Using Theoretical Descriptors in Quantitative Structure-Activity Relationships: Opiate Receptor Activity by Fentanyl-like Compounds

Determination of structural factors affecting binding to mu, kappa and delta opioid receptors

Addiction is a complex behavioral phenomenon in which naturally occurring or synthetic chemicals modulate the response of the reward system through their binding to a variety of neuroreceptors, resulting in compulsive substance-seeking and use despite harmful consequences to the individual. Among these, the opioid receptor (OR) family and more specifically, the mu-opioid receptor (MOR) subtype plays a critical role in the addiction to powerful prescription and illicit drugs such as hydrocodone, oxycodone, fentanyl, cocaine, and methamphetamine (Contet et al. in Curr Opin Neurobiol 14(3):370-378, 2004). Conversely, agonists binding to kappa (KOR) and antagonists binding to delta opioid receptors (DOR) have been reported to induce negative reinforcing effects. As more than 700 new psychoactive substances were illegally sold between 2009 and 2016 (DEA-DCT-DIR-032-18), most of them lacking basic toxicological and pharmacological profiles, molecular modeling approaches that could quickly and reliably fill the gaps in our knowledge would be highly desirable tools for determining the effects of these synthetics. Here, we report accurate 3D-spectrometric data-activity relationship classification models for large and diverse datasets of MOR, KOR and DOR binders with areas under the receiver operating characteristic curve for the "blind" prediction sets exceeding 0.88. Structural features associated with (selective) binding to MOR, KOR and/or DOR were identified. These models could assist regulatory agencies in evaluating the health risks associated with the use of unprofiled substances as well as to help the pharmaceutical industry in its search for new drugs to combat addiction.

Modeling drug-melanin interaction with theoretical linear solvation energy relationships

The affinity of drugs and other xenobiotic agents for melanin is a well-known phenomenon, often occurring with serious physiological consequences. For example, the interaction of anti-psychotic drugs with neuromelanin may play a pivotal role in the induction of extrapyramidal movement disorders associated with the chronic administration of phenothiazine and other neuroleptic agents. Little, however, is known about the complete nature of melanin-drug binding and the impact of these interactions on the physico-chemical properties of melanin. Data, such as binding affinities, can be analyzed using recently developed computational methods that combine mathematical models of chemical structure with statistical analysis. In particular, theoretical linear solvation energy relationships provide a convenient model for understanding and predicting biological, chemical, and physical properties. By using this modeling technique, drug-melanin binding of a set of 16 compounds has been analyzed with correlation analysis and a set of theoretical molecular parameters in order to better understand and characterize drug-melanin interactions. The resulting correlation equation supports a charge transfer model for drug-melanin complex formation and can also be used to estimate binding constants for related compounds.