Every drug, from morphine to ibuprofen, has a standard dose — a sort of one-size-fits all recommendation. But a new study suggests that when it comes to drug doses, “one size fits all” rarely applies.
Stanford Medicine professor Russ Altman, MD, PhD, and a team of scientists found that almost everyone (99.5% of individuals) is likely to have an abnormal or “atypical” response to at least one therapeutic drug. This, at least, is the case for people in the United Kingdom, as the study’s data came from the UK Biobank, a project that collects, studies and shares data.
The research found that nearly a quarter of the study’s participants had been prescribed a drug for which they were predicted to have an atypical response, based on their genetic makeup. On average, participants were predicted to have an atypical response to 10 drugs… Continue reading.
As the COVID-19 pandemic continues to infect people across the world, a technological application already familiar to many in the biotech field is lending a key supporting role in the fight to treat and stop it: artificial intelligence (AI).
AI is currently being used by many companies to identify and screen existing drugs that could be repurposed to treat COVID-19, aid clinical trials, sift through trial data, and scour through patient electronic medical records (EMRs). The power of AI in COVID-19 is that it is being used to generate actionable information—some of which would be impossible without AI—much more quickly than before.
A simple definition of AI is the ability of a computer to rapidly think and learn. AI utilizes machine learning to analyze large amounts of data. It can also model predictions, screen virtually and develop insights that can be used to advance R&D and make patient medical assessments… Continue reading.
Precision medicine, which leverages a patient’s genetics to help make medical decisions, has the potential to revolutionize medicine. Its applications are numerous: from predicting who may have an adverse reaction to a medication, to allowing targeted therapies of cancer with particular mutations. In 2015, President Obama’s State of the Union announced an initiative to further our understanding of precision medicine and to build the infrastructure to implement it. An important part of this initiative is building a large diverse research cohort to help discover disease-gene and drug-gene associations. The key word is diverse – because genetic risk factors can be population-specific. In the past, individuals of African, Hispanic, and Middle Eastern ancestry have been understudied. Only by including individuals from all different ancestral backgrounds can we hope to implement precision medicine in an inclusive way.
In 2011, Russ Altman’s research group was pondering the importance of inclusive precision medicine when it became clear that several studies examining the baseline genetic variation across the globe, 1000 Genomes and the International HapMap Project, had an underrepresentation of Middle Eastern populations. As a scientist of Iranian descent who had undergone direct-to-consumer genotyping with 23andMe, I wondered how to make sense of my data when no baseline genetic study of the Iranian population existed. When scientists Dr. Mostafa Ronaghi and Dr. Pardis Sabeti approached Dr. Altman’s group about the idea of creating such a baseline, I was immediately interested. Through the generous support of the PARSA Foundation, we began our journey to create a genetic baseline of the Iranian population.
Increasingly, innovation sparks from creative connections across disciplines. Drawing from deep expertise in several branches of science, some of our TEDMED speakers employ their own interdisciplinary knowledge to create breakthrough technology that is advancing healthcare and our understanding of human potential.
Russ Altman, professor of bioengineering, genetics, medicine and computer science at Stanford, uses machine learning as well. He and his team strive to understand drug actions at the molecular, cellular, organism and population levels, including how genetic variation impacts drug response. “There is a shortage of new drugs in the pipeline, despite our ability to make unprecedented measurements about molecules, cells, organs, individuals and populations,” Russ explains, adding that he wants to use these measurement data to change the way we discover and evaluate drugs.
Russ says that he feels “privileged to work at the intersection of biology and computer science.” He was pursuing a graduate degree in biophysics in 1984, when Apple introduced the first Mac. “I was so excited by this computer (which I couldn’t afford) that I changed my PhD program to Medical Information Sciences, and bought a used Apple III (that’s right – !!!), and never looked back,” he shared with us.
Teri Klein and Russ Altman have received NIH funding to expand two ongoing projects that publish information about the connection between patients’ genetics and their responses to prescription drugs.
The National Institutes of Health has awarded School of Medicine researchers Teri Klein, PhD, and Russ Altman, MD, PhD, $14 million in funding for two projects that will advance the practice of precision health.
Altman, professor of bioengineering, of medicine and of genetics, and Klein, senior research scientist, are the principal investigators for a four-year, $10 million grant from the National Institute of General Medical Sciences to expand their premier precision-health resource PharmGKB knowledge base, now in its 15th year. PharmGKB provides comprehensive information about how genetics affects drug response in individuals.
People can react very differently to the same drugs. For example, the enzyme CYP2D6 is involved in metabolizing hundreds of prescription drugs. One drug that CYP2D6 metabolizes is the opiate painkiller codeine, which CYP2D6 converts into morphine — the active form of the painkiller. Most people have just two copies of the CYP2D6 gene, but some of us have more. Extra copies of the gene pump out so much of the enzyme that codeine and other drugs are metabolized far more rapidly than prescribing physicians expect. People with more than two copies of the gene can convert a standard dose of codeine to morphine so rapidly that they may overdose.
Russ Altman, MD, PhD, professor of bioengineering, of genetics and of biomedical informatics research, was elected for contributions in the field of bioinformatics, particularly for analysis of targets for drug action and of the impact of human variation on drug responses. Altman, who holds the Kenneth Fong Professorship, is interested in the analysis of protein and RNA structure and function, as well as in applying systems biology concepts to pharmacology and personalized medicine.
Researchers at the Stanford University School of Medicine and Microsoft Research have revealed that the Internet search history of consumers can yield information on the unreported side effects of drugs or drug combinations.
By analyzing 12 months of search history from 6 million Internet users who consented to share anonymized logs of their Web searches for research purposes, the team was able to pinpoint an interaction between two drugs that was unknown at the time of data collection.
“Seeking health information is a major use of the Internet now,” said co-author of the new paper Russ Altman, MD, PhD, Stanford professor of bioengineering, of genetics and of medicine. “So we thought people are likely typing in drugs they are taking and the side effects they are experiencing and that there must be a way for us to use this data.”
The study was published March 6 in the Journal of the American Medical Informatics Association.
Using data drawn from queries entered into Google, Microsoft and Yahoo search engines, scientists at Microsoft, Stanford and Columbia University have for the first time been able to detect evidence of unreported prescription drug side effects before they were found by the Food and Drug Administration’s warning system.
Using automated software tools to examine queries by six million Internet users taken from Web search logs in 2010, the researchers looked for searches relating to an antidepressant, paroxetine, and a cholesterol lowering drug, pravastatin. They were able to find evidence that the combination of the two drugs caused high blood sugar.
The study, which was reported in the Journal of the American Medical Informatics Association on Wednesday, is based on data-mining techniques similar to those employed by services like Google Flu Trends, which has been used to give early warning of the prevalence of the sickness to the public.
A week ago, you started a new prescription medication for acne. Today, you feel dizzy and short of breath and have difficulty concentrating. Your symptoms are not listed in the package insert as possible side effects of the drug, but why else would you be feeling so odd?
Unfortunately, there’s no easy answer. Clinical trials are designed to show that a drug is safe and effective. But even the largest trials can’t identify irksome or even dangerous side effects experienced by only a tiny proportion of those people taking the drug. They also aren’t designed to study how drugs interact with one another in the human body — a consideration that becomes increasingly important as people age and their medicine cabinets begin to overflow.
Now researchers at the Stanford University School of Medicine have devised a computer algorithm that enabled them to swiftly sift through millions of reports to the U.S. Food and Drug Administration by patients and their physicians and identify “true” drug side effects. The method also worked to identify previously unsuspected interactions between pairs of drugs, most notably that antidepressants called SSRIs interact with a common blood pressure medication to significantly increase the risk of a potentially deadly heart condition.
A widely used combination of two common medications may cause unexpected increases in blood glucose levels, according to a study conducted at the Stanford University School of Medicine, Vanderbilt University and Harvard Medical School. Researchers were surprised at the finding because neither of the two drugs — one, an antidepressant marketed as Paxil, and the other, a cholesterol-lowering medication called Pravachol — has a similar effect alone.
The increase is more pronounced in people who are diabetic, and in whom the control of blood sugar levels is particularly important. It’s also apparent in pre-diabetic laboratory mice exposed to both drugs. The researchers speculate that between 500,000 and 1 million people in this country may be taking the two medications simultaneously.
The researchers’ study relied on an adverse-event reporting database maintained by the U.S. Food and Drug Administration and on sophisticated electronic medical records used by each of the three participating institutions. They used data-mining techniques to identify patterns of associations in large populations that would not be readily apparent to physicians treating individual patients.
“These kinds of drug interactions are almost certainly occurring all of the time, but, because they are not part of the approval process by the Food and Drug Administration, we can only learn about them after the drugs are on the market,” said Russ Altman, MD, PhD, professor of bioengineering, of genetics and of medicine at Stanford.