Turnover modeling of non-esterified fatty acids in rats after multiple intravenous infusions of nicotinic acid.

The objective of this investigation was to use a pharmacokinetic (PK)/pharmacodynamic (PD) approach to describe and evaluate a PK model of nicotinic acid (NiAc) in guinea pigs and a PD feedback model of changes in non-esterified fatty acid (NEFA) concentrations in rats following multiple intravenous infusions of NiAc at different rates and durations of inhouse and literature (NEFA after extravascular NiAc dosing) data. Serial arterial blood samples were taken for evaluation of NiAc exposure in guinea pigs and NEFA in rats. The biophase kinetics of NiAc was assumed to impact on NEFA turnover with feedback incorporated via an inhibitory moderator compartment. The response acted linearly on the production of moderator, which then acted inversely on the turnover rate of response. The potency, expressed as the amount of NiAc in the biophase causing a 50 % inhibitory effect (ID(50)), was 6.5 nmol +/- 31 % and the half-life of response (t(1/2, kout)) 2 min +/- 18 %. The half-life of tolerance (t(1/2, ktol)) was 9 min +/- 27 %. The model can be used to provide information about factors that determine the time course of NEFA response following different rates and routes of administration of NiAc or NiAc analogues.

State-of-the-art tools for computational site of metabolism predictions: comparative analysis, mechanistical insights, and future applications.

In drug design, it is crucial to have reliable information on how a chemical entity behaves in the presence of metabolizing enzymes. This requires substantial experimental efforts. Consequently, being able to predict the likely site/s of metabolism in any compound, synthesized or virtual, would be highly beneficial and time efficient. In this work, six different methodologies for predictions of the site of metabolism (SOM) have been compared and validated using structurally diverse data sets of drug-like molecules with well-established metabolic pattern in CYP3A4, CYP2C9, or both. Three of the methods predict the SOM based on the ligand's chemical structure, two additional methods use structural information of the enzymes, and the sixth method combines structure and ligand similarity and reactivity. The SOM is correctly predicted in 50 to 90% of the cases, depending on method and enzyme, which is an encouraging rate. We also discuss the underlying mechanisms of cytochrome P450 metabolism in the light of the results from this comparison.