Impact of genetically supported target selection on R&amp;D productivity

Hurle, Mark R.; Nelson, Matthew R.; Agarwal, Pankaj; Cardon, Lon R.

doi:10.1038/nrd.2016.164

Cited by 22 publications

(9 citation statements)

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“…We discarded indications which were diagnostic in nature or unspecified, mapping the remainder to Medical Subject Headings (MeSH) [ 71 ]. We also observed that only 24% of the failures reported in Pharmaprojects are clinical failures, suggesting a clinical success rate of nearly 35%, much higher than typically cited [ 69 ].…”

Section: Methodsmentioning

confidence: 51%

“…Only features derived from tissue expression datasets were promising predictors of success versus failure in phase III, specifically, mean mRNA expression and standard deviation of expression across tissues. Although these features were significant at a false discovery rate cut-off of 0.05, their effect sizes were too small to be useful for classification of the majority of untested targets, however, even a two-fold improvement in target quality can dramatically increase R&D productivity [ 69 ]. We identified 943 targets predicted to be twice as likely to fail in phase III clinical trials as past phase III targets, and, therefore, should be flagged as having unfavorable expression characteristics.…”

Section: Discussionmentioning

confidence: 99%

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Systematic interrogation of diverse Omic data reveals interpretable, robust, and generalizable transcriptomic features of clinically successful therapeutic targets

2018

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Target selection is the first and pivotal step in drug discovery. An incorrect choice may not manifest itself for many years after hundreds of millions of research dollars have been spent. We collected a set of 332 targets that succeeded or failed in phase III clinical trials, and explored whether Omic features describing the target genes could predict clinical success. We obtained features from the recently published comprehensive resource: Harmonizome. Nineteen features appeared to be significantly correlated with phase III clinical trial outcomes, but only 4 passed validation schemes that used bootstrapping or modified permutation tests to assess feature robustness and generalizability while accounting for target class selection bias. We also used classifiers to perform multivariate feature selection and found that classifiers with a single feature performed as well in cross-validation as classifiers with more features (AUROC = 0.57 and AUPR = 0.81). The two predominantly selected features were mean mRNA expression across tissues and standard deviation of expression across tissues, where successful targets tended to have lower mean expression and higher expression variance than failed targets. This finding supports the conventional wisdom that it is favorable for a target to be present in the tissue(s) affected by a disease and absent from other tissues. Overall, our results suggest that it is feasible to construct a model integrating interpretable target features to inform target selection. We anticipate deeper insights and better models in the future, as researchers can reuse the data we have provided to improve methods for handling sample biases and learn more informative features. Code, documentation, and data for this study have been deposited on GitHub at https://github.com/arouillard/omic-features-successful-targets.

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Section: Methodsmentioning

confidence: 51%

Section: Discussionmentioning

confidence: 99%

Systematic interrogation of diverse Omic data reveals interpretable, robust, and generalizable transcriptomic features of clinically successful therapeutic targets

2018

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“… 55 These discoveries highlight known and novel factors in pathways critical to bone biology (that is, Wnt, for mesenchymal stem cell differentiation) as well as potential new factors and biological pathways that might constitute future drug targets. 56 …”

Section: Discussionmentioning

confidence: 99%

Assessment of the genetic and clinical determinants of fracture risk: genome wide association and mendelian randomisation study

Trajanoska

Morris

Oei

et al. 2018

BMJ

211

250

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ObjectivesTo identify the genetic determinants of fracture risk and assess the role of 15 clinical risk factors on osteoporotic fracture risk.DesignMeta-analysis of genome wide association studies (GWAS) and a two-sample mendelian randomisation approach.Setting25 cohorts from Europe, United States, east Asia, and Australia with genome wide genotyping and fracture data.ParticipantsA discovery set of 37 857 fracture cases and 227 116 controls; with replication in up to 147 200 fracture cases and 150 085 controls. Fracture cases were defined as individuals (>18 years old) who had fractures at any skeletal site confirmed by medical, radiological, or questionnaire reports. Instrumental variable analyses were performed to estimate effects of 15 selected clinical risk factors for fracture in a two-sample mendelian randomisation framework, using the largest previously published GWAS meta-analysis of each risk factor.ResultsOf 15 fracture associated loci identified, all were also associated with bone mineral density and mapped to genes clustering in pathways known to be critical to bone biology (eg, SOST, WNT16, and ESR1) or novel pathways (FAM210A, GRB10, and ETS2). Mendelian randomisation analyses showed a clear effect of bone mineral density on fracture risk. One standard deviation decrease in genetically determined bone mineral density of the femoral neck was associated with a 55% increase in fracture risk (odds ratio 1.55 (95% confidence interval 1.48 to 1.63; P=1.5×10−68). Hand grip strength was inversely associated with fracture risk, but this result was not significant after multiple testing correction. The remaining clinical risk factors (including vitamin D levels) showed no evidence for an effect on fracture.ConclusionsThis large scale GWAS meta-analysis for fracture identified 15 genetic determinants of fracture, all of which also influenced bone mineral density. Among the clinical risk factors for fracture assessed, only bone mineral density showed a major causal effect on fracture. Genetic predisposition to lower levels of vitamin D and estimated calcium intake from dairy sources were not associated with fracture risk.

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“…89,129 There are strong indications that GWAS loci are enriched with genes that are successfully targeted with existing drugs 130,131 and that a drug development pipeline built on support from human genetics will be more efficient and lead to improvements in clinical development success. 131,132 For example, the proportion of drug targets with genetic support from GWAS or the Online Mendelian Inheritance in Man database increases significantly across stages in the drug development pipeline; from ≈2% in the preclinical stage to ≈8% for approved drugs—a proportion that is even higher for drugs treating diabetes mellitus and metabolic diseases. 131…”

Section: Can Genetics Increase Efficiency Of the T2d Drug Developmentmentioning

confidence: 99%

Human Genetics of Obesity and Type 2 Diabetes Mellitus

Ingelsson

McCarthy

2018

Circ: Genomic and Precision Medicine

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Type 2 diabetes mellitus (T2D) and obesity already represent 2 of the most prominent risk factors for cardiovascular disease, and are destined to increase in importance given the global changes in lifestyle. Ten years have passed since the first round of genome-wide association studies for T2D and obesity. During this decade, we have witnessed remarkable developments in human genetics. We have graduated from the despair of candidate gene-based studies that generated few consistently replicated genotype-phenotype associations, to the excitement of an exponential harvest of loci robustly associated with medical outcomes through ever larger genome-wide association study meta-analyses. As well as discovering hundreds of loci, genome-wide association studies have provided transformative insights into the genetic architecture of T2D and other complex traits, highlighting the extent of polygenicity and the tiny effect sizes of many common risk alleles. Genome-wide association studies have also provided a critical starting point for discovering new biology relevant to these traits. Expectations are high that these discoveries will foster development of more effective strategies for intervention, through optimization of precision medicine approaches. In this article, we review current knowledge and provide suggestions for the next steps in genetic research for T2D and obesity. We focus on four areas relevant to precision medicine: genetic architecture, pharmacogenetics and other gene-environment interactions, mechanistic inference, and drug development. As we describe, the genetic architecture of complex traits has major implications for the prospects of precision medicine, rendering some anticipated approaches decidedly unrealistic. We highlight obstacles to the translation of human genetic findings into mechanism inference but are optimistic that, as these are overcome, there is untapped potential for novel drugs and more effective strategies for treating and preventing T2D and obesity.

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Impact of genetically supported target selection on R&D productivity

Cited by 22 publications

References 1 publication

Systematic interrogation of diverse Omic data reveals interpretable, robust, and generalizable transcriptomic features of clinically successful therapeutic targets

Systematic interrogation of diverse Omic data reveals interpretable, robust, and generalizable transcriptomic features of clinically successful therapeutic targets

Assessment of the genetic and clinical determinants of fracture risk: genome wide association and mendelian randomisation study

Human Genetics of Obesity and Type 2 Diabetes Mellitus

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