Systems Approaches in Risk Assessment

Lesko, Lawrence J.; Zheng, Songmao; Schmidt, Stephan

doi:10.1038/clpt.2013.29

Cited by 18 publications

(19 citation statements)

References 60 publications

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“…Top‐down approaches usually start at a high level of organization and become increasingly more complex to better characterize the underlying biological system. Bottom‐up approaches, on the other hand, start with the “bottom” elements of the organism (e.g., genes or proteins and their known interactions), and work their way up to the patient‐phenotype level . Both approaches have advantages and limitations, which have given rise to hybrid approaches such as pharmacokinetic/enhanced‐pharmacodynamic (PK/ePD) models, that represent a synergy between the two approaches.…”

Section: Systems Pharmacology Modelsmentioning

confidence: 99%

Sobol Sensitivity Analysis: A Tool to Guide the Development and Evaluation of Systems Pharmacology Models

et al. 2015

CPT Pharmacom & Syst Pharma

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325

218

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A systems pharmacology model typically integrates pharmacokinetic, biochemical network, and systems biology concepts into a unifying approach. It typically consists of a large number of parameters and reaction species that are interlinked based upon the underlying (patho)physiology and the mechanism of drug action. The more complex these models are, the greater the challenge of reliably identifying and estimating respective model parameters. Global sensitivity analysis provides an innovative tool that can meet this challenge. CPT Pharmacometrics Syst. Pharmacol. (2015) 4, 69–79; doi:10.1002/psp4.6; published online 25 February 2015

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Section: Systems Pharmacology Modelsmentioning

confidence: 99%

Sobol Sensitivity Analysis: A Tool to Guide the Development and Evaluation of Systems Pharmacology Models

et al. 2015

CPT Pharmacom & Syst Pharma

Self Cite

325

218

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“…For instance, toxicity databases now contain large amounts of chemical and biological information through data consolidation (e.g. ACToR [19], Bio2RDF [49], OpenPHACTS[50], PredPharmTox [51]; see [11] for table of databases).…”

Section: Integrative Approach Combining Cheminformatics and Bioinformmentioning

confidence: 99%

Integrative Approaches for Predicting In Vivo Effects of Chemicals from their Structural Descriptors and the Results of Short-Term Biological Assays

Low¹,

Sedykh²,

Rusyn³

et al. 2014

CTMC

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Cheminformatics approaches such as Quantitative Structure Activity Relationship (QSAR) modeling have been used traditionally for predicting chemical toxicity. In recent years, high throughput biological assays have been increasingly employed to elucidate mechanisms of chemical toxicity and predict toxic effects of chemicals in vivo. The data generated in such assays can be considered as biological descriptors of chemicals that can be combined with molecular descriptors and employed in QSAR modeling to improve the accuracy of toxicity prediction. In this review, we discuss several approaches for integrating chemical and biological data for predicting biological effects of chemicals in vivo and compare their performance across several data sets. We conclude that while no method consistently shows superior performance, the integrative approaches rank consistently among the best yet offer enriched interpretation of models over those built with either chemical or biological data alone. We discuss the outlook for such interdisciplinary methods and offer recommendations to further improve the accuracy and interpretability of computational models that predict chemical toxicity.

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“…The concept of “signal detection” using secondary data resources should be considered a hypothesis‐generating step to evaluate purported formulation or safety issues and not necessarily sufficient for regulatory action. As previously mentioned, these methods are just 1 pillar in a 3‐pillar approach, including (1) analysis of real‐world data to detect and verify signals, (2) physiologically based absorption PK models to identify pre‐absorption parameters with sensitivity to result in different PK profiles, and (3) PK/pharmacodynamic models to link changes in exposure with clinical response . Given the robustness and rapidity of claims, signals can be evaluated within months of a new medication or formulation.…”

Section: Discussionmentioning

confidence: 99%

“…In collaboration with the FDA, our research team has formed an integrated approach to assess generic drug performance using 3 evidence pillars: (1) analysis of real-world data to detect and mechanistically assess the signals, (2) physiologically based absorption PK models to identify preabsorption parameters with sensitivity to result in different PK profiles, and (3) PK/pharmacodynamic models to link changes in exposure with clinical response. 24 Together, this translational research approach can provide reciprocal validation and lead to hypothesis generation or confirmation ( Figure 1). 24 This work aimed to develop a framework to use real-world health care data to detect and verify signals related to generic drugs, that is, the first pillar of the integrated approach.…”

mentioning

confidence: 99%

“…24 Together, this translational research approach can provide reciprocal validation and lead to hypothesis generation or confirmation ( Figure 1). 24 This work aimed to develop a framework to use real-world health care data to detect and verify signals related to generic drugs, that is, the first pillar of the integrated approach. Such a framework could be implemented in active surveillance to generate hypotheses or be used to confirm observations from other evidence pillars (ie, reciprocal validation).…”

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confidence: 99%

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Real‐World Data Approaches for Early Detection of Potential Safety and Effectiveness Signals for Generic Substitution: A Metoprolol Extended‐Release Case Study

Brown

Henriksen

Vozmediano

et al. 2019

The Journal of Clinical Pharma

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Real‐world spontaneous adverse event reports and administrative health care data were utilized as one part of a multipronged approach to verify surveillance signals related to generic drug formulations. This study used metoprolol succinate extended release as a historic case example from which to develop an analytic framework. The US Food and Drug Administration Adverse Event Reporting System was utilized for disproportionality analyses and to identify outcomes of interest. Claims data were analyzed for generic uptake, proportion of prescriptions with “dispense as written” orders, time to discontinuation or switching, and relative rates of clinical events. Adverse Event Reporting System data showed that the Medical Dictionary for Regulatory Activities terms for product quality were higher for generic metoprolol cases and that a number of clinical events were increased that could be side effects of high or low variability in drug levels. Compared to amlodipine‐benazepril, which also had a first‐approved generic at the same time, market share data showed that metoprolol succinate had lower utilization and more prescriptions written as dispense as written. Switching and discontinuation were generally higher for metoprolol users compared to amlodipine‐benazepril users. Finally, clinical event rates were generally higher for generic versus brand metoprolol but lower for the same comparison for amlodipine‐benazepril users. In the claims‐based analyses, the 90‐day period immediately after generic entry provided stronger signal capture than using the entire study period. This analytic framework can be implemented to actively monitor new generic formulations for potential bioequivalence failures. Signals from these analyses require confirmation (eg, via pharmacometric analyses) to be informative for regulatory action.

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Systems Approaches in Risk Assessment

Cited by 18 publications

References 60 publications

Sobol Sensitivity Analysis: A Tool to Guide the Development and Evaluation of Systems Pharmacology Models

Sobol Sensitivity Analysis: A Tool to Guide the Development and Evaluation of Systems Pharmacology Models

Integrative Approaches for Predicting In Vivo Effects of Chemicals from their Structural Descriptors and the Results of Short-Term Biological Assays

Real‐World Data Approaches for Early Detection of Potential Safety and Effectiveness Signals for Generic Substitution: A Metoprolol Extended‐Release Case Study

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