), and Dapagliflozin (DAP) were evaluated by a facile TLC-spectrodensitometric method. The developed method was validated and employed for simultaneous determination of the investigated drugs in their synthetic quaternary mixture, single- and multi-component tablets, and human plasma. The separation of the cited drugs was achieved using silica gel G 60F254-TLC plates and a mobile system consisting of n-butanol: water: glacial acetic acid (7: 3: 1, v/v/v). After scanning at 234 nm, good linearities (10.0–2000.0 ng/band for each drug) and correlation coefficients (r = 0.99882–0.99972) with lower limits of detection and quantitation (2.17–3.58 and 6.57–10.85 ng/band, respectively) were statistically calculated. The obtained recoveries (98.35–101.38%) proved the wide applicability of the established method for concurrent estimation of the studied antidiabetics in fixed-dose combination tablets and human plasma. Besides, the present work was extended to estimate the lipophilicity parameters of the targeted drugs. Molecular lipophilicity (RM), relative lipophilicity (RM0), and lipophilic descriptor (C0) were calculated for MET, LIN, EMP, and DAP. Good correlations (r = 0.8729–0.9933) between the chromatographic retention data and molecular descriptors of the studied drugs were attained. The obtained results confirmed the poor lipophilicity of MET and LIN compared to EMP and DAP. Lastly, understanding the lipophilicity of the cited drugs may be promising for the future design of safer and more effective formulations for diabetes mellitus, cancer, and Alzheimer’s disease. Over and above, this work may be further applied to QSAR studies.

Retention indices for naturally-occurring chiral and achiral compounds on common gas chromatography chiral stationary phases

Chiral gas chromatography columns were initially developed for enantiomeric separations, however, they are increasingly being used for routine analyses of natural samples which also include many achiral compounds. Retention index (RI) data have been essential in the identification of natural compounds using standard columns when authentic standards are not available, but scarce RI information exists for chiral columns. In this work, we present linear RI data for 81 compounds (chiral and achiral) commonly found in biological samples for the two most common chiral stationary phases, Cyclodex-B and CycloSil-B, calculated with an alkane hydrocarbon series. In addition, we compared the resolution of various isomer pairs between the two chiral stationary phases and found better overall performance with CycloSil-B. Finally, we tested the applicability of our data set by calculating RI values for numerous compounds in leaf extracts of two Monarda fistulosa chemotypes on both columns and found good agreement. We discuss the relevance of chiral chromatographic retention data in a chemical ecology framework and call for the development of a comprehensive chiral RI database.

Transfer of gas chromatographic retention data among poly(siloxane) columns by quantitative structure-retention relationships based on molecular descriptors of both solutes and stationary phases

In the present study, computational molecular descriptors of 90 saturated esters and seven poly(siloxane) stationary phases with different polarity (SE-30, OV-7, DC-710, OV-25, XE-60, OV-225 and Silar-5CP) were combined into quantitative structure-retention relationship (QSRR) models aimed at predicting the Kováts retention indices (RIs) of the solutes. The molecular descriptors (174) of the stationary phases included in the models were computed using Dragon software from poly(siloxane) oligomers made of 20 siloxane units reflecting the nominal composition of the stationary phase, whereas 439 molecular descriptors were adopted to represent the esters. Different QSRR models were generated by means of Partial Least Squares (PLS) regression to assess the accuracy of this approach in predicting the RIs of unexplored solutes both in known and external stationary phases. After calibration of each PLS model, the descriptors were selected/discarded according to their relevance, evaluated by Covariance Selection (CovSel), and the PLS models were re-built, which resulted in a noticeable improvement of their predictive ability. Firstly, all the available data were equally divided into a training and a test set; the model built on the calibration set was used to predict the RIs of the validation observations. Successively, seven diverse PLS models were created following a “leave-one-column-out” fashion procedure, each one finalized to the estimation of the RIs of the 90 esters associated with a single stationary phase, whereas the calibration model was calculated on the remaining data. All the estimated models provided successful results on the external stationary phase, and predictive performance further increased after variable selection based on CovSel analysis. The final models provided a Root Mean Square Error in Cross Validation (RMSECV) in the range 12–20, a Root Mean Square Error in Prediction (RMSEP) in the range 11–26, and Mean Absolute Percentage Errors in Prediction (MAMEPs) in the range 0.7–1.5, revealing accurate cross-column prediction. Eventually, to test the robustness of the proposed approach, the 90 solutes were equally partitioned into a calibration and a test set and two further QSSR strategies were applied. The first PLS model was calibrated on all the seven stationary phases and the RIs of the 45 external solutes in the same seven columns were simultaneously predicted. The last QSRR approach followed a “leave-one-column-out” scheme and RI of 45 test solutes on an external stationary phase was predicted by a PLS model calibrated with the data of the 45 remaining solutes and the six left stationary phases. After selection of the significant molecular descriptors, PLS regression provided RMSECV values in the range 6–19, RMSEPs in the range 10–14, and MAPEPs in the range 0.9–2.4, revealing the suitability of the approach to deduce the RI of unknown solutes in uncharted stationary phases.

Graph Convolutional Networks for Improved Prediction and Interpretability of Chromatographic Retention Data.

Machine learning is a popular technique to predict the retention times of molecules based on descriptors. Descriptors and associated labels (e.g., retention times) of a set of molecules can be used to train a machine learning algorithm. However, descriptors are fixed molecular features which are not necessarily optimized for the given machine learning problem (e.g., to predict retention times). Recent advances in molecular machine learning make use of so-called graph convolutional networks (GCNs) to learn molecular representations from atoms and their bonds to adjacent atoms to optimize the molecular representation for the given problem. In this study, two GCNs were implemented to predict the retention times of molecules for three different chromatographic data sets and compared to seven benchmarks (including two state-of-the art machine learning models). Additionally, saliency maps were computed from trained GCNs to better interpret the importance of certain molecular sub-structures in the data sets. Based on the overall observations of this study, the GCNs performed better than all benchmarks, either significantly outperforming them (5-25% lower mean absolute error) or performing similar to them (<5% difference). Saliency maps revealed a significant difference in molecular sub-structures that are important for predictions of different chromatographic data sets (reversed-phase liquid chromatography vs hydrophilic interaction liquid chromatography).

The use of biomimetic chromatography to predict acute aquatic toxicity of pharmaceutical compounds

The potential of biomimetic chromatography to predict ecotoxicological endpoints of pharmaceutical compounds was investigated. For this purpose, a data set of previously and newly measured chromatographic retention data for 36 structurally diverse drugs was used. Standardized retention times were measured on the immobilized artificial membrane, human serum albumin, and alpha-1-acid glycoprotein stationary phases. As ecotoxicological endpoints, half-maximal lethal concentration values of fish and half-maximal effective concentration (immobilization) values of a water flea (Daphnia magna spp.) determined with a two-day static method were considered. Ecotoxicity values correlated with octanol-water partitioning and the positive charge of compounds contributed even more to the toxicity. Models based on membrane partition exhibited the best statistics and predictive performance, attributed to lipophilicity and membrane electrostatic interactions. Alpha-1-acid glycoprotein binding led to satisfactory models, owing to its function as a binder of neutral and basic lipophilic compounds. Albumin binding, however, did not result in sound models, as it is governed by lipophilicity and the negative charge of compounds, contrary to the mechanism of toxicity. Both membrane and alpha-1-acid glycoprotein models were superior statistically from those derived from the octanol-water system. Overall, membrane and alpha-1-acid glycoprotein retention can be suggested as promising indices to assess the ecotoxicological risk of drugs.

Evaluation of drug-likeness and ADME properties of sunscreens and preservatives using reversed-phase thin layer chromatographic retention data and calculated descriptors

RP-18 TLC chromatography was used to evaluate the pharmacokinetic properties (volume of distribution, VD; plasma protein binding, %PPB; the ability to cross the blood-brain barrier expressed as log PS and log BB) of several cosmetic raw materials – sunscreen and preservatives. The majority of these compounds are intended for topical use on skin and their drug-likeness and the ability to cross biological barriers are undesired. The retention parameters RM0, S, PC1 and RM75 % obtained for mobile phases containing six organic modifiers (methanol, acetonitrile, THF, acetone, dioxane, DMF) were used as the sole descriptors or combined with calculated physicochemical properties (PSA, MW, VM) of studied compounds.The chromatographic parameters considered in this study are, generally speaking, good predictors of the compounds’ pharmacokinetic properties VD, %PPB and log PS. RM75 % and the novel parameters derived from it (RM75 %/MW and RM75 %/VM) can be considered time- and cost-effective alternatives to the chromatographic parameters obtained by extrapolation or interpolation methods. In the case of some pharmacokinetic properties investigated in this study additional descriptors (PSA) have a significant influence on the quality of correlations.

Long-chain syn-1-phenylalkane-1,3-diyl diacetates, related phenylalkane derivatives, and sec-alcohols, all possessing dominantly iso-branched chain termini, and 2/3-methyl-branched fatty acids from Primula veris L. (Primulaceae) wax

Herein, the results of the first study of non-flavonoid constituents of aboveground surface-wax washings of Primula veris L. (Primulaceae) are presented. Chromatography of the washings yielded a minor fraction composed of n-, iso-, and anteiso-series of long-chained syn-1-phenylalkane-1,3-diyl diacetates, 3-oxo-1-phenylalkan-1-yl acetates, 1-phenylalkane-1,3-diones, 1-hydroxy-1-phenylalkan-3-ones, sec-alcohols (2- to 10-alkanols), and n-, iso-, anteiso-, 2-methylalkanoic and 3-methylalkanoic acids; 118 of these constituents represent up to now unreported natural compounds. The structural/stereochemical elucidation was accomplished by the synthesis of authentic standards, derivatization reactions, the use of gas chromatographic retention data and detailed 1D and 2D-NMR analyses of the obtained complex chromatographic fraction. Primula veris produces unusually high amounts of branched long-chained metabolites (>60%) except for the fatty acids where the percentage of branched isomers is comparable to the ones with n-chains. Noteworthy is the fact that long-chained α- and β-methyl substituted fatty acids were detected herein for the first time in the kingdom Plantae.

Detection of a secondary organic aerosol tracer derived from personal care products

Decamethylcyclopentasiloxane (D5) is frequently used in personal care products (PCPs). In the gas phase, D5 is oxidized to form 1-hydroxynonamethylcyclopentasiloxane (D4TOH), which can partition to the particle phase. Numerous studies have reported secondary organic aerosol (SOA) formation via hydroxyl (OH) radical-initiated oxidation of D5. It is expected that PCPs have a significant impact on SOA, but the extent has not yet been investigated. To date, no studies have reported the occurrence of PCP-derived SOA in ambient particulate matter. This study examined fine particulate matter (PM2.5) collected in Atlanta, GA and Houston, TX and determined D4TOH was present in 28 of 29 and 33 of 46 ambient PM2.5 samples, respectively. Gas chromatographic retention data in the form of the Kováts index is reported for the first time to aid others in identifying this compound in order to assess the impact of PCPs on SOA formation. The estimated concentration of D4TOH ranged from 16 to 185 pg m-3 in Atlanta and 19–206 pg m-3 in Houston. Synthetic musks were also detected in Atlanta and Houston PM2.5 samples, which is consistent with PCPs impacting urban air quality. Because of its specificity and demonstrated detectability, D4TOH may be useful as a tracer of PCP-derived secondary organic aerosol.

Pharmacological classification of anticancer drugs applying chromatographic retention data and chemometric analysis

The combination of chromatography and chemometrics has been introduced in order to provide information about drug analytes, biological macromolecules, the stationary phase, whose properties are related to molecular pharmacology and rational drug design. In this work a pharmacological classification was made in order to find strategy for predicting an activity of anticancer drugs. Principal Component Analysis (PCA) method has been employed to build some relationship models between the following: lipophilicity parameters of selected antitumor drugs obtained by chromatographic analysis, some molecular modeling descriptors from HyperChem software, lipophilicity parameters calculated by proper program, biological activity, as well as mechanism of actions and therapeutic properties of selected anticancer drugs. The most significant influence on factors value had parameters, which described compounds’ lipophilicity. Important debt on antitumor properties had some other molecular descriptors, which were considered in this paper (e.g. topological, physico-chemical, and energy ones). In addition, distribution of individual drugs on plots determined by two principal components had shown points in good accordance with their chemical structures and cytostatic activity as well as their mechanism of actions and therapeutic properties.

Measurements of Activity Coefficients at Infinite Dilution for Organic Solutes in the Ionic Liquids N-Ethyl- and N-Octyl-N-methylmorpholinium Bis(trifluoromethanesulfonyl)imide. A Useful Tool for Solvent Selection.

In recent years, many papers describing ionic liquids (IL) as promising solvents in separation techniques have been published. The conscious choice of appropriate ionic liquid as absorption media in effective extraction of selected types of analytes requires deeper understanding of the analyte−IL interactions. Therefore, intensive research is conducted to determine the values of activity coefficient at infinite dilution, which allows us to characterize the nature of these interactions. Based on the inverse gas chromatography retention data, activity coefficients at infinite dilution of 48 different organic compounds in the ionic liquids N-ethyl-N-methylmorpholinium bis(trifluoromethanesulfonyl)imide [C2C1Mor][TFSI] and N-octyl-N-methylmorpholinium bis(trifluoromethanesulfonyl)imide [C8C1Mor][TFSI] were determined. The measurements covered a broad range of volatile organic compounds, including n-alkanes, n-alkenes, n-alkynes, alcohols, aldehydes, ketones, aromatic compounds and common polar solvents, representing different types of interactions. Activity coefficients at infinite dilution were measured in the temperature range from 313.15 to 363.15 K. The excess partial molar enthalpies and entropies at infinite dilution were determined. Selectivity at infinite dilution was also calculated for exemplary separation processes in the hexane/benzene system. The obtained results were analyzed and compared with literature data for ionic liquids containing the same anion [TFSI]¯ and different cations. The study results indicate that some potential applications of the investigated ionic liquids in separation problems exist.

Separation optimization in HPLC analysis implemented in R programming language

A complete package of functions in the R-language has been written for professional separation optimization of complex mixtures of ionized and/or non-ionized solutes. The package includes functions for (a) base-line correction of experimentally recorded chromatograms, (b) modeling of chromatographic peak shapes and retention data, (c) prediction of the retention time of the test analytes and/or their chromatograms, and (d) separation optimization under either isocratic or single and/or double gradient elution conditions by changing the organic modifier(s) content and/or eluent pH. The optimization functions presented in this study offer two different modes for selection of optimal separation conditions: automatic and manual mode. In the automatic mode, the optimal separation conditions are determined by maximizing the resolution within separation time preset by the analyst. In the manual mode, the optimal separation conditions are selected via scatter or contour plots. The foreknowledge of the precise dependence of resolution and separation time upon one or two retention parameters of interest, provided by the proposed computer-assisted separation optimization method, gives chromatographers a feel of confidence for the selection of the optimal conditions for a desired separation. An illustrative video given in the Supplementary material may encourage a novice practitioner in R (software) programming language to follow the proposed separation optimization procedure in a real HPLC analysis.

Advances in structure elucidation of low molecular weight carbohydrates by liquid chromatography-multiple-stage mass spectrometry analysis

Oligosaccharides are gaining importance because of their beneficial properties in human health. They normally appear in natural and synthetic products as complex mixtures of different monomeric units, glycosidic linkages and degrees of polymerization, being disaccharides and trisaccharides usually the most abundant ones. Although liquid chromatography-mass spectrometry is a useful technique for oligosaccharides analysis, the similarity of their structures makes difficult their characterization. Moreover, there is still scarce information about the relationship between carbohydrate chemical structure, mass spectra and chromatographic data. Then, in this work, chromatographic parameters for 23 disaccharides with different linkages and monomeric units (glucose, galactose, mannose and fructose) were determined using porous graphitized and hydrophilic interaction liquid chromatography columns. Moreover, diagnostic ions of these disaccharides obtained by tandem mass spectra (MS2) were established by stepwise linear discriminant analysis. The relationship between carbohydrate chemical structure and their chromatographic retention data and characteristic ions obtained by multiple-stage mass spectrometry (MSn) was successful in establishing some specific criteria that allowed the characterization of trisaccharides with different structural features.

Use of HPLC retention to investigate new P descriptors designed to represent ion-π interactions

Computational methods increasingly provide access for Quantitative-Structure-Activity Relationship (QSAR) models to molecular descriptors that have minimal identifiable or experimentally accessible chemical/physical properties. However, molecular information inherent in chromatographic data suggests chromatography as a convenient experimental platform for legitimizing newly proposed molecular descriptors. In this study, liquid chromatographic retention data collected on a trimethylammonium (TAM) anion exchange stationary phase was used to evaluate two new molecular descriptors, P− and P+ developed to account for contributions from ion-π interactions. In the Linear Solvation Energy Relationship (LSER) model, the P− and P+ parameter replaced E. Incorporation of the new parameters was most impactful on S (sconv= −0.173 ± 0.070 vs. smod -0.115 ± 0.081). The remaining coefficients (v, b, c) were within experimental error of their respective coefficients in the conventional model. Nonparametric statistical analysis revealed comparable goodness of fits for the conventional and modified models. Finally, comparison of the contribution of individual interaction modes for substituted benzenes (log kbenzene-X) relative to benzene (log kbenzene) conducted with the new model was consistent with chemical intuition for the model-attributed ion − π and other individual interaction modes. Thus, adoption of P descriptors into the LSER model provides a convenient means for exploring models for weak ion − π interactions.

Descriptors for terpene esters from chromatographic and partition measurements: Estimation of human odor detection thresholds

We have used gas chromatographic retention data together with other data to obtain Abraham descriptors for 30 terpene esters. These include the air-water partition coefficient, as log Kw, for which no experimental values are available for any terpene ester. The other descriptors are the ester dipolarity, S, the hydrogen bond basicity, B, (the ester hydrogen bond acidity is zero for the esters studied), and L the logarithm of the air-hexadecane partition coefficient. Both S and B are larger than those for simple aliphatic esters, as expected from the terpene ester structures that include ring systems and ethylenic double bonds. These descriptors can then be used to obtain a large number of physicochemical and environmental properties of terpene esters. We have analyzed experimental results on human odor detection thresholds and have constructed another equation for the calculation of these thresholds, to go with a previous equation that we have reported. Then the descriptors for terpene esters can be used to estimate the important odor detection thresholds.