Mendelian randomization for studying the effects of perturbing drug targets

Dipender Gill^{1

2

3

4

5}, Marios K Georgakis⁶, Venexia M Walker^{7

8

9}, A Floriaan Schmidt^{10

11}, Apostolos Gkatzionis¹², Daniel F Freitag¹³, Chris Finan^{10

14

15}, Aroon D Hingorani^{10

14

15}, Joanna M M Howson³, Stephen Burgess^{12

16}, Daniel I Swerdlow¹⁰, George Davey Smith^{7

8

17}, Michael V Holmes¹⁸, Martin Dichgans^{6

19

20}, Robert A Scott²¹, Jie Zheng^{7

8}, Bruce M Psaty^{22

23}, Neil M Davies^{7

8

24}

Affiliations

¹ Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK.
² Centre for Pharmacology and Therapeutics, Department of Medicine, Imperial College London, London, UK.
³ Novo Nordisk Research Centre, Oxford, UK.
⁴ Clinical Pharmacology and Therapeutics Section, Institute of Medical and Biomedical Education and Institute for Infection and Immunity, St George's, University of London, London, UK.
⁵ Clinical Pharmacology Group, Pharmacy and Medicines Directorate, St George's University Hospitals NHS Foundation Trust, London, UK.
⁶ Institute for Stroke and Dementia Research (ISD), University Hospital of Ludwig-Maximilians-University (LMU), Munich, Germany.
⁷ Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK.
⁸ Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.
⁹ Department of Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
¹⁰ Institute of Cardiovascular Science, Faculty of Population Health, University College London, London, UK.
¹¹ Department of Cardiology, Division Heart and Lungs, University Medical Center Utrecht, Utrecht, The Netherlands.
¹² Medical Research Council Biostatistics Unit, University of Cambridge, Cambridge, UK.
¹³ Bayer Pharmaceuticals, Open Innovation & Digital Technologies, Wuppertal, Germany.
¹⁴ UCL British Heart Foundation Research Acceleratorversity College London, London, UK.
¹⁵ UCL Hospitals, NIHR Biomedical Research Centre, London, UK.
¹⁶ Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
¹⁷ NIHR Bristol Biomedical Research Centre, University of Bristol, Bristol, UK.
¹⁸ Medical Research Council Population Health Research Unit, University of Oxford, Oxford, UK.
¹⁹ Munich Cluster for Systems Neurology (SyNergy), Munich, Germany.
²⁰ German Centre for Neurodegenerative Diseases (DZNE), Munich, Germany.
²¹ Human Genetics, GlaxoSmithKline, Stevenage, UK.
²² Cardiovascular Health Research Unit, Departments of Medicine, Epidemiology and Health Services, University of Washington, Seattle, WA, USA.
²³ Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA.
²⁴ K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway.

PMID: 33644404
PMCID: PMC7903200
DOI: 10.12688/wellcomeopenres.16544.2

Review

Mendelian randomization for studying the effects of perturbing drug targets

Dipender Gill et al. Wellcome Open Res. 2021.

. 2021 Feb 10:6:16.

doi: 10.12688/wellcomeopenres.16544.2. eCollection 2021.

Authors

Affiliations

¹ Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK.
² Centre for Pharmacology and Therapeutics, Department of Medicine, Imperial College London, London, UK.
³ Novo Nordisk Research Centre, Oxford, UK.
⁴ Clinical Pharmacology and Therapeutics Section, Institute of Medical and Biomedical Education and Institute for Infection and Immunity, St George's, University of London, London, UK.
⁵ Clinical Pharmacology Group, Pharmacy and Medicines Directorate, St George's University Hospitals NHS Foundation Trust, London, UK.
⁶ Institute for Stroke and Dementia Research (ISD), University Hospital of Ludwig-Maximilians-University (LMU), Munich, Germany.
⁷ Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK.
⁸ Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.
⁹ Department of Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
¹⁰ Institute of Cardiovascular Science, Faculty of Population Health, University College London, London, UK.
¹¹ Department of Cardiology, Division Heart and Lungs, University Medical Center Utrecht, Utrecht, The Netherlands.
¹² Medical Research Council Biostatistics Unit, University of Cambridge, Cambridge, UK.
¹³ Bayer Pharmaceuticals, Open Innovation & Digital Technologies, Wuppertal, Germany.
¹⁴ UCL British Heart Foundation Research Acceleratorversity College London, London, UK.
¹⁵ UCL Hospitals, NIHR Biomedical Research Centre, London, UK.
¹⁶ Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
¹⁷ NIHR Bristol Biomedical Research Centre, University of Bristol, Bristol, UK.
¹⁸ Medical Research Council Population Health Research Unit, University of Oxford, Oxford, UK.
¹⁹ Munich Cluster for Systems Neurology (SyNergy), Munich, Germany.
²⁰ German Centre for Neurodegenerative Diseases (DZNE), Munich, Germany.
²¹ Human Genetics, GlaxoSmithKline, Stevenage, UK.
²² Cardiovascular Health Research Unit, Departments of Medicine, Epidemiology and Health Services, University of Washington, Seattle, WA, USA.
²³ Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA.
²⁴ K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway.

PMID: 33644404
PMCID: PMC7903200
DOI: 10.12688/wellcomeopenres.16544.2

Abstract

Drugs whose targets have genetic evidence to support efficacy and safety are more likely to be approved after clinical development. In this paper, we provide an overview of how natural sequence variation in the genes that encode drug targets can be used in Mendelian randomization analyses to offer insight into mechanism-based efficacy and adverse effects. Large databases of summary level genetic association data are increasingly available and can be leveraged to identify and validate variants that serve as proxies for drug target perturbation. As with all empirical research, Mendelian randomization has limitations including genetic confounding, its consideration of lifelong effects, and issues related to heterogeneity across different tissues and populations. When appropriately applied, Mendelian randomization provides a useful empirical framework for using population level data to improve the success rates of the drug development pipeline.

Keywords: Drugs; Genetics; Mendelian randomization.

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Conflict of interest statement

Competing interests: DG is employed part-time by Novo Nordisk. DFF is an employee of Bayer AG, Germany. JMMH is an employee of Novo Nordisk. DIS is an employee of Silence Therapeutics plc. MVH has collaborated with Boehringer Ingelheim in research, and in accordance with the policy of the Clinical Trial Service Unit and Epidemiological Studies Unit (University of Oxford), did not accept any personal payment. RAS is an employee and shareholder in GlaxoSmithKline. BMP serves on the Steering Committee of the Yale Open Data Access Project funded by Johnson & Johnson. NMD reports funding from the Global Research Awards into Nicotine Dependence (GRAND) which is an independent grant awarding body funded by Pfizer. All other authors declare no conflicts of interest.

Figures

**Figure 1.. Principles of Mendelian randomization studies (MR) studying drug effects.**
MR makes use of genetic variants located within or close to a gene encoding a drug target (e.g. at *HMGCR* encoding the drug target of statins) that lead to downstream effects similar to the desired drug response (e.g. lowering of low-density lipoprotein [LDL] cholesterol) in order to explore effects on clinical outcomes (e.g. risk of coronary artery disease). SNP: single-nucleotide polymorphism.

**Figure 2.. Potential strategies for selecting genetic variants as instruments for a protein drug target.**
Variants within or close to the drug target gene might be selected on the basis of their associations with gene expression and levels of the target protein or known downstream functions such as effects on metabolite levels or biomarkers. Notably, variants influencing gene expression and protein levels do not always influence the function of the derived protein product and might not translate to downstream effects comparable to those achieved by the pharmacological modulation of the drug target.

**Figure 3.. Comparison between Mendelian randomization (MR) study for drug effects and randomized clinical trial (RCT).**
Similar to the randomization process of RCTs, the random allocation of alleles at a drug target gene in MR studies allows the distribution of individuals to groups that differ only regarding the downstream effects of the drug target and not other confounders. While the random allocation of alleles in MR studies happens at conception and leads to lifelong effects, the randomization in RCTs typically happens later in life and focuses on the effects of short-term interventions.

See this image and copyright information in PMC

References

1. Santos R, Ursu O, Gaulton A, et al. : A comprehensive map of molecular drug targets. Nat Rev Drug Discov. 2017;16(1):19–34. 10.1038/nrd.2016.230 - DOI - PMC - PubMed
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Mendelian randomization for studying the effects of perturbing drug targets

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Mendelian randomization for studying the effects of perturbing drug targets

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Conflict of interest statement

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