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Diagnostic and Drug Discovery Initiative for Alzheimer’s Disease

Final Report Summary - D3I4AD (Diagnostic and Drug Discovery Initiative for Alzheimer’s Disease)

The overall aim of the scientific work package described in this D3i4AD project is to develop chemical biology tools to better understand the role of cellular prion protein (PrPC) in Alzeheimer’s Disease (AD) and its interactions with other proteins including, but not limited to, amyloid beta (AB1-42) oligomers, BACE, Fyn, Cav-1 and Tau etc. Harnessing this understanding in order to develop novel chemical entities for diagnostic and therapeutic applications is the specific goal of the partnership. The research programme of the D3i4AD project comprises four complementary work packages and will bring key resources, personnel and infrastructure from beneficiaries to the D3i4AD project:

-Work Package 1 investigates the biophysical characterisation between PrPC and its binding partners.
-Work Package 2 works to develop computational system engineering tools to virtually simulate the influence of PrPC and interaction partners in a global AD network landscape.
-Work Package 3 involves the design, synthesis and testing of novel chemical probes/inhibitors to perturb the interaction of PrPC and its binding partners.
-Work Package 4 focuses on translational exploration of novel chemical entities in diagnostic and therapeutic applications.

Description and results of the work performed, achieved and chemical biology tools developed during the project are summarised as follows:

1. Biological assays to determine expression of prion protein PrPC and its potential interaction partner, AB1-42, in HEK293 (human) and N2a (mouse) cells have been developed and evaluated using western blot and other biophysical techniques. Cell cycle analysis data suggested that the PRNP gene is overexpressed in the G1 and terminal differentiation phases. The binding of AB1-42 oligomers to PrPC on the surface of live cells was seen using flow cytometry and ICC (WP 3)
2. Cell-based assays for Alzheimer Disease research, drug discovery and remediation were developed using differentiated iPS from healthy and AD patients to characterise the gene and protein expression levels of PrPC and all interacting parties including AB1-42, PSD, mGluR5, NMDA-R, Fyn and pTau. Neurons from iPSC derived from AD patients showed AB1-42 is produced, binding and AD pathology. Mechanistic investigation showed that the expression level of PrPC is reduced in AD and a new hypothesis “differential expression of PrPC during the neural development could be involved in the earliest stages of AD” was proposed. (WP 1 and 3)
3. Development of a cell line (over)expressing PrPC and APP showed that PRNP is found in the highest concentrations in the order as follows: Glia > Rat Cortical Neurons (RCN) > Microglia > iCells > HEK > SH-SY5Y 32 > Sh-SY5Y 13. FYN, BACE and MAPT are ubiquitous in human cells, while mGluR5 (GRM5) and the NMDA subunits (GRIN1, 2A and 2B) have limited expression. (WP 1 and 3)
4. A cell lines that expresses AB1-42 was developed using APPSwe/Lon and PSEN1 #E9 mutations in HEK cells. In addition, natural amyloid-beta (AB1-42) oligomers were produced from Chinese Hamster Ovary cells stably transfected with cDNA encoding APP751, an amyloid precursor protein that contains the Val717Phe familial Alzheimer’s disease mutation. (WP 1 and 3)
5. A SPR-based direct binding assay was developed using various coupling methods to evaluate the immobilisation of both PrPC and AB1-42 including conventional amide coupling on CM5 chip. It was found that His-Tag immobilization worked better than amino coupling immobilization. (WP 1 and 3)
6. Bexarotene, a compound that is known for its interaction with active Aβ, was used as a tool compound to test the activity of the Aβ oligomers via a 1H CPMG NMR experiment. A 19F-NMR CPMG experiment was used to evaluate the mixtures of computationally selected binders to AB1-42 in the presence and absence of Aβ. An STD screen was used as an orthogonal binding study with the added ability to elucidate non-fluorine containing binders. (WP 1 and 3)

1. A deep neural network program has been developed that is able to perform both classification and regression on chemical data. The performance of deep neural networks and Macau, was tested on sparse chemical data sets providing a broad overview of how performance deteriorates as data becomes sparser (the results of this analysis led to a publication in Journal of Chemoinformatics). However, these models were not able to predict accurately the results of the phenotypic assays because of the difference in chemical composition between the data from phenotypic screens and the bioactivity data. (WP 2)
2. Network modelling tool for design and discovery of compounds that modulate the effect of PrPC in signalling pathways associated with Alzheimer's disease was built with sophisticated causal reasoning algorithms and used to select compounds for screening in cellular model. Also, docking models of PrPC and AB1-42oligomers were constructed and used to propose binding models and possible mutants to test these experimentally. Methodology was developed for accurate docking with success in blind D3R challenge. (WP 2)
3. Conformal prediction and confidence interval method developed and validated on in-house Lilly data sets. The average PI size of CPs is correlated with assay variability and model accuracy and, thus, more accurate assays and models are more likely to yield useful PIs. The selection of the reliability method for normalisation needs to consider the (prior) correlation of reliability estimates to residual error in order to ensure that PI size accurately represent prediction error. (WP 2)
4. A novel computational structure based generation tool in target based drug discovery investigated whether a new de-novo design tool can create compounds that are synthetically feasible; active; reduce drug development timeline through informed design; and outside of the medicinal chemists ‘toolkit’ of sidechains using AChE as trial drug target resulting in several workflows to enable the automated creation of 2D and 3D models (WP 2)

1. Synthesis of potent and selective butyrylcholinesterase purine nucleosides for the investigation of sugar protection towards BBB crossing were studied and subsequently characterized with 2D-NMR spectroscopy. The most potent purine moiety as butyrylcholinesterase inhibitor and copper chelators were identified. The addition of the benzamide fragment to the molecule to increase the copper chelating activity has also been performed. (WP 3)
2. Design and discovery of over 200 compounds that modulate the effect of PrPC in signalling pathways associated with Alzheimer's disease were selected with cheminformatics and networks mapping techniques to be tested in SPR assay. Molecular modelling techniques are also employed to assist the design on protein constructs for the SPR assay. A model of the AD network involving a number of proteins known to be important in AD disease progression, including PrPC, Tau, Fyn, mGluR5 and NR2B was established. Workflows for finding all known molecular probes for the proteins in the interaction network have been developed and an initial set of predicted compounds that will be used in the cell based assays have been compiled. (WP 2 and 3)
3. New approaches for the Synthesis of molecular entities for the investigation of C-glycosyl polyphenols to interact with AB1-42 and prion protein PrPC have been developed for therapeutics. Small library of 50 compounds have been screened and several leads identified as leads against neurodegeneration, with no toxicity in Caco-cells assay and the ability to cross BBB. (WP 3)
4. Synthesis and characterization of new benzamide protected purine nucleosides bearing deoxy glycosyl groups to investigate copper chelating activity useful to investigate the role of metals binding in the PrP folding/misfolding and aggregation process and in the interaction with AB oligomers. Anomeric configuration and linkage of glycosyl group to base for the best chelating Cu2+selectivity was shown to depend on the presence/absence of the hydroxymethyl group. (WP 3)
5. Ruthenium-based copper probes for surface enhanced copper detection and interaction with PrPC on its octarepeat peptide were synthesised to establish how they interfere with NMDA receptor activity with effective and selective responses. However, the compound has proved difficult in isolation and reproducibility in synthesis. (WP 3)

1. Luminescent metal complex-based probes that bind to biomolecules to aid in the development of novel diagnostic or therapeutic agents were tested and results showed that on/off effects of these probes are specific for the metal ions. (WP 4)
2. Robust, well-expressed Adhiron proteins to attach desired diagnostic molecules for Alzheimer’s disease to magnetic nanoparticles (MNP) were developed. Copper chelating ligands were attached to MNP and will be used to measure free Cu2+ ions in the blood. (WP 4)
3. The design and synthesis and characterisation (MS and H1 NMR) of chemical sensors ((E)-S-(3-hydroxy-4-(((8-hydroxyquinolin-5-yl)imino)methyl)phenyl)dimethylcarbamothioate) to investigate the relationship between copper and Alzheimer's disease have been carried out. (WP 4)
4. Evaluation of natural products and their derivatives as probes for detecting copper and zinc
indicates candidates (AM13 and MMN5) to be used as fluorescent probes for Fe2+ and Cu2+ respectively and warrant further investigation using a body fluid like human serum. (WP 4)

The originality and innovative aspects of the proposed project include that it is the first programme that investigates the complex interactions between prion protein, PrPC, and AD targets at this level of detail using a suite of multidisciplinary and trans-sectorial chemical biology techniques; it will generate new knowledge on the role of prion protein in AD; new strategies for next generation of diagnostic and therapeutic development will be evolved; new PET for AD diagnosis capable of rapid detection with high sensitivity will be developed; novel lead compounds against AD will be obtained for further development.


CONTACT DETAILS: Professor Beining Chen, CI (University of Sheffield, Sheffield UK S3 7HF);