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Ita K, Prinze J. Machine learning for skin permeability prediction: random forest and XG boost regression. J Drug Target 2024; 32:57-65. [PMID: 37962433 DOI: 10.1080/1061186x.2023.2284096] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Accepted: 11/09/2023] [Indexed: 11/15/2023]
Abstract
Background: Machine learning algorithms that can quickly and easily estimate skin permeability (Kp) are increasingly being used in drug delivery research. The linear free energy relationship (LFER) developed by Abraham is a practical technique for predicting Kp. The permeability coefficients and Abraham solute descriptor values for 175 organic compounds have been documented in the scientific literature.Purpose: The purpose of this project was to use a publicly available dataset to make skin permeability predictions using the random forest and XBoost regression techniques.Methods: We employed Pandas-based methods in JupyterLab to predict permeability coefficient (Kp) from solute descriptors (excess molar refraction [E], combined dipolarity/polarizability [S], overall solute hydrogen bond acidity and basicity [A and B], and the McGowan's characteristic molecular volume [V]).Results: The random forest and XG Boost regression models established statistically significant association between the descriptors and the skin permeability coefficient.
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Affiliation(s)
- Kevin Ita
- College of Pharmacy, Touro University, Vallejo, CA, USA
| | - Joyce Prinze
- College of Pharmacy, Touro University, Vallejo, CA, USA
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2
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Kahsay BN, Moeller L, Wohlrab J, Neubert RHH, Gebre-Mariam T. Delivery of small hydrophilic molecules across the stratum corneum: Identification of model systems and parameters to study topical delivery of free amino acids. Int J Pharm 2024; 661:124372. [PMID: 38909923 DOI: 10.1016/j.ijpharm.2024.124372] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2024] [Revised: 06/18/2024] [Accepted: 06/20/2024] [Indexed: 06/25/2024]
Abstract
Free amino acids (FAAs) constitute the largest component (∼40 %) of the so-called natural moisturizing factors of the skin. Their level declines in dry skin conditions and one strategy to overcome this problem may involve the topical delivery of FAAs through appropriate strategy. The objective of the present study was therefore to identify alternative skin models and study the corneocyte-water partition coefficients (KCOR/W) and permeation coefficient (KP) of 18 FAAs. The KCOR/W was studied using standard protocols and the permeation studies were conducted using Franz diffusion cell. The results indicate that the FAAs have high partitioning behavior to the corneocytes. The KCOR/W values of the human COR and that of pig ear skin were better correlated with each other than that of keratin isolated from chicken feathers. The presence of lipid in the stratum corneum (SC), initial concentration of the FAAs, and permeation enhancers affect the KCOR/W. The FAAs have low permeation into the SC which suggests the need for permeation enhancers in designing dosage form containing these compounds. Even though the investigated mathematical models show good prediction of the Kp values, better prediction could be obtained by considering factors such as the possible entrapment of the FAAs by the CORs.
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Affiliation(s)
- Birhanu Nigusse Kahsay
- Institute of Applied Dermatopharmacy, Martin Luther University Halle-Wittenberg, Weinbergweg 23 06120, Halle (Saale), Germany; Department of Biopharmaceutics & Pharmaceutical Technology, Institute of Pharmaceutical & Biomedical Sciences, Johannes Gutenberg University, Staudinger Weg 5 D-55099, Mainz, Germany
| | - Lucie Moeller
- Department of Systemic Environmental Biotechnology, Helmholtz Centre for Environmental Research, Permoserstrasse 15 04318, Leipzig, Germany
| | - Johannes Wohlrab
- Department of Dermatology and Venereology, Martin Luther University Halle-Wittenberg, Ernst-Grube-Str. 40 06120, Halle (Saale), Germany
| | - Reinhard H H Neubert
- Institute of Applied Dermatopharmacy, Martin Luther University Halle-Wittenberg, Weinbergweg 23 06120, Halle (Saale), Germany.
| | - Tsige Gebre-Mariam
- Department of Pharmaceutics and Social Pharmacy, School of Pharmacy, College of Health Sciences, Addis Ababa University, P.O. Box 9086, Addis Ababa, Ethiopia.
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3
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Intasiri A, Illa SE, Prertprawnon S, Wang S, Li L, Bell TW, Li D. Comparison of in vitro membrane permeabilities of diverse environmental chemicals with in silico predictions. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 933:173244. [PMID: 38750756 DOI: 10.1016/j.scitotenv.2024.173244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Revised: 04/29/2024] [Accepted: 05/12/2024] [Indexed: 05/19/2024]
Abstract
The parallel artificial membrane permeability assay (PAMPA) is widely used for estimating biomembrane permeabilities of experimental drugs in pharmaceutical research. However, there are few reports of studies using PAMPA to measure membrane permeabilities of chemicals of environmental concern (CECs) outside the pharmaceutical domain, many of which differ substantially from drugs in their physicochemical properties. We applied PAMPA methods simulating gastrointestinal (PAMPA-GIT) and blood-brain barrier (PAMPA-BBB) membranes under consistent conditions to 51 CECs, including some pharmaceuticals. A backward stepwise multivariate linear regression was implemented to explore the correlation between the differences of measured permeabilities from PAMPA-GIT and PAMPA-BBB and Abraham solute descriptors. In addition, a previously reported in silico model was evaluated by comparing predicted and measured permeability results. PAMPA-GIT and PAMPA-BBB experimental permeability results agreed relatively well. The backward stepwise multivariate linear regression identified excess molar refraction and polarizability to be significant at the 0.10 level in predicting the differences between PAMPA-GIT and PAMPA-BBB. The in silico model performed well - with predicted permeability of most compounds within two-fold of experimentally measured values. We found that CECs pose experimental challenges to the PAMPA method in terms of having lower solubility and lower stability compared to most drugs.
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Affiliation(s)
- Amarawan Intasiri
- Department of Chemistry, University of Nevada, 1664 N. Virginia Street, Reno, NV 89557, USA
| | - Siena E Illa
- School of Public Health, University of Nevada, 1664 N. Virginia Street, Reno, NV 89557, USA
| | - Supadach Prertprawnon
- Department of Chemistry, University of Nevada, 1664 N. Virginia Street, Reno, NV 89557, USA
| | - Shenghong Wang
- School of Public Health, University of Nevada, 1664 N. Virginia Street, Reno, NV 89557, USA
| | - Li Li
- School of Public Health, University of Nevada, 1664 N. Virginia Street, Reno, NV 89557, USA
| | - Thomas W Bell
- Department of Chemistry, University of Nevada, 1664 N. Virginia Street, Reno, NV 89557, USA
| | - Dingsheng Li
- School of Public Health, University of Nevada, 1664 N. Virginia Street, Reno, NV 89557, USA.
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4
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Zhang Z, Sangion A, Wang S, Gouin T, Brown T, Arnot JA, Li L. Chemical Space Covered by Applicability Domains of Quantitative Structure-Property Relationships and Semiempirical Relationships in Chemical Assessments. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024. [PMID: 38263624 PMCID: PMC10882972 DOI: 10.1021/acs.est.3c05643] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/25/2024]
Abstract
A significant number of chemicals registered in national and regional chemical inventories require assessments of their potential "hazard" concerns posed to humans and ecological receptors. This warrants knowledge of their partitioning and reactivity properties, which are often predicted by quantitative structure-property relationships (QSPRs) and other semiempirical relationships. It is imperative to evaluate the applicability domain (AD) of these tools to ensure their suitability for assessment purpose. Here, we investigate the extent to which the ADs of commonly used QSPRs and semiempirical relationships cover seven partitioning and reactivity properties of a chemical "space" comprising 81,000+ organic chemicals registered in regulatory and academic chemical inventories. Our findings show that around or more than half of the chemicals studied are covered by at least one of the commonly used QSPRs. The investigated QSPRs demonstrate adequate AD coverage for organochlorides and organobromines but limited AD coverage for chemicals containing fluorine and phosphorus. These QSPRs exhibit limited AD coverage for atmospheric reactivity, biodegradation, and octanol-air partitioning, particularly for ionizable organic chemicals compared to nonionizable ones, challenging assessments of environmental persistence, bioaccumulation capability, and long-range transport potential. We also find that a predictive tool's AD coverage of chemicals depends on how the AD is defined, for example, by the distance of a predicted chemical from the centroid of the training chemicals or by the presence or absence of structural features.
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Affiliation(s)
- Zhizhen Zhang
- School of Public Health, University of Nevada, Reno, Reno, Nevada 89557, United States
| | | | - Shenghong Wang
- School of Public Health, University of Nevada, Reno, Reno, Nevada 89557, United States
| | - Todd Gouin
- TG Environmental Research, Sharnbrook, Bedford MK44 1PL, U.K
| | - Trevor Brown
- ARC Arnot Research & Consulting, Toronto, Ontario M4M 1W4, Canada
| | - Jon A Arnot
- ARC Arnot Research & Consulting, Toronto, Ontario M4M 1W4, Canada
- Department of Physical and Environmental Sciences, University of Toronto Scarborough, Toronto, Ontario M1C 1A4, Canada
- Department of Pharmacology and Toxicology, University of Toronto, Toronto, Ontario M5S 1A8, Canada
| | - Li Li
- School of Public Health, University of Nevada, Reno, Reno, Nevada 89557, United States
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5
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Aakash A, Kulsoom R, Khan S, Siddiqui MS, Nabi D. Novel Models for Accurate Estimation of Air-Blood Partitioning: Applications to Individual Compounds and Complex Mixtures of Neutral Organic Compounds. J Chem Inf Model 2023; 63:7056-7066. [PMID: 37956246 PMCID: PMC10685450 DOI: 10.1021/acs.jcim.3c01288] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Revised: 10/23/2023] [Accepted: 10/25/2023] [Indexed: 11/15/2023]
Abstract
The air-blood partition coefficient (Kab) is extensively employed in human health risk assessment for chemical exposure. However, current Kab estimation approaches either require an extensive number of parameters or lack precision. In this study, we present two novel and parsimonious models to accurately estimate Kab values for individual neutral organic compounds, as well as their complex mixtures. The first model, termed the GC×GC model, was developed based on the retention times of nonpolar chemical analytes on comprehensive two-dimensional gas chromatography (GC×GC). This model is unique in its ability to estimate the Kab values for complex mixtures of nonpolar organic chemicals. The GC×GC model successfully accounted for the Kab variance (R2 = 0.97) and demonstrated strong prediction power (RMSE = 0.31 log unit) for an independent set of nonpolar chemical analytes. Overall, the GC×GC model can be used to estimate Kab values for complex mixtures of neutral organic compounds. The second model, termed the partition model (PM), is based on two types of partition coefficients: octanol to water (Kow) and air to water (Kaw). The PM was able to effectively account for the variability in Kab data (n = 344), yielding an R2 value of 0.93 and root-mean-square error (RMSE) of 0.34 log unit. The predictive power and explanatory performance of the PM were found to be comparable to those of the parameter-intensive Abraham solvation models (ASMs). Additionally, the PM can be integrated into the software EPI Suite, which is widely used in chemical risk assessment for initial screening. The PM provides quick and reliable estimation of Kab compared to ASMs, while the GC×GC model is uniquely suited for estimating Kab values for complex mixtures of neutral organic compounds. In summary, our study introduces two novel and parsimonious models for the accurate estimation of Kab values for both individual compounds and complex mixtures.
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Affiliation(s)
- Ahmad Aakash
- Institute
of Environmental Science and Engineering (IESE), School of Civil and
Environmental Engineering (SCEE), National
University of Sciences and Technology (NUST), H-12, 48000 Islamabad, Pakistan
| | - Ramsha Kulsoom
- Institute
of Environmental Science and Engineering (IESE), School of Civil and
Environmental Engineering (SCEE), National
University of Sciences and Technology (NUST), H-12, 48000 Islamabad, Pakistan
| | - Saba Khan
- Institute
of Environmental Science and Engineering (IESE), School of Civil and
Environmental Engineering (SCEE), National
University of Sciences and Technology (NUST), H-12, 48000 Islamabad, Pakistan
| | - Musab Saeed Siddiqui
- Institute
of Environmental Science and Engineering (IESE), School of Civil and
Environmental Engineering (SCEE), National
University of Sciences and Technology (NUST), H-12, 48000 Islamabad, Pakistan
| | - Deedar Nabi
- Institute
of Environmental Science and Engineering (IESE), School of Civil and
Environmental Engineering (SCEE), National
University of Sciences and Technology (NUST), H-12, 48000 Islamabad, Pakistan
- GEOMAR
Helmholtz Center for Ocean Research, Wischhofstrasse 1-3, 24148 Kiel, Germany
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Song K, Yang X, Wang Y, Wan Z, Wang J, Wen Y, Jiang H, Li A, Zhang J, Lu S, Fan B, Guo S, Ding Y. Addressing new chemicals of emerging concern (CECs) in an indoor office. ENVIRONMENT INTERNATIONAL 2023; 181:108259. [PMID: 37839268 DOI: 10.1016/j.envint.2023.108259] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Revised: 09/27/2023] [Accepted: 10/09/2023] [Indexed: 10/17/2023]
Abstract
Indoor pollutants change over time and place. Exposure to hazardous organics is associated with adverse health effects. This work sampled gaseous organics by Tenax TA tubes in two indoor rooms, i.e., an office set as samples, and the room of chassis dynamometer (RCD) set as backgrounds. Compounds are analyzed by a thermal desorption comprehensive two-dimensional gas chromatography-quadrupole mass spectrometer (TD-GC × GC-qMS). Four new chemicals of emerging concern (CECs) are screened in 469 organics quantified. We proposed a three-step pipeline for CECs screening utilizing GC × GC including 1) non-target scanning of organics with convincing molecular structures and quantification results, 2) statistical analysis between samples and backgrounds to extract useful information, and 3) pixel-based property estimation to evaluate the contamination potential of addressed chemicals. New CECs spotted in this work are all intermediate volatility organic compounds (IVOCs), containing mintketone, isolongifolene, β-funebrene, and (5α)-androstane. Mintketone and sesquiterpenes may be derived from the use of volatile chemical products (VCPs), while (5α)-androstane is probably human-emitted. The occurrence and contamination potential of the addressed new CECs are reported for the first time. Non-target scanning and the measurement of IVOCs are of vital importance to get a full glimpse of indoor organics.
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Affiliation(s)
- Kai Song
- State Environmental Protection Key Laboratory of Vehicle Emission Control and Simulation, Chinese Research Academy of Environmental Sciences, Beijing 100012, China; Vehicle Emission Control Center, Chinese Research Academy of Environmental Sciences, Beijing 100012, China; State Key Joint Laboratory of Environmental Simulation and Pollution Control, International Joint Laboratory for Regional Pollution Control, Ministry of Education (IJRC), College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China; Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science & Technology, Nanjing 210044, China
| | - Xinping Yang
- State Environmental Protection Key Laboratory of Vehicle Emission Control and Simulation, Chinese Research Academy of Environmental Sciences, Beijing 100012, China; Vehicle Emission Control Center, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
| | - Yunjing Wang
- State Environmental Protection Key Laboratory of Vehicle Emission Control and Simulation, Chinese Research Academy of Environmental Sciences, Beijing 100012, China; Vehicle Emission Control Center, Chinese Research Academy of Environmental Sciences, Beijing 100012, China.
| | - Zichao Wan
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, International Joint Laboratory for Regional Pollution Control, Ministry of Education (IJRC), College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China
| | - Junfang Wang
- State Environmental Protection Key Laboratory of Vehicle Emission Control and Simulation, Chinese Research Academy of Environmental Sciences, Beijing 100012, China; Vehicle Emission Control Center, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
| | - Yi Wen
- China Automotive Technology and Research Center (CATARC), Beijing 100176, China
| | - Han Jiang
- State Environmental Protection Key Laboratory of Vehicle Emission Control and Simulation, Chinese Research Academy of Environmental Sciences, Beijing 100012, China; Vehicle Emission Control Center, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
| | - Ang Li
- China Automotive Technology and Research Center (CATARC), Beijing 100176, China
| | | | - Sihua Lu
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, International Joint Laboratory for Regional Pollution Control, Ministry of Education (IJRC), College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China
| | - Baoming Fan
- TECHSHIP (Beijing) Technology Co., LTD, Beijing 100039, China
| | - Song Guo
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, International Joint Laboratory for Regional Pollution Control, Ministry of Education (IJRC), College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China; Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science & Technology, Nanjing 210044, China.
| | - Yan Ding
- State Environmental Protection Key Laboratory of Vehicle Emission Control and Simulation, Chinese Research Academy of Environmental Sciences, Beijing 100012, China; Vehicle Emission Control Center, Chinese Research Academy of Environmental Sciences, Beijing 100012, China.
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7
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Sobańska AW, Brzezińska E. Immobilized Keratin HPLC Stationary Phase-A Forgotten Model of Transdermal Absorption: To What Molecular and Biological Properties Is It Relevant? Pharmaceutics 2023; 15:pharmaceutics15041172. [PMID: 37111656 PMCID: PMC10144615 DOI: 10.3390/pharmaceutics15041172] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 03/28/2023] [Accepted: 04/04/2023] [Indexed: 04/29/2023] Open
Abstract
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.
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Affiliation(s)
- Anna Weronika Sobańska
- Department of Analytical Chemistry, Faculty of Pharmacy, Medical University of Lodz, ul. Muszyńskiego 1, 90-151 Lodz, Poland
| | - Elżbieta Brzezińska
- Department of Analytical Chemistry, Faculty of Pharmacy, Medical University of Lodz, ul. Muszyńskiego 1, 90-151 Lodz, Poland
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Aakash A, Nabi D. Reliable prediction of sensory irritation threshold values of organic compounds using new models based on linear free energy relationships and GC×GC retention parameters. CHEMOSPHERE 2023; 313:137339. [PMID: 36423720 DOI: 10.1016/j.chemosphere.2022.137339] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/21/2022] [Revised: 11/18/2022] [Accepted: 11/19/2022] [Indexed: 06/16/2023]
Abstract
The human sensory irritation threshold (SIT) is an important biochemical parameter for the exposure assessment of organic air pollutants. First, we recalibrated the Abraham solvation models (ASMs) for 9 SIT endpoints by curating 720 individual experimental SIT values to find an accurate and parsimonious ASM variant, which exhibited root mean square error (RMSE) = 0.174-0.473 log unit. Second, we report linear free energy relationships - henceforth called partition models (PMs) - which exploit the correlations of 9 SIT endpoints with the linear combinations of partition coefficients for octanol-water and air-water systems showing RMSE = 0.221-0.591 log unit. These PMs can easily be integrated into widely used EPI-Suite™ screening tool. The explanatory and predictive performance of PMs were like parameter-intensive ASMs. Third, we present GC × GC models that are based on the retention times of the nonpolar analytes on the comprehensive two-dimensional gas chromatography (GC × GC), which successfully described the SIT variance (R2=0.959-0.996) and depicted a strong predictive power (RMSE = 0.359-0.660 log unit) for an independent set of nonpolar analytes. Taken together, PMs allow easy SIT screening of organic chemicals compared to ASMs. Unlike ASMs, our GC × GC models can be applied to estimate SIT of complex nonpolar mixtures.
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Affiliation(s)
- Ahmad Aakash
- Institute of Environmental Science and Engineering (IESE), School of Civil and Environmental Engineering (SCEE), National University of Sciences and Technology (NUST), H-12, Islamabad, Pakistan; Environment and Agriculture Laboratory, School of Interdisciplinary Engineering & Sciences (SINES), National University of Sciences and Technology (NUST), H-12, Islamabad, Pakistan
| | - Deedar Nabi
- Institute of Environmental Science and Engineering (IESE), School of Civil and Environmental Engineering (SCEE), National University of Sciences and Technology (NUST), H-12, Islamabad, Pakistan; Environment and Agriculture Laboratory, School of Interdisciplinary Engineering & Sciences (SINES), National University of Sciences and Technology (NUST), H-12, Islamabad, Pakistan.
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9
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Waters LJ, Quah XL. Predicting skin permeability using HuskinDB. Sci Data 2022; 9:584. [PMID: 36151144 PMCID: PMC9508232 DOI: 10.1038/s41597-022-01698-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Accepted: 09/14/2022] [Indexed: 11/16/2022] Open
Abstract
A freely accessible database has recently been released that provides measurements available in the literature on human skin permeation data, known as the ‘Human Skin Database – HuskinDB’. Although this database is extremely useful for sourcing permeation data to help with toxicity and efficacy determination, it cannot be beneficial when wishing to consider unlisted, or novel compounds. This study undertakes analysis of the data from within HuskinDB to create a model that predicts permeation for any compound (within the range of properties used to create the model). Using permeability coefficient (Kp) data from within this resource, several models were established for Kp values for compounds of interest by varying the experimental parameters chosen and using standard physicochemical data. Multiple regression analysis facilitated creation of one particularly successful model to predict Kp through human skin based only on three chemical properties. The model transforms the dataset from simply a resource of information to a more beneficial model that can be used to replace permeation testing for a wide range of compounds.
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Affiliation(s)
- Laura J Waters
- School of Applied Sciences, University of Huddersfield, Queensgate, Huddersfield, HD1 3DH, UK.
| | - Xin Ling Quah
- School of Applied Sciences, University of Huddersfield, Queensgate, Huddersfield, HD1 3DH, UK
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10
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Khawar MI, Mahmood A, Nabi D. Exploring the role of octanol-water partition coefficient and Henry's law constant in predicting the lipid-water partition coefficients of organic chemicals. Sci Rep 2022; 12:14936. [PMID: 36056200 PMCID: PMC9440013 DOI: 10.1038/s41598-022-19452-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Accepted: 08/29/2022] [Indexed: 11/19/2022] Open
Abstract
Partition coefficients for storage lipid-water (logKlw) and phospholipid-water (logKpw) phases are key parameters to understand the bioaccumulation and toxicity of organic contaminants. However, the published experimental databases of these properties are dwarfs and current estimation approaches are cumbersome. Here, we present partition models that exploit the correlations of logKlw, and of logKpw with the linear combinations of the octanol-water partition coefficient (logKow) and the dimensionless Henry's law constant (air-water partition coefficient, logKaw). The calibrated partition models successfully describe the variations in logKlw data (n = 305, R2 = 0.971, root-mean-square-error (rmse) = 0.375), and in logKpw data (n = 131, R2 = 0.953, rmse = 0.413). With the inputs of logKow and logKaw estimated from the U.S. EPA's EPI Suite, our models of logKlw and logKpw have exhibited rmse = 0.52 with respect to experimental values indicating suitability of these models for inclusion in the EPI Suite. Our models perform similar to or better than the previously reported models such as one parameter partition models, Abraham solvation models, and models based on quantum-chemical calculations. Taken together, our models are robust, easy-to-use, and provide insight into variations of logKlw and logKpw in terms of hydrophobicity and volatility trait of chemicals.
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Affiliation(s)
- Muhammad Irfan Khawar
- Institute of Environmental Science and Engineering (IESE), School of Civil and Environmental Engineering (SCEE), National University of Sciences and Technology (NUST), Islamabad, H-12, Pakistan
- Environment and Agriculture Laboratory, School of Interdisciplinary Engineering and Sciences (SINES), National University of Sciences and Technology (NUST), Islamabad, H-12, Pakistan
| | - Azhar Mahmood
- School of Natural Sciences (SNS), National University of Sciences and Technology (NUST), Islamabad, H-12, Pakistan
| | - Deedar Nabi
- Institute of Environmental Science and Engineering (IESE), School of Civil and Environmental Engineering (SCEE), National University of Sciences and Technology (NUST), Islamabad, H-12, Pakistan.
- Environment and Agriculture Laboratory, School of Interdisciplinary Engineering and Sciences (SINES), National University of Sciences and Technology (NUST), Islamabad, H-12, Pakistan.
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11
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IAM Chromatographic Models of Skin Permeation. MOLECULES (BASEL, SWITZERLAND) 2022; 27:molecules27061893. [PMID: 35335257 PMCID: PMC8952769 DOI: 10.3390/molecules27061893] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Revised: 03/12/2022] [Accepted: 03/12/2022] [Indexed: 12/20/2022]
Abstract
Chromatographic retention factor log kIAM obtained from IAM HPLC chromatography with buffered aqueous mobile phases and calculated molecular descriptors (surface area—Sa; molar volume—VM; polar surface area—PSA; count of freely rotable bonds—FRB; H-bond acceptor count—HA; energy of the highest occupied molecular orbital—EHOMO; energy of the lowest unoccupied orbital—ELUMO; and polarizability—α) obtained for a group of 160 structurally unrelated compounds were tested in order to generate useful models of solutes’ skin permeability coefficient log Kp. It was established that log kIAM obtained in the conditions described in this study is not sufficient as a sole predictor of the skin permeability coefficient. Simple put, potentially useful models based on log kIAM and readily available calculated descriptors, accounting for 85 to 91% of the total variability, were generated using Multiple Linear Regression (MLR).The models proposed in the study were tested on a group of 20 compounds with known experimental log Kp values.
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12
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Investigation of partition coefficients and fingerprints of atmospheric gas- and particle-phase intermediate volatility and semi-volatile organic compounds using pixel-based approaches. J Chromatogr A 2022; 1665:462808. [PMID: 35032735 DOI: 10.1016/j.chroma.2022.462808] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2021] [Revised: 01/05/2022] [Accepted: 01/05/2022] [Indexed: 11/21/2022]
Abstract
Ambient gas- and particle-phase intermediate volatility and semi-volatile organic compounds (I/SVOCs) of Beijing were analyzed by a thermal desorption comprehensive two-dimensional gas chromatography quadrupole mass spectrometry (TD-GC × GC-qMS). A pixel-based scheme combing the integration-based approach was applied for partition coefficients estimation and fingerprints identification. Blob-by-blob recognition was firstly utilized to characterize I/SVOCs from the molecular level. 412 blobs in gas-phase and 460 blobs in particle-phase were resolved, covering a total response of 47.5% and 43.5%. A large pool of I/SVOCs was found with a large diversity of chemical classes in both gas- and particle-phase. Acids (8.5%), b-alkanes (5.8%), n-alkanes (C8-C25, 5.3%), and aromatics (4.4%) were dominant in gas-phase while esters (7.0%, including volatile chemical product compounds, VCPs), n-alkanes (C9-C34, 5.7%), acids (4.6%), and siloxanes (3.6%) were abundant in particle-phase. Air pollutants were then evaluated by a two-parameter linear free energy relationship (LFER) model, which could be further implemented in the two-dimensional volatility basis set (2D-VBS) model. Multiway principal component analysis (MPCA) and partial least squares-discriminant analysis (PLS-DA) implied that naphthalenes, phenol, propyl-benzene isomers, and oxygenated volatile organic compounds (OVOCs) were key components in the gas-phase under different pollution levels. This work gives more insight into property estimation and fingerprints identification for complex ambient samples.
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