ENPP1 Inhibitor Design via Computational Screening
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Solution Overview
Problem
There is a need for effective ENPP1 modulators for therapeutic applications such as anti-viral, anti-bacterial, immunotherapy, and anti-cancer treatments due to ENPP1's role in regulating inflammatory responses and tissue calcification.
Innovation Solution
Development of compounds represented by Formula (I) and Formula (II), which are ENPP1 inhibitors, administered as pharmaceutical compositions to inhibit ENPP1 activity, thereby treating conditions like cancer.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If ENPP1 inhibitors are developed for therapeutic applications, then anti-viral, anti-bacterial, immunotherapy, and anti-cancer effects are achieved, but the complexity of drug development and clinical validation increases
Solution Approach 1:
The patent performs preliminary computational screening and in silico validation to identify potential ENPP1 inhibitors before experimental testing. This preliminary action filters out ineffective compounds early, reducing the complexity and cost of subsequent experimental validation while maintaining high therapeutic efficacy prospects
Solution Approach 2:
The patent uses computational models and molecular docking simulations as intermediaries between theoretical drug design and experimental validation. These computational tools serve as mediators to predict enzyme-inhibitor interactions, reducing the need for extensive trial-and-error experimentation
2Reliability
If ENPP1 activity is inhibited to treat cancer and inflammatory conditions, then therapeutic benefits are achieved, but off-target effects and toxicity risks may increase
Solution Approach 1:
The patent designs ENPP1 inhibitors with specific molecular characteristics that enable selective binding to the ENPP1 active site while avoiding other phosphodiesterase family members. The compounds exhibit local quality in their interaction profile, showing high affinity for ENPP1 but minimal binding to other targets, thereby reducing off-target effects
Solution Approach 2:
The patent employs iterative optimization based on computational feedback from molecular docking scores and predicted binding affinities. This feedback mechanism allows continuous refinement of inhibitor structures to enhance selectivity for ENPP1 while minimizing interactions with other enzymes, reducing toxicity risks
Data Source
AI summary
The disclosure provides enantiomeric quinoline and aza-quinazolines based on the ENPP1 modulators of Formula (I) or Formula (II) and salts thereof, and their use for the modulation of ENPP1 activity.