The micellar liquid chromatography technique and quantitative retention (structure)–activity relationships method were used to predict properties of carbamic and phenoxyacetic acids derivatives, newly synthesized in our laboratory and considered as potential pesticides. Important properties of the test substances characterizing their potential significance as pesticides as well as threats to humans were considered: the volume of distribution, the unbonded fractions, the blood–brain distribution, the rate of skin and cell permeation, the dermal absorption, the binding to human serum albumin, partitioning between water and plants’ cuticles, and the lethal dose. Pharmacokinetic and toxicity parameters were predicted as functions of the solutes’ lipophilicities and the number of hydrogen bond donors, the number of hydrogen bond acceptors, and the number of rotatable bonds. The equations that were derived were evaluated statistically and cross-validated. Important features of the molecular structure influencing the properties of the tested substances were indicated. The QSAR models that were developed had high predictive ability and high reliability in modeling the properties of the molecules that were tested. The investigations highlighted the applicability of combined chromatographic technique and QS(R)ARs in modeling the important properties of potential pesticides and reducing unethical animal testing.

The quantitative structure–activity relationship (QSAR) methodology was used to predict the blood–brain permeability (log BB) for 65 synthetic heterocyclic compounds tested as promising drug candidates. The compounds were characterized by different descriptors: lipophilicity, parachor, polarizability, molecular weight, number of hydrogen bond acceptors, number of rotatable bonds, and polar surface area. Lipophilic properties of the compounds were evaluated experimentally by micellar liquid chromatography (MLC). In the experiments, sodium dodecyl sulfate (SDS) as the effluent component and the ODS-2 column were used. Using multiple linear regression and leave-one-out cross-validation, we derived the statistically significant and highly predictive quantitative structure–activity relationship models. Thus, this study provides valuable information on the expected properties of the substances that can be used as a support tool in the design of new therapeutic agents.

No abstract

The Quantitative Structure-Activity Relationships (QSAR) methodology was utilized to predict the biological properties, including protein binding, plasma and brain unbound fractions, blood-brain barrier permeability, intestinal permeability, and lethal dose, of a series of newly synthesized s-triazines considered as potential herbicides. The Over-Pressured Layer Chromatography (OPLC) technique, employing reversed-phase systems, was applied to determine the lipophilicities of the substances, characterized by the retention parameters RM0. In the QSAR methodology, the chromatographic lipophilicity parameters (RM0), along with polarizability (α) and molecular weight (MW), were used as independent variables. Multiple linear regression was employed to derive the Quantitative Structure-Activity Relationships, which were subsequently validated, and their statistical significance was demonstrated.