On the role of polarizability in QSAR

First report on chemometrics-driven multilayered lead prioritization in addressing oxysterol-mediated overexpression of G protein-coupled receptor 183

Contemporary research has convincingly demonstrated that upregulation of G protein-coupled receptor 183 (GPR183), orchestrated by its endogenous agonist, 7α,25-dihydroxyxcholesterol (7α,25-OHC), leads to the development of cancer, diabetes, multiple sclerosis, infectious, and inflammatory diseases. A recent study unveiled the cryo-EM structure of 7α,25-OHC bound GPR183 complex, presenting an untapped opportunity for computational exploration of potential GPR183 inhibitors, which served as our inspiration for the current work. A predictive and validated two-dimensional QSAR model using genetic algorithm (GA) and multiple linear regression (MLR) on experimental GPR183 inhibition data was developed. QSAR study highlighted that structural features like dissimilar electronegative atoms, quaternary carbon atoms, and CH2RX fragment (X: heteroatoms) influence positively, while the existence of oxygen atoms with a topological separation of 3, negatively affects GPR183 inhibitory activity. Post assessment of true external set prediction capability, the MLR model was deployed to screen 12,449 DrugBank compounds, followed by a screening pipeline involving molecular docking, druglikeness, ADMET, protein-ligand stability assessment using deep learning algorithm, molecular dynamics, and molecular mechanics. The current findings strongly evidenced DB05790 as a potential lead for prospective interference of oxysterol-mediated GPR183 overexpression, warranting further in vitro and in vivo validation.

Integrating molecular descriptors for enhanced prediction: Shedding light on the potential of pH to model hydrated electron reaction rates for organic compounds

Hydrated electron reaction rate constant (ke-aq) is an important parameter to determine reductive degradation efficiency and to mitigate the ecological risk of organic compounds (OCs). However, OC species morphology and the concentration of hydrated electrons (e-aq) in water vary with pH, complicating OC fate assessment. This study introduced the environmental variable of pH, to develop models for ke-aq for 701 data points using 3 descriptor types: (i) molecular descriptors (MD), (ii) quantum chemical descriptors (QCD), and (iii) the combination of both (MD + QCD). Models were screened using 2 descriptor screening methods (MLR and RF) and 14 machine learning (ML) algorithms. The introduction of QCDs that characterized the electronic structure of OCs greatly improved the performance of models while ensuring the need for fewer descriptors. The optimal model MLR-XGBoost(MD + QCD), which included pH, achieved the most satisfactory prediction: R2tra = 0.988, Q2boot = 0.861, R2test = 0.875 and Q2test = 0.873. The mechanistic interpretation using the SHAP method further revealed that QCDs, polarizability, volume, and pH had a great influence on the reductive degradation of OCs by e-aq. Overall, the electrochemical parameters (QCDs, pH) related to the solvent and solute are of significance and should be considered in any future ML modeling that assesses the fate of OCs in aquatic environment.

Making the Case for Quantum Mechanics in Predictive Toxicology─Nearly 100 Years Too Late?

The use of quantum mechanics (QM) has long been the norm to study covalent-binding phenomena in chemistry and biochemistry. The pharmaceutical industry leverages QM models explicitly in covalent drug discovery and implicitly to characterize short-range interactions in noncovalent binding. Predictive toxicology has resisted widespread adoption of QM, including in the pharmaceutical industry, despite its obvious relevance to the metabolic processes in the upstream of adverse outcome pathways and advances in both QM methods and computational resources, which support fit-for-purpose applications in reasonable timeframes. Here, we make the case for embracing QM as an indispensable part of a toxicologist's toolkit. We argue that QM provides the necessary orthogonality to alert-based expert systems and traditional QSARs, consistent with calls for animal-free integrated testing strategies for safety assessments of commercial chemicals. We outline existing roadblocks to this transition, including the need to train model developers in QM and the shift toward service-based toxicity models that utilize high-performance computing clusters. Lastly, we describe recent examples of successful implementations of QM in hazard assessments and propose how in silico toxicology can be further advanced by integrating QM with artificial intelligence.

Theoretical investigation on hydrolysis mechanism of cis-platin analogous Pt(II)/Pd(II) complex by DFT calculation and molecular docking approach for their interaction with DNA & HSA

The properties to be an active drug candidate of the complex Pt(TEEDA)Cl₂, C1; Pd(TEEDA)Cl₂, C2 and their hydrolysed product [Pt(TEEDA)(OH₂)₂]²⁺, C1' and [Pd(TEEDA)(OH₂)₂]²⁺, C2' were predicted by Lipinski's rule of 5 and PASS (prediction of activity spectra for substances) web tool. Their structural profile, HOMO-LUMO energy and electronic potential surface ware analysed by DFT calculation. Their TD-DFT spectra were compared with experimental UV-Vis spectra. The hydrolysis mechanisms of C1 & C2 to the diaqua form C1' and C2' were extensively investigated by DFT method in different levels of theory and using CPCM/water model and compared with recognised Pt based anticancer drugs. All the stationary states, including the transition state for the reactions were identified by the DFT calculation. The IRC calculation confirmed that the transition states are well connected and corelate with reactants and products. Interaction of the complexes with DNA & HSA was also investigated by molecular docking study.

The study on QSAR and relations between molecular descriptors of 5, 8-quinoline quinones derivatives

Abstract In this study, some electronic, hydrophobic and thermochemical parameters of 28 different 5,8-quinolinequinone derivatives having diverse substituents have been calculated by using DFT (B3LYP)/6-31G(d, p) method and basis set. Relationships between different molecular descriptors have been studied by using molecular polarizability (α), dipole moment (μ), EHOMO, ELUMO, molecular volume (Vm), ionization potential (IP), electron affinity (EA), electronegativity (χ), molecular hardness (η), molecular softness (S), electrophilic index (ω), molar refractivity (MR), octanol–water partition coefficient (log P), thermochemical properties (entropy (Se), capacity of heat (C)); as to investigate activity relationships with molecular structure. In addition, the QSAR/QSPR between molecular properties and biological activity (anti-proliferative and anti-inflammatory activity) has been investigated, where R, R2, F, P and RMSE have taken into account in order to find a statistically correct model in QSAR studies. The dependence of the electronegativity parameter on both electronic and thermochemical parameters is found to be the most correlated parameter.

Insight into the structural requirements of gelatinases (MMP-2 and MMP-9) inhibitors by multiple validated molecular modelling approaches: Part II

Inhibition of the matrix metalloproteinases (MMPs) is effective against metastasis of secondary tumours. Previous MMP inhibitors have failed in clinical trials due to their off-target toxicity in solid tumours. Thus, newer MMP inhibitors now have paramount importance. Here, different molecular modelling techniques were applied on a dataset of 110 gelatinase (MMP-2 and MMP-9) inhibitors. The objectives of the present study were to identify structural fingerprints for gelatinase inhibition and also to develop statistically validated QSAR models for the screening and prediction of different derivatives as MMP-2 (gelatinase A) and MMP-9 (gelatinase B) inhibitors. The Bayesian classification study provided the ROC values for the training set of 0.837 and 0.815 for MMP-2 and MMP-9, respectively. The linear model also produced the leave-one-out cross-validated Q² of 0.805 (eq. 1, MMP-2) and 0.724 (eq. 2, MMP-9), an r² of 0.845 (eq. 1, MMP-2) and 0.782 (eq. 2, MMP-9), an r²_Pred of 0.806 (eq. 1, MMP-2) and 0.732 (eq. 2, MMP-9). Similarly, non-linear learning models were also statistically significant and reliable. Overall, this study may help in the rational design of newer compounds with higher gelatinase inhibition to fight against both primary and secondary cancers in future.

Computational investigation of interaction between titanocene dichloride and nanoclusters (B12N12, B12P12, Al12N12 and Al12P12)

This study investigated the interactions between B12N12, B12P12, Al12N12 and Al12P12 nanoclusters and titanocene dichloride anticancer drug complex using B3P86 functional. The bonding interaction between the nano-clusters and anticancer drug were examined through energy decomposition analysis (EDA). A good quadratic equation between interaction energy and molar volume (Vm) were provided. Charge transfer between fragments were illustrated with electrophilicity-based charge transfer (ECT). According to calculations, the values of heat of formation of the studied systems were negative (exothermic), which shows that these molecules are thermodynamically stable. The relationship between molar refractivity (MR) and Vm presented linear correlation.

QSPR Models for the Molar Refraction, Polarizability and Refractive Index of Aliphatic Carboxylic Acids Using the ZEP Topological Index

The molar refraction, polarizability, and refractive index for a series of monocarboxylic, dicarboxylic, and unsaturated monocarboxylic acids, having a symmetric or asymmetric structure, were investigated by the application of quantitative structure property relationship (QSPR) technique. We used a linear regression method and a single molecular descriptor, the ZEP topological index, calculated in a simple manner, with the help of weighted electronic distances, and also calculated on the basis of the chemical structure of the molecules. The high-quality performance and predictive ability of the QSPR models obtained were validated by means of specific validation techniques: y-randomization test, the leave-one-out cross validation procedure, and external validation. The investigated properties are well modeled (with r2 > 0.99) by the ZEP index, using the regression analysis as a statistical tool for developing reliable QSPR models. Our approach provides an alternative technique to the existing additive methods for predicting the molar refraction and polarizability of carboxylic acids, which is essentially based on the summation of atom and/or functional group contributions or bond contributions, and of some correction increments.

Insight into the structural requirement of aryl sulphonamide based gelatinases (MMP-2 and MMP-9) inhibitors - Part I: 2D-QSAR, 3D-QSAR topomer CoMFA and Naïve Bayes studies - First report of 3D-QSAR Topomer CoMFA analysis for MMP-9 inhibitors and jointly inhibitors of gelatinases together

Gelatinases [gelatinase A - matrix metalloproteinase-2 (MMP-2), gelatinase B - matrix metalloproteinase-9 (MMP-9)] play key roles in many disease conditions including cancer. Despite some research work on gelatinases inhibitors both jointly and individually had been reported, challenges still exist in achieving potency as well as selectivity. Here in part I of a series of work, we have reported the structural requirement of some arylsulfonamides. In particular, regression-based 2D-QSARs, topomer CoMFA (comparative molecular field analysis) and Bayesian classification models were constructed to refine structural features for attaining better gelatinase inhibitory activity. The 2D-QSAR models exhibited good statistical significance. The descriptors nsssN, SHBint6, SHBint7, PubchemFP629 were directly correlated with the MMP-2 binding affinities whereas nsssN, SHBint10 and AATS2i were directly proportional to MMP-9 binding affinities. The topomer CoMFA results indicated that the steric and electrostatic fields play key roles in gelatinase inhibition. The established Naïve Bayes prediction models were evaluated by fivefold cross validation and an external test set. Furthermore, important molecular descriptors related to MMP-2 and MMP-9 binding affinities and some active/inactive fragments were identified. Thus, these observations may be helpful for further work of aryl sulphonamide based gelatinase inhibitors in future.

Molecular structure and quantum descriptors of cefradine by using vibrational spectroscopy (IR and Raman), NBO, AIM, chemical reactivity and molecular docking

This study aims to investigate the structural and vibrational features of cefradine (the first-generation cephalosporin antibiotic) based on spectroscopic experiments and theoretical quantum chemical approach. The fundamental structural aspects of cefradine have been examined based on optimized geometry, spectroscopic behavior, intermolecular interaction, chemical reactivity, intramolecular hydrogen bonding, and molecular docking analysis. The most stable minimum energy conformer of the title molecule was identified by performing a one-dimensional potential energy surface scan along the rotational bonds at B3LYP/6-311++G (d,p) level of theory. The vibrational features of the molecule and information about the coupled modes were predicted. The chemical reactivity and stability of all the possible conformers of cefradine were estimated based on the HOMO-LUMO energy gap and NBO approach. The overall picture of accumulation of charges on individual atoms of the molecule was predicted by molecular electrostatic potential (MEP) surface map which in turn identifies the nucleophilic and electrophilic region or sites. The quantitative analysis of electrophilicity and nucleophilicity indices was done by Hirshfeld charge analysis and it was found that N8 atom is the most prominent site for nucleophilic attack while C14 atom is feasible for electrophilic attack. QTAIM study has also been performed to investigate the nature and strength of hydrogen bonding interactions. Besides, molecular docking studies were performed to examine the active binding residues of the target.

Nitrofurantoin-melamine monohydrate (cocrystal hydrate): Probing the role of H-bonding on the structure and properties using quantum chemical calculations and vibrational spectroscopy

Cocrystal monohydrate of nitrofurantoin (NF) with melamine (MELA) has been studied as NF is an antibacterial drug used for the treatment of urinary tract infections. The structure of nitrofurantoin-melamine-monohydrate (NF-MELA-H₂O) is characterized by FT-IR and FT-Raman spectroscopy. The energies and vibrational frequencies of the optimized structures calculated using quantum chemical calculations. Supported by normal coordinate analyses and potential energy distributions (PEDs), the complete vibrational assignments recommended for the observed fundamentals of cocrystal hydrate. With the aim of inclusion of all the H-bond interactions, dimer of NF-MELA-H₂O has been studied as only two molecules of cocrystal hydrate are present in the unit cell. By the study of dimeric model consistent assignment of the FT-IR and FT-Raman spectrum obtained. H-bonds are of essential importance in an extensive range of molecular sciences. The vibrational analyses depict existence of H-bonding (O-H⋯N) between water O-H and pyridyl N atom of MELA in both monomer and dimer. To probe the strength and nature of H-bonding in monomer and dimer, topological parameters such as electron density (ρ_BCP), Laplacian of electron density (∇²ρ_BCP), total electron energy density (H_BCP) and H-bond energy (E_HB) at bond critical points (BCP) are evaluated by quantum theory of atoms in molecules (QTAIM). Natural bond orbitals (NBOs) analyses are carried out to study especially the intra and intermolecular H-bonding and their second order stabilization energy (E⁽²⁾). The value of HOMO-LUMO energy band gap for NF-MELA-H₂O (monomer and dimer both) is less than NF, showing more chemical reactivity for NF-MELA-H₂O. Chemical reactivity has been described with the assistance of electronic descriptors. Global electrophilicity index (ω = 7.3992 eV) shows that NF-MELA-H₂O behaves as a strong electrophile than NF. The local reactivity descriptors analyses such as Fukui functions, local softnesses and electrophilicity indices performed to determine the reactive sites within NF-MELA-H₂O. In MEP map of NF-MELA (monomer and dimer) electronegative regions are about NO₂ and C=O group of NF, although the electropositive regions are around NH₂, N-H group and H₂O molecule. Molar refractivity (MR) value of NF-MELA-H₂O (monomer and dimer) lies within the range set by Lipinski's modified rules. This study could set as an example to study the H-bond interactions in pharmaceutical cocrystals.

Quantitative Structure-Activity Relationship Study of Camptothecin Derivatives as Anticancer Drugs Using Molecular Descriptors

Aim and Objective:

A Quantitative Structure-Activity Relationship (QSAR) has been widely developed to derive a correlation between chemical structures of molecules to their known activities. In the present investigation, QSAR models have been carried out on 76 Camptothecin (CPT) derivatives as anticancer drugs to develop a robust model for the prediction of physicochemical properties.

Materials and Methods:

A training set of 60 structurally diverse CPT derivatives was used to construct QSAR models for the prediction of physiochemical parameters such as Van der Waals surface area (SvdW), Van der Waals Volume (VvdW), Molar Refractivity (MR) and Polarizability (α). The QSAR models were optimized using Multiple Linear Regression (MLR) analysis. A test set of 16 compounds was evaluated using the defined models.

:

The Genetic Algorithm And Multiple Linear Regression Analysis (GA-MLR) were used to select the descriptors derived from the Dragon software to generate the correlation models that relate the structural features to the studied properties.

Results:

QSAR models were used to delineate the important descriptors responsible for the properties of the CPT derivatives. The statistically significant QSAR models derived by GA-MLR analysis were validated by Leave-One-Out Cross-Validation (LOOCV) and test set validation methods. The multicollinearity and autocorrelation properties of the descriptors contributed in the models were tested by calculating the Variance Inflation Factor (VIF) and the Durbin–Watson (DW) statistics.

Conclusion:

The predictive ability of the models was found to be satisfactory. Thus, QSAR models derived from this study may be helpful for modeling and designing some new CPT derivatives and for predicting their activity.

Study of molecular structure and hydrogen bond interactions in dipfluzine-benzoic acid (DIP-BEN) cocrystal using spectroscopic and quantum chemical method

The purpose of this article is to predict the molecular structure of the cocrystal of dipfluzine-benzoic acid (DIP-BEN) through computational approach (DFT calculations) and validate it using vibrational spectroscopic studies. The molecular structure of the DIP-BEN cocrystal has been predicted by forming models on the basis of the active sites available to form H-bonds between dipfluzine (DIP) and benzoic acid (BEN). Conformational study has been performed and potential energy surface scans are plotted around the flexible bonds of the cocrystal molecule and three stable conformers have been obtained. Quantum theory of atoms in molecules (QTAIM) explains that all the interactions are medium and partially covalent in nature. Natural bond orbital analysis of the second order perturbation theory of the Fock matrix suggests that interactions LP (2) O2 → σ*(O74H75) and LP (2) F1 → σ* (O89H90) are responsible for the stabilization of the molecule. The HOMO and LUMO energies and electronic charge transfer (ECT) confirms that charge flows from BEN to DIP. Global reactivity descriptor parameters suggest that DIP-BEN cocrystal is softer, thus more reactive in comparison to DIP. Local reactivity descriptor parameter is used to predict reactive sites of the cocrystal. The experimental and theoretical results support the formation of cocrystal through strong hydrogen bond (O89H90⋯F1 and O74H75⋯O2) interactions present in cocrystal.

Spectroscopic (FT-IR, FT-Raman, and 13C SS-NMR) and quantum chemical investigations to provide structural insights into nitrofurantoin–4-hydroxybenzoic acid cocrystals

Non-covalent interactions contribute considerably to the stability of cocrystals and have appreciable effects on their molecular geometry as well.

Vibrational spectra, hydrogen bonding interactions and chemical reactivity analysis of nicotinamide–citric acid cocrystals by an experimental and theoretical approach

The hydrogen bond interactions in the cocrystal lead to spatial arrangements enhancing the physicochemical properties.

The Internal Relation between Quantum Chemical Descriptors and Empirical Constants of Polychlorinated Compounds

Quantum chemical descriptors and empirical parameters are two different types of chemical parameters that play the fundamental roles in chemical reactivity and model development. However, previous studies have lacked detail regarding the relationship between quantum chemical descriptors and empirical constants. We selected polychlorinated biphenyls (PCBs) as an object to investigate the intrinsic correlation between 16 quantum chemical descriptors and Hammett constants. The results exhibited extremely high linearity for ∑ with Qxx/yy/zz, α and EHOMO based on the meta-position grouping. Polychlorinated dibenzodioxins (PCDDs) and polychlorinated naphthalenes (PCNs) congeners, as two independent compounds, validated the reliability of the relationship. The meta-substituent grouping method between ∑ and α was successfully used to predict the rate constant (k) for •OH oxidation of PCBs, as well as the octanol/water partition coefficient (logKOW) and aqueous solubility (-logSW) of PCDDs, and exhibited excellent agreement with experimental measurements. Revealing the intrinsic correlation underlying the empirical constant and quantum chemical descriptors can develop simpler and higher efficient model application in predicting the environmental behavior and chemical properties of compounds.

A novel model to predict gas-phase hydroxyl radical oxidation kinetics of polychlorinated compounds

In this study, a novel model based on aromatic meta-substituent grouping was presented to predict the second-order rate constants (k) for OH oxidation of PCBs in gas-phase. Since the oxidation kinetics are dependent on the chlorination degree and position, we hypothesized that it may be more accurate for k value prediction if we group PCB congeners based on substitution positions (i.e., ortho (o), meta (m), and para (p)). To test this hypothesis, we examined the correlation of polarizability (α), a quantum chemical based descriptor for k values, with an empirical Hammett constant (σ⁺) on each substitution position. Our result shows that α is highly linearly correlated to ∑σ_o,m,p⁺ based on aromatic meta-substituents leading to the grouping based predictive model. With the new model, the calculated k values exhibited an excellent agreement with experimental measurements, and greater predictive power than the quantum chemical based quantitative structure activity relationship (QSAR) model. Further, the relationship of α and ∑σ_o,m,p⁺ for PCDDs congeners, together with highest occupied molecular orbital (HOMO) distribution, were used to validate the aromatic meta-substituent grouping method. This newly developed model features a combination of good predictability of quantum chemical based QSAR model and simplicity of Hammett relationship, showing a great potential for fast and computational tractable prediction of k values for gas-phase OH oxidation of polychlorinated compounds.

Study of molecular interactions and chemical reactivity of the nitrofurantoin–3-aminobenzoic acid cocrystal using quantum chemical and spectroscopic (IR, Raman, 13C SS-NMR) approaches

Inquiries of structural reactivity, molecular interactions and vibrational characterization of drugs are essential in understanding their behaviour.

Study of molecular structure, chemical reactivity and H-bonding interactions in the cocrystal of nitrofurantoin with urea

The cocrystal of nitrofurantoin with urea (C₈H₆N₄O₅)·(CH₄N₂O), a non-ionic supramolecular complex, has been studied.

Rate constants of hydroxyl radical oxidation of polychlorinated biphenyls in the gas phase: A single-descriptor based QSAR and DFT study

The second-order rate constants (k) of hydroxyl radical (·OH) with polychlorinated biphenyls (PCBs) in the gas phase are of scientific and regulatory importance for assessing their global distribution and fate in the atmosphere. Due to the limited number of measured k values, there is a need to model the k values for unknown PCBs congeners. In the present study, we developed a quantitative structure-activity relationship (QSAR) model with quantum chemical descriptors using a sequential approach, including correlation analysis, principal component analysis, multi-linear regression, validation, and estimation of applicability domain. The result indicates that the single descriptor, polarizability (α), plays an important role in determining the reactivity with a global standardized function of lnk = -0.054 × α ‒ 19.49 at 298 K. In order to validate the QSAR predicted k values and expand the current k value database for PCBs congeners, an independent method, density functional theory (DFT), was employed to calculate the kinetics and thermodynamics of the gas-phase ·OH oxidation of 2,4',5-trichlorobiphenyl (PCB31), 2,2',4,4'-tetrachlorobiphenyl (PCB47), 2,3,4,5,6-pentachlorobiphenyl (PCB116), 3,3',4,4',5,5'-hexachlorobiphenyl (PCB169), and 2,3,3',4,5,5',6-heptachlorobiphenyl (PCB192) at 298 K at B3LYP/6-311++G**//B3LYP/6-31 + G** level of theory. The QSAR predicted and DFT calculated k values for ·OH oxidation of these PCB congeners exhibit excellent agreement with the experimental k values, indicating the robustness and predictive power of the single-descriptor based QSAR model we developed.

Molecular structure, spectral analysis and hydrogen bonding analysis of ampicillin trihydrate: a combined DFT and AIM approach

The optimized structure and active sites of ampicillin trihydrate calculated using monomeric and dimeric models.

Uptake and translocation of organophosphates and other emerging contaminants in food and forage crops

Emerging contaminants in wastewater and sewage sludge spread on agricultural soil can be transferred to the human food web directly by uptake into food crops or indirectly following uptake into forage crops. This study determined uptake and translocation of the organophosphates tris(1-chloro-2-propyl) phosphate (TCPP) (log Kow 2.59), triethyl-chloro-phosphate (TCEP) (log Kow 1.44), tributyl phosphate (TBP) (log Kow 4.0), the insect repellent N,N-diethyl toluamide (DEET) (log Kow 2.18) and the plasticiser N-butyl benzenesulfonamide (NBBS) (log Kow 2.31) in barley, wheat, oilseed rape, meadow fescue and four cultivars of carrot. All species were grown in pots of agricultural soil, freshly amended contaminants in the range of 0.6-1.0 mg/kg dry weight, in the greenhouse. The bioconcentration factors for root (RCF), leaf (LCF) and seed (SCF) were calculated as plant concentration in root, leaf or seed over measured initial soil concentration, both in dry weight. The chlorinated flame retardants (TCEP and TCPP) displayed the highest bioconcentration factors for leaf and seed but did not show the same pattern for all crop species tested. For TCEP, which has been phased out due to toxicity but is still found in sewage sludge and wastewater, LCF was 3.9 in meadow fescue and 42.3 in carrot. For TCPP, which has replaced TCEP in many products and also occurs in higher residual levels in sewage sludge and wastewater, LCF was high for meadow fescue and carrot (25.9 and 17.5, respectively). For the four cultivars of carrot tested, the RCF range for TCPP and TCEP was 10-20 and 1.7-4.6, respectively. TCPP was detected in all three types of seeds tested (SCF, 0.015-0.110). Despite that DEET and NBBS have log Kow in same range as TCPP and TCEP, generally lower bioconcentration factors were measured. Based on the high translocation of TCPP and TCEP to leaves, especially TCPP, into meadow fescue (a forage crop for livestock animals), ongoing risk assessments should be conducted to investigate the potential effects of these compounds in the food web.

Assessment of hydroxylated metabolites of polychlorinated biphenyls as potential xenoestrogens: a QSAR comparative analysis∗

Alternative methods, including quantitative structure-activity relationships (QSAR), are being used increasingly when appropriate data for toxicity evaluation of chemicals are not available. Approximately 40 mono-hydroxylated polychlorinated biphenyls (OH-PCBs) have been identified in humans. They represent a health and environmental concern because some of them have been shown to have agonist or antagonist interactions with human hormone receptors. This could lead to modulation of steroid hormone receptor pathways and endocrine system disruption. We performed QSAR analyses using available estrogenic activity (human estrogen receptor ER alpha) data for 71 OH-PCBs. The modelling was performed using multiple molecular descriptors including electronic, molecular, constitutional, topological, and geometrical endpoints. Multiple linear regressions and recursive partitioning were used to best fit descriptors. The results show that the position of the hydroxyl substitution, polarizability, and meta adjacent un-substituted carbon pairs at the phenolic ring contribute towards greater estrogenic activity for these chemicals. These comparative QSAR models may be used for predictive toxicity, and identification of health consequences of PCB metabolites that lack empirical data. Such information will help prioritize such molecules for additional testing, guide future basic laboratory research studies, and help the health/risk assessment community understand the complex nature of chemical mixtures.

A systematic investigation of quaternary ammonium ions as asymmetric phase-transfer catalysts. Application of quantitative structure activity/selectivity relationships

Although the synthetic utility of asymmetric phase-transfer catalysis continues to expand, the number of proven catalyst types and design criteria remains limited. At the origin of this scarcity is a lack in understanding of how catalyst structural features affect the rate and enantioselectivity of phase transfer catalyzed reactions. Described in this paper is the development of quantitative structure-activity relationships (QSAR) and -selectivity relationships (QSSR) for the alkylation of a protected glycine imine with libraries of quaternary ammonium ion catalysts. Catalyst descriptors including ammonium ion accessibility, interfacial adsorption affinity, and partition coefficient were found to correlate meaningfully with catalyst activity. The physical nature of the descriptors was rationalized through differing contributions of the interfacial and extraction mechanisms to the reaction under study. The variation in the observed enantioselectivity was rationalized employing a comparative molecular field analysis (CoMFA) using both the steric and electrostatic fields of the catalysts. A qualitative analysis of the developed model reveals preferred regions for catalyst binding to afford both configurations of the alkylated product.