[I am an employee of Celgene. All opinions expressed here are my own.]
A meeting was recently convened to discuss a roadmap for understanding the genetics of common diseases (search Twitter for #cdcoxf18). I presented my vision of a genetics dose-response portal (slides here; link to related 2018 ASHG talk here). The organizers (@RachelGLiao, @markmccarthyoxf, @ceclindgren, Rory Collins [Oxford], Judy Cho [New York], @NancyGenetics, @dalygene, @eric_lander) asked participants to share their vision. I thought I would blog about my mine.
You’ll notice my vision is ambitious. Nonetheless, I believe these objectives are feasible to accomplish within a 3-year (Phase 1) and 7-year (Phase 2) time frame. Phase 1 would start immediately and would guide projects for Phase 2. In reality, many aspects of Phase 1 are already underway today (e.g., GWAS catalogue at EBI; Global Alliance for Genomics and Health [GA4GH] data sharing methods). Phase 2 consists of two parts: federation of global biobanks and experimental validation of variants, genes and pathways. Some components of Phase 2 could start today (e.g., exome sequencing in >100,000 cases selected from existing case-control cohorts and biobanks; human knockout project). As with Phase 1, many components of Phase 2 are already underway (federation of existing biobanks [e.g.,…
A new genetics initiative was announced today: the creation of FinnGen (press release here). FinnGen’s goal is to generate sequence and GWAS data on up to 500,000 individuals with linked clinical data and consented for recall. There are many applications for such a resource, including drug discovery and development. In this blog, I want to first describe the application of PheWAS for drug discovery and development, and then introduce FinnGen as a new PheWAS resource (see FinnGen slide deck here).
[Disclaimer: I am an employee of Celgene. The views expressed here are my own.]
PheWAS turns GWAS on its head. While GWAS tests millions of genetic variants for association to a single trait, PheWAS does the opposite: tests hundreds (if not thousands) of traits for association with a single genetic variant. This approach is primarily relevant for those genetic variants with an unambiguous functional consequence – for example, a variant associated with disease risk or a variant that completely abrogates gene function. There are useful online resources (see here), as well as several nice recent reviews by Josh Denny and colleagues, which provide additional background on PheWAS (see here, here).
Work that originated from my academic lab represents the first example of PheWAS for drug discovery – in particular, how to use PheWAS to predict on-target adverse drug events (ADEs) and to select indications for clinical trials (see 2015 PLoS One publication here).…
Last week Alnylam reported positive news on Phase 3 outcomes for their RNA interference (RNAi) therapy to treat patients with a rare genetic cause of amyloidosis with polyneuropathy (see here). I tweeted the following:
The 20-year journey from scientific discovery to positive Phase 3 clinical trial data got me thinking about other novel therapeutic modalities. Was twenty years a long time or typical for an innovative therapeutic modality? Where are other promising modalities on their journey to regulatory approval? Is the biopharmaceutical industry on the cusp of a series of innovative modalities that could change the therapeutic landscape for patients? How will these new modalities improve our ability to test therapeutic hypotheses?
[Disclaimer: I am an employee of Celgene. The views expressed here are my own.]
To explore these questions, I decided to review different novel therapeutic modalities, which I am defining as those other than small molecules, protein therapeutics (e.g., insulin) and traditional vaccines. This decision was practical, as the amount of literature in these modalities is expansive.
For each new modality, I asked whether a drug has been approved by either a European or US regulatory agency (EMA and FDA, respectively). If a drug has been approved, I reviewed the time from seminal scientific discovery (which sometimes is clear, sometimes is not) to the approved therapy.…
A new sickle cell anemia gene therapy study published in the New England Journal of Medicine (see here, here) gives hope to patients and the concept of rapidly programmable therapeutics based on causal human biology. But how close are we really?
It takes approximately 5-7 years to advance from a therapeutic hypothesis to an early stage clinical trial, and an additional 4-7 years of late stage clinical studies to advance to regulatory approval. This is simply too long, too inefficient and too expensive.
But how can timelines be shortened?
In the current regulatory environment, it is difficult to compress late stage development timelines. This leaves the time between target selection (or “discovery”) and early clinical trials (ideally clinical proof-of-concept, or “PoC”) as an important time to gain efficiencies. Further, discovery to PoC is an important juncture for minimizing failure rates in late development and delivering value to patients in the real world (see here).
Here, I argue that rapidly programmable therapeutics based a molecular understanding of the causal disease process is key to compressing the discovery to PoC timeline.
Imagine a world where the molecular basis of disease is completely understood. For common diseases, germline genetics contributes approximately two-thirds of risk; for rare diseases, germline genetics contributes nearly 100% of risk.…
[Disclaimer: I am a Merck/MSD employee. The opinions I am expressing are my own and do not necessarily represent the position of my employer.]
STRENGTHS AND WEAKNESSES OF THE CURRENT INDUSTRY MODEL
In the near-term (10-years), I suspect that the pharma model of late development and commercialization will likely persist, as the cost and complexity of getting a drug approved is difficult by other mechanisms. Over time, however, new ways of performing late-stage trials will likely evolve. Drugs that are today in the early R&D pipeline will drive this evolution. If drugs look like they do today, dominated by small molecules and biologics with high probability of failure in Phase II/III, then the current model will likely continue with incremental improvements in efficiency. However, if new therapeutic modalities emerge (CRISPR, mRNA, microbiome, etc) and/or the probability of success in Phase II/III improves substantially, then the model of late development and commercialization will be forced to evolve, too.…
Inevitably when I post a blog on “human biology” I get a series of comments about the importance of non-human model organisms in drug discovery and development. My position is clear: pick targets based on causal human biology, and then use whatever means necessary to advance a drug discovery program to the clinic.
Very often, non-human model organisms are the “whatever means necessary” to understand mechanism of action. For example, while human genetic studies identified PCSK9 as an important regulator of LDL cholesterol, mouse studies were critical to understand that PCSK9 acts via binding to LDL receptor (LDLR) on the surface of cells (see here). As a consequence, therapeutic antibodies were designed to block circulating PCSK9 from the blood and increase LDLR-mediated removal of circulating LDL (and hopefully to protect from cardiovascular disease).
Moreover, non-human animal models are necessary to understand in vivo pharmacology and safety of therapeutic molecules before advancing into human clinical trials.
Beyond drug discovery, of course, studies from non-human animal models provide fundamental biological insights. Without studies of prokaryotic organisms, for example, we would not have powerful genome-editing tools such as CRISPR-Cas9. Without decades of work on mouse embryonic stem cells, we would not have human induced pluripotent stem cells (iPSCs).…
This week’s theme is genes to function for drug screens…with a macabre theme of zombies! As more genes are discovered through GWAS and large-scale sequencing in humans, there is a pressing need to understand function. There are at least two steps: (1) fine-mapping the most likely causal genes and causal variants; and (2) functional interrogation of causal genes and causal variants to move towards a better understanding of causal human biology for drug screens (“from genes to screens”).
Genome-editing represents one very powerful tool, and the latest article from the laboratory of Feng Zhang at the Broad Institute takes genome-editing to a new level (see Genetic Engineering & Biotechnology News commentary here). They engineer the dead!
Since its introduction in late 2012, the CRISPR-Cas9 gene-editing technology has revolutionized the ways scientists can apply to interrogate gene functions. Using a catalytically inactive Cas9 protein (dead Cas9, dCas9) tethered to an engineered single-guide RNA (sgRNA) molecule, the authors demonstrated the ability to conduct robust gain-of-function genetic screens through programmable, targeted gene activation.
Earlier this year, the laboratories of Stanley Qi, Jonathan Weissman and others \ reported the use of dCas9 conjugated with a transcriptional activator for gene activation (see Cell paper here).…