Compound lipophilicity as a descriptor to predict binding affinity (1/K(m)) in mammals

The utilisation of structural descriptors to predict metabolic constants of xenobiotics in mammals

Quantitative structure-activity relationships (QSARs) were developed to predict the Michaelis-Menten constant (Km) and the maximum reaction rate (Vmax) of xenobiotics metabolised by four enzyme classes in mammalian livers: alcohol dehydrogenase (ADH), aldehyde dehydrogenase (ALDH), flavin-containing monooxygenase (FMO), and cytochrome P450 (CYP). Metabolic constants were gathered from the literature and a genetic algorithm was employed to select at most six predictors from a pool of over 2000 potential molecular descriptors using two-thirds of the xenobiotics in each enzyme class. The resulting multiple linear models were cross-validated using the remaining one-third of the compounds. The explained variances (R(2)adj) of the QSARs were between 50% and 80% and the predictive abilities (R(2)ext) between 50% and 60%, except for the Vmax QSAR of FMO with both R(2)adj and R(2)ext less than 30%. The Vmax values of FMO were independent of substrate chemical structure because the rate-limiting step of its catalytic cycle occurs before compound oxidation. For the other enzymes, Vmax was predominantly determined by functional groups or fragments and electronic properties because of the strong and chemical-specific interactions involved in the metabolic reactions. The most relevant predictors for Km were functional groups or fragments for the enzymes metabolising specific compounds (ADH, ALDH and FMO) and size and shape properties for CYP, likely because of the broad substrate specificity of CYP enzymes. The present study can be helpful to predict the Km and Vmax of four important oxidising enzymes in mammals and better understand the underlying principles of chemical transformation by liver enzymes.

Atomic insights into distinct hormonal activities of Bisphenol A analogues toward PPARγ and ERα receptors

Bisphenol A analogues (BPAs) belong to a wide variety of large volume chemicals with diverse applications yet emerging environmental concerns. Limited experimental data have demonstrated that BPAs with different halogenation patterns distinctly affect the agonistic activities toward proliferator-activated receptor (PPAR)γ and estrogen receptors (ER)α. Understanding the modes of action of BPAs toward different receptors is essential, however, the underlying molecular mechanism is still poorly understood. Here we probed the molecular recognition process of halogenated BPAs including TBBPA, TCBPA, BPAF, BPC, triBBPA, diBBPA, and monoBBPA toward PPARγ and ERα by molecular modeling, especially the impact of different halogen patterns. Increasing bromination at phenolic rings of BPAs was found highly correlated with electrostatic interactions (R(2) = 0.978 and 0.865 toward PPARγ and ERα, respectively) and van der Waals interactions (R(2) = 0.995 and 0.994 toward PPARγ and ERα, respectively). More halogenated phenolic rings at 3,5-positions of BPAs increase the shielding of the hormonally active phenolic OH and markedly decrease electrostatic interactions favorable for agonistic activities toward PPARγ, but unfavorable for agonistic activities toward ERα. The halogenation at the phenolic rings of BPAs exerts more impact on molecular electrostatic potential distribution than halogenation at the bridging alkyl moiety. Different halogenations further alter hydrogen bond interactions of BPAs and induce conformational changes of PPARγ ligand binding domain (LBD) and ERα LBD, specifically affecting the stabilization of helix H12 attributable to the different agonistic activities. Our results indicate that structural variations in halogenation patterns result in different interactions of BPAs with PPARγ LBD and ERα LBD, potentially causing distinct agonistic/antagonistic toxic effects. The various halogenation patterns should be fully considered for the design of future environmentally benign chemicals with reduced toxicities and desired properties.

Mechanistically-based QSARs to describe metabolic constants in mammals

Biotransformation is one of the processes which influence the bioaccumulation of chemicals. The enzymatic action of metabolism involves two processes, i.e. the binding of the substrate to the enzyme followed by a catalytic reaction, which are described by the Michaelis-Menten constant (Km) and the maximum rate (Vmax). Here, we developed Quantitative Structure-Activity Relationships (QSARs) for Log(1/Km) and LogVmax for substrates of four enzyme classes. We focused on oxidations catalysed by alcohol dehydrogenase (ADH), aldehyde dehydrogenase (ALDH), flavin-containing monooxygenase (FMO) and cytochrome P450 (CYP) in mammals. The chemicals investigated were xenobiotics, including alcohols, aldehydes, pesticides and drugs. We applied general linear models for this purpose, employing descriptors related to partitioning, geometric characteristics, and electronic properties of the substrates, which can be interpreted mechanistically. The explained variance of the QSARs varied between 20% and 70%, and it was larger for Log(1/Km) than for LogVmax. The increase of 1/Km with compound logP and size suggests that weak interactions are important, e.g. by substrate binding via desolvation processes. The importance of electronic factors for 1/Km was described in relation to the catalytic mechanism of the enzymes. Vmax was particularly influenced by electronic properties, such as dipole moment and energy of the lowest unoccupied molecular orbital. This can be explained by the nature of the catalysis, characterised by the cleavage and formation of covalent or ionic bonds (strong interactions). The present study may be helpful to understand the underlying principles of the chemical specific activity of four important oxidising enzymes.