ABSTRACT:The blood-brain barrier (BBB) is a biological barrier that protects the brain from neurotoxic agents and regulates the influx and efflux of molecules required for its correct function. This stringent regulation hampers the passage of brain parenchyma-targeting drugs across the BBB. BBB shuttles have been proposed as a way to overcome this hurdle since these peptides can not only cross the BBB but also carry molecules which would otherwise be unable to cross the barrier unaided.Here we developed a new high-throughput screening methodology to identify new peptide BBB shuttles in a broadly unexplored chemical space. By introducing D-amino acids, this approach screens only protease-resistant peptides. This methodology combines combinatorial chemistry for peptide library synthesis, in vitro models mimicking the BBB for library evaluation, and state-of-the-art mass spectrometry techniques to identify those peptides able to cross the in vitro assays. BBB shuttle synthesis was performed by the mix-andsplit technique to generate a library based on the following: Ac-D-Arg-XXXXX-NH 2 , where X were: D-Ala (a), D-Arg (r), D-Ile (i), D-Glu (e), D-Ser (s), D-Trp (w), or D-Pro (p). The assays used comprised the in vitro cell-based BBB assay (mimicking both active and passive transport) and the PAMPA (mimicking only passive diffusion). The identification of candidates was determined using a twostep mass spectrometry approach combining LTQ-Orbitrap and Q-trap mass spectrometers.Identified sequences were postulated to cross the BBB models. We hypothesized that some sequences cross the BBB through passive diffusion mechanisms and others through other mechanisms, including paracellular flux and active transport.These results provide a new set of BBB shuttle peptide families. Furthermore, the methodology described is proposed as a consistent approach to search for protease-resistant therapeutic peptides. KEYWORDS:Blood-brain barrier (BBB), mass spectrometry (MS), LTQ-Orbitrap, Q-trap, single reaction monitoring (SRM), in vitro cell-based BBB model, parallel artificial membrane permeability assay (PAMPA), mix-and-split, solid-phase peptide synthesis (SPPS).

Fragment-based drug discovery is widely applied both in industrial and in academic screening programs. Several screening techniques rely on NMR to detect binding of a fragment to a target. NMR-based methods are among the most sensitive techniques and have the further advantage of yielding a low rate of false positives and negatives. However, NMR is intrinsically slower than other screening techniques; thus, to increase throughput in NMR-based screening, researchers often assay mixtures of fragments, rather than single fragments. Herein we present a fast and straightforward computer-aided method to design mixtures of fragments taken from a library that have minimized NMR signal overlap. This approach enables direct identification of one or several active fragments without the need for deconvolution. Our approach entails encoding of NMR spectra into a computer-readable format that we call a fingerprint, and minimizing the global signal overlap through a Monte Carlo algorithm. The scoring function used favors a homogenous distribution of the global signal overlap. The method does not require additional experimental work: the only data required are NMR spectra, which are generally recorded for each compound as a quality control measure before its insertion into the library.