Identification and Mitigation of a Reactive Metabolite Liability Associated with Aminoimidazoles

Srivastava, Abhishek; Ramachandran, Sreekanth; Hameed, Shahul; Ahuja, Vijay Kamal; Hosagrahara, Vinayak

doi:10.1021/tx500212c

Cited by 15 publications

(20 citation statements)

References 29 publications

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“…Four example molecules are visualized. Top center, an antimalarial drug candidate . Top right, a voltage-gated sodium channel inhibitor drug candidate .…”

Section: Resultsmentioning

confidence: 99%

Deep Learning to Predict the Formation of Quinone Species in Drug Metabolism

Hughes

Swamidass

2017

Chem. Res. Toxicol.

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Many adverse drug reactions are thought to be caused by electrophilically reactive drug metabolites that conjugate to nucleophilic sites within DNA and proteins, causing cancer or toxic immune responses. Quinone species, including quinone-imines, quinone-methides, and imine-methides, are electrophilic Michael acceptors that are often highly reactive and comprise over 40% of all known reactive metabolites. Quinone metabolites are created by cytochromes P450 and peroxidases. For example, cytochromes P450 oxidize acetaminophen to N-acetyl-p-benzoquinone imine, which is electrophilically reactive and covalently binds to nucleophilic sites within proteins. This reactive quinone metabolite elicits a toxic immune response when acetaminophen exceeds a safe dose. Using a deep learning approach, this study reports the first published method for predicting quinone formation: the formation of a quinone species by metabolic oxidation. We model both one- and two-step quinone formation, enabling accurate quinone formation predictions in nonobvious cases. We predict atom pairs that form quinones with an AUC accuracy of 97.6%, and we identify molecules that form quinones with 88.2% AUC. By modeling the formation of quinones, one of the most common types of reactive metabolites, our method provides a rapid screening tool for a key drug toxicity risk. The XenoSite quinone formation model is available at http://swami.wustl.edu/xenosite/p/quinone.

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“…Four example molecules are visualized. Top center, an antimalarial drug candidate . Top right, a voltage-gated sodium channel inhibitor drug candidate .…”

Section: Resultsmentioning

confidence: 99%

Deep Learning to Predict the Formation of Quinone Species in Drug Metabolism

Hughes

Swamidass

2017

Chem. Res. Toxicol.

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“…Each site of reactivity is labeled with a white circle. Top right, an antimalarial drug candidate metabolite, 44 and bottom right, gefitinib. 45 …”

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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“…Notably, another mPGES‐1 inhibitor that shared metabolic activation of the imidazole ring was also associated with DILI in one healthy subject with aminotransferase concentration >10× ULN in a separate multiple ascending dose study . Metabolic activation of different aminoimidazole compounds also involves the formation of epoxide in vitro , albeit via a different metabolic route. These data suggest that epoxide formation of the imidazole moiety may have played a role in the development of hepatotoxicity.…”

Section: Discussionmentioning

confidence: 95%

Dose‐dependent acute liver injury with hypersensitivity features in humans due to a novel microsomal prostaglandin E synthase 1 inhibitor

Jin

Regev

Kam

et al. 2017

Brit J Clinical Pharma

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AIMSLY3031207, a novel microsomal prostaglandin E synthase 1 inhibitor, was evaluated in a multiple ascending dose study after nonclinical toxicology studies and a single ascending dose study demonstrated an acceptable toxicity, safety and tolerability profile. METHODSHealthy subjects were randomized to receive LY3031207 (25, 75 and 275 mg), placebo or celecoxib (400 mg) once daily for 28 days. The safety, tolerability and pharmacokinetic and pharmacodynamic profiles of LY3031207 were evaluated. RESULTSThe study was terminated when two subjects experienced drug-induced liver injury (DILI) after they had received 225 mg LY3031207 for 19 days. Liver biopsy from these subjects revealed acute liver injury with eosinophilic infiltration. Four additional DILI cases were identified after LY3031207 dosing had been stopped. All six DILI cases shared unique presentations of hepatocellular injury with hypersensitivity features and demonstrated a steep dose-dependent trend. Prompt discontinuation of the study drug and supportive medical care resulted in full recovery. Metabolites from metabolic activation of the imidazole ring were observed in plasma and urine samples from all subjects randomized to LY3031207 dosing. CONCLUSIONSThis study emphasized the importance of careful safety monitoring and serious adverse events management in phase I trials. Metabolic activation of the imidazole ring may be involved in the development of hepatotoxicity of LY3031207.

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Identification and Mitigation of a Reactive Metabolite Liability Associated with Aminoimidazoles

Cited by 15 publications

References 29 publications

Deep Learning to Predict the Formation of Quinone Species in Drug Metabolism

Deep Learning to Predict the Formation of Quinone Species in Drug Metabolism

Modeling Reactivity to Biological Macromolecules with a Deep Multitask Network

Dose‐dependent acute liver injury with hypersensitivity features in humans due to a novel microsomal prostaglandin E synthase 1 inhibitor

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