Cheminformatics Screening for Selective PPAR-δ Agonists

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Solution Overview

Problem

There are no current clinical studies on selective PPAR-δ agonists or their combination with other protein receptors, limiting their therapeutic application for chronic diseases such as obesity, diabetes, and atherosclerosis.

Innovation Solution

Development of novel PPAR-δ agonists through a cheminformatics-based model using the ISE algorithm, screening the ENAMINE database, and identifying compounds with high binding affinities, such as GNF-0242 and GNF-8065, which are tested for their efficacy as PPAR-δ modulators.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If selective PPAR-δ agonists are developed for therapeutic application, then treatment efficacy for chronic diseases is improved, but lack of clinical studies and approved drugs limits their current applicability

Engineering Contradiction:

Improvetreatment efficacyVSAvoidclinical applicability

Core Design Contradiction:

ReliabilityVSAdaptability or versatility

Solution Approach 1:

The patent performs preliminary computational screening and identification of PPAR-δ agonists before clinical studies can be conducted. By using in silico methods to pre-identify and characterize potential agonists with predicted nanomolar affinity, the research prepares a foundation for future clinical applications that would otherwise be limited by the lack of known compounds to test.

Inventive Principle:

Principle #10Preliminary action

2Productivity

If computational screening methods are used to identify PPAR-δ agonists, then discovery efficiency is improved, but validation through in vitro testing is required to confirm activity

Engineering Contradiction:

Improvediscovery efficiencyVSAvoidvalidation time

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The computational screening serves as a preliminary filtering step that identifies the most promising compounds before they undergo time-consuming in vitro validation. By pre-ranking compounds based on predicted binding affinity and selecting only the top candidates for experimental testing, the method maximizes discovery efficiency while minimizing the time and resources required for validation.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The in silico screening method is self-validating in that it uses the same PPAR-δ binding site structural information to both identify and predict the activity of agonists. The computational model serves its own validation needs by providing a consistent framework for both compound identification and affinity prediction, reducing the need for extensive preliminary screening.

Inventive Principle:

Principle #25Self-service

Applied Scientific Principles

This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.

Function Achieved in This Case

The identified compounds demonstrate good to excellent binding affinities, with several in the nanomolar range, offering potential therapeutic benefits for obesity, type 2 diabetes, atherosclerosis, and other chronic diseases by modulating PPAR-δ activity.

Implementation Method 1

Development of novel PPAR-δ agonists through a cheminformatics-based model using the ISE algorithm, screening the ENAMINE database, and identifying compounds with high binding affinities

Methodology Applied

Scientific EffectCheminformatics-based modeling:

Implementation Method 2

Methodology Applied

Scientific EffectMolecular binding:

Data Source

PatentUS11771702B2Agonists of PPAR-δ

Publication Date: 2023.10.03 GOLDBLUM AMIRAM

AI summary

Disclosed are PPAR receptor modulators and their uses in medicine. The novel agonists for PPAR-δ were found by screening molecules through a chemoinformatics-based model. The novel agonists for PPAR-δ are proposed for use in medicine.