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Hartford, CT, United States

Grant
Agency: Department of Health and Human Services | Branch: | Program: SBIR | Phase: Phase II | Award Amount: 1.25M | Year: 2011

DESCRIPTION (provided by applicant): Currently 30 million Americans with elevated cholesterol receive statin therapy, making statins the most prescribed drug class in the U.S. Statin Induction and Neuro-Myopathy (SINM), the balance of potency and safety, is the main clinical management challenge of these drugs, particularly when treatment targets are aggressive requiring LDL cholesterol levels below 100 mg/dl. In medical practice, Neuro-Myopathy presents as a constellation of neuromuscular side effects, including myalgia (muscle aches, cramps, weakness) and myopathy (muscular injury monitored by serum creatine kinase (CK) elevation). Neuro-myopathy is more frequent at the higher doses required for treating advanced cardiovascular disease and varies in extentbetween individual statins and from patient to patient. Statin usage is ultimately limited by toxicity. Clearly there is an urgent need to simultaneously avert side effects and optimize lipid lowering at the outset of treatment to bolster success in lowering cardiovascular disease risk in literally millions of patients. Recognizing that clinicians balance safety with efficacy when prescribing statins, our research group and others have established genetic markers that are valid candidates for a panel of safety and efficacy markers. The SINM PhyzioType System is the first diagnostic tool to integrate statin safety and efficacy markers for clinical use. The SINM PhyzioType system can predict the safety and efficacy of the pre-eminent statin drugs (namely, atorvastatin [Lipitor(R)], simvastatin [Zocor(R)], and rosuvastatin [Crestor(R)]-which together account for 85% of the U.S. market share8) according to the genome of each patient, enabling selection of the optimal drug for each patient. Alternatively if a patient's genomic profile proves to be incompatible with statins, the clinician can opt to prescribe another drug class. This Phase II Renewal Program is entitled SYSTEM FOR DNA-GUIDED OPTIMIZATION AND PERSONALIZATION OF STATIN THERAPY. Our previous Fast-Track SBIR Program enabled the discovery of gene markers and configuration of predictive bioclinical algorithms, and it advanced the clinical development of the SINM PhyzioType product closer to commercialization. The proposed Phase II Renewal Program will validate the performance of the SINM PhyzioType product in a prospective study of an independent population of 400 patients naove to statin therapy or who had not received statins for at least 3 months. Allocated funds will be used to validate the product forpersonalized clinical management of statins, to develop a medical informatics interface enabling use of the product by clinicians (Personalized Health Portal) and to prepare the SINM PhyzioType for commercialization. With this project, Genomas will continue to enlist some of the most highly qualified lipid clinical specialists in the world, including Paul D. Thompson, M.D., of Hartford Hospital's Division of Cardiology, John P. Kane, M.D., of the Cardiovascular Research Institute at University of California at San Francisco, and Bruce Gordon, M.D., of the Rogosin Institute at New York Presbyterian Medical Center for this Program. Theodore Holford, Ph.D., of the Yale School of Medicine will serve as a consultant in biostatistics. The expected outcome is final development of the Genomas product SINM PhyzioTypeTM System, that predicts the variable lipid-altering efficacy and the risk of drug-induced neuromuscular side effects that arise in the substantial segment of patients receiving statins. With data collected Phase II Renewal funding, Genomas will be able to serve confirmatory proof that the PhyzioType product is a reliable, reproducible and cost-effective product enabling physicians to optimize treatment strategies in lipid disorders while avoiding neuromyopathy. The goal is to enable clinicians to deploy a genetic decision support system to manage statins, prescribe these drugs on a DNA-guided, personalized basis and effectively lower the risk of cardiovascular disease for each patient. PUBLIC HEALTHRELEVANCE: In the proposed Phase II Renewal Program, the SINM PhyzioTypeTM System for optimization and personalization of statin therapy will be validated through a prospective study to finalize the product ahead of commercialization. This validation study will serve confirmatory proof that the SINM PhyzioTypeTM System is a reliable, reproducible and cost-effect product enabling physicians to optimize treatment strategies in lipid disorders and minimize detrimental neuromuscular side effects. This pioneering product, which represents the first tool to integrate statin safety and efficacy markers for clinical use, will advance the practice of personalized medicine and reduce side effects while elevating therapeutic benefits of the country's most popular drugs that are already on the market.


Grant
Agency: Department of Health and Human Services | Branch: | Program: SBIR | Phase: Phase II | Award Amount: 1.35M | Year: 2009

DESCRIPTION (provided by applicant): Schizophrenia (SZ) is a severely debilitating mental illnesses. It is highly heritable, with concordance rates of 50-80% in monozygotic twins and substantial familial clustering. Phenomenology-based diagnostic systems in psychiatry have serious limitations resulting in classifications lacking clear boundaries and biological basis. With respect to SZ, there is extensive overlap with other disorders on many dimensions, including symptoms, neurophysiology, brain imaging, cognition, and pharmacotherapy. Our eventual goal is to develop a novel product termed Phyziotype , which will substantially improve the diagnosis of SZ by accessing the substantial contribution (50-80%) of genetic factors to the disease. The Phyziotype consists of a multi-gene ensemble of single nucleotide polymorphisms (SNPs) which, interpreted with a biomathematical algorithm, may predict the onset of SZ, more clearly delineate its diagnosis from related disorders, and distinguish between potential etiological subtypes. This Phase II Program is concerned with the discovery of the MRI DNA markers that form the foundation of the Phyziotype. The discovery of predictive biomarkers through association studies based on conventional psychiatric phenotypes has been limited by clinical confounders and the small effect sizes for individual markers. We hypothesize that endophenotypes , known subclinical vulnerability phenotypes, are more strongly associated to individual genes than clinical observations, and provide a powerful tool to discover predictive biomarkers. Functional magnetic resonance imaging (fMRI) of the brain is a versatile technique for measuring important psychiatric endophenotypes. This Program will utilize fMRI endophenotypes for association screening with total genome SNP arrays to identify MRI DNA markers relevant to the diagnosis of SZ. These biomarkers will advance our understanding of genetic risk factors and neurophysiology and may stimulate novel approaches to pharmaceutical development by refining psychiatric diagnosis. This Phase II program integrates the substantial physiogenomic capabilities of Genomas with the leading fMRI research of Dr. Godfrey Pearlson of Hartford Hospital's Institute of Living and the advanced neuroinformatics capabilities of Dr. Vincent Calhoun at the MIND Institute and the University of New Mexico. With ready access to a rich patient population at the Institute of Living and the MIND Research Network, the team has already integrated neuroimaging and physiogenomics as a novel platform for molecular analysis of mental illness, leading to two publications in 'Human Brain Mapping' and 'Annals of Biomedical Engineering'. This Phase II SBIR Program extends these studies to include 500 SZ patients and 250 healthy controls. All participants will be genotyped for 1,072,820 SNPs and 13,298 copy number variants using total genome arrays. Focused and Hypothesis-free Modes of physiogenomic analysis with fMRI endophenotypes will be used to discover MRI DNA markers for SZ. Physiogenomic models incorporating multiple SNPs will be developed as research prototypes for a Phyziotype system. PUBLIC HEALTH RELEVANCE: Schizophrenia (SZ) is a severely debilitating mental illnesses, currently afflicting 2.4 million American adults. The Phase II program will utilize fMRI endophenotypes and total genome SNP screening to identify MRI DNA markers relevant to schizophrenia. MRI DNA markers will eventually allow the development of systems that refine the clinical diagnosis of mental illness and may predict the individual susceptibility to schizophrenia and other disorders. The development of the actual diagnostic product will be pursued during Phase III as an FDA-approved diagnostic system for personalized mental health.


Grant
Agency: Department of Health and Human Services | Branch: | Program: SBIR | Phase: Phase II | Award Amount: 449.18K | Year: 2007

DESCRIPTION (provided by applicant): Atypical antipsychotic drugs (AAPs) are indicated in the treatment for schizophrenia, bipolar disorder, psychotic depression and other psychiatric disorders. Their drawback is drug-induced metabolic derangements including weight gain, hyperlipidemia, and diabetic risks. These diabetic metabolic symptoms (DiMS) vary widely between drugs and from patient to patient. We propose to develop a novel product termed "Physiotype" to deliver personalized information for each patient on the drug- specific risks among aripiprazole, olanzapine, quetiapine, risperidone, and ziprasidone. The Physiotype consists of a multi-gene ensemble of single nucleotide polymorphisms (SNPs) that, interpreted with a biomathematical algorithm, may explain most of the inter-individual differences in DiMS among the 5 AAPs The proprietary physiogenomics technology and state-of-the-art genotyping laboratories of Genomas will be integrated with the clinical resources of the Institute of Living (Hartford CT) and of the University of Kentucky (Lexington KY), through subcontracts, respectively, to Dr. John Goethe and Dr. Jose de Leon. Our goal in Phase II is to discover SNPs predictive of differences in DiMS side effects between these 5 AAPs and to develop them into predictive diagnostic products for psychiatrists in their practice. We will recruit 200 patients treated by each of the 5 AAPs, characterize their weight and lipid profiles, and obtain their DNA for creation of a clinical registry and DNA repository. We will determine each patient's genotype at 100,000 SNPs covering all ~30,000 genes and also evolutionary conserved regions for a comprehensive, hypothesis-free search for genetic markers of DiMS. In Phase I, the collaborators have already accumulated a registry and DNA repository of 374 AAP-treated patients and their DNA. We have genotyped DNA from olanzapine- and risperidone-treated patients in the repository for an array of 384 SNPs in 222 cardiovascular, metabolic and psychiatric candidate genes and performed physiogenomic predictive modeling. We have discovered novel drug-specific DiMS markers for olanzapine and risperidone including the apolipoprotein E and leptin receptor genes, respectively. We have developed a prototype Physiotype and tested it in an independent psychiatric population. The Physiotype predicted that ~20% of patients have the most weight associated with risperidone and ~80% with olanzapine, which is consistent with known olanzapine average effects, and also pinpoints the greater risperidone-specific risk for many individuals. In Phase III, a prospective randomized trial of all 5 AAPs is planned as part of FDA review of a Physiotype device. The Physiotype will assist psychiatrists to avoid side effects by guiding drug selection for each patient according to innate characteristics unraveled and interpreted directly from the person's own DNA. The proposed program will develop DNA diagnostic products to enhance safety of atypical antipsychotic drugs (AAPs) and improve the medical management of schizophrenia and related disorders leading to better outcomes. As of now, the development of AAP side effects is unpredictable, potentially disabling to the patient, and discourages patient compliance. The products will enable DNA- guided medicine: the determination of which AAP is most suitable and the implementation of clinical safeguards, individualized to each patient, using his/her personal genome.


Grant
Agency: Department of Health and Human Services | Branch: | Program: SBIR | Phase: Phase I | Award Amount: 143.34K | Year: 2008

DESCRIPTION (provided by applicant): Statins are the most prescribed drugs in the world. Their efficacy in primary and secondary prevention of cardiovascular disease as well as beneficial pleiotrophic and anti-inflammatory effects have fostered increasingl y aggressive usage and dosage. Their main clinically relevant safety risk is statin-induced myopathy (SIM) evidenced clinically as a constellation of neuromuscular side effects (hereinafter NMSEs). NMSEs are disabling to 3-20% of patients on statins, requi re alteration of therapy, and reduce compliance. NMSEs include myalgias (muscle aches, cramps, weakness) and myositis (monitored by elevation of serum creatine kinase [CK] activity). NMSEs vary in extent between drugs and from patient to patient. We will d evelop a novel product termed SIM PhyzioType system to provide clinicians with individualized information for each patient on the safest statin drug among atorvastatin, simvastatin, and rosuvastatin, the 3 most prescribed statins. The PhyzioType consists of a multi-SNP (single nucleotide polymorphism) ensemble that, interpreted with a biomathematical algorithm, predicts drug response. As part of our preliminary work, we have genotyped 242 statin-treated patients with a targeted array of 384 SNPs from 222 c ardiovascular and neuromuscular candidate genes, and performed physiogenomic associations. We have developed a prototype PhyzioType system incorporating predictive models for myalgia, serum CK activity, and LDLc reduction for atorvastatin and simvastatin p atients. We have discovered a mechanistic link between vascular homeostasis and CK elevation, and between serotonin receptors and myalgia. These results have been published in Pharmacogenomics and Muscle and Nerve. For this SBIR Program, the physiogenomics technology and state-of-the-art genotyping laboratories of Genomas will be combined with the clinical experience and resources of Drs. Paul Thompson, Alan Wu, and Bruce Gordon, respectively, at Hartford Hospital, Univ. California San Francisco and Rogosin Institute, through institutional subcontracts. We will recruit to obtain 250 patients treated with each drug and use existing clinical records to characterize their NMSE and LDLc responses. We will use physiogenomics to identify those SNPs that differenti ate the risk of NMSEs among the 3 statins and combine them into the SIM PhyzioType system. In Phase I, we will continue genotyping with the hypothesis-driven array of 384 SNPs. In Phase II, we will incorporate a hypothesis-free approach by genotyping each patient at 550,000 SNPs with a total genome array covering all ~30,000 genes on all chromosomes and the mitochondrion. This work will also contribute to the pharmacology of SIM and unravel new pharmaceutical targets. We will create and validate the SIM Phy zioType system with clinically useful prediction of NMSEs and potency for each of the 3 statins. In Phase III a prospective trial is planned for FDA approval of the SIM PhyzioType product.


Grant
Agency: Department of Health and Human Services | Branch: | Program: SBIR | Phase: Phase II | Award Amount: 1.01M | Year: 2008

DESCRIPTION (provided by applicant): Statins are the most prescribed drugs in the world. Their efficacy in primary and secondary prevention of cardiovascular disease as well as beneficial pleiotrophic and anti-inflammatory effects have fostered increasingl y aggressive usage and dosage. Their main clinically relevant safety risk is statin-induced myopathy (SIM) evidenced clinically as a constellation of neuromuscular side effects (hereinafter NMSEs). NMSEs are disabling to 3-20% of patients on statins, requi re alteration of therapy, and reduce compliance. NMSEs include myalgias (muscle aches, cramps, weakness) and myositis (monitored by elevation of serum creatine kinase [CK] activity). NMSEs vary in extent between drugs and from patient to patient. We will d evelop a novel product termed SIM PhyzioType system to provide clinicians with individualized information for each patient on the safest statin drug among atorvastatin, simvastatin, and rosuvastatin, the 3 most prescribed statins. The PhyzioType consists of a multi-SNP (single nucleotide polymorphism) ensemble that, interpreted with a biomathematical algorithm, predicts drug response. As part of our preliminary work, we have genotyped 242 statin-treated patients with a targeted array of 384 SNPs from 222 c ardiovascular and neuromuscular candidate genes, and performed physiogenomic associations. We have developed a prototype PhyzioType system incorporating predictive models for myalgia, serum CK activity, and LDLc reduction for atorvastatin and simvastatin p atients. We have discovered a mechanistic link between vascular homeostasis and CK elevation, and between serotonin receptors and myalgia. These results have been published in Pharmacogenomics and Muscle and Nerve. For this SBIR Program, the physiogenomics technology and state-of-the-art genotyping laboratories of Genomas will be combined with the clinical experience and resources of Drs. Paul Thompson, Alan Wu, and Bruce Gordon, respectively, at Hartford Hospital, Univ. California San Francisco and Rogosin Institute, through institutional subcontracts. We will recruit to obtain 250 patients treated with each drug and use existing clinical records to characterize their NMSE and LDLc responses. We will use physiogenomics to identify those SNPs that differenti ate the risk of NMSEs among the 3 statins and combine them into the SIM PhyzioType system. In Phase I, we will continue genotyping with the hypothesis-driven array of 384 SNPs. In Phase II, we will incorporate a hypothesis-free approach by genotyping each patient at 550,000 SNPs with a total genome array covering all ~30,000 genes on all chromosomes and the mitochondrion. This work will also contribute to the pharmacology of SIM and unravel new pharmaceutical targets. We will create and validate the SIM Phy zioType system with clinically useful prediction of NMSEs and potency for each of the 3 statins. In Phase III a prospective trial is planned for FDA approval of the SIM PhyzioType product.

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