The next generation of genomicists - PowerPoint Presentation

Slide1

The next generation of genomicists

Gerald J. Wyckoff

Training modesAre we training

genomicists to deal with the right problems?Are the training modalities we’re using proper?What questions should we be looking at?

Problems in Drug Discovery

FDA protocols.Success in animals before phased clinical drug trials**Most successful drugs target very conserved evolutionarily, probably in part for this reason

Personalized medicine

(Disclosure: I’m the founder of

Zorilla

Research, commercializing software from my lab for Drug Discovery and

OneHealth markets)

Drug Discovery

While costs for many technologies are dropping quickly…The latest report from the Tufts CSDD says costs for bringing a successful drug to market are $2.5B USD

$1B USD is prior to clinical trials

Nature, 9 March 2014

The next generation of genomicists

Embrace the need for new modes of thinkingPut genomics and evolutionary genomics at the center of drug development and discovery

Develop new models

Current Approach is Wrong

Our In silico work needs to go beyond docking to look at picking targets in an intelligent manner

Not all targets are going to allow successful development

An overwhelming number of Human alleles were suboptimal

1

Proteins that are the targets off successful drug development are very conservative- even though disease genes are often very rapidly evolving

2

Recently, data showed that common variants- even in supposedly “normal” individuals- may play a roll in some

multigenic

diseases

3

Not all disease genes are potential targets

Taken together, this means that there are many disease causal or contributing alleles (gene variants) in the population that fall below the threshold of interest for drug discovery efforts

But a precision medicine approach, which is where the industry is heading, will require drugs against a broad number of pre-existing conditions; not something the current drug pipeline is geared to do

1

Fay JC, Wyckoff GJ, Wu CI. Positive and negative selection on the human genome.

Genetics

. 2001;158(3):1227-1234.

2

Qing Zhang, Ada

Solidar

, Nicholas J.

Murgolo

,

Wynand

Alkema, Wei Ding, Peter M.

Groenen

, Jonathan R. Greene, Eric L. Gustafson,

Jan

Klomp

, Ellie D. Norris, Ping

Qiu

and Gerald J. Wyckoff, 2012. Selective Constraint: A Hallmark of Genes Successfully Targeted for Pharmaceutical Development.

American Journal of Drug Discovery and Development, 2: 184-193.

3

Ubadah

Sabbagh

,

Saman

Mullegama

, and Gerald J. Wyckoff, “Identification and Evolutionary Analysis of Potential Candidate Genes in a Human Eating Disorder,”

BioMed

Research International, vol. 2016, Article ID 7281732, 11 pages, 2016. doi:10.1155/2016/7281732

Not Just Docking

The “disease first” approach for Drug Discovery to date WILL NOT suffice in the very near futureWe need to be intelligently choosing eventual targets, not just potential targets

Where do we go from here?

Many deleterious SNPS

Estimates show that each individual carried far more deleterious SNPs (Single Nucleotide Polymorphisms) than are identified as “disease causal”

These are contributing to “genetic load”; another way of looking at it is each of these genes may manifest as a disease, disorder, or proclivity towards disease under certain environmental conditions

These aren’t what we typically think of as “disease causal”, because in many individuals they don’t actually cause disease- because the person wasn’t exposed to a particular environmental factor

Measuring evolution

We use the synonymous (non-amino acid changing) nucleotide changes as a ruler for how much change would take place if there was no selection.We would expect (under neutrality) that there would be as many amino acid changes.

Correct for number of sites.

Called

Ka

/Ks.

Conservative Bias

Qing Zhang, Ada

Solidar

, Nicholas J.

Murgolo

,

Wynand Alkema, Wei Ding, Peter M. Groenen

, Jonathan R. Greene, Eric L. Gustafson, Jan

Klomp

, Ellie D. Norris, Ping

Qiu

and Gerald J. Wyckoff, 2012. Selective Constraint: A Hallmark of Genes Successfully Targeted for Pharmaceutical Development. American Journal of Drug Discovery and Development, 2: 184-193.

Can we actualize this?We wanted to know if we could put this into practice using data from public sources

We were interested in the link between sleep and metabolic disordersNotably, the link between metabolism and neurological function in obesity

Example with GEO

Ubadah

Sabbagh

,

Saman

Mullegama

, and Gerald J. Wyckoff, “Identification and Evolutionary Analysis of Potential Candidate Genes in a Human Eating Disorder,”

BioMed

Research International, vol. 2016, Article ID 7281732, 11 pages, 2016. doi:10.1155/2016/7281732

Results

1,052 genes significantText analysis: about 100 interestingSeveral overlap across all sets:

From Text Analysis:

Evolutionary analysis: Problem

What’s Normal?

Some SNPs identified as “normal” even in reference populations likely aren’t

We see that in a screen looking at potential genes involved with eating disorders.

Some genes have a clear excess of SNPs predicted to be deleterious by programs such as SIFT or

Polyphen

Common Disorder, Common Variant

“The common disease-common variant (often abbreviated CD-CV) hypothesis predicts that common disease-causing alleles, or variants, will be found in all human populations which manifest a given disease. Common variants (not necessarily disease-causing) are known to exist in coding and regulatory sequences of genes. According to the CD-CV hypothesis, some of those variants lead to susceptibility to complex polygenic diseases. Each variant at each gene influencing a complex disease will have a small additive or multiplicative effect on the disease phenotype. These diseases, or traits, are evolutionarily neutral in part because so many genes influence the traits.”

Back to the future!

Wyckoff GJ, Wang W, Wu CI. Rapid evolution of male reproductive genes in the

descent of man. Nature. 2000 Jan 20;403(6767):304-9. PubMed PMID: 10659848.

Where do we go from here?

Better integration between Human and animal data at all levels of drug discovery.Pathway analysisPK/PD analysis

Don’t lose data from failed drug studies.

Evolutionary analysis is key -

OneHealth

Need new tools for aggregating target information.

Help prevent late stage failures in drug development.

Big Data Approach

Training the future

Must utilize new modes to ensure integrated learning across all genomics-related disciplinesContinuing education opportunities for professionals in fields related to pharmaceuticals, medicine“Borrow” OneHealth

concepts- work with Veterinarians.

For Further Information, contact:

wyckoffg@umkc.edu

Acknowledgments

The Wyckoff Lab

Lee Likins, Scott Foy, Ming Yang, Andrew

Skaff

,

Ubadah

Sabbah

,

Saman

Mullegama

, Adam

Younkin

Ada

Solidar

(B-tech Consulting)

The

Miziorko

Lab (UMKC)

Jeff Murphy (Nickel City Software)

Brian

Geisbrecht

(KSU)

And his lab

John Walker (SLU)

Jim

Riviere

(KSU)

Tom

Menees

(UMKC)

Zorilla

Research

John Crowe, Lee Whittaker

NIH 1 R41 GM 088922-01A1

NIH 2 R44 GM097902-02A1 (

Dockhorn

, PI)

NIH 1 R21 AI113552-01 (

Geisbrecht

, PI)

VaSSA

Informatics, LLC for major funding

Digital Sandbox KC

Missouri Technology Corporation

UMKC Fast-Track

UMKC SBS, UMRB, UMKC FRG, KCALSI for additional funding