Octanol/water partition coefficients estimated using retention times in reverse-phase liquid chromatography and calculated in silico as one of the determinant factors for pharmacokinetic parameter estimations of general chemical substances.

The octanol/water partition coefficient P (logP) is a hydrophobicity index and is one of the determining factors for the pharmacokinetics of orally administered substances because it influences membrane permeability. To illustrate the wide-ranging variety of compounds in the chemical space, a two-dimensional data plot consisting of 25 blocks was previously proposed based on a substance's in silico chemical descriptors. The logP values of approximately 200 diverse chemicals (test plus reference compounds covering all 25 blocks of the chemical space) were estimated experimentally using retention times in reverse-phase liquid chromatography; these values were compared with those of authentic reference compounds with established logP values (available for 17 of 60 reference substances in the Organization for Economic Co-operation and Development Test Guideline 117). The logP values of 140 of 165 chemicals successfully estimated using four different mobile phase conditions (pH 2, 4, 7, and 10 for molecular forms) correlated significantly with those calculated using the in silico packages ChemDraw and ACD/Percepta (r > 0.72). Although substances that neighbored authentic compounds in the chemical space had precisely correlated logP values estimated experimentally and in silico, some compounds that were more distant from authentic substances showed lower logP values than those estimated in silico. These results indicate that additional authentic reference materials with wider ranging chemical diversity and their logP values from reverse-phase liquid chromatography should be included in the international test guidance to promote simple and reliable estimation of octanol/water partition coefficients, which are important determinant factors for the pharmacokinetics of general chemicals.

A single mutation in the mosquito (Aedes aegypti) olfactory receptor 8 causes loss of function to 1-octen-3-ol.

The host-seeking behavior of mosquitoes have long been established to be primarily odor-mediated. In this process, olfactory receptors (Ors) play a critical role. 1-Octen-3-ol is a common volatile compound that is attractive to hematophagous arthropods such as mosquitos. The olfactory receptor 8 (AaOr8) on the tip of the stylet and maxillary palp of Aedes aegypti is tuned to 1-octen-3-ol, which is required for mosquitoes to quickly find blood vessels from a vertebrate host. However, little is known about the interaction of AaOr8 with 1-octen-3-ol which was studied in vivo and in silico in this study. The molecular binding poses and energies between ligands and the receptor were investigated. Three mutants of AaOr8 were cloned and compared with in vivo calcium imaging utilizing heterologous expression systems. As a result, our findings imply that a genetic disruption including targeted modification of Ors genes may be used to reduce mosquito bites.

Interpretable attention-based multi-encoder transformer based QSPR model for assessing toxicity and environmental impact of chemicals.

The rising demand from consumer goods and pharmaceutical industry is driving a fast expansion of newly developed chemicals. The conventional toxicity testing of unknown chemicals is expensive, time-consuming, and raises ethical concerns. The quantitative structure-property relationship (QSPR) is an efficient computational method because it saves time, resources, and animal experimentation. Advances in machine learning have improved chemical analysis in QSPR studies, but the real-world application of machine learning-based QSPR studies was limited by the unexplainable 'black box' feature of the machine learnings. In this study, multi-encoder structure-to-toxicity (S2T)-transformer based QSPR model was developed to estimate the properties of polychlorinated biphenyls (PCBs) and endocrine disrupting chemicals (EDCs). Simplified molecular input line entry systems (SMILES) and molecular descriptors calculated by the Dragon 6 software, were simultaneously considered as input of QSPR model. Furthermore, an attention-based framework is proposed to describe the relationship between the molecular structure and toxicity of hazardous chemicals. The S2T-transformer model achieved the highest R2 scores of 0.918, 0.856, and 0.907 for logarithm of octanol-water partition coefficient (Log KOW), octanol-air partition coefficient (Log KOA), and bioconcentration factor (Log BCF) estimation of PCBs, respectively. Moreover, the attention weights were able to properly interpret the lateral (meta, para) chlorination associated with PCBs toxicity and environmental impact.

Ability of biomimetic chromatography and physicochemical systems to predict the skin permeation of neutral compounds. A comparison study.

Five in vitro physicochemical systems have been evaluated in terms of its ability to emulate the skin permeation of neutral compounds: the permeation in two different PAMPA membranes, the classical octanol-water partition coefficient, and two biomimetic chromatography systems, one based in cerasome electrokinetic chromatography and another based in reversed-phase liquid chromatography measurements. The coefficients of the solvation parameter model equation of the mentioned systems have been compared to the ones of the skin permeation process through different comparison parameters. Moreover, a method to predict whether a physicochemical system is able to emulate satisfactorily a biological one, just by the analysis of the equation coefficients has been developed. Results reveal that the two PAMPA systems are a good choice to emulate directly the skin permeation of neutral compounds. Instead, the other three systems need a volume correction term to provide a satisfactory emulation. However, after the correction, all the evaluated systems show a similar ability to emulate well skin permeation, as predicted.

Intermolecular Interactions, Solute Descriptors, and Partition Properties of Neutral Per- and Polyfluoroalkyl Substances (PFAS).

The environmental partition properties of perfluoroalkyl and polyfluoroalkyl substances (PFAS) must be understood for their transport and fate analysis. In this study, isothermal gas chromatographic (GC) retention times of 60 neutral PFAS were measured using four columns with different stationary phase polarities, which indicated varying polar interactions exerted by these substances. The GC data were combined with new octanol/water partition coefficient data from this study and existing partition coefficient data from the literature and used to determine the polyparameter linear free energy relationship (PP-LFER) solute descriptors. A complete set of the solute descriptors was obtained for 47 PFAS, demonstrating the characteristic intermolecular interaction properties, such as hydrogen bonding capabilities influenced by the electron-withdrawing perfluoroalkyl group. The partition coefficients between octanol and water, air and water, and octanol and air predicted by the PP-LFER models agreed with those predicted by the quantum chemically based model COSMOtherm, suggesting that both models are highly accurate for neutral PFAS and can fill the current large data gaps in partition property data. A chemical partitioning space plot was generated by using the PP-LFER-predicted partition coefficients, showing the primary importance of the air phase for the environmental distribution of nonpolar and weakly polar PFAS and the increasing significance of organic phases with increasing PFAS polarity.

The effect of descriptor database selection on the physicochemical characterization and prediction of water-air, octanol-air and octanol-water partition constants using the solvation parameter model.

The distribution of neutral compounds in biphasic separation systems can be described by the solvation parameter model using six solute properties, or descriptors. These descriptors (McGowan's characteristic volume, excess molar refraction, dipolarity/polarizability, hydrogen-bond acidity and basicity, and the gas-liquid partition constant on n-hexadecane at 298.15 K) are curated in two publicly accessible databases for hundreds (WSU compound descriptor database) or thousands (Abraham compound descriptor database). These databases were developed independently using different approaches resulting in descriptor values that vary for many compounds. Previously, it was shown that the two descriptor databases are not interchangeable, and the WSU descriptor database consistently demonstrated improved model performance for chromatographic systems where the uncertainty in the dependent variable was minimized by suitable quality control and calibration procedures. In this report we wish to evaluate whether the same conclusions are true for models with a dependent variable containing significant measurement uncertainty. To evaluate this hypothesis, we assembled databases for water-air, octanol-air, and octanol-water partition constants reported by multiple laboratories using various measurement methods. It was found that database selection has little effect on model quality or model predictive capability but significantly affects the assignment of the contribution of individual intermolecular interactions to the dependent variable. The latter information is database specific, and a quantitative comparison of system constants should be restricted to models using the same compound descriptor database.

Cross-Species Evaluation of Bioaccumulation Thresholds for Air-Breathing Animals.

In air-breathing organisms, an organic chemical's susceptibility to elimination via urinary excretion and respiratory exhalation can be judged on the basis of the octanol-water partition ratio (KOW) and the octanol-air partition ratio (KOA), respectively. Current regulations specify that chemicals with KOW values of <102 and KOA values of <105 may be screened as non-bioaccumulative in air breathers. Here we used a model-based approach to evaluate whether these thresholds are consistent with a biomagnification factor of 1 for 141 different mammals, birds, and reptiles. Animals with lower rates of respiration (e.g., manatees and sloths) and those ingesting high-lipid diets (e.g., polar bears and carnivorous birds) were predicted to be able to biomagnify persistent chemicals with KOA values of <105. This was also observed for several temperate reptiles due to their lower respiration rates and internal temperatures. Protective KOA thresholds were determined to be <104.85 for mammals, <104.60 for birds, <104.60 for reptiles at >25 °C, and <103.95 for reptiles at ≤25 °C. For all animals, urination alone was not efficient to prevent the biomagnification of any organic chemical. For chemicals with KOW values of <101, we found that biomagnification of persistent chemicals was constrained by the water-air partition ratio (KWA) rather than KOA. Differences in physiology may need to be considered in bioaccumulation assessments of air-breathing species.

Predicting partitioning from low density polyethylene to blood and adipose tissue by linear solvation energy relationship models.

The variety of polymers utilized in medical devices demands for testing of extractables and leachables according to ISO 10993-18:2020 in combination with ISO 10993-1:2018. The extraction of the materials involves the use of organic solvents as well as aqueous buffers to cover a wide range of polarity and pH-values, respectively. To estimate patient exposure to chemicals leaching from a polymer in direct body contact, simulating solvents are applied to best mimic the solubilization and partitioning behavior of the related tissue or body fluid. Here we apply linear solvation energy relationship (LSER) models to predict blood/water and adipose tissue/water partition coefficients. We suggest this predictive approach to project levels of potential leachables, design extraction experiments, and to identify the optimal composition of simulating extraction solvents. We compare our predictions to LSER predictions for commonly applied surrogates like ethanol/water mixtures, butanol, and octanol as well as olive oil, butanone, 1,4-dioxane for blood and adipose tissue, respectively. We therefore selected a set of 26 experimentally determined blood/water partition coefficients and 33 adipose tissue/water partition coefficients, where we demonstrate that based on the root mean squared error rmse the LSER approach performs better than surrogates like octanol or butanol and equally well as 60:40 ethanol/water for blood. For adipose tissue/water partitioning, the experimentally determined octanol/water partition coefficient performs best but the rmse is at the same range as our LSER approach based on experimentally determined descriptors. Further, we applied our approach for 248 extractables where we calculated blood/low density polyethylene (LDPE) and adipose tissue/LDPE partition coefficients. By this approach, we successfully identified chemicals of potential interest to a toxicological evaluation based on the total risk score.

How Azide Ion/Hydrazoic Acid Passes Through Biological Membranes: An Experimental and Computational Study.

Hydrazoic acid (HN3) and its deprotonated form azide ion (N3-) (AHA) are toxic because they inhibit the cytochrome c oxidase complex IV (CoX IV) embedded in the inner mitochondrial membrane that forms part of the enzyme complexes involved in cellular respiration. Critical to its toxicity is the inhibition of CoX IV in the central nervous system and cardiovascular system. Hydrazoic acid is an ionizable species and its affinity for membranes, and the associated permeabilities, depend on the pH values of aqueous media on both sides of the membranes. In this article, we address the permeability of AHA through the biological membrane. In order to understand the affinity of the membrane for the neutral and ionized form of azide, we measured the octanol/water partition coefficients at pH values of 2.0 and 8.0, which are 2.01 and 0.00034, respectively. Using a Parallel Artificial Membrane Permeability Assay (PAMPA) experiment, we measured the effective permeability through the membrane, which is logPe - 4.97 and - 5.26 for pH values of 7.4 and pH 8.0, respectively. Experimental permeability was used to validate theoretical permeability, which was estimated by numerically solving a Smoluchowski equation for AHA diffusion through the membrane. We demonstrated that the rate of permeation through the cell membrane of 8.46·104 s-1 is much higher than the rate of the chemical step of CoX IV inhibition by azide of 200 s-1. The results of this study show that transport through the membrane does not represent the rate-limiting step and therefore does not control the rate of CoX IV inhibition in the mitochondria. However, the observed dynamics of azide poisoning is controlled by circulatory transport that takes place on a time scale of minutes.

Building Machine Learning Small Molecule Melting Points and Solubility Models Using CCDC Melting Points Dataset.

Predicting solubility of small molecules is a very difficult undertaking due to the lack of reliable and consistent experimental solubility data. It is well known that for a molecule in a crystal lattice to be dissolved, it must, first, dissociate from the lattice and then, second, be solvated. The melting point of a compound is proportional to the lattice energy, and the octanol-water partition coefficient (log P) is a measure of the compound's solvation efficiency. The CCDC's melting point dataset of almost one hundred thousand compounds was utilized to create widely applicable machine learning models of small molecule melting points. Using the general solubility equation, the aqueous thermodynamic solubilities of the same compounds can be predicted. The global model could be easily localized by adding additional melting point measurements for a chemical series of interest.

Lipophilicity Modulations by Fluorination Correlate with Membrane Partitioning.

Bioactive compounds generally need to cross membranes to arrive at their site of action. The octanol-water partition coefficient (lipophilicity, logPOW ) has proven to be an excellent proxy for membrane permeability. In modern drug discovery, logPOW and bioactivity are optimized simultaneously, for which fluorination is one of the relevant strategies. The question arises as to which extent the often subtle logP modifications resulting from different aliphatic fluorine-motif introductions also lead to concomitant membrane permeability changes, given the difference in molecular environment between octanol and (anisotropic) membranes. It was found that for a given compound class, there is excellent correlation between logPOW values with the corresponding membrane molar partitioning coefficients (logKp ); a study enabled by novel solid-state 19 F NMR MAS methodology using lipid vesicles. Our results show that the factors that cause modulation of octanol-water partition coefficients similarly affect membrane permeability.

Using Machine Learning To Predict Partition Coefficient (Log P) and Distribution Coefficient (Log D) with Molecular Descriptors and Liquid Chromatography Retention Time.

During preclinical evaluations of drug candidates, several physicochemical (p-chem) properties are measured and employed as metrics to estimate drug efficacy in vivo. Two such p-chem properties are the octanol-water partition coefficient, Log P, and distribution coefficient, Log D, which are useful in estimating the distribution of drugs within the body. Log P and Log D are traditionally measured using the shake-flask method and high-performance liquid chromatography. However, it is challenging to measure these properties for species that are very hydrophobic (or hydrophilic) owing to the very low equilibrium concentrations partitioned into octanol (or aqueous) phases. Moreover, the shake-flask method is relatively time-consuming and can require multistep dilutions as the range of analyte concentrations can differ by several orders of magnitude. Here, we circumvent these limitations by using machine learning (ML) to correlate Log P and Log D with liquid chromatography (LC) retention time (RT). Predictive models based on four ML algorithms, which used molecular descriptors and LC RTs as features, were extensively tested and compared. The inclusion of RT as an additional descriptor improves model performance (MAE = 0.366 and R2 = 0.89), and Shapley additive explanations analysis indicates that RT has the highest impact on model accuracy.

Octanol/Air Partition Coefficient─A General-Purpose Fragment Model to Predict Log Koa from Molecular Structure.

The octanol/air partition coefficient Koa is important for assessing the bioconcentration of airborne xenobiotics in foliage and in air-breathing organisms. Moreover, Koa informs about compound partitioning to aerosols and indoor dust, and complements the octanol/water partition coefficient Kow and the air/water partition coefficient Kaw for multimedia fate modeling. Experimental log Koa at 25 °C has been collected from literature for 2161 compounds with molecular weights from 16 to 959 Da. The curated data set covers 18.2 log units (from -1.0 to 17.2). A newly developed fragment model for predicting log Koa from molecular structure outperforms COSMOtherm, EPI-Suite KOAWIN, OPERA, and linear solvation energy relationships (LSERs) regarding the root-mean-squared error (rms) and the maximum negative and positive errors (mne and mpe) (rms: 0.57 vs 0.86 vs 1.09 vs 1.19 vs 1.05-1.53, mne: -2.55 vs -3.95 vs -7.51 vs -7.54 vs (-5.63) - (-7.34), mpe: 2.91 vs 5.97 vs 7.54 vs 4.24 vs 6.89-10.2 log units). The prediction capability, statistical robustness, and sound mechanistic basis are demonstrated through initial separation into a training and prediction set (80:20%), mutual leave-50%-out validation, and target value scrambling in terms of temporarily wrong compound-Koa allocations. The new general-purpose model is implemented in a fully automatized form in the ChemProp software available to the public. Regarding Koa indirectly determined through Kow and Kaw, a new approach is developed to convert from wet to dry octanol, enabling higher consistency in experimental (and thus also predicted) Koa.

Nα -Methylation of arginine: Implications for cell-penetrating peptides.

The field of cell-penetrating peptides is dominated by the use of oligomers of arginine residues. Octanol-water partitioning in the presence of an anionic lipid is a validated proxy for cell-penetrative efficacy. Here, we add one, two, or three N-methyl groups to Ac-Arg-NH2 and examine the effects on octanol-water partitioning. In the absence of an anionic lipid, none of these arginine derivatives can be detected in the octanol layer. In the presence of sodium dodecanoate, however, increasing N-methylation correlates with increasing partitioning into octanol, which is predictive of higher cell-penetrative ability. We then evaluated fully Nα -methylated oligoarginine peptides and observed an increase in their cellular penetration compared with canonical oligoarginine peptides in some contexts. These findings indicate that a simple modification, Nα -methylation, can enhance the performance of cell-penetrating peptides.

Modelling the octanol-air partition coefficient of aromatic pollutants based on the solvation free energy and the dimer effect.

In this study, generalized predictive models were developed to estimate KOA of four kinds of aromatic pollutants based on the calculated solvation free energy and taking the dimer effect into account. Uncorrected log KOA values, which were directly estimated from the calculated solvation free energy of individual molecules, underestimated experimental values, and the deviation increased with increasing log KOA. Dimers were found to greatly affect the apparent KOA values of these aromatic pollutants, which were driven by π-π interactions. London dispersion and exchange-repulsion terms were identified to be dominant components of the underlying π-π interactions. It is interesting to find that the π-π interactions of polybrominated diphenyl ethers correlate with not only the molecular polarizability but also the size of opposing aromatic surfaces, which leads to a different trend of π-π interactions from other aromatic pollutants. A universal quantitative structure-activity relationship model was developed to estimate the proportion of dimers based on five molecular structural descriptors relevant to the π-π interactions. After calibration with the dimer effect, estimations of log KOA were consistent with experimental values. Therefore, the dimer effect should be taken into consideration when investigating the partition behavior of aromatic pollutants, and the solvation free energy model could be an alternative method for the prediction of KOA.

Predicting octanol/water partition coefficients and pKa for the SAMPL7 challenge using the SM12, SM8 and SMD solvation models.

Blind predictions of octanol/water partition coefficients and pKa at 298.15 K for 22 drug-like compounds were made for the SAMPL7 challenge. Octanol/water partition coefficients were predicted from solvation free energies computed using electronic structure calculations with the SM12, SM8 and SMD solvation models. Within these calculations we compared the use of gas- and solution-phase optimized geometries of the solute. Based on these calculations we found that in general the use of solution phase-optimized geometries increases the affinity of the solutes for water as compared to octanol, with the use of gas-phase optimized geometries resulting in the better agreement with experiment. The pKa is computed using the direct approach, scaled solvent-accessible surface model, and the inclusion of an explicit water molecule, where the latter two methods have previously been shown to offer improved predictions as compared to the direct approach. We find that the use of an explicit water molecule provides superior predictions, and that the predicted macroscopic pKa is sensitive to the employed microstates.

Exploring the role of octanol-water partition coefficient and Henry's law constant in predicting the lipid-water partition coefficients of organic chemicals.

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.

Bioaccumulation Screening of Neutral Hydrophobic Organic Chemicals in Air-Breathing Organisms Using In Vitro Rat Liver S9 Biotransformation Assays.

To advance methods for bioaccumulation assessment of organic substances in air-breathing organisms, the present study developed an in vitro approach for screening neutral hydrophobic organic substances for their bioaccumulation potential in air-breathing organisms consisting of (1) depletion assays for chemicals in rat liver S9 subcellular fractions, (2) in vitro-in vivo extrapolation, and (3) whole-organism bioaccumulation modeling to assess the biomagnification potential of neutral organic substances in the rat. Testing of the in vitro method on 14 test chemicals of potentially biomagnifying substances showed that the bioassays could be conducted with a high level of reproducibility and that in vitro-derived elimination rate constants were in good agreement with in vivo-determined elimination rate constants in the rat. Exploring the potential of the in vitro approach for screening organic chemicals for bioaccumulation in air-breathing organisms indicated that chemical substances that exhibit a depletion rate constant in the S9 in vitro bioassay ≥0.3 h-1 are not expected to biomagnify in rats independent of their octanol-water partitioning coefficient (KOW ) or octanol-air partitioning coefficient (KOA ). The high level of reproducibility achieved in the test, combined with the good agreement between in vitro-derived and in vivo-determined depuration rates, suggests that the in vitro approach in combination with a KOA - and KOW -based screening approach has good potential for screening chemicals in commerce for their bioaccumulation potential in air-breathing organisms in a cost-effective and expedient manner, especially if the bioassay can be automated. Environ Toxicol Chem 2022;41:2565-2579. © 2022 SETAC.

AlphaLogD determination: An optimized Reversed-Phase Liquid Chromatography method to measure lipophilicity on neutral and basic small and Beyond-Rule-of-Five compounds.

Lipophilicity can be measured with different methods, such as Shake-Flask or liquid chromatography. HPLC presents the advantage of overcoming solubility issues and therefore extending the range of lipophilicity to high values. A specific HPLC method, called ELogD, had been developed 20 years ago on a C16-amide stationary phase, enhancing hydrophobic and hydrogen bond interactions to mimic octanol-water partition. The emergence of novel stationary phases and the need for a less complex mobile phase have led to the development of a new HPLC assay called alphaLogD, applicable to neutral and basic compounds at pH 7.4, that combines superficially porous particles with a high number of equilibriums between solutes and stationary phase, leading to a lower number of isocratic methods to determine the logk'w at a higher throughput. Statistical studies have been run to successfully evaluate the alphaLogD method compared to the Shake-Flask method and to allow this lipophilicity measurement into the so-called Beyond-Rule-of-5-molecules space.

Comparison of logP and logD correction models trained with public and proprietary data sets.

In drug discovery, partition and distribution coefficients, logP and logD for octanol/water, are widely used as metrics of the lipophilicity of molecules, which in turn have a strong influence on the bioactivity and bioavailability of potential drugs. There are a variety of established methods, mostly fragment or atom-based, to calculate logP while logD prediction generally relies on calculated logP and pKa for the estimation of neutral and ionized populations at a given pH. Algorithms such as ClogP have limitations generally leading to systematic errors for chemically related molecules while pKa estimation is generally more difficult due to the interplay of electronic, inductive and conjugation effects for ionizable moieties. We propose an integrated machine learning QSAR modeling approach to predict logD by training the model with experimental data while using ClogP and pKa predicted by commercial software as model descriptors. By optimizing the loss function for the ClogD calculated by the software, we build a correction model that incorporates both descriptors from the software and available experimental logD data. Additionally, we calculate logP from the logD model using the software predicted pKa's. Here, we have trained models using publicly or commercial available logD data to show that this approach can improve on commercial software predictions of lipophilicity. When applied to other logD data sets, this approach extends the domain of applicability of logD and logP predictions over commercial software. Performance of these models favorably compare with models built with a larger set of proprietary logD data.