Optimised method to estimate octanol water distribution coefficient (logD) in a high throughput format

Immobilised artificial membrane liquid chromatography vs liposome electrokinetic capillary chromatography: Suitability in drug/bio membrane partitioning studies and effectiveness in the assessment of the passage of drugs through the respiratory mucosa

This study pioneers a comparison of the application of biomimetic techniques, immobilised artificial membrane liquid chromatography (IAM LC) and liposome electrokinetic capillary chromatography (LEKC), for the prediction of pulmonary drug permeability. The pulmonary absorption profiles of 26 structurally unrelated drug-like molecules were evaluated using their IAM hydrophobicity index (CHI IAM) measured in IAM LC, and the logarithm of distribution constants (log KLEKC) derived from the LEKC experiments. Lipophilicity (phospholipids) parameters obtained from IAM LC and most LEKC analyses were linearly related to the n-octanol/water partitioning coefficients of the neutral forms (i.e., log Po/w values) to a moderate extent. However, the relationship with distribution coefficients at the experimental pH (7.4) (i.e., log D7.4) were weaker overall for IAM LC data and sigmoidal for some liposome compositions (phosphatidyl choline (PC): phosphatidyl inositol (PI) 85:15 mol% and 90:10 mol%) and concentrations (4 mM) in LEKC. This suggests that phospholipid partitioning supports both hydrophobic and electrostatic interactions occurring between ionised drugs and charged phospholipid moieties. The latter interactions are original when compared to those taking place in the more established n-octanol/water partitioning systems. A stronger correlation (R2 > 0.65) was identified between the LEKC retention parameters, and the experimental apparent lung permeability (i.e., log Papp values) as opposed to the values obtained by IAM LC. Therefore, LEKC offers unprecedented advantages over IAM LC in simulating cell membrane partitioning processes in the pulmonary delivery of drugs. Although LEKC has the advantage of more effectively simulating the electrostatic and hydrophobic forces in drug/pulmonary membrane interactions in vitro, the technique is unsuitable for analysing highly hydrophilic neutral or anionic compounds at the experimental pH. Conversely, IAM LC is useful for analysing compounds spanning a wider range of lipophilicity. Its simpler and more robust implementation, and propensity for high-throughput automation make it a favourable choice for researchers in drug development and pharmacological studies.

Application of machine learning models for property prediction to targeted protein degraders

Machine learning (ML) systems can model quantitative structure-property relationships (QSPR) using existing experimental data and make property predictions for new molecules. With the advent of modalities such as targeted protein degraders (TPD), the applicability of QSPR models is questioned and ML usage in TPD-centric projects remains limited. Herein, ML models are developed and evaluated for TPDs' property predictions, including passive permeability, metabolic clearance, cytochrome P450 inhibition, plasma protein binding, and lipophilicity. Interestingly, performance on TPDs is comparable to that of other modalities. Predictions for glues and heterobifunctionals often yield lower and higher errors, respectively. For permeability, CYP3A4 inhibition, and human and rat microsomal clearance, misclassification errors into high and low risk categories are lower than 4% for glues and 15% for heterobifunctionals. For all modalities, misclassification errors range from 0.8% to 8.1%. Investigated transfer learning strategies improve predictions for heterobifunctionals. This is the first comprehensive evaluation of ML for the prediction of absorption, distribution, metabolism, and excretion (ADME) and physicochemical properties of TPD molecules, including heterobifunctional and molecular glue sub-modalities. Taken together, our investigations show that ML-based QSPR models are applicable to TPDs and support ML usage for TPDs' design, to potentially accelerate drug discovery.

Emerging contaminants as unintentional substances in paper and board: A case of unexpected pharmaceuticals detection in the paper recycling chain

The contamination of paper products by various chemicals has been reported on a global level, but to date, no published research has investigated pharmaceutical contamination of paper-based products. In this study, pharmaceutical analysis was conducted on 42 samples collected from various points of the recycled paper value chain in Cape Town, South Africa, which included the various grades that may be included in the manufacturing of recycled paperboard. The analysis was achieved by ultrasonic-assisted extraction of paper samples before detection by UHPLC-Q Orbitrap. Quantification limits ranged from 1.15 pg/g for ketoprofen to 46.07 pg/g for methocarbamol. Pharmaceuticals identified in newspaper samples were dexamethasone, ketoprofen, and 17β-estradiol. The latter was also detected in paper shopping bags (up to 697.49 ng/g), infant bathtub packaging (280.62 ng/g), battery packaging (137.43 ng/g), and an egg carton (170.47 ng/g). Carbamazepine was also prominent with its concentration reaching 13.02 ng/g in a vegetable box. Suspect screening tentatively identified 14 additional pharmaceuticals in paper samples, with minocycline, prazepam, and anabolic steroids appearing more prominently. This pioneering study indicated that unintentional pharmaceutical exposure had expanded beyond environmental media to consumer products.

Nonclassical Zwitterions as a Design Principle to Reduce Lipophilicity without Impacting Permeability

The ionization of bioactive molecules impacts many ADME-relevant physicochemical properties, in particular, solubility, lipophilicity, and permeability. Ampholytes contain both acidic and basic groups and are distinguished as ordinary ampholytes and zwitterions. An influential review states that zwitterions only exist if the acidic pKa is significantly lower than the basic pKa. Through concordance of measured and calculated pKa and log P, we show that the zwitterionic behavior of several marketed drugs and natural products occurs despite a low or negative ΔpKa. These nonclassical zwitterions are characterized by a weak acidic and basic pKa and conjugation through an extended aromatic system, often including pseudorings via intramolecular hydrogen bonds. In contrast to most classical zwitterions, nonclassical zwitterions can exhibit excellent permeability. As permeability and lipophilicity are typically correlated, the combination of low lipophilicity and high permeability makes nonclassical zwitterions an attractive design principle in medicinal chemistry.

Sensitive fluorescence detection based on dimeric G-quadruplex combined with enzyme-assisted solid-phase microextraction of streptomycin in honey

Streptomycin (STR), an aminoglycoside antibiotic with the potential to persist in honey and other food products, may induce allergy, toxicity and antibiotic resistance in humans. In this study, we developed a solid-phase microextraction (SPME) biosensor based on a quartz rod that was modified with double-stranded DNA structures consisting of partially complementary G-rich base DNA strand and STR aptamer. The STR isolated by SPME initially bound to the aptamer. Then the remaining double-stranded DNA structures were cleaved by the Nt.BstNBI enzyme, resulting in release of G-quadruplex dimers. The latter formed a complex with thioflain T fluorescent dye, resulting in an amplified fluorescence response. The method exhibited high sensitivity (a limit of detection of 10.84 pM), wide linear range (0.05 nM ∼ 500 nM (with determination coefficient > 0.99)), and simple operation, making it suitable and convenient for STR detection. Successful STR determination in genuine honey samples was demonstrated.

Discovery of Ligands for TRIM58, a Novel Tissue-Selective E3 Ligase

Redirecting E3 ligases to neo-substrates, leading to their proteasomal disassembly, known as targeted protein degradation (TPD), has emerged as a promising alternative to traditional, occupancy-driven pharmacology. Although the field has expanded tremendously over the past years, the choice of E3 ligases remains limited, with an almost exclusive focus on CRBN and VHL. Here, we report the discovery of novel ligands to the PRY-SPRY domain of TRIM58, a RING ligase that is specifically expressed in erythroid precursor cells. A DSF screen, followed by validation using additional biophysical methods, led to the identification of TRIM58 ligand TRIM-473. A basic SAR around the chemotype was established by utilizing a competitive binding assay employing a short FP peptide probe derived from an endogenous TRIM58 substrate. The X-ray co-crystal structure of TRIM58 in complex with TRIM-473 gave insights into the binding mode and potential exit vectors for bifunctional degrader design.

Occurrence, fate, and potential risk of pharmaceutical pollutants in agriculture: Challenges and environmentally friendly solutions

In recent years, pharmaceutical active compounds (PhACs) have attained global prevalence. The behavior of PhACs in agricultural soils is complex and depends on several factors, such as the nature of the compounds and their physicochemical characteristics, which affect their fate and potential threats to human health, ecosystems, and the environment. The detection of residual pharmaceutical content is possible in both agricultural soils and environmental matrices. PhACs are commonly found in agricultural soil, with concentrations varying significantly, ranging from as low as 0.048 ng g-1 to as high as 1420.76 mg kg-1. The distribution and persistence of PhACs in agriculture can lead to the leaching of these toxic pollutants into surface water, groundwater, and vegetables/plants, resulting in human health risks and environmental pollution. Biological degradation or bioremediation plays a critical role in environmental protection and efficiently eliminates contamination by hydrolytic and/or photochemical reactions. Membrane bioreactors (MBRs) have been investigated as the most recent approach for the treatment of emerging persistent micropollutants, including PhACs, from wastewater sources. MBR- based technologies have proven to be effective in eliminating pharmaceutical compounds, achieving removal rates of up to 100%. This remarkable outcome is primarily facilitated by the processes of biodegradation and metabolization. In addition, phytoremediation (i.e., constructed wetlands), microalgae-based technologies, and composting can be highly efficient in remediating PhACs in the environment. The exploration of key mechanisms involved in pharmaceutical degradation has revealed a range of approaches, such as phytoextraction, phytostabilization, phytoaccumulation, enhanced rhizosphere biodegradation, and phytovolatilization. The well-known advanced/tertiary removal of sustainable sorption by biochar, activated carbon, chitosan, etc. has high potential and yields excellent quality effluents. Adsorbents developed from agricultural by-products have been recognized to eliminate pharmaceutical compounds and are cost-effective and eco-friendly. However, to reduce the potentially harmful impacts of PhACs, it is necessary to focus on advanced technologies combined with tertiary processes that have low cost, high efficiency, and are energy-saving to remove these emerging pollutants for sustainable development.

Recent advances on molecular dynamics-based techniques to address drug membrane permeability with atomistic detail

Several factors affect the passive membrane permeation of small molecules, including size, charge, pH, or the presence of specific chemical groups. Understanding these features is paramount to identifying or designing drug candidates with optimal ADMET properties and this can be achieved through experimental/knowledge-based methodologies or using computational approaches. Empirical methods often lack detailed information about the underlying molecular mechanism. In contrast, Molecular Dynamics-based approaches are a powerful strategy, providing an atomistic description of this process. This technique is continuously growing, featuring new related methodologies. In this work, the recent advances in this research area will be discussed.

Predicting Oral Absorption for Compounds Outside the Rule of Five Property Space

The estimation of the extent of absorption of drug candidates intended for oral drug delivery is an important selection criteria in drug discovery. The use of cell-based transwell assays examining flux across cell-monolayers (e.g., Caco-2 or MDCK cells) usually provide satisfactory predictions of the extent of absorption in vivo. These predictions often fall short of expection for molecules outside the traditional low molecular weight property space. In this manuscript the transwell permeability assay was modified to circumvent potential issues that can be encountered when evaluating the aforementioned drug molecules. Particularly, the addition of albumin in the acceptor compartment to reduce potential binding to cells and the acceptor compartment, improved the predictive power of the assay. Cellular binding and lysosomal trapping effects are significantly reduced for larger molecules, particularly lipophilic bases under these more physiological conditions, resulting in higher recovery values and a better prediction power. The data indicate that lysosomal trapping does not impact the rate of absorption of lipophilic bases in general but is rather an exception. Finally, compounds believed to permeate by passive mechanisms were used in a calibration curve for the effective prediction of the fraction absorbed of molecules of interest in current medicinal chemistry efforts.

Artificial intelligence in the early stages of drug discovery

Although the use of computational methods within the pharmaceutical industry is well established, there is an urgent need for new approaches that can improve and optimize the pipeline of drug discovery and development. In spite of the fact that there is no unique solution for this need for innovation, there has recently been a strong interest in the use of Artificial Intelligence for this purpose. As a matter of fact, not only there have been major contributions from the scientific community in this respect, but there has also been a growing partnership between the pharmaceutical industry and Artificial Intelligence companies. Beyond these contributions and efforts there is an underlying question, which we intend to discuss in this review: can the intrinsic difficulties within the drug discovery process be overcome with the implementation of Artificial Intelligence? While this is an open question, in this work we will focus on the advantages that these algorithms provide over the traditional methods in the context of early drug discovery.

Evaluation of 5H-Thiazolo[3,2-α]pyrimidin-5-ones as Potential GluN2A PET Tracers

We describe here our efforts to develop a PET tracer for imaging GluN2A-containing NMDA receptors, based on a 5H-thiazolo[3,2-α]pyrimidin-5-one scaffold. The metabolic stability and overall properties could be optimized satisfactorily, although binding affinities remained a limiting factor for in vivo imaging. We nevertheless identified 7-(((2-fluoroethyl)(3-fluorophenyl)amino)-methyl)-3-(2-(hydroxymethyl)cyclopropyl)-2-methyl-5H-thiazolo-[3,2-α]pyrimidin-5-one ([¹⁸ F]7b) as a radioligand providing good-quality images in autoradiographic studies, as well as a tritiated derivative, 2-(7-(((2-fluoroethyl)(4-fluorophenyl)amino)methyl)-2-methyl-5-oxo-5H-thiazolo[3,2-α]pyrimidin-3-yl)cyclopropane-1-carbonitrile ([³ H₂ ]15b), which was used for the successful development of a radioligand binding assay. These are valuable new tools for the study of GluN2A-containing NMDA receptors, and for the optimization of allosteric modulators binding to the pharmacophore located at the dimer interface of the GluN1-GluN2A ligand-binding domain.

Conformational Ensembles of Antibodies Determine Their Hydrophobicity

A major challenge in the development of antibody biotherapeutics is their tendency to aggregate. One root cause for aggregation is exposure of hydrophobic surface regions to the solvent. Many current techniques predict the relative aggregation propensity of antibodies via precalculated scales for the hydrophobicity or aggregation propensity of single amino acids. However, those scales cannot describe the nonadditive effects of a residue’s surrounding on its hydrophobicity. Therefore, they are inherently limited in their ability to describe the impact of subtle differences in molecular structure on the overall hydrophobicity. Here, we introduce a physics-based approach to describe hydrophobicity in terms of the hydration free energy using grid inhomogeneous solvation theory (GIST). We apply this method to assess the effects of starting structures, conformational sampling, and protonation states on the hydrophobicity of antibodies. Our results reveal that high-quality starting structures, i.e., crystal structures, are crucial for the prediction of hydrophobicity and that conformational sampling can compensate errors introduced by the starting structure. On the other hand, sampling of protonation states only leads to good results when combined with high-quality structures, whereas it can even be detrimental otherwise. We conclude by pointing out that a single static homology model may not be adequate for predicting hydrophobicity.

Solvation Thermodynamics in Different Solvents: Water-Chloroform Partition Coefficients from Grid Inhomogeneous Solvation Theory

Reliable information on partition coefficients plays a key role in drug development, as solubility decisively affects bioavailability. In a physicochemical context, the partition coefficient of a solute between two different solvents can be described as a function of solvation free energies. Hence, substantial scientific efforts have been made toward accurate predictions of solvation free energies in various solvents. The grid inhomogeneous solvation theory (GIST) facilitates the calculation of solvation free energies. In this study, we introduce an extended version of the GIST algorithm, which enables the calculation for chloroform in addition to water. Furthermore, GIST allows localization of enthalpic and entropic contributions. We test our approach by calculating partition coefficients between water and chloroform for a set of eight small molecules. We report a Pearson correlation coefficient of 0.96 between experimentally determined and calculated partition coefficients. The capability to reliably predict partition coefficients between water and chloroform and the possibility to localize their contributions allow the optimization of a compound's partition coefficient. Therefore, we presume that this methodology will be of great benefit for the efficient development of pharmaceuticals.

Inverse liquid-solid chromatography to evaluate drug interactions with organosilane-modified polydimethylsiloxane for use in body-on-a-chip systems

Body-on-a-chip and organ-on-a-chip systems utilize polydimethylsiloxane (PDMS) because of the relative suitability of the material for fabrication of microfluidic channels and chambers used in these devices. However, hydrophobic molecules, especially therapeutic compounds, tend to adsorb to PDMS, which may distort the dose-response curves that feed into the pharmacokinetic/pharmacodynamic models used to translate preclinical data into predictions of clinical outcomes. Surface modification by organosilanes is one method being explored to modify PDMS, but the effect of organosilanes on drug adsorption isotherms is not well characterized. We utilized Inverse Liquid-Solid Chromatography to characterize the adsorption parameters of the drugs acetaminophen, diclofenac, and verapamil with native PDMS and organosilane-modified (fluoropolymer (13F) and polyethylene glycol) PDMS surfaces, to correlate the modifications with changes in drug adsorption. It was determined that the organosilane modifications significantly changed the energy of adsorption of the test drug utilizing our methodology.

ReSCoSS: a flexible quantum chemistry workflow identifying relevant solution conformers of drug-like molecules

Conformational equilibria are at the heart of drug design, yet their energetic description is often hampered by the insufficient accuracy of low-cost methods. Here we present a flexible and semi-automatic workflow based on quantum chemistry, ReSCoSS, designed to identify relevant conformers and predict their equilibria across different solvent environments in the Conductor-like Screening Model for Real Solvents (COSMO-RS) framework. We demonstrate the utility and accuracy of the workflow through conformational case studies on several drug-like molecules from literature where relevant conformations are known. We further show that including ReSCoSS conformers significantly improves COSMO-RS based predictions of physicochemical properties over single-conformation approaches. ReSCoSS has found broad adoption in the in-house drug discovery and development work streams and has contributed to establishing quantum-chemistry methods as a strategic pillar in ligand discovery.

Characterization of Drug-Polymer Adsorption Isotherms in Body-on-a-Chip Systems by Inverse Liquid-Solid Chromatography

Body-on-a-chip and human-on-a-chip systems are currently being used to augment and could eventually replace animal models in drug discovery and basic biological research. However, hydrophobic molecules, especially therapeutic compounds, tend to adsorb to the polymer materials used to create these microfluidic platforms, which may distort the dose-response curves that feed into pharmacokinetic/pharmacodynamic (PK/PD) models, which translate preclinical data into predictions of clinical outcomes. Inverse liquid-solid chromatography paired with a numerical optimization based on the Langmuir model of adsorption was used to characterize the adsorption isotherm parameters of drugs to polydimethylsiloxane (PDMS) and polymethylmethacrylate (PMMA), polymers commonly used in these platforms. The adsorption isotherms were then compared against concentration measurements of drugs recirculated in these platforms. This research further illustrates the point that by quantifying drug or drug candidate interactions before system dosing and including this data in the PK/PD models, then polymers used in these platforms need not be limited to "less-adsorbing" materials.

Prediction of Partition Coefficients of Environmental Toxins Using Computational Chemistry Methods

The partitioning of compounds between aqueous and other phases is important for predicting toxicity. Although thousands of octanol-water partition coefficients have been measured, these represent only a small fraction of the anthropogenic compounds present in the environment. The octanol phase is often taken to be a mimic of the inner parts of phospholipid membranes. However, the core of such membranes is typically more hydrophobic than octanol, and other partition coefficients with other compounds may give complementary information. Although a number of (cheap) empirical methods exist to compute octanol-water (log k _OW) and hexadecane-water (log k _HW) partition coefficients, it would be interesting to know whether physics-based models can predict these crucial values more accurately. Here, we have computed log k _OW and log k _HW for 133 compounds from seven different pollutant categories as well as a control group using the solvation model based on electronic density (SMD) protocol based on Hartree-Fock (HF) or density functional theory (DFT) and the COSMO-RS method. For comparison, XlogP3 (log k _OW) values were retrieved from the PubChem database, and KowWin log k _OW values were determined as well. For 24 of these compounds, log k _OW was computed using potential of mean force (PMF) calculations based on classical molecular dynamics simulations. A comparison of the accuracy of the methods shows that COSMO-RS, KowWin, and XlogP3 all have a root-mean-square deviation (rmsd) from the experimental data of ≈0.4 log units, whereas the SMD protocol has an rmsd of 1.0 log units using HF and 0.9 using DFT. PMF calculations yield the poorest accuracy (rmsd = 1.1 log units). Thirty-six out of 133 calculations are for compounds without known log k _OW, and for these, we provide what we consider a robust prediction, in the sense that there are few outliers, by averaging over the methods. The results supplied may be instrumental when developing new methods in computational ecotoxicity. The log k _HW values are found to be strongly correlated to log k _OW for most compounds.

Development and Validation of Liquid Chromatography-Based Methods to Assess the Lipophilicity of Cytotoxic Platinum(IV) Complexes

Lipophilicity is a crucial parameter for drug discovery, usually determined by the logarithmic partition coefficient (Log P) between octanol and water. However, the available detection methods have restricted the widespread use of the partition coefficient in inorganic medicinal chemistry, and recent investigations have shifted towards chromatographic lipophilicity parameters, frequently without a conversion to derive Log P. As high-performance liquid chromatography (HPLC) instruments are readily available to research groups, a HPLC-based method is presented and validated to derive the partition coefficient of a set of 19 structurally diverse and cytotoxic platinum(IV) complexes exhibiting a dynamic range of at least four orders of magnitude. The chromatographic lipophilicity parameters φ0 and Log kw were experimentally determined for the same set of compounds, and a correlation was obtained that allows interconversion between the two lipophilicity scales, which was applied to an additional set of 34 platinum(IV) drug candidates. Thereby, a φ0 = 58 corresponds to Log P = 0. The same approaches were successfully evaluated to determine the distribution coefficient (Log D) of five ionisable platinum(IV) compounds to sample pH-dependent effects on the lipophilicity. This study provides straight-forward HPLC-based methods to determine the lipophilicity of cytotoxic platinum(IV) complexes in the form of Log P and φ0 that can be interconverted and easily expanded to other metal-based compound classes.

Impact of Dynamically Exposed Polarity on Permeability and Solubility of Chameleonic Drugs Beyond the Rule of 5

Conformational flexibility has been proposed to significantly affect drug properties outside rule-of-5 (Ro5) chemical space. Here, we investigated the influence of dynamically exposed polarity on cell permeability and aqueous solubility for a structurally diverse set of drugs and clinical candidates far beyond the Ro5, all of which populated multiple distinct conformations as revealed by X-ray crystallography. Efflux-inhibited (passive) Caco-2 cell permeability correlated strongly with the compounds' minimum solvent-accessible 3D polar surface areas (PSA), whereas aqueous solubility depended less on the specific 3D conformation. Inspection of the crystal structures highlighted flexibly linked aromatic side chains and dynamically forming intramolecular hydrogen bonds as particularly effective in providing "chameleonic" properties that allow compounds to display both high cell permeability and aqueous solubility. These structural features, in combination with permeability predictions based on the correlation to solvent-accessible 3D PSA, should inspire drug design in the challenging chemical space far beyond the Ro5.

A confidence predictor for logD using conformal regression and a support-vector machine

Lipophilicity is a major determinant of ADMET properties and overall suitability of drug candidates. We have developed large-scale models to predict water–octanol distribution coefficient (logD) for chemical compounds, aiding drug discovery projects. Using ACD/logD data for 1.6 million compounds from the ChEMBL database, models are created and evaluated by a support-vector machine with a linear kernel using conformal prediction methodology, outputting prediction intervals at a specified confidence level. The resulting model shows a predictive ability of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hbox {Q}^{2}=0.973$$\end{document}Q2=0.973 and with the best performing nonconformity measure having median prediction interval of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\pm ~0.39$$\end{document}±0.39 log units at 80% confidence and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\pm ~0.60$$\end{document}±0.60 log units at 90% confidence. The model is available as an online service via an OpenAPI interface, a web page with a molecular editor, and we also publish predictive values at 90% confidence level for 91 M PubChem structures in RDF format for download and as an URI resolver service.

Accessing lipophilicity of drugs with biomimetic models: A comparative study using liposomes and micelles

Lipophilicity is a physicochemical property of crucial importance in drug discovery and drug design. Biomimetic models, such as liposomes and micelles, constitute a valuable tool for the assessment of lipophilicity through the determination of partition coefficients (log K_p). However, the lack of standardization hampers the judgment about which model or method has the best and broadest passive drug permeation predictive capacity. This work provides a comparative analysis between the methodologies based on biomimetic models to determine the partition coefficient (log K_p). For that purpose, a set of reference substances preconized by the Organization for Economic Cooperation and Development (OECD) guidelines was used. The biomimetic models employed were liposomes and micelles composed by 1,2-dimyristoyl-sn-glycero-3-phosphorylcholine (DMPC) and hexadecylphosphocholine (HePC), respectively. Both lipids were used as representative phospholipids of natural membranes. The partition coefficients between biomimetic models and aqueous phases were determined by derivative spectroscopy at physiological conditions (37 °C and pH 7.4). The partition coefficients obtained using biomimetic models are quite different and more reliable than the ones obtained using an octanol/water system. Comparing the performance of the two biomimetic models, micelles revealed to be suitable only for substances with high molar absorption coefficient and log K_p > 3, but in general liposomes are the best model for accessing lipophilicity of drugs. Furthermore, a comparison between experimental data and the partition coefficients determined by the computational method COSMOmic is also provided and discussed. As a final summarizing result, a decision tree is provided in order to guide the selection of a tool for assessing the lipophilicity of drugs.

Investigating Intracellular Localisation and Cytotoxicity Trends for Neutral and Cationic Iridium Tetrazolato Complexes in Live Cells

A family of five neutral cyclometalated iridium(III) tetrazolato complexes and their methylated cationic analogues have been synthesised and characterised. The complexes are distinguished by variations of the substituents or degree of π conjugation on either the phenylpyridine or tetrazolato ligands. The photophysical properties of these species have been evaluated in organic and aqueous media, revealing predominantly a solvatochromic emission originating from mixed metal-to-ligand and ligand-to-ligand charge transfer excited states of triplet multiplicity. These emissions are characterised by typically long excited-state lifetimes (∼hundreds of ns), and quantum yields around 5-10 % in aqueous media. Methylation of the complexes caused a systematic red-shift of the emission profiles. The behaviour and the effects of the different complexes were then examined in cells. The neutral species localised mostly in the endoplasmic reticulum and lipid droplets, whereas the majority of the cationic complexes localised in the mitochondria. The amount of complexes found within cells does not depend on lipophilicity, which potentially suggests diverse uptake mechanisms. Methylated analogues were found to be more cytotoxic compared to the neutral species, a behaviour that might to be linked to a combination of uptake and intracellular localisation.