Structural alert/reactive metabolite concept as applied in medicinal chemistry to mitigate the risk of idiosyncratic drug toxicity: a perspective based on the critical examination of trends in the top 200 drugs marketed in the United States

Aminomethyl-Derived Beta Secretase (BACE1) Inhibitors: Engaging Gly230 without an Anilide Functionality

A growing subset of β-secretase (BACE1) inhibitors for the treatment of Alzheimer’s disease (AD) utilizes an anilide chemotype that engages a key residue (Gly230) in the BACE1 binding site. Although the anilide moiety affords excellent potency, it simultaneously introduces a third hydrogen bond donor that limits brain availability and provides a potential metabolic site leading to the formation of an aniline, a structural motif of prospective safety concern. We report herein an alternative aminomethyl linker that delivers similar potency and improved brain penetration relative to the amide moiety. Optimization of this series identified analogues with an excellent balance of ADME properties and potency; however, potential drug–drug interactions (DDI) were predicted based on CYP 2D6 affinities. Generation and analysis of key BACE1 and CYP 2D6 crystal structures identified strategies to obviate the DDI liability, leading to compound 16, which exhibits robust in vivo efficacy as a BACE1 inhibitor.

Latest Advances Towards Ras Inhibition: A Medicinal Chemistry Perspective

Owing to their high occurrence rate across many human cancers and their lack of druggability so far, mutant forms of the signaling protein Ras are currently among the most attractive (and elusive) oncology targets. This strong appeal explains the sustained effort in the field, and the ensuing progress has rekindled optimism regarding the discovery of Ras inhibitors. In this Minireview, we discuss the most recent advances towards irreversible inhibitors, and highlight approaches to inhibitors of Ras-effector interactions that have been overshadowed by the current focus on direct Ras inhibition. At the same time, we provide a critical assessment from a medicinal chemistry perspective.

Computer-aided discovery of anti-HIV agents

A review is provided on efforts in our laboratory over the last decade to discover anti-HIV agents. The work has focused on computer-aided design and synthesis of non-nucleoside inhibitors of HIV-1 reverse transcriptase (NNRTIs) with collaborative efforts on biological assaying and protein crystallography. Numerous design issues were successfully addressed including the need for potency against a wide range of viral variants, good aqueous solubility, and avoidance of electrophilic substructures. Computational methods including docking, de novo design, and free-energy perturbation (FEP) calculations made essential contributions. The result is novel NNRTIs with picomolar and low-nanomolar activities against wild-type HIV-1 and key variants that also show much improved solubility and lower cytotoxicity than recently approved drugs in the class.

Accessing Drug Metabolites via Transition-Metal Catalyzed C-H Oxidation: The Liver as Synthetic Inspiration

Can classical and modern chemical C-H oxidation reactions complement biotransformation in the synthesis of drug metabolites? We have surveyed the literature in an effort to try to answer this important question of major practical significance in the pharmaceutical industry. Drug metabolites are required throughout all phases of the drug discovery and development process; however, their synthesis is still an unsolved problem. This Review, not intended to be comprehensive or historical, highlights relevant applications of chemical C-H oxidation reactions, electrochemistry and microfluidic technologies to drug templates in order to access drug metabolites, and also highlights promising reactions to this end. Where possible or appropriate, the contrast with biotransformation is drawn. In doing so, we have tried to identify gaps where they exist in the hope to spur further activity in this very important research area.

Integrated analysis of microRNA and mRNA expression profiles highlights the complex and dynamic behavior of toosendanin-induced liver injury in mice

Triterpenoid Toosendanin (TSN) exhibits a plenty of pharmacological effects in human and great values in agriculture. However, the hepatotoxicity caused by TSN or Melia-family plants containing TSN used in traditional Chinese medicine has been reported, and the mechanisms of TSN-induced liver injury (TILI) still remain largely unknown. In this study, the dose- and time-dependent effects of TSN on mice liver were investigated by an integrated microRNA-mRNA approach as well as the general toxicological assessments. As the results, the dose- and time-dependent liver injury and alterations in global microRNA and mRNA expressions were detected. Particularly, 9-days 80 mg/kg TSN exposure caused most serious liver injury in mice, and the hepatic adaptation to TILI was unexpectedly observed after 21-days 80 mg/kg TSN administration. Based on the pathway analysis of the intersections between predicted targets of differentially expressed microRNAs and differentially expressed mRNAs at three time points, it revealed that TILI may be caused by glutathione depletion, mitochondrial dysfunction and lipid dysmetabolism, ultimately leading to hepatocytes necrosis in liver, while liver regeneration may play an important role in the hepatic adaptation to TILI. Our results demonstrated that the integrated microRNA-mRNA approach could provide new insight into the complex and dynamic behavior of TILI.

Metabolic profile and safety of piperlongumine

Piperlongumine (PPL), a natural plant product, has been extensively studied in cancer treatment going up on clinical trials. Since the first report related to its use on cancer research (in 2011) around 80 papers have been published in less than 10 years, but a gap still remaining. There are no metabolism studies of PPL in human organism. For the lack of a better view, here, the CYP450 in vitro oxidation of PPL was described for the first time. In addition, the enzymatic kinetic data, the predicted in vivo parameters, the produced metabolites, the phenotyping study and possible piperlongumine-drug interactions in vivo is presented.

A Model to predict severity of drug-induced liver injury in humans

Drug-induced liver injury (DILI) is a major public health concern, and improving its prediction remains an unmet challenge. Recently, we reported the Rule-of-2 (RO2) and found lipophilicity (logP ≥3) and daily dose ≥100 mg of oral medications to be associated with significant risk for DILI; however, the RO2 failed to estimate grades of DILI severity. In an effort to develop a quantitative metrics, we analyzed the association of daily dose, logP, and formation of reactive metabolites (RM) in a large set of Food and Drug Administration-approved oral medications and found factoring RM into the RO2 to highly improve DILI prediction. Based on these parameters and by considering n = 354 drugs, an algorithm to assign a DILI score was developed. In univariate and multivariate logistic regression analyses the algorithm (i.e., DILI score model) defined the relative contribution of daily dose, logP, and RM and permitted a quantitative assessment of risk of clinical DILI. Furthermore, a clear relationship between calculated DILI scores and DILI risk was obtained when applied to three independent studies. The DILI score model was also functional with drug pairs defined by similar chemical structure and mode of action but divergent toxicities. Specifically, for drug pairs where the RO2 failed, the DILI score correctly identified toxic drugs. Finally, the model was applied to n = 159 clinical cases collected from the National Institutes of Health's LiverTox database to demonstrate that the DILI score correlated with the severity of clinical outcome.

CONCLUSIONS

Based on daily dose, lipophilicity, and RM, a DILI score algorithm was developed that provides a scale of assessing the severity of DILI risk in humans associated with oral medications. (Hepatology 2016;64:931-940).

Automatically updating predictive modeling workflows support decision-making in drug design

Using predictive models for early decision-making in drug discovery has become standard practice. We suggest that model building needs to be automated with minimum input and low technical maintenance requirements. Models perform best when tailored to answering specific compound optimization related questions. If qualitative answers are required, 2-bin classification models are preferred. Integrating predictive modeling results with structural information stimulates better decision making. For in silico models supporting rapid structure–activity relationship cycles the performance deteriorates within weeks. Frequent automated updates of predictive models ensure best predictions. Consensus between multiple modeling approaches increases the prediction confidence. Combining qualified and nonqualified data optimally uses all available information. Dose predictions provide a holistic alternative to multiple individual property predictions for reaching complex decisions.

MARK inhibitors: Declaring a No-Go decision on a chemical series based on extensive DMPK experimentation

Attempts to optimize pharmacokinetic properties in a promising series of pyrrolopyrimidinone MARK inhibitors for the treatment of Alzheimer's disease are described. A focus on physical properties and ligand efficiency while prosecuting this series afforded key tool compounds that revealed a large discrepancy in the rat in vitro-in vivo DMPK (Drug Metabolism/Pharmacokinetics) correlation. These differences prompted an in vivo rat disposition study employing a radiolabeled representative of the series, and the results from this experiment justified the termination of any further optimization efforts.

Alarms about structural alerts

Structural alerts are widely accepted in chemical toxicology and regulatory decision support as a simple and transparent means to flag potential chemical hazards or group compounds into categories for read-across. However, there has been a growing concern that alerts disproportionally flag too many chemicals as toxic, which questions their reliability as toxicity markers. Conversely, the rigorously developed and properly validated statistical QSAR models can accurately and reliably predict the toxicity of a chemical; however, their use in regulatory toxicology has been hampered by the lack of transparency and interpretability. We demonstrate that contrary to the common perception of QSAR models as "black boxes" they can be used to identify statistically significant chemical substructures (QSAR-based alerts) that influence toxicity. We show through several case studies, however, that the mere presence of structural alerts in a chemical, irrespective of the derivation method (expert-based or QSAR-based), should be perceived only as hypotheses of possible toxicological effect. We propose a new approach that synergistically integrates structural alerts and rigorously validated QSAR models for a more transparent and accurate safety assessment of new chemicals.

Metabolism and bioactivation of the tricyclic antidepressant amitriptyline in human liver microsomes and human urine

Aim: Amitriptyline is a widely used tricyclic antidepressant, but the metabolic studies were conducted almost 20 years ago using high-performance liquid chromatography coupled with ultraviolet detector or radiolabeled methods. Results: First, multiple ion monitoring (MIM)- enhanced product ion (EPI) scan was used to obtain the diagnostic ions or neutral losses in human liver microsome incubations with amitriptyline. Subsequently, predicted multiple reaction monitoring (MRM)-EPI scan was used to identify the metabolites in human urine with the diagnostic ions or neutral losses. Finally, product ion filtering and neutral loss filtering were used as the data mining tools to screen metabolites. Consequently, a total of 28 metabolites were identified in human urine after an oral administration using LC–MS/MS. Conclusion: An integrated workflow using LC–MS/MS was developed to comprehensively profile the metabolites of amitriptyline in human urine, in which five N-acetyl-l-cysteine conjugates were characterized as tentative biomarkers for idiosyncratic toxicity.

Development of a cell viability assay to assess drug metabolite structure-toxicity relationships

Many adverse drug reactions are caused by the cytochrome P450 (CYP)-dependent activation of drugs into reactive metabolites. In order to reduce attrition due to metabolism-induced toxicity and to improve the safety of drug candidates, we developed a simple cell viability assay by combining a bioactivation system (human CYP3A4, CYP2D6 and CYP2C9) with Hep3B cells. We screened a series of drugs to explore structural motifs that may be responsible for CYP450-dependent activation caused by reactive metabolite formation, which highlighted specific liabilities regarding certain phenols and anilines.

[Recent findings regarding the mechanism of idiosyncratic drug toxicity]

Animal experiments cannot predict the probability of idiosyncratic drug toxicity; consequently, an important goal of the pharmaceutical industry is to develop a new methodology for preventing this form of drug reaction. Although the mechanism remains unclear, immune reactions are likely involved in the toxic processes underlying idiosyncratic drug toxicity: the drug is first activated into a chemically reactive metabolite that binds covalently to proteins and then acts as an immunogen. Therefore, screening tests to detect chemically reactive metabolites are conducted early during drug development and typically involve trapping with glutathione. More quantitative methods are then used in a later stage of drug development and frequently employ (14)Cor (3)H-labeled compounds. It has recently been demonstrated that a zone classification system can be used to separate risky drugs from likely safe drugs: by plotting the amount of each protein-bound reactive metabolite in vitro against the dose levels in vivo, the risk associated with each drug candidate can be assessed. A mechanism for idiosyncratic drug-induced hepatotoxicity was proposed by analogy to virus-induced hepatitis, in which cytotoxic T lymphocytes play an important role. This mechanism suggests that polymorphism in human leukocyte antigens is involved in idiosyncrasy, and a strong correlation with a specific genotype of human leukocyte antigens has been found in many cases of idiosyncratic drug toxicity. Therefore, gene biomarkers hold promise for reducing the clinical risk and prolonging the life cycle of otherwise useful drugs.

Case Characterization, Clinical Features and Risk Factors in Drug-Induced Liver Injury

Idiosyncratic drug-induced liver injury (DILI) caused by xenobiotics (drugs, herbals and dietary supplements) presents with a range of both phenotypes and severity, from acute hepatitis indistinguishable of viral hepatitis to autoimmune syndromes, steatosis or rare chronic vascular syndromes, and from asymptomatic liver test abnormalities to acute liver failure. DILI pathogenesis is complex, depending on the interaction of drug physicochemical properties and host factors. The awareness of risk factors for DILI is arising from the analysis of large databases of DILI cases included in Registries and Consortia networks around the world. These networks are also enabling in-depth phenotyping with the identification of predictors for severe outcome, including acute liver failure and mortality/liver transplantation. Genome wide association studies taking advantage of these large cohorts have identified several alleles from the major histocompatibility complex system indicating a fundamental role of the adaptive immune system in DILI pathogenesis. Correct case definition and characterization is crucial for appropriate phenotyping, which in turn will strengthen sample collection for genotypic and future biomarkers studies.

1,3-diphenylpropan-1-ones as allosteric modulators of α7 nACh receptors with analgesic and antioxidant properties

Nicotine acethylcholine receptors (nAChRs) play critical roles in cognitive processes, neuroprotection and inflammation.

RESULTS

According to their substituents, 1,3-diphenylpropan-1-one derivatives act as α7 nAChRs negative allosteric modulators (NAM, OMe) or Type I positive allosteric modulators (PAMs, OH). Compounds 7 and 31 were the most effective (989 and 666% enhancement of ACh-induced currents) and potent (EC50: 12.9 and 6.85 μM) PAMs. They exhibited strong radical scavenging values. Compound 31, selective over other neuronal nAChR subtypes and with acceptable pharmacokinetic profile, showed antinociceptive effects in a model of inflammatory pain.

CONCLUSION

Compound 31 is a novel, potent and selective α7 nAChR PAM, displaying antioxidant and analgesic activities. The 1,3-diphenylpropan-1-one scaffold could be the base toward more advanced type I PAMs for the treatment of nAChR-mediated diseases.

High content analysis assay for prediction of human hepatotoxicity in HepaRG and HepG2 cells

Drug-induced liver injury (DILI) results in the termination of drug development or withdrawal of a drug from the market. The establishment of a predictive, high-throughput preclinical test system to evaluate potential clinical DILI is therefore required. Here, we established a high content analysis (HCA) assay in human hepatocyte cell lines such as the HepaRG with normal expression levels of CYP enzymes and HepG2 with extremely low expression levels of CYP enzymes. Clinical DILI or non-DILI compounds were evaluated for reactive oxygen species (ROS) production, glutathione (GSH) consumption, and mitochondrial membrane potential (MMP) attenuation. A proportion of DILI compounds induced ROS generation, GSH depletion, and MMP dysfunction, which was consistent with reported mechanisms of DILI of these compounds. In particular, DILI compounds that deplete GSH via reactive metabolites exhibited a more marked decrease in intracellular GSH or increase in ROS production in HepaRG cells than in HepG2 cells. Comparison of the two cell lines with different levels of CYP expression might help clarify the contribution of metabolism to hepatocyte toxicity. These results suggest that the HCA assay in HepaRG and HepG2 cells might help improve the accuracy of evaluating clinical DILI potential during drug screening.

Mechanism of isoniazid-induced hepatotoxicity: then and now

Isoniazid (INH) remains a mainstay for the treatment of tuberculosis despite the fact that it can cause liver failure. Previous mechanistic hypotheses have classified this type of drug-induced liver injury (DILI) as 'metabolic idiosyncrasy' which was thought not to involve an immune response and was mainly due to the bioactivation of the acetylhydrazine metabolite. However, more recent studies support an alternative hypothesis, specifically, that INH itself is directly bioactivated to a reactive metabolite, which in some patients leads to an immune response and liver injury. Furthermore, there appear to be two phenotypes of INH-induced liver injury. Most cases involve mild liver injury, which resolves with immune tolerance, while other cases appear to have a more severe phenotype that is associated with the production of anti-drug/anti-CYP P450 antibodies and can progress to liver failure.

The design, synthesis and evaluation of low molecular weight acidic sulfonamides as URAT1 inhibitors for the treatment of gout

A series of low molecular weight and synthetically facile acidic sulfonamides that are potent and selective URAT1 inhibitors is described.

Detection and characterization of ponatinib reactive metabolites by liquid chromatography tandem mass spectrometry and elucidation of bioactivation pathways

This study involved the identification and characterization of ponatinib by LC-MS/MS. Additionally ponatinib reactive metabolites were also investigated using KCN as a trapping agent, showing the formation of four reactive cyano adducts.

Nano-NiFe2O4 catalyzed microwave assisted one-pot regioselective synthesis of novel 2-alkoxyimidazo[1,2-a]pyridines under aerobic conditions

Here, we have demonstrated regioselective synthesis of novel 2-alkoxy-3-arylimidazo[1,2-a]pyridines using nano-NiFe₂O₄ as reusable catalyst via one-pot sequential aza-Michael addition, Fe-promoted ligand transfer and C–H imination reactions.

Metabolic activation and drug-induced liver injury: in vitro approaches for the safety risk assessment of new drugs

Drug-induced liver injury (DILI) is a significant leading cause of hepatic dysfunction, drug failure during clinical trials and post-market withdrawal of approved drugs. Many cases of DILI are unexpected reactions of an idiosyncratic nature that occur in a small group of susceptible individuals. Intensive research efforts have been made to understand better the idiosyncratic DILI and to identify potential risk factors. Metabolic bioactivation of drugs to form reactive metabolites is considered an initiation mechanism for idiosyncratic DILI. Reactive species may interact irreversibly with cell macromolecules (covalent binding, oxidative damage), and alter their structure and activity. This review focuses on proposed in vitro screening strategies to predict and reduce idiosyncratic hepatotoxicity associated with drug bioactivation. Compound incubation with metabolically competent biological systems (liver-derived cells, subcellular fractions), in combination with methods to reveal the formation of reactive intermediates (e.g., formation of adducts with liver proteins, metabolite trapping or enzyme inhibition assays), are approaches commonly used to screen the reactivity of new molecules in early drug development. Several cell-based assays have also been proposed for the safety risk assessment of bioactivable compounds. Copyright © 2015 John Wiley & Sons, Ltd.

Toxicology Strategies for Drug Discovery: Present and Future

Attrition due to nonclinical safety represents a major issue for the productivity of pharmaceutical research and development (R&D) organizations, especially during the compound optimization stages of drug discovery and the early stages of clinical development. Focusing on decreasing nonclinical safety-related attrition is not a new concept, and various approaches have been experimented with over the last two decades. Front-loading testing funnels in Discovery with in vitro toxicity assays designed to rapidly identify unfavorable molecules was the approach adopted by most pharmaceutical R&D organizations a few years ago. However, this approach has also a non-negligible opportunity cost. Hence, significant refinements to the "fail early, fail often" paradigm have been proposed recently to reflect the complexity of accurately categorizing compounds with early data points without taking into account other important contextual aspects, in particular efficacious systemic and tissue exposures. This review provides an overview of toxicology approaches and models that can be used in pharmaceutical Discovery at the series/lead identification and lead optimization stages to guide and inform chemistry efforts, as well as a personal view on how to best use them to meet nonclinical safety-related attrition objectives consistent with a sustainable pharmaceutical R&D model. The scope of this review is limited to small molecules, as large molecules are associated with challenges that are quite different. Finally, a perspective on how several emerging technologies may impact toxicity evaluation is also provided.

Mapping the effects of drugs on the immune system

Understanding how drugs affect the immune system has consequences for treating disease and minimizing unwanted side effects. Here we present an integrative computational approach for predicting interactions between drugs and immune cells in a system-wide manner. The approach matches gene sets between transcriptional signatures to determine their similarity. We apply the method to model the interactions between 1,309 drugs and 221 immune cell types and predict 69,995 known and novel interactions. The resulting immune-cell pharmacology map is used to predict how 5 drugs influence 4 immune cell types in humans and mice. To validate the predictions, we analyzed patient records and examined cell population changes from in vivo experiments. Our method offers a tool for screening thousands of interactions to identify relationships between drugs and the immune system.

Computational toxicology

Predictive toxicology plays a critical role in reducing the failure rate of new drugs in pharmaceutical research and development. Despite recent gains in our understanding of drug-induced toxicity, however, it is urgent that the utility and limitations of our current predictive tools be determined in order to identify gaps in our understanding of mechanistic and chemical toxicology. Using recently published computational regression analyses of in vitro and in vivo toxicology data, it will be demonstrated that significant gaps remain in early safety screening paradigms. More strategic analyses of these data sets will allow for a better understanding of their domain of applicability and help identify those compounds that cause significant in vivo toxicity but which are currently mis-predicted by in silico and in vitro models. These ‘outliers’ and falsely predicted compounds are metaphorical lighthouses that shine light on existing toxicological knowledge gaps, and it is essential that these compounds are investigated if attrition is to be reduced significantly in the future. As such, the modern computational toxicologist is more productively engaged in understanding these gaps and driving investigative toxicology towards addressing them.

A novel cell-based assay for the evaluation of immune- and inflammatory-related gene expression as biomarkers for the risk assessment of drug-induced liver injury

Drug-induced liver injury (DILI) is a major problem in drug development. Although some in vitro methods assessing DILI risk that utilize hepatic cell death or cellular stress as markers have been developed, the predictive ability of these tests is low. In this study, we sought to develop a novel cell-based assay for the risk assessment of DILI that considers drug metabolism as well as immune- and inflammatory-related gene expression. To accomplish this goal, human hepatoma HepaRG or HepG2 cells were treated with 96 drugs with different clinical DILI risks. The conditioned media were subsequently used to treat human promyelocytic leukemia HL-60 cells, and the mRNA expression levels of immune- and inflammatory-related genes in the cells were measured. An area under the receiver operating characteristic curve (ROC-AUC) was calculated to evaluate the predictive performance of the mRNA levels as markers to discriminate DILI risk. The expression of interleukin-8 (IL-8) in HL-60 cells treated with conditioned media from HepaRG cells (HL-60/HepaRG) exhibited the highest ROC-AUC value of 0.758, followed by the expression of IL-1β in HL-60/HepaRG (ROC-AUC: 0.726). Notably, the ROC-AUC values of these genes were higher in HL-60/HepaRG than in HL-60/HepG2, which suggests that HL-60/HepaRG has a higher potential for detecting the metabolic activation of drugs. An integrated score calculated from the levels of S100 calcium-binding protein A9 (S100A9), IL-1β, and IL-8 more precisely determined the DILI risks than individual gene expression did. The developed cell-based assay that utilizes immune-related gene expression would aid in the assessment of potential DILI risks.

The Art and Science of Diagnosing and Managing Drug-induced Liver Injury in 2015 and Beyond

Drug-induced liver injury (DILI) remains a leading reason why new compounds are dropped from further study or are the subject of product warnings and regulatory actions. Hy's Law of drug-induced hepatocellular jaundice causing a case-fatality rate or need for transplant of 10% or higher has been validated in several large national registries, including the ongoing, prospective U.S. Drug-Induced Liver Injury Network. It serves as the basis for stopping rules in clinical trials and in clinical practice. Because DILI can mimic all known causes of acute and chronic liver disease, establishing causality can be difficult. Histopathologic findings are often nonspecific and rarely, if ever, considered pathognomonic. A daily drug dose >50-100 mg is more likely to be hepatotoxic than does <10 mg, especially if the compound is highly lipophilic or undergoes extensive hepatic metabolism. The quest for a predictive biomarker to replace alanine aminotransferase is ongoing. Markers of necrosis and apoptosis such as microRNA-122 and keratin 18 may prove useful in identifying patients at risk for severe injury when they initially present with a suspected acetaminophen overdose. Although a number of drugs causing idiosyncratic DILI have HLA associations that may allow for pre-prescription testing to prevent hepatotoxicity, the cost and relatively low frequency of injury among affected patients limit the current usefulness of such genome-wide association studies. Alanine aminotransferase monitoring is often recommended but has rarely been shown to be an effective method to prevent serious DILI. Guidelines on the diagnosis and management of DILI have recently been published, although specific therapies remain limited. The LiverTox Web site has been introduced as an interactive online virtual textbook that makes the latest information on more than 650 agents available to clinicians, regulators, and drug developers alike.

Understanding P450-mediated Bio-transformations into Epoxide and Phenolic Metabolites

Adverse drug reactions are commonly the result of cytochrome P450 enzymes (CYPs) converting the drugs into reactive metabolites. Thus, information about the CYP bioactivation of drugs would not only provide insight into metabolic stability, but also into the potential toxicity. For example, oxidation of phenyl rings may lead to either toxic epoxides or safer phenols. Herein, we demonstrate that the potential to form reactive metabolites is encoded primarily in the properties of the molecule to be oxidized. While the enzyme positions the molecule inside the binding pocket (selects the site of metabolism), the subsequent reaction is only dependent on the substrate itself. To test this hypothesis, we used this observation as a predictor of drug inherent toxicity. This approach was used to successfully identify the formation of reactive metabolites in over 100 drug molecules. These results provide a new perspective on the impact of functional groups on aromatic oxidation of drugs and their effects on toxicity.