Metabolism of (−)-(S)-nicotine in the isolated perfused rabbit lung

Aislaitner, G.; Bello, A.; Tan, Soo Choon; Hutt, Andrew J.; Marriott, Christopher; Gorrod, J. W.

doi:10.1007/bf03190976

Cited by 5 publications

(4 citation statements)

References 10 publications

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“…Right, four example molecules are visualized with their protein and GSH reactivity predictions indicated by colored shading. From top to bottom, a TRPV1 antagonist metabolite, 46 a nitroso metabolite of 2-amino-1-methyl-6-phenylimidazo[4,5- b ]pyridine (formed during cooking of meat), 47 a nicotine iminium ion metabolite (reactive with cyanide), 48 and a benoxaprofen acyl glucuronide metabolite (a nonsteroidal anti-inflammatory drug withdrawn due to hepatotoxicity). 49 Sites of reactivity are indicated by white circles.…”

Section: Resultsmentioning

confidence: 99%

Modeling Reactivity to Biological Macromolecules with a Deep Multitask Network

et al. 2016

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Most small-molecule drug candidates fail before entering the market, frequently because of unexpected toxicity. Often, toxicity is detected only late in drug development, because many types of toxicities, especially idiosyncratic adverse drug reactions (IADRs), are particularly hard to predict and detect. Moreover, drug-induced liver injury (DILI) is the most frequent reason drugs are withdrawn from the market and causes 50% of acute liver failure cases in the United States. A common mechanism often underlies many types of drug toxicities, including both DILI and IADRs. Drugs are bioactivated by drug-metabolizing enzymes into reactive metabolites, which then conjugate to sites in proteins or DNA to form adducts. DNA adducts are often mutagenic and may alter the reading and copying of genes and their regulatory elements, causing gene dysregulation and even triggering cancer. Similarly, protein adducts can disrupt their normal biological functions and induce harmful immune responses. Unfortunately, reactive metabolites are not reliably detected by experiments, and it is also expensive to test drug candidates for potential to form DNA or protein adducts during the early stages of drug development. In contrast, computational methods have the potential to quickly screen for covalent binding potential, thereby flagging problematic molecules and reducing the total number of necessary experiments. Here, we train a deep convolution neural network—the XenoSite reactivity model—using literature data to accurately predict both sites and probability of reactivity for molecules with glutathione, cyanide, protein, and DNA. On the site level, cross-validated predictions had area under the curve (AUC) performances of 89.8% for DNA and 94.4% for protein. Furthermore, the model separated molecules electrophilically reactive with DNA and protein from nonreactive molecules with cross-validated AUC performances of 78.7% and 79.8%, respectively. On both the site- and molecule-level, the model’s performances significantly outperformed reactivity indices derived from quantum simulations that are reported in the literature. Moreover, we developed and applied a selectivity score to assess preferential reactions with the macromolecules as opposed to the common screening traps. For the entire data set of 2803 molecules, this approach yielded totals of 257 (9.2%) and 227 (8.1%) molecules predicted to be reactive only with DNA and protein, respectively, and hence those that would be missed by standard reactivity screening experiments. Site of reactivity data is an underutilized resource that can be used to not only predict if molecules are reactive, but also show where they might be modified to reduce toxicity while retaining efficacy. The XenoSite reactivity model is available at .

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Section: Resultsmentioning

confidence: 99%

Modeling Reactivity to Biological Macromolecules with a Deep Multitask Network

et al. 2016

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“…Isolated lung models have repeatedly been employed for evaluating pharmacokinetic and pharmacodynamic parameters of agents of interest, mostly after admixture to the perfusion medium (Aislaitner et al, 1997;Valodia and Syce, 2000). In the current study, we focused on iloprost, a stable prostacyclin analog, and compared the inhalative to the intravascular route of administration.…”

Section: Discussionmentioning

confidence: 99%

Pharmacokinetics and Metabolism of Infused versus Inhaled Iloprost in Isolated Rabbit Lungs

Schermuly

Schulz²,

Ghofrani³

et al. 2002

J Pharmacol Exp Ther

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Iloprost is a potent prostacyclin analog, which has been shown to exert beneficial effects in several vascular disorders. Inhalation of aerosolized iloprost was found to cause selective pulmonary vasodilatation, and this approach is under current investigation for treatment of chronic pulmonary hypertension. The present study investigated pharmacokinetics and metabolism of aerosolized iloprost in isolated buffer-perfused rabbit lungs, compared with intravascular administration of the prostanoid. After buffer admixture of iloprost, a steady decline of perfusate concentrations of the intact prostanoid was noted (half-life ϳ3.5 h), mostly attributable to progressive metabolism to dinor-and tetranoriloprost. Inhaled iloprost rapidly entered the intravascular compartment, with peak buffer concentrations being noted after 30 min (bioavailability ϳ63%). Compared with infused iloprost, significantly more rapid metabolism to dinorand tetranoriloprost was noted for iloprost administered via the inhalative route of application. However, the percentage of the nebulized agent that enters the intravascular space as intact iloprost displays the same clearance rate as directly perfusateadmixed prostanoid. We conclude that a high percentage of inhaled iloprost rapidly enters the intravascular compartment in intact rabbit lungs. The lung is capable of metabolizing iloprost via ␤-oxidation, and more rapid appearance of dinor-and tetranoriloprost is noted for the inhalative as compared with the intravascular route of iloprost administration.

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“…In an isolated perfused rabbit lung, NIC was found to be transformed mainly to COT (34%), nicotine 1’-N-oxide (14%), 3-hydroxycotinine (10%) and cotinine 1’-N-oxide (3%) (Aislaitner et al, 1997). Nornicotine and norcotinine were detected in only low amounts.…”

Section: Discussionmentioning

confidence: 99%

Nicotine metabolism in pregnant and nonpregnant rabbits

Tutka¹,

Dempsey²,

Jacob³

et al. 2008

CNTR

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Smoking remains a major public health concern during pregnancy and is associated with numerous adverse effects. Recently the clearance of nicotine (NIC) and cotinine (COT) was shown to be substantially increased in pregnant women compared to non-pregnant controls. The present study investigated the usefulness of the rabbit for studying the molecular basis for the observed changes in NIC and COT disposition during pregnancy. NIC was largely metabolized to COT in rabbit liver microsomes (approximately 50% of total metabolism) with significant amounts of nicotine-N'-oxide and nornicotine also being detected. The conversion of NIC to COT was also detected in rabbit placental and fetal liver microsomes albeit at only a fraction of the rate in adult rabbit liver microsomes. The major products of COT metabolism in rabbit liver microsomes were 5'-hydroxycotinine, cotinine-N'-oxide and norcotinine. Differences between human and rabbit liver were most apparent for COT, with the major human metabolite 3'-hydroxycotinine, being formed at only low levels in rabbit liver microsomes. Pregnancy had no effect on the metabolism of NIC or on the expression of CYP2A6 immunoreactive proteins in rabbit liver microsomes. These studies provide a complete quantitative assessment of NIC metabolism in rabbit liver microsomes and suggest that the rabbit may not be an appropriate animal model to study the effects of pregnancy on NIC and COT metabolism. However, a molecular understanding of these effects is essential for prediction of the pharmacological and toxicological consequences of smoking during pregnancy.

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Metabolism of (−)-(S)-nicotine in the isolated perfused rabbit lung

Cited by 5 publications

References 10 publications

Modeling Reactivity to Biological Macromolecules with a Deep Multitask Network

Modeling Reactivity to Biological Macromolecules with a Deep Multitask Network

Pharmacokinetics and Metabolism of Infused versus Inhaled Iloprost in Isolated Rabbit Lungs

Nicotine metabolism in pregnant and nonpregnant rabbits

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