From Dynamics to Diagnosis and Therapy: A Multiscale Computational Framework for MALT1-Targeted Cancer Theranostics

Area of Science:

• Biochemistry
• Medicinal Chemistry
• Computational Chemistry

Background:

• Cancer remains a leading global cause of death, necessitating advanced therapeutic and diagnostic strategies.
• MALT1 is a crucial biomarker implicated in NF-κB signaling, vital for immune responses and linked to various cancer developments.
• Current research lacks chemical agents for simultaneous cancer diagnosis and therapy targeting MALT1.

Purpose of the Study:

• To design and optimize a novel theranostic agent targeting the MALT1 biomarker for cancer treatment and diagnosis.
• To explore the potential of MALT1 as a target for developing innovative cancer theranostic drugs.

Main Methods:

• Biased molecular dynamics (MD) simulations.
• Convolutional variational autoencoders (CVAEs).
• Quantum calculations.
• Synthesis and testing of phenothiazine-based compounds (Compound 1 and Compound 2).

Main Results:

• Compound 2 demonstrated a significant MALT1 allosteric inhibition ratio of 71%, more than doubling Compound 1's 29% inhibition.
• Both compounds exhibited environment-dependent fluorescence (cyan enol to blue enol emission), serving as a potential molecular signature for signaling.
• The study highlighted the significance of enol emission in Excited-State Intramolecular Proton Transfer (ESIPT) probes, often overlooked in favor of keto emission.

Conclusions:

• The developed theranostic agent (Compound 2) shows promising potential for cancer treatment and diagnosis by targeting the MALT1 allosteric pocket.
• The findings provide a robust theoretical framework for designing future MALT1-targeting theranostic agents and can be applied to other biological systems.
• This research advances the development of targeted cancer therapies with integrated diagnostic capabilities.