Solvent polarity and hydrophobicity of solutes are two sides of the same coin

The hydrophobicity of solutes measures the intensity of a solute's interaction with aqueous environment. The aqueous environment may change with its composition, leading to changes in its solvent properties largely characterized by polarity. As a result, the relative hydrophobicity of a solute is a function of the solute structure and the properties of the water-based solvent determined by the total composition of the aqueous phase. This aspect is commonly ignored by medicinal chemists even though it is essential for drug distribution between different biological tissues. Partitioning of solutes in aqueous two-phase systems provides the relative hydrophobicity estimates for any water-soluble compounds that can be used to improve predictions of the toxicity and other biological effects of these compounds.

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.

Linear Relationships between Partition Coefficients of Different Organic Compounds and Proteins in Aqueous Two-Phase Systems of Various Polymer and Ionic Compositions

Analysis of the partition coefficients of small organic compounds and proteins in different aqueous two-phase systems under widely varied ionic compositions shows that logarithms of partition coefficients for any three compounds or proteins or two organic compounds and one protein are linearly interrelated, although for protein(s) there are ionic compositions when the linear fit does not hold. It is suggested that the established interrelationships are due to cooperativity of different types of solute-solvent interactions in aqueous media. This assumption is confirmed by analysis of distribution coefficients of various drugs in octanol-buffer systems with varied ionic compositions of the buffer. Analysis of the partition coefficients characterizing distribution of variety of drugs between blood and different tissues of rats in vivo reported in the literature showed that the above assumption is correct and enabled us to identify the tissues with the components of which the drug(s) may engage in presumably direct interactions. It shows that the suggested assumption is valid for even complex biological systems.

Experimental versus theoretical log D7.4 , pKa and plasma protein binding values for benzodiazepines appearing as new psychoactive substances

The misuse of benzodiazepines as new psychoactive substances is an increasing problem around the world. Basic physicochemical and pharmacokinetic data is required on these substances to interpret and predict their effects upon humans. Experimental log D7.4 , pKa and plasma protein binding values were determined for 11 benzodiazepines that have recently appeared as new psychoactive substances (3-hydroxyphenazepam, 4'-chlorodiazepam, desalkylflurazepam, deschloroetizolam, diclazepam, etizolam, flubromazepam, flubromazolam, meclonazepam, phenazepam, and pyrazolam) and compared with values generated by various software packages (ACD/I-lab, MarvinSketch, ADMET Predictor and PreADMET). ACD/I-LAB returned the most accurate values for log D7.4 and plasma protein binding while ADMET Predictor returned the most accurate values for pKa . Large variations in predictive errors were observed between compounds. Experimental values are currently preferable and desirable as they may aid with the future 'training' of predictive models for these new psychoactive substances.

Analysis of the distribution of organic compounds and drugs between biological tissues in the framework of solute partitioning in aqueous two-phase systems

Distribution of organic compounds between different biological tissues may be considered in the framework of solute partitioning in aqueous two-phase systems.