Human Genomics and Drug Development

Improving the odds of drug development success through human genomics: modelling study

Scientific Reports ◽

10.1038/s41598-019-54849-w ◽

2019 ◽

Vol 9 (1) ◽

Cited By ~ 9

Author(s):

Aroon D. Hingorani ◽

Valerie Kuan ◽

Chris Finan ◽

Felix A. Kruger ◽

Anna Gaulton ◽

...

Keyword(s):

Drug Development ◽

Target Identification ◽

Preclinical Studies ◽

Success Rates ◽

Protein Coding ◽

Human Genomics ◽

Genome Wide ◽

Drug Target Identification ◽

Development Failure

AbstractLack of efficacy in the intended disease indication is the major cause of clinical phase drug development failure. Explanations could include the poor external validity of pre-clinical (cell, tissue, and animal) models of human disease and the high false discovery rate (FDR) in preclinical science. FDR is related to the proportion of true relationships available for discovery (γ), and the type 1 (false-positive) and type 2 (false negative) error rates of the experiments designed to uncover them. We estimated the FDR in preclinical science, its effect on drug development success rates, and improvements expected from use of human genomics rather than preclinical studies as the primary source of evidence for drug target identification. Calculations were based on a sample space defined by all human diseases – the ‘disease-ome’ – represented as columns; and all protein coding genes – ‘the protein-coding genome’– represented as rows, producing a matrix of unique gene- (or protein-) disease pairings. We parameterised the space based on 10,000 diseases, 20,000 protein-coding genes, 100 causal genes per disease and 4000 genes encoding druggable targets, examining the effect of varying the parameters and a range of underlying assumptions, on the inferences drawn. We estimated γ, defined mathematical relationships between preclinical FDR and drug development success rates, and estimated improvements in success rates based on human genomics (rather than orthodox preclinical studies). Around one in every 200 protein-disease pairings was estimated to be causal (γ = 0.005) giving an FDR in preclinical research of 92.6%, which likely makes a major contribution to the reported drug development failure rate of 96%. Observed success rate was only slightly greater than expected for a random pick from the sample space. Values for γ back-calculated from reported preclinical and clinical drug development success rates were also close to the a priori estimates. Substituting genome wide (or druggable genome wide) association studies for preclinical studies as the major information source for drug target identification was estimated to reverse the probability of late stage failure because of the more stringent type 1 error rate employed and the ability to interrogate every potential druggable target in the same experiment. Genetic studies conducted at much larger scale, with greater resolution of disease end-points, e.g. by connecting genomics and electronic health record data within healthcare systems has the potential to produce radical improvement in drug development success rate.

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Flipping the odds of drug development success through human genomics

10.1101/170142 ◽

2017 ◽

Author(s):

Aroon D. Hingorani ◽

Valerie Kuan ◽

Chris Finan ◽

Felix A. Kruger ◽

Anna Gaulton ◽

...

Keyword(s):

Monoclonal Antibodies ◽

Drug Development ◽

Drug Target ◽

Molecular Targets ◽

Pharmacological Action ◽

Common Disease ◽

Human Genomics ◽

Small Molecule Drugs ◽

Development Failure ◽

Druggable Targets

AbstractDrug development depends on accurately identifying molecular targets that both play a causal role in a disease and are amenable to pharmacological action by small molecule drugs or bio-therapeutics, such as monoclonal antibodies.Errors in drug target specification contribute to the extremely high rates of drug development failure.Integrating knowledge of genes that encode druggable targets with those that influence susceptibility to common disease has the potential to radically improve the probability of drug development success.

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