The Experts below are selected from a list of 255228 Experts worldwide ranked by ideXlab platform
Volker M. Lauschke - One of the best experts on this subject based on the ideXlab platform.
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Integrating rare genetic variants into pharmacogenetic Drug Response predictions.
Human Genomics, 2018Co-Authors: Magnus Ingelman-sundberg, Souren Mkrtchian, Yitian Zhou, Volker M. LauschkeAbstract:Variability in genes implicated in Drug pharmacokinetics or Drug Response can modulate treatment efficacy or predispose to adverse Drug reactions. Besides common genetic polymorphisms, recent sequencing projects revealed a plethora of rare genetic variants in genes encoding proteins involved in Drug metabolism, transport, and Response. To understand the global importance of rare pharmacogenetic gene variants, we mapped the variability in 208 pharmacogenes by analyzing exome sequencing data from 60,706 unrelated individuals and estimated the importance of rare and common genetic variants using a computational prediction framework optimized for pharmacogenetic assessments. Our analyses reveal that rare pharmacogenetic variants were strongly enriched in mutations predicted to cause functional alterations. For more than half of the pharmacogenes, rare variants account for the entire genetic variability. Each individual harbored on average a total of 40.6 putatively functional variants, rare variants accounting for 10.8% of these. Overall, the contribution of rare variants was found to be highly gene- and Drug-specific. Using warfarin, simvastatin, voriconazole, olanzapine, and irinotecan as examples, we conclude that rare genetic variants likely account for a substantial part of the unexplained inter-individual differences in Drug metabolism phenotypes. Combined, our data reveal high gene and Drug specificity in the contributions of rare variants. We provide a proof-of-concept on how this information can be utilized to pinpoint genes for which sequencing-based genotyping can add important information to predict Drug Response, which provides useful information for the design of clinical trials in Drug development and the personalization of pharmacological treatment.
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Rare genetic variants in cellular transporters, metabolic enzymes, and nuclear receptors can be important determinants of interindividual differences in Drug Response.
Genetics in Medicine, 2016Co-Authors: Mikael Kozyra, Magnus Ingelman-sundberg, Volker M. LauschkeAbstract:Rare genetic variants in cellular transporters, metabolic enzymes, and nuclear receptors can be important determinants of interindividual differences in Drug Response
Melissa C Skala - One of the best experts on this subject based on the ideXlab platform.
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quantitative optical imaging of primary tumor organoid metabolism predicts Drug Response in breast cancer
Cancer Research, 2014Co-Authors: Alex J Walsh, Rebecca S Cook, Melinda E Sanders, Luigi Aurisicchio, Gennaro Ciliberto, Carlos L Arteaga, Melissa C SkalaAbstract:There is a need for technologies to predict the efficacy of cancer treatment in individual patients. Here, we show that optical metabolic imaging of organoids derived from primary tumors can predict the therapeutic Response of xenografts and measure antitumor Drug Responses in human tumor–derived organoids. Optical metabolic imaging quantifies the fluorescence intensity and lifetime of NADH and FAD, coenzymes of metabolism. As early as 24 hours after treatment with clinically relevant anticancer Drugs, the optical metabolic imaging index of responsive organoids decreased ( P P −6 ), with no change in Drug-resistant organoids. Drug Response in xenograft-derived organoids was validated with tumor growth measurements in vivo and staining for proliferation and apoptosis. Heterogeneous cellular Responses to Drug treatment were also resolved in organoids. Optical metabolic imaging shows potential as a high-throughput screen to test the efficacy of a panel of Drugs to select optimal Drug combinations. Cancer Res; 74(18); 5184–94. ©2014 AACR .
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quantitative optical imaging of primary tumor organoid metabolism predicts Drug Response in breast cancer
Cancer Research, 2014Co-Authors: Alex J Walsh, Rebecca S Cook, Melinda E Sanders, Luigi Aurisicchio, Gennaro Ciliberto, Carlos L Arteaga, Melissa C SkalaAbstract:There is a need for technologies to predict the efficacy of cancer treatment in individual patients. Here, we show that optical metabolic imaging of organoids derived from primary tumors can predict the therapeutic Response of xenografts and measure antitumor Drug Responses in human tumor-derived organoids. Optical metabolic imaging quantifies the fluorescence intensity and lifetime of NADH and FAD, coenzymes of metabolism. As early as 24 hours after treatment with clinically relevant anticancer Drugs, the optical metabolic imaging index of responsive organoids decreased (P < 0.001) and was further reduced when effective therapies were combined (P < 5 × 10(-6)), with no change in Drug-resistant organoids. Drug Response in xenograft-derived organoids was validated with tumor growth measurements in vivo and staining for proliferation and apoptosis. Heterogeneous cellular Responses to Drug treatment were also resolved in organoids. Optical metabolic imaging shows potential as a high-throughput screen to test the efficacy of a panel of Drugs to select optimal Drug combinations. Cancer Res; 74(18); 5184-94. ©2014 AACR.
Magnus Ingelman-sundberg - One of the best experts on this subject based on the ideXlab platform.
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Integrating rare genetic variants into pharmacogenetic Drug Response predictions.
Human Genomics, 2018Co-Authors: Magnus Ingelman-sundberg, Souren Mkrtchian, Yitian Zhou, Volker M. LauschkeAbstract:Variability in genes implicated in Drug pharmacokinetics or Drug Response can modulate treatment efficacy or predispose to adverse Drug reactions. Besides common genetic polymorphisms, recent sequencing projects revealed a plethora of rare genetic variants in genes encoding proteins involved in Drug metabolism, transport, and Response. To understand the global importance of rare pharmacogenetic gene variants, we mapped the variability in 208 pharmacogenes by analyzing exome sequencing data from 60,706 unrelated individuals and estimated the importance of rare and common genetic variants using a computational prediction framework optimized for pharmacogenetic assessments. Our analyses reveal that rare pharmacogenetic variants were strongly enriched in mutations predicted to cause functional alterations. For more than half of the pharmacogenes, rare variants account for the entire genetic variability. Each individual harbored on average a total of 40.6 putatively functional variants, rare variants accounting for 10.8% of these. Overall, the contribution of rare variants was found to be highly gene- and Drug-specific. Using warfarin, simvastatin, voriconazole, olanzapine, and irinotecan as examples, we conclude that rare genetic variants likely account for a substantial part of the unexplained inter-individual differences in Drug metabolism phenotypes. Combined, our data reveal high gene and Drug specificity in the contributions of rare variants. We provide a proof-of-concept on how this information can be utilized to pinpoint genes for which sequencing-based genotyping can add important information to predict Drug Response, which provides useful information for the design of clinical trials in Drug development and the personalization of pharmacological treatment.
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Rare genetic variants in cellular transporters, metabolic enzymes, and nuclear receptors can be important determinants of interindividual differences in Drug Response.
Genetics in Medicine, 2016Co-Authors: Mikael Kozyra, Magnus Ingelman-sundberg, Volker M. LauschkeAbstract:Rare genetic variants in cellular transporters, metabolic enzymes, and nuclear receptors can be important determinants of interindividual differences in Drug Response
Russ B Altman - One of the best experts on this subject based on the ideXlab platform.
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Drug Response pharmacogenetics for 200 000 uk biobank participants
bioRxiv, 2020Co-Authors: Gregory Mcinnes, Russ B AltmanAbstract:Pharmacogenetics studies how genetic variation leads to variability in Drug Response. Guidelines for selecting the right Drug and right dose to patients based on their genetics are clinically effective, but are still widely unused. For some Drugs, the normal clinical decision making process may lead to the optimal dose of a Drug that minimizes side effects and maximizes effectiveness. Without measurements of genotype, physicians and patients may observe and adjust dosage in a manner that reflects the underlying genetics. The emergence of genetic data linked to longitudinal clinical data in large biobanks offers an opportunity to confirm known pharmacogenetic interactions as well as discover novel associations by investigating outcomes from normal clinical practice. Here we use the UK Biobank to search for pharmacogenetic interactions among 200 Drugs and 9 genes among 200,000 participants. We identify associations between pharmacogene phenotypes and Drug maintenance dose as well as side effect incidence. We find support for several known Drug-gene associations as well as novel pharmacogenetic interactions.
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bioinformatics and variability in Drug Response a protein structural perspective
Journal of the Royal Society Interface, 2012Co-Authors: Jennifer L Lahti, Grace W Tang, Emidio Capriotti, Tianyun Liu, Russ B AltmanAbstract:Marketed Drugs frequently perform worse in clinical practice than in the clinical trials on which their approval is based. Many therapeutic compounds are ineffective for a large subpopulation of patients to whom they are prescribed; worse, a significant fraction of patients experience adverse effects more severe than anticipated. The unacceptable risk–benefit profile for many Drugs mandates a paradigm shift towards personalized medicine. However, prior to adoption of patient-specific approaches, it is useful to understand the molecular details underlying variable Drug Response among diverse patient populations. Over the past decade, progress in structural genomics led to an explosion of available three-dimensional structures of Drug target proteins while efforts in pharmacogenetics offered insights into polymorphisms correlated with differential therapeutic outcomes. Together these advances provide the opportunity to examine how altered protein structures arising from genetic differences affect protein–Drug interactions and, ultimately, Drug Response. In this review, we first summarize structural characteristics of protein targets and common mechanisms of Drug interactions. Next, we describe the impact of coding mutations on protein structures and Drug Response. Finally, we highlight tools for analysing protein structures and protein–Drug interactions and discuss their application for understanding altered Drug Responses associated with protein structural variants.
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an integrative method for scoring candidate genes from association studies application to warfarin dosing
BMC Bioinformatics, 2010Co-Authors: Nicholas P Tatonetti, Joel T Dudley, Hersh Sagreiya, Atul J Butte, Russ B AltmanAbstract:Background A key challenge in pharmacogenomics is the identification of genes whose variants contribute to Drug Response phenotypes, which can include severe adverse effects. Pharmacogenomics GWAS attempt to elucidate genotypes predictive of Drug Response. However, the size of these studies has severely limited their power and potential application. We propose a novel knowledge integration and SNP aggregation approach for identifying genes impacting Drug Response. Our SNP aggregation method characterizes the degree to which uncommon alleles of a gene are associated with Drug Response. We first use pre-existing knowledge sources to rank pharmacogenes by their likelihood to affect Drug Response. We then define a summary score for each gene based on allele frequencies and train linear and logistic regression classifiers to predict Drug Response phenotypes.
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the pharmacogenetics research network from snp discovery to clinical Drug Response
Clinical Pharmacology & Therapeutics, 2007Co-Authors: Kathleen M. Giacomini, Claire M Brett, M E Dolan, Russ B Altman, Daniel F Hayes, Teri E Klein, Julie A. Johnson, David A Flockhart, Neal L. Benowitz, Ronald M. KraussAbstract:The NIH Pharmacogenetics Research Network (PGRN) is a collaborative group of investigators with a wide range of research interests, but all attempting to correlate Drug Response with genetic variation. Several research groups concentrate on Drugs used to treat specific medical disorders (asthma, depression, cardiovascular disease, addiction of nicotine, and cancer), whereas others are focused on specific groups of proteins that interact with Drugs (membrane transporters and phase II Drug-metabolizing enzymes). The diverse scientific information is stored and annotated in a publicly accessible knowledge base, the Pharmacogenetics and Pharmacogenomics Knowledge base (PharmGKB). This report highlights selected achievements and scientific approaches as well as hypotheses about future directions of each of the groups within the PGRN. Seven major topics are included: informatics (PharmGKB), cardiovascular, pulmonary, addiction, cancer, transport, and metabolism.
Carlos L Arteaga - One of the best experts on this subject based on the ideXlab platform.
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quantitative optical imaging of primary tumor organoid metabolism predicts Drug Response in breast cancer
Cancer Research, 2014Co-Authors: Alex J Walsh, Rebecca S Cook, Melinda E Sanders, Luigi Aurisicchio, Gennaro Ciliberto, Carlos L Arteaga, Melissa C SkalaAbstract:There is a need for technologies to predict the efficacy of cancer treatment in individual patients. Here, we show that optical metabolic imaging of organoids derived from primary tumors can predict the therapeutic Response of xenografts and measure antitumor Drug Responses in human tumor–derived organoids. Optical metabolic imaging quantifies the fluorescence intensity and lifetime of NADH and FAD, coenzymes of metabolism. As early as 24 hours after treatment with clinically relevant anticancer Drugs, the optical metabolic imaging index of responsive organoids decreased ( P P −6 ), with no change in Drug-resistant organoids. Drug Response in xenograft-derived organoids was validated with tumor growth measurements in vivo and staining for proliferation and apoptosis. Heterogeneous cellular Responses to Drug treatment were also resolved in organoids. Optical metabolic imaging shows potential as a high-throughput screen to test the efficacy of a panel of Drugs to select optimal Drug combinations. Cancer Res; 74(18); 5184–94. ©2014 AACR .
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quantitative optical imaging of primary tumor organoid metabolism predicts Drug Response in breast cancer
Cancer Research, 2014Co-Authors: Alex J Walsh, Rebecca S Cook, Melinda E Sanders, Luigi Aurisicchio, Gennaro Ciliberto, Carlos L Arteaga, Melissa C SkalaAbstract:There is a need for technologies to predict the efficacy of cancer treatment in individual patients. Here, we show that optical metabolic imaging of organoids derived from primary tumors can predict the therapeutic Response of xenografts and measure antitumor Drug Responses in human tumor-derived organoids. Optical metabolic imaging quantifies the fluorescence intensity and lifetime of NADH and FAD, coenzymes of metabolism. As early as 24 hours after treatment with clinically relevant anticancer Drugs, the optical metabolic imaging index of responsive organoids decreased (P < 0.001) and was further reduced when effective therapies were combined (P < 5 × 10(-6)), with no change in Drug-resistant organoids. Drug Response in xenograft-derived organoids was validated with tumor growth measurements in vivo and staining for proliferation and apoptosis. Heterogeneous cellular Responses to Drug treatment were also resolved in organoids. Optical metabolic imaging shows potential as a high-throughput screen to test the efficacy of a panel of Drugs to select optimal Drug combinations. Cancer Res; 74(18); 5184-94. ©2014 AACR.
