Extending the limitations in the prediction of PAMPA permeability with machine learning algorithms.

Gastrointestinal absorption is a key factor amongst the ADME-related (absorption, distribution, metabolism and excretion) pharmacokinetic properties; therefore, it has a major role in drug discovery and drug safety determinations. The Parallel Artificial Membrane Permeability Assay (PAMPA) can be considered as the most popular and well-known screening assay for the measurement of gastrointestinal absorption. Our study provides quantitative structure-property relationship (QSPR) models based on experimental PAMPA permeability data for almost four hundred diverse molecules, which is a great extension of the applicability of the models in the chemical space. Two- and three-dimensional molecular descriptors were applied for the model building in every case. We have compared the performance of a classical partial least squares regression (PLS) model with two major machine learning algorithms: artificial neural networks (ANN) and support vector machine (SVM). Due to the applied gradient pH in the experiments, we have calculated the descriptors for the model building at pH values of 7.4 and 6.5, and compared the effect of pH on the performance of the models. After a complex validation protocol, the best model had an R2=0.91 for the training set, and R2= 0.84 for the external test set. The developed models are capable for the robust and fast prediction of new compounds with an excellent accuracy compared to the previous QSPR models.

Natural Lipid Extracts as an Artificial Membrane for Drug Permeability Assay: In Vitro and In Silico Characterization.

In vitro non-cellular permeability models such as the parallel artificial membrane permeability assay (PAMPA) are widely applied tools for early-phase drug candidate screening. In addition to the commonly used porcine brain polar lipid extract for modeling the blood-brain barrier's permeability, the total and polar fractions of bovine heart and liver lipid extracts were investigated in the PAMPA model by measuring the permeability of 32 diverse drugs. The zeta potential of the lipid extracts and the net charge of their glycerophospholipid components were also determined. Physicochemical parameters of the 32 compounds were calculated using three independent forms of software (Marvin Sketch, RDKit, and ACD/Percepta). The relationship between the lipid-specific permeabilities and the physicochemical descriptors of the compounds was investigated using linear correlation, Spearman correlation, and PCA analysis. While the results showed only subtle differences between total and polar lipids, permeability through liver lipids highly differed from that of the heart or brain lipid-based models. Correlations between the in silico descriptors (e.g., number of amide bonds, heteroatoms, and aromatic heterocycles, accessible surface area, and H-bond acceptor-donor balance) of drug molecules and permeability values were also found, which provides support for understanding tissue-specific permeability.

Cyclodextrin-enabled nepafenac eye drops with improved absorption open a new therapeutic window.

Nepafenac is a highly effective NSAID used for treating postoperative ocular inflammation and pain after cataract surgery and its advantage over conventional topical NSAIDs has been proved many times. However, Nevanac® is a suspension eye drop, which clearly lacks patient compliance causing irritation, blurred vision, foreign body sensation along with problematic dosage due to its sticky, inhomogeneous consistence. In this study, nepafenac containing eye drops were prepared using hydroxypropyl-ß-cyclodextrin to ensure complete dissolution of nepafenac, sodium hyaluronate to provide mucoadhesion and adequate viscosity and a preservative-free officinal formula, Oculogutta Carbomerae containing carbomer (just like Nevanac®), therefore providing a similar base for the new formulations. According to an experimental design, 11 formulations were tested in vitro including two reference formulations by measuring their viscosity, mucoadhesion, drug release and corneal permeability. Finally, two formulations were found promising and investigated further on porcine eyes ex vivo and corneal distribution of nepafenac was determined by RAMAN mapping. The results showed that one formulation possessed better bioavailability ex vivo than Nevanac® 0.1 % suspension, while the other formulation containing only 60 % of the original dose were ex vivo equivalent with Nevanac® opening the way to nepafenac-containing eye drops with better patient compliance in the future.

Synthesis, Pharmacokinetic Characterization and Antioxidant Capacity of Carotenoid Succinates and Their Melatonin Conjugates.

Carotenoid succinates were synthesized from hydroxy carotenoids and were coupled to a commercially available derivative of melatonin via amide bond for producing more powerful anti-oxidants and yet new hybrid lipophilic bifunctional molecules with additional therapeutic effects. The coupling reactions produced conjugates in acceptable to good yields. Succinylation increased the water solubility of the carotenoids, while the conjugation with melatonin resulted in more lipophilic derivatives. The conjugates showed self-assembly in aqueous medium and yielded relatively stable colloidal solutions in phosphate-buffered saline. Antioxidant behavior was measured with ABTS and the FRAP methods for the carotenoids, the carotenoid succinates, and the conjugates with melatonin. A strong dependence on the quality of the solvent was observed. TEAC values of the new derivatives in phosphate-buffered saline were found to be comparable to or higher than those of parent carotenoids, however, synergism was observed only in FRAP assays.

Synthesis, complex formation and corneal permeation of cyclodextrin-modified, thiolated poly(aspartic acid) as self-gelling formulation of dexamethasone.

The present study aimed at developing a potential in situ gellable dexamethasone (DXM) eye drop. Poly(aspartic acid) (PASP) derivatives were synthesized with dual functionality to improve the solubility of DXM, and to achieve in situ gelation. First, amine-modified ß-cyclodextrin (CD) was attached to polysuccinimide (PSI), second, thiol functionalities were added by the reaction of cysteamine and succinimide rings. Finally, the PSI derivatives were hydrolysed to the corresponding PASP derivatives to get water-soluble polymers. Phase-solubility studies confirmed the complexation ability of CD-containing PASP derivatives. In situ gelation and the effect of the CD immobilization on this behaviour were characterized by rheological measurements. The solubilizing effect of CD was confirmed by kinetic solubility measurements, whereas in vitro corneal permeability assay (corneal-PAMPA) measurements were performed to determine in vitro permeability and flux values. The effect of the PASP derivatives on permeation strongly depended on chemical composition and polymer concentration.

A corneal-PAMPA-based in silico model for predicting corneal permeability.

The capability to predict corneal permeability based on physicochemical parameters has always been a desirable objective of ophthalmic drug development. However, previous work has been limited to cases where either the diversity of compounds used was lacking or the performance of the models was poor. Our study provides extensive quantitative structure-property relationship (QSPR) models for corneal permeability predictions. The models involved in vitro corneal permeability measurements of 189 diverse compounds. Preliminary analysis of data showed that there is no significant correlation between corneal-PAMPA (Parallel Artificial Membrane Permeability Assay) permeability values and other pharmacokinetically relevant in silico drug transport parameters like Caco-2, jejunal permeability and blood-brain partition coefficient (logBB). Two different QSPR models were developed: one for corneal permeability and one for corneal membrane retention, based on experimental corneal-PAMPA permeability data. Partial least squares regression was applied for producing the models, which contained classical molecular descriptors and ECFP fingerprints in combination. A complex validation protocol (including internal and external validation) was carried out to provide robust and appropriate predictions for the permeability and membrane retention values. Both models had an overall fit of R2 > 0.90, including R2-values not lower than 0.85 for validation runs, and provide quick and accurate predictions of corneal permeability values for a diverse set of compounds.

Corneal-PAMPA: A novel, non-cell-based assay for prediction of corneal drug permeability.

The present study was aimed to develop an in vitro non-cell-based method, for the measurement of corneal permeability. To this avail, the parallel artificial membrane permeability assay (PAMPA) was used for studying the effects of composition of the artificial lipid membrane, the DMSO cosolvent content of the donor phase as well as different buffer solutions in the model. Based on experimental corneal permeability values of 25 active pharmaceutical ingredients (APIs), a final model with good predictive ability (R2 = 0.880) was developed and validated. The optimized conditions for the Corneal-PAMPA were the following: iso-pH conditions using phosphate buffer saline (PBS pH 7.4) without cosolvent, phosphatidylcholine (10.7 w/v %; without cholesterol) dissolved in a mixture of hexane:dodecane:chloroform = 70:25:5 (v/v) as an artificial membrane, 4 h incubation of the PAMPA plates at 35 °C. The model's applicability in the case of diluted solutions of eye drops was also demonstrated.