Predictable metabolism

A. M. ter laak, N. P. E. Vermeulen, Molecular-modeling approaches to predict metabolism and toxicity, in Pharmacokinetic Optimization in Drug Research, B. Testa, H. van de Water-beemd, G. Folkers, R. Guy (Eds.), Wiley-VCH, Weinheim, 2001,... 

Computational methods including both metabolism databases and predictive metabolism software can be used to aid bioanalytical groups in suggesting all possible potential metabolite masses before identification by mass spectroscopy (MS) [116,117]. This approach can also combine specialized MS spectra feature prediction software that will use the outputs from databases and prediction software and make comparisons with the molecular masses observed... 

Ekins S. In silica approaches to predicting metabolism, toxicology and beyond. Biochem Soc Trans 2003 31 611-4. 

Boyer S, Zamora I. New methods in predictive metabolism. J Comput-Aided... 

Plant use is less biotechnologically advanced and fundamentally more complex. The first generation of plant metabolic engineering met with mixed success and produced unanticipated results — problems that are not necessarily restricted to manipulation of carotenogenesis. The reason is that predictive metabolic engineering relies on the establishment of both needed tools and an information infrastructure... 

Darvas F. Predicting metabolic pathways by logic programming. J Mol Graph 1988 6 80-86. 

Boyer, S. and Zamora, I. (2002) New methods in predictive metabolism. Journal of Computer-Aided Molecular Design, 16 (5/6), 403 113. 

A number of approaches are available or under development to predict metabolism, including expert systems such as MetabolExpert (Compudrug), Meteor (Lhasa), MetaFore [42] and the databases Metabolite (MDL) and Metabolism (Synopsys) [43]. Ultimately such programs may be linked to computer-aided toxicity prediction based on quantitative structure-toxicity relationships and expert systems for toxicity evaluation such as DEREK (Lhasa) (see also Chapter 8) [44]. 

A number of reports also describe the prediction of mechanism-based inhibition (MBI) [17,18]. In this type of model, MBI is determined in part by spectral shift and inactivation kinetics. Jones et al. applied computational pharmacophores, recursive partitioning and logistic regression in attempts to predict metabolic intermediate complex (MIC) formation from structural inputs [17]. The development of models that accurately predict MIC formation will provide another tool to help reduce the overall risk of DDI [19]. 

Jones, D.R., Ekins, S., Li, L. and Hall, S.D. (2007) Computational approaches that predict metabolic intermediate complex formation with CYP3A4 ( + b5). Drug Metabolism and Disposition The Biolo ccd Fate of Chemicals, 35, 1466-1475. 

The Screening Hypothesis seeks to explain the evolution of NPs when the chances of any one NP benefiting the producer are indeed very low. This simple hypothesis predicts that certain metabolic traits which favour the generation and retention of NP diversity will be retained during the evolution of NP metabolism. The most important of these predicted metabolic traits is the ability of enzymes making NPs to either accept... 

Kutchan T. (2005). Predictive metabolic engineering in plants still full of surprises. Trends in Biotechnology, 23,381-3. 

N), 32 mg/m (RA) upon repeated dosing) hepatobiliary and renal (predicted metabolic processes)... 

The guarded hot-plate method can be modified to perform dry and wet heat transfer testing (sweating skin model). Some plates contain simulated sweat glands and use a pumping mechanism to deliver water to the plate surface. Thermal comfort properties that can be determined from this test are do, permeability index (T), and comfort limits. Permeability index indicates moisture—heat permeability through the fabric on a scale of 0 (completely impermeable) to 1 (completdy permeable). This parameter indicates the effect of skin moisture on heat loss. Comfort limits are the predicted metabolic activity levds that may be sustained while maintaining body thermal comfort in the test environment. 

Amot, J.A., Meylan, W., Tunkel, J., Howard, P.H., Mackay, D., Bonnell, M. and Boethling, R.S. (2009) A quantitative structure-activity relationship for predicting metabolic biotransformation rates for organic chemicals in fish. Environ Toxicol Chem, 28, 1168-1177. 

Long, A. and J. D. Walker. 2003. Quantitative structure-activity relationships for predicting metabolism and modeling cytochrome p450 enzyme activitiEsiviron. Toxicol. Cherr22 1894-1899. 

Although there is considerable information about metabolic transformation rates of a variety of organic substances in aquatic organisms, it is often difficult to predict metabolic transformation rates in organisms under field conditions. 

Over the past decade there has been a substantial improvement in the ability to predict metabolism-based in vivo drug interactions from kinetic data obtained in vitro. This advance has been most evident for interactions that occur at the level of cytochrome P450 (CYP)-catalyzed oxidation and reflects the availability of human tissue samples, cDNA-expressed CYPs, and well-defined substrates and inhibitors of individual enzymes. The most common paradigm in the prediction of in vivo drug interactions has been first to determine the enzyme selectivity of a suspected inhibitor and subsequently to estimate the constant that quantifies the potency of reversible inhibition in vitro. This approach has been successful in identifying clinically important potent competitive inhibitors, such as quinidine, fluoxetine, and itraconazole. However, there is a continuing concern that a number of well-established and clinically important CYP-mediated drug interactions are not predictable from the classical approach that assumes reversible mechanisms of inhibition are ubiquitous. 

Zvinavashe E, Murk AJ, Rietjens M (2008) Promises and pitfalls of quantitative structure-activity relationship approaches for predicting metabolism and toxicity. Chem Res Toxicol 21(12) 2229-2236... 

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