Identifying the reactive metabolites of tyrosine kinase inhibitors in a comprehensive approach: Implications for drug‐drug interactions and hepatotoxicity

Use of Structural Alerts for Reactive Metabolites in the Application SpotRM

Reactive metabolite (RM) formation is widely accepted as playing a crucial role in causing idiosyncratic adverse drug reactions (IADRs), where the liver is most affected. An important goal of drug design is to avoid selection of drug candidates giving rise to RMs and therefore risk causing problems later on involving IADRs. The simplest, initial approach is to avoid test structures that have substructures known or strongly suspected to be associated with IADRs. However, as is evident from the many case reports of IADRs, in most cases a clear association with any (bio)chemical mechanism is lacking, which makes it hard to establish any structure-toxicity relationship. Separate studies of RM formation, in vitro and in vivo, have led to likely evidence and to establishing many structural alerts (SAs) that can be used for fast selection/deselection of planned test compounds. As a background to a discussion of the concept, 25 kinase inhibitor drugs with known problems of hepatotoxicity were probed against a set of SAs contained in the application SpotRM. A clear majority of the probed drugs show liabilities as evident by being flagged by more than one of the fairly established types of SAs. At the same time, no clear SAs were found in three drugs, which is discussed in the broader context of usefulness and selection tactics of SAs in drug design.

Higher risk of hepatotoxicity associated with cabozantinib in cancer patients

BACKGROUND

The efficacy of cabozantinib has attracted interest in various solid tumors. The primary aim of this study was to evaluate the risk of hepatotoxicity associated with cabozantinib in the patients with cancer.

METHODS

PubMed, Cochrane, and EMBASE databases were searched for published randomized controlled trials (RCTs) from inception to September 9, 2023. The mainly outcomes were all-grade and grade ≥3 elevation of aspartate aminotransferase (AST) and alanine aminotransferase (ALT), expressed as relative risk (RR) and 95% confidence interval (CI). All data were pooled using fixed-effect or random-effects models according to the heterogeneity of the included RCTs.

RESULTS

Among the 922 records identified, 8 RCTs incorporating 2613 patients with cancer were included. For patients receiving cabozantinib, the relative risks of all-grade AST elevation (RR, 2.63; 95% CI, 2.16-3.20, P < 0.001), all-grade ALT elevation (RR, 2.89; 95% CI, 2.31-3.60, P < 0.001), grade ≥3 AST elevation (RR, 2.26; 95% CI, 1.34-3.83, P = 0.002), and grade ≥3 ALT elevation (RR, 3.40; 95% CI, 1.65-7.01, P < 0.001) were higher than those of patients who did not receive cabozantinib group. Further subgroup analysis showed that the relative risk of hepatotoxicity associated with cabozantinib was higher than that in the other TKIs (erlotinib, sunitinib, and sorafenib) and the non-TKI drug groups (everolimus, prednisone, mitoxantrone, and paclitaxel).

CONCLUSIONS

Compared with other solid tumor drugs, such as everolimus, sorafenib, sunitinib, paclitaxel, mitoxantrone-prednisone et al., cabozantinib has a higher risk of hepatotoxicity.

Integrating network pharmacology and drug side-effect data to explore mechanism of liver injury-induced by tyrosine kinase inhibitors

Tyrosine kinase inhibitors (TKIs) are highly efficient small-molecule anticancer drugs. Despite the specificity and efficacy of TKIs, they can produce off-target effects, leading to severe liver toxicity, and even some of them are labeled as black box hepatotoxicity. Thus, we focused on representative TKIs associated with severe hepatic adverse events, namely lapatinib, pazopanib, regorafenib, and sunitinib as objections of study, then integrated drug side-effect data from United State Food and Drug Administration (U.S. FDA) and network pharmacology to elucidate mechanism underlying TKI-induced liver injury. Based on network pharmacology, we constructed a specific comorbidity module of high risk of serious adverse effects and created drug-disease networks. Enrichment analysis of the networks revealed the depletion of all-trans-retinoic acid and the involvement of down-regulation of the HSP70 family-mediated endoplasmic reticulum (ER) stress as key factors in TKI-induced liver injury. These results were further verified by transcription data. Based on the target prediction results of drugs and reactive metabolites, we also shed light on the association between toxic metabolites and severe hepatic adverse reactions, and thinking HSPA8, HSPA1A, CYP1A1, CYP1A2 and CYP3A4 were potential therapeutic or preventive targets against TKI-induced liver injury. In conclusion, our research provides comprehensive insights into the mechanism underlying severe liver injury caused by TKIs, offering a better understanding of how to enhance patient safety and treatment efficacy.

Bioactivation and reactivity research advances - 2022 year in review‡

With the 50th year mark since the launch of Drug Metabolism and Disposition journal, the field of drug metabolism and bioactivation has advanced exponentially in the past decades (Guengerich 2023).This has, in a major part, been due to the continued advances across the whole spectrum of applied technologies in hardware, software, machine learning (ML), and artificial intelligence (AI). LC-MS platforms continue to evolve to support key applications in the field, and automation is also improving the accuracy, precision, and throughput of these supporting assays. In addition, sample generation and processing is being aided by increased diversity and quality of reagents and bio-matrices so that what is being analyzed is more relevant and translatable. The application of in silico platforms (applied software, ML, and AI) is also making great strides, and in tandem with the more traditional approaches mentioned previously, is significantly advancing our understanding of bioactivation pathways and how these play a role in toxicity. All of this continues to allow the area of bioactivation to evolve in parallel with associated fields to help bring novel or improved medicines to patients with urgent or unmet needs.Shuai Wang and Cyrus Khojasteh, on behalf of the authors.

The role of cytochrome P450 3A4-mediated metabolism in sorafenib and lapatinib hepatotoxicity

Tyrosine kinase inhibitors (TKIs) are increasingly popular drugs used to treat more than a dozen different diseases, including some forms of cancer. Despite having fewer adverse effects than traditional chemotherapies, they are not without risks. Liver injury is a particular concern. Of the FDA-approved TKIs, approximately 40% cause hepatotoxicity. However, little is known about the underlying pathophysiology. The leading hypothesis is that TKIs are converted by cytochrome P450 3A4 (CYP3A4) to reactive metabolites that damage proteins. Indeed, there is strong evidence for this bioactivation of TKIs in in vitro reactions. However, the actual toxic effects are underexplored. Here, we measured the cytotoxicity of several TKIs in primary mouse hepatocytes, HepaRG cells, and HepG2 cells with and without CYP3A4 modulation. To our surprise, the data indicate that CYP3A4 increases resistance to sorafenib and lapatinib hepatotoxicity. The results have implications for the mechanism of toxicity of these drugs in patients and underline the importance of selecting an appropriate experimental model.

Reported hepatotoxicity and hepatotoxicity guidance in the product information of protein kinase inhibitors in oncology registered at the European Medicines Agency

Protein kinase inhibitors (PKIs) used in oncology can induce severe and even fatal hepatotoxicity. Several PKIs are registered within a certain class to target a specific kinase. No systematic comparison of the reported hepatotoxicity and clinical guidance for monitoring and management of hepatotoxic events between the various PKI summaries of product characteristics (SmPC) is yet available. A systematic analysis of data on 21 hepatotoxicity parameters obtained from the SmPCs and European public assessment reports (EPARs) of European Medicines Agency-approved antineoplastic PKIs (n = 55) has been conducted. The median reported incidence (range) of all grades of aspartate aminotransferase (AST) elevations was 16.9% (2.0%-86.4%) for PKI monotherapy, with 2.1% (0.0%-10.3%) being grade 3/4 and for all grades alanine aminotransferase (ALT) elevations 17.6% (2.0%-85.5%), with 3.0% (0.0%-25.0%) being grade 3/4. Fatalities due to hepatotoxicity were reported for 22 out of 47 PKIs (monotherapy) and for 5 out of 8 PKIs (combination therapy). A maximum grade of grade 4 and grade 3 hepatotoxicity was reported for 45% (n = 25) and 6% (n = 3), respectively. Liver parameter monitoring recommendations were present in 47 of the 55 SmPCs. Dose reductions were recommended for 18 PKIs. Discontinuation was recommended for patients meeting Hy's law criteria (16 out of 55 SmPCs). Severe hepatotoxic events are reported in approximately 50% of the analyzed SmPCs and EPARs. Differences in the degree of hepatotoxicity are apparent. Although liver parameter monitoring recommendations are present in the vast majority of the analyzed PKI SmPCs, the clinical guidance for hepatotoxicity was not standardized.

Metabolic activation of tyrosine kinase inhibitors: recent advance and further clinical practice

At present, receptor tyrosine kinase signaling-related pathways have been successfully mediated to inhibit tumor proliferation and promote anti-angiogenesis effects for cancer therapy. Tyrosine kinase inhibitors (TKIs), a group of novel chemotherapeutic agents, have been applied to treat diverse malignant tumors effectively. However, the latent toxic and side effects of TKIs, such as hepatotoxicity and cardiotoxicity, limit their use in clinical practice. Metabolic activation has the potential to lead to toxic effects. Numerous TKIs have been demonstrated to be transformed into chemically reactive/potentially toxic metabolites following cytochrome P450-catalyzed activation, which causes severe adverse reactions, including hepatotoxicity, cardiotoxicity, skin toxicity, immune injury, mitochondria injury, and cytochrome P450 inactivation. However, the precise mechanisms of how these chemically reactive/potentially toxic species induce toxicity remain poorly understood. In addition, we present our viewpoints that regulating the production of reactive metabolites may decrease the toxicity of TKIs. Exploring this topic will improve understanding of metabolic activation and its underlying mechanisms, promoting the rational use of TKIs. This review summarizes the updated evidence concerning the reactive metabolites of TKIs and the associated toxicities. This paper provides novel insight into the safe use of TKIs and the prevention and treatment of multiple TKIs adverse effects in clinical practice.

The Ascension of Targeted Covalent Inhibitors

Covalent drugs have made a major impact on human health but until recently were shunned by the pharmaceutical industry over concerns about the potential for toxicity. A resurgence has occurred driven by the clinical success of targeted covalent inhibitors (TCIs), with eight drugs approved over the past decade. The opportunity to create unique drugs by exploiting the covalent mechanism of action has enabled clinically decisive target product profiles to be achieved. TCIs have revolutionized the treatment paradigm for non-small-cell lung cancer and chronic lymphocytic leukemia. This Perspective will highlight the clinical and financial success of this class of drugs and provide early insight into toxicity, a key factor that had hindered progress in the field. Further innovation in the TCI approach, including expanding beyond cysteine-directed electrophiles, kinases, and cancer, highlights the broad opportunity to deliver a new generation of breakthrough therapies.

A Risk Scoring System Utilizing Machine Learning Methods for Hepatotoxicity Prediction One Year After the Initiation of Tyrosine Kinase Inhibitors

Background

There is currently no method to predict tyrosine kinase inhibitor (TKI) -induced hepatotoxicity. The purpose of this study was to propose a risk scoring system for hepatotoxicity induced within one year of TKI administration using machine learning methods.

Methods

This retrospective, multi-center study analyzed individual data of patients administered different types of TKIs (crizotinib, erlotinib, gefitinib, imatinib, and lapatinib) selected in five previous studies. The odds ratio and adjusted odds ratio from univariate and multivariate analyses were calculated using a chi-squared test and logistic regression model. Machine learning methods, including five-fold cross-validated multivariate logistic regression, elastic net, and random forest were utilized to predict risk factors for the occurrence of hepatotoxicity. A risk scoring system was developed from the multivariate and machine learning analyses.

Results

Data from 703 patients with grade II or higher hepatotoxicity within one year of TKI administration were evaluated. In a multivariable analysis, male and liver metastasis increased the risk of hepatotoxicity by 1.4-fold and 2.1-fold, respectively. The use of anticancer drugs increased the risk of hepatotoxicity by 6.0-fold. Patients administered H2 blockers or PPIs had a 1.5-fold increased risk of hepatotoxicity. The area under the receiver-operating curve (AUROC) values of machine learning methods ranged between 0.73-0.75. Based on multivariate and machine learning analyses, male (1 point), use of H2 blocker or PPI (1 point), presence of liver metastasis (2 points), and use of anticancer drugs (4 points) were integrated into the risk scoring system. From a training set, patients with 0, 1, 2-3, 4-7 point showed approximately 9.8%, 16.6%, 29.0% and 61.5% of risk of hepatotoxicity, respectively. The AUROC of the scoring system was 0.755 (95% CI, 0.706-0.804).

Conclusion

Our scoring system may be helpful for patient assessment and clinical decisions when administering TKIs included in this study.

Design, Synthesis, and Biological Evaluation of Pyrano[2,3-c]-pyrazole-Based RalA Inhibitors Against Hepatocellular Carcinoma

The activation of Ras small GTPases, including RalA and RalB, plays an important role in carcinogenesis, tumor progress, and metastasis. In the current study, we report the discovery of a series of 6-sulfonylamide-pyrano [2,3-c]-pyrazole derivatives as novel RalA inhibitors. ELISA-based biochemical assay results indicated that compounds 4k–4r suppressed RalA/B binding capacities to their substrates. Cellular proliferation assays indicated that these RalA inhibitors potently inhibited the proliferation of HCC cell lines, including HepG2, SMMC-7721, Hep3B, and Huh-7 cells. Among the evaluated compounds, 4p displayed good inhibitory capacities on RalA (IC₅₀ = 0.22 μM) and HepG2 cells (IC₅₀ = 2.28 μM). Overall, our results suggested that a novel small-molecule RalA inhibitor with a 6-sulfonylamide-pyrano [2, 3-c]-pyrazole scaffold suppressed autophagy and cell proliferation in hepatocellular carcinoma, and that it has potential for HCC-targeted therapy.

Permeabilized cryopreserved human hepatocytes as an exogenous metabolic system in a novelmetabolism-dependent cytotoxicity assay (MDCA) for the evaluation of metabolic activation anddetoxification of drugs associated with drug induced liver injuries: Results with acetaminophen,amiodarone, cyclophosphamide, ketoconazole, nefazodone, and troglitazone

We report here a novel in vitro experimental system, the metabolism-dependent cytotoxicity assay (MDCA), for the definition of the roles of hepatic drug metabolism in toxicity. MDCA employs permeabilized cofactor-supplemented cryopreserved human hepatocytes (MetMax{trade mark, serif} human hepatocytes, MMHH), as an exogenous metabolic activating system, and HEK-293 cells, a cell line devoid of drug metabolizing enzyme activity, as target cells for the quantification of drug toxicity. The assay was performed in the presence and absence of cofactors for key drug metabolism pathways known to play key roles in drug toxicity: NADPH/NAD+ for phase 1 oxidation, UDPGA for UGT mediated glucuronidation, PAPS for SULT mediated sulfation, and GSH for GST mediated GSH conjugation. Six drugs with clinically significant hepatoxicity, resulting in liver failure or a need for liver transplantation: acetaminophen, amiodarone, cyclophosphamide, ketoconazole, nefazodone and troglitazone were evaluated. All six drugs exhibited cytotoxicity enhancement by NADPH, suggesting metabolic activation via phase 1 oxidation. Attenuation of cytotoxicity by UDPGA was observed for acetaminophen, ketoconazole and troglitazone, by PAPS for acetaminophen, ketoconazole and troglitazone, and by GSH for all six drugs. Our results suggest that MDCA can be applied towards the elucidation of metabolic activation and detoxification pathways, providing information that can be applied in drug development to guide structure optimization to reduce toxicity and to aid the assessment of metabolism-based risk factors for drug toxicity. GSH detoxification represents an endpoint for the identification of drugs forming cytotoxic reactive metabolites, a key property of drugs with idiosyncratic hepatotoxicity. Significance Statement Application of the metabolism-dependent cytotoxicity assay (MDCA) for the elucidation of the roles of metabolic activation and detoxification pathways in drug toxicity may provide information to guide structure optimization in drug development to reduce hepatotoxic potential, and to aid the assessment of metabolism-based risk factors. GSH detoxification represents an endpoint for the identification of drugs forming cytotoxic reactive metabolites may be applied towards the evaluation of idiosyncratic hepatotoxicity.

Association predictions of genomics, proteinomics, transcriptomics, microbiome, metabolomics, pathomics, radiomics, drug, symptoms, environment factor, and disease networks: A comprehensive approach

Currently, the research of multi-omics, such as genomics, proteinomics, transcriptomics, microbiome, metabolomics, pathomics, and radiomics, are hot spots. The relationship between multi-omics data, drugs, and diseases has received extensive attention from researchers. At the same time, multi-omics can effectively predict the diagnosis, prognosis, and treatment of diseases. In essence, these research entities, such as genes, RNAs, proteins, microbes, metabolites, pathways as well as pathological and medical imaging data, can all be represented by the network at different levels. And some computer and biology scholars have tried to use computational methods to explore the potential relationships between biological entities. We summary a comprehensive research strategy, that is to build a multi-omics heterogeneous network, covering multimodal data, and use the current popular computational methods to make predictions. In this study, we first introduce the calculation method of the similarity of biological entities at the data level, second discuss multimodal data fusion and methods of feature extraction. Finally, the challenges and opportunities at this stage are summarized. Some scholars have used such a framework to calculate and predict. We also summarize them and discuss the challenges. We hope that our review could help scholars who are interested in the field of bioinformatics, biomedical image, and computer research.

Chromatographic bioanalytical assays for targeted covalent kinase inhibitors and their metabolites

Deriving from targeted kinase inhibitors (TKIs), targeted covalent kinase inhibitors (TCKIs) are a new class of TKIs that are covalently bound to their target residue of kinase receptors. Currently, there are many new TCKIs under clinical development besides afatinib, ibrutinib, osimertinib, neratinib, acalabrutinib, dacomitinib, and zanubrutinib that are already approved by the FDA. Subsequently, there is an increasing demand for bioanalytical methods to qualitatively and quantitively investigate those compounds, leading to a number of papers reporting the development, validation, and use of bioanalytical methods for TCKIs. Most publications describe the technological set up of analytical methods that allow quantification of TCKIs in various biomatrices such as plasma, cerebrospinal fluid, urine, tissue, and liver microsomes. In addition, the identification of metabolites and biotransformation pathways of new TCKIs has gained more interest in recent years. We provide an overview of bioanalytical methods of this new class of TCKIs. The included issues are sample pretreatment, chromatographic separation, detection, and method validation. In the scope of bioanalysis of TCKIs, protein precipitation is mostly applied to treat the biological matrices sample. Liquid chromatographic in reversed-phase mode (RPLC) and mass detection with triple quadrupole (QqQ) are the most often utilized separation and quantitative detection modes, respectively. There may be a possibility of increased use of the high-resolution mass spectrometry (HRMS) for qualitative investigation purposes in the future. We also found that US FDA and EMA guidelines are the most common guidelines employed as validation framework for the bioanalytical methods of TCKIs.

Potential role of drug metabolizing enzymes in chemotherapy-induced gastrointestinal toxicity and hepatotoxicity

INTRODUCTION

Toxicity of chemotherapy drugs is the leading cause of poor therapeutic outcome in many cancer patients. Gastrointestinal (GI) toxicity and hepatotoxicity are among the most common side effects of current chemotherapies. Emerging studies indicate that many chemotherapy-induced toxicities are driven by drug metabolism, but very few reviews summarize the role of drug metabolism in chemotherapy-induced GI toxicity and hepatotoxicity. In this review, we highlighted the importance of drug metabolizing enzymes (DMEs) in chemotherapy toxicity.

AREAS COVERED

Our review demonstrated that altered activity of DMEs play important role in chemotherapy-induced GI toxicity and hepatotoxicity. Besides direct changes in catalytic activities, the transcription of DMEs is also affected by inflammation, cell-signaling pathways, and/or by drugs in cancer patients due to the disease etiology.

EXPERT OPINION

More studies should focus on how DMEs are altered during chemotherapy treatment, and how such changes affect the metabolism of chemotherapy drug itself. This mutual interaction between chemotherapies and DMEs can lead to excessive exposure of parent drug or toxic metabolites which ultimately cause GI adverse effect.

Approaches to mitigate the risk of serious adverse reactions in covalent drug design

INTRODUCTION

Covalent inhibition of target proteins using high affinity ligands bearing weakly electrophilic warheads is being adopted increasingly as design strategy in the discovery of novel therapeutics, and several covalent drugs have now received regulatory approval for indications in oncology. Experience to date with targeted covalent inhibitors has led to a number of design principles that underlie the safety and efficacy of this increasingly important class of molecules.

AREAS COVERED

A review is provided of the current status of the covalent drug approach, emphasizing the unique benefits and attendant risks associated with reversible and irreversible binders. Areas of application beyond inhibition of tyrosine kinases are presented, and design considerations to de-risk covalent inhibitors with respect to undesirable off-target effects are discussed.

EXPERT OPINION

High selectivity for the intended protein target has emerged as a key consideration in mitigating safety risks associated with widespread proteome reactivity. Powerful chemical proteomics-based techniques are now available to assess selectivity in a drug discovery setting. Optimizing pharmacokinetics to capitalize on the intrinsically high potency of covalent drugs should lead to low daily doses and greater safety margins, while minimizing susceptibility to metabolic activation likewise will attenuate the risk of covalent drug toxicity.

Potential for pharmacokinetic interactions between Schisandra sphenanthera and bosutinib, but not imatinib: in vitro metabolism study combined with a physiologically-based pharmacokinetic modelling approach

AIMS

This study aimed to investigate the potential interaction between Schisandra sphenanthera, imatinib and bosutinib combining in vitro and in silico methods.

METHODS

In vitro metabolism of imatinib and bosutinib using recombinant enzymes and human liver microsomes were investigated in the presence and absence of Schisandra lignans. Physiologically-based pharmacokinetic (PBPK) models for the lignans accounting for reversible and mechanism-based inhibitions and induction of CYP3A enzymes were built in the Simcyp Simulator (version 17) and evaluated for their capability to predict interactions with midazolam and tacrolimus. Their potential effect on systemic exposures of imatinib and bosutinib were predicted using PBPK in silico simulations.

RESULTS

Schisantherin A and schisandrol B, but not schisandrin A, potently inhibited CYP3A4-mediated metabolism of imatinib and bosutinib. All three compounds showed a strong reversible inhibition on CYP2C8 enzyme with k_i of less than 0.5 μmol L^-1 . The verified PBPK models were able to describe the increase in systemic exposure of midazolam and tacrolimus due to co-administration of S. sphenanthera, consistent with the reported changes in the corresponding clinical interaction study (AUC ratio of 2.0 vs 2.1 and 2.4 vs 2.1, respectively). The PBPK simulation predicted that at recommended dosing regimens of S. sphenanthera, co-administration would result in an increase in bosutinib exposure (AUC ratio 3.0) but not in imatinib exposure.

CONCLUSION

PBPK models for Schisandra lignans were successfully developed. Interaction between imatinib and Schisandra lignans was unlikely to be of clinical importance. Conversely, S. sphenanthera at a clinically-relevant dose results in a predicted three-fold increase in bosutinib systemic exposure.

Metabolic profiling of tyrosine kinase inhibitor nintedanib using metabolomics

Nintedanib is a promising tyrosine kinase inhibitor for clinically treating idiopathic pulmonary fibrosis (IPF). Some clinical cases reported that nintedanib treatment can cause hepatotoxicity and myocardial toxicity. U. S. FDA warns the potential drug-drug interaction when it is co-administrated with other drugs. In order to understand the potential toxicity of nintedanib and avoid drug-drug interaction, the metabolism of nintedanib was systematically investigated in human liver microsomes and mice using metabolomics approach, and the toxicity of metabolites was predicted by ADMET lab. Nineteen metabolites were detected in vivo and in vitro metabolism, and 8 of them were undescribed. Calculated partition coefficients (Clog P) were used to distinguish the isomers of nintedanib metabolites in this study. The major metabolic pathways of nintedanib majorly included hydroxylation, demethylation, glucuronidation, and acetylation reactions. The ADMET prediction indicated that nintedanib was a substrate of the cytochrome P450 3A4 (CYP3A4) and P-glycoprotein (P-gp). And nintedanib and most of its metabolites might possess potential hepatotoxicity and cardiotoxicity. This study provided a global view of nintedanib metabolism, which could be used to understand the mechanism of adverse effects related to nintedanib and its potential drug-drug interaction.

Involvement of Pazopanib and Sunitinib Aldehyde Reactive Metabolites in Toxicity and Drug-Drug Interactions in Vitro and in Patient Samples

Tyrosine kinase inhibitors (TKI) are targeted anticancer drugs that have been successfully developed over the past 2 decades. To date, many of them (around 70%) require warnings for liver injury and five of them, including pazopanib and sunitinib, have Black Box Warning (BBW) labels. Although TKI-induced hepatotoxicity is the first cause of drug failures in clinical trials, BBW labels, and market withdrawals, the underlying mechanisms remain unclear. However, the recent discovery of new reactive metabolites (RM) with aldehyde structures during pazopanib and sunitinib metabolism offers new perspectives for investigating their involvement in the toxicity of these two TKI. These hard electrophiles have a high reactivity potential toward proteins and are thought to be responsible for cytochrome P450 inactivation, drug-drug interactions (DDI), and liver toxicity. We report here, for the first time, the presence of these aldehyde RM in human plasma samples obtained during drug monitoring. Docking experiments in the CYP3A4 active site were performed and showed that pazopanib and sunitinib fitting in the catalytic site are in accordance with their regioselective oxidation to aldehydes. They also suggested that aldehyde RM may react with lysine and arginine residues. Based on these results, we studied the reactivity of the aldehyde RM toward lysine and arginine residues as potential targets on the protein framework to better understand how these RM could be involved in liver toxicity and drug-drug interactions. Adduct formation with different hepatic and plasma proteins was investigated by LC-MS/MS, and adducts between pazopanib or sunitinib aldehyde derivatives and lysine residues on both CYP3A4 and plasma proteins were indeed shown for the first time.