Plenge Lab
Date posted: September 27, 2023 | Author: | No Comments »

Categories: Drug Discovery Human Genetics

At the beginning of the summer, I had the opportunity to join the team at Royalty Pharma for a great event at MIT (link here). It was an interesting time for me as I was thinking about the new role I was about to take at Bristol Myers Squibb. While I had certainly been “a leader” for several years now, I was pushing myself to think through and question my perspectives on R&D now that I was taking on increasing responsibilities as “the leader” of the research organization.

And so the presentation opportunity with Royalty really pushed me to articulate my views in a way that would hopefully resonate with and inspire others. I titled my presentation “Bullseye.Aim.Fire” and then renamed it “Increasing R&D Productivity to Deliver Transformational Medicines” so the topic would be more obvious. What I’m really sharing in the presentation is my fundamental belief about R&D, linking together several factors that I see as mission critical.

To me, it really all comes down to causal human biology. In order to be successful, we must understand the cause-and-effect relationship between perturbing a particular biological target with a medicine and the outcome that will then impact human physiology.…

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Date posted: January 28, 2023 | Author: | No Comments »

Categories: Drug Discovery Human Genetics

For me, the most enjoyable aspect of discovery research is exploring the unknown. It is about having a big idea; believing in that big idea based on a scientific belief framework; coming to a crossroads in the validity of the big idea, which is usually marked by deep uncertainty and skepticism; making a data-driven scientific decision to proceed (or not) to the next inflection point of testing the big idea; and ultimately arriving at a conclusion of whether the big idea is true.

Unfortunately, most of these scientific adventure stories are lost in the way we communicate about science. We tell a story to communicate the final message – we have a new medicine that is effective in treating patients – as that is the cleanest way to communicate to an audience not familiar with the gory details of the discovery. Such retrospective narratives are also the simplest way to communicate the validity of the big idea, not the tortuous and often complicated path to arrive at truth.

But such retrospective narratives don’t capture the immensely personal nature of our research discoveries. Moreover, such retrospective narratives often make the big idea seem preordained or obvious, when the big idea was anything but.…

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Date posted: January 7, 2022 | Author: | No Comments »

Categories: Drug Discovery Embedded Genomics Human Genetics Precision Medicine

[ I am an employee of Bristol Myers Squibb. The views expressed here are my own, assuming I am real and not a humanoid. ]

In the original Blade Runner (1982), Harrison Ford’s character, Deckard, implements a fictitious Voight-Kampff test to measure bodily functions such as heart rate and pupillary dilation in response to emotionally provocative questions. The purpose: to establish “truth”, i.e., determine whether an individual is a human or a bioengineered humanoid known as a replicant.

While the Voight-Kampff test was used to establish truth for humans vs replicants, the concept of “truth” is central to neural networks used in machine learning and artificial intelligence (AI). And for AI to be effective in drug discovery and development, it is critical to ask a fundamental question: what is “truth” in drug discovery and development?

 

INTRODUCTION

I recently read the book Genius Makers by Cade Metz and was reminded of the long history of machine learning, neural networks, and artificial intelligence (AI). This is a field more than 60 years in the making, with slow growth for the first 50 years – AI was founded as an academic discipline in 1956 – and exponential growth in the last 10. The original mathematical framework of neural networks was created in the 50’s (perceptron), 60’s and 70’s (backpropagation), but went largely unappreciated outside of academics, as the practical applications were few and far between.…

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Date posted: May 28, 2021 | Author: | No Comments »

Categories: Drug Discovery Human Genetics

[Disclaimer: I am an employee of Bristol Myers Squibb. The views expressed here are my own.]

One of my favorite questions to ask is: “What captures your imagination? At a recent family dinner, responses were varied but encouraging for the next generation: black swan events, comparative anatomy & human physiology, space exploration & intelligent life beyond our planet, and more. My response was programmable therapeutics, a topic which I have blogged about in the past.

In this blog I define programmable therapeutics and provide a few recent examples (severe combined immune deficiency and mRNA vaccines). As you will see, programmable therapeutics is more than pure imagination – we are seeing this new concept evolve before our very eyes.

What is the concept of programmable therapeutics?

While there are different definitions of the concept of programmable therapeutics (see a16z talk; programmable cells; synthetic biology; CRISPR base editing), my definition of programmable therapeutics relates to a platform with modular components that can shorten the time from new target to drug candidate and ultimately regulatory trials that can lead to an approved medicine.

For most drug development programs, the identification of a drug target represents the start of a long journey that is highly artisanal.…

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Date posted: April 8, 2020 | Author: | No Comments »

Categories: Drug Discovery Human Genetics Immunogenomics

[I am an employee of BMS. The views expressed here are my own.]

The blog is long, so I will start with an executive summary. (You can download a pdf copy of the blog here.) Pharmacologic intervention has the opportunity to impact disease progression in the SARS-CoV-2 / COVID-19 crisis. Repurposing of approved therapies is the fastest way to impact patients today, as these medicines have regulatory approval to enable investigator-initiated trials and have a manufacturing process to ensure drug supply. Here, I focus on a specific clinical inflection point in COVID-19 disease progression – hospitalized patients early in their disease course and with signs of a maladaptive immune response, with the intervention intended to prevent disease progression and admission to the ICU.  Based on an understanding of disease biology today – which is still quite limited – this clinical inflection point is due to a “maladaptive immune response” seen early in the disease course in patients who later progress to critical illness. Rigorous clinical trials are required to test therapeutic hypotheses related to repurposed therapies, which need to be done in a clinical setting caring for extremely sick patients. Finally, I describe additional research that is required to understand the biology of SARS-CoV-2 / COVID-19, and how such research (e.g.,…

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Date posted: January 12, 2020 | Author: | No Comments »

Categories: Drug Discovery Embedded Genomics Human Genetics Immunogenomics

[I am an employee of Bristol-Myers Squibb. The views expressed here are my own.]

One of my predictions for the next decade – the “clear view” decade – is that we will have the ability to click on any gene in the human genome to generate function-phenotype maps. These maps should enable drug discovery by informing on mechanism, magnitude and markers of target perturbation. In particular, I have championed an “allelic series” model, whereby genes with a series of alleles are used to derived genetic dose-response curves (see here, here).

During a recent presentation to my former colleagues at the Division of Genetics at Brigham & Women’s Hospital (BHW, slides here), I discussed important assumptions underlying this model:

  1. Large-scale sequence data will identify a range of protein-coding variants associated with traits of medical interest that are suitable surrogates for drug discovery (allelic series architecture assumption).
  2. It will be possible to use high-throughput functional assays to interrogate the impact of trait-associated variants on cell physiology for the majority of genes in the genome (functional readout assumption).
  3. Large-scale biobanks will emerge to enable testing of these same trait-associated variants for pleiotropic effects across a wide-variety of clinical phenotypes in the real world (PheWAS assumption).

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Date posted: September 28, 2019 | Author: | No Comments »

Categories: Drug Discovery Embedded Genomics Human Genetics

[I am an employee of Celgene. The views expressed here are my own.]

In the Wizard of Oz, Dorothy clicks her heels and hopes for re-entry from her dream world by repeating, “There’s no place like homethere’s no place like home…” I often feel that many in the genetics community look at their human genetics data with the same youthful optimism as Dorothy – clicking their genetic heels and wishing “my genetic discovery will become a drugmy genetic discovery will become a drug…” But without rigor and discipline, such heel-clicking won’t overcome many of the challenges that face drug hunters along the tortuous journey from a genetic idea to a new medicine.

In this blog, I discuss a recent study on the genetics of multiple sclerosis (MS) published in Science (see here). This is a beautiful study that substantially advances the genetic landscape of patients with a devastating disease. However, the study falls short in terms of the application of human genetics to drug discovery. To chart a course for the future, I introduce the concept of mechanism, magnitude and markers (oh my!), which I refer to as the three M’s. …

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Date posted: March 26, 2019 | Author: | No Comments »

Categories: Drug Discovery Human Genetics

[Disclaimer: I am an employee of Celgene. The views expressed here are my own.]

Human genetics offers the potential to identify drug targets and to inform decision-making on the journey to an approved drug. A recent study by Ference et al in the New England Journal of Medicine (NEJM) provides an example of human genetics in action. While most of the study focuses on Mendelian randomization to establish a relation among ACLY genetic variation, LDL cholesterol levels, and cardiovascular events, in this blog I focus on a topic highlighted in the companion NEJM editorial: human genetics to predict on-target adverse drug events (see NEJM editorial here).

First, what is the framework for the application of human genetics to predict on-target adverse drug events (ADEs)?  Briefly, human genetics can predict on-target toxicity if the following criteria are met: (1) unambiguous association of genetic variant to a clinical phenotype that is a surrogate for drug efficacy and toxicity; (2) unambiguous relationship between disease-associated variant and implicated gene that is the target of the therapeutic intervention; (3) quantitative assessment of gene function and clinical phenotypes of efficacy and toxicity to estimate a “genotype-phenotype dose-response” relationship; and (4) confidence that the therapeutic intervention mimics the mechanism of action of the disease-associated variant.…

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Date posted: January 6, 2019 | Author: | No Comments »

Categories: Drug Discovery Embedded Genomics Human Genetics

[I am an employee of Celgene. All views expressed here are my own.]

At the 2018 Annual Atlas Ventures Retreat (AVR), I participated in a panel on Digital Health (along with David Schenkhein, John Reed, Scott Brun). The panel discussion was led by Michael Ringel, who also provide an excellent introduction to Digital Health (his slides here). While there are many aspects to digital health, we focused on the application to drug discovery and development.  In this blog, the main point I want to emphasize is that I believe that the digital health tipping point will occur when products that benefit patients (e.g., therapeutics) facilitate the integration of digital health initiatives that currently reside in silos.

What is digital health in relation to drug discovery & development? There are many different definitions with many different components, and this, in essence, is part of the challenge (see Figure below). In early discovery biology, digital health represents various data types (e.g., human genetics, ‘omics data, cell models) and analytical methods (e.g., simple regression, machine learning, artificial intelligence).  In late discovery biology, digital health includes sophisticated analytical methods for in silico drug design and organoid models to recapitulate the human system for pre-clinical testing.…

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Date posted: December 9, 2018 | Author: | No Comments »

Categories: Drug Discovery Embedded Genomics Human Genetics

[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.,…

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Date posted: October 21, 2018 | Author: | No Comments »

Categories: Drug Discovery Embedded Genomics Human Genetics Precision Medicine

[Disclaimer: I am an employee of Celgene. The views reported here are my own.]

I presented at the PharmacoGenomics Research Network (PGRN) portion of the 2018 ASHG meeting (link to my slides here).  A major theme from my talk was that precision medicine holds promise for advancing novel therapies, but that implementation of pharmacogenomics (PGx) will happen by design not by accident. Here is what I mean – and why our health care systems need to build for this future state today.

PGx by design – PGx by design starts at the very beginning of the drug discovery journey, when the choice is made to develop a therapeutic molecule against a target or a pathway. A precision medicine hypothesis is carried forward into the design of a therapeutic molecule (“matching modality with mechanism”), pre-clinical biomarkers to measure pharmacodynamic responses, and early proof-of-concept clinical studies in defined patient subsets. Late-stage clinical development is performed in these patient subsets, and regulatory approval is obtained with a label that defines this patient subset. Health care systems will essentially be required to incorporate precision medicine into patient care.

There are emerging examples of PGx by design. Indeed, there are an increasing number of FDA approvals that fit with the PGx by design model (see figure below).…

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Date posted: August 28, 2018 | Author: | No Comments »

Categories: Embedded Genomics Human Genetics Precision Medicine

[I am an employee of Celgene. All views expressed here are my own.]

What is the clinical significance of residing within the tail of a distribution for disease risk? A new study published in Nature Genetics uses a composite polygenic score to measure extremes of genetic risk(see original article here). The authors make the bold statement: “it is time to contemplate the inclusion of polygenic risk prediction in clinical care”. In this plengegen.com blog, I briefly review the paper, frame the impact of the study in terms of “long tails”, and propose how genetic tails may be used as part of a healthcare system reimagined.

The premise of the paper is that a genome-wide polygenic score (GPS) – a composite genetic test that includes thousands and sometimes millions of genetic variants – can identify a small number of individuals from the general population that have an elevated risk.  The study applies polygenic risk scores to five common diseases but spends most attention to coronary artery disease (CAD). For each disease, the increase in risk is approximately 3- to 5-fold higher among individuals at the extreme of the polygenic tail compared to those in the general population – see Figure 2a (and below) for CAD, where ~8% of the general population is at a 3-fold increase in risk based on a polygenic risk score.…

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Date posted: June 7, 2018 | Author: | No Comments »

Categories: Drug Discovery Human Genetics

[Disclaimer: I am an employee of Celgene. The views reported here are my own.]

I recently participated in a Harvard Medical School Executive Education course on human genetics and drug discovery (link here, slides here and here). My presentation concluded with a short discussion on emerging resources such as Phenome-Wide Association Studies (PheWAS) to predict adverse drug events and guide indication selection, and protein quantitative trait loci (pQTLs) for Mendelian randomization. In this blog, I highlight briefly our recent Nature publication on pQTLs, “Genomic atlas of the human plasma proteome” (here), which represents a new public resource for drug discovery.

Human genetic targets are endowed with favorable properties, one of which is the ability to use genetic tools for nature’s randomized control trial. Central to this concept is Mendelian randomization, a method that uses human genetic variants as an instrument to examine the causal effect of a modifiable exposure (e.g., protein biomarker) on disease in observational studies (reviewed here and recent Nature Reviews Genetics here).

Proteins provide an ideal paradigm for Mendelian randomization analysis for drug discovery, as proteins are under proximal genetic control and represent the targets of most approved drugs.

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Date posted: May 1, 2018 | Author: | No Comments »

Categories: Drug Discovery Human Genetics

[Disclaimer: I am an employee of Celgene. The views reported here are my own.]

On a recent family vacation to Cumberland Island, a 9,800-acre barrier island off the coast of Georgia, I was mesmerized by the dense forest of live oak trees covered with Spanish moss. Upon first glance, the branches of these magnificent trees extend chaotically in all directions, and it is difficult to discern where the trees begin and end. But upon closer inspection, the root structure can be identified, moss disentangled, and the overall complexity unraveled.

These craggy oak trees serve as metaphor for our complex human biological ecosystem: a dense forest of molecules with gnarled branches of pathways meandering in all directions, without an obvious root structure of human disease. Extending the metaphor further, the oak trees make the point that I see as one of the most difficult aspect of drug discovery and development: understanding root cause of disease, and matching therapeutic modality and biological mechanism to prevent or cure devastating illness.

In this blog, I highlight two recent publications that underscore the importance of matching modality and mechanism. The first article, published in the New England Journal of Medicine, reported clinical data on 22 patients with beta-thalassemia treated with ex vivo gene therapy (here).…

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Date posted: April 13, 2018 | Author: | No Comments »

Categories: Drug Discovery Embedded Genomics Human Genetics

[Disclaimer: I am an employee of Celgene. The views reported here are my own.]

Drug research and development (R&D) is a slow, arduous process. As readers of this blog know, it takes >10 years and upwards of $2.5 billion dollars to bring new therapies to patients in need. An aspiration of the biopharmaceutical ecosystem is to shorten cycle times and increase probability of success, thereby dramatically improving the efficiency of R&D.

One potential solution is to use human genetics to pick targets, understand molecular mechanism, select pharmacodynamics biomarkers, and identify patients most likely to respond to treatment (see Science Translational Medicine article here). While intuitively appealing and supported by retrospective analyses (here), it is not yet routinely implemented in most R&D organizations (although see Amgen blog here; Regeneron study below). Indeed, human genetics often represents an inconvenient path to a new therapeutic, as it takes substantial effort to understand the molecular mechanism responsible for genetic risk and many such targets are difficult to drug.

But what if…

…it were possible to go from gene variant to therapeutic hypothesis instantly via in silico analysis;

…it were possible to select an “off-the shelf” therapeutic molecule that recapitulates a human genetic mutation, and take this molecule into humans almost immediately, with limited pre-clinical testing;

…it were possible to select pharmacodynamics (PD) biomarkers that capture underlying human physiology, and to measure those PD biomarkers in a small, human proof-of-mechanism clinical trial;

…it were possible to model the magnitude of effect of a therapeutic intervention relative to existing standard-of-care, and thereby to estimate the commercial market of an as-yet-to-be-approved drug?…

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Date posted: December 28, 2017 | Author: | No Comments »

Categories: Drug Discovery Embedded Genomics Human Genetics

Over the holidays my family participated in an Escape Room, a live puzzle adventure game. We worked as a team to solve riddles, find clues and, over the course of 60-minutes, complete an old town bank heist. Many of the successful clues came from unexpected places – coordinates on maps, numbers inscribed in hidden places, and physical features of the room itself. Other clues seemed promising, but ultimately led to dead ends. In the end, everything came together and we escaped with only seconds to spare.

And so it goes with the invention of new medicines. The approval of a new medicine is an Escape Room of sorts, but over the course of decades not minutes. And like an Escape Room, clues can come from unexpected places, with some leading to new insights and others leading to dead ends.

I was in an Escape Room state-of-mind as I read a Science Translational Medicine article that developed a system to differentiate blood cells into microglia-like cells to study gene variants implicated in neurodegenerative disorders (here). In this blog, I provide a brief summary of the study, and then describe the potentially interesting phenomenon of genetically driven tissue-specific pathogenicity.…

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Date posted: December 19, 2017 | Author: | No Comments »

Categories: Drug Discovery Embedded Genomics Human Genetics Precision Medicine

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

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).…

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Date posted: June 19, 2017 | Author: | No Comments »

Categories: Drug Discovery Human Genetics

A new manuscript by Jonathan Pritchard and colleagues published in Cell (see here) has garnered a lot of attention from the genetics community (see here, here, here, here, here). In this blog, I add to the ongoing commentary. I first summarize the main conclusions of the manuscript, and then I discuss the implications for drug discovery and development. For the latter, the three main points are: (1) “core genes” represent good drug targets, especially if they harbor a series of alleles that link function to phenotype; (2) regulatory networks identified by “peripheral genes” point to specific cell types and mechanism that can be used for phenotypic screens; and (3) new approaches are needed to drug cellular networks – what I will refer to as “circuit pharmacology” – as the bulk of drug discovery today is an attempt to reduce complex mechanisms to individual drug targets.

Here is a brief summary of the main conclusions of the manuscript.

  1. There is a small number of “core genes” that “provide mechanistic insights into disease biology and may suggest druggable targets.” How these core genes are defined, however, remains to be determined. The manuscript suggests a few approaches, including: genes with large effect size variants from GWAS and genes with an allelic series, especially those with lower-frequency variants of larger effects.

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Date posted: April 30, 2017 | Author: | No Comments »

Categories: Drug Discovery Human Genetics Immunogenomics

A recent study in the New England Journal of Medicine provides genetic support for a pharmacologically validated target, BAFF, in the treatment of systemic lupus erythematosus.  But can human genetics also be used to estimate the target dose and a therapeutic window?

As readers of plengegen.com know, I am constantly on the lookout for published studies that provide insight into the utility of human genetics for drug discovery and development.  This past week there was a great post from Francis Collins on the role of the NIH in the discovery (in part via human genetics) and development of tofacitinib (see here), anakinra and potentially novel targets (e.g., STING) for inflammatory diseases (here).  Nature Reviews Drug Discovery published a News & Analysis on PCSK9 as a “fertile testing ground for new drug modalities including long-acting RNA interference drugs, vaccines against self-antigens, CRISPR therapeutics and small molecules that control ribosomal activity” (here).  New York City released information about a new public health initiative, The NYC Macroscope, which will use electronic health records (EHRs) to track conditions managed by primary care practices that are important to public health..and one day may be linked to genetic data for discovery research (that is me just speculating).…

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Date posted: April 23, 2017 | Author: | No Comments »

Categories: Drug Discovery Embedded Genomics Human Genetics

In response to an original research article published in Nature by Sekar Kathiresan and colleagues (see here), I penned a News & Views piece for Nature (here), a blog for the Timmerman Report (here, here), and a podcast for BBC Inside Science (here). An important theme for drug discovery & development is that human knockouts can rule-in and to rule-out drug targets.  For human knock-out data, the key concept is to understand the effect of maximum genetic perturbation on human physiology.

  1. Rule-in drug targets: As has been described by Matt Nelson and colleagues from GlaxoSmithKline (see 2015 Nature Genetics), and David Cook and colleagues from AstraZeneca (see 2014 Nature Reviews Drug Discovery), therapeutic molecules developed against targets with human genetic data are more likely to lead to regulatory approval than those without.  PCSK9 represents the poster child for human genetic knockouts in drug discovery & development (see my plengegen.com blog here).  But there are many other examples, too.
  1. Rule-out drug targets: But human genetics can also rule-out drug targets or mechanisms that are nominated through animal models, human epidemiology or other approaches.  A prominent example is related to raising HDL cholesterol, the so-called “good cholesterol”.

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