Disproportionality analysis of stomatitis associated with anticancer drugs using Japanese Adverse Drug Event Reporting Database.

INTRODUCTION: Anticancer drug-induced stomatitis can affect a patient's quality of life and the continuation of drug treatment. Although there have been reports of the occurrence of stomatitis associated with anticancer agents in clinical trials, few Japanese participants have been enrolled in clinical trials and have not been sufficiently investigated. In addition, there has been little attention on research on anticancer drugs associated with stomatitis by patient stratification with different carcinogenic sites. Therefore, the aim of this study was to determine the disproportionality associated with stomatitis for various types of anticancer drugs in different types of cancer patients using the Japan Spontaneous Adverse Event Reporting Database (JADER). METHODS: The aim of this study was to identify the disproportionality of stomatitis by analyzing the type of anticancer drug and cancer patients using the Japanese Pharmacovigilance Database. Data obtained from spontaneous reports of adverse events with more than 10 stomatitis outbreaks reported in the Japanese Adverse Drug Event Report database (JADER) between April 2004 and March 2023 were analyzed. The safety signal for an adverse event was defined as the lower limit of the 95% confidence interval of the reporting odds ratio of >1. RESULTS: There were 6178 reports of drugs associated with stomatitis. Among these, 41 drugs were suggested to be associated with stomatitis, and 41 drugs were detected as signals. These drugs were classified based on their efficacy: antipyrimidines (six drugs), folate metabolism antagonists (three drugs), alkylating agents (four drugs), platinum (three drugs), topoisomerase inhibitors (three drugs), microtubule inhibitors (three drugs), mTOR inhibitors (two drugs), kinase inhibitors (seven drugs), anti-growth factor antibodies (five drugs) immune checkpoint inhibitors (one drug), and others (four drugs). CONCLUSION: The drugs that may be associated with stomatitis were cell cycle-dependent drugs, epidermal growth factor receptor-tyrosine kinase inhibitors, and mTOR inhibitors. Thus, the use of JADER suggests that anti-growth factor antibodies and immune checkpoint inhibitors may be associated with stomatitis development.

Synthesis and Biological Evaluation of 2-Azolylmethylene-3-(2H)-benzofuranone Derivatives as Potent Monoamine Oxidases Inhibitors.

A series of 2-azolylmethylene-3-(2H)-benzofuranone derivatives, 2-indolylmethylene-3-(2H)-benzofuranone and 2-pyrrolylmethylene-3-(2H)-benzofuranone derivatives, were synthesized, and their monoamine oxidase (MAO) A and B inhibitory activities were evaluated. Compounds 1b, 3b, 6b, 7b, and 10b showed strong inhibitory activity against MAO-A, and compound 3b showed the highest potency and selectivity, with an IC50 value of 21 nM and a MAO-A selectivity index of 48. Compounds 3c, 4c, 9a, 9c, 10c, 11a, and 11c showed strong inhibitory activity against MAO-B, and compound 4c showed the highest potency and selectivity, with an IC50 value of 16 nM and a MAO-B selectivity index of >1100. Further analysis of these compounds indicated that compound 3b for MAO-A and compound 4c for MAO-B were competitive inhibitors, with Ki values of 10 and 6.1 nM, respectively. Furthermore, computational analyses, such as quantitative structure-activity relationship (QSAR) analysis of the 2-azolylmethylene-3-(2H)-benzofuranone derivatives conducting their pIC50 values with the Molecular Operating Environment (MOE) and Mordred, and molecular docking analysis using MOE-Dock supported that the 2-azolylmethylene-3-(2H)-benzofuranone derivatives are a privileged scaffold for the design and development of novel MAO inhibitors.

Progress in Predicting Ames Test Outcomes from Chemical Structures: An In-Depth Re-Evaluation of Models from the 1st and 2nd Ames/QSAR International Challenge Projects.

The Ames/quantitative structure-activity relationship (QSAR) International Challenge Projects, held during 2014-2017 and 2020-2022, evaluated the performance of various predictive models. Despite the significant insights gained, the rules allowing participants to select prediction targets introduced ambiguity in model performance evaluation. This reanalysis identified the highest-performing prediction model, assuming a 100% coverage rate (COV) for all prediction target compounds and an estimated performance variation due to changes in COV. All models from both projects were evaluated using balance accuracy (BA), the Matthews correlation coefficient (MCC), the F1 score (F1), and the first principal component (PC1). After normalizing the COV, a correlation analysis with these indicators was conducted, and the evaluation index for all prediction models in terms of the COV was estimated. In total, using 109 models, the model with the highest estimated BA (76.9) at 100% COV was MMI-VOTE1, as reported by Meiji Pharmaceutical University (MPU). The best models for MCC, F1, and PC1 were all MMI-STK1, also reported by MPU. All the models reported by MPU ranked in the top four. MMI-STK1 was estimated to have F1 scores of 59.2, 61.5, and 63.1 at COV levels of 90%, 60%, and 30%, respectively. These findings highlight the current state and potential of the Ames prediction technology.

Adverse Event Profile of Azacitidine: Analysis by Route of Administration Using Japanese Pharmacovigilance Database.

INTRODUCTION: Azacitidine is a useful drug for myelodysplastic syndromes and acute myeloid leukemia. In clinical trials, hematologic toxicity and infection have been observed as adverse events (AEs) of this drug. However, information on the time to onset of high risk AEs and subsequent outcomes, as well as differences in the frequency of AEs due to the route of administration is lacking. In this study, we investigated azacitidine-induced AEs comprehensively using the Japanese Adverse Event Reporting Database (JADER) published by the Pharmaceuticals and Medical Devices Agency, with disproportionate analysis of AE incidence trends, time to onset, and subsequent outcomes. In addition, we analyzed the differences in AEs by route of administration and the number of days until the occurrence of AEs and generated hypotheses. METHODS: The study used JADER data reported from April 2004 to June 2022. Risk estimation was conducted using reported odds ratio. A signal was detected when the lower limit of the 95% confidence interval of the calculated ROR was ≥1. RESULTS: A total of 34 signals were detected as AEs due to azacitidine. Among them, 15 were hematologic toxicities and 10 were infections, which demonstrated a particularly high rate of death. Signals of AEs such as tumor lysis syndrome (TLS) and cardiac failure, which have been described in case reports, were also detected, and the rate of death after onset was high. In addition, more AEs generally occurred within the first month of treatment. CONCLUSION: The results of this study suggest that more attention should be paid to cardiac failure, hematologic toxicity, infection, and TLS. Because many patients in clinical trials have discontinued treatment due to serious AEs before the therapeutic effect became apparent, appropriate supportive care, dose reduction, and drug withdrawal are important for the continuation of treatment.

Analysis of drug-induced liver-related adverse event trend reporting between 1997 and 2019.

AIM: This study aimed to analyze the current trends of drug-induced liver-related adverse events in the Food and Drug Administration Adverse Event Reporting System (FAERS) and Japanese Adverse Drug Event Report (JADER) databases. METHODS: The characteristics of implicated drugs were investigated by analyzing big data on drug-induced liver-related adverse events over the past 20 years in FAERS, comparing drug rankings between the JADER and FAERS databases, and calculating rankings of drugs inducing liver-related adverse events using the Medical Dictionary for Regulatory Activities Terminology. RESULTS: In the 452 272 cases registered in FAERS from 1997 to 2019, warfarin, paracetamol, and adalimumab were the drugs most related to drug-induced liver injury (DILI). In the 38 919 cases registered in JADER from 2004 to 2019, sorafenib, nivolumab, and herbal extracts were the drugs most related to DILI. No associations were found between the top 30 drugs in either of the two databases. Notably, the number of drug-induced liver-related adverse event reports and total adverse events has sharply increased in recent years. CONCLUSIONS: Although liver-related adverse events are largely caused by host immunity and other constitutional factors, differences in primary diseases, countries, and historical backgrounds lead to differences in the number of reports. Securing an appropriate database and a mechanism to collect real-time information on the frequency of adverse drug reactions is warranted.

Impact of Drugs and Patient Characteristics on Life Expectancy during the Induction Phase of Dialysis.

Various factors affect the prognosis of dialysis patients. Analysis of the drugs used and clinical and demographic characteristics of the patient at the time of dialysis initiation is a useful means of estimating prognosis. In this study, we investigated the drugs used by dialysis patients during the induction phase of dialysis and performed a detailed analysis of variables predictive of prognosis. Patients who underwent dialysis between June 1998 and January 2019 and died during this period were included in the study (n = 118). The induction phase of dialysis was defined as the first month after dialysis began. Dialysis duration was defined as the time between dialysis initiation and death. A univariate regression analysis was performed, with dialysis duration as the objective variable and the drugs used during the induction phase of dialysis, blood laboratory values, age at start of dialysis, sex, body height, body weight, medical history and cause of death as the explanatory variables. In addition, multiple logistic regression analysis with stepwise variable selection of significant factors was performed to determine the factors related to dialysis duration. Antihypertensives, hemoglobin (Hb), and age at start of dialysis were found to have significant effects on dialysis duration. It was posited that antihypertensives prolong dialysis duration, thereby improving life expectancy. The regression model developed allowed estimation of prognosis based on the drugs used during the induction phase of dialysis and patient characteristics. These findings may be used to improve drug adherence in dialysis patients and guide physicians in their treatment.

Ensemble Learning, Deep Learning-Based and Molecular Descriptor-Based Quantitative Structure-Activity Relationships.

A deep learning-based quantitative structure-activity relationship analysis, namely the molecular image-based DeepSNAP-deep learning method, can successfully and automatically capture the spatial and temporal features in an image generated from a three-dimensional (3D) structure of a chemical compound. It allows building high-performance prediction models without extracting and selecting features because of its powerful feature discrimination capability. Deep learning (DL) is based on a neural network with multiple intermediate layers that makes it possible to solve highly complex problems and improve the prediction accuracy by increasing the number of hidden layers. However, DL models are too complex when it comes to understanding the derivation of predictions. Instead, molecular descriptor-based machine learning has clear features owing to the selection and analysis of features. However, molecular descriptor-based machine learning has some limitations in terms of prediction performance, calculation cost, feature selection, etc., while the DeepSNAP-deep learning method outperforms molecular descriptor-based machine learning due to the utilization of 3D structure information and the advanced computer processing power of DL.

Comprehensive Analysis of Ixazomib-Induced Adverse Events Using the Japanese Pharmacovigilance Database.

BACKGROUND: Ixazomib is an orally available proteasome inhibitor for multiple myeloma with adverse effects such as gastrointestinal symptoms, skin rashes, and thrombocytopenia reported in clinical trials and post-marketing surveillance, resulting in treatment discontinuation. However, comprehensive adverse event (AE) assessments for ixazomib are lacking. OBJECTIVES: Herein, we aimed to determine the frequency and risk of AEs associated with ixazomib in Japanese patients using the Japanese Adverse Event Reporting Database (JADER). Additionally, the time to onset and post hoc outcomes of unique AEs were clarified. METHODS: To investigate the association between ixazomib and AEs, we analyzed the JADER database, comprising voluntary AE reports submitted to the Pharmaceuticals and Medical Devices Agency, between April 2004 and June 2021. AEs with ≥10 reports were included in the analysis, and criteria for the presence of AE signals were defined as meeting the requirements of proportional report ratio ≥2 and χ2 ≥ 4. Characteristic AEs were analyzed considering time to onset and onset outcomes. RESULTS: Of 34 extracted AEs, 18 presented AE signals. The 12 post hoc outcomes with fatality rates ≥10% included septic shock (50.0%), infection (41.2%), heart failure (16.7%), pneumonia (14.2%), and tumor necrosis syndrome (13.3%). A median of the time to onset showed that 11 of the 18 AEs occurred from ixazomib initiation to approximately 1 month later. CONCLUSION: Our results suggest that ixazomib may increase the incidence of 18 AEs, 11 of which occurred within the first month of treatment. Furthermore, 8 AEs were found to have potentially fatal outcomes at a rate of ≥10%. Therefore, monitoring AEs during the first month of treatment appears necessary.

Evaluation of Cardiac Adverse Events Associated with Carfilzomib Using a Japanese Real-World Database.

BACKGROUND: Carfilzomib is a proteasome inhibitor widely used for the treatment of multiple myeloma. However, cardiac adverse events (CAEs) are a serious side effect of carfilzomib administration. Observational studies based on systematic reviews and real-world data have revealed that the risk of CAEs tends to be high. However, there have been no reports on the incidence of CAEs associated with carfilzomib in Japanese patients. Furthermore, there have been no reports on the timing and post-event outcomes of CAEs. OBJECTIVES: The purpose of this study was to identify the trends in carfilzomib-associated adverse events, the time to onset of CAEs, and the clinical outcomes after the occurrence of CAEs using the Japanese Adverse Drug Event Report (JADER) database. METHOD: We analyzed data from the JADER database, which contains reports of spontaneous adverse events submitted to the Pharmaceutical and Medical Device Agency, between April 2004 and December 2020. The relative risk of adverse events was estimated using the reporting odds ratio. The time to onset and post-event outcomes were evaluated for adverse cardiotoxic events with >10 reports. RESULTS: The reporting rate was significantly higher for all 6 detected CAEs. A time-to-onset histogram of the 6 CAEs showed that they all occurred early after carfilzomib administration. The median time of onset of heart failure, congestive heart failure, and acute heart failure was approximately 2 weeks after treatment. The adverse events with the largest proportion of fatal clinical outcomes were acute heart failure (26%) and heart failure (9.5%). CONCLUSIONS: This study suggests that the early signs and symptoms of potential fatal heart failure should be monitored during carfilzomib treatment.

Comprehensive Analysis of Bortezomib-Induced Adverse Events Using the Japanese Real-World Database.

BACKGROUND: Bortezomib is used as first-line therapy for multiple myeloma. Observational studies based on the FDA Adverse Event Reporting System database analysis and systematic reviews indicate that the incidence of peripheral neuropathy (PN) and tumor lysis syndrome (TLS) tends to be higher with bortezomib than that of other drugs. In a comprehensive analysis assessing drugs that cause PN in Japanese patients, the incidence of bortezomib-induced adverse events (AEs) was reportedly high. However, a comprehensive assessment of bortezomib is lacking. OBJECTIVES: The purpose of this study was to determine the frequency of bortezomib AEs in Japanese patients and to determine the incidence, time to onset, and post hoc outcomes of unique AEs using the Japanese Adverse Drug Event Report database. METHOD: To investigate the association between bortezomib and AEs, we analyzed the Japanese Adverse Drug Event Report database, which contains spontaneous AE reports submitted to the Pharmaceuticals and Medical Devices Agency from April 2004 to December 2020. Criteria indicating the presence of an AE signal were met when the following requirements were fulfilled: proportional reporting ratios ≥2 and χ2 ≥ 4. Time to onset and post-event outcomes were analyzed for characteristic AEs. RESULTS: Among 26 extracted AEs, 13 presented AE signals. The post-exposure outcomes of 12 AEs showed fatal outcomes at rates exceeding 10%, including cardiac failure (30%), lung disorder (24%), pneumonia (18%), and TLS (10%). Furthermore, a histogram of time to onset revealed that the 12 AEs were concentrated from the beginning to approximately 1 month after bortezomib administration. The median onset times for cardiac failure, lung disorder, pneumonia, and TLS were 28, 13, 42, and 5 days, respectively. CONCLUSIONS: Cardiac failure, lung disorder, pneumonia, and TLS had a higher rate of fatal clinical outcomes after onset than other AEs. These AEs exhibited a greater onset tendency in the early post-dose period. This study suggests that there is a need to monitor signs of cardiac failure, lung disorder, pneumonia, and TLS, potentially resulting in serious outcomes.

Investigation of carbonic anhydrase inhibitory effects and cytotoxicities of pyrazole-based hybrids carrying hydrazone and zinc-binding benzenesulfonamide pharmacophores.

Pyrazole-based carbohydrazone hybrids have been considered to be a remarkable class of compounds in pharmaceutical chemistry. Here, we reported bioactivities of 4-(3-(2-(arylidene)hydrazin-1-carbonyl)-5-phenyl-1H-pyrazol-1-yl)benzenesulfonamides (1-27) towards CA isoenzymes (hCA I, hCA II, hCA IX) and human oral squamous cell carcinoma cell line. Compounds 19 (Ki = 10.1 nM, hCA I/hCA IX = 749.6), 22 (Ki = 18.5 nM, hCA I/hCA IX = 429.2), 26 (Ki = 14.5 nM, hCA I/hCA IX = 596.9), 27 (Ki = 21.5 nM, hCA I/hCA IX = 413.1) were more potent and selective inhibitors of cancer-associated hCA IX isoenzyme. Compounds 22 and 26 were also found to be approximately three times more selective hCA IX inhibitors over off-target hCA II at low nanomolar. Compounds 19, 22, 23, 24, and 26 with IC50 of 1.6-1.7 µM showed potent cytotoxicity against human oral squamous cell carcinoma cell line as compared with human gingival fibroblast, producing the tumor-specificity value over 100. This was due to its cytostatic growth inhibition accompanied by a slight but significant dose-dependent increase in cell shrinkage and subG1 cell accumulation and marginal activation of caspase 3 substrates. Bioassay results showed that carbohydrazone-based hybrids could be useful candidates to design novel anticancer compounds and selective carbonic anhydrase inhibitors.

Construction of a prediction model for drug removal rate in hemodialysis based on chemical structures.

In designing drug dosing for hemodialysis patients, the removal rate (RR) of the drug by hemodialysis is important. However, acquiring the RR is difficult, and there is a need for an estimation method that can be used in clinical settings. In this study, the RR predictive model was constructed using the RR of known drugs by quantitative structure-activity relationship (QSAR) analysis. Drugs were divided into a model construction drug set (75%) and a model validation drug set (25%). The RR was collected from 143 medicines. The objective variable (RR) and chemical structural characteristics (descriptors) of the drug (explanatory variable) were used to construct a prediction model using partial least squares (PLS) regression and artificial neural network (ANN) analyses. The determination coefficients in the PLS and ANN methods were 0.586 and 0.721 for the model validation drug set, respectively. QSAR analysis successfully constructed dialysis RR prediction models that were comparable or superior to those using pharmacokinetic parameters. Considering that the RR dataset contains potential errors, we believe that this study has achieved the most reliable RR prediction accuracy currently available. These predictive RR models can be achieved using only the chemical structure of the drug. This model is expected to be applied at the time of hemodialysis.

Comprehensive analysis of everolimus-induced adverse events using the Japanese real-world database.

WHAT IS KNOWN AND OBJECTIVES: As for adverse events (AEs) caused by everolimus, findings from clinical trials and post-marketing surveillance have reported interstitial lung disease, hyperglycaemia, cardiovascular disease, etc. However, these reports are limited to incidence, and detailed studies on the risk of occurrence, time to onset and post-event clinical outcomes are only related to hyperglycaemia. The purpose of this study was to perform a comprehensive analysis of adverse events during everolimus therapy in patients with renal cell carcinoma (RCC) using the Japanese Adverse Event Report database. METHODS: Data reported between April 2004 and June 2021 in the Japanese Adverse Drug Event Report database were extracted for use. The reported odds ratio, time to onset and post-event course were analysed for the top 30 adverse events reported. RESULTS AND DISCUSSION: Among the top 30 adverse events, 23 adverse event signals were detected and classified into seven categories: lung-related AEs, haematological-related AEs, cancer progression, blood glucose-related AEs, hepatic-related AEs, renal-related AEs and others. The lung-related adverse events category was the most common, and the proportion of fatal outcomes after the occurrence of two adverse events related to infectious pneumonia was more than 10%. WHAT IS NEW AND CONCLUSION: A comprehensive survey of adverse events associated with everolimus administration using the pharmacovigilance database revealed that pulmonary and haematological AEs are frequently reported. The results suggest that attention should be paid to the occurrence of lung disorders because they may lead to fatal outcomes.

A Deep Learning-Based Quantitative Structure-Activity Relationship System Construct Prediction Model of Agonist and Antagonist with High Performance.

Molecular design and evaluation for drug development and chemical safety assessment have been advanced by quantitative structure-activity relationship (QSAR) using artificial intelligence techniques, such as deep learning (DL). Previously, we have reported the high performance of prediction models molecular initiation events (MIEs) on the adverse toxicological outcome using a DL-based QSAR method, called DeepSnap-DL. This method can extract feature values from images generated on a three-dimensional (3D)-chemical structure as a novel QSAR analytical system. However, there is room for improvement of this system's time-consumption. Therefore, in this study, we constructed an improved DeepSnap-DL system by combining the processes of generating an image from a 3D-chemical structure, DL using the image as input data, and statistical calculation of prediction-performance. Consequently, we obtained that the three prediction models of agonists or antagonists of MIEs achieved high prediction-performance by optimizing the parameters of DeepSnap, such as the angle used in the depiction of the image of a 3D-chemical structure, data-split, and hyperparameters in DL. The improved DeepSnap-DL system will be a powerful tool for computer-aided molecular design as a novel QSAR system.

Nuclear Receptor and Stress Response Pathways Associated with Antineoplastic Agent-Induced Diarrhea.

In severe cases, antineoplastic agent-induced diarrhea may be life-threatening; therefore, it is necessary to determine the mechanism of toxicity and identify the optimal management. The mechanism of antineoplastic agent-induced diarrhea is still unclear but is often considered to be multifactorial. The aim of this study was to determine the molecular initiating event (MIE), which is the initial interaction between molecules and biomolecules or biosystems, and to evaluate the MIE specific to antineoplastic agents that induce diarrhea. We detected diarrhea-inducing drug signals based on adjusted odds ratios using the Food and Drug Administration Adverse Event Reporting System. We then used the quantitative structure-activity relationship platform of Toxicity Predictor to identify potential MIEs that are specific to diarrhea-inducing antineoplastic agents. We found that progesterone receptor antagonists were potential MIEs associated with diarrhea. The findings of this study may help improve the prediction and management of antineoplastic agent-induced diarrhea.

Tumor-Specificity, Neurotoxicity, and Possible Involvement of the Nuclear Receptor Response Pathway of 4,6,8-Trimethyl Azulene Amide Derivatives.

BACKGROUND: Very few papers covering the anticancer activity of azulenes have been reported, as compared with those of antibacterial and anti-inflammatory activity. This led us to investigate the antitumor potential of fifteen 4,6,8-trimethyl azulene amide derivatives against oral malignant cells. METHODS: 4,6,8-Trimethyl azulene amide derivatives were newly synthesized. Anticancer activity was evaluated by tumor-specificity against four human oral squamous cell carcinoma (OSCC) cell lines over three normal oral cells. Neurotoxicity was evaluated by cytotoxicity against three neuronal cell lines over normal oral cells. Apoptosis induction was evaluated by Western blot and cell cycle analyses. RESULTS: Among fifteen derivatives, compounds 7, 9, and 15 showed the highest anticancer activity, and relatively lower neurotoxicity than doxorubicin, 5-fluorouracil (5-FU), and melphalan. They induced the accumulation of a comparable amount of a subG1 population, but slightly lower extent of caspase activation, as compared with actinomycin D, used as an apoptosis inducer. The quantitative structure-activity relationship analysis suggests the significant correlation of tumor-specificity with a 3D shape of molecules, and possible involvement of inflammation and hormone receptor response pathways. CONCLUSIONS: Compounds 7 and 15 can be potential candidates of a lead compound for developing novel anticancer drugs.

Novel Unsymmetric 3,5-Bis(benzylidene)-4-piperidones That Display Tumor-Selective Toxicity.

Two series of novel unsymmetrical 3,5-bis(benzylidene)-4 piperidones 2a-f and 3a-e were designed as candidate antineoplastic agents. These compounds display potent cytotoxicity towards two colon cancers, as well as several oral squamous cell carcinomas. These compounds are less toxic to various non-malignant cells giving rise to large selectivity index (SI) figures. Many of the compounds are also cytotoxic towards CEM lymphoma and HL-60 leukemia cells. Representative compounds induced apoptotic cell death characterized by caspase-3 activation and subG1 accumulation in some OSCC cells, as well as the depolarization of the mitochondrial membrane potential in CEM cells. A further line of inquiry was directed to finding if the SI values are correlated with the atomic charges on the olefinic carbon atoms. The potential of these compounds as antineoplastic agents was enhanced by an ADME (absorption, distribution, metabolism, and excretion) evaluation of five lead molecules, which revealed no violations.

A Molecular Image-Based Novel Quantitative Structure-Activity Relationship Approach, Deepsnap-Deep Learning and Machine Learning.

The quantitative structure-activity relationship (QSAR) approach has been used in numerous chemical compounds as in silico computational assessment for a long time. Further, owing to the high-performance modeling of QSAR, machine learning methods have been developed and upgraded. Particularly, the three- dimensional structure of chemical compounds has been gaining increasing attention owing to the representation of a large amount of information. However, only many of feature extraction is impossible to build models with the high-ability of the prediction. Thus, suitable extraction and effective selection of features are essential for models with excellent performance. Recently, the deep learning method has been employed to construct prediction models with very high performance using big data, especially, in the field of classification. Therefore, in this study, we developed a molecular image-based novel QSAR approach, called DeepSnap-Deep learning approach for designing high-performance models. In addition, this DeepSnap-Deep learning approach outperformed the conventional machine learnings when they are compared. Herein, we discuss the advantage and disadvantages of the machine learnings as well as the availability of the DeepSnap-Deep learning approach.

The Risk Factors Associated with Immune Checkpoint Inhibitor-Related Pneumonitis.

BACKGROUND: Pneumonitis is a serious adverse event in patients treated with immune checkpoint inhibitors (ICIs), with a mortality rate of up to 20%. The risk factors for ICI-related pneumonitis remain unclear due to the scarce data and infrequent event rate of 0-10% for all grades in patients using ICIs. OBJECTIVES: This study evaluated the risk factors for ICI-related pneumonitis using the United States Food and Drug Administration (US FDA) Adverse Event Reporting System (FAERS) database. METHOD: To investigate the association between pneumonitis and ICIs, the FAERS database, which contains spontaneous adverse event reports submitted to the US FDA, was utilized. Data between January 2014 and December 2019 were collected. Univariate logistic regression analysis with covariates, including age, sex, and ICI use, was performed to assess the risk of ICI-related pneumonitis. The relative risk of pneumonitis was estimated using by the odds ratio. RESULTS: We identified 4,248,808 reports, including 51,166 cases of those who received eight different ICIs. Nivolumab was the most common ICI (n = 27,273 of 51,166 [53.3%] patients). Reporting rates of pneumonitis were significantly high in ICI users (odds ratio 29.48; 95% confidence interval [CI], 27.49-31.62). Univariate logistic regression analysis showed that pneumonitis risk was significantly associated with age. Age ≤60 years old was associated with an increase in the reported frequency of pneumonitis. CONCLUSIONS: Our data suggest that the risk of ICI-related pneumonitis may increase in certain populations, including younger age (age <60 years) and ICIs users. These patients require careful monitoring.

Synthesis and biological evaluation of 3-styrylchromone derivatives as selective monoamine oxidase B inhibitors.

A series of 3-styrylchromone derivatives was synthesized and evaluated for monoamine oxidase (MAO) A and B inhibitory activities. Most of all derivatives inhibited MAO-B selectively, except compound 21. Compound 19, which had a methoxy group at R2 on the chromone ring and chlorine at R4 on phenyl ring, potently inhibited MAO-B, with an IC50 value of 2.2 nM. Compound 1 showed the highest MAO-B selectivity, with a selectivity index of >3700. Further analysis of these compounds indicated that compounds 1 and 19 were reversible and mixed-type MAO-B inhibitors, suggesting that their mode of action may be through tight-binding inhibition to MAO-B. Quantitative structure-activity relationship (QSAR) analyses of the 3-styrylchromone derivatives were conducted using their pIC50 values, through Molecular Operating Environment (MOE) and Dragon. There were 1796 descriptors of MAO-B inhibitory activity, which showed significant correlations (P < 0.05). Further investigation of the 3-styrylchromone structures as useful scaffolds was performed through three-dimensional-QSAR studies using AutoGPA, which is based on the molecular field analysis algorithm using MOE. The MAO-B inhibitory activity model constructed using pIC50 value index exhibited a determination coefficients (R2) of 0.972 and a Leave-One-Out cross-validated determination coefficients (Q2) of 0.914. These data suggest that the 3-styrylchromone derivatives assessed herein may be suitable for the design and development of novel MAO inhibitors.