Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Systems pharmacology augments drug safety surveillance.

ABSTRACT: Small molecule drugs are the foundation of modern medical practice, yet their use is limited by the onset of unexpected and severe adverse events (AEs). Regulatory agencies rely on postmarketing surveillance to monitor safety once drugs are approved for clinical use. Despite advances in pharmacovigilance methods that address issues of confounding bias, clinical data of AEs are inherently noisy. Systems pharmacology-the integration of systems biology and chemical genomics-can illuminate drug mechanisms of action. We hypothesize that these data can improve drug safety surveillance by highlighting drugs with a mechanistic connection to the target phenotype (enriching true positives) and filtering those that do not (depleting false positives). We present an algorithm, the modular assembly of drug safety subnetworks (MADSS), to combine systems pharmacology and pharmacovigilance data and significantly improve drug safety monitoring for four clinically relevant adverse drug reactions.

SUBMITTER: Lorberbaum T

PROVIDER: S-EPMC4325423 | biostudies-literature | 2015 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Systems pharmacology augments drug safety surveillance.

Lorberbaum T T Nasir M M Keiser M J MJ Vilar S S Hripcsak G G Tatonetti N P NP

Clinical pharmacology and therapeutics 20141220 2

Small molecule drugs are the foundation of modern medical practice, yet their use is limited by the onset of unexpected and severe adverse events (AEs). Regulatory agencies rely on postmarketing surveillance to monitor safety once drugs are approved for clinical use. Despite advances in pharmacovigilance methods that address issues of confounding bias, clinical data of AEs are inherently noisy. Systems pharmacology-the integration of systems biology and chemical genomics-can illuminate drug mech ...[more]

PMID: 25670520

Similar Datasets

Systems pharmacology of adverse event mitigation by drug combinations.

Project description:Drugs are designed for therapy, but medication-related adverse events are common, and risk/benefit analysis is critical for determining clinical use. Rosiglitazone, an efficacious antidiabetic drug, is associated with increased myocardial infarctions (MIs), thus limiting its usage. Because diabetic patients are often prescribed multiple drugs, we searched for usage of a second drug ("drug B") in the Food and Drug Administration's Adverse Event Reporting System (FAERS) that could mitigate the risk of rosiglitazone ("drug A")-associated MI. In FAERS, rosiglitazone usage is associated with increased occurrence of MI, but its combination with exenatide significantly reduces rosiglitazone-associated MI. Clinical data from the Mount Sinai Data Warehouse support the observations from FAERS. Analysis for confounding factors using logistic regression showed that they were not responsible for the observed effect. Using cell biological networks, we predicted that the mitigating effect of exenatide on rosiglitazone-associated MI could occur through clotting regulation. Data we obtained from the db/db mouse model agreed with the network prediction. To determine whether polypharmacology could generally be a basis for adverse event mitigation, we analyzed the FAERS database for other drug combinations wherein drug B reduced serious adverse events reported with drug A usage such as anaphylactic shock and suicidality. This analysis revealed 19,133 combinations that could be further studied. We conclude that this type of crowdsourced approach of using databases like FAERS can help to identify drugs that could potentially be repurposed for mitigation of serious adverse events.

| S-EPMC3963511 | biostudies-literature

Cassia auriculata: Aspects of Safety Pharmacology and Drug Interaction.

Project description:Safety pharmacology studies help in identifying preclinical adverse drug reactions. We carried out routine safety pharmacology with focus on cardiovascular variables and pharmacokinetic herb-drug interaction studies on rats fed with standardized traditional hydro-alcoholic extract and technology-based supercritical extract of Cassia auriculata for 12 weeks. Our studies indicate that both these extracts are pharmacologically safe and did not show any significant adverse reactions at the tested doses. The traditional hydro-alcoholic extract did not show any significant effect on pharmacokinetics; however, the technology-based supercritical extract caused a significant reduction in absorption of metformin. Our results indicate the need to include pharmacokinetic herb-drug interaction studies as evidence for safety especially for technology-based extracts.

| S-EPMC3137796 | biostudies-literature

The promises of quantitative systems pharmacology modelling for drug development.

Project description:Recent growth in annual new therapeutic entity (NTE) approvals by the U.S. Food and Drug Administration (FDA) suggests a positive trend in current research and development (R&D) output. Prior to this, the cost of each NTE was considered to be rising exponentially, with compound failure occurring mainly in clinical phases. Quantitative systems pharmacology (QSP) modelling, as an additional tool in the drug discovery arsenal, aims to further reduce NTE costs and improve drug development success. Through in silico mathematical modelling, QSP can simulate drug activity as perturbations in biological systems and thus understand the fundamental interactions which drive disease pathology, compound pharmacology and patient response. Here we review QSP, pharmacometrics and systems biology models with respect to the diseases covered as well as their clinical relevance and applications. Overall, the majority of modelling focus was aligned with the priority of drug-discovery and clinical trials. However, a few clinically important disease categories, such as Immune System Diseases and Respiratory Tract Diseases, were poorly covered by computational models. This suggests a possible disconnect between clinical and modelling agendas. As a standard element of the drug discovery pipeline the uptake of QSP might help to increase the efficiency of drug development across all therapeutic indications.

| S-EPMC5064996 | biostudies-literature

Integrated systems pharmacology analysis of clinical drug-induced peripheral neuropathy.

Project description:A systems pharmacology approach was undertaken to define and identify the proteins/genes significantly associated with clinical incidence and severity of drug-induced peripheral neuropathy (DIPN). Pharmacological networks of 234 DIPN drugs, their known targets (both intended and unintended), and the intermediator proteins/genes interacting with these drugs via their known targets were examined. A permutation test identified 230 DIPN-associated intermediators that were enriched with apoptosis and stress response genes. Neuropathy incidence and severity were curated from drug labels and literature and were used to build a predictive model of DIPN using a regression tree algorithm, based on the drug targets and their intermediators. DIPN drugs whose targets interacted with both v-myc avian myelocytomatosis viral oncogene homolog (MYC) and proliferating cell nuclear antigen-associated factor (PAF15) were associated with a neuropathy incidence of 38.1%, whereas drugs interacting only with MYC had an incidence of 2.9%. These results warrant further investigation in order to develop a predictive tool for the DIPN potential of a new drug.

| S-EPMC4051377 | biostudies-literature

Translational systems pharmacology-based predictive assessment of drug-induced cardiomyopathy.

Project description:Drug-induced cardiomyopathy contributes to drug attrition. We compared two pipelines of predictive modeling: (1) applying elastic net (EN) to differentially expressed genes (DEGs) of drugs; (2) applying integer linear programming (ILP) to construct each drug's signaling pathway starting from its targets to downstream proteins, to transcription factors, and to its DEGs in human cardiomyocytes, and then subjecting the genes/proteins in the drugs' signaling networks to EN regression. We classified 31 drugs with availability of DEGs into 13 toxic and 18 nontoxic drugs based on a clinical cardiomyopathy incidence cutoff of 0.1%. The ILP-augmented modeling increased prediction accuracy from 79% to 88% (sensitivity: 88%; specificity: 89%) under leave-one-out cross validation. The ILP-constructed signaling networks of drugs were better predictors than DEGs. Per literature, the microRNAs that reportedly regulate expression of our six top predictors are of diagnostic value for natural heart failure or doxorubicin-induced cardiomyopathy. This translational predictive modeling might uncover potential biomarkers.

| S-EPMC5869547 | biostudies-literature

Autophagy and Apoptosis Specific Knowledgebases-guided Systems Pharmacology Drug Research.

Project description:BACKGROUND:Autophagy and apoptosis are the basic physiological processes in cells that clean up aged and mutant cellular components or even the entire cells. Both autophagy and apoptosis are disrupted in most major diseases such as cancer and neurological disorders. Recently, increasing attention has been paid to understand the crosstalk between autophagy and apoptosis due to their tightly synergetic or opposite functions in several pathological processes. OBJECTIVE:This study aims to assist autophagy and apoptosis-related drug research, clarify the intense and complicated connections between two processes, and provide a guide for novel drug development. METHODS:We established two chemical-genomic databases which are specifically designed for autophagy and apoptosis, including autophagy- and apoptosis-related proteins, pathways and compounds. We then performed network analysis on the apoptosis- and autophagy-related proteins and investigated the full protein-protein interaction (PPI) network of these two closely connected processes for the first time. RESULTS:The overlapping targets we discovered show a more intense connection with each other than other targets in the full network, indicating a better efficacy potential for drug modulation. We also found that Death-associated protein kinase 1 (DAPK1) is a critical point linking autophagy- and apoptosis-related pathways beyond the overlapping part, and this finding may reveal some delicate signaling mechanism of the process. Finally, we demonstrated how to utilize our integrated computational chemogenomics tools on in silico target identification for small molecules capable of modulating autophagy- and apoptosis-related pathways. CONCLUSION:The knowledge-bases for apoptosis and autophagy and the integrated tools will accelerate our work in autophagy and apoptosis-related research and can be useful sources for information searching, target prediction, and new chemical discovery.

| S-EPMC6774909 | biostudies-literature

Parsing interindividual drug variability: an emerging role for systems pharmacology.

Project description:There is notable interindividual heterogeneity in drug response, affecting both drug efficacy and toxicity, resulting in patient harm and the inefficient utilization of limited healthcare resources. Pharmacogenomics is at the forefront of research to understand interindividual drug response variability, but although many genotype-drug response associations have been identified, translation of pharmacogenomic associations into clinical practice has been hampered by inconsistent findings and inadequate predictive values. These limitations are in part due to the complex interplay between drug-specific, human body and environmental factors influencing drug response and therefore pharmacogenomics, whilst intrinsically necessary, is by itself unlikely to adequately parse drug variability. The emergent, interdisciplinary and rapidly developing field of systems pharmacology, which incorporates but goes beyond pharmacogenomics, holds significant potential to further parse interindividual drug variability. Systems pharmacology broadly encompasses two distinct research efforts, pharmacologically-orientated systems biology and pharmacometrics. Pharmacologically-orientated systems biology utilizes high throughput omics technologies, including next-generation sequencing, transcriptomics and proteomics, to identify factors associated with differential drug response within the different levels of biological organization in the hierarchical human body. Increasingly complex pharmacometric models are being developed that quantitatively integrate factors associated with drug response. Although distinct, these research areas complement one another and continual development can be facilitated by iterating between dynamic experimental and computational findings. Ultimately, quantitative data-derived models of sufficient detail will be required to help realize the goal of precision medicine.

| S-EPMC4696409 | biostudies-literature

Systems-pharmacology dissection of a drug synergy in imatinib-resistant CML.

Project description:Occurrence of the BCR-ABL(T315I) gatekeeper mutation is among the most pressing challenges in the therapy of chronic myeloid leukemia (CML). Several BCR-ABL inhibitors have multiple targets and pleiotropic effects that could be exploited for their synergistic potential. Testing combinations of such kinase inhibitors identified a strong synergy between danusertib and bosutinib that exclusively affected CML cells harboring BCR-ABL(T315I). To elucidate the underlying mechanisms, we applied a systems-level approach comprising phosphoproteomics, transcriptomics and chemical proteomics. Data integration revealed that both compounds targeted Mapk pathways downstream of BCR-ABL, resulting in impaired activity of c-Myc. Using pharmacological validation, we assessed that the relative contributions of danusertib and bosutinib could be mimicked individually by Mapk inhibitors and collectively by downregulation of c-Myc through Brd4 inhibition. Thus, integration of genome- and proteome-wide technologies enabled the elucidation of the mechanism by which a new drug synergy targets the dependency of BCR-ABL(T315I) CML cells on c-Myc through nonobvious off targets.

| S-EPMC4038039 | biostudies-literature

Systems pharmacology: network analysis to identify multiscale mechanisms of drug action.

Project description:Systems approaches have long been used in pharmacology to understand drug action at the organ and organismal levels. The application of computational and experimental systems biology approaches to pharmacology allows us to expand the definition of systems pharmacology to include network analyses at multiple scales of biological organization and to explain both therapeutic and adverse effects of drugs. Systems pharmacology analyses rely on experimental "omics" technologies that are capable of measuring changes in large numbers of variables, often at a genome-wide level, to build networks for analyzing drug action. A major use of omics technologies is to relate the genomic status of an individual to the therapeutic efficacy of a drug of interest. Combining pathway and network analyses, pharmacokinetic and pharmacodynamic models, and a knowledge of polymorphisms in the genome will enable the development of predictive models of therapeutic efficacy. Network analyses based on publicly available databases such as the U.S. Food and Drug Administration's Adverse Event Reporting System allow us to develop an initial understanding of the context within which molecular-level drug-target interactions can lead to distal effectors in a process that results in adverse phenotypes at the organ and organismal levels. The current state of systems pharmacology allows us to formulate a set of questions that could drive future research in the field. The long-term goal of such research is to develop polypharmacology for complex diseases and predict therapeutic efficacy and adverse event risk for individuals prior to commencement of therapy.

| S-EPMC3619403 | biostudies-literature

Systems pharmacology identifies drug targets for Stargardt disease-associated retinal degeneration.

Project description:A systems pharmacological approach that capitalizes on the characterization of intracellular signaling networks can transform our understanding of human diseases and lead to therapy development. Here, we applied this strategy to identify pharmacological targets for the treatment of Stargardt disease, a severe juvenile form of macular degeneration. Diverse GPCRs have previously been implicated in neuronal cell survival, and crosstalk between GPCR signaling pathways represents an unexplored avenue for pharmacological intervention. We focused on this receptor family for potential therapeutic interventions in macular disease. Complete transcriptomes of mouse and human samples were analyzed to assess the expression of GPCRs in the retina. Focusing on adrenergic (AR) and serotonin (5-HT) receptors, we found that adrenoceptor α 2C (Adra2c) and serotonin receptor 2a (Htr2a) were the most highly expressed. Using a mouse model of Stargardt disease, we found that pharmacological interventions that targeted both GPCR signaling pathways and adenylate cyclases (ACs) improved photoreceptor cell survival, preserved photoreceptor function, and attenuated the accumulation of pathological fluorescent deposits in the retina. These findings demonstrate a strategy for the identification of new drug candidates and FDA-approved drugs for the treatment of monogenic and complex diseases.

| S-EPMC3859412 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data