INDOLE-3-CARBINOL (I3C) DERIVATIVES: DESIGN, SYNTHESIS AND POTENTIAL BIOLOGICAL APPLICATIONS

Indole-3-carbinol (I3C) is a natural compound derived from cruciferous glucosinolates, widely recognized for its diverse biological activities, including antitumor, antiviral, anti-inflammatory, and antimicrobial effects. However, its instability under acidic conditions and the resulting formation of a complex mixture of oligomers limit its bioavailability and direct therapeutic use. Among the main condensation products, 3,3′-diindolylmethane (3,3′-DIM) and its synthetic isomer 2,3′-diindolylmethane (2,3′-DIM) have shown remarkable pharmacological potential, encouraging the design of structurally stable and chemically tunable analogues. This thesis’s project considers the design and synthesis of new bioactive libraries of I3C derivatives, including bisindoles, combining organic and medicinal chemistry strategies. Chapters 2 and 3 describe the Suzuki–Miyaura and Mizoroki–Heck reactions applied to the marine compound 2,2-bis(6-bromo-1H-indol-3-yl)ethanamine, aiming to develop structurally diversified analogues with enhanced antileishmanial activity and reduced toxicity toward human cells. Careful optimization of the catalytic system enabled selective functionalization while preserving the indole scaffold and improving the biological safety profile. Chapters 4 and 5 report the synthesis of new I3C and 2,3′-DIM analogues designed to enhance chemical stability, lipophilicity, and bioavailability. The introduced structural modifications yielded more stable and potentially more effective compounds suitable for further pharmacological and toxicological studies. Chapter 6 presents an innovative methodology aiming to obtain more three-dimensional bonds such as C(sp3)–C(sp2) via flow photochemistry, optimized through a Bayesian Optimization process, and potentially able to expand the accessible chemical space in medicinal chemistry, thus improving molecular interactions with biological targets. This integrated experimental and machine learning approach allow the rapid identification of optimal and sustainable reaction conditions.