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

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.

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Genomics-driven drug discovery based on disease-susceptibility genes

Inflammation and Regeneration ◽

10.1186/s41232-021-00158-7 ◽

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.

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Type 2 Diabetes Subtype Responsive to ACCORD Intensive Glycemia Treatment

10.2337/figshare.14109806.v2 ◽

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 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. Research Design and Methods 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. Results We identified four clinical groupings in the intensive glycemia arm, and clinical group 4 (C4) displayed fewer CVD outcomes (HR=0.34, P=2.01x10-3) and microvascular outcomes (HR=0.86, P=.015) than standard treatment. A single nucleotide polymorphism, rs220721, in MAS1, reached suggestive significance with C4 (P=4.34x10-7). 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, P=4.02x10-6), but not for microvascular outcomes (P<.05). Mendelian randomization indicated causality between the PS, on-trial HbA1c, and reduction in CVD outcomes (P<.05). Conclusions 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.

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Fi-score: a novel approach to characterise protein topology and aid in drug discovery studies

10.1101/2020.06.25.171124 ◽

2020 ◽

Author(s):

Austė Kanapeckaitė ◽

Claudia Beaurivage ◽

Matthew Hancock ◽

Erik Verschueren

Keyword(s):

Dihedral Angle ◽

Target Selection ◽

Gaussian Mixture Models ◽

Drug Repurposing ◽

Gaussian Mixture ◽

Rational Drug Design ◽

Drug Candidates ◽

Novel Approach ◽

Protein Fingerprinting ◽

Rational Drug

AbstractTarget evaluation is at the centre of rational drug design and biologics development. In order to successfully engineer antibodies, T-cell receptors or small molecules it is necessary to identify and characterise potential binding or contact sites on therapeutically relevant target proteins. Currently, there are numerous challenges in achieving a better docking precision as well as characterising relevant sites. We devised a first-of-its-kind in silico protein fingerprinting approach based on dihedral angle and B-factor distribution to probe binding sites and sites of structural importance. In addition, we showed that the entire protein regions or individual structural subsets can be profiled using our derived fi-score based on amino acid dihedral angle and B-factor distribution. We further described a method to assess the structural profile and extract information on sites of importance using machine learning Gaussian mixture models. In combination, these biophysical analytical methods could potentially help to classify and systematically analyse not only targets but also drug candidates that bind to specific sites which would greatly improve pre-screening stage, target selection and drug repurposing efforts in finding other matching targets.

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Proteome-wide Mendelian randomization in global biobank meta-analysis reveals multi-ancestry drug targets for common diseases

10.1101/2022.01.09.21268473 ◽

2022 ◽

Author(s):

Huiling Zhao ◽

humaira Rasheed ◽

Therese Haugdahl Nost ◽

Yoonsu Cho ◽

Yi Liu ◽

...

Keyword(s):

Drug Targets ◽

Mendelian Randomization ◽

Meta Analysis ◽

Drug Repurposing ◽

Population Data ◽

Limited Data ◽

Specific Effects ◽

Clinical Trial Evidence ◽

Males And Females ◽

Trial Evidence

Proteome-wide Mendelian randomization (MR) shows value in prioritizing drug targets in Europeans, but limited data has made identification of causal proteins in other ancestries challenging. Here we present a multi-ancestry proteome-wide MR analysis pipeline based on cross-population data from the Global Biobank Meta-analysis Initiative (GBMI). We estimated the causal effects of 1,545 proteins on eight complex diseases in up to 32,658 individuals of African ancestries and 1.22 million individuals of European ancestries. We identified 45 and seven protein-disease pairs with MR and genetic colocalization evidence in the two ancestries respectively. 15 protein-disease pairs showed evidence of differential effects between males and females. A multi-ancestry MR comparison identified two protein-disease pairs with MR evidence of an effect in both ancestries, seven pairs with European-specific effects and seven with African-specific effects. Integrating these MR signals with observational and clinical trial evidence, we were able to evaluate the efficacy of one existing drug, identify seven drug repurposing opportunities and predict seven novel effects of proteins on diseases. Our results highlight the value of proteome-wide MR in informing the generalisability of drug targets across ancestries and illustrate the value of multi-cohort and biobank meta-analysis of genetic data for drug development.

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Literature-Wide Association Studies (LWAS) for a Rare Disease: Drug Repurposing for Inflammatory Breast Cancer

Molecules ◽

10.3390/molecules25173933 ◽

2020 ◽

Vol 25 (17) ◽

pp. 3933

Author(s):

Xiaojia Ji ◽

Chunming Jin ◽

Xialan Dong ◽

Maria S. Dixon ◽

Kevin P. Williams ◽

...

Keyword(s):

Breast Cancer ◽

Inflammatory Breast Cancer ◽

Association Studies ◽

Effective Means ◽

Drug Repurposing ◽

Disease Association ◽

Vector Representation ◽

Novel Approach ◽

Similarity Maps ◽

Biological Concepts

Drug repurposing is an effective means for rapid drug discovery. The aim of this study was to develop and validate a computational methodology based on Literature-Wide Association Studies (LWAS) of PubMed to repurpose existing drugs for a rare inflammatory breast cancer (IBC). We have developed a methodology that conducted LWAS based on the text mining technology Word2Vec. 3.80 million “cancer”-related PubMed abstracts were processed as the corpus for Word2Vec to derive vector representation of biological concepts. These vectors for drugs and diseases served as the foundation for creating similarity maps of drugs and diseases, respectively, which were then employed to find potential therapy for IBC. Three hundred and thirty-six (336) known drugs and three hundred and seventy (370) diseases were expressed as vectors in this study. Nine hundred and seventy (970) previously known drug-disease association pairs among these drugs and diseases were used as the reference set. Based on the hypothesis that similar drugs can be used against similar diseases, we have identified 18 diseases similar to IBC, with 24 corresponding known drugs proposed to be the repurposing therapy for IBC. The literature search confirmed most known drugs tested for IBC, with four of them being novel candidates. We conclude that LWAS based on the Word2Vec technology is a novel approach to drug repurposing especially useful for rare diseases.

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Network-Based Approaches Reveal Potential Therapeutic Targets for Host-Directed Antileishmanial Therapy Driving Drug Repurposing

Microbiology Spectrum ◽

10.1128/spectrum.01018-21 ◽

2021 ◽

Author(s):

J. Eduardo Martinez-Hernandez ◽

Zaynab Hammoud ◽

Alessandra Mara de Sousa ◽

Frank Kramer ◽

Rubens L. do Monte-Neto ◽

...

Keyword(s):

Gene Regulatory Networks ◽

Regulatory Networks ◽

Biological Process ◽

Expression Profiles ◽

Therapeutic Targets ◽

Drug Repurposing ◽

Novel Approach ◽

Gene Regulatory ◽

Molecular Functions ◽

Approved Drugs

This work opens a new path to fight parasites by targeting host molecular functions by repurposing available and approved drugs. We created a novel approach to identify key proteins involved in any biological process by combining gene regulatory networks and expression profiles.

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Controlling astrocyte-mediated synaptic pruning signals for schizophrenia drug repurposing with Deep Graph Networks

10.1101/2021.10.07.463459 ◽

2021 ◽

Author(s):

Alessio Gravina ◽

Jennifer L. Wilson ◽

Davide Bacciu ◽

Kevin J. Grimes ◽

Corrado Priami

Keyword(s):

High Throughput Screening ◽

Clinical Utility ◽

Large Scale ◽

Drug Repurposing ◽

The United States ◽

Novel Approach ◽

Potential Clinical Utility ◽

Abnormal Brain ◽

Synaptic Pruning ◽

Selection Of

Schizophrenia is a debilitating psychiatric disorder, leading to both physical and social morbidity. Worldwide 1% of the population is struggling with the disease, with 100,000 new cases annually only in the United States. Despite its importance, the goal of finding effective treatments for schizophrenia remains a challenging task, and previous work conducted expensive large-scale phenotypic screens. This work investigates the benefits of Machine Learning for graphs to optimize drug phenotypic screens and predict compounds that mitigate abnormal brain reduction induced by excessive glial phagocytic activity in schizophrenia subjects. Given a compound and its concentration as input, we propose a method that predicts a score associated with three possible compound effects, i.e., reduce, increase, or not influence phagocytosis. We leverage a high-throughput screening to prove experimentally that our method achieves good generalization capabilities. The screening involves 2218 compounds at five different concentrations. Then, we analyze the usability of our approach in a practical setting, i.e., prioritizing the selection of compounds in the SWEETLEAD library. We provide a list of 64 compounds from the library that have the most potential clinical utility for glial phagocytosis mitigation. Lastly, we propose a novel approach to computationally validate their utility as possible therapies for schizophrenia.

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Finding genetically-supported drug targets for Parkinson’s disease using Mendelian randomization of the druggable genome

Nature Communications ◽

10.1038/s41467-021-26280-1 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Catherine S. Storm ◽

Demis A. Kia ◽

Mona M. Almramhi ◽

Sara Bandres-Ciga ◽

Chris Finan ◽

...

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Drug Targets ◽

Molecular Mechanisms ◽

Mendelian Randomization ◽

Disease Risk ◽

Target Identification ◽

Causal Effect ◽

Age At Onset ◽

Drug Repurposing

AbstractParkinson’s disease is a neurodegenerative movement disorder that currently has no disease-modifying treatment, partly owing to inefficiencies in drug target identification and validation. We use Mendelian randomization to investigate over 3,000 genes that encode druggable proteins and predict their efficacy as drug targets for Parkinson’s disease. We use expression and protein quantitative trait loci to mimic exposure to medications, and we examine the causal effect on Parkinson’s disease risk (in two large cohorts), age at onset and progression. We propose 23 drug-targeting mechanisms for Parkinson’s disease, including four possible drug repurposing opportunities and two drugs which may increase Parkinson’s disease risk. Of these, we put forward six drug targets with the strongest Mendelian randomization evidence. There is remarkably little overlap between our drug targets to reduce Parkinson’s disease risk versus progression, suggesting different molecular mechanisms. Drugs with genetic support are considerably more likely to succeed in clinical trials, and we provide compelling genetic evidence and an analysis pipeline to prioritise Parkinson’s disease drug development.

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Type 2 Diabetes Subtype Responsive to ACCORD Intensive Glycemia Treatment

10.2337/figshare.14109806.v1 ◽

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 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. Research Design and Methods 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. Results We identified four clinical groupings in the intensive glycemia arm, and clinical group 4 (C4) displayed fewer CVD outcomes (HR=0.34, P=2.01x10-3) and microvascular outcomes (HR=0.86, P=.015) than standard treatment. A single nucleotide polymorphism, rs220721, in MAS1, reached suggestive significance with C4 (P=4.34x10-7). 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, P=4.02x10-6), but not for microvascular outcomes (P<.05). Mendelian randomization indicated causality between the PS, on-trial HbA1c, and reduction in CVD outcomes (P<.05). Conclusions 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.

Download Full-text