ABSTRACT
This study addresses the need for accurate structural data regarding the toxicity of fragrances in sanitizers and disinfectants. We compare the predictive and descriptive (model stability) potential of multiple linear regression (MLR) and partial least squares (PLS) models optimized through variable selection (VS). A novel hybrid chaotic neural network algorithm with competitive learning (CCLNNA)-PLS modeling strategy can offer specific optimization with satisfactory results, even for a limited dataset. While also exploring the preliminary comparative analysis, the goal is to introduce an adapted novel CCLNNA optimization strategy for VS, inspired by neural networks, along with exploring the influence of the percentage of significant descriptors in the optimization function to enhance the final model's capabilities. We analyzed an available dataset of 24 molecules, incorporating ADMET and PaDEL descriptors as predictor variables, to explore the relationship between the response/target variable (pLC50) and the meticulously optimized set of descriptors. The suitability of the selected PLS models (cross- and external-validated accuracy combined with percentage of significant descriptors at a level equal to or >80 %) underscores the importance of expanding the dataset to amplify the validation protocols, thus enhancing future model reliability and environmental impact.
Subject(s)
Disinfectants , Neural Networks, Computer , Disinfectants/toxicity , Least-Squares Analysis , Algorithms , Perfume , Linear ModelsSubject(s)
Acetates , Odorants , Perfume , Humans , Perfume/toxicity , Perfume/chemistry , Animals , Risk Assessment , Acetates/toxicity , Acetates/chemistry , Endpoint Determination , Consumer Product Safety , No-Observed-Adverse-Effect Level , Toxicity Tests , Esters/toxicity , Esters/chemistry , Databases, ChemicalSubject(s)
Perfume , Phenethylamines , Animals , Humans , Consumer Product Safety , Databases, Chemical , Endpoint Determination , No-Observed-Adverse-Effect Level , Odorants , Perfume/toxicity , Perfume/chemistry , Risk Assessment , Toxicity Tests , Phenethylamines/chemistry , Phenethylamines/toxicitySubject(s)
Odorants , Pentanols , Perfume , Animals , Humans , Butanols/toxicity , Butanols/chemistry , Consumer Product Safety , Databases, Chemical , Endpoint Determination , No-Observed-Adverse-Effect Level , Perfume/toxicity , Perfume/chemistry , Risk Assessment , Toxicity Tests , Pentanols/chemistry , Pentanols/toxicityABSTRACT
Industrial biotechnology and biocatalysis can provide very effective synthetic tools to increase the sustainability of the production of fine chemicals, especially flavour and fragrance (F&F) ingredients, the market demand of which has been constantly increasing in the last years. One of the most important transformations in F&F chemistry is the reduction of CC bonds, typically carried out with metal-catalysed hydrogenations or hydride-based reagents. Its biocatalytic counterpart is a competitive alternative, showcasing a range of advantages such as excellent chemo-, regio- and stereoselectivity, ease of implementation, mild reaction conditions and modest environmental impact. In the present review, the application of biocatalysed alkene reductions (from microbial fermentations with wild-type strains to engineered isolated ene-reductase enzymes) to synthetic processes useful for the F&F industry will be described, highlighting not only the exquisite stereoselectivity achieved, but also the overall improvement when chirality is not involved. Multi-enzymatic cascades involving CC bioreductions are also examined, which allow much greater chemical complexity to be built in one-pot biocatalytic systems.
