Novel generic UPLC/MS/MS method for high throughput analysis applied to permeability assessment in early Drug Discovery.

A novel generic ultra performance liquid chromatography-tandem mass spectrometric (UPLC/MS/MS) method for the high throughput quantification of samples generated during permeability assessment (PAMPA) has been developed and validated. The novel UPLC/MS/MS methodology consists of two stages. Firstly, running a 1.5min isocratic method, compound-specific multiple reaction monitoring (MRM) methods were automatically prepared. In a second stage, samples were analyzed by a 1.5min generic gradient UPLC method on a BEH C18 column (50mmx2.1mm). Compounds were detected with a Waters Micromass Quattro Premier mass spectrometer operating in positive electrospray ionization using the compound-specific MRM methods. The linearity for the validation compounds (caffeine, propranolol, ampicillin, atenolol, griseofulvin and carbamazepine) typically ranges from 3.05nM to 12,500nM and the limits of detection for all generically developed methods are in the range between 0.61nM and 12nM in an aqueous buffer. The novel generic methodology was successfully introduced within early Drug Discovery and resulted in a four-fold increase of throughput as well as a significant increase in sensitivity compared to other in-house generic LC/MS methods.

AC and DC electrospinning of hydroxypropylmethylcellulose with polyethylene oxides as secondary polymer for improved drug dissolution.

Alternating current electrospinning (ACES) capable to reach multiple times higher specific productivities than widely used direct current electrospinning (DCES) was investigated and compared with DCES to prepare drug-loaded formulations based on one of the most widespread polymeric matrix used for commercialized pharmaceutical solid dispersions, hydroxypropylmethylcellulose 2910 (HPMC). In order to improve the insufficient spinnability of HPMC (both with ACES and DCES) polyethylene oxide (PEO) as secondary polymer with intense ACES activity was introduced into the electrospinning solution. Different grades of this polymer used at as low concentrations in the fibers as 0.1% or less enabled the production of high quality HPMC-based fibrous mats without altering its physicochemical properties remarkably. Increasing concentrations of higher molecular weight PEOs led to the thickening of fibers from submicronic diameters to several microns of thickness. ACES fibers loaded with the poorly water-soluble model drug spironolactone were several times thinner than drug-loaded fibers prepared with DCES in spite of the higher feeding rates applied. The amorphous HPMC-based fibers with large surface area enhanced the dissolution of spironolactone significantly, the presence of small amounts of PEO did not affect the dissolution rate. The presented results confirm the diverse applicability of ACES, a novel technique to prepare fibrous drug delivery systems.

Lubricant-Induced Crystallization of Itraconazole From Tablets Made of Electrospun Amorphous Solid Dispersion.

Investigation of downstream processing of nanofibrous amorphous solid dispersions to generate tablet formulation is in a quite early phase. Development of high speed electrospinning opened up the possibility to study tableting of electrospun solid dispersions (containing polyvinylpyrrolidone-vinyl acetate and itraconazole [ITR] in this case). This work was conducted to investigate the influence of excipients on dissolution properties and the feasibility of scaled-up rotary press tableting. The dissolution rates from tablets proved to be mainly composition dependent. Magnesium stearate acted as a nucleation promoting agent (providing an active hydrophobic environment for crystallization of ITR) hindering the total dissolution of ITR. This crystallization process proved to be temperature dependent as well. However, the extent of dissolution of more than 95% was realizable when a less hydrophobic lubricant, sodium stearyl fumarate (soluble in the medium), was applied. Magnesium stearate induced crystallization even if it was put in the dissolution medium next to proper tablets. After optimization of the composition, scaled-up tableting on a rotary press was carried out. Appropriate dissolution of ITR from tablets was maintained for 3 months at 25°C/60% relative humidity. HPLC measurements confirmed that ITR was chemically stable both in the course of downstream processing and storage.

Alternating current electrospinning for preparation of fibrous drug delivery systems.

Alternating current electrospinning (ACES) was compared to direct current electrospinning (DCES) for the preparation of drug-loaded nanofibrous mats. It is generally considered that DCES is the solely technique to produce nanofibers using the electrostatic force from polymer solutions, however, less studied and also capable ACES provides further advantages such as increased specific productivities. A poorly water-soluble drug (carvedilol) was incorporated into the fibers based on three different polymeric matrices (an acid-soluble terpolymer (Eudragit(®) E), a base-soluble copolymer (Eudragit(®) L 100-55) and a nonionic homopolymer (polyvinylpyrrolidone K90)) to improve the dissolution of the weak base drug under different pH conditions. Morphology and fiber diameter evaluation showed similar electrospun fibers regardless the type of the high voltage and the major differences in feeding rates. The amorphous ACES and DCES fibers provided fast and total drug dissolutions in all cases. The presented results show that ACES can be a more feasible novel alternative to formulate fibers for drug delivery purposes.

Incorporation of drugs in an amorphous state into electrospun nanofibers composed of a water-insoluble, nonbiodegradable polymer.

Electrostatic spinning was applied to the preparation of drug-laden nonbiodegradable nanofiber for potential use in topical drug administration and wound healing. The specific aim of these studies was to assess whether these systems might be of interest as delivery systems for poorly water-soluble drugs. Itraconazole and ketanserin were selected as model compounds while a segmented polyurethane (PU) was selected as the nonbiodegradable polymer. For both itraconazole and ketanserin, an amorphous nanodispersion with PU was obtained when the drug/polymer solutions were electrospun from dimethylformide (DMF) and dimethylacetamide (DMAc), respectively. The collected nonwoven fabrics were shown to release the drugs at various rates and profiles based on the nanofiber morphology and drug content. Data were generated using a specially designed release apparatus based around a rotating cylinder. At low drug loading, itraconazole was released from the nanofibers as a linear function of the square root of time suggesting Fickian kinetics. No initial drug burst was observed. A biphasic release pattern was observed for ketanserin in which two sequential linear components were noted. These release phases may be temporally correlated with (1) drug diffusion through the polymer and (2) drug diffusion through formed aqueous pores.

Usefulness of a novel Caco-2 cell perfusion system. I. In vitro prediction of the absorption potential of passively diffused compounds.

A simple, reliable, and user friendly system was established to cultivate Caco-2 cell monolayer for epithelial transport studies. After an initial growth period of 1 week in a CO(2) incubator, Caco-2 cells were cultivated in an automated continuous perfusion system (Minucells and Minutissue, Germany). Medium was constantly renewed at the apical and basal side of the monolayers, which resulted in a continuous supply of nutrients as well as in a continuous removal of metabolite wastes. The monolayers obtained with the new perfusion culture system were evaluated to estimate the passive transport properties of a series of model compounds. The results produced were compared to those of monolayers obtained with the standard 21-day system. The integrity of cell monolayers was checked by measuring transepithelial electrical resistance (TEER) and by the transport of the paracellular leakage marker sodium fluorescein. The results of confocal microscopy as well as TEER measurements indicated the formation of a monolayer on various support filters. The growth and differentiation of Caco-2 cells were highly dependent upon the individual support filters and extracellular matrix proteins used for Caco-2 attachment. The permeability coefficients of several model compounds across Caco-2 cells obtained with the perfusion system were approximately two-fold higher than those obtained using the traditional 21-day Snapwell-based cultures. A good correlation was found between the transport of passively diffused drugs across Caco-2 monolayers differentiated in the perfusion system and the transport according to the standard method. The rank ordering of high permeable model compounds tested through Caco-2 monolayers, differentiated in a perfusion system, was similar to the standard 21-day culture method.

Application of PAMPA-models to predict BBB permeability including efflux ratio, plasma protein binding and physicochemical parameters.

This study examines whether algorithms to predict brain penetration of 88 drug candidates could benefit from inclusion of PAMPA data such as P(eff), flux and membrane retention. Specifically the ability to fit experimentally derived LogBB data with PAMPA information and compound related physicochemical and structural parameters was assessed. Collected data were analyzed by partial least square analysis and various regression models for LogBB. Four PAMPA methodologies were evaluated in this study including: (1) a PAMPA-BLM (black lipid membrane) model, (2) a PAMPA-DS (double sink) model, (3) a PAMPA-BBB (blood-brain barrier) model and (4) a PAMPA-BBB-UWL (unstirred water layer). Additionally, plasma protein binding (PPB) experiments and a Caco-2 assay were performed to determine the unbound fraction in plasma and the efflux ratio, respectively, for subsets of the selected compounds. This information was combined with the obtained PAMPA data in an effort to improve the predictions of LogBB. Taken in aggregate, the results presented, suggest that the PAMPA-BLM parameters are the most important contributors to predict the LogBB. The optimized multiple linear regression (MLR) relationship including the PAMPA-BLM properties demonstrated a slightly improved prediction compared to the model without the PAMPA-BLM parameters. Including the plasma protein binding of 15 compounds resulted in a significantly improved PAMPA-BLM prediction of LogBB, while integrating the efflux ratio with PAMPA-BLM or PAMPA-BBB P(eff) values, resulted in improved classification of brain permeable [BBB+(LogBB>/=0)] and impermeable [BBB-(LogBB<0)] compounds.

Application of PAMPA-models to predict BBB permeability including efflux ratio, plasma protein binding and physicochemical parameters.

This study examines whether algorithms to predict brain penetration of 88 drug candidates could benefit from inclusion of PAMPA data such as Peff, flux and membrane retention. Specifically the ability to fit experimentally derived LogBB data with PAMPA information and compound related physicochemical and structural parameters was assessed. Collected data were analyzed by partial least square analysis and various regression models for LogBB. Four PAMPA methodologies were evaluated in this study including: (1) a PAMPA-BLM (black lipid membrane) model, (2) a PAMPA-DS (double sink) model, (3) a PAMPA-BBB (blood-brain barrier) model and (4) a PAMPA-BBB-UWL (unstirred water layer). Additionally, plasma protein binding (PPB) experiments and a Caco-2 assay were performed to determine the unbound fraction in plasma and the efflux ratio, respectively, for subsets of the selected compounds. This information was combined with the obtained PAMPA data in an effort to improve the predictions of LogBB. Taken in aggregate, the results presented, suggest that the PAMPA-BLM parameters are the most important contributors to predict the LogBB. The optimized multiple linear regression (MLR) relationship including the PAMPA-BLM properties demonstrated a slightly improved prediction compared to the model without the PAMPA-BLM parameters. Including the plasma protein binding of 15 compounds resulted in a significantly improved PAMPA-BLM prediction of LogBB, while integrating the efflux ratio with PAMPA-BLM or PAMPA-BBB Peff values, resulted in improved classification of brain permeable [BBB + (LogBB >or= 0)] and impermeable [BBB--(LogBB < 0)] compounds.

Evaluation of various PAMPA models to identify the most discriminating method for the prediction of BBB permeability.

The Parallel Artificial Membrane Permeability Assay (PAMPA) has been successfully introduced into the pharmaceutical industry to allow useful predictions of passive oral absorption. Over the last 5 years, researchers have modified the PAMPA such that it can also evaluate passive blood-brain barrier (BBB) permeability. This paper compares the permeability of 19 structurally diverse, commercially available drugs assessed in four different PAMPA models: (1) a PAMPA-BLM (black lipid membrane) model, (2) a PAMPA-DS (Double Sink) model, (3) a PAMPA-BBB model and (4) a PAMPA-BBB-UWL (unstirred water layer) model in order to find the most discriminating method for the prediction of BBB permeability. Both the PAMPA-BBB model and the PAMPA-BLM model accurately identified compounds which pass the BBB (BBB+) and those which poorly penetrate the BBB (BBB-). For these models, BBB+ and BBB- classification ranges, in terms of permeability values, could be defined, offering the opportunity to validate the paradigm with in vivo data. The PAMPA models were subsequently applied to a set of 14 structurally diverse internal J&J candidates with known log (brain/blood concentration) (LogBB) values. Based on these LogBB values, BBB classifications were established (BBB+: LogBB0 >or=; BBB-: LogBB<0). PAMPA-BLM resulted in three false positive identifications, while PAMPA-BBB misclassified only one compound. Additionally, a Caco-2 assay was performed to determine the efflux ratio of all compounds in the test set. The false positive that occurred in both models was shown to be related to an increased efflux ratio. Both the PAMPA-BLM and the PAMPA-BBB models can be used to predict BBB permeability of compounds in combination with an assay that provides p-gp efflux data, such as the Caco-2 assay.

Prediction of blood-brain barrier penetration of poorly soluble drug candidates using surface activity profiling.

The aim of this study was to determine whether transepithelial transport across the blood-brain barrier (BBB) [expressed as the logarithm of blood/brain partitioning coefficient (logbb)] could be correlated to surface tension properties for a series of new chemical entities (NCEs) having extremely low solubility in aqueous media. Surface tension data were generated by the "Du Nouy maximum pull force method" using an automated, small volume Kibron Delta 8 Multi-channel tensiometer. Using the surface pressure/concentration profiles, parameters such as the maximum surface pressure, cross-sectional area and the air-water partitioning coefficient were calculated for the individual compounds and correlated with their in vivo logbb values. A good linear correlation (R(2)=0.8669) between logbb and cross-sectional area was observed, suggesting a morphological analogy between the molecular orientation at the air-water interface and the anisotropic cellular bilayer of the blood-brain barrier.

In vivo, in vitro and in silico methods for small molecule transfer across the BBB.

The inability of molecules to permeate the BBB is a significant source of attrition in Central Nervous System (CNS) drug discovery. Given the increasing medical drivers for new and improved CNS drugs, small molecule transfer across the BBB is attracting a heightened awareness within pharmaceutical industry and medical fields. In order to assess the potential for small CNS molecules to permeate the BBB, a variety of methods and models, from in silico to in vivo going through in vitro models are developed as predictive tools in drug discovery. This review gives a comprehensive overview of different approaches currently considered in drug discovery to circumvent the lack of small molecule transfer through the BBB, together with their inherent advantages and disadvantages. Particularly, special attention is drawn to in silico models, with a detailed and contemporary point of view on prediction tools and guidelines for rational design.