DDI2 Is a Ubiquitin-Directed Endoprotease Responsible for Cleavage of Transcription Factor NRF1.

The Ddi1/DDI2 proteins are ubiquitin shuttling factors, implicated in a variety of cellular functions. In addition to ubiquitin-binding and ubiquitin-like domains, they contain a conserved region with similarity to retroviral proteases, but whether and how DDI2 functions as a protease has remained unknown. Here, we show that DDI2 knockout cells are sensitive to proteasome inhibition and accumulate high-molecular weight, ubiquitylated proteins that are poorly degraded by the proteasome. These proteins are targets for the protease activity of purified DDI2. No evidence for DDI2 acting as a de-ubiquitylating enzyme was uncovered, which could suggest that it cleaves the ubiquitylated protein itself. In support of this idea, cleavage of transcription factor NRF1 is known to require DDI2 activity in vivo. We show that DDI2 is indeed capable of cleaving NRF1 in vitro but only when NRF1 protein is highly poly-ubiquitylated. Together, these data suggest that DDI2 is a ubiquitin-directed endoprotease.

The Aspartic Protease Ddi1 Contributes to DNA-Protein Crosslink Repair in Yeast.

Naturally occurring or drug-induced DNA-protein crosslinks (DPCs) interfere with key DNA transactions if not repaired in a timely manner. The unique family of DPC-specific proteases Wss1/SPRTN targets DPC protein moieties for degradation, including stabilized topoisomerase-1 cleavage complexes (Top1ccs). Here, we describe that the efficient DPC disassembly requires Ddi1, another conserved predicted protease in Saccharomyces cerevisiae. We found Ddi1 in a genetic screen of the tdp1 wss1 mutant defective in Top1cc processing. Ddi1 is recruited to a persistent Top1cc-like DPC lesion in an S phase-dependent manner to assist in the eviction of crosslinked protein from DNA. Loss of Ddi1 or its putative protease activity hypersensitizes cells to DPC trapping agents independently from Wss1 and 26S proteasome, implying its broader role in DPC repair. Among the potential Ddi1 targets, we found the core component of Pol II and show that its genotoxin-induced degradation is impaired in ddi1. We propose that the Ddi1 protease contributes to DPC proteolysis.

Proteolytic activation of angiomotin by DDI2 promotes angiogenesis.

The scaffolding protein angiomotin (AMOT) is indispensable for vertebrate embryonic angiogenesis. Here, we report that AMOT undergoes cleavage in the presence of lysophosphatidic acid (LPA), a lipid growth factor also involved in angiogenesis. AMOT cleavage is mediated by aspartic protease DNA damage-inducible 1 homolog 2 (DDI2), and the process is tightly regulated by a signaling axis including neurofibromin 2 (NF2), tankyrase 1/2 (TNKS1/2), and RING finger protein 146 (RNF146), which induce AMOT membrane localization, poly ADP ribosylation, and ubiquitination, respectively. In both zebrafish and mice, the genetic inactivation of AMOT cleavage regulators leads to defective angiogenesis, and the phenotype is rescued by the overexpression of AMOT-CT, a C-terminal AMOT cleavage product. In either physiological or pathological angiogenesis, AMOT-CT is required for vascular expansion, whereas uncleavable AMOT represses this process. Thus, our work uncovers a signaling pathway that regulates angiogenesis by modulating a cleavage-dependent activation of AMOT.

Removal of RTF2 from Stalled Replisomes Promotes Maintenance of Genome Integrity.

The protection and efficient restart of stalled replication forks is critical for the maintenance of genome integrity. Here, we identify a regulatory pathway that promotes stalled forks recovery from replication stress. We show that the mammalian replisome component C20orf43/RTF2 (homologous to S. pombe Rtf2) must be removed for fork restart to be optimal. We further show that the proteasomal shuttle proteins DDI1 and DDI2 are required for RTF2 removal from stalled forks. Persistence of RTF2 at stalled forks results in fork restart defects, hyperactivation of the DNA damage signal, accumulation of single-stranded DNA (ssDNA), sensitivity to replication drugs, and chromosome instability. These results establish that RTF2 removal is a key determinant for the ability of cells to manage replication stress and maintain genome integrity.

IK-DDI: a novel framework based on instance position embedding and key external text for DDI extraction.

Determining drug-drug interactions (DDIs) is an important part of pharmacovigilance and has a vital impact on public health. Compared with drug trials, obtaining DDI information from scientific articles is a faster and lower cost but still a highly credible approach. However, current DDI text extraction methods consider the instances generated from articles to be independent and ignore the potential connections between different instances in the same article or sentence. Effective use of external text data could improve prediction accuracy, but existing methods cannot extract key information from external data accurately and reasonably, resulting in low utilization of external data. In this study, we propose a DDI extraction framework, instance position embedding and key external text for DDI (IK-DDI), which adopts instance position embedding and key external text to extract DDI information. The proposed framework integrates the article-level and sentence-level position information of the instances into the model to strengthen the connections between instances generated from the same article or sentence. Moreover, we introduce a comprehensive similarity-matching method that uses string and word sense similarity to improve the matching accuracy between the target drug and external text. Furthermore, the key sentence search method is used to obtain key information from external data. Therefore, IK-DDI can make full use of the connection between instances and the information contained in external text data to improve the efficiency of DDI extraction. Experimental results show that IK-DDI outperforms existing methods on both macro-averaged and micro-averaged metrics, which suggests our method provides complete framework that can be used to extract relationships between biomedical entities and process external text data.

SSF-DDI: a deep learning method utilizing drug sequence and substructure features for drug-drug interaction prediction.

BACKGROUND: Drug-drug interactions (DDI) are prevalent in combination therapy, necessitating the importance of identifying and predicting potential DDI. While various artificial intelligence methods can predict and identify potential DDI, they often overlook the sequence information of drug molecules and fail to comprehensively consider the contribution of molecular substructures to DDI. RESULTS: In this paper, we proposed a novel model for DDI prediction based on sequence and substructure features (SSF-DDI) to address these issues. Our model integrates drug sequence features and structural features from the drug molecule graph, providing enhanced information for DDI prediction and enabling a more comprehensive and accurate representation of drug molecules. CONCLUSION: The results of experiments and case studies have demonstrated that SSF-DDI significantly outperforms state-of-the-art DDI prediction models across multiple real datasets and settings. SSF-DDI performs better in predicting DDI involving unknown drugs, resulting in a 5.67% improvement in accuracy compared to state-of-the-art methods.

Assessing and mitigating pH-mediated DDI risks in drug development - formulation approaches and clinical considerations.

pH-mediated drug-drug interactions (DDI) is a prevalent DDI in drug development, especially for weak base compounds with highly pH-dependent solubility. FDA has released a guidance on the evaluation of pH-mediated DDI assessments using in vitro testing and clinical studies. Currently, there is no common practice of ways of testing across the academia and industry. The development of biopredictive method and physiologically-based biopharmaceutics modeling (PBBM) approaches to assess acid-reducing agent (ARA)-DDI have been proven with accurate prediction and could decrease drug development burden, inform clinical design and potentially waive clinical studies. Formulation strategies and careful clinical design could help mitigate the pH-mediated DDI to avoid more clinical studies and label restrictions, ultimately benefiting the patient. In this review paper, a detailed introduction on biorelevant dissolution testing, preclinical and clinical study requirement and PBPK modeling approaches to assess ARA-DDI are described. An improved decision tree for pH-mediated DDI is proposed. Potential mitigations including clinical or formulation strategies are discussed.

Transporter-mediated drug-drug interactions: regulatory guidelines, in vitro and in vivo methodologies and translation, special populations, and the blood-brain barrier.

This review, part of a special issue on drug-drug interactions (DDIs) spearheaded by the International Society for the Study of Xenobiotics (ISSX) New Investigators, explores the critical role of drug transporters in absorption, disposition, and clearance in the context of DDIs. Over the past two decades, significant advances have been made in understanding the clinical relevance of these transporters. Current knowledge on key uptake and efflux transporters that affect drug disposition and development is summarized. Regulatory guidelines from the FDA, EMA, and PMDA that inform the evaluation of potential transporter-mediated DDIs are discussed in detail. Methodologies for preclinical and clinical testing to assess potential DDIs are reviewed, with an emphasis on the utility of physiologically based pharmacokinetic (PBPK) modeling. This includes the application of relative abundance and expression factors to predict human pharmacokinetics (PK) using preclinical data, integrating the latest regulatory guidelines. Considerations for assessing transporter-mediated DDIs in special populations, including pediatric, hepatic, and renal impairment groups, are provided. Additionally, the impact of transporters at the blood-brain barrier (BBB) on the disposition of CNS-related drugs is explored. Enhancing the understanding of drug transporters and their role in drug disposition and toxicity can improve efficacy and reduce adverse effects. Continued research is essential to bridge remaining gaps in knowledge, particularly in comparison with cytochrome P450 (CYP) enzymes.

AMDE: a novel attention-mechanism-based multidimensional feature encoder for drug-drug interaction prediction.

The properties of the drug may be altered by the combination, which may cause unexpected drug-drug interactions (DDIs). Prediction of DDIs provides combination strategies of drugs for systematic and effective treatment. In most of deep learning-based methods for predicting DDI, encoded information about the drugs is insufficient in some extent, which limits the performances of DDIs prediction. In this work, we propose a novel attention-mechanism-based multidimensional feature encoder for DDIs prediction, namely attention-based multidimensional feature encoder (AMDE). Specifically, in AMDE, we encode drug features from multiple dimensions, including information from both Simplified Molecular-Input Line-Entry System sequence and atomic graph of the drug. Data experiments are conducted on DDI data set selected from Drugbank, involving a total of 34 282 DDI relationships with 17 141 positive DDI samples and 17 141 negative samples. Experimental results show that our AMDE performs better than some state-of-the-art baseline methods, including Random Forest, One-Dimension Convolutional Neural Networks, DeepDrug, Long Short-Term Memory, Seq2seq, Deepconv, DeepDDI, Graph Attention Networks and Knowledge Graph Neural Networks. In practice, we select a set of 150 drugs with 3723 DDIs, which are never appeared in training, validation and test sets. AMDE performs well in DDIs prediction task, with AUROC and AUPRC 0.981 and 0.975. As well, we use Torasemide (DB00214) as an example and predict the most likely drug to interact with it. The top 15 scores all have been reported with clear interactions in literatures.

Application of Artificial Intelligence in Drug-Drug Interactions Prediction: A Review.

Drug-drug interactions (DDI) are a critical aspect of drug research that can have adverse effects on patients and can lead to serious consequences. Predicting these events accurately can significantly improve clinicians' ability to make better decisions and establish optimal treatment regimens. However, manually detecting these interactions is time-consuming and labor-intensive. Utilizing the advancements in Artificial Intelligence (AI) is essential for achieving accurate forecasts of DDIs. In this review, DDI prediction tasks are classified into three types according to the type of DDI prediction: undirected DDI prediction, DDI events prediction, and Asymmetric DDI prediction. The paper then reviews the progress of AI for each of these three prediction tasks in DDI and provides a summary of the data sets used as well as the representative methods used in these three prediction directions. In this review, we aim to provide a comprehensive overview of drug interaction prediction. The first section introduces commonly used databases and presents an overview of current research advancements and techniques across three domains of DDI. Additionally, we introduce classical machine learning techniques for predicting undirected drug interactions and provide a timeline for the progression of the predicted drug interaction events. At last, we debate the difficulties and prospects of AI approaches at predicting DDI, emphasizing their potential for improving clinical decision-making and patient outcomes.

Donor-derived mold infections in lung transplant recipients: The importance of active surveillance.

Unexpected donor-derived fungal infections represent a rare but potentially fatal complication in lung transplant (Tx) recipients. Timely communication of the results of donor cultures and prompt treatment of recipients are crucial to mitigate the consequences of donor-derived transmissions. In this prospective cohort study, all consecutive patients who underwent lung transplantation from 2015 to 2022 were included. In December 2015, a Local Active Surveillance System has been implemented to provide biovigilance of donor culture results and optimize recipients' management. The aim of this study is to investigate the incidence of unexpected, mold-positive cultures among lung donors and the rate of transmission to recipients. Furthermore, management strategies and outcome of recipients with mold transmission are described. In case of isolation of the same mold in donor and recipient cultures, when possible, transmission was confirmed by dendrogram analysis. During the study period, 82 lung Tx were performed from 80 donors. The prevalence of donors with "unexpected" mold isolation from the respiratory tract was 3.75% (3/80). Isolated molds were Aspergillus niger, Rhizopus oryzae, and Aspergillus flavus. Transmissions occurred in all the three cases (100%) with a mean time of 5 days from lung Tx but none of the recipients developed invasive mold disease. Our Local Active Surveillance System allowed prompt recognition of lung donors unexpected mold colonization. Even though transmission occurred, introduction of early targeted antifungal therapy prevented potential catastrophic consequence of mold donor-derived infection in the immediate post-Tx period.

High-throughput assay to simultaneously evaluate activation of CYP3A and the direct and time-dependent inhibition of CYP3A, CYP2C9, and CYP2D6 using liquid chromatography-tandem mass spectrometry.

In the early stages of drug discovery, adequate evaluation of the potential drug-drug interactions (DDIs) of drug candidates is important. Several CYP3A activators are known to lead to underestimation of DDIs. These compounds affect midazolam 1'-hydroxylation but not midazolam 4-hydroxylation.We used both metabolic reactions of midazolam to evaluate the activation and inhibition of CYP3A activators simultaneously. For our CYP inhibition assay using cocktail probe substrates, simultaneous liquid chromatography-tandem mass spectrometry monitoring of 1'-hydroxymidazolam and 4-hydroxymidazolam for CYP3A was established in addition to monitoring of 4-hydroxydiclofenac and 1'-hydroxybufuralol for CYP2C9 and CYP2D6.The results of our cocktail inhibition assay were well correlated with those of a single inhibition assay, as were the estimated inhibition parameters for typical CYP3A inhibitors. In our assay, a proprietary compound that activated midazolam 1'-hydroxylation and tended to inhibit 4-hydroxylation was evaluated along with known CYP3A activators. All compounds were well characterised by comparison of the results of midazolam 1'- and 4-hydroxylation.In conclusion, our CYP cocktail inhibition assay can detect CYP3A activation and assess the direct and time-dependent inhibition potentials for CYP3A, CYP2C9, and CYP2D6. This method is expected to be very efficient in the early stages of drug discovery.

Docetaxel, cyclophosphamide, and epirubicin: application of PBPK modeling to gain new insights for drug-drug interactions.

The new adjuvant chemotherapy of docetaxel, epirubicin, and cyclophosphamide has been recommended for treating breast cancer. It is necessary to investigate the potential drug-drug Interactions (DDIs) since they have a narrow therapeutic window in which slight differences in exposure might result in significant differences in treatment efficacy and tolerability. To guide clinical rational drug use, this study aimed to evaluate the DDI potentials of docetaxel, cyclophosphamide, and epirubicin in cancer patients using physiologically based pharmacokinetic (PBPK) models. The GastroPlus™ was used to develop the PBPK models, which were refined and validated with observed data. The established PBPK models accurately described the pharmacokinetics (PKs) of three drugs in cancer patients, and the predicted-to-observed ratios of all the PK parameters met the acceptance criterion. The PBPK model predicted no significant changes in plasma concentrations of these drugs during co-administration, which was consistent with the observed clinical phenomenon. Besides, the verified PBPK models were then used to predict the effect of other Cytochrome P450 3A4 (CYP3A4) inhibitors/inducers on these drug exposures. In the DDI simulation, strong CYP3A4 modulators changed the exposure of three drugs by 0.71-1.61 fold. Therefore, patients receiving these drugs in combination with strong CYP3A4 inhibitors should be monitored regularly to prevent adverse reactions. Furthermore, co-administration of docetaxel, cyclophosphamide, or epirubicin with strong CYP3A4 inducers should be avoided. In conclusion, the PBPK models can be used to further investigate the DDI potential of each drug and to develop dosage recommendations for concurrent usage by additional perpetrators or victims.

Assessment of drug-drug interaction of dapagliflozin with LCZ696 based on an LC-MS/MS method.

The co-administration of dapagliflozin (DPF) and sacubitril/valsartan (LCZ696) has emerged as a promising therapeutic approach for managing heart failure. Given that DPF and LCZ696 are substrates for P-glycoprotein, there is a plausible potential for drug-drug interactions when administered concomitantly. To investigate the pharmacokinetic changes when these drugs are co-administered, we have established and validated a liquid chromatography-tandem mass spectrometry (LC-MS/MS) method capable of simultaneously detecting DPF, LBQ657 (the active metabolite of sacubitril) and valsartan in rat plasma. This method has demonstrated selectivity, sensitivity, and accuracy. Drug-drug interactions were examined by the LC-MS/MS method. The mechanisms were investigated using everted intestinal sac models and Caco-2 cells. The results showed that DPF significantly increased the area under the curve (AUC(0-t)) (3,563.3 ± 651.7 vs. 7,146.5 ± 1,714.9 h µg/L) of LBQ657 (the active metabolite of sacubitril) and the AUC(0-t) (24,022.4 ± 6,774.3 vs. 55,728.3 ± 32,446.3 h µg/L) of valsartan after oral co-administration. Dapagliflozin significantly increased the amount of LBQ657 and valsartan in intestinal sacs by 1- and 1.25-fold at 2.25 h. Caco-2 cell uptake studies confirmed that P-glycoprotein is the transporter involved in this interaction. This finding enhances the understanding of drug-drug interactions in the treatment of heart failure and provides a guidence for clinical therapy.

Icaritin exhibits potential drug-drug interactions through the inhibition of human UDP-glucuronosyltransferase in vitro.

Icaritin is a prenylflavonoid derivative of the genus Epimedium (Berberidaceae) and has a variety of pharmacological actions. Icaritin is approved by the National Medical Products Administration as an anticancer drug that exhibits efficacy and safety advantages in patients with hepatocellular carcinoma cells. This study aimed to evaluate the inhibitory effects of icaritin on UDP-glucuronosyltransferase (UGT) isoforms. 4-Methylumbelliferone (4-MU) was employed as a probe drug for all the tested UGT isoforms using in vitro human liver microsomes (HLM). The inhibition potentials of UGT1A1 and 1A9 in HLM were further tested by employing 17ß-estradiol (E2) and propofol (PRO) as probe substrates, respectively. The results showed that icaritin inhibits UGT1A1, 1A3, 1A4, 1A7, 1A8, 1A10, 2B7, and 2B15. Furthermore, icaritin exhibited a mixed inhibition of UGT1A1, 1A3, and 1A9, and the inhibition kinetic parameters (Ki) were calculated to be 3.538, 2.117, and 0.306 (µM), respectively. The inhibition of human liver microsomal UGT1A1 and 1A9 both followed mixed mechanism, with Ki values of 2.694 and 1.431 (µM). This study provides supporting information for understanding the drug-drug interaction (DDI) potential of the flavonoid icaritin and other UGT-metabolized drugs in clinical settings. In addition, the findings provide safety evidence for DDI when liver cancer patients receive a combination therapy including icaritin.

Domains in Action: Understanding Ddi1's Diverse Functions in the Ubiquitin-Proteasome System.

The ubiquitin-proteasome system (UPS) is a pivotal cellular mechanism responsible for the selective degradation of proteins, playing an essential role in proteostasis, protein quality control, and regulating various cellular processes, with ubiquitin marking proteins for degradation through a complex, multi-stage process. The shuttle proteins family is a very unique group of proteins that plays an important role in the ubiquitin-proteasome system. Ddi1, Dsk2, and Rad23 are shuttle factors that bind ubiquitinated substrates and deliver them to the 26S proteasome. Besides mediating the delivery of ubiquitinated proteins, they are also involved in many other biological processes. Ddi1, the least-studied shuttle protein, exhibits unique physicochemical properties that allow it to play non-canonical functions in the cells. It regulates cell cycle progression and response to proteasome inhibition and defines MAT type of yeast cells. The Ddi1 contains UBL and UBA domains, which are crucial for binding to proteasome receptors and ubiquitin respectively, but also an additional domain called RVP. Additionally, much evidence has been provided to question whether Ddi1 is a classical shuttle protein. For many years, the true nature of this protein remained unclear. Here, we highlight the recent discoveries, which shed new light on the structure and biological functions of the Ddi1 protein.

Assessment of TGx-DDI genes for genotoxicity in a comprehensive panel of chemicals.

BACKGROUND: The TGx-DDI biomarker identifies transcripts specifically induced by primary DNA damage. Profiling similarity of TGx-DDI signatures can allow clustering compounds by genotoxic mechanism. This transcriptomics-based approach complements conventional toxicology testing by enhancing mechanistic resolution. METHODS: Unsupervised hierarchical clustering and t-distributed stochastic neighbor embedding (tSNE) were utilized to assess similarity of publicly-available per- and polyfluoroalkyl substances (PFAS) and ToxCast chemicals based on TGx-DDI modulation. TempO-seq transcriptomic data after highest chemical concentrations were analyzed. RESULTS: Clustering discriminated between genotoxic and non-genotoxic compounds while drawing similarity among chemicals with shared mechanisms. PFAS largely clustered distinctly from classical mutagens. However, dynamic range across PFAS types and durations indicated variable potential for DNA damage. tSNE visualization reinforced phenotypic groupings, with genotoxins clustering separately from non-DNA damaging agents. DISCUSSION: Unsupervised learning approaches applied to TGx-DDI profiles effectively categorizes chemical genotoxicity potential, aiding elucidation of biological response pathways. This transcriptomics-based strategy gives further insight into the role and effect of individual TGx-DDI biomarker genes and complements existing assays by enhancing mechanistic resolution. Overall, TGx-DDI biomarker profiling holds promise for predictive safety screening.

Mammalian Ddi2 is a shuttling factor containing a retroviral protease domain that influences binding of ubiquitylated proteins and proteasomal degradation.

Although several proteasome subunits have been shown to bind ubiquitin (Ub) chains, many ubiquitylated substrates also associate with 26S proteasomes via "shuttling factors." Unlike the well-studied yeast shuttling factors Rad23 and Dsk2, vertebrate homologs Ddi2 and Ddi1 lack a Ub-associated domain; therefore, it is unclear how they bind Ub. Here, we show that deletion of Ddi2 leads to the accumulation of Ub conjugates with K11/K48 branched chains. We found using affinity copurifications that Ddi2 binds Ub conjugates through its Ub-like domain, which is also required for Ddi2 binding to proteasomes. Furthermore, in cell extracts, adding Ub conjugates increased the amount of Ddi2 associated with proteasomes, and adding Ddi2 increased the binding of Ub conjugates to purified proteasomes. In addition, Ddi2 also contains a retroviral protease domain with undefined cellular roles. We show that blocking the endoprotease activity of Ddi2 either genetically or with the HIV protease inhibitor nelfinavir increased its binding to Ub conjugates but decreased its binding to proteasomes and reduced subsequent protein degradation by proteasomes leading to further accumulation of Ub conjugates. Finally, nelfinavir treatment required Ddi2 to induce the unfolded protein response. Thus, Ddi2 appears to function as a shuttling factor in endoplasmic reticulum-associated protein degradation and delivers K11/K48-ubiquitylated proteins to the proteasome. We conclude that the protease activity of Ddi2 influences this shuttling factor activity, promotes protein turnover, and helps prevent endoplasmic reticulum stress, which may explain nelfinavir's ability to enhance cell killing by proteasome inhibitors.

DDI2 promotes tumor metastasis and resists antineoplastic drugs-induced apoptosis in colorectal cancer.

The normal colorectal mucosa undergoes precancerous lesions that can develop over time into colorectal cancer (CRC). In the stage of precancerous lesions, DNA replication stress may lead to genome instability. We have performed whole-exome sequencing on genomic DNA obtained from three cases of CRC tissues and identified a novel frameshift mutation of DNA damage inducible 1 homolog 2 gene (DDI2, c. 854 del T). To date, there is no direct evidence that DDI2 is involved in the carcinogenesis of CRC. In this study, we demonstrated that DDI2 is upregulated in the early stage of CRC based on clinical samples and public databases. We also found that 5FU, a standard chemotherapeutic agent for CRC treatment, increased DDI2 mRNA levels in a dose-dependent manner. Depression of DDI2 inhibited CRC cell proliferation, migration and invasion both in vitro and in vivo. Transcriptome sequencing revealed that DDI2 was involved in the mitogen-activated protein kinase (MAPK) pathway. Furthermore, DDI2 resists a MAPK kinase (MEK) inhibitor (trametinib) and a PolyADP-ribose polymerase 1 (PARP1) inhibitor (talazoparib) induced apoptosis in CRC cells. Thus, our results indicate that DDI2 may play a vital role in the carcinogenesis of CRC and could serve as a promising therapeutic target for CRC.

Effect of capmatinib on the pharmacokinetics of substrates of CYP3A (midazolam) and CYP1A2 (caffeine) in patients with MET-dysregulated solid tumours.

BACKGROUND: Preclinical studies showed that capmatinib reversibly inhibits cytochrome P450 (CYP) 3A4 and CYP1A2 in a time-dependent manner. In this study, we evaluated the effect of capmatinib on the exposure of sensitive substrates of CYP3A (midazolam) and CYP1A2 (caffeine) in patients with mesenchymal-epithelial transition (MET)-dysregulated solid tumours. Besides pharmacokinetics, we assessed treatment response and safety. METHODS: This open-label, multicentre, single-sequence study consisted of a molecular prescreening period, a screening/baseline period of ≤28 days and a drug-drug interaction (DDI) phase of 12 days. On day 1 of the DDI phase, 37 patients received a single oral dose of midazolam 2.5 mg and caffeine 100 mg as a two-drug cocktail. Capmatinib 400 mg bid was administered from day 4 on a continuous dosing schedule. On day 9 of the DDI phase, patients were re-exposed to midazolam and caffeine. After the DDI phase, patients received capmatinib on continuous 21-day cycles until disease progression at the discretion of the investigator. RESULTS: A 22% (90% confidence interval [CI] 7-38%) increase in the midazolam maximum plasma concentration (Cmax ) was noted when administered with capmatinib, but this was deemed not clinically meaningful. Co-administration with capmatinib resulted in 134% (90% CI 108-163%) and 122% (90% CI 95-153%) increases in the caffeine area under the plasma concentration-time curve from time zero to infinity (AUCinf ) and area under the plasma concentration-time curve from time zero to the last measurable point (AUClast ), respectively, with no change in Cmax . Adverse events were consistent with the known capmatinib safety profile. No new safety signals were reported in this study. CONCLUSION: The data from this study demonstrated that capmatinib is a moderate CYP1A2 inhibitor. Capmatinib administration did not cause any clinically relevant changes in midazolam exposure.