Looking back, it’s hard to believe the Human Genome Project — a herculean endeavor in the 90s and early 2000s to sequence the genetic instruction manual for humans — faced pushback. At the time, critics argued the 15-year effort would deplete resources from more fruitful drug development.
But now that it’s been two decades since the project’s completion, few argue against its successes, which helped catapult science, and particularly drug discovery, into a new era.
“The $3 billion that we spent on the Human Genome Project, I doubt there are very many investments that we've made that have had a higher ROI,” Aaron Mitchell, global R&D solution area lead at the consulting firm ZS, said of the project.
Few industry nooks have been left untouched by the project’s discoveries — as it ushered in a new, biology-first approach to R&D that moved from typical labs “with pipettes and test tubes” into labs with computers and data, and led to vast improvements in sequencing technology that have in turn fueled the identification of disease targets and the advent of personalized medicine.
“Those things are actually happening from 40 years ago where (they) seemed like science fiction,” said Ian Dunham, director of the Open Targets Initiative and the lead researcher responsible for sequencing the first complete human chromosome.
Still, while the project has led to monumental shifts in the diagnosis and treatment of diseases like cancer, a long road remains before all its original goals, including “cheaply and easily scanning a person’s genome at a moment’s notice” and developing truly personalized drugs, may fully be realized.
“The whole point of sequencing the genome was to enable these medical and drug discovery processes down the line.”
Director, Open Targets Initiative
“Have we reached the promise that we set out with the Human Genome Project? We absolutely have not,” Mitchell said of the progress. “There's so much more work to be done. But I think we are seeing a lot of the advancements.”
To get to those next frontiers, experts argue that increased industry collaboration, similar to that what took place during the Human Genome Project, and investments in artificial intelligence are needed to overcome existing roadblocks.
The next 20 years
While precision medicine exists today “in the sense that you have a diagnosis that's individual to a person and you can tell the reasons why they have the symptoms they do,” Dunham contends that similarly personalized treatments will be enabled with “wider sequencing efforts” that include much more data.
Efforts like the NIH’s All of Us Research Program and the U.K.’s initiative to map the genome of 5 million people across its health system could help researchers uncover broader trends on how disease outcomes and drug effectiveness are impacted by genotypes.
However, the next step must also go beyond looking at the genome to other systems in the body, Mitchell argued.
“What we've seen is that once we understand the genome, we also must understand the transcriptome and metabolome and the proteome and other levels of human biology that follow from our genetic understanding,” he said. “Until we do understand biology at that level, it would be really hard for us to fulfill the promise of precision medicine.”
Research is already underway to investigate these pathways. For instance, Dunham’s Open Targets initiative — comprising a pre-competitive consortium of five drug companies and two academic centers, recently drew a map showing the networks of proteins that interact with genes linked to therapeutic areas. The goal of the project was to identify new drug targets or potentially opportunities to repurpose existing medications.
But as more data is collected, Mitchell said the industry is at a turning point of figuring out how to store, organize and process it all to find actionable insights. And he expects reaching the next level will require more than just human ingenuity.
“We've already exceeded the capacity of the human mind in terms of our understanding of biology in the amount of information data that we have, and we now need to move to artificial intelligence for us to be able to leverage this data to generate these new breakthroughs,” he said.
Dunham agreed, arguing that AI “can play a big role” in “obtaining the traits and phenotypes and diseases that are associated with patients,” from unstructured data, like clinical trial data, which he sees as one of the next steps in the pursuit of personalized medicine.
With the promise of AI also comes challenges, though, and to overcome these both experts suggested the industry look to the roots of the Human Genome Project.
In the original project, around 5% of the budget was devoted to understanding the ethical considerations associated with genetics, Mitchell said. As the industry considers greater adoption of AI to support the evolution of that research, similar effort should be put toward designing ethical guardrails for the technology.
“Companies are doing what they need to do to live up to the legal requirements of using that data. But you have to ask the question: Are they living up to the ethical requirements?” he asked. "Do we need to maybe look beyond what’s legal and look into what’s right?” he said, pointing out that ethical considerations should play a role in the future of genomic data.
Additionally, Dunham said the Human Genome Project found success partially because all partners involved were “committed to releasing the data early on.”
While that spirit of cooperation and sharing naturally broke down as the research moved into the clinic, he said breaking down those barriers will once again be important “because ultimately, there's more data than people can legitimately reasonably handle and it's better to have more eyes on it.”
And although the path to fulfilling the mission of the genome project appears daunting, Dunham said he’s motivated by the results the industry is already seeing.
“The whole point of sequencing the genome was to enable these medical and drug discovery processes down the line,” he said. “That's where we are and that's been revolutionary in terms of what we can do next in genomes and drug discovery.”