Optimization of reaction parameters for the electrochemical oxidation of lidocaine with a Design of Experiments approach

N-Dealkylation of Amines

N-dealkylation, the removal of an N-alkyl group from an amine, is an important chemical transformation which provides routes for the synthesis of a wide range of pharmaceuticals, agrochemicals, bulk and fine chemicals. N-dealkylation of amines is also an important in vivo metabolic pathway in the metabolism of xenobiotics. Identification and synthesis of drug metabolites such as N-dealkylated metabolites are necessary throughout all phases of drug development studies. In this review, different approaches for the N-dealkylation of amines including chemical, catalytic, electrochemical, photochemical and enzymatic methods will be discussed.

Nanoporous Gold Catalyst for the Oxidative N-dealkylation of Drug Molecules: A Method for Synthesis of N-dealkylated Metabolites

A novel method for the selective catalytic N‐dealkylation of drug molecules on a nanoporous gold (NPG) catalyst producing valuable N‐dealkylated metabolites and intermediates is described. Drug metabolites are important chemical entities at every stage of drug discovery and development, from exploratory discovery to clinical development, providing the safety profiles and the ADME (adsorption, distribution, metabolism, and elimination) of new drug candidates. Synthesis was carried out in aqueous solution at 80 °C using air (oxygen source) as oxidant, in single step with good isolated yields. Different examples examined in this study showed that aerobic catalytic N‐dealkylation of drug molecules on NPG has a broad scope supporting N‐deethylation, N‐deisopropylation and N‐demethylation, converting either 3° amines to 2° amines, or 2° amines to 1° amines.

Voltammetric Behaviour of Drug Molecules as a Predictor of Metabolic Liabilities

Electron transfer plays a vital role in drug metabolism and underlying toxicity mechanisms. Currently, pharmaceutical research relies on pharmacokinetics (PK) and absorption, distribution, metabolism, elimination and toxicity (ADMET) measurements to understand and predict drug reactions in the body. Metabolic stability (and toxicity) prediction in the early phases of the drug discovery and development process is key in identifying a suitable lead compound for optimisation. Voltammetric methods have the potential to overcome the significant barrier of new drug failure rates, by giving insight into phase I metabolism events which can have a direct bearing on the stability and toxicity of the parent drug being dosed. Herein, we report for the first time a data-mining investigation into the voltammetric behaviour of reported drug molecules and their correlation with metabolic stability (indirectly measured via t½), as a potential predictor of drug stability/toxicity in vivo. We observed an inverse relationship between oxidation potential and drug stability. Furthermore, we selected and prepared short- (<10 min) and longer-circulation (>2 h) drug molecules to prospectively survey the relationship between oxidation potential and stability.

Normal boundary intersection applied as multivariate and multiobjective optimization in the treatment of amoxicillin synthetic solution

Amoxicillin is a useful antibiotic to combat bacterial infections. However, this drug can cause serious problems when discarded in waterways due to its great bioaccumulation potential. This compound can be treated via advanced oxidation processes (AOPs), which are capable of converting amoxicillin into carbon dioxide and water. In this context, the use of ozone as an oxidizer has excelled in amoxicillin degradation. This paper aims at treating a synthetic solution of amoxicillin (0.1 g L^-1) in a reactor with ozone bubbling. A Design of Experiment (DoE) with a response surface known as Central Composite Design (CCD) was used to optimize the treatment process. In addition, a Normal Boundary Intersection (NBI) method was used in the construction of a Pareto boundary chart. Results after 1-h treatment showed a reduction of 53% of the initial organic matter from a designed model using factors, such as pH, ozone generator power, and O₃ flow. A model was built from the CCD with score of 0.9929. Thus, the model was able to represent the real scenario with confidence.