A non-linear structure–property model for octanol–water partition coefficient

Yerramsetty, Krishna; Neely, Brian J.; Gasem, Khaled A. M.

doi:10.1016/j.fluid.2012.07.001

Cited by 9 publications

(7 citation statements)

References 55 publications

(63 reference statements)

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“…Models of environmental partitioning of organic chemicals generally have better performance than those associated with bioaccumulation or toxicokinetic parameters, with RMSEs ranging from 0.4 to 0.7 being reported (Kipka & Di Toro, 2009, 2011a, 2011b; Kuo & Di Toro, 2013a, 2013b). An uncertainty of ±1 log unit is also generally expected for prediction models of fundamental physicochemical properties such as log organic–carbon partition coefficient (Kipka & Di Toro, 2011a, 2011b), log K OW (Yerramsetty et al, 2012), and log organic–air partition coefficient (Fu et al, 2016; Meylan & Howard, 2005). Comparing the error in log k M,est with available bioaccumulation, toxicokinetic, and partitioning models clearly shows that the proposed k M,est approximation is working well with good accuracy.…”

Section: Resultsmentioning

confidence: 99%

Determination of In Vivo Biotransformation Kinetics Using Early‐Time Biota Concentrations

Kuo

Toro

2021

Enviro Toxic and Chemistry

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Technical challenges have hampered the characterization of biotransformation kinetics-a critical link in understanding and predicting the toxicokinetics and ecotoxicology of organic compounds. A shortcut approach to characterize the in vivo biotransformation rate constant (k M ) with incomplete pathway or metabolite details was proposed. The value of k M can be derived as ( ), with f PC (t) being the molar equivalent fraction of the parent compound (PC) at an early time t in both constant exposure and decay source chemical uptake scenarios. The approximation-based k M values agreed well with k M values derived from rigorous fitting or toxicokinetic modeling (n = 42, root mean square error = 0.30) with accuracy exceeding those of typical toxicokinetic or partitioning models. The method is accurate when sampling time is adequately resolved (i.e., t < ln(2)/k M ) but will likely produce biased k M values with improper time-averaging. The approximate equation yields consistent theoretical expectations for fast and slow biotransformation reactions and is fully compatible with standard bioaccumulation and toxicity testing protocols. The simplification strategy circumvents statistical complications and numerical issues inherent in regressing or modeling the toxicokinetics of multimetabolite systems and may be adapted to similar problems at other physiological scales or ecotoxicological contexts. The method can help advance interspecies comparison of chemical metabolism and support the development of in vitro-in vivo extrapolations and in silico models needed for building next-generation ecological and health risk-assessment practices.

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Section: Resultsmentioning

confidence: 99%

Determination of In Vivo Biotransformation Kinetics Using Early‐Time Biota Concentrations

Kuo

Toro

2021

Enviro Toxic and Chemistry

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“…The external test set data were only used to assess the predictive quality of the developed model. Please refer to our previous works ,, for a detailed description on descriptor reduction, data classification, and model development …”

Section: Qspr Methodologymentioning

confidence: 99%

“…In this step, a large number of generated descriptors are reduced to smaller significant descriptor subsets. The algorithm applied in this work employs evolutionary programming and differential Please refer to our previous works 18,34,35 for a detailed description on descriptor reduction, data classification, and model development. 33 3.5.…”

Section: Industrial and Engineering Chemistry Researchmentioning

confidence: 99%

A Comparative Study of QSPR Generalized Activity Coefficient Model Parameters for Vapor–Liquid Equilibrium Mixtures

Gebreyohannes

Dadmohammadi

Neely

et al. 2016

Ind. Eng. Chem. Res.

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Generalized activity coefficient models are often essential for predicting the extent of liquid nonideality in a mixture in the absence of experimental data. This work is focused on generalizing the interaction parameters of three widely used activity coefficient models, nonrandom two-liquid (NRTL), universal quasi-chemical (UNIQUAC), and Wilson. Specifically, we applied a theory-framed quantitative structure−property relationship (TF-QSPR) modeling approach for the purpose of generalization. In this modeling approach, theoretical frameworks, such as the NRTL model, are used to describe the phase behavior properties, and QSPR methodology is used to generalize the binary interaction parameters of the models. In this study, a binary VLE database consisting of 916 systems was compiled and employed to develop the QSPR models. Interaction parameters of the NRTL, UNIQUAC, and Wilson models were determined by performing data regression analyses. QSPR models were developed to predict the interaction parameters found in the regression analyses. The structural descriptors of the molecules were used as inputs in the QSPR models. The phase equilibria properties estimated using the generalized QSPR models resulted in about 2 times the error as compared to the results found in the data regression analyses. Overall, the quality of property predictions from the QSPR models is comparable to those of the UNIFAC-2006 group-contribution model when all of its group-interaction parameters are available; however, the UNIFAC model produced worse predictions when such parameters are lacking. Thus, our methodology offers a viable complement when UNIFAC parameters are missing.

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“…A detailed discussion on this approach can be found in our previous works. 13,28,29 The initial step in the model development process is to divide the entire data set into four subsets (training, validation, internal test, and external test sets) with a proportion of 50% for the training set, 15% for the internal validation set, 10% for the internal test set, and the remaining 25% for the external test set. The data division was performed by ensuring that there is adequate representation of all the functional-group interactions in all the data sets.…”

Section: Qspr Methodologymentioning

confidence: 99%

“…The hybrid approach uses evolutionary programming (EP) and differential evolution (DE) as a wrapper around artificial neural networks (ANNs) to identify the best descriptor subsets from the initial molecular descriptors pool. A detailed discussion on this approach can be found in our previous works. ,, …”

Section: Qspr Methodologymentioning

confidence: 99%

Generalized Interaction Parameter for the Modified Nonrandom Two-Liquid (NRTL) Activity Coefficient Model

Gebreyohannes

Neely

Gasem

2014

Ind. Eng. Chem. Res.

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Activity coefficients are used in phase equilibria calculations to account for the nonideal behavior of liquids in a mixture. In our previous work, we proposed a modification to the widely used nonrandom two liquid (NRTL) activity coefficient model that reduced the number of parameters from two to one. The current work is an extension to the previous work, and it focuses on generalizing the parameter of the modified NRTL (mNRTL1) model using a theory-framed quantitative structure–property relationship (QSPR) modeling approach, where the mNRTL1 model was used as a theoretical framework to develop the behavior model, and QSPR was used to generalize the substance-specific parameter of the model. In this work, a VLE database consisting of 916 binary systems with a wide range of functional-group interactions was assembled. Data regression analyses were performed to determine the parameter of the mNRTL1 model. The structural descriptors of the molecules were used as inputs in the QSPR model to predict the regressed parameter. The generalized QSPR model produced equilibrium property predictions within two times the errors obtained through regression of the experimental data. The newly developed QSPR model resulted in comparable phase equilibria predictions to that of the group-contribution method, UNIFAC. In comparison, the QSPR model has less than 10% of the number of parameters used by UNIFAC and is applicable to a wider range of systems.

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A non-linear structure–property model for octanol–water partition coefficient

Cited by 9 publications

References 55 publications

Determination of In Vivo Biotransformation Kinetics Using Early‐Time Biota Concentrations

Determination of In Vivo Biotransformation Kinetics Using Early‐Time Biota Concentrations

A Comparative Study of QSPR Generalized Activity Coefficient Model Parameters for Vapor–Liquid Equilibrium Mixtures

Generalized Interaction Parameter for the Modified Nonrandom Two-Liquid (NRTL) Activity Coefficient Model

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