In addition to the solubilization of poorly water-soluble, highly lipophilic drugs, lipid nanoemulsions bear potential for drug targeting approaches. This requires that the drug remains within the emulsion droplets until they reach the site of action. Since drug release is rather controlled by the lipophilicity of the drug than by the formulation, this study systematically investigated the influence of drug lipophilicity on the course of drug transfer in (physiological) acceptor media. An increase in drug lipophilicity, according to ClogD/P values, was achieved by the formation of lipophilic prodrugs of 5-phenylanthranilic acid - a potential pathoblocker. The range of substances was supplemented by orlistat, lumefantrine and cholesteryl acetate as model drugs. Drug transfer from supercooled trimyristin nanodroplets was determined via differential scanning calorimetry by monitoring their onset crystallization temperature, which decreases linearly with increasing drug content. Release of the model (pro)drugs ranged from burst to hardly any release in the order of the ClogD/P values. Except for cholesteryl acetate, the results were in line with the lipophilicity of the model (pro)drugs estimated by their retention times on a reversed-phase HPLC column under isocratic conditions. An approximate prediction of drug release kinetics was, thus, possible by logP calculations and, to a limited extent, also by reversed-phase HPLC. A further finding was the increased drug loading capacity of the lipid nanoemulsion for lipophilic prodrugs, if the structural changes of the parent compound were accompanied by a lower melting point.

OBJECTIVE: The goal was to assess, for lipophilic drugs, the impact of logP on human volume of distribution at steady-state (VDss) predictions, including intermediate fut and Kp values, from six methods: Oie-Tozer, Rodgers-Rowland (tissue-specific Kp and only muscle Kp), GastroPlus, Korzekwa-Nagar, and TCM-New.
METHODS: A sensitivity analysis with focus on logP was conducted by keeping pKa and fup constant for each of four drugs, while varying logP. VDss was also calculated for the specific literature logP values. Error prediction analysis was conducted by analyzing prediction errors by source of logP values, drug, and overall values.
RESULTS: The Rodgers-Rowland methods were highly sensitive to logP values, followed by GastroPlus and Korzekwa-Nagar. The Oie-Tozer and TCM-New methods were only modestly sensitive to logP. Hence, the relative performance of these methods depended upon the source of logP value. As logP values increased, TCM-New and Oie-Tozer were the most accurate methods. TCM-New was the only method that was accurate regardless of logP value source. Oie-Tozer provided accurate predictions for griseofulvin, posaconazole, and isavuconazole; GastroPlus for itraconazole and isavuconazole; Korzekwa-Nagar for posaconazole; and TCM-New for griseofulvin, posaconazole, and isavuconazole. Both Rodgers-Rowland methods provided inaccurate predictions due to the overprediction of VDss.
CONCLUSIONS: TCM-New was the most accurate prediction of human VDss across four drugs and three logP sources, followed by Oie-Tozer. TCM-New showed to be the best method for VDss prediction of highly lipophilic drugs, suggesting BPR as a favorable surrogate for drug partitioning in the tissues, and which avoids the use of fup.

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.

The antimicrobial quantitative structure-activity relationship of plant flavonoids against Gram-positive bacteria was established in our previous works, and the cell membrane was confirmed as a major site of action. To investigate whether plant flavonoids have similar antibacterial effects and mechanisms against both Gram-negative and Gram-positive bacteria, here, the minimum inhibitory concentrations (MICs) of 37 plant flavonoids against Escherichia coli were determined using the microdilution broth method, and then the correlation between their lipophilic parameter ACD/LogP or LogD7.40 value and their MIC was analyzed. Simultaneously, the correlation between the ACD/LogP or LogD7.40 value and the MIC of 46 plant flavonoids reported in the literature against E. coli was also analyzed. Both sets of results showed that there is a significant correlation between the LogP value and the MIC of plant flavonoids against Gram-negative bacteria. However, it is difficult to effectively predict the MIC of plant flavonoids against Gram-negative bacteria from their lipophilic parameters. By comparing two regression curves derived from plant flavonoids against Gram-negative and Gram-positive bacteria, it was further discovered that the antibacterial activities of most plant flavonoids against Gram-negative bacteria are stronger than those against Gram-positive bacteria when their LogP values are less than approximately 3.0, but the opposite is true when their LogP values are more than approximately 3.6. Moreover, this comparison also suggests that unlike mainly acting on the cell membrane of Gram-positive bacteria, plant flavonoids have multiple mechanisms against Gram-negative species, while the cell membrane is also an important action site among them. Combined with the correlation analyses between the enzyme inhibitory activity and the LogP value of the reported flavonoids, it was further suggested that DNA gyrase is another important target of plant flavonoids against Gram-negative bacteria.

Bisphenol A (BPA) is a widely used plastic material, but its potential endocrine disrupting effect has restricted its use. The BPA alternatives have raised concerns. This study aimed to compare inhibitory potencies of 11 BPA analogues on human and rat placental aromatase (CYP19A1). The inhibitory potency on human CYP19A1 ranged from bisphenol H (IC50, 0.93 μM) to tetramethyl BPA and tetrabromobisphenol S (ineffective at 100 μM) when compared to BPA (IC50, 73.48 μM). Most of them were mixed/competitive inhibitors and inhibited estradiol production in human BeWo cells. Molecular docking analysis showed all BPA analogues bind to steroid active site or in between steroid and heme of CYP19A1 and form a hydrogen bond with catalytic residue Met374. Pharmacophore analysis showed that there were 4 hydrophobic regions for BPA analogues, with bisphenol H occupying 4 regions. Bivariate correlation analysis showed that LogP (lipophilicity) and LogS (water solubility) of BPA analogues were correlated with their IC50 values. Computerized drug metabolism and pharmacokinetics analysis showed that bisphenol H, tetrabromobisphenol A, and tetrachlorobisphenol A had low solubility, which might explain their weaker inhibition on estradiol production on BeWo cells. In conclusion, BPA analogues mostly can inhibit CYP19A1 and the lipophilicity determines their inhibitory strength.

Organotypic and microphysiological systems (MPS) that can emulate the molecular phenotype and function of human tissues, such as liver, are increasingly used in preclinical drug development. However, despite their improved predictivity, drug development success rates have remained low with most compounds failing in clinical phases despite promising preclinical data. Here, it is tested whether absorption of small molecules to polymers commonly used for MPS fabrication can impact preclinical pharmacological and toxicological assessments and contribute to the high clinical failure rates. To this end, identical devices are fabricated from eight different MPS polymers and absorption of prototypic compounds with different physicochemical properties are analyzed. It is found that overall absorption is primarily driven by compound hydrophobicity and the number of rotatable bonds. However, absorption can differ by >1000-fold between polymers with polydimethyl siloxane (PDMS) being most absorptive, whereas polytetrafluoroethylene (PTFE) and thiol-ene epoxy (TEE) absorbed the least. Strikingly, organotypic primary human liver cultures successfully flagged hydrophobic hepatotoxins in lowly absorbing TEE devices at therapeutically relevant concentrations, whereas isogenic cultures in PDMS devices are resistant, resulting in false negative safety signals. Combined, these results can guide the selection of MPS materials and facilitate the development of preclinical assays with improved translatability.

The use of alternative substances to replace bisphenol A (BPA) has been encouraged. The objective of this study was to evaluate the effects of BPA and 9 BPA alternatives on human and rat aromatase (CYP19A1) in human and rat placental microsomes. The results revealed that bisphenol A, AP, B, C, E, F, FL, S, and Z, and 4,4\'-thiodiphenol (TDP) inhibited human CYP19A1 and bisphenol A, AP, B, C, FL, Z, and TDP inhibited rat CYP19A1. The IC50 values of human CYP19A1 ranged from 3.3 to 172.63 μM and those of rat CYP19A1 ranged from 2.20 to over 100 μM. BPA alternatives were mixed/competitive inhibitors and inhibited estradiol production in BeWo placental cells. Molecular docking analysis showed that BPA alternatives bind to the domain between heme and steroid and form a hydrogen bond with catalytic residue Met374. Pharmacophore analysis showed that there were one hydrogen bond donor, one hydrophobic region, and one ring aromatic hydrophobic region. Bivariate correlation analysis showed that molecular weight, alkyl atom weight, and LogP of BPA alternatives were inversely correlated with their IC50 values. In conclusion, BPA alternatives can inhibit human and rat CYP19A1 and the lipophilicity and the substituted alkyl size determines their inhibitory strength.

Due to the observed increase in the importance of computational methods in determining selected physicochemical parameters of biologically active compounds that are key to understanding their ADME/T profile, such as lipophilicity, there is a great need to work on accurate and precise in silico models based on some structural descriptors, such as topological indices for predicting lipophilicity of certain anti-androgenic and hypouricemic agents and their derivatives, for which the experimental lipophilicity parameter is not accurately described in the available literature, e.g., febuxostat, oxypurinol, ailanthone, abiraterone and teriflunomide. Therefore, the following topological indices were accurately calculated in this paper: Gutman (M, Mν), Randić (0χ, 1χ, 0χν, 1χν), Wiener (W), Rouvray-Crafford (R) and Pyka (A, 0B, 1B) for the selected anti-androgenic drugs (abiraterone, bicalutamide, flutamide, nilutamide, leflunomide, teriflunomide, ailanthone) and some hypouricemic compounds (allopurinol, oxypurinol, febuxostat). Linear regression analysis was used to create simple linear correlations between the newly calculated topological indices and some physicochemical parameters, including lipophilicity descriptors of the tested compounds (previously obtained by TLC and theoretical methods). Our studies confirmed the usefulness of the obtained linear regression equations based on topological indices to predict ADME/T important parameters, such as lipophilicity descriptors of tested compounds with anti-androgenic and hypouricemic effects. The proposed calculation method based on topological indices is fast, easy to use and avoids valuable and lengthy laboratory experiments required in the case of experimental ADME/T studies.

A long-lasting goal of computational biochemists, medicinal chemists, and structural biologists has been the development of tools capable of deciphering the molecule-molecule interaction code that produces a rich variety of complex biomolecular assemblies comprised of the many different simple and biological molecules of life: water, small metabolites, cofactors, substrates, proteins, DNAs, and RNAs. Software applications that can mimic the interactions amongst all of these species, taking account of the laws of thermodynamics, would help gain information for understanding qualitatively and quantitatively key determinants contributing to the energetics of the bimolecular recognition process. This, in turn, would allow the design of novel compounds that might bind at the intermolecular interface by either preventing or reinforcing the recognition. HINT, hydropathic interaction, was a model and software code developed from a deceptively simple idea of Donald Abraham with the close collaboration with Glen Kellogg at Virginia Commonwealth University. HINT is based on a function that scores atom-atom interaction using LogP, the partition coefficient of any molecule between two phases; here, the solvents are water that mimics the cytoplasm milieu and octanol that mimics the protein internal hydropathic environment. This review summarizes the results of the extensive and successful collaboration between Abraham and Kellogg at VCU and the group at the University of Parma for testing HINT in a variety of different biomolecular interactions, from proteins with ligands to proteins with DNA.

The logarithm of n-octanol-water partition coefficient (logP) is frequently used as an indicator of lipophilicity in drug discovery, which has substantial impacts on the absorption, distribution, metabolism, excretion, and toxicity of a drug candidate. Considering that the experimental measurement of the property is costly and time-consuming, it is of great importance to develop reliable prediction models for logP. In this study, we developed a transfer free energy-based logP prediction model-FElogP. FElogP is based on the simple principle that logP is determined by the free energy change of transferring a molecule from water to n-octanol. The underlying physical method to calculate transfer free energy is the molecular mechanics-Poisson Boltzmann surface area (MM-PBSA), thus this method is named as free energy-based logP (FElogP). The superiority of FElogP model was validated by a large set of 707 structurally diverse molecules in the ZINC database for which the measurement was of high quality. Encouragingly, FElogP outperformed several commonly-used QSPR or machine learning-based logP models, as well as some continuum solvation model-based methods. The root-mean-square error (RMSE) and Pearson correlation coefficient (R) between the predicted and measured values are 0.91 log units and 0.71, respectively, while the runner-up, the logP model implemented in OpenBabel had an RMSE of 1.13 log units and R of 0.67. Given the fact that FElogP was not parameterized against experimental logP directly, its excellent performance is likely to be expanded to arbitrary organic molecules covered by the general AMBER force fields.