The combination of microspotting of analytical and internal standards, matrix sublimation, and recently developed software for quantitative mass spectrometry imaging has been used to develop a high-resolution method for the determination of terbinafine hydrochloride in the epidermal region of a full thickness living skin equivalent model. A quantitative assessment of the effect of the addition of the penetration enhancer (dimethyl isosorbide (DMI)) to the delivery vehicle has also been performed, and data have been compared to those obtained from LC-MS/MS measurements of homogenates of isolated epidermal tissue. At 10% DMI, the levels of signal detected for the drug in the epidermis were 0.20 ± 0.072 mg/g tissue for QMSI and 0.28 ± 0.040 mg/g tissue for LC-MS/MS at 50% DMI 0.69 ± 0.23 mg/g tissue for QMSI and 0.66 ± 0.057 mg/g tissue for LC-MS/MS. Comparison of means and standard deviations indicates no significant difference between the values obtained by the two methods.

A 3D cell culture is an artificially created environment in which cells are permitted to grow/interact with their surroundings in all three dimensions. Derived from 3D cell culture, organoids are generally small-scale constructs of cells that are fabricated in the laboratory to serve as 3D representations of in vivo tissues and organs. Due to regulatory, economic and societal issues concerning the use of animals in scientific research, it seems clear that the use of 3D cell culture and organoids in for example early stage studies of drug efficacy and toxicity will increase. The combination of such 3D tissue models with mass spectrometry imaging provides a label-free methodology for the study of drug absorption/penetration, drug efficacy/toxicity, and drug biotransformation. In this article, some of the successes achieved to date and challenges to be overcome before this methodology is more widely adopted are discussed.

We report for the first time label-free quantification of xenobiotic metabolizing enzymes (XME), transporters, redox enzymes, proteases and nucleases in six human skin explants and a 3D living skin equivalent model from LabSkin. We aimed to evaluate the suitability of LabSkin as an alternative to animal testing for the development of topical formulations. More than 2000 proteins were identified and quantified from total cellular protein. Alcohol dehydrogenase 1C (ADH1C), the most abundant phase I XME in human skin, and glutathione S-transferase pi 1 (GSTP1), the most abundant phase II XME in human skin, were present in similar abundance in LabSkin. Several esterases were quantified and esterase activity was confirmed in LabSkin using substrate-based mass spectrometry imaging. No cytochrome P450 (CYP) activity was observed for the substrates tested, in agreement with the proteomics data, where the cognate CYPs were absent in both human skin and LabSkin. Label-free protein quantification allowed insights into other related processes such as redox homeostasis and proteolysis. For example, the most abundant antioxidant enzymes were thioredoxin (TXN) and peroxiredoxin-1 (PRDX1). This systematic determination of functional equivalence between human skin and LabSkin is a key step towards the construction of a representative human in vitro skin model, which can be used as an alternative to current animal-based tests for chemical safety and for predicting dosage of topically administered drugs.

Breast cancer is a global health issue affecting 2.3 million women per year, causing death in over 600,000. Mammography (and biopsy) is the gold standard for screening and diagnosis. Whilst effective, this test exposes individuals to radiation, has limitations to its sensitivity and specificity and may cause moderate to severe discomfort. Some women may also find this test culturally unacceptable. This proof-of-concept study, combining bottom-up proteomics with Matrix Assisted Laser Desorption Ionisation Mass Spectrometry (MALDI MS) detection, explores the potential for a non-invasive technique for the early detection of breast cancer from fingertip smears. A cohort of 15 women with either benign breast disease (n = 5), early breast cancer (n = 5) or metastatic breast cancer (n = 5) were recruited from a single UK breast unit. Fingertips smears were taken from each patient and from each of the ten digits, either at the time of diagnosis or, for metastatic patients, during active treatment. A number of statistical analyses and machine learning approaches were investigated and applied to the resulting mass spectral dataset. The highest performing predictive method, a 3-class Multilayer Perceptron neural network, yielded an accuracy score of 97.8% when categorising unseen MALDI MS spectra as either the benign, early or metastatic cancer classes. These findings support the need for further research into the use of sweat deposits (in the form of fingertip smears or fingerprints) for non-invasive screening of breast cancer.