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Tatonetti Lab Connects Drug Interactions to Deadly Heart Condition

Nicholas Tatonetti
Nicholas Tatonetti is an assistant professor in the Department of Biomedical Informatics and Department of Systems Biology.

A team of Columbia University Medical Center (CUMC) scientists led by Nicholas Tatonetti has identified several drug combinations that may lead to a potentially fatal type of heart arrhythmia known as torsades de pointes (TdP). The key to the discovery was a new bioinformatics pipeline called DIPULSE (Drug Interaction Prediction Using Latent Signals and EHRs), which builds on previous methods Tatonetti developed for identifying drug-drug interactions (DDIs) in observational data sets. The results are reported in a new paper in the journal Drug Safety and are covered in a detailed multimedia feature published by the Chicago Tribune.

The algorithm mined data contained in the US FDA Adverse Event Reporting System (FAERS) to identify latent signals of DDIs that cause QT interval prolongation, a disturbance in the electrical cycle that coordinates the heartbeat. It then validated these predictions by looking for their signatures in electrocardiogram results contained in a large collection of electronic health records at Columbia. Interestingly, the drugs the investigators identified do not cause the condition on their own, but only when taken in specific combinations.

Previously, no reliable methods existed for identifying these kinds of combinations. Although the findings are preliminary, the retrospective confirmation of many of DIPULSE’s predictions in actual patient data suggests its effectiveness, and the investigators plan to test them experimentally in the near future.

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New Algorithm Accurately Predicts Drug Mechanism of Action

DeMAND graphical abstract
By analyzing drug-induced changes in disease-specific patterns of gene expression, a new algorithm called DeMAND identifies the genes involved in implementing a drug's effects. The method could help predict undesirable off-target interactions, suggest ways of regulating a drug's activity, and identify novel therapeutic uses for FDA-approved drugs, three critical challenges in drug development.

Researchers in the Columbia University Department of Systems Biology have developed an efficient and accurate method for determining a drug’s mechanism of action — the cellular machinery through which it produces its pharmacological effect. Considering that most drugs, including widely used ones, act in ways that are not completely understood at the molecular level, this accomplishment addresses a key challenge to drug development. The new approach also holds great potential for improving drugs’ effectiveness, identifying better combination therapies, and avoiding dangerous drug-induced side effects.

According to Andrea Califano, the Clyde and Helen Wu Professor of Chemical Systems Biology and co-senior author on the study, “This new methodology makes it possible for the first time to generate a genome-wide footprint of the proteins that are responsible for implementing or modulating the activity of a drug. The accuracy of the method has been the most surprising result, with up to 80% of the identified proteins confirmed by experimental assays.”

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Clinical Trials for Cancer, One Patient at a Time

Reposted from the Columbia University Medical Center Newsroom. Find the original article here .

Cancer bottlenecks
In an N-of-1 study, researchers at Columbia University use techniques from systems biology to analyze genomic information from an individual patient’s tumor. The goal is to identify key genes, called master regulators  (green circles), which, while not mutated, are nonetheless necessary for the survival of cancer cells. 

Columbia University Medical Center (CUMC) researchers are developing a new approach to cancer clinical trials, in which therapies are designed and tested one patient at a time. The patient’s tumor is “reverse engineered” to determine its unique genetic characteristics and to identify existing U.S. Food and Drug Administration (FDA)-approved drugs that may target them.

Rather than focusing on the usual mutated genes, only a very small number of which can be used to guide successful therapeutic strategies, the method analyzes the regulatory logic of the cell to identify genes and gene pairs that are critical for the survival of the tumor but are not critical for normal cells. FDA-approved drugs that inhibit these genes are then tested in a mouse model of the patient’s tumor and, if successful, considered as potential therapeutic agents for the patient — a journey from bedside to bench and back again that takes about six to nine months.

“We are taking a rather different approach to tailor therapy to the individual cancer patient,” said principal investigator Andrea Califano, PhD, Clyde and Helen Wu Professor of Chemical Systems Biology and chair of CUMC’s new Department of Systems Biology. “If we have learned one thing about this disease, it’s that it has tremendous heterogeneity both across patients and within individual patients. When we expect different patients with the same tumor subtype or different cells within the same tumor to respond the same way to a treatment, we make a huge simplification. Yet this is how clinical studies are currently conducted. To address this problem, we are trying to understand how tumors are regulated one at a time. Eventually, we hope to be able to treat patients not on an individual basis, but based on common vulnerabilities of the cancer cellular machinery, of which genetic mutations are only indirect evidence. Genetic alterations are clearly responsible for tumorigenesis but control points in molecular networks may be better therapeutic targets.”

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The Exposome: Connecting Environmental Factors to Human Disease

Some factors in the expo some

The exposome incorporates factors such as the environment we inhabit, the food we eat, and the drugs we take.

Although genomics has dramatically improved our understanding of the molecular origins of certain human genetic diseases, our health is also influenced by exposures to our surrounding environment. Molecules found in food, air and water pollution, and prescription drugs, for example, interact with genetic, molecular, and physiologic features within our bodies in highly personalized ways. The nature of these relationships is important in determining who is immune to such exposures and who becomes sick because of them.

In the past, methods for studying this interface have been limited because of the complexity of the problem. After all, how could we possibly cross-reference a lifetime’s worth of exposures with individual genetic profiles in any kind of meaningful way? Recently, however, an explosion in the generation of quantitative data related to the environment, health, and genetics — along with new computational methods based in machine learning and bioinformatics — have made this landscape ripe for exploration.

At this year’s South by Southwest Interactive Festival in Austin, Texas, Department of Systems Biology Assistant Professor Nicholas Tatonetti and his collaborator Chirag Patel (Harvard Medical School) discussed the remarkable new opportunities that “big data” approaches offer for investigating this landscape. Driving Tatonetti and Patel’s approach is a concept called the exposome. First proposed by Christopher Wild (University of Leeds) in 2005, an exposome represents all of the environmental exposures a person has experienced during his or her life that could play a role in the onset of chronic diseases. Tatonetti and Chirag’s presentation highlighted how investigation of the exposome has become tractable, as well as the important roles that individuals can play in supporting this effort.

In the following interview, Dr. Tatonetti discusses some of the approaches his team is using to explore the exposome, and how the project has evolved out of his previous research.

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Columbia Researchers Will Use Master Regulators to Reclassify Cancer Subtypes

Andrea Califano and Aris Floratos
Andrea Califano and Aris Floratos will lead an effort to reclassify tumors catalogued in TCGA according to their master regulators.

Andrea Califano and Aris Floratos, faculty members in the Columbia University Department of Systems Biology, have received a two-year, $624,236 subcontract to develop a new classification system of cancer subtypes. The agreement was awarded through a subcontract from Leidos Biomedical Research, Inc., which operates the Frederick National Laboratory for Cancer Research for the federal government.  

By performing an integrative analysis of genomic data from the Cancer Genome Atlas (TCGA) and proteomic data from the National Cancer Institute’s Clinical Proteomic Tumor Analysis Consortium (CPTAC), the researchers plan to recategorize tumors collected in TCGA based on the master regulator genes that determine their state. This is in contrast to other approaches based on expression of genes that reflect tissue lineage and proliferative processes. In addition, the team will link the genetics of each tumor sample to the specific master regulators that determine its state using a recently published novel algorithm (DIGGIT). Ultimately, the project aims to provide a more useful catalog of pan-cancer subtypes that could help to identify biomarkers and therapeutic targets for specific kinds of tumors, and ultimately provide a resource to guide the next generation of precision medicine.

“We have to reevaluate the way in which we organize tumors within subtypes, using both gene expression data and mutational data,” says Dr. Califano. “Right now the common approach is to classify tumor types based on rather generic genes that are differentially expressed between subtypes. But most of these genes play no role in actually driving the disease. We want to shift the emphasis and classify tumors based on the genes that truly regulate tumor state and survival.”

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Target Identified for Reversing Drug Resistance in Acute Lymphoblastic Leukemia

Reversing glucocorticoid resistance

A representative example of tumor load analysis using bioluminescence imaging in mice following xenograft with T-ALL. Treatment with either MK2206 or dexamethasone showed limited efficacy, while combination treatment saw near complete elimination of tumor cells.

In a paper published in Cancer Cell, a team of researchers led by Adolfo Ferrando and Andrea Califano at Columbia University has identified the protein kinase AKT as a target for reversing resistance to glucocorticoid therapy in patients with acute lymphoblastic leukemia (ALL).  

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Web Search Logs Reveal Adverse Drug Interactions

Searches for hyperglycemia-related terms

Percentage of users in each of the three user groups searching for hyperglycemia-related terms, computed per week over 12 months of search log data. Background refers to the fraction of all searchers who search for hyperglycemia-related symptoms or terminology independent of the presence of the drugs in the users’ search histories.

Although the US Food and Drug Organization and other agencies collect and analyze reports on adverse drug effects, alerts for single drugs and drug-drug interactions are often delayed due to the time it takes to accumulate evidence. Columbia University Department of Systems Biology faculty member Nicholas Tatonetti, in collaboration with investigators at Stanford University and Microsoft Research, hypothesized that Internet users can provide early clues of adverse drug events as they seek information on the web concerning symptoms they are experiencing. A new paper explains their results.

As a test, Tatonetti and colleagues asked whether it would be possible to detect evidence of an interaction between the antidepressant paroxetine and the anti-cholesterol drug pravastatin by analyzing web search logs from 2010. As a postfoc at Stanford, Tatonetti and colleagues used a data mining algorithm to analyze FDA adverse event reporting records, and retroactively found this combination to be associated with hyperglycemia (high blood sugar) in some patients. In this new project, the researchers analyzed the search logs of millions of Internet users from a period before the above association was identified to see how often they entered search terms related to hyperglycemia and to one or both medications under investigation. (Participants in this study opted in by voluntarily installing a web browser extension that tracked their activity anonymously.)

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Columbia Genome Center Holds High-Throughput Screening and Chemistry Symposium

High-throughput screening’s ability to perform thousands of experiments efficiently and under carefully controlled conditions has made it an important tool for basic and translational biological research. At Columbia University, the JP Sulzberger Columbia Genome Center and the Chemical Probe Synthesis Facility provide a flexible platform for researchers interested in applying high-throughput experimentation in their work. On December 17, 2012, the Genome Center hosted a symposium to spotlight its capabilities in high-throughput screening, to explain the important role that synthetic chemistry plays in high-throughput screening, and to describe some recent research projects at Columbia that have utilized these tools.

As High-Throughput Screening Core Scientific Director Charles Karan explained, the Genome Center operates a suite of advanced technologies for automated liquid handling, robotic assay implementation, and high-throughput, high-content microscopy. The Genome Center also offers Columbia University researchers access to several large collections for conducting high-throughput screens. These include the Columbia Cell Line Encyclopedia, which includes 850 cancer cell lines collected from around the world, as well as a chemical diversity library curated by researchers in the Chemical Probe Synthesis Facility. This “tool chest” gives Columbia investigators access to a pre-selected set of compounds that have been predicted to result in the highest quality potential hits. Karan also reported that the Genome Center recently negotiated an arrangement with Sigma Aldrich to give access to the company’s shRNA clones to researchers at Columbia at greatly discounted rates.

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Study of Cancer Metabolism Identifies Potential Drug Targets to Starve Tumors

Figure

Tumor-induced mRNA expression changes for individual biochemical reactions in central metabolism. 

A large study analyzing gene expression data from 22 cancer types has identified a broad spectrum of metabolic expression changes associated with cancer. The analysis, led by Dennis Vitkup, first author Jie Hu, a postdoctoral research scientist in the Vitkup lab, with a multi-institutional group of collaborators, also identified hundreds of potential drug targets that could cut off a tumor’s fuel supply or interfere with its ability to synthesize essential elements necessary for tumor growth. The study has just been published in the online edition of Nature Biotechnology .

As Columbia University Medical Center reports:

The results should ramp up research into drugs that interfere with cancer metabolism, a field that dominated cancer research in the early 20th century and has recently undergone a renaissance.

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