Key Takeaways:
- Pharmaceutical development is high-risk and resource-intensive, with a 90% failure rate in clinical trials, often due to inadequate efficacy, toxicity, drug properties, or commercial viability.
- Incorporating human genetic evidence doubles drug approval rates, paving the way for innovative therapies and new molecular entities.
- Techniques like GWASs and PheWAS linking genetic data to phenotypic data enhance drug development by identifying associations between rare alleles and diseases.
- Published human genetic studies, primarily centered on individuals of European descent, hinder our understanding of genetic diversity and impede the development of new therapies suitable for diverse populations; therefore, establishing study cohorts with under-represented populations is crucial for promoting health equality and identifying novel drug targets based on diverse genetic variants.
- The Alliance for Genomic Discovery (AGD) aims to reshape drug development by sequencing 250,000 diverse samples, providing a powerful resource for pharmaceutical members to correlate genetic variations with clinical outcomes and, in turn, enabling these companies to better serve a global population.
The Struggle to Discover New Therapies
Discovering and developing pharmaceuticals is a resource-intensive and high-risk endeavor, sometimes spanning 15 years with costs exceeding $2 billion for their approval (Hinkson et al., 2020). Shockingly, about nine out of ten potential therapies, upon progressing to clinical trials, fail before approval (Dowden & Munro, 2019; Sun et al., 2022). The four primary contributors to the staggering 90% failure rate in drug development are inadequate clinical efficacy, unmanageable toxicity, suboptimal drug-like properties and a lack of commercial viability (Dowden & Munro, 2019; Harrison, 2016; Sun et al., 2022). To increase the chances of a drug target passing these critical checkpoints, considerable endeavors can be directed towards incorporating human genetic evidence into drug development.
In the drug development pipeline, all compounds before entering clinical phases must undergo rigorous testing in animal models, providing significant evidence of their potential to treat diseases. However, despite promising results in preclinical studies, the translation of efficacy and safety from animal models to human clinical trials is often elusive. Integrating human genetic evidence into the drug development process has recently emerged as a crucial strategy to navigate this challenge. Drugs grounded in such evidence exhibit a twofold increase in approval rates (Nelson et al., 2015), contributing to a higher prevalence of first-in-class therapies and new molecular entities (NMEs) (King et al., 2019). This not only accelerates the approval process but also streamlines the discovery of more effective and targeted treatments. Leveraging human genetic data empowers researchers with valuable insights into the genetic basis of diseases, facilitating the identification of better drug targets. The substantial presence of genetic evidence in FDA-approved drugs in 2021 (Ochoa et al., 2022) underscores its instrumental role in advancing drug discovery and fostering the emergence of innovative pharmaceutical solutions.
Linking Genetics to Clinical Data for Drug Discovery
To incorporate genetics into therapeutic development, researchers can link the genetic code of an individual to their Electronic Health Records (EHRs). Researchers can use techniques like Genome-wide association studies (GWASs), Phenome-wide association studies (PheWAS), Mendelian Randomization or Loss/Gain-of-Function Variants to discover associations between
rare alleles and human disease (Krebs & Milani, 2023). Using these techniques, drugs tailored for Mendelian disorders have achieved notable success in clinical trials and approvals (Heilbron et al., 2021). For instance, the genetic disease Autosomal dominant hypercholesterolemia (ADH) confers an increased risk of coronary artery disease (CAD) through elevated levels of plasmatic low-density lipoprotein (LDL). By linking phenotypic data with genetic data, researchers were able to identify the association of the PCSK9 gene with high LDL levels (Abifadel et al., 2003). This kickstarted a series of studies that culminated in the approval of two monoclonal antibodies that inhibit PCSK9, Repatha (Evolocumab) and Praluent (Alirocumab) (Krebs & Milani, 2023; Robinson et al., 2015) with their treatment reducing the rate of major adverse cardiovascular events by half (Kaddoura et al., 2020). Indeed, therapies derived from these kinds of impactful rare alleles exhibit a 6-7.2 times greater likelihood of receiving approval due to their substantial effect on symptoms (Nelson et al., 2015; King et al., 2019). However, for many prevalent diseases, heritable risk is predominantly associated with numerous common variants, each having smaller individual effect sizes. This intricate genetic landscape complicates the identification of therapeutic targets, making the discovery of new avenues for therapy challenging and necessitating new strategies.
So far, a disproportionate number of published human genetic studies have centered on individuals of European descent (Fatumo et al., 2022). However, this narrow focus restricts our understanding to a limited diversity of alleles and genetic disorders, hindering the development of new therapies. To promote health equality, it’s crucial to establish study cohorts that include under‐represented populations. After all, individuals of European descent represent only a fraction of the total human genetic variation (Heilbron et al., 2021). Diverse cohorts represent unique opportunities for identifying novel drug targets based on genetic variants that are less frequent or even absent in people of European ancestry. Genetic discoveries will have greater discovery power in populations where a disease is more prevalent and, hence, with larger disease cohorts; at the same time, these discoveries will be more relevant and beneficial for these populations.
Founding the Alliance for Genomic Discovery
This need to identify rare genetic variants in diverse patient cohorts has driven the collaboration of NashBio and Illumina Inc. to establish AGD. AGD, comprising eight member organizations—AbbVie, Amgen, AstraZeneca, Bayer, Merck, Bristol Myers Squibb (BMS), GlaxoSmithKline Pharmaceuticals (GSK), and Novo Nordisk (Novo)—aims to expedite therapeutic development through whole-genome sequencing (WGS) 250,000 samples from Vanderbilt University Medical Center’s (VUMC) biobank repository, BioVU®. As the first phase in AGD, deCODE genetics performed WGS on the first 35,000 VUMC samples, primarily made up of DNA from individuals of African ancestry. Moving forward, deCODE/Amgen will sequence the remaining samples for the Alliance members to have access to the resulting data for drug discovery and therapeutic development. The WGS data will then be linked with structured EHR data from NashBio and VUMC, creating a valuable resource for pharmaceutical members to correlate genetic variations with clinical outcomes. To learn more about how AGD aims to accelerate drug discovery and to hear directly from the alliance members, click here.
Summary
AGD marks a pivotal step in reshaping drug development, offering a solution to the challenges plaguing the pharmaceutical industry. With a staggering 90% failure rate in clinical trials, the incorporation of human genetic evidence into drug development by AGD aims to increase the approval likelihood of drug targets, fostering the discovery of more effective and targeted treatments. AGD also aims to address the limitations of existing genetic resources and studies. The WGS of 250,000 samples, encompassing diverse populations and linked with structured EHR data, provides pharmaceutical members with a powerful resource. This not only accelerates drug discovery but also facilitates the development of tailored therapies. AGD represents a significant step toward healthcare equality, highlighting the importance of diverse genetic studies in progressing drug discovery for the benefit of all people.
References
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