Advanced approaches for mitigating impact of pre-treatment generated inhibitors in lignocellulosic hydrolysates: A comprehensive review

Abstract

Second-generation (2G) bioethanol generation from lignocellulosic biomass (LCB) is a renewable and sustainable alternative to fossil fuels. However, the recalcitrant character of LCB, requires physicochemical pretreatment, which generates some toxic inhibitors (furans, carboxylic acids, and phenolic compounds). These inhibitors inhibit microbial growth during fermentation by disrupting cellular redox balance, damaging membranes integrity, and inhibiting metabolic activities, leading to decreased ethanol yield and productivity. This review discusses generation and toxicity mechanism of these inhibitors, and detoxification strategies such as physical, chemical, and biological to hybrid approaches. It also highlights microbial bioprospecting, metabolic engineering, and adaptive evolution for developing robust inhibitor-tolerant strains. Integration of systems biology tools (such as genomics, transcriptomics, proteomics, metabolomics, and fluxomics) with artificial intelligence (AI)-based modeling is emphasized for revealing intricate cellular stress response pathway to guide précised strain engineering. Key challenges are, optimizing pretreatment method to minimize inhibitors, while maximizing cellulose accessibility for low-lignin biomass, however for high-lignin biomass an efficient cost-effective detoxification strategy is essential. Moreover, microbial inhibitors tolerance and fermentation strategies for overcoming synergistic toxicity are also critical. Therefore, future research should focus on integrating multi-omics approaches, statistical/AI-driven optimization, and leveraging synthetic biology for developing commercially viable and environmentally friendly 2G bioethanol biorefineries. To the best of our knowledge, this is the first review that comprehensively summarizes inhibitors generation, toxicity mechanism, detoxification strategies, robust strain development strategies, and system biology- AI integration for advancing sustainable lignocellulosic bioethanol production.