Systems pharmacology of adverse event mitigation by drug combinations

Unveiling the hidden ocular risks of isotretinoin: a comprehensive FAERS-Based analysis

OBJECTIVE

While extensive research has been conducted on isotretinoin's systemic side effects, studies focusing on its ocular side effects remain limited and often lack substantial sample sizes. To address this gap, we conducted a comprehensive investigation of isotretinoin-related ocular toxicity using data from the FAERS spanning 2004 to 2024.

METHODS

After excluding duplicate and incomplete records from the FAERS database, we identified 760 eye-related adverse event reports from a total of 45,258 isotretinoin-related entries. We employed the Reporting Odds Ratio (ROR) method to assess the risk of ocular problems. Additionally, we examined the onset timing of eye toxicity.

RESULTS

Among the 760 reports analyzed, dry eye emerged as the most frequently reported condition (n = 222), although it did not exhibit the strongest association. The ROR was observed for night blindness (ROR = 35.8, 95% CI = 29.66-43.21), indicating a significant risk. This finding underscores the need to focus on isotretinoin's impact on the retina and fundus, especially since night blindness and vision loss can manifest as early as the first day of treatment.

CONCLUSION

These findings prompt new recommendations for safety monitoring by clinicians. However, additional clinical and fundamental research is essential to substantiate these observations and further elucidate the effects of isotretinoin on ocular health.

Advanced AI-Driven Prediction of Pregnancy-Related Adverse Drug Reactions

Ensuring drug safety during pregnancy is critical due to the potential risks to both the mother and fetus. However, the exclusion of pregnant women from clinical trials complicates the assessment of adverse drug reactions (ADRs) in this population. This study aimed to develop and validate risk prediction models for pregnancy-related ADRs of drugs using advanced Machine Learning (ML) and Deep Learning (DL) techniques, leveraging real-world data from the FDA Adverse Event Reporting System. We explored three methods─Information Component, Reporting Odds Ratio, and 95% confidence interval of ROR─for classifying drugs into high-risk and low-risk categories. DL models, including Directed Message Passing Neural Networks (DMPNN), Graph Neural Networks, and Graph Convolutional Networks, were developed and compared to traditional ML models like Random Forest, Support Vector Machines, and XGBoost. Among these, the DMPNN model, which integrated molecular graph information and molecular descriptors, exhibited the highest predictive performance, particularly at the preferred term level. The model was validated against external data sets from SIDER and DailyMed, demonstrating strong generalizability. Additionally, the model was applied to assess the risk of 22 oral hypoglycemic drugs, and potential substructure alerts for pregnancy-related ADRs were identified. These findings suggest that the DMPNN model is a valuable tool for predicting ADRs in pregnant women, offering significant advancement in drug safety assessment and providing crucial insights for safer medication use during pregnancy.

MDNNSyn: A Multi-Modal Deep Learning Framework for Drug Synergy Prediction

Synergistic drug combination prediction tasks based on the computational models have been widely studied and applied in the cancer field. However, most of models only consider the interactions between drug pairs and specific cell lines, without taking into account the multiple biological relationships of drug-drug and cell line-cell line that also largely affect synergistic mechanisms. To this end, here we propose a multi-modal deep learning framework, termed MDNNSyn, which adequately applies multi-source information and trains multi-modal features to infer potential synergistic drug combinations. MDNNSyn extracts topology modality features by implementing the multi-layer hypergraph neural network on drug synergy hypergraph and constructs semantic modality features through similarity strategy. A multi-modal fusion network layer with gated neural network is then employed for synergy score prediction. MDNNSyn is compared to five classic and state-of-the-art prediction methods on DrugCombDB and Oncology-Screen datasets. The model achieves area under the curve (AUC) scores of 0.8682 and 0.9013 on two datasets, an improvement of 3.70 % and 2.71 % over the second-best model. Case study indicates that MDNNSyn is capable of detecting potential synergistic drug combinations.

DDSBC: A Stacking Ensemble Classifier-Based Approach for Breast Cancer Drug-Pair Cell Synergy Prediction

Breast cancer (BC) ranks as a leading cause of mortality among women worldwide, with incidence rates continuing to rise. The quest for effective treatments has led to the adoption of drug combination therapy, aiming to enhance drug efficacy. However, identifying synergistic drug combinations remains a daunting challenge due to the myriad of potential drug pairs. Current research leverages machine learning (ML) and deep learning (DL) models for drug-pair synergy prediction and classification. Nevertheless, these models often underperform on specific cancer types, including BC, as they are trained on data spanning various cancers without any specialization. Here, we introduce a stacking ensemble classifier, the drug-drug synergy for breast cancer (DDSBC), tailored explicitly for BC drug-pair cell synergy classification. Unlike existing models that generalize across cancer types, DDSBC is exclusively developed for BC, offering a more focused approach. Our comparative analysis against classical ML methods as well as DL models developed for drug synergy prediction highlights DDSBC's superior performance across test and independent datasets on BC data. Despite certain metrics where other methods narrowly surpass DDSBC by 1-2%, DDSBC consistently emerges as the top-ranked model, showcasing significant differences in scoring metrics and robust performance in ablation studies. DDSBC's performance and practicality position it as a preferred choice or an adjunctive validation tool for identifying synergistic or antagonistic drug pairs in BC, providing valuable insights for treatment strategies.

MMFSyn: A Multimodal Deep Learning Model for Predicting Anticancer Synergistic Drug Combination Effect

Combination therapy aims to synergistically enhance efficacy or reduce toxic side effects and has widely been used in clinical practice. However, with the rapid increase in the types of drug combinations, identifying the synergistic relationships between drugs remains a highly challenging task. This paper proposes a novel deep learning model MMFSyn based on multimodal drug data combined with cell line features. Firstly, to ensure the full expression of drug molecular features, multiple modalities of drugs, including Morgan fingerprints, atom sequences, molecular diagrams, and atomic point cloud data, are extracted using SMILES. Secondly, for different modal data, a Bi-LSTM, gMLP, multi-head attention mechanism, and multi-scale GCNs are comprehensively applied to extract the drug feature. Then, it selects appropriate omics features from gene expression and mutation omics data of cancer cell lines to construct cancer cell line features. Finally, these features are combined to predict the synergistic anti-cancer drug combination effect. The experimental results verify that MMFSyn has significant advantages in performance compared to other popular methods, with a root mean square error of 13.33 and a Pearson correlation coefficient of 0.81, which indicates that MMFSyn can better capture the complex relationship between multimodal drug combinations and omics data, thereby improving the synergistic drug combination prediction.

In-silico approaches to assessing multiple high-level drug-drug and drug-disease adverse drug effects

INTRODUCTION

Pharmacovigilance plays a pivotal role in monitoring adverse events (AEs) related to chemical substances in human/animal populations. With increasing spontaneous-reporting systems, researchers turned to in-silico approaches to efficiently analyze drug safety profiles. Here, we review in-silico methods employed for assessing multiple drug-drug/drug-disease AEs covered by comparative analyses and visualization strategies.

AREAS COVERED

Disproportionality, involving multi-stage statistical methodologies and data processing, identifies safety signals among drug-AE pairs. By stratifying data based on disease indications/demographics, researchers address confounders and assess drug safety. Comparative analyses, including clustering techniques and visualization techniques, assess drug similarities, patterns, and trends, calculate correlations, and identify distinct toxicities. Furthermore, we conducted a thorough Scopus search on 'pharmacovigilance,' yielding 5,836 publications spanning 2003 to 2023.

EXPERT OPINION

Pharmacovigilance relies on diverse data sources, presenting challenges in the integration of in-silico approaches and requiring compliance with regulations and AI adoption. Systematic use of statistical analyses enables identifications of potential risks with drugs. Frequentist and Bayesian methods are used in disproportionalities, each with its strengths and weaknesses. Integration of pharmacogenomics with pharmacovigilance enables personalized medicine, with AI further enhancing patient engagement. This multidisciplinary approach holds promise, improving drug efficacy and safety, and should be a core mission of One-Health studies.

Emerging therapeutic drug monitoring technologies: considerations and opportunities in precision medicine

In recent years, the development of sensor and wearable technologies have led to their increased adoption in clinical and health monitoring settings. One area that is in early, but promising, stages of development is the use of biosensors for therapeutic drug monitoring (TDM). Traditionally, TDM could only be performed in certified laboratories and was used in specific scenarios to optimize drug dosage based on measurement of plasma/blood drug concentrations. Although TDM has been typically pursued in settings involving medications that are challenging to manage, the basic approach is useful for characterizing drug activity. TDM is based on the idea that there is likely a clear relationship between plasma/blood drug concentration (or concentration in other matrices) and clinical efficacy. However, these relationships may vary across individuals and may be affected by genetic factors, comorbidities, lifestyle, and diet. TDM technologies will be valuable for enabling precision medicine strategies to determine the clinical efficacy of drugs in individuals, as well as optimizing personalized dosing, especially since therapeutic windows may vary inter-individually. In this mini-review, we discuss emerging TDM technologies and their applications, and factors that influence TDM including drug interactions, polypharmacy, and supplement use. We also discuss how using TDM within single subject (N-of-1) and aggregated N-of-1 clinical trial designs provides opportunities to better capture drug response and activity at the individual level. Individualized TDM solutions have the potential to help optimize treatment selection and dosing regimens so that the right drug and right dose may be matched to the right person and in the right context.

Drug-drug interaction assessment based on a large-scale spontaneous reporting system for hepato- and renal-toxicity, and thrombocytopenia with concomitant low-dose methotrexate and analgesics use

BACKGROUND

Methotrexate (MTX) is the cornerstone of rheumatoid arthritis (RA) treatment and is highly effective with low-dose intermittent administration. MTX is occasionally used in combination with non-steroidal anti-inflammatory drugs (NSAIDs) and acetaminophen (APAP)/paracetamol for pain or inflammation control. With MTX treatment, the side effects, such as hepatotoxicity, renal failure, and myelosuppression should be considered. These are also seen with analgesics treatment.

METHODS

We used a large spontaneously reported adverse event database (FAERS [JAPIC AERS]) to analyze whether the reporting of adverse events increased upon MTX and analgesic therapy in patients with RA.

RESULTS

After identifying RA cases, the crude reporting odds ratios (cRORs) for hepatotoxicity, renal failure, and thrombocytopenia associated with the use of MTX, APAP, or NSAIDs were calculated by disproportionality analysis, which revealed significantly higher cRORs for these events. No analgesics showed consistent positive signals for drug-drug interaction (DDI) with concomitant low-dose MTX analyzed using four algorithms for DDI interaction (the Ω shrinkage measure, additive or multiplicative, and combination risk ratio models). However, in renal failure and thrombocytopenia, loxoprofen (Ω025 = 0.08) and piroxicam (Ω025 = 0.46), and ibuprofen (Ω025 = 0.74) and ketorolac (Ω025 = 3.52), respectively, showed positive signals in the Ω shrinkage measure model, and no consistency was found among adverse events or NSAIDs.

CONCLUSIONS

Studies using spontaneous reporting systems have limitations such as reporting bias or lack of patient background; however, the results of our comprehensive analysis support the results of previous clinical or epidemiological studies. This study also demonstrated the usefulness of FAERS for DDI assessment.

Integrative Research of Neuropharmacology and Informatics Pharmacology for Mental Disorder

Mental illness poses a huge social burden, accounting for approximately 14% of all deaths. Depression, a major component of mental illness, affects approximately 300 million people worldwide, mainly in developed countries, and is not only a major social burden but also a cause of suicide. The social burden of depression is estimated to increase further in developing countries, and overcoming it is a pressing issue for all countries, including Japan. Although clinical evidence has demonstrated the efficacy of serotonergic neurotransmission enhancers in the treatment of depression, the full picture of their therapeutic effects has not yet been fully elucidated. In this review, we show that the hyperactivity of serotonin neurons, especially those in the dorsal raphe nucleus, is commonly induced by various antidepressants within a period corresponding to the onset of their clinical efficacy. We established quantitative prediction methods for pharmacological activity using only chemical structures to translate the biological understanding of mental disorders, including major depressive disorders, into clinically effective therapeutics. Our method exhibited better performance than the previously reported methods of quantitative prediction, while targeting a larger number of proteins. Our article suggests the importance of integrative neuropharmacology and informatics-based pharmacology studies to understand the biological basis of mental disorders and facilitate drug development for these disorders.

Drug Repositioning Using Computer-aided Drug Design (CADD)

Drug repositioning is a method of using authorized drugs for other unusually complex diseases. Compared to new drug development, this method is fast, low in cost, and effective. Through the use of outstanding bioinformatics tools, such as computer-aided drug design (CADD), computer strategies play a vital role in the re-transformation of drugs. The use of CADD's special strategy for target-based drug reuse is the most promising method, and its realization rate is high. In this review article, we have particularly focused on understanding the various technologies of CADD and the use of computer-aided drug design for target-based drug reuse, taking COVID-19 and cancer as examples. Finally, it is concluded that CADD technology is accelerating the development of repurposed drugs due to its many advantages, and there are many facts to prove that the new ligand-targeting strategy is a beneficial method and that it will gain momentum with the development of technology.

Drug Repurposing Using FDA Adverse Event Reporting System (FAERS) Database

Drug repurposing is an emerging approach to reassigning existing pre-approved therapies for new indications. The FDA Adverse Event Reporting System (FAERS) is a large database of over 28 million adverse event reports submitted by medical providers, patients, and drug manufacturers and provides extensive drug safety signal data. In this review, four common drug repurposing strategies using FAERS are described, including inverse signal detection for a single disease, drug-drug interactions that mitigate a target ADE, identifying drug-ADE pairs with opposing gene perturbation signatures and identifying drug-drug pairs with congruent gene perturbation signatures. The purpose of this review is to provide an overview of these different approaches using existing successful applications in the literature. With the fast expansion of adverse drug event reports, FAERS-based drug repurposing represents a promising strategy for discovering new uses for existing therapies.

A CRISPR-drug perturbational map for identifying compounds to combine with commonly used chemotherapeutics

Combination chemotherapy is crucial for successfully treating cancer. However, the enormous number of possible drug combinations means discovering safe and effective combinations remains a significant challenge. To improve this process, we conduct large-scale targeted CRISPR knockout screens in drug-treated cells, creating a genetic map of druggable genes that sensitize cells to commonly used chemotherapeutics. We prioritize neuroblastoma, the most common extracranial pediatric solid tumor, where ~50% of high-risk patients do not survive. Our screen examines all druggable gene knockouts in 18 cell lines (10 neuroblastoma, 8 others) treated with 8 widely used drugs, resulting in 94,320 unique combination-cell line perturbations, which is comparable to the largest existing drug combination screens. Using dense drug-drug rescreening, we find that the top CRISPR-nominated drug combinations are more synergistic than standard-of-care combinations, suggesting existing combinations could be improved. As proof of principle, we discover that inhibition of PRKDC, a component of the non-homologous end-joining pathway, sensitizes high-risk neuroblastoma cells to the standard-of-care drug doxorubicin in vitro and in vivo using patient-derived xenograft (PDX) models. Our findings provide a valuable resource and demonstrate the feasibility of using targeted CRISPR knockout to discover combinations with common chemotherapeutics, a methodology with application across all cancers.

Patient-Specific Pharmacokinetics and Dasatinib Nephrotoxicity

BACKGROUND

Dasatinib has been associated with nephrotoxicity. We sought to examine the incidence of proteinuria on dasatinib and determine potential risk factors that may increase dasatinib-associated glomerular injury.

METHODS

We examined glomerular injury through urine albumin-creatinine ratio (UACR) in 82 patients with chronic myelogenous leukemia who were on tyrosine-kinase inhibitor therapy for at least 90 days. t tests were used to compare mean differences in UACR, while regression analysis was used to assess the effects of drug parameters on proteinuria development while on dasatinib. We assayed plasma dasatinib pharmacokinetics using tandem mass spectroscopy and further described a case study of a patient who experienced nephrotic-range proteinuria while on dasatinib.

RESULTS

Participants treated with dasatinib ( n =32) had significantly higher UACR levels (median 28.0 mg/g; interquartile range, 11.5-119.5) than participants treated with other tyrosine-kinase inhibitors ( n =50; median 15.0 mg/g; interquartile range, 8.0-35.0; P < 0.001). In total, 10% of dasatinib users exhibited severely increased albuminuria (UACR >300 mg/g) versus zero in other tyrosine-kinase inhibitors. Average steady-state concentrations of dasatinib were positively correlated with UACR ( ρ =0.54, P = 0.03) and duration of treatment ( P = 0.003). There were no associations with elevated BP or other confounding factors. In the case study, kidney biopsy revealed global glomerular damage with diffuse foot process effacement that recovered on termination of dasatinib treatment.

CONCLUSIONS

Exposure to dasatinib was associated with a significant chance of developing proteinuria compared with other similar tyrosine-kinase inhibitors. Dasatinib plasma concentration significantly correlated with higher risk of developing proteinuria while receiving dasatinib.

PODCAST

This article contains a podcast at https://dts.podtrac.com/redirect.mp3/www.asn-online.org/media/podcast/CJASN/2023_09_08_CJN0000000000000219.mp3.

An Overview of the Potential Medicinal and Pharmaceutical Properties of Ru(II)/(III) Complexes

This review examines the existing knowledge about Ru(II)/(III) ion complexes with a potential application in medicine or pharmacy, which may offer greater potential in cancer chemotherapy than Pt(II) complexes, which are known to cause many side effects. Hence, much attention has been paid to research on cancer cell lines and clinical trials have been undertaken on ruthenium complexes. In addition to their antitumor activity, ruthenium complexes are under evaluation for other diseases, such as type 2 diabetes, Alzheimer's disease and HIV. Attempts are also being made to evaluate ruthenium complexes as potential photosensitizers with polypyridine ligands for use in cancer chemotherapy. The review also briefly examines theoretical approaches to studying the interactions of Ru(II)/Ru(III) complexes with biological receptors, which can facilitate the rational design of ruthenium-based drugs.

Dasatinib nephrotoxicity correlates with patient-specific pharmacokinetics

Introduction

Dasatinib has been associated with nephrotoxicity. We sought to examine the incidence of proteinuria on dasatinib and determine potential risk factors that may increase dasatinib-associated glomerular injury.

Methods

We examine glomerular injury via urine albumin-to-creatinine ratio (UACR) in 101 chronic myelogenous leukemia patients who were on tyrosine-kinase inhibitor (TKI) therapy for at least 90 days. We assay plasma dasatinib pharmacokinetics using tandem mass spectroscopy, and further describe a case study of a patient who experienced nephrotic-range proteinuria while on dasatinib.

Results

Patients treated with dasatinib (n= 32) had significantly higher UACR levels (median 28.0 mg/g, IQR 11.5 - 119.5) than patients treated with other TKIs (n=50; median 15.0 mg/g, IQR 8.0 - 35.0; p < 0.001). In total, 10% of dasatinib users exhibited severely increased albuminuria (UACR > 300 mg/g) versus zero in other TKIs. Average steady state concentrations of dasatinib were positively correlated with UACR (ρ = 0.54, p = 0.03) as well as duration of treatment ( p =0.003). There were no associations with elevated blood pressure or other confounding factors. In the case study, kidney biopsy revealed global glomerular damage with diffuse foot process effacement that recovered upon termination of dasatinib treatment.

Conclusions

Exposure to dasatinib is associated a significant chance of developing proteinuria compared to other similar TKIs. Dasatinib plasma concentration significantly correlates with increased risk of developing proteinuria while receiving dasatinib. Screening for renal dysfunction and proteinuria is strongly advised for all dasatinib patients.

Lisinopril prevents bullous pemphigoid induced by dipeptidyl peptidase 4 inhibitors via the Mas receptor pathway

Recent studies have suggested that dipeptidyl peptidase 4 (DPP4) inhibitors increase the risk of development of bullous pemphigoid (BP), which is the most common autoimmune blistering skin disease; however, the associated mechanisms remain unclear, and thus far, no therapeutic targets responsible for drug-induced BP have been identified. Therefore, we used clinical data mining to identify candidate drugs that can suppress DPP4 inhibitor-associated BP, and we experimentally examined the underlying molecular mechanisms using human peripheral blood mononuclear cells (hPBMCs). A search of the US Food and Drug Administration Adverse Event Reporting System and the IBM^® MarketScan^® Research databases indicated that DPP4 inhibitors increased the risk of BP, and that the concomitant use of lisinopril, an angiotensin-converting enzyme inhibitor, significantly decreased the incidence of BP in patients receiving DPP4 inhibitors. Additionally, in vitro experiments with hPBMCs showed that DPP4 inhibitors upregulated mRNA expression of MMP9 and ACE2, which are responsible for the pathophysiology of BP in monocytes/macrophages. Furthermore, lisinopril and Mas receptor (MasR) inhibitors suppressed DPP4 inhibitor-induced upregulation of MMP9. These findings suggest that the modulation of the renin-angiotensin system, especially the angiotensin1-7/MasR axis, is a therapeutic target in DPP4 inhibitor-associated BP.

Methotrexate-related adverse events and impact of concomitant treatment with folic acid and tumor necrosis factor-alpha inhibitors: An assessment using the FDA adverse event reporting system

Methotrexate (MTX) is an essential anti-rheumatic drug used to treat rheumatoid arthritis (RA). Prevention or management of adverse reactions, including interstitial lung disease (ILD), hepatotoxicity, myelosuppression, and infection, remains fundamental for safe MTX therapy. Using the Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) (JAPIC AERS), we performed disproportionality analyses of adverse events related to MTX use and the impact of concomitant medications. Upon analyzing all reported cases in FAERS between 1997 and 2019, the crude reporting odds ratios (cRORs; 95% confidence intervals) for ILD, hepatotoxicity, myelosuppression, and tuberculosis (TB) in relation to MTX use were 4.00 (3.83-4.17), 1.99 (1.96-2.02), 3.66 (3.58-3.74), and 7.97 (7.65-8.3), respectively. Combining MTX with folic acid (FA) or tumor necrosis factor-alpha inhibitors (TNFis) tended to reduce cRORs for these adverse events (except for TB). Multiple logistic regression analysis in patients with RA was conducted to calculate adjusted reporting odds ratios (aRORs) for age, sex, and MTX treatment patterns (MTX alone and combined with FA and TNFi). Higher age (except for hepatotoxicity) and male sex were significantly associated with adverse events. Combining FA or TNFi with MTX reduced aRORs for MTX-related hepatotoxicity and myelosuppression; in contrast, the effect of FA was not obvious in ILD or TB. Although studies assessing spontaneous reporting systems have limitations such as reporting bias, data from our logistic regression analysis demonstrated that adding FA to MTX-based therapy could help reduce the dose-dependent adverse events of MTX, thereby providing clinical evidence that supports the beneficial effect of FA. This study also demonstrated the usefulness of FAERS in comparing adverse events based on treatment patterns.

Increased expression of glutathione peroxidase 3 prevents tendinopathy by suppressing oxidative stress

Tendinopathy, a degenerative disease, is characterized by pain, loss of tendon strength, or rupture. Previous studies have identified multiple risk factors for tendinopathy, including aging and fluoroquinolone use; however, its therapeutic target remains unclear. We analyzed self-reported adverse events and the US commercial claims data and found that the short-term use of dexamethasone prevented both fluoroquinolone-induced and age-related tendinopathy. Rat tendons treated systemically with fluoroquinolone exhibited mechanical fragility, histological change, and DNA damage; co-treatment with dexamethasone attenuated these effects and increased the expression of the antioxidant enzyme glutathione peroxidase 3 (GPX3), as revealed via RNA-sequencing. The primary role of GPX3 was validated in primary cultured rat tenocytes treated with fluoroquinolone or H₂O₂, which accelerates senescence, in combination with dexamethasone or viral overexpression of GPX3. These results suggest that dexamethasone prevents tendinopathy by suppressing oxidative stress through the upregulation of GPX3. This steroid-free approach for upregulation or activation of GPX3 can serve as a novel therapeutic strategy for tendinopathy.

Identification of prophylactic drugs for oxaliplatin-induced peripheral neuropathy using big data

BACKGROUND

Drug repositioning is a cost-effective method to identify novel disease indications for approved drugs; it requires a shorter developmental period than conventional drug discovery methods. We aimed to identify prophylactic drugs for oxaliplatin-induced peripheral neuropathy by drug repositioning using data from large-scale medical information and life science information databases.

METHODS

Herein, we analyzed the reported data between 2007 and 2017 retrieved from the FDA's database of spontaneous adverse event reports (FAERS) and the LINCS database provided by the National Institute of Health. The efficacy of the drug candidates for oxaliplatin-induced peripheral neuropathy obtained from the database analysis was examined using a rat model of peripheral neuropathy. Additionally, we compared the incidence of peripheral neuropathy in patients who received oxaliplatin at the Tokushima University Hospital, Japan. The effects of statins on the animal model were examined in six-week-old male Sprague-Dawley rats and seven or eight-week-old male BALB/C mice. Retrospective medical chart review included clinical data from Tokushima University Hospital from April 2009 to March 2018.

RESULTS

Simvastatin, indicated for dyslipidemia, significantly reduced the severity of peripheral neuropathy and oxaliplatin-induced hyperalgesia. In the nerve tissue of model rats, the mRNA expression of Gstm1 increased with statin administration. A retrospective medical chart review using clinical data revealed that the incidence of peripheral neuropathy decreased with statin use.

CONCLUSION AND RELEVANCE

Thus, drug repositioning using data from large-scale basic and clinical databases enables the discovery of new indications for approved drugs with a high probability of success.

Preventive effect of rebamipide on NSAID-induced lower gastrointestinal tract injury using FAERS and JADER

Non-steroidal anti-inflammatory drugs (NSAIDs) are widely used for their antipyretic, analgesic, and anti-inflammatory properties. However, various aspects of NSAID-induced lower gastrointestinal tract injury remain unclear, and effective prophylaxis has not been established. Based on its pharmacological effect and clinical trials, rebamipide may prevent lower gastrointestinal tract injury, although this evidence is limited by the small scale of trials. The present study used the FDA Adverse Event Reporting System (FAERS) and the Japanese Adverse Event Reporting Database (JADER) to assess the efficacy of rebamipide in combination with loxoprofen and diclofenac in preventing NSAID-induced lower gastrointestinal tract injury. The calculated reporting odds ratio and 95% confidence interval (CI) for rebamipide in combination with loxoprofen and diclofenac were 1.15 (95% CI 0.88–1.51) and 1.28 (95% CI 0.82–2.01) for FAERS, and 0.50 (95% CI 0.35–0.71) and 0.43 (95% CI 0.27–0.67) for JADER, respectively. No signal was detected when combining drugs. These results suggest a prophylactic effect of rebamipide on NSAID-induced lower gastrointestinal tract injury.

From cell lines to cancer patients: personalized drug synergy prediction

MOTIVATION

Combination drug therapies are effective treatments for cancer. However, the genetic heterogeneity of the patients and exponentially large space of drug pairings pose significant challenges for finding the right combination for a specific patient. Current in silico prediction methods can be instrumental in reducing the vast number of candidate drug combinations. However, existing powerful methods are trained with cancer cell line gene expression data, which limits their applicability in clinical settings. While synergy measurements on cell line models are available at large scale, patient-derived samples are too few to train a complex model. On the other hand, patient-specific single-drug response data are relatively more available.

RESULTS

In this work, we propose a deep learning framework, Personalized Deep Synergy Predictor (PDSP), that enables us to use the patient-specific single drug response data for customizing patient drug synergy predictions. PDSP is first trained to learn synergy scores of drug pairs and their single drug responses for a given cell line using drug structures and large scale cell line gene expression data. Then, the model is fine-tuned for patients with their patient gene expression data and associated single drug response measured on the patient ex vivo samples. In this study, we evaluate PDSP on data from three leukemia patients and observe that it improves the prediction accuracy by 27% compared to models trained on cancer cell line data.

AVAILABILITY AND IMPLEMENTATION

PDSP is available at https://github.com/hikuru/PDSP.

Drug-Repositioning Approaches Based on Medical and Life Science Databases

Drug repositioning is a drug discovery strategy in which an existing drug is utilized as a therapeutic agent for a different disease. As information regarding the safety, pharmacokinetics, and formulation of existing drugs is already available, the cost and time required for drug development is reduced. Conventional drug repositioning has been dominated by a method involving the search for candidate drugs that act on the target molecules of an organism in a diseased state through basic research. However, recently, information hosted on medical information and life science databases have been used in translational research to bridge the gap between basic research in drug repositioning and clinical application. Here, we review an example of drug repositioning wherein candidate drugs were found and their mechanisms of action against a novel therapeutic target were identified via a basic research method that combines the findings retrieved from various medical and life science databases.

Machine learning approaches for drug combination therapies

Drug combination therapy is a promising strategy to treat complex diseases such as cancer and infectious diseases. However, current knowledge of drug combination therapies, especially in cancer patients, is limited because of adverse drug effects, toxicity and cell line heterogeneity. Screening new drug combinations requires substantial efforts since considering all possible combinations between drugs is infeasible and expensive. Therefore, building computational approaches, particularly machine learning methods, could provide an effective strategy to overcome drug resistance and improve therapeutic efficacy. In this review, we group the state-of-the-art machine learning approaches to analyze personalized drug combination therapies into three categories and discuss each method in each category. We also present a short description of relevant databases used as a benchmark in drug combination therapies and provide a list of well-known, publicly available interactive data analysis portals. We highlight the importance of data integration on the identification of drug combinations. Finally, we address the advantages of combining multiple data sources on drug combination analysis by showing an experimental comparison.

Drug Repurposing Prediction and Validation From Clinical Big Data for the Effective Treatment of Interstitial Lung Disease

Interstitial lung diseases (ILDs) are a group of respiratory disorders characterized by chronic inflammation and fibrosis of the pulmonary interstitial tissues. Although the etiology of ILD remains unclear, some drug treatments are among the primary causes of ILD. In the present study, we analyzed the FDA Adverse Event Reporting System and JMDC Inc. insurance claims to identify a coexisting drug that reduced the incidence of ILD associated with the use of an anti-arrhythmic agent, amiodarone, and found that the thrombin inhibitor dabigatran prevented the amiodarone-induced ILD in both clinical datasets. In an experimental validation of the hypothesis, long-term oral treatment of mice with amiodarone caused a gradual decrease in body weight caused by respiratory insufficiency. In the lungs of amiodarone-treated mice, infiltration of macrophages was observed in parallel with a delayed upregulation of the platelet-derived growth factor receptor α gene. In contrast, co-treatment with dabigatran significantly attenuated these amiodarone-induced changes indicative of ILD. These results suggest that dabigatran is effective in preventing drug-induced ILD. This combinatorial approach of drug repurposing based on clinical big data will pave the way for finding a new treatment with high clinical predictability and a well-defined molecular mechanism.

DeepDDS: deep graph neural network with attention mechanism to predict synergistic drug combinations

MOTIVATION

Drug combination therapy has become an increasingly promising method in the treatment of cancer. However, the number of possible drug combinations is so huge that it is hard to screen synergistic drug combinations through wet-lab experiments. Therefore, computational screening has become an important way to prioritize drug combinations. Graph neural network has recently shown remarkable performance in the prediction of compound-protein interactions, but it has not been applied to the screening of drug combinations.

RESULTS

In this paper, we proposed a deep learning model based on graph neural network and attention mechanism to identify drug combinations that can effectively inhibit the viability of specific cancer cells. The feature embeddings of drug molecule structure and gene expression profiles were taken as input to multilayer feedforward neural network to identify the synergistic drug combinations. We compared DeepDDS (Deep Learning for Drug-Drug Synergy prediction) with classical machine learning methods and other deep learning-based methods on benchmark data set, and the leave-one-out experimental results showed that DeepDDS achieved better performance than competitive methods. Also, on an independent test set released by well-known pharmaceutical enterprise AstraZeneca, DeepDDS was superior to competitive methods by more than 16% predictive precision. Furthermore, we explored the interpretability of the graph attention network and found the correlation matrix of atomic features revealed important chemical substructures of drugs. We believed that DeepDDS is an effective tool that prioritized synergistic drug combinations for further wet-lab experiment validation.

AVAILABILITY AND IMPLEMENTATION

Source code and data are available at https://github.com/Sinwang404/DeepDDS/tree/master.

Inferring Synergistic Drug Combinations Based on Symmetric Meta-Path in a Novel Heterogeneous Network

Combinatorial drug therapy is a promising way for treating cancers, which can reduce drug side effects and improve drug efficacy. However, due to the large-scale combinatorial space, it is difficult to quickly and effectively identify novel synergistic drug combinations for further implementing combinatorial drug therapy. The computational method of fusing multi-source knowledge is a time- and cost-efficient strategy to infer synergistic drug combinations for testing. However, for the existing computational methods of inferring synergistic drug combinations, it still remains a challenging to effectively combine multi-source information to achieve the desired results. Hence, in this study, we developed a novel Inference method of Synergistic Drug Combinations based on Symmetric Meta-Path (ISDCSMP), which can systematically and accurately prioritize synergistic drug combinations in a novel drug-target heterogeneous network integrating multi-source information. In the experiment, ISDCSMP outperformed the state-of-the-art methods in terms of AUC and precision on the benchmark dataset in five-fold cross validation. Moreover, we further illustrated performances of different ways for obtaining the combination coefficients, and analyzed the influences of the maximum meta-path length. The performances of various single meta-paths were described in five-fold cross validation. Finally, we confirmed the practical usefulness of ISDCSMP with the predicted novel synergistic drug combinations. The source code of ISDCSMP is available at https://github.com/KDDing/ISDCSMP.

Striatal TRPV1 activation by acetaminophen ameliorates dopamine D2 receptor antagonist-induced orofacial dyskinesia

Antipsychotics often cause tardive dyskinesia, an adverse symptom of involuntary hyperkinetic movements. Analysis of the US Food and Drug Administration Adverse Event Reporting System and JMDC insurance claims revealed that acetaminophen prevented the dyskinesia induced by dopamine D₂ receptor antagonists. In vivo experiments further showed that a 21-day treatment with haloperidol increased the number of vacuous chewing movements (VCMs) in rats, an effect that was inhibited by oral acetaminophen treatment or intracerebroventricular injection of N-(4-hydroxyphenyl)-arachidonylamide (AM404), an acetaminophen metabolite that acts as an activator of the transient receptor potential vanilloid 1 (TRPV1). In mice, haloperidol-induced VCMs were also mitigated by treatment with AM404 applied to the dorsal striatum, an effect not seen in TRPV1-deficient mice. Acetaminophen prevented the haloperidol-induced decrease in the number of c-Fos⁺preproenkephalin⁺ striatal neurons in wild-type mice but not in TRPV1-deficient mice. Finally, chemogenetic stimulation of indirect pathway medium spiny neurons in the dorsal striatum decreased haloperidol-induced VCMs. These results suggest that acetaminophen activates the indirect pathway neurons by activating TRPV1 channels via AM404.

Investigation of drugs affecting hypertension in bevacizumab-treated patients and examination of the impact on the therapeutic effect

Background

In patients treated with bevacizumab, hypertension may be a biomarker of therapeutic efficacy. However, it is not clear whether drugs that control blood pressure influence bevacizumab's efficacy. In this study, we investigated drugs that may affect hypertension in bevacizumab‐treated patients and examined the impact on the therapeutic effect.

Patients and methods

We analyzed 3,724,555 reports from the third quarter of 2010 to the second quarter of 2015. All data were obtained from the Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) analysis. In this retrospective cohort study, we investigated a total of 58 patients diagnosed with colorectal cancer and treated for the first time with bevacizumab containing XELOX or mFOLFOX6 at The University of Tokushima Hospital between January 2010 and December 2015. The effect of the treatment was evaluated according to Response Evaluation Criteria in Solid Tumors version 1.0. Thereafter, the effect was confirmed using Gene Expression Omnibus (GEO) and cultured cells.

Results

There are few reports in FAERS of hypertension in patients treated with omeprazole on bevacizumab. Based on the chart review, patients who used proton pump inhibitors (PPI) had a lower response to treatment than those who did not (response rate: 25% vs 50%). Furthermore, experiments on GEO and cell lines suggested that induction of vascular endothelial growth factor (VEGF) gene expression by PPIs is the cause of the reduced therapeutic effect.

Conclusion

PPIs prevent hypertension in bevacizumab‐treated patients but may reduce bevacizumab's anti‐tumoral effects by inducing VEGF expression.

Combining PARP Inhibition with Platinum, Ruthenium or Gold Complexes for Cancer Therapy

Platinum drugs are heavily used first-line chemotherapeutic agents for many solid tumours and have stimulated substantial interest in the biological activity of DNA-binding metal complexes. These complexes generate DNA lesions which trigger the activation of DNA damage response (DDR) pathways that are essential to maintain genomic integrity. Cancer cells exploit this intrinsic DNA repair network to counteract many types of chemotherapies. Now, advances in the molecular biology of cancer has paved the way for the combination of DDR inhibitors such as poly (ADP-ribose) polymerase (PARP) inhibitors (PARPi) and agents that induce high levels of DNA replication stress or single-strand break damage for synergistic cancer cell killing. In this review, we summarise early-stage, preclinical and clinical findings exploring platinum and emerging ruthenium anti-cancer complexes alongside PARPi in combination therapy for cancer and also describe emerging work on the ability of ruthenium and gold complexes to directly inhibit PARP activity.

Transcriptomic profiling of human cardiac cells predicts protein kinase inhibitor-associated cardiotoxicity

Kinase inhibitors (KIs) represent an important class of anti-cancer drugs. Although cardiotoxicity is a serious adverse event associated with several KIs, the reasons remain poorly understood, and its prediction remains challenging. We obtain transcriptional profiles of human heart-derived primary cardiomyocyte like cell lines treated with a panel of 26 FDA-approved KIs and classify their effects on subcellular pathways and processes. Individual cardiotoxicity patient reports for these KIs, obtained from the FDA Adverse Event Reporting System, are used to compute relative risk scores. These are then combined with the cell line-derived transcriptomic datasets through elastic net regression analysis to identify a gene signature that can predict risk of cardiotoxicity. We also identify relationships between cardiotoxicity risk and structural/binding profiles of individual KIs. We conclude that acute transcriptomic changes in cell-based assays combined with drug substructures are predictive of KI-induced cardiotoxicity risk, and that they can be informative for future drug discovery.

Cardiotoxic adverse events associated with kinase inhibitors are a growing concern in clinical oncology. Here the authors use cellular transcriptomic responses of human cardiomyocytes treated with protein kinase inhibitors and the associated drug structural signatures to determine an integrated predictive signature of cardiotoxicity.

Combining Allosteric and Orthosteric Drugs to Overcome Drug Resistance

Historically, most drugs target protein orthosteric sites. The gradual emergence of resistance hampers their therapeutic effectiveness, posing a challenge to drug development. Coadministration of allosteric and orthosteric drugs provides a revolutionary strategy to circumvent drug resistance, as drugs targeting the topologically distinct allosteric sites can restore or even enhance the efficacy of orthosteric drugs. Here, we comprehensively review the latest successful examples of such combination treatments against drug resistance, with a focus on their modes of action and the underlying structural mechanisms. Our work supplies an innovative insight into such promising methodology against the recalcitrant drug resistance conundrum and will be instructive for future clinical therapeutics.

Development of a novel aortic dissection mouse model and evaluation of drug efficacy using in-vivo assays and database analyses

OBJECTIVE

Aortic dissection is a life-threatening disease. At present, the only therapeutic strategies available are surgery and antihypertensive drugs. Moreover, the molecular mechanisms underlying the onset of aortic dissection are still unclear. We established a novel aortic dissection model in mice using pharmacologically induced endothelial dysfunction. We then used the Japanese Adverse Drug Event Report database to investigate the role of pitavastatin in preventing the onset of aortic dissection.

METHODS AND RESULTS

To induce endothelial dysfunction, Nω-nitro-L-arginine methyl ester, a nitric oxide synthase inhibitor, was administered to C57BL/6 mice. Three weeks later, angiotensin II (Ang II) and β-aminopropionitrile (BAPN), a lysyl oxidase inhibitor, were administered with osmotic mini-pumps. False lumen formation was used as the pathological determinant of aortic dissection. The incidences of aortic dissection and death from aneurysmal rupture were significantly higher in the Nω-nitro-L-arginine methyl ester, Ang II, and BAPN (LAB) group than they were in the Ang II and BAPN (AB) group.Pitavastatin was administered orally to LAB mice. It significantly lowered the incidences of dissection and rupture. It also decreased inflammation and medial degradation, both of which were exacerbated in the LAB group. The Japanese Adverse Drug Event Report database analysis indicated that there were 113 cases of aortic dissection out of 95 090 patients (0.12%) not receiving statins but only six cases out of 16 668 patients receiving statins (0.04%) (odds ratio: 0.30; P = 0.0043).

CONCLUSION

Our results suggest that endothelial dysfunction is associated with the onset of aortic dissection and pitavastatin can help prevent this condition.

Inverse Association between Metformin and Amiodarone-Associated Extracardiac Adverse Events

Background: The association between metformin and amiodarone-induced adverse events was examined using spontaneous adverse event database. Additionally, the association between other antidiabetic drugs and amiodarone-induced adverse events were also examined. Methods: A total of 6,153,696 reports from the first quarter of 2004 through the fourth quarter of 2015 were downloaded from the US Food and Drug Administration adverse event reporting system. Reporting odds ratio (ROR) and information component (IC) were used to detect associations between antidiabetic drugs and amiodarone-associated adverse events. Additionally, subset data analysis was performed to investigate whether the use of antidiabetic drugs further increased or decreased the risk of adverse events in patients receiving amiodarone therapy. Next, the RORs were adjusted for coadministered antidiabetic drugs using logistic regression analysis. Results: By whole dataset analysis, significant inverse associations were found between metformin and interstitial lung disease (ROR 0.84, 95% confidence interval [CI] 0.79-0.90; IC -0.24, 95% CI -0.33 to -0.15). In the subset data analysis, metformin (ROR 0.62, 95%CI 0.43-0.89; IC -0.63, 95%CI -1.14 to -0.11), sulfonylureas (ROR 0.53, 95%CI 0.32-0.85; IC -0.85, 95%CI -1.53 to -0.17), and dipeptidyl peptidase-4 (DPP-4) inhibitors (ROR 0.25, 95%CI 0.08-0.78; IC -1.66, 95%CI -3.08 to -0.23) were inversely associated with hyperthyroidism. Additionally, metformin (ROR 0.43, 95%CI 0.33-0.57; IC -1.09, 95%CI -1.49 to -0.69), sulfonylureas (ROR 0.64, 95%CI 0.48-0.86; IC -0.59, 95%CI -1.00 to -0.17), and DPP-4 inhibitors (ROR 0.47, 95%CI 0.27-0.81; IC -0.99, 95%CI -1.76 to -0.22) were inversely associated with interstitial lung disease. In the logistic regression analyses, DPP-4 inhibitors (adjusted ROR 0.32, 95% CI 0.10-1.00) and metformin (adjusted ROR 0.46, 95% CI 0.34-0.62) were inversely associated with amiodarone-associated hyperthyroidism and interstitial lung disease, respectively. Conclusion: Metformin is a candidate drug to reduce the risk of amiodarone-induced hyperthyroidism and interstitial lung disease.

High-dimensionality Data Analysis of Pharmacological Systems Associated with Complex Diseases

It is widely accepted that molecular reductionist views of highly complex human physiologic activity, e.g., the aging process, as well as therapeutic drug efficacy are largely oversimplifications. Currently some of the most effective appreciation of biologic disease and drug response complexity is achieved using high-dimensionality (H-D) data streams from transcriptomic, proteomic, metabolomics, or epigenomic pipelines. Multiple H-D data sets are now common and freely accessible for complex diseases such as metabolic syndrome, cardiovascular disease, and neurodegenerative conditions such as Alzheimer's disease. Over the last decade our ability to interrogate these high-dimensionality data streams has been profoundly enhanced through the development and implementation of highly effective bioinformatic platforms. Employing these computational approaches to understand the complexity of age-related diseases provides a facile mechanism to then synergize this pathologic appreciation with a similar level of understanding of therapeutic-mediated signaling. For informative pathology and drug-based analytics that are able to generate meaningful therapeutic insight across diverse data streams, novel informatics processes such as latent semantic indexing and topological data analyses will likely be important. Elucidation of H-D molecular disease signatures from diverse data streams will likely generate and refine new therapeutic strategies that will be designed with a cognizance of a realistic appreciation of the complexity of human age-related disease and drug effects. We contend that informatic platforms should be synergistic with more advanced chemical/drug and phenotypic cellular/tissue-based analytical predictive models to assist in either de novo drug prioritization or effective repurposing for the intervention of aging-related diseases. SIGNIFICANCE STATEMENT: All diseases, as well as pharmacological mechanisms, are far more complex than previously thought a decade ago. With the advent of commonplace access to technologies that produce large volumes of high-dimensionality data (e.g., transcriptomics, proteomics, metabolomics), it is now imperative that effective tools to appreciate this highly nuanced data are developed. Being able to appreciate the subtleties of high-dimensionality data will allow molecular pharmacologists to develop the most effective multidimensional therapeutics with effectively engineered efficacy profiles.

Assessment of the cardiovascular adverse effects of drug-drug interactions through a combined analysis of spontaneous reports and predicted drug-target interactions

Adverse drug effects (ADEs) are one of the leading causes of death in developed countries and are the main reason for drug recalls from the market, whereas the ADEs that are associated with action on the cardiovascular system are the most dangerous and widespread. The treatment of human diseases often requires the intake of several drugs, which can lead to undesirable drug-drug interactions (DDIs), thus causing an increase in the frequency and severity of ADEs. An evaluation of DDI-induced ADEs is a nontrivial task and requires numerous experimental and clinical studies. Therefore, we developed a computational approach to assess the cardiovascular ADEs of DDIs. This approach is based on the combined analysis of spontaneous reports (SRs) and predicted drug-target interactions to estimate the five cardiovascular ADEs that are induced by DDIs, namely, myocardial infarction, ischemic stroke, ventricular tachycardia, cardiac failure, and arterial hypertension. We applied a method based on least absolute shrinkage and selection operator (LASSO) logistic regression to SRs for the identification of interacting pairs of drugs causing corresponding ADEs, as well as noninteracting pairs of drugs. As a result, five datasets containing, on average, 3100 potentially ADE-causing and non-ADE-causing drug pairs were created. The obtained data, along with information on the interaction of drugs with 1553 human targets predicted by PASS Targets software, were used to create five classification models using the Random Forest method. The average area under the ROC curve of the obtained models, sensitivity, specificity and balanced accuracy were 0.837, 0.764, 0.754 and 0.759, respectively. The predicted drug targets were also used to hypothesize the potential mechanisms of DDI-induced ventricular tachycardia for the top-scoring drug pairs. The created five classification models can be used for the identification of drug combinations that are potentially the most or least dangerous for the cardiovascular system.

Metabolomics-Driven Exploration of the Chemical Drug Space to Predict Combination Antimicrobial Therapies

Alternative to the conventional search for single-target, single-compound treatments, combination therapies can open entirely new opportunities to fight antibiotic resistance. However, combinatorial complexity prohibits experimental testing of drug combinations on a large scale, and methods to rationally design combination therapies are lagging behind. Here, we developed a combined experimental-computational approach to predict drug-drug interactions using high-throughput metabolomics. The approach was tested on 1,279 pharmacologically diverse drugs applied to the gram-negative bacterium Escherichia coli. Combining our metabolic profiling of drug response with previously generated metabolic and chemogenomic profiles of 3,807 single-gene deletion strains revealed an unexpectedly large space of inhibited gene functions and enabled rational design of drug combinations. This approach is applicable to other therapeutic areas and can unveil unprecedented insights into drug tolerance, side effects, and repurposing. The compendium of drug-associated metabolome profiles is available at https://zampierigroup.shinyapps.io/EcoPrestMet, providing a valuable resource for the microbiological and pharmacological communities.

Rational discovery of dual-indication multi-target PDE/Kinase inhibitor for precision anti-cancer therapy using structural systems pharmacology

Many complex diseases such as cancer are associated with multiple pathological manifestations. Moreover, the therapeutics for their treatments often lead to serious side effects. Thus, it is needed to develop multi-indication therapeutics that can simultaneously target multiple clinical indications of interest and mitigate the side effects. However, conventional one-drug-one-gene drug discovery paradigm and emerging polypharmacology approach rarely tackle the challenge of multi-indication drug design. For the first time, we propose a one-drug-multi-target-multi-indication strategy. We develop a novel structural systems pharmacology platform 3D-REMAP that uses ligand binding site comparison and protein-ligand docking to augment sparse chemical genomics data for the machine learning model of genome-scale chemical-protein interaction prediction. Experimentally validated predictions systematically show that 3D-REMAP outperforms state-of-the-art ligand-based, receptor-based, and machine learning methods alone. As a proof-of-concept, we utilize the concept of drug repurposing that is enabled by 3D-REMAP to design dual-indication anti-cancer therapy. The repurposed drug can demonstrate anti-cancer activity for cancers that do not have effective treatment as well as reduce the risk of heart failure that is associated with all types of existing anti-cancer therapies. We predict that levosimendan, a PDE inhibitor for heart failure, inhibits serine/threonine-protein kinase RIOK1 and other kinases. Subsequent experiments and systems biology analyses confirm this prediction, and suggest that levosimendan is active against multiple cancers, notably lymphoma, through the direct inhibition of RIOK1 and RNA processing pathway. We further develop machine learning models to predict cancer cell-line's and a patient's response to levosimendan. Our findings suggest that levosimendan can be a promising novel lead compound for the development of safe, effective, and precision multi-indication anti-cancer therapy. This study demonstrates the potential of structural systems pharmacology in designing polypharmacology for precision medicine. It may facilitate transforming the conventional one-drug-one-gene-one-disease drug discovery process and single-indication polypharmacology approach into a new one-drug-multi-target-multi-indication paradigm for complex diseases.

Disruption of podocyte cytoskeletal biomechanics by dasatinib leads to nephrotoxicity

Nephrotoxicity is a critical adverse event that leads to discontinuation of kinase inhibitor (KI) treatment. Here we show, through meta-analyses of FDA Adverse Event Reporting System, that dasatinib is associated with high risk for glomerular toxicity that is uncoupled from hypertension, suggesting a direct link between dasatinib and podocytes. We further investigate the cellular effects of dasatinib and other comparable KIs with varying risks of nephrotoxicity. Dasatinib treated podocytes show significant changes in focal adhesions, actin cytoskeleton, and morphology that are not observed with other KIs. We use phosphoproteomics and kinome profiling to identify the molecular mechanisms of dasatinib-induced injury to the actin cytoskeleton, and atomic force microscopy to quantify impairment to cellular biomechanics. Furthermore, chronic administration of dasatinib in mice causes reversible glomerular dysfunction, loss of stress fibers, and foot process effacement. We conclude that dasatinib induces nephrotoxicity through altered podocyte actin cytoskeleton, leading to injurious cellular biomechanics.

Network-based prediction of drug combinations

Drug combinations, offering increased therapeutic efficacy and reduced toxicity, play an important role in treating multiple complex diseases. Yet, our ability to identify and validate effective combinations is limited by a combinatorial explosion, driven by both the large number of drug pairs as well as dosage combinations. Here we propose a network-based methodology to identify clinically efficacious drug combinations for specific diseases. By quantifying the network-based relationship between drug targets and disease proteins in the human protein-protein interactome, we show the existence of six distinct classes of drug-drug-disease combinations. Relying on approved drug combinations for hypertension and cancer, we find that only one of the six classes correlates with therapeutic effects: if the targets of the drugs both hit disease module, but target separate neighborhoods. This finding allows us to identify and validate antihypertensive combinations, offering a generic, powerful network methodology to identify efficacious combination therapies in drug development.

Irinotecan-induced neutropenia is reduced by oral alkalization drugs: analysis using retrospective chart reviews and the spontaneous reporting database

PURPOSE

SN-38, an active metabolite of irinotecan, is reabsorbed by the intestinal tract during excretion, causing diarrhoea and neutropenia. In addition, the association between blood levels of SN-38 and neutropenia has been reported previously, and the rapid excretion of SN-38 from the intestinal tract is considered to prevent neutropenia. Oral alkalization drugs are used as prophylactic agents for suppressing SN-38 reabsorption. The relationship between oral alkalization drugs and neutropenia, however, has not been well studied. The aim of this study was to investigate the relationship between oral alkalization drugs and neutropenia in irinotecan-treated patients.

METHODS AND RESULTS

Patients with cervical or ovarian cancer were administered irinotecan and investigated by medical chart reviews to determine whether oral alkalization drugs were effective at ameliorating irinotecan-induced neutropenia. The drug combination in the oral alkalization drugs-ursodeoxycholic acid, magnesium oxide, and sodium hydrogen carbonate-significantly improved neutrophil counts and reduced dose intensity compared with those of non-users. In the large-scale Japanese Adverse Drug Event Report database, the reporting odds ratio of irinotecan-induced neutropenia was significantly lower when irinotecan had been given in combination with oral alkalization drugs.

CONCLUSIONS

These data indicate that oral alkalization drugs may reduce the frequency of neutropenia caused by irinotecan administration, making it possible to increase the dose safely.

Network Pharmacology Analysis of Damnacanthus indicus C.F.Gaertn in Gene-Phenotype

Damnacanthus indicus C.F.Gaertn is known as Huci in traditional Chinese medicine. It contains a component having anthraquinone-like structure which is a part of the many used anticancer drugs. This study was to collect the evidence of disease-modulatory activities of Huci by analyzing the published literature on the chemicals and drugs. A list of its compounds and direct protein targets is predicted by using Bioinformatics Analysis Tool for Molecular Mechanism of TCM. A protein-protein interaction network using links between its directed targets and the other known targets was constructed. The DPT-associated genes in net were scrutinized by WebGestalt. Exploring the cancer genomics data related to Huci through cBio Portal. Survival analysis for the overlap genes is done by using UALCAN. We got 16 compounds and it predicts 62 direct protein targets and 100 DPTs and they were identified for these compounds. DPT-associated genes were analyzed by WebGestalt. Through the enrichment analysis, we got top 10 identified KEGG pathways. Refined analysis of KEGG pathways showed that one of these ten pathways is linked to Rap1 signaling pathway and another one is related to breast cancer. The survival analysis for the overlap genes shows the significant negative effect of these genes on the breast cancer patients. Through the research results of Damnacanthus indicus C.F.Gaertn, it is shown that medicine network pharmacology may be regarded as a new paradigm for guiding the future studies of the traditional Chinese medicine in different fields.

Onset timing of statin-induced musculoskeletal adverse events and concomitant drug-associated shift in onset timing of MAEs

To evaluate the onset timing of musculoskeletal adverse events (MAEs) that develop during statin monotherapy and to determine whether concomitant drugs used concurrently with statin therapy shifts the onset timing of MAEs. Cases in which statins (atorvastatin, rosuvastatin, simvastatin, lovastatin, fluvastatin, pitavastatin, and pravastatin) were prescribed were extracted from the US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) Data Files. The onset timing of MAEs during statin monotherapy was evaluated by determining the difference between statin start date and MAE onset date. The use of concomitant drugs with statin therapy was included in the analysis. Statins used in combination with concomitant drugs were compared with statin monotherapy to determine if the use of concomitant drugs shifted the onset timing of MAEs. The onset of MAEs was significantly faster with atorvastatin and rosuvastatin than with simvastatin. A difference in onset timing was not detected with other statins because the number of cases was too small for analysis. When evaluating concomitant drug use, the concomitant drugs that shifted the onset timing of MAEs could not be detected. Statins with strong low-density lipoprotein cholesterol-lowering effects (atorvastatin and rosuvastatin) contributed not only to a high risk of MAE onset, but also to a shorter time-to-onset. No concomitant drug significantly shifted the onset timing of MAEs when used concurrently with statins.

Applications of Ruthenium Complex in Tumor Diagnosis and Therapy

Ruthenium complexes are a new generation of metal antitumor drugs that are currently of great interest in multidisciplinary research. In this review article, we introduce the applications of ruthenium complexes in the diagnosis and therapy of tumors. We focus on the actions of ruthenium complexes on DNA, mitochondria, and endoplasmic reticulum of cells, as well as signaling pathways that induce tumor cell apoptosis, autophagy, and inhibition of angiogenesis. Furthermore, we highlight the use of ruthenium complexes as specific tumor cell probes to dynamically monitor the active biological component of the microenvironment and as excellent photosensitizer, catalyst, and bioimaging agents for phototherapies that significantly enhance the diagnosis and therapeutic effect on tumors. Finally, the combinational use of ruthenium complexes with existing clinical antitumor drugs to synergistically treat tumors is discussed.

Systematic synthesis of a 6-component organic-salt alloy of naftopidil, and pentanary, quaternary and ternary multicomponent crystals

The single-crystal X-ray structure of a 6-component organic-salt alloy (hexanary) of naftopidil (1) (an active pharmaceutical ingredient) with benzoic acid (2) and four different hydroxy-substituted benzoic acids, i.e. salicylic acid (3), 2,3-di-hydroxybenzoic acid (4), 2,4-di-hydroxybenzoic acid (5) and 2,6-di-hydroxybenzoic acid (6), is reported. The hexanary assembly originates from the observation that the binary salts of naftopidil with the above acids are isostructural. In addition to the 6-component solid, we also describe five 5-component, ten 4-component, and ten 3-component organic-salt alloys of naftopidil (1) with carboxylic acids (2)-(6). These alloys were obtained from different combinations of the acids with the drug. The synthetic design of the multicomponent organic alloys is based on the rationale of geometrical factors (shape and size) and chemical interactions (hydrogen bonds). The common supramolecular synthon in all these crystal structures was the cyclic N+-H⋯O- and O-H⋯O hydrogen-bonded motif of (9) graph set between the 2-hy-droxyammonium group of naftopidil and the carboxyl-ate anion. This ionic synthon is strong and robust, directing the isostructural assembly of naftopidil with up to five different carboxylic acids in the crystal structure together with the lower-level multicomponent adducts. Solution crystallization by slow evaporation provided the multicomponent organic salts and alloys which were characterized by a combination of single-crystal X-ray diffraction, powder X-ray diffraction, NMR and differential scanning calorimetry techniques.

Systems biology primer: the basic methods and approaches

Systems biology is an integrative discipline connecting the molecular components within a single biological scale and also among different scales (e.g. cells, tissues and organ systems) to physiological functions and organismal phenotypes through quantitative reasoning, computational models and high-throughput experimental technologies. Systems biology uses a wide range of quantitative experimental and computational methodologies to decode information flow from genes, proteins and other subcellular components of signaling, regulatory and functional pathways to control cell, tissue, organ and organismal level functions. The computational methods used in systems biology provide systems-level insights to understand interactions and dynamics at various scales, within cells, tissues, organs and organisms. In recent years, the systems biology framework has enabled research in quantitative and systems pharmacology and precision medicine for complex diseases. Here, we present a brief overview of current experimental and computational methods used in systems biology.

An integrative approach using real-world data to identify alternative therapeutic uses of existing drugs

Different computational approaches are employed to efficiently identify novel repositioning possibilities utilizing different sources of information and algorithms. It is critical to propose high-valued candidate-repositioning possibilities before conducting lengthy in vivo validation studies that consume significant resources. Here we report a novel multi-methodological approach to identify opportunities for drug repositioning. We performed analyses of real-world data (RWD) acquired from the United States Food and Drug Administration’s Adverse Event Reporting System (FAERS) and the claims database maintained by the Japan Medical Data Center (JMDC). These analyses were followed by cross-validation through bioinformatics analyses of gene expression data. Inverse associations revealed using disproportionality analysis (DPA) and sequence symmetry analysis (SSA) were used to detect potential drug-repositioning signals. To evaluate the validity of the approach, we conducted a feasibility study to identify marketed drugs with the potential for treating inflammatory bowel disease (IBD). Primary analyses of the FAERS and JMDC claims databases identified psycholeptics such as haloperidol, diazepam, and hydroxyzine as candidates that may improve the treatment of IBD. To further investigate the mechanistic relevance between hit compounds and disease pathology, we conducted bioinformatics analyses of the associations of the gene expression profiles of these compounds with disease. We identified common biological features among genes differentially expressed with or without compound treatment as well as disease-perturbation data available from open sources, which strengthened the mechanistic rationale of our initial findings. We further identified pathways such as cytokine signaling that are influenced by these drugs. These pathways are relevant to pathologies and can serve as alternative targets of therapy. Integrative analysis of RWD such as those available from adverse-event databases, claims databases, and transcriptome analyses represent an effective approach that adds value to efficiently identifying potential novel therapeutic opportunities.

Systems Pharmacology: Defining the Interactions of Drug Combinations

The majority of diseases are associated with alterations in multiple molecular pathways and complex interactions at the cellular and organ levels. Single-target monotherapies therefore have intrinsic limitations with respect to their maximum therapeutic benefits. The potential of combination drug therapies has received interest for the treatment of many diseases and is well established in some areas, such as oncology. Combination drug treatments may allow us to identify synergistic drug effects, reduce adverse drug reactions, and address variability in disease characteristics between patients. Identification of combination therapies remains challenging. We discuss current state-of-the-art systems pharmacology approaches to enable rational identification of combination therapies. These approaches, which include characterization of mechanisms of disease and drug action at a systems level, can enable understanding of drug interactions at the molecular, cellular, physiological, and organismal levels. Such multiscale understanding can enable precision medicine by promoting the rational development of combination therapy at the level of individual patients for many diseases.

Implications of Off-Target Serotoninergic Drug Activity: An Analysis of Serotonin Syndrome Reports Using a Systematic Bioinformatics Approach

Study Objective

Serotonergic adverse drug events (ADEs) are caused by enhanced intrasynaptic concentrations of 5‐hydroxytryptamine (5‐HT). No systematic process currently exists for evaluating cumulative 5‐HT and off‐target toxicity of serotonergic drugs. The primary study aim was to create a Serotonergic Expanded Bioactivity Matrix (SEBM) by using a molecular bioinformatics, polypharmacologic approach for assessment of the participation of individual 5‐HT drugs in serotonin syndrome (SS) reports.

Data Sources

Publicly available databases including the U.S. Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS), ChEMBL, DrugBank, PubChem, and Kyoto Encyclopedia of Genes and Genomes (KEGG) were queried for computational and pharmacologic data.

Design

An in‐house bioinformatics TargetSearch program ( http://dxulab.org/software) was used to characterize 71 serotonergic drugs interacting at 13 serotonin receptor subtypes and serotonin reuptake transporter protein (SERT). In addition, off‐target interactions at norepinephrine transporter (NET), monoamine oxidase (MAO), and muscarinic receptors were included to define seven polypharmacological drug cohorts. Serotonin syndrome reports for each serotonergic drug were extracted from FAERS by using the Sternbach and Hunter criteria.

Measurements and Main Results

A proportional reporting adverse drug reaction (ADR) ratio (PRR) was calculated from each drug's total ADEs and SS case reports and aggregated by drug bioactivity cohorts. Triple‐receptor interactions had a disproportionately higher number of SS cases using both the Hunter criteria (mean PRR 1.72, 95% CI 1.05–2.39) and Sternbach (mean PRR 1.54, 95% CI 1.29–1.79). 5‐Hydroxytryptamine agonists were associated with a significantly lower proportion of SS cases using the Hunter and Sternbach criteria, respectively (mean PRR 0.49, 95% CI 0.17–0.81 and mean PRR 0.49, 95% CI 0.15–0.83). Drugs with disproportionately higher participation in SS vary considerably between the two diagnostic criteria.

Conclusion

The SEBM model suggests a possible polypharmacological role in SS. Although further research is needed, off‐target receptor activity may help explain differences in severity of toxicity and clinical presentation.