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Live Conference Coverage @Medcitynews Converge 2018 @Philadelphia: Promising Drugs and Breaking Down Silos

Posted in CANCER BIOLOGY & Innovations in Cancer Therapy, Cancer Prevention: Research & Programs, Clinical & Translational, Disease Biology, Small Molecules in Development of Therapeutic Drugs, FDA Regulatory Affairs, Health Economics and Outcomes Research, Pharmaceutical Discovery, Pharmaceutical Drug Discovery, Pharmaceutical Industry Competitive Intelligence, Uncategorized, Value-based Drug Pricing, tagged , , , , , , , , , , , , , , , , , , , , , , , , on July 12, 2018| Leave a Comment »

Live Conference Coverage @Medcitynews Converge 2018 @Philadelphia: Promising Drugs and Breaking Down Silos

Reporter: Stephen J. Williams, PhD

Promising Drugs, Pricing and Access

The drug pricing debate rages on. What are the solutions to continuing to foster research and innovation, while ensuring access and affordability for patients? Can biosimilars and generics be able to expand market access in the U.S.?

Moderator: Bunny Ellerin, Director, Healthcare and Pharmaceutical Management Program, Columbia Business School
Speakers:
Patrick Davish, AVP, Global & US Pricing/Market Access, Merck
Robert Dubois M.D., Chief Science Officer and Executive Vice President, National Pharmaceutical Council
Gary Kurzman, M.D., Senior Vice President and Managing Director, Healthcare, Safeguard Scientifics
Steven Lucio, Associate Vice President, Pharmacy Services, Vizient

What is working and what needs to change in pricing models?

Robert:  He sees so many players in the onStevencology space discovering new drugs and other drugs are going generic (that is what is working).  However are we spending too much on cancer care relative to other diseases (their initiative Going Beyond the Surface)

Steven:  the advent of biosimilars is good for the industry

Patrick:  large effort in oncology, maybe too much (750 trials on Keytruda) and he says pharma is spending on R&D (however clinical trials take large chunk of this money)

Robert: cancer has gotten a free ride but cost per year relative to benefit looks different than other diseases.  Are we overinvesting in cancer or is that a societal decision

Gary:  maybe as we become more specific with precision medicines high prices may be a result of our success in specifically targeting a mutation.  We need to understand the targeted drugs and outcomes.

Patrick: “Cancer is the last big frontier” but he says prices will come down in most cases.  He gives the example of Hep C treatment… the previous only therapeutic option was a very toxic yearlong treatment but the newer drugs may be more cost effective and safer

Steven: Our blockbuster drugs could diffuse the expense but now with precision we can’t diffuse the expense over a large number of patients

President’s Cancer Panel Recommendation

Six recommendations

  1. promoting value based pricing
  2. enabling communications of cost
  3. financial toxicity
  4. stimulate competition biosimilars
  5. value based care
  6. invest in biomedical research

Patrick: the government pricing regime is hurting.  Alot of practical barriers but Merck has over 200 studies on cost basis

Robert:  many concerns/impetus started in Europe on pricing as they are a set price model (EU won’t pay more than x for a drug). US is moving more to outcomes pricing. For every one health outcome study three studies did not show a benefit.  With cancer it is tricky to establish specific health outcomes.  Also Medicare gets best price status so needs to be a safe harbor for payers and biggest constraint is regulatory issues.

Steven: They all want value based pricing but we don’t have that yet and there is a challenge to understand the nuances of new therapies.  Hard to align all the stakeholders together so until some legislation starts to change the reimbursement-clinic-patient-pharma obstacles.  Possibly the big data efforts discussed here may help align each stakeholders goals.

Gary: What is the data necessary to understand what is happening to patients and until we have that information it still will be complicated to determine where investors in health care stand at in this discussion

Robert: on an ICER methods advisory board: 1) great concern of costs how do we determine fair value of drug 2) ICER is only game in town, other orgs only give recommendations 3) ICER evaluates long term value (cost per quality year of life), budget impact (will people go bankrupt)

4) ICER getting traction in the public eye and advocates 5) the problem is ICER not ready for prime time as evidence keeps changing or are they keeping the societal factors in mind and they don’t have total transparancy in their methodology

Steven: We need more transparency into all the costs associated with the drug and therapy and value-based outcome.  Right now price is more of a black box.

Moderator: pointed to a recent study which showed that outpatient costs are going down while hospital based care cost is going rapidly up (cost of site of care) so we need to figure out how to get people into lower cost setting

Breaking Down Silos in Research

“Silo” is healthcare’s four-letter word. How are researchers, life science companies and others sharing information that can benefit patients more quickly? Hear from experts at institutions that are striving to tear down the walls that prevent data from flowing.

Moderator: Vini Jolly, Executive Director, Woodside Capital Partners
Speakers:
Ardy Arianpour, CEO & Co-Founder, Seqster @seqster
Lauren Becnel, Ph.D., Real World Data Lead for Oncology, Pfizer
Rakesh Mathew, Innovation, Research, & Development Lead, HealthShareExchange
David Nace M.D., Chief Medical Officer, Innovaccer

Seqster: Seqster is a secure platform that helps you and your family manage medical records, DNA, fitness, and nutrition data—all in one place. Founder has a genomic sequencing background but realized sequence  information needs to be linked with medical records.

HealthShareExchange.org :

HealthShare Exchange envisions a trusted community of healthcare stakeholders collaborating to deliver better care to consumers in the greater Philadelphia region. HealthShare Exchange will provide secure access to health information to enable preventive and cost-effective care; improve quality of patient care; and facilitate care transitions. They have partnered with multiple players in healthcare field and have data on over 7 million patients.

Innovacer

Data can be overwhelming, but it doesn’t have to be this way. To drive healthcare efficiency, we designed a modular suite of products for a smooth transition into a data-driven world within 4 weeks. Why does it take so much money to move data around and so slowly?

What is interoperatibility?

Ardy: We knew in genomics field how to build algorithms to analyze big data but how do we expand this from a consumer standpoint and see and share your data.

Lauren: how can we use the data between patients, doctors, researchers?  On the research side genomics represent only 2% of data.  Silos are one issue but figuring out the standards for data (collection, curation, analysis) is not set. Still need to improve semantic interoperability. For example Flatiron had good annotated data on male metastatic breast cancer.

David: Technical interopatabliltiy (platform), semantic interopatability (meaning or word usage), format (syntactic) interopatibility (data structure).  There is technical interoperatiblity between health system but some semantic but formats are all different (pharmacies use different systems and write different prescriptions using different suppliers).  In any value based contract this problem is a big issue now (we are going to pay you based on the quality of your performance then there is big need to coordinate across platforms).  We can solve it by bringing data in real time in one place and use mapping to integrate the format (need quality control) then need to make the data democratized among players.

Rakesh:  Patients data should follow the patient. Of Philadelphia’s 12 health systems we had a challenge to make data interoperatable among them so tdhey said to providers don’t use portals and made sure hospitals were sending standardized data. Health care data is complex.

David: 80% of clinical data is noise. For example most eMedical Records are text. Another problem is defining a patient identifier which US does not believe in.

 

 

 

 

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Diabetic Retinopathy

Posted in Auditory and vision, Blindness, Clinical & Translational, Curation, Diabetes Mellitus, Disease Biology, Disease Biology, Small Molecules in Development of Therapeutic Drugs, Endocrine Diseases, Immunology, Immunotherapy, Innovations, International Global Work in Pharmaceutical, Monoclonal antibody therapy, tagged , , , , on November 16, 2015| Leave a Comment »

Diabetic Retinopathy

Larry H. Bernstein, MD, FCAP, Curator

LPBI

 

Lucentis effective for proliferative diabetic retinopathy

NIH-funded clinical trial marks first major advance in therapy in 40 years.

http://www.nih.gov/news-events/news-releases/lucentis-effective-proliferative-diabetic-retinopathy

Illustration of ruptured blood vessels

http://www.nih.gov/sites/default/files/styles/featured_media_breakpoint-large-extra/public/news-events/news-releases/2015/20151116-eye-blood-vessels.jpg

Abnormal blood vessels bleeding into the center of the eye due to proliferative diabetic retinopathy.

https://youtu.be/jPoCIa0_1po

A clinical trial funded by the National Institutes of Health has found that the drug ranibizumab (Lucentis) is highly effective in treating proliferative diabetic retinopathy. The trial, conducted by the Diabetic Retinopathy Clinical Research Network (DRCR.net) compared Lucentis with a type of laser therapy called panretinal or scatter photocoagulation, which has remained the gold standard for proliferative diabetic retinopathy since the mid-1970s. The findings demonstrate the first major therapy advance in nearly 40 years.

“These latest results from the DRCR Network provide crucial evidence for a safe and effective alternative to laser therapy against proliferative diabetic retinopathy,” said Paul A. Sieving, M.D., Ph.D., director of NIH’s National Eye Institute (NEI), which funded the trial.  The results were published online today in the Journal of the American Medical Association.

Treating abnormal retinal blood vessels with laser therapy became the standard treatment for proliferative diabetic retinopathy after the NEI announced results of the Diabetic Retinopathy Study in 1976. Although laser therapy effectively preserves central vision, it can damage night and side vision; so, researchers have sought therapies that work as well or better than laser but without such side effects.

A complication of diabetes, diabetic retinopathy can damage blood vessels in the light-sensitive retina in the back of the eye. As the disease worsens, blood vessels may swell, become distorted and lose their ability to function properly. Diabetic retinopathy becomes proliferative when lack of blood flow in the retina increases production of a substance called vascular endothelial growth factor, which can stimulate the growth of new, abnormal blood vessels. These new vessels are prone to bleeding into the center of the eye, often requiring a surgical procedure called a vitrectomy to clear the blood. The abnormal blood vessels can also cause scarring and retinal detachment. Lucentis is among several drugs that block the effects of vascular endothelial growth factor.

About 7.7 million U.S. residents have diabetic retinopathy, a leading cause of blindness among working-age Americans. Among these, about 1.5 percent have PDR.

The DRCR.net enrolled 305 participants (394 eyes) with proliferative diabetic retinopathy in one or both eyes at 55 clinical sites across the country. Eyes were assigned randomly to treatment with Lucentis or laser. For participants who enrolled both eyes in the study, one eye was assigned to the laser group and the other was assigned to the Lucentis group. About half of the eyes assigned to the laser group required more than one round of laser treatment. In the other group, Lucentis (0.5 mg/0.05 ml) was given via injections into the eye once per month for three consecutive months, and then as needed until the disease resolved or stabilized.

Because Lucentis is commonly used to treat diabetic macular edema—the build-up of fluid in the central area of the retina—the study permitted the use of Lucentis for diabetic macular edema in the laser group, if necessary. Slightly more than half (53 percent) of eyes in the laser group received Lucentis injections to treat diabetic macular edema. About 6 percent of eyes in the Lucentis group received laser therapy, mostly to treat retinal detachment or bleeding.

At two years, vision in the Lucentis group improved by about half a line on an eye chart compared with virtually no change in the laser group. There was little change in side vision with injection (average worsening of 23 decibels) but a substantial loss of side vision with laser (average worsening of 422 decibels).   The vitrectomy rate was lower in the Lucentis group (8 of 191 eyes) than in the laser group (30 of 203 eyes).

Rates of serious systemic adverse events, including cardiac arrest and stroke, were similar between the two groups. One patient in the Lucentis group developed endophthalmitis, an infection in the eye. Other side effects were low, with little difference between treatment groups.

“Lucentis should be considered a viable treatment option for people with proliferative diabetic retinopathy, especially for individuals needing anti-vascular endothelial growth factor for diabetic macular edema,” said Jeffrey G. Gross, M.D., of the Carolina Retina Center in Columbia, South Carolina, who chaired the study. Dr. Gross presented results November 13, 2015, at the annual meeting of the American Academy of Ophthalmology in Las Vegas.

In addition to treating proliferative diabetic retinopathy, the report suggests Lucentis may even help prevent diabetic macular edema from occurring. Among people without diabetic macular edema at the start of the study, only 9 percent of Lucentis-treated eyes developed diabetic macular edema during the study, compared with 28 percent in the laser group. The DRCR.net will continue to follow patients in this study for a total of five years.

The DRCR.net is dedicated to facilitating multicenter clinical research of diabetic eye disease. The network formed in 2002 and comprises more than 350 physicians practicing at more than 140 clinical sites across the country. For more information, visit the DRCR.net website at http://drcrnet.jaeb.org/(link is external).

The study was funded by NEI grants EY14231, EY23207, EY18817.

Lucentis was provided by Genentech. Additional research funding for this study was provided by the National Institute of Diabetes and Digestive and Kidney Diseases, also a part of the NIH.

The study is registered as NCT01489189 at ClinicalTrials.gov(link is external).

The NEI provides information about diabetic eye disease at http://www.nei.nih.gov/health/diabetic/.

Information about diabetes is available through the National Diabetes Education Program, www.ndep.nih.gov/.

View an NEI video about the study at https://youtu.be/jPoCIa0_1po(link is external).

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Personalized Medicine: The need for a Shared Language in Medicine

Posted in Biological Networks, Gene Regulation and Evolution, Biomarkers & Medical Diagnostics, Cell Biology, Signaling & Cell Circuits, Genome Biology, Personalized and Precision Medicine & Genomic Research, Population Health Management, Genetics & Pharmaceutical, tagged , , , , , , on October 12, 2015|

Personalized Medicine – The California Initiative

Curator: Demet Sag, PhD, CRA, GCP

Are we there yet?  Life is a journey so the science.

Governor Brown announced Precision Medicine initiative for California on April 14, 2015.  UC San Francisco is hosting the two-year initiative, through UC Health, which includes UC’s five medical centers, with $3 million in startup funds from the state. The public-private initiative aims to leverage these funds with contributions from other academic and industry partners.

With so many campuses spread throughout the state and so much scientific, clinical and computational expertise, the UC system has the potential to bring it all together, said Atul Butte, MD, PhD, who is leading the initiative.

At the beginning of 2015 President Obama signed this initiative and assigned people to work on this project.

Previously NCI Director Harold Varmus, MD said that “Precision medicine is really about re-engineering the diagnostic categories for cancer to be consistent with its genomic underpinnings, so we can make better choices about therapy,” and “In that sense, many of the things we’re proposing to do are already under way.”

The proposed initiative has two main components:

  • a near-term focus on cancers and
  • a longer-term aim to generate knowledge applicable to the whole range of health and disease.

Both components are now within our reach because of advances in basic research, including molecular biology, genomics, and bioinformatics. Furthermore, the initiative taps into converging trends of increased connectivity, through social media and mobile devices, and Americans’ growing desire to be active partners in medical research.

Since the human genome is sequenced it became clear that actually there are few genes than expected and shared among organisms to accomplish same or similar core biological functions.  As a result, knowledge of the biological role of such shared proteins in one organism can be transferred to another organism.

It was necessary to generate a dynamic yet controlled standardized collection of information with ever changing and accumulating data. It was called Gene Ontology Consortium. Three independent ontologies can be reached at  (http://www.geneontology.org) developed based on :

  1. biological process,
  2. molecular function and
  3. cellular component.

We need a common language for annotation for a functional conservation. Genesis of the grand biological unification made it possible to complete the genomic sequences of not only human but also the main model organisms and more:

·         the budding yeast, Saccharomyces cerevisiae, completed in 1996

·         the nematode worm Caenorhabditis elegans, completed in 1998

·         the fruitfly Drosophila melanogaster,

·         the flowering plant Arabidopsis thaliana

·         fission yeast Schizosaccharomyces pombe

·         the mouse , Mus musculus

On the other hand, as we know there are allelic variations that underlie common diseases and complete genome sequencing for many individuals with and without disease is required.  However, there are advantages and disadvantages as we can carry out partial surveys of the genome by genotyping large numbers of common SNPs in genome-wide association studies but there are problems such as computing the data efficiently and sharing the information without tempering privacy. Therefore we should be mindful about few main conditions including:

  1. models of the allelic architecture of commondiseases,
  2. sample size,
  3. map density and
  4. sample-collection biases.

This will lead into the cost control and efficiency while identifying genuine disease-susceptibility loci. The genome-wide association studies (GWAS) have progressed from assaying fewer than 100,000 SNPs to more than one million, and sample sizes have increased dramatically as the search for variants that explain more of the disease/trait heritability has intensified.

In addition, we must translate this sequence information from genomics locus of the genes to function with related polymorphism of these genes so that possible patterns of the gene expression and disease traits can be matched. Then, we may develop precision technologies for:

  1. Diagnostics
  2. Targeted Drugs and Treatments
  3. Biomarkers to modulate cells for correct functions

With the knowledge of:

  1. gene expression variations
  2. insight in the genetic contribution to clinical endpoints ofcomplex disease and
  3. their biological risk factors,
  4. share etiologic pathways

therefore, requires an understanding of both:

  • the structure and
  • the biology of the genome.

These studies demonstrated hundreds of associations of common genetic variants with over 80 diseases and traits collected under a controlled online resource.  However, identifying published GWAS can be challenging as a simple PubMed search using the words “genome wide association studies”  may be easily populated with un-relevant  GWAS.

National Human Genome Research Institute (NHGRI) Catalog of Published Genome-Wide Association Studies (http://www.genome.gov/gwastudies), an online, regularly updated database of SNP-trait associations extracted from published GWAS was developed.

Therefore, sequencing of a human genome is a quite undertake and requires tools to make it possible:

  • to explore the genetic component incomplex diseases and
  • to fully understand the genetic pathways contributing tocomplex disease

The rapid increase in the number of GWAS provides an unprecedented opportunity to examine the potential impact of common genetic variants on complex diseases by systematically cataloging and summarizing key characteristics of the observed associations and the trait/disease associated SNPs (TASs) underlying them.

With this in mind, many forms can be established:

  1. to describe the features of this resource and the methods we have used to produce it,
  2. to provide and examine key descriptive characteristics of reported TASs such as estimated risk allele frequencies and odds ratios,
  3. to examine the underlying functionality of reported risk loci by mapping them to genomic annotation sets and assessing overrepresentation via Monte Carlo simulations and
  4. to investigate the relationship between recent human evolution and human disease phenotypes.

This procedure has no clear path so there are several obstacles in the actual functional variant that is often unknown. This may be due to:

  1. trait/disease associated SNPs (TASs),
  2. a well known SNP+ strong linkage disequilibrium (LD) with the TAS,
  3. an unknown common SNP tagged by a haplotype
  4. rare single nucleotide variant tagged by a haplotype on which the TAS occurs, or
  5. Copy Number variation (CNV), a linked copy number variant.

There can be other factors such as

  • Evolution,
  • Natural Selection
  • Environment
  • Pedigree
  • Epigenetics

Even though heritage is another big factor, the concept of heritability and its definition as an estimable, dimensionless population parameter as introduced by Sewall Wright and Ronald Fisher almost a century ago.

As a result, heritability gain interest since it allows us to compare of the relative importance of genes and environment to the variation of traits within and across populations. The heritability is an ongoing mechanism and  remains as a key:

  • to selection in evolutionary biology and agriculture, and
  • to the prediction of disease risk in medicine.

Table 1.

Reported TASs associated with two or more distinct traits

Chromosomal region Rs number(s) Attributed genes Associated traits reported in catalog
1p13.2 rs2476601, rs6679677 PTPN22 Crohn’s disease, type 1 diabetes, rheumatoid arthritis
1q23.2 rs2251746, rs2494250 FCER1A Serum IgE levels, select biomarker traits (MCP1)
2p15 rs1186868, rs1427407 BCL11A Fetal hemoglobin, F-cell distribution
2p23.3 rs780094 GCKR CRP, lipids, waist circumference
6p21.33 rs3131379, rs3117582 HLA / MHC region Systemic lupus erythematosus, lung cancer, psoriasis, inflammatory bowel disease, ulcerative colitis, celiac disease, rheumatoid arthritis, juvenile idiopathic arthritis, multiple sclerosis, type 1 diabetes
6p22.3 rs6908425, rs7756992, rs7754840, rs10946398, rs6931514 CDKAL1 Crohn’s disease, type 2 diabetes
6p25.3 rs1540771, rs12203592, rs872071 IRF4 Freckles, hair color, chronic lymphocytic leukemia
6q23.3 rs5029939, rs10499194 TNFAIP3 Systemic lupus erythematosus, rheumatoid arthritis
7p15.1 rs1635852, rs864745 JAZF1 Height, type 2 diabetes*
8q24.21 rs6983267 Intergenic Prostate or colorectal cancer, breast cancer
9p21.3 rs10811661, rs1333040, rs10811661, rs10757278, rs1333049 CDKN2A, CDKN2B Type 2 diabetes, intracranial aneurysm, myocardial infarction
9q34.2 rs505922, rs507666, rs657152 ABO Protein quantitative trait loci (TNF-α), soluble ICAM-1, plasma levels of liver enzymes (alkaline phosphatase)
12q24 rs1169313, rs7310409, rs1169310, rs2650000 HNF1A Plasma levels of liver enzyme (GGT), C-reactive protein, LDL cholesterol
16q12.2 rs8050136, rs9930506, rs6499640, rs9939609, rs1121980 FTO Type 2 diabetes, body mass index or weight
17q12 rs7216389, rs2872507 ORMDL3 Asthma, Crohn’s disease
17q12 rs4430796 TCF2 Prostate cancer, type 2 diabetes
18p11.21 rs2542151 PTPN2 Type 1 diabetes, Crohn’s disease
19q13.32 rs4420638 APOE, APOC1, APOC4 Alzheimer’s disease, lipids

* The well known association of JAZF1 with prostate cancer was reported with a p value of 2 × 10−6 (18), which did not meet the threshold of 5 × 10−8 for this analysis.

PMC full text: Proc Natl Acad Sci U S A. 2009 Jun 9; 106(23): 9362–9367.

Published online 2009 May 27. doi:  10.1073/pnas.0903103106

.

Table 2

Allele-Frequency Data for Nine Reproducible Associations

frequency
gene diseasea SNP associated alleleb Europeand Africane δf FST reference(s)c
CTLA4 T1DM Thr17Ala Ala .38 (1,670) .209 (402) .171 .06 Osei-Hyiaman et al. 2001; Lohmueller et al. 2003
DRD3 Schizophrenia Ser9Gly Ser/Ser .67 (202) .116 (112) .554 .458 Crocq et al. 1996; Lohmueller et al.2003
AGT Hypertension Thr235Met Thr .42 (3,034) .91 (658) .49 .358 Rotimi et al. 1996; Nakajima et al.2002
PRNP CJD Met129Val Met .72 (138) .556 (72) .164 .049 Hirschhorn et al. 2002; Soldevila et al. 2003
F5 DVT Arg506Gln Gln .044 (1,236) .00 (251) .044 .03 Rees et al. 1995; Hirschhorn et al.2002
HFE HFE Cys382Tyr Tyr .038 (2,900) .00 (806) .038 .024 Feder et al. 1996; Merryweather-Clarke et al. 1997
MTHFR DVT C677T T .3 (188) .066 (468) .234 .205 Schneider et al. 1998; Ray et al.2002
PPARG T2DM Pro12Ala Pro .925 (120) 1.0 (120) .075 .067 Altshuler et al. 2000; HapMap Project
KCNJ11 T2DM Asp23Lys Lys .36 (96) .09 (98) .27 .182 Florez et al. 2004

aCJD = Creutzfeldt-Jacob disease; DVT = deep venous thrombosis; HFE = hemochromatosis; T1DM = type I diabetes; T2DM = type II diabetes.

bThe associated allele is the SNP associated with disease, regardless of whether it is the derived or the ancestral allele. The frequencies for this allele are given.

cThe reference that claims this to be a reproducible association, as well as the reference from which the allele frequencies were taken. For allele frequencies obtained from a meta-analysis, only the reference claiming reproducible association is given.

dAllele frequency obtained from the literature involving a European population. Either the general population frequency or the frequency in control groups in an association study was used. To reduce bias, when a control frequency was used for Europeans, a control frequency was also used for Africans. The total number of chromosomes surveyed is given in parentheses after each frequency.

eAllele frequency obtained from the literature involving a West African population. The total number of chromosomes surveyed is given in parentheses after each frequency.

fδ = The difference in the allele frequency between Europeans and Africans.

Table 3

PMC full text:

 Am J Hum Genet. 2006 Jan; 78(1): 130–136.

Published online 2005 Nov 16. doi:  10.1086/499287

Copyright/License ►Request permission to reuse

Allele-Frequency Data for 39 Reported Associations

frequency
gene disease/phenotypea SNP associated alleleb Europeand Africane δf FST referencec
ADRB1 MI Arg389Gly Arg .717 (46) .467 (30) .251 .1 Iwai et al. 2003
ALOX5AP MI, stroke rs10507391 T .682 (44) .159 (44) .523 .425 Helgadottir et al. 2004
CAT Hypertension −844 (C/T) Tg .714 (42) .659 (44) .055 0 Jiang et al. 2001
CCR2 AIDS susceptibility Ile64Val Val .87 (46) .813 (48) .057 0 Smith et al. 1997
CD36 Malaria Y to stop Stop 0 (46) .083 (48) .083 .062 Aitman et al. 2000
F13 MI Val34Leu Val .762 (42) .795 (44) .033 0 Kohler et al. 1999
FGA Pulmonary embolism Thr312Ala Ala .2 (40) .5 (42) .3 .159 Carter et al. 2000
GP1BA CAD Thr145Met Met .022 (46) .167 (48) .145 .095 Gonzalez-Conejero et al.1998
ICAM1 MS Lys469Glu Lys .643 (42) .875 (48) .232 .12 Nejentsev et al. 2003
ICAM1 Malaria Lys29Met Met 0 (46) .354 (48) .354 .335 Fernandez-Reyes et al.1997
IFNGR1 Hp infection −56 (C/T) T .455 (44) .604 (48) .15 .023 Thye et al. 2003
IL13 Asthma −1055 (C/T) T .196 (46) .25 (44) .054 0 van der Pouw Kraan et al. 1999
IL13 Bronchial asthma Arg110Gln Gln .273 (44) .119 (42) .154 .05 Heinzmann et al. 2003
IL1A AD −889 (C/T) T .295 (44) .391 (46) .096 0 Nicoll et al. 2000
IL1B Gastric cancer −31 (C/T) T .826 (46) .375 (48) .451 .335 El-Omar et al. 2000
IL3 RA −16 (C/T) C .739 (46) .875 (48) .136 .037 Yamada et al. 2001
IL4 Asthma −590 (T/C) T .174 (46) .708 (48) .534 .436 Noguchi et al. 1998
IL4R Asthma Gln576Arg Arg .295 (44) .565 (46) .27 .118 Hershey et al. 1997
IL6 Juvenile arthritis −174 (C/G) G .5 (44) 1 (46) .5 .494 Fishman et al. 1998
IL8 RSV bronchiolitis −251 (T/A) Th .659 (44) .229 (48) .43 .301 Hull et al. 2000
ITGA2 MI 807 (C/T) T .316 (38) .25 (48) .066 0 Moshfegh et al. 1999
LTA MI Thr26Asn Asn .357 (42) .5 (44) .143 .018 Ozaki et al. 2002
MC1R Fair skin Val92Met Met .068 (44) 0 (44) .068 .047 Valverde et al. 1995
NOS3 MI Glu298Asp Asp .5 (44) .136 (44) .364 .247 Shimasaki et al. 1998
PLAU AD Pro141Leu Pro .659 (44) .979 (48) .32 .287 Finckh et al. 2003
PON1 CAD Arg192Gln Arg .174 (46) .727 (44) .553 .461 Serrato and Marian 1995
PON2 CAD Cys311Ser Ser .826 (46) .762 (42) .064 0 Sanghera et al. 1998
PTGS2 Colon cancer −765 (G/C) C .238 (42) .292 (48) .054 0 Koh et al. 2004
PTPN22i RA Arg620Trp Trp .084 (1,120) .024 (818) .059 .03 Begovich et al. 2004
SELE CAD Ser128Arg Arg .091 (44) .021 (48) .07 .025 Wenzel et al. 1994
SELL IgA nephropathy Pro238Ser Ser .065 (46) .333 (48) .268 .183 Takei et al. 2002
SELP MI Thr715Pro Thr .864 (44) .977 (44) .114 .063 Herrmann et al. 1998
SFTPB ARDS Ile131Thr Thr .5 (44) .348 (46) .152 .025 Lin et al. 2000
SPD RSV infection Met11Thr Met .568 (44) .478 (46) .09 0 Lahti et al. 2002
TF AD Pro570Ser Pro .957 (46) .935 (46) .022 0 Zhang et al. 2003
THBD MI Ala455Val Ala .87 (46) .848 (46) .022 0 Norlund et al. 1997
THBS4 MI Ala387Pro Pro .341 (44) .083 (48) .258 .166 Topol et al. 2001
TNFA Infectious disease −308 (A/G) A .182 (44) .205 (44) .023 0 Bayley et al. 2004
VCAM1 Stroke in SCD Gly413Ala Gly 1 (46) .938 (48) .063 .041 Taylor et al. 2002

aAD = Alzheimer disease; AIDS = acquired immunodeficiency syndrome; ARDS = acute respiratory distress syndrome; CAD = coronary artery disease; Hp = Helicobacter pylori; MI = myocardial infarction; MS = multiple sclerosis; RA = rheumatoid arthritis; RSV = respiratory syncytial virus; SCD = sickle cell disease.

bThe associated allele is the SNP associated with disease, regardless of whether it is the derived or the ancestral allele. The frequencies for this allele are given.

cThe reference that reported association with the listed disease/phenotype.

dFrequency obtained from the Seattle SNPs database for the European sample. The total number of chromosomes surveyed is given in parentheses after each frequency.

eFrequency obtained from the Seattle SNPs database for the African American sample. The total number of chromosomes surveyed is given in parentheses after each frequency.

fδ = The difference in the allele frequency between African Americans and Europeans.

gAssociated allele in database is A.

hAssociated allele in reference is A.

iThis SNP was not from the Seattle SNPs database; instead, allele frequencies from Begovich et al. (2004) were used.

They reported that “The SNPs associated with common disease that we investigated do not show much higher levels of differentiation than those of random SNPs. Thus, in these cases, ethnicity is a poor predictor of an individual’s genotype, which is also the pattern for random variants in the genome. This lends support to the hypothesis that many population differences in disease risk are environmental, rather than genetic, in origin. However, some exceptional SNPs associated with common disease are highly differentiated in frequency across populations, because of either a history of random drift or natural selection. The exceptional SNPs  are located in AGT, DRD3, ALOX5AP, ICAM1, IL1B, IL4, IL6, IL8, and PON1. Of note, evidence of selection has been observed for AGT (Nakajima et al. 2004), IL4(Rockman et al. 2003), IL8 (Hull et al. 2001), and PON1 (Allebrandt et al. 2002). Yet, for the vast majority of the common-disease–associated polymorphisms we examined, ethnicity is likely to be a poor predictor of an individual’s genotype.”

In 2002The International HapMap Project was launched:

  • to provide a public resource
  • to accelerate medical genetic research.

Two Hapmap projects were completed. In phase I the objective was to genotype at least one common SNP every 5 kilobases (kb) across the euchromatic portion of the genome in 270 individuals from four geographically diverse population. In Phase II of the HapMap Project, a further 2.1 million SNPs were successfully genotyped on the same individuals.

The re-mapping of SNPs from Phase I of the project identified 21,177 SNPs that had an ambiguous position or some other feature indicative of low reliability; these are not included in the filtered Phase II data release. All genotype data are available from the HapMap Data Coordination Center (http://www.hapmap.org) and dbSNP (http://www.ncbi.nlm.nih.gov/SNP).

In the Phase II HapMap we identified 32,996 recombination hotspots3,6,36 (an increase of over 50% from Phase I) of which 68% localized to a region of≤5 kb. The median map distance induced by a hotspot is 0.043 cM (or one crossover per 2,300 meioses) and the hottest identified, on chromosome 20, is 1.2 cM (one crossover per 80 meioses). Hotspots account for approximately 60% of recombination in the human genome and about 6% of sequence (Supplementary Fig. 6).

In addition to many previously identified regions in HapMap Phase I including LARGE, SYT1 andSULT1C2 (previously called SULT1C1), about  200 regions identified from the Phase II HapMap that include many established cases of selection, such as the genes HBB andLCT, the HLA region, and an inversion on chromosome 17. Finally, in the future, whole-genome sequencing will provide a natural convergence of technologies to type both SNP and structural variation. Nevertheless, until that point, and even after, the HapMap Project data will provide an invaluable resource for understanding the structure of human genetic variation and its link to phenotype.

 

FUNCTIONAL GENOMICS AND DATA FOR MEDICINE:  BIOINFORMATICS/COMPUTER BIOLOGY

HMM libraries, such as PANTHER, Pfam, and SMART, are used primarily to recognize and annotate conserved motifs in protein sequences.

In the genomic era, one of the fundamental goals is to characterize the function of proteins on a large scale.

PANTHER, for relating protein sequence relationships to function relationships in a robust and accurate way under two main parts:

  • the PANTHER library (PANTHER/LIB)- collection of “books,” each representing a protein family as a multiple sequence alignment, a Hidden Markov Model (HMM), and a family tree.
  • the PANTHER index (PANTHER/X)- ontology for summarizing and navigating molecular functions and biological processes associated with the families and subfamilies.

PANTHER can be applied on three areas of active research:

  • to report the size and sequence diversity of the families and subfamilies, characterizing the relationship between sequence divergence and functional divergence across a wide range of protein families.
  • use the PANTHER/X ontology to give a high-level representation of gene function across the human and mouse genomes.
  • to rank missense single nucleotide polymorphisms (SNPs), on a database-wide scale, according to their likelihood of affecting protein function.

PRINTS is a compendium of protein motif ‘fingerprints’. A fingerprint is defined as a group of motifs excised from conserved regions of a sequence alignment, whose diagnostic power or potency is refined by iterative databasescanning (in this case the OWL composite sequence database).

The information contained within PRINTS is distinct from, but complementary to the consensus expressions stored in the widely-used PROSITE dictionary of patterns.

However, the position-specific amino acid probabilities in an HMM can also be used to annotate individual positions in a protein as being conserved (or conserving a property such as hydrophobicity) and therefore likely to be required for molecular function. For example, a mutation (or variant) at a conserved position is more likely to impact the function of that protein.

In addition, HMMs from different subfamilies of the same family can be compared with each other, to provide hypotheses about which residues may mediate the differences in function or specificity between the subfamilies.

Several computational algorithms and databases for comparing protein sequences developed and matured:

  1. particularly Hidden Markov Models (HMM;Krogh et al. 1994; Eddy 1996) and
  2. PSI-BLAST (Altschul et al. 1997),

The profile has a different amino acid substitution vector at each position in the profile, based on the pattern of amino acids observed in a multiple alignment of related sequences.

Profile methods combine algorithms with databases: A group of related sequences is used to build a statistical representation of corresponding positions in the related proteins. The power of these methods therefore increases as new sequences are added to the database of known proteins.

Multiple sequence alignments (Dayhoff et al. 1974) and profiles have allowed a systematic study of related sequences. One of the key observations is that some positions are “conserved,” that is, the amino acid is invariant or restricted to a particular property (such as hydrophobicity), across an entire group of related sequences.

The dependence of profile and pattern-matching approaches (Jongeneel et al. 1989) on sequence databases led to the development of databases of profiles

  1. BLOCKS,Henikoff and Henikoff 1991;
  2. PRINTS,Attwood et al. 1994) and
  3. patterns (Prosite,Bairoch 1991) that could be searched in much the same way as sequence databases.

Among the most widely used protein family databases are

  1. Pfam (Sonnhammer et al. 1997;Bateman et al. 2002) and
  2. SMART (Schultz et al. 1998;Letunic et al. 2002), which combine expert analysis with the well-developed HMM formalism for statistical modeling of protein families (mostly families of related protein domains).

Either knowing its family membership to predict its function, or subfamily within that family is enough (Hannenhalli and Russell 2000).

  • Phylogenetic trees (representing the evolutionary relationships between sequences) and
  • dendrograms (tree structures representing the similarity between sequences) (e.g.,Chiu et al. 1985; Rollins et al. 1991).

The PANTHER/LIB HMMs can be viewed as a statistical method for scoring the “functional likelihood” of different amino acid substitutions on a wide variety of proteins. Because it uses evolutionarily related sequences to estimate the probability of a given amino acid at a particular position in a protein, the method can be referred to as generating “position-specific evolutionary conservation” (PSEC) scores.

Schematic illustration of the process for building PANTHER families.

  1. Family clustering.
  2. Multiple sequence alignment (MSA), family HMM, and family tree building.
  3. Family/subfamily definition and naming.
  4. Subfamily HMM building.
  5. Molecular function and biological process association.

Of these, steps 1, 2, and 4 are computational, and steps 3 and 5 are human-curated (with the extensive aid of software tools).

 

 

Further Reading

Human Phenome Project: Freimer N., Sabatti C. The human phenome project. Nat. Genet. 2003;34:15–21.

Jones R., Pembrey M., Golding J., Herrick D. The search for genenotype/phenotype associations and the phenome scan. Paediatr. Perinat. Epidemiol. 2005;19:264–275.

Stearns F.W. One hundred years of pleiotropy: A retrospective. Genetics.2010;186:767–773.

Welch J.J., Waxman D. Modularity and the cost of complexity. Evolution.2003;57:1723–1734.

Albert A.Y., Sawaya S., Vines T.H., Knecht A.K., Miller C.T., Summers B.R., Balabhadra S., Kingsley D.M., Schluter D. The genetics of adaptive shape shift in stickleback: Pleiotropy and effect size. Evolution. 2008;62:76–85.

Brem R.B., Yvert G., Clinton R., Kruglyak L. Genetic dissection of transcriptional regulation in budding yeast. Science. 2002;296:752–755.

Morley M., Molony C.M., Weber T.M., Devlin J.L., Ewens K.G., Spielman R.S., Cheung V.G. Genetic analysis of genome-wide variation in human gene expression. Nature. 2004;430:743–747. [PMC free article] [PubMed]

Wagner G.P., Zhang J. The pleiotropic structure of the genotype-phenotype map: The evolvability of complex organisms. Nat. Rev. Genet. 2011;12:204–213.

Cooper Z.N., Nelson R.M., Ross L.F. Informed consent for genetic research involving pleiotropic genes: An empirical study of ApoE research. IRB. 2006;28:1–11.

 

Model Organisms:

Worm Sequencing Consortium. The C. elegans Sequencing Consortium Genome sequence of the nematode C. elegans: a platform for investigating biology. Science.1998;282:2012–2018.

Adams MD, et al. The genome sequence of Drosophila melanogaster. Science.2000;287:2185–2195.

Meinke DW, et al. Arabidopsis thaliana: a model plant for genome analysis. Science. 1998;282:662–682. [PubMed]

Chervitz SA, et al. Using the Saccharomyces Genome Database (SGD) for analysis of protein similarities and structure. Nucleic Acids Res. 1999;27:74–78.

The FlyBase Consortium The FlyBase database of the Drosophila Genome Projects and community literature. Nucleic Acids Res. 1999;27:85–88.

Blake JA, et al. The Mouse Genome Database (MGD): expanding genetic and genomic resources for the laboratory mouse. Nucleic Acids Res. 2000;28:108–111.

Ball CA, et al. Integrating functional genomic information into the Saccharomyces Genome Database. Nucleic Acids Res. 2000;28:77–80.

Venter, J.C., Adams, M.D., Myers, E.W., Li, P.W., Mural, R.J., Sutton, G.G., Smith, H.O., Yandell, M., Evans, C.A., Holt, R.A., et al. 2001. The sequence of the human genome. Science 291: 1304–1351.

Lander, E.S., Linton, L.M., Birren, B., Nusbaum, C., Zody, M.C., Baldwin, J., Devon, K., Dewar, K., Doyle, M., FitzHugh, W., et al. 2001. Initial sequencing and analysis of the human genome. Nature 409: 860–921.

Mi, H., Vandergriff, J., Campbell, M., Narechania, A., Lewis, S., Thomas, P.D., and Ashburner, M. 2003. Assessment of genome-wide protein function classification for Drosophila melanogaster. Genome Res.

Ashburner, M., Ball, C.A., Blake, J.A., Botstein, D., Butler, H., Cherry, J.M., Davis, A.P., Dolinski, K., Dwight, S.S., Eppig, J.T., et al. The Gene Ontology Consortium. 2000. Gene ontology: Tool for the unification of biology. Nat. Genet. 25: 25–29.

 

Computational Biology

Attwood TK, Beck ME, Bleasby AJ, Parry-Smith DJ. PRINTS–a database of protein motif fingerprints. Nucleic Acids Res. 1994 Sep;22(17):3590-6.

Obenauer JC, Yaffe MB. Computational prediction of protein-protein interactions.

Methods Mol Biol. 2004;261:445-68. Review.

Aitken A. Protein consensus sequence motifs. Mol Biotechnol. 1999 Oct;12(3):241-53. Review.

Bork P, Koonin EV. Protein sequence motifs. Curr Opin Struct Biol. 1996 Jun;6(3):366-76. Review.

Hodgman TC. The elucidation of protein function by sequence motif analysis.  Comput Appl Biosci. 1989 Feb;5(1):1-13. Review.

Altschul, S.F., Madden, T.L., Schäffer, A.A., Zhang, J., Zhang, Z., Miller, W., and Lipman, D.J. 1997. Gapped BLAST and PSI-BLAST: A new generation of protein database search programs. Nucleic Acids Res. 25: 3389–3402.

Spencer CC, et al. The influence of recombination on human genetic diversity.PLoS Genet. 2006;2:e148.

Petes TD. Meiotic recombination hot spots and cold spots. Nature Rev. Genet.2001;2:360–369.

Griffiths RC, Tavaré S. The age of a mutation in a general coalescent tree. Stoch Models. 1998;14:273–295. doi: 10.1080/15326349808807471.

Gauderman WJ. Sample size requirements for matched case-control studies of gene-environment interaction. Stat Med. 2002;21(1):35–50. doi: 10.1002/sim.973.

Attwood, T.K., Beck, M.E., Bleasby, A.J., and Parry-Smith, D.J. 1994. PRINTS—A database of protein motif fingerprints. Nucleic Acids Res. 22: 3590–3596.

Bairoch, A. 1991. PROSITE: A dictionary of sites and patterns in proteins. Nucleic Acids Res. 19 Suppl: 2241–2245.

Bairoch, A. and Apweiler, R. 2000. The SWISS-PROT protein sequence database and its supplement TrEMBL in 2000. Nucleic Acids Res. 28: 45–48.

Bateman, A., Birney, E., Cerruti, L., Durbin, R., Etwiller, L., Eddy, S.R., Griffiths-Jones, S., Howe, K.L., Marshall, M., and Sonnhammer, E.L. 2002. The Pfam protein families database. Nucleic Acids Res. 30: 276–280.

Sonnhammer, E.L., Eddy, S.R., and Durbin, R. 1997. Pfam: A comprehensive database of protein domain families based on seed alignments. Proteins 28:405–420.

Swets, J.A. 1988. Measuring the accuracy of diagnostic systems. Science 240:1285–1293. [PubMed]

Thomas, P.D., Kejariwal, A., Campbell, M.J., Mi, H., Diemer, K., Guo, N., Ladunga, I., Ulitsky-Lazareva, B., Muruganujan, A., Rabkin, S., et al. 2003. PANTHER: A browsable database of gene products organized by biological function, using curated protein family and subfamily classification. Nucleic Acids Res. 31: 334–341.

HUGO Gene Nomenclature Committee (2011). HGNC Database.http://www.genenames.org/.

 

Population Genomics, GWAS, Inheritance, Heritability, Migration, Selection  an Evolution:

Dayhoff, M.O., Barker, W.C., and McLaughlin, P.J. 1974. Inferences from protein and nucleic acid sequences: Early molecular evolution, divergence of kingdoms and rates of change. Orig. Life 5: 311–330.

Joseph Lachance Disease-associated alleles in genome-wide association studies are enriched for derived low frequency alleles relative to HapMap and neutral expectations BMC Med Genomics. 2010; 3: 57.

Joseph Lachance, Sarah A. Tishkoff  Biased Gene Conversion Skews Allele Frequencies in Human Populations, Increasing the Disease Burden of Recessive Alleles  Am J Hum Genet. 2014 October 2; 95(4): 408-420.

Hemalatha Kuppusamy, Helga M. Ogmundsdottir, Eva Baigorri, Amanda Warkentin, Hlif Steingrimsdottir, Vilhelmina Haraldsdottir, Michael J. Mant, John Mackey, James B. Johnston, Sophia Adamia, Andrew R. Belch, Linda M. Pilarski Inherited Polymorphisms in Hyaluronan Synthase 1 Predict Risk of Systemic B-Cell Malignancies but Not of Breast Cancer  PLoS One. 2014; 9(6): e100691.

Joseph Lachance, Sarah A. Tishkoff  Population Genomics of Human Adaptation

Annu Rev Ecol Evol Syst. Author manuscript; available in PMC 2014 November 5.

Published in final edited form as: Annu Rev Ecol Evol Syst. 2013 November; 44: 123–143

Joseph Lachance, Sarah A. Tishkoff SNP ascertainment bias in population genetic analyses: Why it is important, and how to correct it  Bioessays.

Erik Corona, Rong Chen, Martin Sikora, Alexander A. Morgan, Chirag J. Patel, Aditya Ramesh, Carlos D. Bustamante, Atul J. Butte Analysis of the Genetic Basis of Disease in the Context of Worldwide Human Relationships and Migration PLoS Genet. 2013 May; 9(5): e1003447.

Olga Y. Gorlova, Jun Ying, Christopher I. Amos, Margaret R. Spitz, Bo Peng, Ivan P. Gorlov J Derived SNP Alleles Are Used More Frequently Than Ancestral Alleles As Risk-Associated Variants In Common Human Diseases Bioinform Comput Biol.

Ani Manichaikul, Wei-Min Chen, Kayleen Williams, Quenna Wong, Michèle M. Sale, James S. Pankow, Michael Y. Tsai, Jerome I. Rotter, Stephen S. Rich, Josyf C. Mychaleckyj  Analysis of Family- and Population-Based Samples in Cohort Genome-Wide Association Studies Hum Genet.

Altshuler D, Daly MJ, Lander ES. Genetic mapping in human disease. Science. 2008; 322(5903):881–888. doi: 10.1126/science.1156409.

Wellcome Trust Case Control Consortium. Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature. 2007;447(7145):661–678. doi: 10.1038/nature05911.

Kotowski IK, Pertsemlidis A, Luke A, Cooper RS, Vega GL, Cohen JC, Hobbs HH. A spectrum of PCSK9 Alleles contributes to plasma levels of low-density lipoprotein cholesterol. American Journal of Human Genetics.2006;78(3):410–422. doi: 10.1086/500615.

Tomlinson I, Webb E, Carvajal-Carmona L, Broderick P, Kemp Z, Spain S, Penegar S, Chandler I, Gorman M, Wood W. et al. A genome-wide association scan of tag SNPs identifies a susceptibility variant for colorectal cancer at 8q24.21. Nature Genetics. 2007;39(8):984–988. doi: 10.1038/ng2085.

Todd JA, Walker NM, Cooper JD, Smyth DJ, Downes K, Plagnol V, Bailey R, Nejentsev S, Field SF, Payne F. et al. Robust associations of four new chromosome regions from genome-wide analyses of type 1 diabetes. Nature Genetics. 2007;39(7):857–864. doi: 10.1038/ng2068.

Manolio TA, Collins FS, Cox NJ, Goldstein DB, Hindorff LA, Hunter DJ, McCarthy MI, Ramos EM, Cardon LR, Chakravarti A. et al. Finding the missing heritability of complex diseases. Nature. 2009;461(7265):747–753. doi: 10.1038/nature08494.

Maher B. Personal genomes: The case of the missing heritability. Nature.2008;456(7218):18–21. doi: 10.1038/456018a.

Clark AG, Boerwinkle E, Hixson J, Sing CF. Determinants of the success of whole-genome association testing. Genome Res. 2005;15(11):1463–1467. doi: 10.1101/gr.4244005.

Clarke AJ, Cooper DN. GWAS: heritability missing in action? European Journal of Human Genetics. 2010;18:859–861. doi: 10.1038/ejhg.2010.35.

Moore JH, Williams SM. Epistasis and its implications for personal genetics. Am J Hum Genet. 2009;85(3):309–320. doi: 10.1016/j.ajhg.2009.08.006.

Goldstein DB. Common genetic variation and human traits. N Engl J Med.2009;360(17):1696–1698. doi: 10.1056/NEJMp0806284.

Hirschhorn JN. Genomewide association studies–illuminating biologic pathways. N Engl J Med. 2009;360(17):1699–1701. doi: 10.1056/NEJMp0808934.

Iles MM. What can genome-wide association studies tell us about the genetics of common disease? PLoS Genet. 2008;4(2):e33. doi: 10.1371/journal.pgen.0040033.

Myles S, Davison D, Barrett J, Stoneking M, Timpson N. Worldwide population differentiation at disease-associated SNPs. BMC Med Genomics.2008;1:22. doi: 10.1186/1755-8794-1-22.

Lohmueller KE, Mauney MM, Reich D, Braverman JM. Variants associated with common disease are not unusually differentiated in frequency across populations. Am J Hum Genet. 2006;78(1):130–136. doi: 10.1086/499287.

Hindorff LA, Sethupathy P, Junkins HA, Ramos EM, Mehta JP, Collins FS, Manolio TA. Potential etiologic and functional implications of genome-wide association loci for human diseases and traits. Proc Natl Acad Sci USA.2009;106(23):9362–9367. doi: 10.1073/pnas.0903103106.

Wang WYS, Barratt BJ, Clayton DG, Todd JA. Genome-wide association studies: Theoretical and practical concerns. Nature Reviews Genetics.2005;6(2):109–118. doi: 10.1038/nrg1522.

Hacia JG, Fan JB, Ryder O, Jin L, Edgemon K, Ghandour G, Mayer RA, Sun B, Hsie L, Robbins C. et al. Determination of ancestral alleles for human single-nucleotide polymorphisms using high-density oligonucleotide arrays. Nat Genet. 1999;22(2):164–167. doi: 10.1038/9674.

Lohmueller KE, Pearce CL, Pike M, Lander ES, Hirschhorn JN. Meta-analysis of genetic association studies supports a contribution of common variants to susceptibility to common disease. Nat Genet. 2003;33(2):177–182. doi: 10.1038/ng1071.

Wang WY, Pike N. The allelic spectra of common diseases may resemble the allelic spectrum of the full genome. Med Hypotheses. 2004;63(4):748–751. doi: 10.1016/j.mehy.2003.12.057.

HapMart. http://hapmart.hapmap.org/BioMart/martview/

Frazer KA, Ballinger DG, Cox DR, Hinds DA, Stuve LL, Gibbs RA, Belmont JW, Boudreau A, Hardenbol P, Leal S. et al. A second generation human haplotype map of over 3.1 million SNPs. Nature.2007;449(7164):851–861. doi: 10.1038/nature06258.

Rotimi CN, Jorde LB. Ancestry and disease in the age of genomic medicine. N Engl J Med. 2010;363(16):1551–1558. doi: 10.1056/NEJMra0911564.

Ganapathy G, Uyenoyama MK. Site frequency spectra from genomic SNP surveys. Theor Popul Biol. 2009;75(4):346–354. doi: 10.1016/j.tpb.2009.04.003.

Nielsen R, Hubisz MJ, Clark AG. Reconstituting the frequency spectrum of ascertained single-nucleotide polymorphism data. Genetics.2004;168(4):2373–2382. doi: 10.1534/genetics.104.031039.

Watterson GA, Guess HA. Is the most frequent allele the oldest? Theor Popul Biol. 1977;11(2):141–160. doi: 10.1016/0040-5809(77)90023-5.

Sherry ST, Ward MH, Kholodov M, Baker J, Phan L, Smigielski EM, Sirotkin K. dbSNP: the NCBI database of genetic variation. Nucleic Acids Res. 2001;29(1):308–311. doi: 10.1093/nar/29.1.308.

Spencer CC, Deloukas P, Hunt S, Mullikin J, Myers S, Silverman B, Donnelly P, Bentley D, McVean G. The influence of recombination on human genetic diversity. PLoS Genet. 2006;2(9):e148. doi: 10.1371/journal.pgen.0020148.

Kimura M. The number of heterozygous nucleotide sites maintained in a finite population due to steady flux of mutations. Genetics. 1969;61(4):893–903.

Johnson AD, O’Donnell CJ. An open access database of genome-wide association results. BMC Med Genet. 2009;10:6. doi: 10.1186/1471-2350-10-6.

Kimura M, Ohta T. The age of a neutral mutant persisting in a finite population. Genetics. 1973;75(1):199–212.

McVean GA, et al. The fine-scale structure of recombination rate variation in the human genome. Science. 2004;304:581–584.

Slatkin M, Rannala B. Estimating the age of alleles by use of intraallelic variability. Am J Hum Genet. 1997;60(2):447–458.

Green R, Krause J, Briggs A, Maricic T, Stenzel U, Kircher M, Patterson N, Li H, Zhai W, Fritz M. et al. A Draft Sequence of the Neanderthal Genome. Science. 2010;328:710–722. doi: 10.1126/science.1188021.

Bamshad M, Wooding SP. Signatures of natural selection in the human genome. Nat Rev Genet. 2003;4(2):99–111. doi: 10.1038/nrg999.

Hernandez RD, Williamson SH, Bustamante CD. Context dependence, ancestral misidentification, and spurious signatures of natural selection. Mol Biol Evol. 2007;24(8):1792–1800. doi: 10.1093/molbev/msm108.

Bustamante CD, et al. Natural selection on protein-coding genes in the human genome. Nature. 2005;437:1153–1157.

 

 

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Novel Macromolecular IV to Oral Delivery Conversion Pathway: Anti-thrombolytic post-surgical – Catalent OptiGel Bio™ Technology

Posted in Coagulation Therapy and Internal Bleeding, Pharmaceutical R&D Investment, tagged , , , , on December 12, 2013| Leave a Comment »

Novel Macromolecular IV to Oral Delivery Conversion Pathway: Anti-thrombolytic post-surgical – Catalent OptiGel Bio™ Technology

Reporter: Aviva Lev-Ari, PhD, RN

Case Study

OptiGel Bio™ Technology Enables IV to Oral Therapy Conversion

Executive Summary

An early-stage biotechnology company had developed a novel macromolecular intravenous (IV) therapy for an anti-thrombolytic post-surgical indication. While the therapy had shown complete absorption via IV, the dose form was not ideal due to a number of factors including manufacturing costs, compliance, and ease of use, as well as as well as the long term treatment requirements. This case study demonstrates how Catalent OptiGel Bio™ technology can provide a pathway for an IV to oral delivery conversion, resulting in enhanced therapies for patients.

The Challenges

Though soluble, the macromolecule presented a number of permeability challenges, which hindered delivery of an active therapeutic dose across the lumen of the small intestine to achieve the desired therapeutic effect.

*Salamat-Miller N et al. , Pharmaceutical Research, 2005, 22(2):245-254

By incorporating OptiGel Bio™ technology and our formulation expertise, an optimized oral therapy was developed combining permeation enhancement and targeted delivery.

physiochemical properties High molecular weight (>2500 Da)Strong negative charge*

Rigid, inflexible geometry*
targeted delivery Functional API must be delivered to the small intestine in order to achieve bioavailability
permeability Mucus layer physical barrierRandom and limited transcellular pathways

“Fence and gate” function of tight junctions
pharmacokinetic profile Oral delivery must reach exposure within therapeutic range

The Catalent Solution

enhanced permeability The first challenge to overcome in development was enhancing the permeability of the macromolecule. A stepwise screening approach utilizing both in vitro and in vivo models yielded lead formulation candidates for further evaluation.

The Catalent Solution

https://kapost-files-prod.s3.amazonaws.com/uploads/direct/1382388405-22-3748/274-01_CaseStudy_OptiGelBio.pdf

enhanced permeability The first challenge to overcome in development was enhancing the permeability of the macromolecule. A stepwise screening approach utilizing both in vitro and in vivo models yielded lead formulation candidates for further evaluation.

Conclusion

Using OptiGel Bio™ technology, we overcame the challenges traditionally associated with the oral delivery of macromolecules and enabled conversion from an IV to a more efficient, more convenient and less invasive oral dose form while maintaining an effective pK profile. Through a multi-step drug delivery screening process and our OptiGel Bio™ technology, we can enable enhanced therapies—resulting in better treatments and more value for innovators, healthcare professionals and patients.

SOURCE

https://kapost-files-prod.s3.amazonaws.com/uploads/direct/1382388405-22-3748/274-01_CaseStudy_OptiGelBio.pdf

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