Blood–brain barrier endogenous transporters as therapeutic targets: a new model for small molecule CNS drug discovery

2015 ◽  
Vol 19 (8) ◽  
pp. 1059-1072 ◽  
Author(s):  
William M Pardridge
Pharmaceutics ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 892
Author(s):  
Elisa L. J. Moya ◽  
Elodie Vandenhaute ◽  
Eleonora Rizzi ◽  
Marie-Christine Boucau ◽  
Johan Hachani ◽  
...  

Central nervous system (CNS) diseases are one of the top causes of death worldwide. As there is a difficulty of drug penetration into the brain due to the blood–brain barrier (BBB), many CNS drugs treatments fail in clinical trials. Hence, there is a need to develop effective CNS drugs following strategies for delivery to the brain by better selecting them as early as possible during the drug discovery process. The use of in vitro BBB models has proved useful to evaluate the impact of drugs/compounds toxicity, BBB permeation rates and molecular transport mechanisms within the brain cells in academic research and early-stage drug discovery. However, these studies that require biological material (animal brain or human cells) are time-consuming and involve costly amounts of materials and plastic wastes due to the format of the models. Hence, to adapt to the high yields needed in early-stage drug discoveries for compound screenings, a patented well-established human in vitro BBB model was miniaturized and automated into a 96-well format. This replicate met all the BBB model reliability criteria to get predictive results, allowing a significant reduction in biological materials, waste and a higher screening capacity for being extensively used during early-stage drug discovery studies.


2020 ◽  
Vol 55 (1) ◽  
Author(s):  
Antje Appelt‐Menzel ◽  
Sabrina Oerter ◽  
Sanjana Mathew ◽  
Undine Haferkamp ◽  
Carla Hartmann ◽  
...  

2016 ◽  
Vol 34 (5) ◽  
pp. 382-393 ◽  
Author(s):  
S. Aday ◽  
R. Cecchelli ◽  
D. Hallier-Vanuxeem ◽  
M.P. Dehouck ◽  
L. Ferreira

Blood ◽  
2004 ◽  
Vol 104 (11) ◽  
pp. 1988-1988 ◽  
Author(s):  
Robert Wild ◽  
Stephen Castaneda ◽  
Christine Flefleh ◽  
Krista Fager ◽  
Ivan Inigo ◽  
...  

Abstract Chronic myeloid leukemia (CML) is a stem cell disorder caused by a constitutively activated tyrosine kinase, the BCR-ABL oncoprotein. Imatinib (STI571, Gleevec) is a small-molecule inhibitor of this kinase that produces clinical remissions in CML patients and is now frontline therapy for this disease. While this agent has a high rate of clinical success in early phases of CML, development of resistance to this drug is increasingly becoming problematic, particularly in later stages of the disease. Moreover, growing evidence suggests that imatinib has very poor penetration of the blood brain barrier, likely due at least partly to its being a substrate of P-glycoprotin (Pgp), resulting in subtherapeutic levels in the CNS. As a result, several clinical cases have been reported where CNS relapses occurred in imatinib treated CML patients despite peripheral blood and bone marrow complete responses (Leis et al., Leuk Lymphoma. 2004 Apr;45(4):695–8). This phenomenon has also been recapitulated in at least one preclinical model, where the limited ability of imatinib to cross the blood-brain barrier allowed the CNS to become a sanctuary for BCR-ABL-induced leukemia (Wolff et al., Blood. 2003 Jun 15;101(12):5010–3). BMS-354825, a small-molecule dual-function SRC/ABL tyrosine kinase inhibitor, was designed to overcome many of the limitations associated with imatinib therapy. BMS-354825 has more than 500-fold increased potency relative to imatinib versus BCR-ABL and more importantly retains activity against 14 of 15 imatinib-resistant BCR-ABL mutants (Shah et al., Science, 2004 Jul 16;305(5682):399–401). In addition, BMS-354825 proved to be equally effective against several preclinically- and clinically-derived tumor models of imatinib resistance (Lee et al., Proceedings of the AACR, Volume 45, March 2004). In the current study, we assessed the efficacy of BMS-354825, which is not a Pgp substrate, in a model of established intracranial CML tumors. SCID-beige mice bearing K562 CML tumors implanted intracranially (2x106 cells per animal) were treated with BMS-354825 orally b.i.d. for a period of up to 40 days. BMS-354825 proved to be exceptionally efficacious resulting in increased lifespan of animals by 450% and 268% for the 15 mg/kg and 5 mg/kg dose levels, respectively. In order to more directly assess the anti-tumor activities of BMS-354825 in this intracranial CML model, we implanted K562 cells stably transfected with the firefly luciferase gene intracranially into SCID-beige animals. Bioluminescent imaging (BLI) then allowed the non-invasive monitoring of in vivo growth of these tumors. BMS-354825 at 15 mg/kg (2qdx14;6 po) achieved tumor regressions and subsequent complete stasis of intracranial K562 growth while animals were on therapy. In summary, these results suggest that BMS-354825 may have therapeutic advantages over imatinib in the management of intracranial CML disease and warrants further clinical investigation.


2012 ◽  
Vol 6 (2) ◽  
pp. 134-144 ◽  
Author(s):  
Rajareddy Kallem ◽  
Chetan P. Kulkarni ◽  
Dakshay Patel ◽  
Megha Thakur ◽  
Michael Sinz ◽  
...  

Author(s):  
Bilal Shaker ◽  
Myeong-Sang Yu ◽  
Jin Sook Song ◽  
Sunjoo Ahn ◽  
Jae Yong Ryu ◽  
...  

Abstract Motivation Identification of blood–brain barrier (BBB) permeability of a compound is a major challenge in neurotherapeutic drug discovery. Conventional approaches for BBB permeability measurement are expensive, time-consuming and labor-intensive. BBB permeability is associated with diverse chemical properties of compounds. However, BBB permeability prediction models have been developed using small datasets and limited features, which are usually not practical due to their low coverage of chemical diversity of compounds. Aim of this study is to develop a BBB permeability prediction model using a large dataset for practical applications. This model can be used for facilitated compound screening in the early stage of brain drug discovery. Results A dataset of 7162 compounds with BBB permeability (5453 BBB+ and 1709 BBB-) was compiled from the literature, where BBB+ and BBB- denote BBB-permeable and non-permeable compounds, respectively. We trained a machine learning model based on Light Gradient Boosting Machine (LightGBM) algorithm and achieved an overall accuracy of 89%, an area under the curve (AUC) of 0.93, specificity of 0.77 and sensitivity of 0.93, when 10-fold cross-validation was performed. The model was further evaluated using 74 central nerve system compounds (39 BBB+ and 35 BBB-) obtained from the literature and showed an accuracy of 90%, sensitivity of 0.85 and specificity of 0.94. Our model outperforms over existing BBB permeability prediction models. Availabilityand implementation The prediction server is available at http://ssbio.cau.ac.kr/software/bbb.


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