scholarly journals Mendelian randomization: a novel approach for the prediction of adverse drug events and drug repurposing opportunities

2017 ◽  
Author(s):  
Venexia M Walker ◽  
George Davey Smith ◽  
Neil M Davies ◽  
Richard M Martin
Keyword(s):  
Adverse Drug Events ◽  
Mendelian Randomization ◽  
Drug Effects ◽  
Drug Repurposing ◽  
Observational Research ◽  
Reverse Causality ◽  
Confounding By Indication ◽  
Causal Inferences ◽  
Novel Approach ◽  
Observational Research Methods

ABSTRACTIdentification of unintended drug effects, specifically drug repurposing opportunities and adverse drug events, maximizes the benefit of a drug and protects the health of patients. However, current observational research methods are subject to several biases. These include confounding by indication, reverse causality, and missing data. We propose that Mendelian randomization (MR) offers a novel approach for the prediction of unintended drug effects. In particular, we advocate the synthesis of evidence from this method and other approaches, in the spirit of triangulation, to improve causal inferences concerning drug effects. MR overcomes some of the limitations associated with the existing methods in this field. Furthermore, it can be applied either pre- or post-approval of the drug and could therefore prevent the potentially harmful exposure of patients in clinical trials and beyond. The potential of MR as a pharmacovigilance and drug repurposing tool is yet to be realized and could both help prevent adverse drug events and identify novel indications for existing drugs in the future.

scholarly journals Mendelian randomization: a novel approach for the prediction of adverse drug events and drug repurposing opportunities

2017 ◽  
Vol 46 (6) ◽  
pp. 2078-2089 ◽  
Author(s):  
Venexia M Walker ◽  
George Davey Smith ◽  
Neil M Davies ◽  
Richard M Martin
Keyword(s):  
Adverse Drug Events ◽  
Mendelian Randomization ◽  
Drug Repurposing ◽  
Novel Approach

Caution against examining the role of reverse causality in Mendelian Randomization

2021 ◽  
Author(s):  
Sharon M. Lutz ◽  
Ann Chen Wu ◽  
John E. Hokanson ◽  
Stijn Vansteelandt ◽  
Christoph Lange
Keyword(s):  
Mendelian Randomization ◽  
Reverse Causality

scholarly journals Genomics-driven drug discovery based on disease-susceptibility genes

2021 ◽  
Vol 41 (1) ◽  
Author(s):  
Kyuto Sonehara ◽  
Yukinori Okada
Keyword(s):  
Drug Discovery ◽  
Drug Targets ◽  
Disease Susceptibility ◽  
Mendelian Randomization ◽  
Association Studies ◽  
Drug Repurposing ◽  
Susceptibility Genes ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Disease Associations

AbstractGenome-wide association studies have identified numerous disease-susceptibility genes. As knowledge of gene–disease associations accumulates, it is becoming increasingly important to translate this knowledge into clinical practice. This challenge involves finding effective drug targets and estimating their potential side effects, which often results in failure of promising clinical trials. Here, we review recent advances and future perspectives in genetics-led drug discovery, with a focus on drug repurposing, Mendelian randomization, and the use of multifaceted omics data.

scholarly journals Framework for identifying drug repurposing candidates from observational healthcare data

JAMIA Open ◽  
2020 ◽  
Author(s):  
Michal Ozery-Flato ◽  
Yaara Goldschmidt ◽  
Oded Shaham ◽  
Sivan Ravid ◽  
Chen Yanover
Keyword(s):  
Causal Inference ◽  
Observational Data ◽  
Prescription Drugs ◽  
Domain Knowledge ◽  
Multiple Testing ◽  
Statistical Significance ◽  
Drug Effects ◽  
Drug Repurposing ◽  
Design Parameters ◽  
Medical Databases

Abstract Objective Observational medical databases, such as electronic health records and insurance claims, track the healthcare trajectory of millions of individuals. These databases provide real-world longitudinal information on large cohorts of patients and their medication prescription history. We present an easy-to-customize framework that systematically analyzes such databases to identify new indications for on-market prescription drugs. Materials and Methods Our framework provides an interface for defining study design parameters and extracting patient cohorts, disease-related outcomes, and potential confounders in observational databases. It then applies causal inference methodology to emulate hundreds of randomized controlled trials (RCTs) for prescribed drugs, while adjusting for confounding and selection biases. After correcting for multiple testing, it outputs the estimated effects and their statistical significance in each database. Results We demonstrate the utility of the framework in a case study of Parkinson’s disease (PD) and evaluate the effect of 259 drugs on various PD progression measures in two observational medical databases, covering more than 150 million patients. The results of these emulated trials reveal remarkable agreement between the two databases for the most promising candidates. Discussion Estimating drug effects from observational data is challenging due to data biases and noise. To tackle this challenge, we integrate causal inference methodology with domain knowledge and compare the estimated effects in two separate databases. Conclusion Our framework enables systematic search for drug repurposing candidates by emulating RCTs using observational data. The high level of agreement between separate databases strongly supports the identified effects.

scholarly journals Circulating Markers Reflect Both Anti- and Pro-Atherogenic Drug Effects in ApoE-Deficient Mice

2008 ◽  
Vol 3 ◽  
pp. BMI.S632 ◽  
Author(s):  
Birong Liao ◽  
Eileen McCall ◽  
Karen Cox ◽  
Chung-Wein Lee ◽  
Shuguang Huang ◽  
...  
Keyword(s):  
Whole Blood ◽  
Plasma Cholesterol ◽  
Drug Effects ◽  
Drug Efficacy ◽  
Vascular Wall ◽  
Atherosclerotic Plaques ◽  
Protein Markers ◽  
Novel Approach ◽  
Coa Reductase

Background Current drug therapy of atherosclerosis is focused on treatment of major risk factors, e.g. hypercholesterolemia while in the future direct disease modification might provide additional benefits. However, development of medicines targeting vascular wall disease is complicated by the lack of reliable biomarkers. In this study, we took a novel approach to identify circulating biomarkers indicative of drug efficacy by reducing the complexity of the in vivo system to the level where neither disease progression nor drug treatment was associated with the changes in plasma cholesterol. Results ApoE-/- mice were treated with an ACE inhibitor ramipril and HMG-CoA reductase inhibitor simvastatin. Ramipril significantly reduced the size of atherosclerotic plaques in brachiocephalic arteries, however simvastatin paradoxically stimulated atherogenesis. Both effects occurred without changes in plasma cholesterol. Blood and vascular samples were obtained from the same animals. In the whole blood RNA samples, expression of MMP9, CD14 and IL-1RN reflected pro-and anti-atherogenic drug effects. In the plasma, several proteins, e.g. IL-1β, IL-18 and MMP9 followed similar trends while protein readout was less sensitive than RNA analysis. Conclusion In this study, we have identified inflammation-related whole blood RNA and plasma protein markers reflecting anti-atherogenic effects of ramipril and pro-atherogenic effects of simwastatin in a mouse model of atherosclerosis. This opens an opportunity for early, non-invasive detection of direct drug effects on atherosclerotic plaques in complex in vivo systems.

scholarly journals How Much Does Economic Growth Contribute to Child Stunting Reductions?

Economies ◽  
2018 ◽  
Vol 6 (4) ◽  
pp. 55 ◽  
Author(s):  
Sébastien Mary
Keyword(s):  
Economic Growth ◽  
Developing Countries ◽  
Causal Effect ◽  
Gdp Per Capita ◽  
Reverse Causality ◽  
Child Stunting ◽  
Percentage Point Increase ◽  
Child Undernutrition ◽  
Novel Approach ◽  
Per Capita

The role of economic growth in reducing child undernutrition remains an open and highly debated question that holds important implications for food security strategies. The empirical evidence has been quite contrasted, primarily in regard to the magnitude of the impacts. Yet, most studies have not (appropriately) accounted for the reverse causality between economic growth and child stunting. Using a dataset of 74 developing countries observed between 1984 and 2014, this paper develops a novel approach accounting for the reverse causal effect of stunting on GDP per capita and finds that the impacts of economic growth are much lower than estimated in most previous studies. A 10% increase in GDP per capita reduces child stunting prevalence by 2.7%. In other words, economic growth is modestly pro-poor. We also estimate that a percentage point increase in child stunting prevalence results in a 0.4% decrease in GDP per capita. A back-of-the-envelope calculation suggests that stunting costs on average about 13.5% of GDP per capita in developing countries.

scholarly journals Type 2 Diabetes Subtype Responsive to ACCORD Intensive Glycemia Treatment

2021 ◽  
Author(s):  
Arshiya Mariam ◽  
Galen Miller-Atkins ◽  
Kevin M. Pantalone ◽  
Robert S. Zimmerman ◽  
John Barnard ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Mendelian Randomization ◽  
Controlled Trial ◽  
Clinical Group ◽  
Cvd Risk ◽  
Group 4 ◽  
Novel Approach ◽  
Genome Wide ◽  
Design And Methods

Objective <p>Current type 2 diabetes (T2D) management contraindicates intensive glycemia treatment in patients with high cardiovascular disease (CVD) risk, and is partially motivated by evidence of harms in the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial. Heterogeneity in response to intensive glycemia treatment has been observed, suggesting potential benefit for some individuals.</p> <p>Research Design and Methods</p> <p>ACCORD was a randomized controlled trial that investigated whether intensively treating glycemia in individuals with T2D reduced CVD outcomes. Using a novel approach to cluster HbA1c trajectories, we identified groups in the intensive glycemia arm with modified CVD risk. Genome-wide analysis and polygenic score (PS) were developed to predict group membership. Mendelian randomization was performed to infer causality. </p> <p>Results</p> <p>We identified four clinical groupings in the intensive glycemia arm, and clinical group 4 (C4) displayed fewer CVD outcomes (HR=0.34, <i>P</i>=2.01x10<sup>-3</sup>) and microvascular outcomes (HR=0.86, <i>P</i>=.015) than standard treatment. A single nucleotide polymorphism, rs220721, in <i>MAS1, </i>reached suggestive significance<i> </i>with C4 (<i>P</i>=4.34x10<sup>-7</sup>). The PS predicted C4 with high accuracy (AUC=0.98), and predicted C4 displayed reduced CVD risk on intensive versus standard glycemia treatment (HR=0.53, <i>P</i>=4.02x10<sup>-6</sup>), but not for microvascular outcomes (<i>P</i><.05). Mendelian randomization indicated causality between the PS, on-trial HbA1c, and reduction in CVD outcomes (<i>P</i><.05). </p> <p>Conclusions</p> <p>We found evidence of a T2D clinical group in ACCORD that benefited from intensive glycemia treatment, and membership of this group can be predicted using genetic variants. This study generates new hypotheses with implications for precision medicine in T2D and represents an important development for this landmark clinical trial warranting further investigation.</p>

scholarly journals Sources of confounding in life course epidemiology

2018 ◽  
Vol 10 (3) ◽  
pp. 299-305 ◽  
Author(s):  
S. Santos ◽  
D. Zugna ◽  
C. Pizzi ◽  
L. Richiardi
Keyword(s):  
Life Course ◽  
Meta Analysis ◽  
Internal Validity ◽  
Observational Research ◽  
Individual Participant Data ◽  
Confounding By Indication ◽  
Life Course Epidemiology ◽  
Combining Data ◽  
Individual Participant ◽  
Different Populations

AbstractIn epidemiologic analytical studies, the primary goal is to obtain a valid and precise estimate of the effect of the exposure of interest on a given outcome in the population under study. A crucial source of violation of the internal validity of a study involves bias arising from confounding, which is always a challenge in observational research, including life course epidemiology. The increasingly popular approach of meta-analyzing individual participant data from several observational studies also brings up to discussion the problem of confounding when combining data from different populations. In this study, we review and discuss the most common sources of confounding in life course epidemiology: (i) confounding by indication, (ii) impact of baseline selection on confounding, (iii) time-varying confounding and (iv) mediator–outcome confounding. We also discuss the issue of addressing confounding in the context of an individual participant data meta-analysis.

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