Computer-Assisted Selective Optimization of Side-Activities-from Cinalukast to a PPARα Modulator

Area of Science:

• Medicinal Chemistry
• Computational Chemistry
• Pharmacology

Background:

• Hit-to-lead optimization is crucial in drug discovery.
• Selective optimization of side-activities (SOSA) offers a focused approach to refining compound properties.
• Cysteinyl leukotriene receptor 1 (CysLT1R) antagonists, like cinalukast, are important therapeutic agents.

Purpose of the Study:

• To investigate the potential of automated computational analogue design for selective optimization of side-activities (SOSA).
• To explore the dual activity profile of cinalukast, a CysLT1R antagonist, with peroxisome proliferator-activated receptor alpha (PPARα) modulatory activity.
• To demonstrate the feasibility of shifting pharmacological activity through computational design and synthesis.

Main Methods:

• Generation of a large virtual library of cinalukast analogues using automated computational methods.
• Classification of virtual compounds for dual activity (PPARα agonism and CysLT1R antagonism) using automated affinity scoring and machine learning algorithms.
• Synthesis and in vitro characterization of a computationally selected analogue.

Main Results:

• A virtual library of approximately 8000 cinalukast analogues was generated and screened computationally.
• Machine learning models successfully predicted dual activity profiles for the analogues.
• The synthesized analogue exhibited a significant shift in activity, with enhanced PPARα activation and reduced CysLT1R antagonism.
• Demonstrated successful application of automated SOSA.

Conclusions:

• Automated computational analogue design is a promising strategy for accelerating hit-to-lead optimization, particularly for SOSA.
• This approach enables the efficient exploration of chemical space to modulate specific pharmacological activities.
• The study validates the potential of computational methods to guide the design of compounds with desired activity profiles.