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Network-Based Assessment of Adverse Drug Reaction Risk in Polypharmacy Using High-Throughput Screening Data.

ABSTRACT: The risk of adverse drug reactions increases in a polypharmacology setting. High-throughput drug screening with transcriptomics applied to human cells has shown that drugs have effects on several molecular pathways, and these affected pathways may be predictive proxy for adverse drug reactions. Depending on the way that different drugs may contribute to adverse drug reactions, different options may exist in the clinical setting. Here, we formulate a network framework to integrate the relationships between drugs, biological functions, and adverse drug reactions based on the high-throughput drug perturbation data from the Library of Integrated Network-Based Cellular Signatures (LINCS) project. We present network-based parameters that indicate whether a given reaction may be related to the effect of a single drug or to the combination of several drugs, as well as the relative risk of adverse drug reaction manifestation given a certain drug combination.

SUBMITTER: de Anda-Jauregui G

PROVIDER: S-EPMC6358820 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature

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Network-Based Assessment of Adverse Drug Reaction Risk in Polypharmacy Using High-Throughput Screening Data.

de Anda-Jáuregui Guillermo G Guo Kai K Hur Junguk J

International journal of molecular sciences 20190117 2

The risk of adverse drug reactions increases in a polypharmacology setting. High-throughput drug screening with transcriptomics applied to human cells has shown that drugs have effects on several molecular pathways, and these affected pathways may be predictive proxy for adverse drug reactions. Depending on the way that different drugs may contribute to adverse drug reactions, different options may exist in the clinical setting. Here, we formulate a network framework to integrate the relationshi ...[more]

PMID: 30658437

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Project description:AimTo develop and test a new tool to assess the avoidability of adverse drug reactions that is suitable for use in paediatrics but which is also applicable to a variety of other settings.MethodsThe study involved multiple phases. Preliminary work involved using the Hallas scale and a modification of the existing Hallas scale, to assess two different sets of adverse drug reaction (ADR) case reports. Phase 1 defined, modified and refined a new tool using multidisciplinary teams. Phase 2 involved the assessment of 50 ADR case reports from a prospective study of paediatric inpatients by individual assessors. Phase 3 compared assessments with the new tool for individuals and groups in comparison to the 'gold standard' (the avoidability outcome set by a panel of senior investigators: an experienced clinical pharmacologist, paediatrician and pharmacist).Main outcome measuresInter-rater reliability (IRR), measure of disagreement and utilization of avoidability categories.ResultsPreliminary work-Pilot phase: results for the original Hallas cases were fair and pairwise kappa scores ranged from 0.21 to 0.36. Results for the modified Hallas cases were poor, pairwise kappa scores ranged from 0.06 to 0.16. Phase 1: on initial use of the new tool, agreement between the two multidisciplinary groups was found on 13/20 cases with a kappa score of 0.29 (95% CI -0.04 to 0.62). Phase 2: the assessment of 50 ADR case reports by six individual reviewers yielded pairwise kappa scores ranging from poor to good 0.12 to 0.75 and percentage exact agreement (%EA) ranged from 52-90%. Phase 3: Percentage exact agreement ranged from 35-70%. Overall, individuals had better agreement with the 'gold standard'.ConclusionAvoidability assessment is feasible but needs careful attention to methods. The Liverpool ADR avoidability assessment tool showed mixed IRR. We have developed and validated a method for assessing the avoidability of ADRs that is transparent, more objective than previous methods and that can be used by individuals or groups.

Project description:IntroductionUnwanted drug interactions with ionic currents in the heart can lead to an increased pro-arrhythmic risk to patients in the clinic. It is therefore a priority for safety pharmacology teams to detect block of cardiac ion channels, and new technologies have enabled the development of automated and high-throughput screening assays using cell lines. As a result of screening multiple ion-channels there is a need to integrate information, particularly for compounds affecting more than one current, and mathematical electrophysiology in-silico action potential models are beginning to be used for this.MethodsWe quantified the variability associated with concentration-effect curves fitted to recordings from high-throughput Molecular Devices IonWorks® Quattro™ screens when detecting block of I(Kr) (hERG), I(Na) (NaV1.5), I(CaL) (CaV1.2), I(Ks) (KCNQ1/minK) and I(to) (Kv4.3/KChIP2.2), and the Molecular Devices FLIPR® Tetra fluorescence screen for I(CaL) (CaV1.2), for control compounds used at AstraZeneca and GlaxoSmithKline. We examined how screening variability propagates through in-silico action potential models for whole cell electrical behaviour, and how confidence intervals on model predictions can be estimated with repeated simulations.ResultsThere are significant levels of variability associated with high-throughput ion channel electrophysiology screens. This variability is of a similar magnitude for different cardiac ion currents and different compounds. Uncertainty in the Hill coefficients of reported concentration-effect curves is particularly high. Depending on a compound's ion channel blocking profile, the uncertainty introduced into whole-cell predictions can become significant.DiscussionOur technique allows confidence intervals to be placed on computational model predictions that are based on high-throughput ion channel screens. This allows us to suggest when repeated screens should be performed to reduce uncertainty in a compound's action to acceptable levels, to allow a meaningful interpretation of the data.

Project description:BackgroundHigh-throughput measurement technologies produce data sets that have the potential to elucidate the biological impact of disease, drug treatment, and environmental agents on humans. The scientific community faces an ongoing challenge in the analysis of these rich data sources to more accurately characterize biological processes that have been perturbed at the mechanistic level. Here, a new approach is built on previous methodologies in which high-throughput data was interpreted using prior biological knowledge of cause and effect relationships. These relationships are structured into network models that describe specific biological processes, such as inflammatory signaling or cell cycle progression. This enables quantitative assessment of network perturbation in response to a given stimulus.ResultsFour complementary methods were devised to quantify treatment-induced activity changes in processes described by network models. In addition, companion statistics were developed to qualify significance and specificity of the results. This approach is called Network Perturbation Amplitude (NPA) scoring because the amplitudes of treatment-induced perturbations are computed for biological network models. The NPA methods were tested on two transcriptomic data sets: normal human bronchial epithelial (NHBE) cells treated with the pro-inflammatory signaling mediator TNFα, and HCT116 colon cancer cells treated with the CDK cell cycle inhibitor R547. Each data set was scored against network models representing different aspects of inflammatory signaling and cell cycle progression, and these scores were compared with independent measures of pathway activity in NHBE cells to verify the approach. The NPA scoring method successfully quantified the amplitude of TNFα-induced perturbation for each network model when compared against NF-κB nuclear localization and cell number. In addition, the degree and specificity to which CDK-inhibition affected cell cycle and inflammatory signaling were meaningfully determined.ConclusionsThe NPA scoring method leverages high-throughput measurements and a priori literature-derived knowledge in the form of network models to characterize the activity change for a broad collection of biological processes at high-resolution. Applications of this framework include comparative assessment of the biological impact caused by environmental factors, toxic substances, or drug treatments.

Dataset Information

Network-Based Assessment of Adverse Drug Reaction Risk in Polypharmacy Using High-Throughput Screening Data.

Publications

Network-Based Assessment of Adverse Drug Reaction Risk in Polypharmacy Using High-Throughput Screening Data.

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