A highly efficient method has been developed to detect and identify reactive metabolites, using stable-isotope trapping combined with ESI-MS/MS neutral loss scanning. A mixture of glutathione (GSH, gamma-glutamylcysteinylglycine) and the stable-isotope labeled compound (GSX, gamma-glutamylcysteinylglycine-(13)C(2)-(15)N) was used at an equal molar ratio to trap reactive metabolites generated in microsomal incubations. Samples resulting from incubations were cleaned and concentrated by SPE, followed by LC-MS/MS analyses using constant neutral loss scanning for 129 Da (the gamma-glutamyl moiety) to detect formed GSH conjugates. Unambiguous identification of glutathione adducts was greatly facilitated by the presence of a unique MS signature of a prominent isotopic doublet that differs in mass by 3 Da. Further structural characterization of conjugates was achieved with high confidence by subsequently acquiring MS/MS spectra that were featured by neutral losses of 75 and 129 Da for GSH adducts and 78 and 129 Da for isotopic GSX adducts. The reliability of this method was vigorously validated using a number of compounds known to form reactive metabolites. Superior sensitivity was demonstrated by the capability of the current approach to identify reactive metabolites at low abundance. Because of the unique isotopic MS signature, ultrafast analyses of reactive metabolites were accomplished by direct injection of cleaned samples into mass spectrometers for neutral loss scanning. More importantly, this study has demonstrated the feasibility of the current method for completely automated detection of reactive metabolites via computer-assisted pattern recognition.

Measurements of the gas phase proton transfer equilibrium [I]: AH + B-= A-+ BH with a pulsed electron high pressure mass spectrometer (PHPMS) lead to AG: and A@ data. These relative acidities are converted to the absolute acidities [2]: AH = A-+ ~+b~calibrationoftherelativeA~~andA@scaletotheknownvaluesforAG~andA@ofareferencecompound(~~1).~arlier determinations that included 16 aliphatic AH are extended in the present work and provide data for 47 aliphatic carboxylic acids. These include halogen, OH, CH30, C2H50, PhO, and NH2 substituted acetic acids, and halogen substituted, unsaturated, and cyclic RC02H of higher carbon number. The effects of the various substituents on the gas phase acidity are briefly examined.

In a drug discovery environment, reasonable go/no-go human in-vivo pharmacokinetic (PK) decisions must be made in a timely manner with a minimum amount of animal in-vivo or in-vitro data. We have investigated the accuracy of the in-vivo correlation between rat and human for the prediction of the total systemic clearance (CL), the volume of distribution at steady state (Vss), and the half-life (t1/2) using simple allometric scaling techniques. We have shown, using a large diverse set of drugs, that a fixed exponent allometric scaling approach can be used to predict human in-vivo PK parameters CL, Vss and t(1/2) solely from rat in-vivo PK data with acceptable accuracy for making go/no-go decisions in drug discovery. Human in-vivo PK predictions can be obtained using the simple allometric scaling relationships CL(Human) approximately = 40 CL(Rat) (L/hr), Vss(Human) approximately = 200 Vss(Rat) (L), and t1/2(Human) approximately = 4 t1/2(Rat) (hr). The average fold error for human CL predictions for N = 176 drugs was 2.25 with 79% of the drugs having a fold error less than 3. The average fold error for human Vss predictions for N = 144 drugs was 1.85 with 84% of the drugs having a fold error less than 3. The average fold error for human t1/2 predictions for N = 145 drugs was 2.05 with 76% of the drugs having a fold error less than 3. Using these simple allometric relationships, the sorting of drug candidates into a low/medium/high/very high human classification scheme was also possible from rat data. Since these simple allometric relationships between rat and human CL, Vss, and t1/2 are reasonably accurate, easy to remember and simple to calculate, these equations should be useful for making early go/no-go in-vivo human PK decisions for drug discovery candidates.

This article is available online at http://www.jlr.org Triglycerides (TGs) are the chief route of transport of dietary fat within chylomicrons and VLDLs, as well as the main form of fuel storage in adipose tissue. TGs are synthesized from one glycerol and three FA molecules, which are attached via ester bonds to the hydroxyl groups of the glycerol backbone. Two major diacylglycerol acyltransferase (DGAT) isozymes, DGAT1 and DGAT2, have been identifi ed. Although both enzymes convert diacylglycerol to TG, they do not share similarity in either their nucleotide or amino acid sequences and have most probably arisen by convergent evolution ( 1, 2 ). Although there are some differences in their tissue distributions, both DGAT1 and DGAT2 are highly expressed in organs that synthesize large amounts of TG, such as the liver, adipose tissue, and small intestine ( 3 ).Studies with genetically altered mice, as well as in vivo suppression of DGAT expression, indicate that both DGAT1 and DGAT2 play important roles in TG synthesis. DGAT1 knockout mice (DGAT1 Ϫ / Ϫ ) have reduced tissue TG levels and exhibit increased sensitivity to insulin and leptin ( 4 ). In addition, they are resistant to high-fat dietinduced obesity as a result of an increase in their metabolic rates ( 4 ). In contrast, knockout mice lacking DGAT2 (DGAT2 Ϫ / Ϫ ) are lipopenic and die soon after birth as a result of profound reductions in substrates for energy metabolism and impaired skin permeability ( 5 ). Hepatic suppression of DGAT2 with antisense oligonucleotides (ASOs) reduced hepatic TG content in rodents ( 6, 7 ), and reversed diet-induced hepatic steatosis and insulin resistance Abstract Diacylglycerol acyltransferase (DGAT) catalyzes the fi nal step in triglyceride (TG) synthesis. There are two isoforms, DGAT1 and DGAT2, with distinct protein sequences and potentially different physiological functions. To date, the ability to determine clear functional differences between DGAT1 and DGAT2, especially with respect to hepatic TG synthesis, has been elusive. To dissect the roles of these two key enzymes, we pretreated HepG2 hepatoma cells with