Yolanda Martín-Biosca

Chiral HPLC method development still relies heavily on trial-and-error screening. We introduce the Efficient Enantioseparation (EES) parameter─a single metric integrating resolution (Rs) and retention (k)─to move from point predictions to full mobile-phase (MP) profile modeling. Using EES as the response, we trained multiple artificial neural networks (ANNs) on 62 variables (molecular descriptors) and 76 objects (structurally diverse neutral and basic compounds chromatographed on a Lux Cellulose-1 column under aqueous-acetonitrile conditions at nine MP compositions). ANNs were optimized with a chaotic competitive-learning optimizer (CCLNNA), then ranked/selected and combined into a consensus model to enhance robustness and limit overfitting. The ANN-consensus model accurately reproduces full EES-MP profiles (R² > 0.9) with lower error dispersion, enabling prospective feasibility checks and single-shot selection of high-EES mobile-phase compositions. External tests on fluoxetine and lormetazepam confirmed prospective utility by anticipating separability at one or more MPs (nominating the MP with maximal EES) or nonseparability across the explored MP range. To our knowledge, this work provides the first proof-of-concept for in silico prediction of full EES-MP profiles in chiral HPLC, enabling intelligent MP selection. Rather than a definitive model, this work evaluates the potential of the strategy: consensus stabilizes learning with limited data and offers greener, actionable guidance that can reduce experiments, reagent consumption, and development time. The framework is extensible to broader chemotypes and stationary and mobile phases; larger data sets could further generalize EES-profile prediction and support intelligent MP selection in sustainable chiral HPLC.

The selection of suitable combinations of chiral stationary phases (CSPs) and mobile phases (MPs) for the enantioresolution of chiral compounds is a complex issue that often requires considerable experimental effort and can lead to significant waste. Linking the structure of a chiral compound to a CSP/MP system suitable for its enantioseparation can be an effective solution to this problem. In this study, we evaluate algorithmic tools for this purpose. Our proposed consensus model, which uses multiple optimized artificial neural networks (ANNs), shows potential as an intelligent recommendation system (IRS) for ranking chromatographic systems suitable for the enantioresolution of chiral compounds with different molecular structures. To evaluate the IRS potential in a proof-of-concept stage, 56 structural descriptors for 56 structurally unrelated chiral compounds across 14 different families are considered. Chromatographic systems under study comprise 7 cellulose and amylose derivative CSPs and acetonitrile or methanol aqueous MPs (14 chromatographic systems in all). The ANNs are optimized using a fit-for-purpose version of the chaotic neural network algorithm with competitive learning (CCLNNA), a novel approach not previously applied in the chemical domain. CCLNNA is adapted to define the inner ANN complexity and perform feature selection of the structural descriptors. A customized target function evaluates the correctness of recommending the appropriate CSP/MP system. The ANN-consensus model exhibits no advisory failures and requires only an experimental attempt to verify the IRS recommendation for complete enantioresolution. This outstanding performance highlights its potential to effectively resolve this problem.

Many of the currently available drugs are chiral compounds that are marketed as racemates or, to a lesser extent, in the form of one of the enantiomers since a pair of enantiomers may have different toxicological and ecotoxicological properties compared to each other. The evaluation of enantioselectivity in biodegradation processes is essential for environmental risk assessment. The objective of this research is to study the enantioselectivity in the biodegradation of two common chiral drugs, citalopram and verapamil, using highly sulphated-γ-cyclodextrin (HS-γ-CD) as chiral selector in Capillary Electrophoresis. Biodegradation experiments were performed in batch mode using a minimal salt medium inoculated with an activated sludge and supplemented with the corresponding enantiomeric mixture. The cultures were incubated at 20 °C for 28 days. Abiotic degradation of verapamil and citalopram enantiomers was also assessed. The concentration of the enantiomers of verapamil and citalopram were monitored using 0.7% and 0.1% m/v HS-γ-CD solutions as chiral selector, respectively. Separations were carried out using the complete filling technique. The results of biodegradability tests indicate that citalopram could be considered potentially persistent while verapamil is presumed to be a non-persistent compound. No evidence of enantioselectivity was observed in any of the biodegradation processes.