A computational quantitative structure-activity relationship study of carbamate anticonvulsants using quantum pharmacological methods

The Buzz on Insecticides: A Review of Uses, Molecular Structures, Targets, Adverse Effects, and Alternatives

Insecticides play a critical role in controlling the spread of insect-borne diseases and preserving crop health. These chemical substances are specifically formulated to kill or manage insect populations. Over the years, various types of insecticides have been developed, including organophosphates, carbamates, pyrethroids, and neonicotinoids, each with unique modes of action, physiological targets, and efficacy. Despite the advantages that insecticides offer, it is imperative to recognize the potential consequences on non-target species, the environment, and human health. It is therefore crucial to follow recommended label instructions and employ integrated pest management practices for the judicious use of insecticides. This review article provides an in-depth examination of the various types of insecticides, including their modes of action, physiological targets, environmental and human health impacts, and alternatives. The aim is to furnish a comprehensive overview of insecticides and to emphasize the significance of responsible and sustainable utilization.

In vitro transport profile of carbamazepine, oxcarbazepine, eslicarbazepine acetate, and their active metabolites by human P-glycoprotein

PURPOSE

Antiepileptic drugs (AEDs) are widely used not only in the treatment of epilepsy but also as treatments for psychiatric disorders. Pharmacoresistance of AEDs in the treatment of epilepsy and psychiatric disorders is a serious problem. Transport of antiepileptic drugs by P-glycoprotein (Pgp, ABCB1, or MDR1), which is overexpressed in the blood-brain barrier, may be a mechanism for resistance of AEDs. For most AEDs, conflicting evidence precludes consensus on whether they are substrates of Pgp. The objective of this study was to evaluate whether analogs and metabolites of the AED carbamazepine are substrates of human Pgp.

METHODS

Polarized cell lines MDCKII and LLC transfected with the human MDR1 gene were used in the bidirectional transport assay and concentration equilibrium transport assay. The expression of Pgp was detected by real-time polymerase chain reaction (PCR) and immunofluorescent staining. Rhodamine-123 uptake was also determined.

KEY FINDINGS

Pgp did not transport carbamazepine, but it did transport its active metabolite carbamazepine-10,11-epoxide. Pgp also pumped eslicarbazepine acetate and oxcarbazepine, as well as their active metabolite (S)-licarbazepine. Transport of the drugs was in the order of ESL>OXC>S-LC>CBZ-E in concentration equilibrium conditions. The transport of these drugs was blocked by Pgp inhibitors tariquidar and verapamil.

SIGNIFICANCE

All carbamazepine analogs or metabolites tested are Pgp substrates, except for carbamazepine. These data suggest that resistance to carbamazepine, oxcarbazepine, or eslicarbazepine acetate may be attributed to increased efflux function of Pgp because they or their active metabolites are Pgp substrates.

A new paradigm for pattern recognition of drugs

A new paradigm is suggested for pattern recognition of drugs. The approach is based on the combined application of the 4D/3D quantitative structure-activity relationship (QSAR) algorithms BiS and ConGO. The first algorithm, BiS/MC (multiconformational), is used for the search for the conformers interacting with a receptor. The second algorithm, ConGO, has been suggested for the detailed study of the selected conformers' electron density and for the search for the electron structure fragments that determine the pharmacophore and antipharmacophore parts of the compounds. In this work we suggest using a new AlteQ method for the evaluation of the molecular electron density. AlteQ describes the experimental electron density (determined by low-temperature highly accurate X-ray analysis) much better than a number of quantum approaches. Herein this is shown using a comparison of the computed electron density with the results of highly accurate X-ray analysis. In the present study the desirability function is used for the first time for the analysis of the effects of the electron structure in the process of pattern recognition of active and inactive compounds. The suggested method for pattern recognition has been used for the investigation of various sets of compounds such as DNA-antimetabolites, fXa inhibitors, 5-HT(1A), and alpha(1)-AR receptors inhibitors. The pharmacophore and antipharmacophore fragments have been found in the electron structures of the compounds. It has been shown that the pattern recognition cross-validation quality for the datasets is unity.

Quantitative structure-activity relationship analysis of functionalized amino acid anticonvulsant agents using k nearest neighbor and simulated annealing PLS methods

We report the development of rigorously validated quantitative structure-activity relationship (QSAR) models for 48 chemically diverse functionalized amino acids with anticonvulsant activity. Two variable selection approaches, simulated annealing partial least squares (SA-PLS) and k nearest neighbor (kNN), were employed. Both methods utilize multiple descriptors such as molecular connectivity indices or atom pair descriptors, which are derived from two-dimensional molecular topology. QSAR models with high internal accuracy were generated, with leave-one-out cross-validated R(2) (q(2)) values ranging between 0.6 and 0.8. The q(2) values for the actual dataset were significantly higher than those obtained for the same dataset with randomly shuffled activity values, indicating that models were statistically significant. The original dataset was further divided into several training and test sets, with highly predictive models providing q(2) values greater than 0.5 for the training sets and R(2) values greater than 0.6 for the test sets. These models were capable of predicting with reasonable accuracy the activity of 13 novel compounds not included in the original dataset. The successful development of highly predictive QSAR models affords further design and discovery of novel anticonvulsant agents.