The technical specificities of the various technologies may determine the success in translating the variant discovery data into actionable information for drug development. In contrast to genotyping arrays, exome and whole genome sequencing identify specific rare disease-associated variants (<<1% allele frequency) which may carry functional effects and be causal in disease. To better understand the potential for genome analysis in drug development, there is a need to spell out the properties of three techniques that are in current use: genome-wide association studies (GWAS) that use high-density genotyping of common variants (>1–5% of allele frequency in the population) and linkage analysis, exome sequencing capturing the coding sequences in ~1.5% of the human genome, and whole genome sequencing achieving good quality coverage of ~85% of the genome. The target genetic perturbation matches the putative drug perturbation endpointsĭruggability, repurposing, chemical matter The target is relevant to in vitro or in vivo experimental endpoints Target identification, validation, mechanism of action The target responds to relevant perturbation(s) Target is pertinent to the cell implicated in pathogenesis Target is pertinent to the disease tissue Identification of individuals in the general population that tolerate heterozygous or homozygous loss of functionĭruggability, consequences of long-term drug action and safety Genetic evidence of association between gene and target (similarity between the clinical trait and the drug indication)Įvidence for severe consequences of genetic variantsĭruggability, consequences on long-term drug action and safety Relevant population data for a given target They are, however, impacting the drug development program at many levels-we illustrate this concept in Table 1 by listing various queries that are now common in target and drug discovery. It should be underscored that data from genomic technologies are not a regular requirement for Investigational New Drug (IND) applications to regulatory agencies such as the US Food and Drug Administration. In fact, these techniques do not compete with each other rather, they are increasingly deployed and interpreted jointly. In this complex data sciences space, it is of value to assess where things are at a specific point in time, the limitations of commonly used technologies, and how such technologies interact. As new technologies develop, they bring not only their unique contributions, but also provide opportunities for linking the new with the old to the benefit of both. Old technologies may fade entirely if they become obsolete or may be retained for a specific use for which the technology remains well suited. Any such effort of course represents a snapshot in time, as technologies are being brought to bear on the problem of diagnosis and treatment of human disease at an amazing rate. The aim of this work is to present progress in the implementation of genomics in drug development.

In parallel to these areas of genomics/omics that we review here, proteomics and metabolomics are also influencing drug development, but are not addressed here. These basic premises are now supported by a rapid expansion of population genomics initiatives (sequencing or genotyping of hundreds of thousands of individuals), in-depth understanding of disease and drug perturbation at the tissue and single-cell level as measured by transcriptome analysis, and by the capacity to screen for loss of function or activation of genes, genome-wide, using CRISPR technologies. Various conceptual approaches and techniques assist target identification, target prioritization and tractability, as well as the prediction of outcomes from pharmacological perturbations. For over 20 years, genomics has been used as a tool for accelerating drug development.

Pinpoint pharma reviews