Chromatographic bioanalysis of antiglaucoma drugs in ocular tissues.

ABSTRACT

Glaucoma is a heterogeneous group of multifactorial optic neuropathies and the leading cause of irreversible blindness and visual impairment. Epidemiological data has estimated that in 2020 there will be more than 80 million individuals affected by the disease worldwide. Nowadays, intraocular pressure (IOP) lowering is carried out mainly by pharmacotherapy, with different drugs. The study of ocular pharmacokinetics of antiglaucoma drugs, crucial for better understanding of ocular distribution, bioavailability, and pharmacodynamic parameters, can benefit the development of antiglaucoma drugs or formulations. Bioanalysis of drugs in ocular matrices is still underestimated, since it is challenging and rarely performed. Therefore, this review summarized the chromatographic methods employed for the quantification of several antiglaucoma drugs in different ocular matrices, discussing bioanalytical steps, such as sample preparation, separation, and detection. Animals and matrices as well as the challenges faced in ocular bioanalysis were also discussed. Ocular bioanalysis has been performed mainly in rabbits, the most adequate animal model for ocular studies. The matrix most used is aqueous humor, because it is cleaner and easier to sample. Sample preparation was carried out primarily employing classic techniques, such as liquid-liquid extraction, protein precipitation, and solid-phase extraction, with conventional solvents and sorbents. Chromatographic separation was achieved predominantly by reversed-phase liquid chromatography. Ultraviolet spectrophotometry and tandem mass spectrometry prevailed for detection, although other techniques, such as fluorimetry, have also been used. It was evidenced that more efforts must be directed towards miniaturized, eco-friendly, and non-terminal sampling for sample preparation. In its turn, ultra high-performance liquid chromatography and mass spectrometry should gain prominence in ocular bioanalysis for separation and detection, respectively, since it combines high separation capacity with selectivity and sensitivity, in addition to being an environmental friendly approach.