Discovery of Lxr Agonists Via Pharmacophore Space Mining
Award last edited on: 1/10/17

Sponsored Program
Awarding Agency
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Principal Investigator
Christian Lang

Company Information

Acelot Inc

5385 Hollister Avenue Suite 111
Santa Barbara, CA 93111
   (805) 617-3610
Location: Single
Congr. District: 24
County: Santa Barbara

Phase I

Contract Number: 1R43AG054350-01A1
Start Date: 9/15/16    Completed: 8/31/17
Phase I year
Phase I Amount
The goal of this proposal is to discover drug-like liver X receptor (LXR) β selective agonists that can halt and reverse the progression of Alzheimer’s disease (AD). Recent work demonstrated that activation of the LXR signaling pathways leads to improved amyloid β (Aβ) turnover, reduction in Aβ plaque area, and the reversal of cognitive, social and olfactory deficits. The research plan consists of two steps. The first step is computer-aided prediction of agonistic LXRβ selective binding, blood brain barrier permeability, and absence of side effects such as mutagenicity or binding to undesired targets. This step relies on a novel method for pharmacophore analysis by examining the joint space of chemical compounds, targets, and chemical/biological properties. This joint space is defined using machine learning on the 3D geometry of spatial arrangement of pharmacophoric points, using attributes such as donors, acceptors, aromatic rings, and charged fragments. The second step consists of biological assays to assess toxicity, brain penetration potential and the ability to induce expression of the corresponding genes. A dozen diverse compounds will have been tested through two iterations of the two steps of in silico prediction and assays at the end of Phase 1. These initial leads will be augmented with additional de novo compounds and further scrutinized via behavioral assays, dose-response studies and more detailed, sophisticated, and mechanistic assays during Phase 2. The outcome of this study will be new drug leads to potentially treat and prevent AD at different stages of cognitive decline and neurodegeneration.

Public Health Relevance Statement:
Interleaving of computer-aided screening of large chemical databases through novel machine learning based models with biological assays will identify drug-like LXRβ selective agonists with minimal side-effects. These agonists will slow and reverse Alzheimer’s Disease.

Project Terms:
Adverse effects; Affect; Agonist; Algorithms; Alzheimer's Disease; American; Amyloid; Animal Model; Area; base; Behavior; Behavioral; Behavioral Assay; Binding; Biochemical; Biological; Biological Assay; Blood - brain barrier anatomy; Brain; Cell Culture Techniques; Charge; Chemicals; clinical application; Clinical Trials; Cognitive; Computer Assisted; computer science; Computer Simulation; Databases; Development; Dose; drug discovery; Drug Kinetics; Drug Targeting; Effectiveness; Future; Genes; Geometry; Goals; Human; Impaired cognition; improved; in vitro Model; in vivo; Joints; Lead; Ligands; Light; lipid metabolism; Liver X Receptor; LXRalpha protein; Machine Learning; Mediating; Memory; Methodology; Methods; Mining; Modeling; Molecular; mouse model; Nerve Degeneration; Neurodegenerative Disorders; neurotoxic; novel; novel therapeutics; Nuclear Receptors; Outcome; Outcome Study; Pathway interactions; Penetration; Peripheral; Permeability; Pharmaceutical Preparations; pharmacophore; Phase; Population; PPAR gamma; prevent; Property; Protein Isoforms; receptor; Receptor Signaling; Research; response; Retinoic Acid Receptor; scaffold; screening; Series; Signal Pathway; social; Staging; Testing; Therapeutic; Toxic eff

Phase II

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Start Date: 00/00/00    Completed: 00/00/00
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