Predicting Skin Permeability by means of Computational Approaches : Reliability and Caveats in Pharmaceutical Studies

© 2019 American Chemical Society.The skin is the main barrier between the internal body environment and the external one. The characteristics of this barrier and its properties are able to modify and affect drug delivery and chemical toxicity parameters. Therefore, it is not surprising that permeability of many different compounds has been measured through several in vitro and in vivo techniques. Moreover, many different in silico approaches have been used to identify the correlation between the structure of the permeants and their permeability, to reproduce the skin behavior, and to predict the ability of specific chemicals to permeate this barrier. A significant number of issues, like interlaboratory variability, experimental conditions, data set building rationales, and skin site of origin and hydration, still prevent us from obtaining a definitive predictive skin permeability model. This review wants to show the main advances and the principal approaches in computational methods used to predict this property, to enlighten the main issues that have arisen, and to address the challenges to develop in future research.Peer reviewedFinal Accepted Versio

Qualitative prediction of blood–brain barrier permeability on a large and refined dataset

The prediction of blood–brain barrier permeation is vitally important for the optimization of drugs targeting the central nervous system as well as for avoiding side effects of peripheral drugs. Following a previously proposed model on blood–brain barrier penetration, we calculated the cross-sectional area perpendicular to the amphiphilic axis. We obtained a high correlation between calculated and experimental cross-sectional area (r = 0.898, n = 32). Based on these results, we examined a correlation of the calculated cross-sectional area with blood–brain barrier penetration given by logBB values. We combined various literature data sets to form a large-scale logBB dataset with 362 experimental logBB values. Quantitative models were calculated using bootstrap validated multiple linear regression. Qualitative models were built by a bootstrapped random forest algorithm. Both methods found similar descriptors such as polar surface area, pKa, logP, charges and number of positive ionisable groups to be predictive for logBB. In contrast to our initial assumption, we were not able to obtain models with the cross-sectional area chosen as relevant parameter for both approaches. Comparing those two different techniques, qualitative random forest models are better suited for blood-brain barrier permeability prediction, especially when reducing the number of descriptors and using a large dataset. A random forest prediction system (ntrees = 5) based on only four descriptors yields a validated accuracy of 88%

Review of QSAR Models and Software Tools for predicting Biokinetic Properties

In the assessment of industrial chemicals, cosmetic ingredients, and active substances in pesticides and biocides, metabolites and degradates are rarely tested for their toxicologcal effects in mammals. In the interests of animal welfare and cost-effectiveness, alternatives to animal testing are needed in the evaluation of these types of chemicals. In this report we review the current status of various types of in silico estimation methods for Absorption, Distribution, Metabolism and Excretion (ADME) properties, which are often important in discriminating between the toxicological profiles of parent compounds and their metabolites/degradation products. The review was performed in a broad sense, with emphasis on QSARs and rule-based approaches and their applicability to estimation of oral bioavailability, human intestinal absorption, blood-brain barrier penetration, plasma protein binding, metabolism and. This revealed a vast and rapidly growing literature and a range of software tools. While it is difficult to give firm conclusions on the applicability of such tools, it is clear that many have been developed with pharmaceutical applications in mind, and as such may not be applicable to other types of chemicals (this would require further research investigation). On the other hand, a range of predictive methodologies have been explored and found promising, so there is merit in pursuing their applicability in the assessment of other types of chemicals and products. Many of the software tools are not transparent in terms of their predictive algorithms or underlying datasets. However, the literature identifies a set of commonly used descriptors that have been found useful in ADME prediction, so further research and model development activities could be based on such studies.JRC.DG.I.6-Systems toxicolog

Validacija topokemijskih modela za predviđanje permeabilnosti kroz krvno-moždanu barijeru

Recently published topochemical models for permeability through the blood-brain barrier were validated and cross-validated in the present study. Five models based on three topochemical indices, Wiener’s topochemical index - a distance-based topochemical descriptor, molecular connectivity topochemical index - an adjacency-based topochemical descriptor and eccentric connectivity topochemical index - an adjacency-cum-distance based topochemical descriptor, for permeability of structurally and chemically diverse molecules through blood-brain barrier were used in the present investigation. A data set comprising 62 structurally and chemically diverse compounds was selected. This data set was divided into two sets of 31 compounds each - one to serve as the validation set and other as the cross-validation set. The values of all the three-topochemical indices in the original as well as in the normalized form for each of the 31 compounds of the validation set were computed using an in house computer program. Resultant data was analyzed and each compound was assigned a permeability characteristic using topochemical models, which was then compared with the reported permeability through the blood-brain barrier. Accuracy of prediction of these models was calculated. The same procedure was similarly followed for the cross-validation set. Studies revealed accuracy of prediction of the order of 7080% during validation. Surprisingly, very high predictability of the order of 7791% was observed during cross-validation. High predictability observed during validation as well as cross-validation authenticates topochemical models for prediction of permeability through the blood-brain barrier.U ovom radu su validirani i unakrsno validirani nedavno objavljeni topokemijski modeli za permeabilnost kroz krvno-moždanu barijeru. Predviđanje prolaska kroz krvno-moždanu barijeru strukturno i kemijski različitih molekula provedeno je na pet modela koji se temelje na tri topološka indeksa, Wienerovom topološkom indeksu, topološkom indeksu molekularne povezanosti i topološkom indeksu ekscentrične povezanosti. Ukupno 62 spoja podijeljena su u dva seta koji su sadržavali 31 spoj. Jedan set upotrebljen je za validaciju, a drugi za unakrsnu validaciju. Vrijednosti svih triju topoloških indeksa u početnom setu i u normaliziranom setu su računate pomoću kompjutorskog programa. Rezultati su analizirani i svakom spoju je pridružena teorijska vrijednost permeabilnosti, koja je zatim uspoređivana s objavljenim eksperimentalnim podacima za permeabilnost kroz krvno-moždanu barijeru. Točnost predviđanja bila je između 70 i 80%. Isti postupak je proveden za unakrsno validacijski set, a točnost je bila iznenađujeće velika (7791%), što ukazuje da se upotrebljeni topokemijski modeli mogu upotrijebiti za predviđanje permeabilnsot kroz krvno-moždanu barijeru

Investigation of the Usability of Some Triazole Derivative Compounds as Drug Active Ingredients by ADME and Molecular Docking Properties

In this study, some important ADME parameters such as physicochemical properties, lipophilicity, water solubility, pharmacokinetics, medicinal chemistry and drug-likeness properties of ten triazole derivative compounds, which may be drug active ingredients, were performed on the SwissADME a web tool worked on-line. Bioavailability radar plotted for each molecule for rapid assessment of drug-likeness. The BOILED-Egg graph was plotted for each molecule to assess passive gastrointestinal absorption (HIA) and brain penetration (BBB) relative to the position of the molecules. SwissTargetPrediction a web tool worked on-line was used to predict the most likely protein targets of molecules. Docking programs have a wide range of applications ranging from computer aided to drug design. Molecules were docked with the determined target protein using the SwissDock a web tool worked on-line

Partial Least Square and Hierarchical Clustering in ADMET Modeling: Prediction of Blood - Brain Barrier Permeation of alpha-Adrenergic and Imidazoline Receptor Ligands

PURPOSE. Rate of brain penetration (logPS), brain/plasma equilibration rate (logPS-brain), and extent of blood-brain barrier permeation (logBB) of 29 alpha-adrenergic and imidazoline-receptors ligands were examined in Quantitative-Structure-Property Relationship (QSPR) study. METHODS. Experimentally determined chromatographic retention data (logKw at pH 4.4, slope (S) at pH 4.4, logKw at pH 7.4, slope (S) at pH 7.4, logKw at pH 9.1, and slope (S) at pH 9.1) and capillary electrophoresis migration parameters (mu(eff) at pH 4.4, mu(eff) at pH 7.4, and mu(eff) at pH 9.1), together with calculated molecular descriptors, were used as independent variables in the QSPR study by use of partial least square (PLS) methodology. RESULTS. Predictive potential of the formed QSPR models, QSPR(logPS), QSPR(logPS-brain), QSPR(logBB), was confirmed by cross- and external validation. Hydrophilicity (Hy) and H-indices (H7m) were selected as significant parameters negatively correlated with both logPS and logPS-brain, while topological polar surface area (TPSA(NO)) was chosen as molecular descriptor negatively correlated with both logPS and logBB. The principal component analysis (PCA) and hierarchical clustering analysis (HCA) were applied to cluster examined drugs based on their chromatographic, electrophoretic and molecular properties. Significant positive correlations were obtained between the slope (S) at pH 7.4 and logBB in A/B cluster and between the logKw at pH 9.1 and logPS in C/D cluster. CONCLUSIONS. Results of the QSPR, clustering and correlation studies could be used as novel tool for evaluation of blood-brain barrier permeation of related alpha-adrenergic/imidazoline receptor ligands

To what extent do cell-penetrating peptides selectively cross the blood-brain barrier?

The blood-brain barrier protects the brain from toxic compounds. Its selective permeability is essential for the optimal function of the central nervous system. Some peptides can cross the blood-brain barrier. On the other hand, cell-penetrating peptides are able to overcome the cell membrane. During this research project, it was investigated whether these cell-penetrating peptides also can cross the blood-brain barrier. The chemical diversity of the already reported cell-penetrating peptides was investigated and a unified response for the extent of cellular uptake of peptides was introduced. Based on this study, a set of cell-penetrating peptides was rationally selected for further research. In order to more objectively compare the quantitative data on the blood-brain barrier influx of peptides, a classification system for blood-brain barrier influx was established. The purity of the selected synthetized cell-penetrating peptides was also investigated, which is essential for obtaining reliable research conclusions. Different chromatographic systems were compared for the analysis of the selected peptides. The investigated cell-penetrating peptides crossed the blood-brain barrier to a different extent. The influx varied from very low to very high and some peptides showed efflux out of the brain. There was no correlation observed between the blood-brain barrier transport kinetics and the extent of cellular uptake. During the aging process, the blood-brain barrier shows an increased permeability and, together with other age-related functional changes, should be taken into account during the development of medicines used by the elderly. Therefore, the current regulatory status of the development of geriatric medicines was investigated

Design of Novel Dopamine Prodrugs - A Computational Approach

Parkinson patients have insufficient dopamine in specific regions of the brain, so attempts have been made to replenish the deficiency in the dopamine. Dopamine itself doesn't cross blood brain barrier, but its precursor, levodopa (LD) is actively transported into the CNS and is converted to dopamine in the brain. The bioavailability of LD is less than 10% with only 1% of administered oral levodopa penetrates the brain. Large doses of levodopa are required because much of the drug is decarboxylated to dopamine in the periphery, resulting in side effects that include nausea, vomiting, cardiac arrhythmias, and hypotension. To minimize the conversion to dopamine (DA) outside the central nervous system (CNS), LD is usually co-administered with peripheral inhibitors of amino acid decarboxylase (carbidopa or benserazide). In spite of that, other central nervous side effects such as dyskinesia, on-off phenomenon and end-of-dose deterioration still remain. In this project, a number of dopamine prodrugs were designed using DFT molecular orbital at B3LYP 6-31G (d, p) levels and molecular mechanics (MM2) calculations aiming to provide prodrugs that are expected to give better bioavailability than the parental drug owing to improved absorption. Furthermore, the proposed prodrugs are believed to be more effective than L-dopa because the latter undergoes decarboxylation in the periphery before reaching the blood–brain barrier. The DFT calculation results revealed that the rate of a proton transfer in processes dopamine ProD 1-ProD 5 is largely dependent on the geometric variations of thereactant (GM) mainly the distance between the two reactive centers, rGM, and the angle of attack α. It was found that systems with low rGM and high α values in their global minimum structures, such as ProD 1 and ProD 2, exhibit much higher rates (lower ∆G‡ ) than these with high rGM and low α values, such as ProD 3-ProD 5 and the rate of the reaction is linearly correlated with rGM and (1/α). Moreover, it was found that the intraconversion rate of the designed dopamine prodrugs is largely determined on the strain energies of the reaction ̓s tetrahedral intermediates(EsINT). Systems having strained tetrahedral intermediates were found to be with low rates and vice versa

Quantitative Structure - Skin permeability Relationships

This paper reviews in silico models currently available for the prediction of skin permeability with the main focus on the quantitative structure-permeability relationship (QSPR) models. A comprehensive analysis of the main achievements in the field in the last decade is provided. In addition, the mechanistic models are discussed and comparative studies that analyse different models are discussed