Physicochemical QSAR analysis of hERG inhibition revisited: towards a quantitative potency prediction.

In an earlier study (Didziapetris R & Lanevskij K (2016). J Comput Aided Mol Des. 30:1175-1188) we collected a database of publicly available hERG inhibition data for almost 6700 drug-like molecules and built a probabilistic Gradient Boosting classifier with a minimal set of physicochemical descriptors (log P, pKa, molecular size and topology parameters). This approach favored interpretability over statistical performance but still achieved an overall classification accuracy of 75%. In the current follow-up work we expanded the database (provided in Supplementary Information) to almost 9400 molecules and performed temporal validation of the model on a set of novel chemicals from recently published lead optimization projects. Validation results showed almost no performance degradation compared to the original study. Additionally, we rebuilt the model using AFT (Accelerated Failure Time) learning objective in XGBoost, which accepts both quantitative and censored data often reported in protein inhibition studies. The new model achieved a similar level of accuracy of discerning hERG blockers from non-blockers at 10 µM threshold, which can be conceived as close to the performance ceiling for methods aiming to describe only non-specific ligand interactions with hERG. Yet, this model outputs quantitative potency values (IC50) and is not tied to a particular classification cut-off. pIC50 from patch-clamp measurements can be predicted with R2 ≈ 0.4 and MAE < 0.5, which enables ligand ranking according to their expected potency levels. The employed approach can be valuable for quantitative modeling of various ADME and drug safety endpoints with a high prevalence of censored data.

Physicochemical QSAR Analysis of Passive Permeability Across Caco-2 Monolayers.

Caco-2 cell line is frequently used as a simplified in vitro model of intestinal absorption. In this study, a database of 1366 Caco-2 permeability coefficients (Pe) for 768 diverse drugs and drug-like compounds was compiled from public sources. The collected data represent permeation rates measured at varying experimental conditions (pH from 4.0 to 8.0, and stirring rates from 0 to >1000 rpm) that presumably account for passive diffusion across mucosal epithelium. These data were subjected to multistep nonlinear regression analysis using a minimal set of physicochemical descriptors (octanol-water log D, pKa, hydrogen bonding potential, and molecular size). The model was constructed in a mechanistic manner incorporating the following components: (i) a hydrodynamic equation of size- and charge-specific along with nonspecific diffusion across the paracellular pathway; (ii) transcellular diffusion represented by thermodynamic membrane/water partitioning ratio; (iii) stirring-dependent limit of maximum achievable permeability due to the presence of unstirred water layer. The obtained model demonstrates good accuracy of log Pe predictions with a residual mean square error <0.5 log units for all training and validation sets. Given its robust performance and straightforward interpretation in terms of simple physicochemical properties, the proposed model may serve as a valuable tool to guide drug discovery efforts toward readily absorbable compounds.

Compilation and physicochemical classification analysis of a diverse hERG inhibition database.

A large and chemically diverse hERG inhibition data set comprised of 6690 compounds was constructed on the basis of ChEMBL bioactivity database and original publications dealing with experimental determination of hERG activities using patch-clamp and competitive displacement assays. The collected data were converted to binary format at 10 µM activity threshold and subjected to gradient boosting machine classification analysis using a minimal set of physicochemical and topological descriptors. The tested parameters involved lipophilicity (log P), ionization (pK a ), polar surface area, aromaticity, molecular size and flexibility. The employed approach allowed classifying the compounds with an overall 75-80 % accuracy, even though it only accounted for non-specific interactions between hERG and ligand molecules. The observed descriptor-response profiles were consistent with common knowledge about hERG ligand binding site, but also revealed several important quantitative trends, as well as slight inter-assay variability in hERG inhibition data. The results suggest that even weakly basic groups (pK a  < 6) might substantially contribute to hERG inhibition potential, whereas the role of lipophilicity depends on the compound's ionization state, and the influence of log P decreases in the order of bases > zwitterions > neutrals > acids. Given its robust performance and clear physicochemical interpretation, the proposed model may provide valuable information to direct drug discovery efforts towards compounds with reduced risk of hERG-related cardiotoxicity.

Improving the prediction of drug disposition in the brain.

INTRODUCTION: Ability to cross the blood-brain barrier is one of the key ADME characteristics of all drug candidates regardless of their target location in the body. While good brain penetration is essential for CNS drugs, it may lead to serious side effects in case of peripherally-targeted molecules. Despite a high demand of computational methods for estimating brain transport early in drug discovery, achieving good prediction accuracy still remains a challenging task. AREAS COVERED: This article reviews various measures employed to quantify brain delivery and recent advances in QSAR approaches for predicting these properties from the compound's structure. Additionally, the authors discuss the classification models attempting to distinguish between permeable and impermeable chemicals. EXPERT OPINION: Recent research in the field of brain penetration modeling showed an increasing understanding of the processes involved in drug disposition, although most models of brain/plasma partitioning still rely on purely statistical considerations. Preferably, new models should incorporate mechanistic knowledge since it is the prerequisite for guiding drug design efforts in the desired direction. To increase the efficiency of computational tools, a broader view is necessary, involving rate and extent of brain penetration, as well as plasma and brain tissue binding strength, instead of relying on any single property.

QSAR analysis of blood-brain distribution: the influence of plasma and brain tissue binding.

The extent of brain delivery expressed as steady-state brain/blood distribution ratio (log BB) is the most frequently used parameter for characterizing central nervous system exposure of drugs and drug candidates. The aim of the current study was to propose a physicochemical QSAR model for log BB prediction. Model development involved the following steps: (i) A data set consisting of 470 experimental log BB values determined in rodents was compiled and verified to ensure that selected data represented drug disposition governed by passive diffusion across blood-brain barrier. (ii) Available log BB values were corrected for unbound fraction in plasma to separate the influence of drug binding to brain and plasma constituents. (iii) The resulting ratios of total brain to unbound plasma concentrations reflecting brain tissue binding were described by a nonlinear ionization-specific model in terms of octanol/water log P and pK(a). The results of internal and external validation demonstrated good predictive power of the obtained model as both log BB and brain tissue binding strength were predicted with residual mean square error of 0.4 log units. The statistical parameters were similar among training and validation sets, indicating that the model is not likely to be overfitted.

Trainable structure-activity relationship model for virtual screening of CYP3A4 inhibition.

A new structure-activity relationship model predicting the probability for a compound to inhibit human cytochrome P450 3A4 has been developed using data for >800 compounds from various literature sources and tested on PubChem screening data. Novel GALAS (Global, Adjusted Locally According to Similarity) modeling methodology has been used, which is a combination of baseline global QSAR model and local similarity based corrections. GALAS modeling method allows forecasting the reliability of prediction thus defining the model applicability domain. For compounds within this domain the statistical results of the final model approach the data consistency between experimental data from literature and PubChem datasets with the overall accuracy of 89%. However, the original model is applicable only for less than a half of PubChem database. Since the similarity correction procedure of GALAS modeling method allows straightforward model training, the possibility to expand the applicability domain has been investigated. Experimental data from PubChem dataset served as an example of in-house high-throughput screening data. The model successfully adapted itself to both data classified using the same and different IC50 threshold compared with the training set. In addition, adjustment of the CYP3A4 inhibition model to compounds with a novel chemical scaffold has been demonstrated. The reported GALAS model is proposed as a useful tool for virtual screening of compounds for possible drug-drug interactions even prior to the actual synthesis.

Ionization-specific QSAR models of blood-brain penetration of drugs.

This study presents a mechanistic QSAR (quantitative structure-activity relationship) analysis of blood-brain barrier (BBB) penetration of drugs and drug-like compounds governed by passive diffusion in rats and mice. The analyzed data included a previously compiled set of almost 200 experimental BBB permeation rates (expressed as log PS values) as well as steady-state brain/plasma distribution ratios for ca. 500 compounds (represented as log BB constants) that were considered free of carrier-mediated transport and other unwanted effects. These data were modeled in terms of nonlinear lipophilicity and ionization dependences. The necessity to separate the influence of drug binding to plasma and brain constituents on the distribution ratio is discussed. Preliminary results demonstrate that, if both the rate and extent of BBB penetration are considered, it is possible to estimate whether a given compound may exhibit central nervous system (CNS) penetration.

Ionization-specific analysis of human intestinal absorption.

This study presents a mechanistic QSAR analysis of human intestinal absorption of drugs and drug-like compounds using a data set of 567 %HIA values. Experimental data represent passive diffusion across intestinal membranes, and are considered to be reasonably free of carrier-mediated transport or other unwanted effects. A nonlinear model was developed relating %HIA to physicochemical properties of drugs (lipophilicity, ionization, hydrogen bonding, and molecular size). The model describes ion-specific intestinal permeability of drugs by both transcellular and paracellular routes, and also accounts for unstirred water layer effects. The obtained model was validated on two external data sets consisting of in vivo human jejunal permeability coefficients (P(eff)) and absorption rate constants (K(a)). Validation results demonstrate good predictive power of the model (RMSE = 0.35-0.45 log units for log K(a) and log P(eff)). High prediction accuracy together with clear physicochemical interpretation (log P, pK(a)) makes this model particularly suitable for use in property-based drug design.

Ionization-specific prediction of blood-brain permeability.

This study presents a mechanistic QSAR analysis of passive blood-brain barrier permeability of drugs and drug-like compounds in rats and mice. The experimental data represented in vivo log PS (permeability-surface area product) from in situ perfusion, brain uptake index, and intravenous administration studies. A data set of 280 log PS values was compiled. A subset of 178 compounds was assumed to be driven by passive transport that is free of plasma protein binding and carrier-mediated effects. This subset was described in terms of nonlinear lipophilicity and ionization dependences, that account for multiple kinetic and thermodynamic effects: (i) drug's kinetic diffusion, (ii) ion-specific partitioning between plasma and brain capillary endothelial cell membranes, and (iii) hydrophobic entrapment in phospholipid bilayer. The obtained QSAR model provides both good statistical significance (RMSE < 0.5) and simple physicochemical interpretations (log P and pKa), thus providing a clear route towards property-based design of new CNS drugs.

The effects of jatrophane derivatives on the reversion of MDR1- and MRP-mediated multidrug resistance in the MDA-MB-231 (HTB-26) cell line.

Multidrug resistance (MDR) of cancer cells can be the result of a variety of mechanisms that are not completely understood. One of the most significant among them concerns altered membrane transport in tumor cells, often referred to as typical or classic MDR. This mechanism is related to the overexpression of a variety of proteins, that belong to the super family of ABC transporters. The aim or this study was to look for new effective modulators of MDR1 and multidrug resistance-associated protein (MRP) transporters. Ten diterpenes based on the jatrophane skeleton, including rearranged polycyclic derivatives, were studied on the MDA-MB-231 (HTB-26) human breast cancer cell line. The majority of those compounds were able to strongly enhance the rhodamine 123 accumulation of the human MDR1 gene transfected mouse lymphoma cell line, as previously described. In the present study, the MDR reversal of the same jatrophanes on MDR1- and MRP- mediated resistance of human breast cancer cells is reported. These cells simultaneously express MDR1 and MRP proteins when identified by monoclonal antibodies. However, in a functional assay, where rhodamine 123 accumulation was measured and verapamil was the traditional positive control, only MRP was active, while MDR1 was inactive. Carboxyfluorescein served as a substrate for MRP-mediated drug efflux, and indomethacine was the positive control used as an inhibitor of MRP in the flow cytometric experiments. The effectivity of various jatrophanes was different on the carboxyfluorescein efflux inhibition of the human breast cancer cells. These results may have importance in the planning of a new type of combination chemotherapy.

Cancer incidence in the workers cohort of textile manufacturing factory in Alytus, Lithuania.

Altogether 14,650 workers employed at least for 1 year a the textile factory in Alytus, Lithuania, were included in the cohort and followed during the period from 1978 to 1997. The standardized incidence ratio (SIR) for men was 1.28. The incidence of esophagus cancer was significant higher (SIR 3.42). It increased only slightly for lung (SIR 1.35). In the women cohort, SIR was 1.05. However, there was a significant increase of the incidence of gallbladder cancer (SIR 3.19). Among textile-processing (spinning and weaving departments) women workers, we found elevated total cancer incidence (SIR 1.35), incidence of breast cancer (SIR 1.49), and cervical cancer (SIR 1.82). In this cohort increased SIR values were observed for more than 10 years since first exposure for all cancer (SIR 1.70) and cervical cancer (SIR 2.44).

Progress in toxinformatics: the challenge of predicting acute toxicity.

Historically, acute toxicity based on LC(50) and LD(50) tests has been analyzed using various quantitative structure-activity relationships (QSARs). The obtained QSAR equations cannot be related to the multiple health effects reflected in the experimental data of analyzed compounds. Therefore little predictive power for novel compounds can be achieved. New methods based on classification SAR (C-SAR) analysis offer new mechanistic knowledge that can be related to new health effects, resulting in better predictive power. To this end, a very careful interpretation of the obtained results is required, implying the use of the existing mechanistic information to the maximum possible extent. The current mini-review aims at determining ways of automated extraction of new mechanistic knowledge from existing data, as well as ways of relating this knowledge to various health effects. A comparison of "statistical induction" and "knowledge-based" approaches is provided. The existing and future developments in predictive acute toxicity are discussed.

Classification structure-activity relations (C-SAR) in prediction of human intestinal absorption.

AB/HIA is a "soft" filter for identifying compounds with poor intestinal membrane permeability. The analyzed data set included over 1000 drug-like compounds with experimental human intestinal absorption (HIA) values. A sequence of recursive partitioning analyses based on multiple physicochemical and structural descriptors led to the derivation of the rule-based algorithm (filter). The obtained rules reveal a simple physicochemical model of intestinal permeability; they also account for the specific effects caused by quarternary nitrogens and biphosphonate groups. Comparison of the observed and predicted values revealed very low percent of disagreement (15% false-positives and 3% false-negatives). The unusual absorption of compounds that deviated from the predicted values was explained in terms of active transport, efflux, chemical stability, chelating ability, and solubility. Most of these effects can be accounted for by new, substructure-specific rules that can be added into the existing filter. This can lead to the development of a reliable theoretical model for predicting human intestinal absorption. If combined with other models for predicting first pass metabolism, the updated AB/HIA filter can be very useful in predicting oral bioavailability.

Classification analysis of P-glycoprotein substrate specificity.

Prediction of P-glycoprotein substrate specificity (S(PGP)) can be viewed as a constituent part of a compound's "pharmaceutical profiling" in drug design. This task is difficult to achieve due to several factors that raised many contradictory opinions: (i) the disparity between the S(PGP) values obtained in different assays, (ii) the confusion between Pgp substrates and inhibitors, (iii) the confusion between lipophilicity and amphiphilicity of Pgp substrates, and (iv) the dilemma of describing class-specific relationships when Pgp has no binding sites of high ligand specificity. In this work, we compiled S(PGP) data for 1000 compounds. All data were represented in a binary format, assigning S(PGP) = 1 for substrates and S(PGP) = 0 for non-substrates. Each value was ranked according to the reliability of experimental assay. Two data sets were considered. Set 1 included 220 compounds with S(PGP) from polarized transport across MDR1 transfected cell monolayers. Set 2 included the entire list of 1000 compounds, with S(PGP) values of generally lower reliability. Both sets were analysed using a stepwise classification structure-activity relationship (C-SAR) method, leading to derivation of simple rules for crude estimation of S(PGP) values. The obtained rules are based on the following factors: (i) compound's size expressed through molar weight or volume, (ii) H-accepting given by the Abraham's beta (that can be crudely approximated by the sum of O and N atoms), and (iii) ionization given by the acid and base pKa values. Very roughly, S(PGP) can be estimated by the "rule of fours". Compounds with (N + O) > or = 8, MW > 400 and acid pKa > 4 are likely to be Pgp substrates, whereas compounds with (N + O) < or = 4, MW < 400 and base pKa < 8 are likely to be non-substrates. The obtained results support the view that Pgp functioning can be compared to a complex "mini-pharmacokinetic" system with fuzzy specificity. This system can be described by a probabilistic version of Abraham's solvation equation, suggesting a certain similarity between Pgp transport and chromatographic retention. The chromatographic model does not work in the case of "marginal" compounds with properties close to the "global" physicochemical cut-offs. In the latter case various class-specific rules must be considered. These can be associated with the "amphiphilicity" and "biological similarity" of compounds. The definition of class-specific effects entails construction of the knowledge base that can be very useful in ADME profiling of new drugs.

[Malignant tumor incidence in employees of the Alytus textile factory (1978-1997)]. / Alytaus tekstiles imones darbuotoju sergamumas piktybiniais navikais (1978-1997).

UNLABELLED: The purpose of this study was to evaluate cancer incidence in the large cotton-manufacturing factory in Lithuania. Altogether 10,198 workers employed at least 1 year in 1969-1997 were included in the cohort and followed during the period 1978/01/01-1997/12/31. National cancer rates were used to calculate the expected number of cancer cases. The overall cancer risk for men was slightly higher than that in the general population (standardized incidence rate (SIR) 1.15, 95% confidence interval (95% CI) 0.98-1.34). A significant increase in the incidence of esophagus (11 observed cases, SIR 3.76, 95% CI 1.88-6.67) and slightly increased of lung (42 observed cases, SIR 1.26, 95% CI 0.91-1.70) cancer became evident. None of the cancer risk showed statistically significant excess cancer incidence in the textile-processing (spinning and weaving) departments (SIR 0.98). In the women cohort the level of the general incidence was very close to expected, standardized incidence rates (SIR) being 0.99 (95% CI 0.88-1.13). However, there was a significant increase in the number of cases of gall bladder (6 observed, SIR 3.19, 95% CI 1.17-6.95). The analysis of the results among textile-processing (spinning and weaving departments) workers indicated the elevated risk of breast cancer (44 observed cases, SIR 1.49, 95% CI 1.08-2.0) and cervical cancer (24 observed cases, SIR 1.68, 95% CI 1.08-2.50). The number of lung cancer cases in this group was a higher, but statistically not significant (5 observed cases, SIR 1.53, 95% CI 0.5-3.58). Increased SIR values were observed for > or = 10 years since the first exposure for all cancers, cervix uteri, ovary and kidney. CONCLUSIONS: The overall cancer risk for men cohort was slightly higher than that in the general population. There was a significant increase in the number of cancer of the esophagus. The overall excess risk in women cohort was only for gall bladder, but for spinners and weavers the elevated risk was for cervix uteri and breast cancer. After 10 years of employment the excess risk was already for all cancers, cervix uteri, ovary and kidney malignant tumors.