ADReCS-Target: target profiles for aiding drug safety research and application

Huang, Li Hong; He, Qiu Shun; Liu, Ke; Cheng, Jiao; Zhong, Min; Chen, Lin Shan; Yao, Libin; Ji, Zhiming

doi:10.1093/nar/gkx899

Cited by 25 publications

(21 citation statements)

References 27 publications

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“…In the future, we will continue to integrate and curate more actionable evidence and drug indications and update the information on clinical trials. Drug efficacy and safety are not only influenced by drug targets, as enzymes, transporters and other proteins can also contribute [ 30 , 31 ]. In addition, drug-drug interactions and even food-drug interactions can change drug responses; therefore, drug ADMET information and drug-drug/food interaction information should be included in the knowledgebase and be taken into account in pharmacotherapy classification.…”

Section: Discussionmentioning

confidence: 99%

OncoPDSS: an evidence-based clinical decision support system for oncology pharmacotherapy at the individual level

Xu¹,

Zhai²,

Huo³

et al. 2020

BMC Cancer

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Background: Precision oncology pharmacotherapy relies on precise patient-specific alterations that impact drug responses. Due to rapid advances in clinical tumor sequencing, an urgent need exists for a clinical support tool that automatically interprets sequencing results based on a structured knowledge base of alteration events associated with clinical implications. Results: Here, we introduced the Oncology Pharmacotherapy Decision Support System (OncoPDSS), a web server that systematically annotates the effects of alterations on drug responses. The platform integrates actionable evidence from several well-known resources, distills drug indications from anti-cancer drug labels, and extracts cancer clinical trial data from the ClinicalTrials.gov database. A therapy-centric classification strategy was used to identify potentially effective and noneffective pharmacotherapies from user-uploaded alterations of multi-omics based on integrative evidence. For each potentially effective therapy, clinical trials with faculty information were listed to help patients and their health care providers find the most suitable one. Conclusions: OncoPDSS can serve as both an integrative knowledge base on cancer precision medicine, as well as a clinical decision support system for cancer researchers and clinical oncologists. It receives multi-omics alterations as input and interprets them into pharmacotherapy-centered information, thus helping clinicians to make clinical pharmacotherapy decisions. The OncoPDSS web server is freely accessible at https://oncopdss.capitalbiobigdata.com.

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Section: Discussionmentioning

confidence: 99%

OncoPDSS: an evidence-based clinical decision support system for oncology pharmacotherapy at the individual level

Xu¹,

Zhai²,

Huo³

et al. 2020

BMC Cancer

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“…Complementary to in silico predictions, evidence from pharmacogenomic and clinical databases was provided. Queried databases included, e.g., PharmGKB [38], PharmVar (available only for CYP2C9, 2C19, 2D6 genes), ADReCS-Target an Adverse Drug Reaction Classification System-Target Profile () [56], which provides comprehensive information about ADRs caused by drug interaction with protein, gene and genetic variation, PheWas Resources (phenome-wide association studies with antineoplastic drugs), Clinvar and dbSNP.…”

Section: Methodsmentioning

confidence: 99%

Use of Germline Genetic Variability for Prediction of Chemoresistance and Prognosis of Breast Cancer Patients

Hlaváč

Kováčová

Elsnerová

et al. 2018

Cancers

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The aim of our study was to set up a panel for targeted sequencing of chemoresistance genes and the main transcription factors driving their expression and to evaluate their predictive and prognostic value in breast cancer patients. Coding and regulatory regions of 509 genes, selected from PharmGKB and Phenopedia, were sequenced using massive parallel sequencing in blood DNA from 105 breast cancer patients in the testing phase. In total, 18,245 variants were identified of which 2565 were novel variants (without rs number in dbSNP build 150) in the testing phase. Variants with major allele frequency over 0.05 were further prioritized for validation phase based on a newly developed decision tree. Using emerging in silico tools and pharmacogenomic databases for functional predictions and associations with response to cytotoxic therapy or disease-free survival of patients, 55 putative variants were identified and used for validation in 805 patients with clinical follow up using KASPTM technology. In conclusion, associations of rs2227291, rs2293194, and rs4376673 (located in ATP7A, KCNAB1, and DFFB genes, respectively) with response to neoadjuvant cytotoxic therapy and rs1801160 in DPYD with disease-free survival of patients treated with cytotoxic drugs were validated and should be further functionally characterized.

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“…with established links to adverse side effects based on a scientific literature search [ 5 , 7 , [19] , [20] , [21] ]. Natural language processing of scientific literature [ 22 , 23 ] and drug labels [24] as well as databases, such as the US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) [25] , OMOP [26] and EU-ADR [27] , further provide resources for machine learning approaches to learn associations between drugs and adverse drug reactions (ADRs) [ 4 , [8] , [9] , [10] , [11] , 22 , 28 , 29 ]. FAERS is a voluntary, post-marketing pharmacovigilance tool that can be used to monitor the clinical and post-marketing performance of drugs.…”

Section: Introductionmentioning

confidence: 99%

Machine learning guided association of adverse drug reactions with in vitro target-based pharmacology

et al. 2020

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Background Adverse drug reactions (ADRs) are one of the leading causes of morbidity and mortality in health care. Understanding which drug targets are linked to ADRs can lead to the development of safer medicines. Methods Here, we analyse in vitro secondary pharmacology of common (off) targets for 2134 marketed drugs. To associate these drugs with human ADRs, we utilized FDA Adverse Event Reports and developed random forest models that predict ADR occurrences from in vitro pharmacological profiles. Findings By evaluating Gini importance scores of model features, we identify 221 target-ADR associations, which co-occur in PubMed abstracts to a greater extent than expected by chance. Amongst these are established relations, such as the association of in vitro hERG binding with cardiac arrhythmias, which further validate our machine learning approach. Evidence on bile acid metabolism supports our identification of associations between the Bile Salt Export Pump and renal, thyroid, lipid metabolism, respiratory tract and central nervous system disorders. Unexpectedly, our model suggests PDE3 is associated with 40 ADRs. Interpretation These associations provide a comprehensive resource to support drug development and human biology studies. Funding This study was not supported by any formal funding bodies.

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ADReCS-Target: target profiles for aiding drug safety research and application

Cited by 25 publications

References 27 publications

OncoPDSS: an evidence-based clinical decision support system for oncology pharmacotherapy at the individual level

OncoPDSS: an evidence-based clinical decision support system for oncology pharmacotherapy at the individual level

Use of Germline Genetic Variability for Prediction of Chemoresistance and Prognosis of Breast Cancer Patients

Machine learning guided association of adverse drug reactions with in vitro target-based pharmacology

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