Introducing Variant Bio: People-Driven Therapeutics
Who we are, our mission, vision, and goals
In 1994, a man in Connecticut was involved in a serious car accident. He was rushed to the hospital for assessment. Broken bones are one of the most common injuries in any type of accident — the unnatural forces the body experiences in an impact frequently result in fractured ribs, hips, pelvises and extremities. So when doctors saw the X-rays they were surprised to see not a single broken bone. Even more shocking: his bones appeared different than any they’d seen before. “Patient X” was referred to a specialist at the Yale Bone Center. Upon further analysis, it was determined that his bones were eight times denser than an average man his age. Otherwise healthy, the only hint that his bones were different came from the swimming pool — he could never stay afloat.
“Patient X” remained little more than a medical curiosity for over half a decade. It wasn’t until the same specialist who treated him heard the story of another Connecticut family with similarly-dense bones that the pieces of the genetic puzzle started coming together. The genetic tools were still primitive, but researchers were able to narrow their search for the cause of these strong bones to a section of chromosome 11.
Meanwhile, around the world researchers were investigating other individuals with unique bones. A team at Case Western Reserve University studying a family with very brittle bones made a breakthrough when they identified a mutation in the LRP5 gene on chromosome 11¹. Perhaps the same gene region responsible for brittle bones could also have something to do with dense bones? With this gene identified, the other researchers rushed to genotype their patients for LRP5 mutations. As it turns out, all of the affected individuals had mutations in or related to LRP5²,³,⁴. The people with dense bones had mutations stopping the body from producing a protein, sclerostin, responsible for breaking down bone⁵. Without it, the body continually builds bone, resulting in the dense bones of the families in Connecticut and elsewhere.
This brings us to April 2019, with the FDA approval of a drug called romosozumab⁶. The development of romosozumab stems directly from the genotyping of families and individuals with rare bone disorders — people like Patient X. Specifically, romosozumab works by blocking the action of the sclerostin protein, resulting in increased bone density. This drug could prove a game-changer for treating the 200 million people worldwide who suffer from brittle bones. Currently, it is approved for post-menopausal women suffering from osteoporosis, who alone account for about eight million Americans.
Outliers are the key
Romosozumab is just one of an emerging class of therapeutics that is harnessing the power of “outlier” individuals. An outlier is a person or group with characteristics placing them on the ends of the biological spectrum — for example, having either extremely brittle or extremely dense bones. Outliers have historically been discarded as rare anomalies, but they may actually hold the keys to drug discovery. By identifying outliers, sequencing their genomes, and exploring the mechanisms behind their unique traits, we can uncover truly novel ways to treat disease. In this way, medicine is leveraging the diversity that nature has already created. In addition to outlier individuals, natural selection has led to some very compelling adaptations in populations, such as Andeans who don’t get altitude sickness,⁷ indigenous Bajau people (“Sea Nomads”) who can free dive up to 70 meters,⁸ and Bangladeshis who are resistant to cholera,⁹ to name a few. These outliers are transforming the way we approach drug development today.
A new way to treat disease
The pharmaceutical development model was invented well before the advent of genetics. As such, for both technical and ethical reasons, it has never been optimized around a “human first” approach. Most drugs are tested with laboratory animals — “model organisms” which are quite different from you and me. The process is incredibly inefficient, and results in only 1 in 10 drugs that make it through actual human clinical trials¹⁰,¹¹.
What if you were able to flip this process on its head? By starting with humans, there is a much higher chance that the drug pathways you are targeting will be effective and safe. This has been proven through recent clinical experience; drugs that have human genetic evidence are twice as likely to succeed in clinical trials¹². Humans should be the model organisms of choice for drug discovery.
If this is a better approach to developing new drugs, why hasn’t it been applied before? Firstly, identifying individuals, families, and populations with unique mutations is like searching for needles in a haystack of eight billion people. Thus, most rare genetic traits have only been identified by happenstance. Secondly, previously available sequencing technology was either not advanced enough to detect small differences in the human genome, or it was prohibitively expensive to do so at scale.
Enter Variant Bio
Change is afoot. Variant Bio, a genomics startup based out of Brooklyn, New York, is attempting to systematize what science previously stumbled upon. The company is focused on the “edges” of human genetic diversity, and already has projects underway in Nepal, New Zealand, The Faroe Islands, and Pakistan, with more than eight others in the works. Most efforts to find genes contributing to traits center around analyzing databases of predominantly European genomes. However, this is problematic because these databases capture just a tiny sliver of global genetic diversity, and few people with unique traits. Instead, Variant works with outliers — extraordinary participants and populations adapted to their environments or who have higher frequencies of certain traits or diseases. The higher frequency of these traits means that finding an association between a trait and a gene requires studying fewer people, thereby keeping study costs low. For example, a recent study found a genetic variant that protects Samoans from diabetes¹³. This variant is present in about 26% of Samoans, so roughly 3,000 people needed to be studied to identify it in this population. Looking for this same variant in a European population, where it is present at a much lower frequency, would have required studying tens of millions of people. Additionally, by strategically sequencing the genomes of outliers, we are more likely to find genes that have strong effects, as these are often driven by a single mutation. Therapeutics that target single genes with strong effects are often more robust and specific, so they make an ideal match for treating disease.
Cutting-edge sequencing technology
In terms of technology, Variant is taking a unique approach to sequencing that allows us to discover entirely new genetic variants in a way that is cost-effective. First, Variant uses whole genome sequencing instead of less comprehensive technology such as microarrays, which only capture a small portion of the genome. Second, by combining high and low coverage sequencing with advanced analytics, Variant is able to reduce costs by 80% versus standard approaches and still identify about 90% of genetic variants. While most industry leaders are eagerly anticipating the arrival of the $100 genome, Variant is already planning for the $10 genome.
An ethical approach
At the root of all genetic health discoveries are the people who share their DNA in the first place. In an effort to counter the unfortunate history of exploiting vulnerable, indigenous, and minority populations in the name of science, Variant was founded on the principle of prioritizing the interests and perspectives of those who partner with us. Before engaging in a single partnership, we first assembled a group of independent ethics advisors. This group guided us in forming our principles of openness and respect for the privacy, beliefs, and cultures of the people with whom we partner. We ensure that the individuals and populations we work with understand that we are a for-profit company, their rights as research partners, how their data is going to be analyzed and their privacy protected, how to easily withdraw from participation, and, most importantly, how are they going to benefit from partnering with us.
An emphasis on benefit sharing
We also ensure that the populations we work with benefit from their partnership with Variant. Our benefit strategy comprises two arms: a short and a long term benefit. In the short term, every population will receive monetary compensation, distributed to the community as a whole, rather than to individuals. The reasoning behind this is that a person’s genetic code is shared with their population, so all members should benefit, regardless of individual participation. With input from the partner populations, these funds will go to local causes supporting capacity building, healthcare, education, and sustainable development. Funds will be distributed regardless of the success of drug development based on any particular population. In the long term, Variant is establishing a non-profit arm that will distribute a portion of revenue across all participating populations.
Far-reaching health benefits
Beyond a monetary benefit, we hope that the work of Variant will leverage the power of genetics to benefit under-represented populations in terms of precision medicine. Precision medicine involves customizing treatment to the individual. However, despite U.S. congressional efforts to diversify clinical trials, the majority of participants in drug studies are men of European ancestry¹⁴,¹⁵. As a result, precision medicine does not currently benefit many people outside of this narrow genetic background. We have already seen this play out: for example, one of the most common drugs used to prevent heart attack and stroke is not effective for 75% of Pacific Islanders because they carry a gene variant that affects their ability to convert the drug to its active form¹⁶. In addition, a lack of diverse data results in a much higher probability of failing to diagnose, or misdiagnosing, genetic diseases in people of color and minority populations.
At the end of the day, human diversity is the currency of genetics, and Variant is prioritizing bringing many different people’s interests and perspectives to the table.
Transforming human health
By taking a global approach to discovering outliers, Variant is harnessing the power of the human genetic code to develop new therapeutics. Our approach starts with people, treats people, and ultimately gives back to people. Following this approach will lead to major therapeutic breakthroughs for diseases with global medical relevance, benefit communities, and ultimately transform human health.
- Boyden, L. M., Mao, J., Belsky, J., Mitzner, L., Farhi, A., Mitnick, M. A., … & Lifton, R. P. (2002). High bone density due to a mutation in LDL-receptor–related protein 5. New England Journal of Medicine, 346(20), 1513–1521.
- Brunkow, M. E., Gardner, J. C., Van Ness, J., Paeper, B. W., Kovacevich, B. R., Proll, S., … & Alisch, R. S. (2001). Bone dysplasia sclerosteosis results from loss of the SOST gene product, a novel cystine knot–containing protein. The American Journal of Human Genetics, 68(3), 577–589.
- Little, R. D., Folz, C., Manning, S. P., Swain, P. M., Zhao, S. C., Eustace, B., … & Benchekroun, Y. (2002). A mutation in the LDL receptor–related protein 5 gene results in the autosomal dominant high–bone-mass trait. The American Journal of Human Genetics, 70(1), 11–19.
- Gong, Y., Slee, R. B., Fukai, N., Rawadi, G., Roman-Roman, S., Reginato, A. M., … & Zacharin, M. (2001). LDL receptor-related protein 5 (LRP5) affects bone accrual and eye development. Cell, 107(4), 513–523.
- Paszty, C., Turner, C. H., & Robinson, M. K. (2010). Sclerostin: a gem from the genome leads to bone‐building antibodies. Journal of Bone and Mineral Research, 25(9), 1897–1904.
- Kolata, G. (2019, April 9). Most Osteoporosis Drugs Don’t Build Bone. This One Does. The New York Times. Retrieved from https://www.nytimes.com/2019/04/09/health/osteoporosis-evenity-bone-amgen.html
- Lindo, J., Haas, R., Hofman, C., Apata, M., Moraga, M., Verdugo, R. A., … & Warinner, C. (2018). The genetic prehistory of the Andean highlands 7000 years BP though European contact. Science Advances, 4(11), eaau4921.
- Ilardo, M. A., Moltke, I., Korneliussen, T. S., Cheng, J., Stern, A. J., Racimo, F., … & van den Munckhof, I. C. (2018). Physiological and genetic adaptations to diving in sea nomads. Cell, 173(3), 569–580.
- Karlsson, E. K., Harris, J. B., Tabrizi, S., Rahman, A., Shlyakhter, I., Patterson, N., … & Sheikh, A. (2013). Natural selection in a Bangladeshi population from the cholera-endemic Ganges river delta. Science Translational Medicine, 5(192), 192ra86–192ra86.
- King, E. A., Davis, J. W., & Degner, J. F. (2019). Are drug targets with genetic support twice as likely to be approved? Revised estimates of the impact of genetic support for drug mechanisms on the probability of drug approval. BioRxiv, 513945.
- Thomas, D. W., Burns, J., Audette, J., Carroll, A., Dow-Hygelund, C., & Hay, M. (2016). Clinical development success rates 2006–2015. San Diego: Biomedtracker/Washington, DC: BIO/Bend: Amplion.
- Nelson, M. R., Tipney, H., Painter, J. L., Shen, J., Nicoletti, P., Shen, Y., … & Cardon, L. R. (2015). The support of human genetic evidence for approved drug indications. Nature Genetics, 47(8), 856.
- Minster, R. L., Hawley, N. L., Su, C. T., Sun, G., Kershaw, E. E., Cheng, H., … & Urban, Z. (2016). A thrifty variant in CREBRF strongly influences body mass index in Samoans. Nature Genetics, 48(9), 1049.
- Oh, S. S., Galanter, J., Thakur, N., Pino-Yanes, M., Barcelo, N. E., White, M. J., … & Borrell, L. N. (2015). Diversity in clinical and biomedical research: a promise yet to be fulfilled. PLoS Medicine, 12(12), e1001918.
- Geller, S. E., Koch, A., Pellettieri, B., & Carnes, M. (2011). Inclusion, analysis, and reporting of sex and race/ethnicity in clinical trials: have we made progress? Journal of Women’s Health, 20(3), 315–320.
- Wu, A. H., White, M. J., Oh, S., & Burchard, E. (2015). The Hawaii clopidogrel lawsuit: the possible effect on clinical laboratory testing. Personalized Medicine, 12(3), 179–181.