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Archive for the ‘Evidence-based decision-making’ Category

Artificial Intelligence (AI) Used to Successfully Determine Most Likely Repurposed Antibiotic Against Deadly Superbug Acinetobacter baumanni

Reporter: Stephen J. Williams, Ph.D.

The World Health Organization has identified 3 superbugs, or infective micororganisms displaying resistance to common antibiotics and multidrug resistance, as threats to humanity:

Three bacteria were listed as critical:

  • Acinetobacter baumannii bacteria that are resistant to important antibiotics called carbapenems. Acinetobacter baumannii are highly-drug resistant bacteria that can cause a range of infections for hospitalized patients, including pneumonia, wound, or blood infections.
  • Pseudomonas aeruginosa, which are resistant to carbapenems. Pseudomonas aeruginosa can cause skin rashes and ear infectious in healthy people but also severe blood infections and pneumonia when contracted by sick people in the hospital.
  • Enterobacteriaceae — a family of bacteria that live in the human gut — that are resistant to both carbepenems and another class of antibiotics, cephalosporins.

 

It has been designated critical need for development of  antibiotics to these pathogens.  Now researchers at Mcmaster University and others in the US had used artificial intelligence (AI) to screen libraries of over 7,000 chemicals to find a drug that could be repurposed to kill off the pathogen.

Liu et. Al. (1) published their results of an AI screen to narrow down potential chemicals that could work against Acinetobacter baumanii in Nature Chemical Biology recently.

Abstract

Acinetobacter baumannii is a nosocomial Gram-negative pathogen that often displays multidrug resistance. Discovering new antibiotics against A. baumannii has proven challenging through conventional screening approaches. Fortunately, machine learning methods allow for the rapid exploration of chemical space, increasing the probability of discovering new antibacterial molecules. Here we screened ~7,500 molecules for those that inhibited the growth of A. baumannii in vitro. We trained a neural network with this growth inhibition dataset and performed in silico predictions for structurally new molecules with activity against A. baumannii. Through this approach, we discovered abaucin, an antibacterial compound with narrow-spectrum activity against A. baumannii. Further investigations revealed that abaucin perturbs lipoprotein trafficking through a mechanism involving LolE. Moreover, abaucin could control an A. baumannii infection in a mouse wound model. This work highlights the utility of machine learning in antibiotic discovery and describes a promising lead with targeted activity against a challenging Gram-negative pathogen.

Schematic workflow for incorporation of AI for antibiotic drug discovery for A. baumannii from 1. Liu, G., Catacutan, D.B., Rathod, K. et al. Deep learning-guided discovery of an antibiotic targeting Acinetobacter baumannii. Nat Chem Biol (2023). https://doi.org/10.1038/s41589-023-01349-8

Figure source: https://www.nature.com/articles/s41589-023-01349-8

Article Source: https://www.nature.com/articles/s41589-023-01349-8

  1. Liu, G., Catacutan, D.B., Rathod, K. et al.Deep learning-guided discovery of an antibiotic targeting Acinetobacter baumanniiNat Chem Biol (2023). https://doi.org/10.1038/s41589-023-01349-8

 

 

For reference to WHO and lists of most pathogenic superbugs see https://www.scientificamerican.com/article/who-releases-list-of-worlds-most-dangerous-superbugs/

The finding was first reported by the BBC.

Source: https://www.bbc.com/news/health-65709834

By James Gallagher

Health and science correspondent

Scientists have used artificial intelligence (AI) to discover a new antibiotic that can kill a deadly species of superbug.

The AI helped narrow down thousands of potential chemicals to a handful that could be tested in the laboratory.

The result was a potent, experimental antibiotic called abaucin, which will need further tests before being used.

The researchers in Canada and the US say AI has the power to massively accelerate the discovery of new drugs.

It is the latest example of how the tools of artificial intelligence can be a revolutionary force in science and medicine.

Stopping the superbugs

Antibiotics kill bacteria. However, there has been a lack of new drugs for decades and bacteria are becoming harder to treat, as they evolve resistance to the ones we have.

More than a million people a year are estimated to die from infections that resist treatment with antibiotics.The researchers focused on one of the most problematic species of bacteria – Acinetobacter baumannii, which can infect wounds and cause pneumonia.

You may not have heard of it, but it is one of the three superbugs the World Health Organization has identified as a “critical” threat.

It is often able to shrug off multiple antibiotics and is a problem in hospitals and care homes, where it can survive on surfaces and medical equipment.

Dr Jonathan Stokes, from McMaster University, describes the bug as “public enemy number one” as it’s “really common” to find cases where it is “resistant to nearly every antibiotic”.

 

Artificial intelligence

To find a new antibiotic, the researchers first had to train the AI. They took thousands of drugs where the precise chemical structure was known, and manually tested them on Acinetobacter baumannii to see which could slow it down or kill it.

This information was fed into the AI so it could learn the chemical features of drugs that could attack the problematic bacterium.

The AI was then unleashed on a list of 6,680 compounds whose effectiveness was unknown. The results – published in Nature Chemical Biology – showed it took the AI an hour and a half to produce a shortlist.

The researchers tested 240 in the laboratory, and found nine potential antibiotics. One of them was the incredibly potent antibiotic abaucin.

Laboratory experiments showed it could treat infected wounds in mice and was able to kill A. baumannii samples from patients.

However, Dr Stokes told me: “This is when the work starts.”

The next step is to perfect the drug in the laboratory and then perform clinical trials. He expects the first AI antibiotics could take until 2030 until they are available to be prescribed.

Curiously, this experimental antibiotic had no effect on other species of bacteria, and works only on A. baumannii.

Many antibiotics kill bacteria indiscriminately. The researchers believe the precision of abaucin will make it harder for drug-resistance to emerge, and could lead to fewer side-effects.

 

In principle, the AI could screen tens of millions of potential compounds – something that would be impractical to do manually.

“AI enhances the rate, and in a perfect world decreases the cost, with which we can discover these new classes of antibiotic that we desperately need,” Dr Stokes told me.

The researchers tested the principles of AI-aided antibiotic discovery in E. coli in 2020, but have now used that knowledge to focus on the big nasties. They plan to look at Staphylococcus aureus and Pseudomonas aeruginosa next.

“This finding further supports the premise that AI can significantly accelerate and expand our search for novel antibiotics,” said Prof James Collins, from the Massachusetts Institute of Technology.

He added: “I’m excited that this work shows that we can use AI to help combat problematic pathogens such as A. baumannii.”

Prof Dame Sally Davies, the former chief medical officer for England and government envoy on anti-microbial resistance, told Radio 4’s The World Tonight: “We’re onto a winner.”

She said the idea of using AI was “a big game-changer, I’m thrilled to see the work he (Dr Stokes) is doing, it will save lives”.

Other related articles and books published in this Online Scientific Journal include the following:

Series D: e-Books on BioMedicine – Metabolomics, Immunology, Infectious Diseases, Reproductive Genomic Endocrinology

(3 book series: Volume 1, 2&3, 4)

https://www.amazon.com/gp/product/B08VVWTNR4?ref_=dbs_p_pwh_rwt_anx_b_lnk&storeType=ebooks

 

 

 

 

 

 

 

 

 

 

  • The Immune System, Stress Signaling, Infectious Diseases and Therapeutic Implications:

 

  • Series D, VOLUME 2

Infectious Diseases and Therapeutics

and

  • Series D, VOLUME 3

The Immune System and Therapeutics

(Series D: BioMedicine & Immunology) Kindle Edition.

On Amazon.com since September 4, 2017

(English Edition) Kindle Edition – as one Book

https://www.amazon.com/dp/B075CXHY1B $115

 

Bacterial multidrug resistance problem solved by a broad-spectrum synthetic antibiotic

The Journey of Antibiotic Discovery

FDA cleared Clever Culture Systems’ artificial intelligence tech for automated imaging, analysis and interpretation of microbiology culture plates speeding up Diagnostics

Artificial Intelligence: Genomics & Cancer

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Joe Biden Announced Science Team Nominations for the New Administration

Reporter: Stephen J. Williams, PhD

Article ID #287: Joe Biden Announced Science Team Nominations for the New Administration. Published on 1/17/2021

WordCloud Image Produced by Adam Tubman

In an announcement televised on C-Span, President Elect Joseph Biden announced his new Science Team to advise on science policy matters, as part of the White House Advisory Committee on Science and Technology. Below is a video clip and the transcript, also available at

https://www.c-span.org/video/?508044-1/president-elect-biden-introduces-white-house-science-team

 

 

COMING UP TONIGHT ON C-SPAN, NEXT, PRESIDENT-ELECT JOE BIDEN AND VICE PRESIDENT-ELECT KAMALA HARRIS ANNOUNCE SEVERAL MEMBERS OF THEIR WHITE HOUSE SCIENCE TEAM. AND THEN SENATE MINORITY LEADER CHUCK SCHUMER TALKS ABOUT THE IMPEACHMENT OF PRESIDENT TRUMP IN THE WEEKLY DEMOCRATIC ADDRESS. AND AFTER THAT, TODAY’S SPEECH BY VICE PRESIDENT MIKE PENCE TO SAILORS AT NAVAL AIR STATION LAMORE IN CALIFORNIA. NEXT, PRESIDENT-ELECT JOE BIDEN AND VICE PRESIDENT-ELECT KAMALA HARRIS ANNOUNCE SEVERAL MEMBERS OF THEIR WHITE HOUSE SCIENCE TEAM. FROM WILMINGTON, DELAWARE, THIS IS ABOUT 40 MINUTES. PRESIDENT-ELECT BIDEN: GOOD AFTERNOON, FOLKS. I WAS TELLING THESE FOUR BRILLIANT SCIENTISTS AS I STOOD IN THE BACK, IN A WAY, THEY — THIS IS THE MOST EXCITING ANNOUNCEMENT THAT I’VE GOTTEN TO MAKE IN THE ENTIRE CABINET RAISED TO A CABINET LEVEL POSITION IN ONE CASE. THESE ARE AMONG THE BRIGHTEST MOST DEDICATED PEOPLE NOT ONLY IN THE COUNTRY BUT THE WORLD. THEY’RE COMPOSED OF SOME OF THE MOST SCIENTIFIC BRILLIANT MINDS IN THE WORLD. WHEN I WAS VICE PRESIDENT AS — I I HAD INTENSE INTEREST IN EVERYTHING THEY WERE DOING AND I PAID ENORMOUS ATTENTION. AND I WOULD — LIKE A KID GOING BACK TO SCHOOL. SIT DOWN AND CAN YOU EXPLAIN TO ME AND THEY WERE — VERY PATIENT WITH ME. AND — BUT AS PRESIDENT, I WANTED YOU TO KNOW I’M GOING TO PAY A GREAT DEAL OF ATTENTION. WHEN I TRAVEL THE WORLD AS VICE PRESIDENT, I WAS OFTEN ASKED TO EXPLAIN TO WORLD LEADERS, THEY ASKED ME THINGS LIKE DEFINE AMERICA. TELL ME HOW CAN YOU DEFINE AMERICA? WHAT’S AMERICA? AND I WAS ON A TIBETAN PLATEAU WITH AT THE TIME WITH XI ZIN PING AND WE HAD AN INTERPRETER CAN I DEFINE AMERICA FOR HIM? I SAID YES, I CAN. IN ONE WORD. POSSIBILITIES. POSSIBILITIES. I THINK IT’S ONE OF THE REASONS WHY WE’VE OCCASIONALLY BEEN REFERRED TO AS UGLY AMERICANS. WE THINK ANYTHING’S POSSIBLE GIVEN THE CHANCE, WE CAN DO ANYTHING. AND THAT’S PART OF I THINK THE AMERICAN SPIRIT. AND WHAT THE PEOPLE ON THIS STAGE AND THE DEPARTMENTS THEY WILL LEAD REPRESENT ENORMOUS POSSIBILITIES. THEY’RE THE ONES ASKING THE MOST AMERICAN OF QUESTIONS, WHAT NEXT? WHAT NEXT? NEVER SATISFIED, WHAT’S NEXT? AND WHAT’S NEXT IS BIG AND BREATHTAKING. HOW CAN — HOW CAN WE MAKE THE IMPOSSIBLE POSSIBLE? AND THEY WERE JUST ASKING QUESTIONS FOR THE SAKE OF QUESTIONS, THEY’RE ASKING THESE QUESTIONS AS CALL TO ACTION. , TO INSPIRE, TO HELP US IMAGINE THE FUTURE AND FIGURE OUT HOW TO MAKE IT REAL AND IMPROVE THE LIVES OF THE AMERICAN PEOPLE AND PEOPLE AROUND THE WORLD. THIS IS A TEAM THAT ASKED US TO IMAGINE EVERY HOME IN AMERICA BEING POWERED BY RENEWABLE ENERGY WITHIN THE NEXT 10 YEARS. OR 3-D IMAGE PRINTERS RESTORING TISSUE AFTER TRAUMATIC INJURIES AND HOSPITALS PRINTING ORGANS FOR ORGAN TRANSPLANTS. IMAGINE, IMAGINE. AND THEY REALLY — AND, YOU KNOW, THEN RALLY, THE SCIENTIFIC COMMUNITY TO GO ABOUT DOING WHAT WE’RE IMAGINING. YOU NEED SCIENCE, DATA AND DISCOVERY WAS A GOVERNING PHILOSOPHY IN THE OBAMA-BIDEN ADMINISTRATION. AND EVERYTHING FROM THE ECONOMY TO THE ENVIRONMENT TO CRIMINAL JUSTICE REFORM AND TO NATIONAL SECURITY. AND ON HEALTH CARE. FOR EXAMPLE, A BELIEF IN SCIENCE LED OUR EFFORTS TO MAP THE HUMAN BRAIN AND TO DEVELOP MORE PRECISE INDIVIDUALIZED MEDICINES. IT LED TO OUR ONGOING MISSION TO END CANCER AS WE KNOW IT, SOMETHING THAT IS DEEPLY PERSONAL TO BOTH MY FAMILY AND KAMALA’S FAMILY AND COUNTLESS FAMILIES IN AMERICA. WHEN PRESIDENT OBAMA ASKED ME TO LEAD THE CANCER MOON SHOT, I KNEW WE HAD TO INJECT A SENSE OF URGENCY INTO THE FIGHT. WE BELIEVED WE COULD DOUBLE THE RATE OF PROGRESS AND DO IN FIVE YEARS WHAT OTHERWISE WOULD TAKE 10. MY WIFE, JILL, AND I TRAVELED AROUND THE COUNTRY AND THE WORLD MEETING WITH THOUSANDS OF CANCER PATIENTS AND THEIR FAMILIES, PHYSICIANS, RESEARCHERS, PHILANTHROPISTS, TECHNOLOGY LEADERS AND HEADS OF STATE. WE SOUGHT TO BETTER UNDERSTAND AND BREAK DOWN THE SILOS AND STOVE PIPES THAT PREVENT THE SHARING OF INFORMATION AND IMPEDE ADVANCES IN CANCER RESEARCH AND TREATMENT WHILE BUILDING A FOCUSED AND COORDINATED EFFORT HERE AT HOME AND ABROAD. WE MADE PROGRESS. BUT THERE’S SO MUCH MORE THAT WE CAN DO. WHEN I ANNOUNCED THAT I WOULD NOT RUN IN 2015 AT THE TIME, I SAID I ONLY HAD ONE REGRET IN THE ROSE GARDEN AND IF I HAD ANY REGRETS THAT I HAD WON, THAT I WOULDN’T GET TO BE THE PRESIDENT TO PRESIDE OVER CANCER AS WE KNOW IT. WELL, AS GOD WILLING, AND ON THE 20TH OF THIS MONTH IN A COUPLE OF DAYS AS PRESIDENT I’M GOING TO DO EVERYTHING I CAN TO GET THAT DONE. I’M GOING TO — GOING TO BE A PRIORITY FOR ME AND FOR KAMALA AND IT’S A SIGNATURE ISSUE FOR JILL AS FIRST LADY. WE KNOW THE SCIENCE IS DISCOVERY AND NOT FICTION. AND IT’S ALSO ABOUT HOPE. AND THAT’S AMERICA. IT’S IN THE D.N.A. OF THIS COUNTRY, HOPE. WE’RE ON THE CUSP OF SOME OF THE MOST REMARKABLE BREAKTHROUGHS THAT WILL FUNDAMENTALLY CHANGE THE WAY OF LIFE FOR ALL LIFE ON THIS PLANET. WE CAN MAKE MORE PROGRESS IN THE NEXT 10 YEARS, I PREDICT, THAN WE’VE MADE IN THE LAST 50 YEARS. AND EXPONENTIAL MOVEMENT. WE CAN ALSO FACE SOME OF THE MOST DIRE CRISES IN A GENERATION WHERE SCIENCE IS CRITICAL TO WHETHER OR NOT WE MEET THE MOMENT OF PERIL AND PROMISE THAT WE KNOW IS WITHIN OUR REACH. IN 1944, FRANKLIN ROOSEVELT ASKED HIS SCIENCE ADVISOR HOW COULD THE UNITED STATES FURTHER ADVANCE SCIENTIFIC RESEARCH IN THE CRITICAL YEARS FOLLOWING THE SECOND WORLD WAR? THE RESPONSE LED TO SOME OF THE MOST GROUND BREAKING DISCOVERIES IN THE LAST 75 YEARS. AND WE CAN DO THAT AGAIN. AND WE CAN DO MORE. SO TODAY, I’M PROUD TO ANNOUNCE A TEAM OF SOME OF THE COUNTRY’S MOST BRILLIANT AND ACCOMPLISHED SCIENTISTS TO LEAD THE WAY. AND I’M ASKING THEM TO FOCUS ON FIVE KEY AREAS. FIRST THE PANDEMIC AND WHAT WE CAN LEARN ABOUT WHAT IS POSSIBLE OR WHAT SHOULD BE POSSIBLE TO ADDRESS THE WIDEST RANGE OF PUBLIC HEALTH NEEDS. SECONDLY, THE ECONOMY, HOW CAN WE BUILD BACK BETTER TO ENSURE PROSPERITY IS FULLY SHARED ALL ACROSS AMERICA? AMONG ALL AMERICANS? AND THIRDLY, HOW SCIENCE HELPS US CONFRONT THIS CLIMATE CRISIS WE FACE IN AMERICA AND THE WORLD BUT IN AMERICA HOW IT HELPS US CONFRONT THE CLIMATE CRISIS WITH AMERICAN JOBS AND INGENUITY. AND FOURTH, HOW CAN WE ENSURE THE UNITED STATES LEADS THE WORLD IN TECHNOLOGIES AND THE INDUSTRIES THAT THE FUTURE THAT WILL BE CRITICAL FOR OUR ECONOMIC PROSPERITY AND NATIONAL SECURITY? ESPECIALLY WITH THE INTENSE INCREASED COMPETITION AROUND THE WORLD FROM CHINA ON? AND FIFTH, HOW CAN WE ASSURE THE LONG-TERM HEALTH AND TRUST IN SCIENCE AND TECHNOLOGY IN OUR NATION? YOU KNOW, THESE ARE EACH QUESTIONS THAT CALL FOR ACTION. AND I’M HONORED TO ANNOUNCE A TEAM THAT IS ANSWERING THE CALL TO SERVE. AS THE PRESIDENTIAL SCIENCE ADVISOR AND DIRECTOR OF THE OFFICE OF SCIENCE AND TECHNOLOGY POLICY, I NOMINATE ONE OF THE MOST BRILLIANT GUYS I KNOW, PERSONS I KNOW, DR. ERIC LANDER. AND THANK YOU, DOC, FOR COMING BACK. THE PIONEER — HE’S A PIONEER IN THE STIFFING COMMUNITY. PRINCIPAL LEADER IN THE HUMAN GENOME PROJECT. AND NOT HYPERBOLE TO SUGGEST THAT DR. LANDER’S WORK HAS CHANGED THE COURSE OF HUMAN HISTORY. HIS ROLE IN HELPING US MAP THE GENOME PULLED BACK THE CURTAIN ON HUMAN DISEASE, ALLOWING SCIENTISTS, EVER SINCE, AND FOR GENERATIONS TO COME TO EXPLORE THE MOLECULAR BASIS FOR SOME OF THE MOST DEVASTATING ILLNESSES AFFECTING OUR WORLD. AND THE APPLICATION OF HIS PIONEERING WORK AS — ARE POISED TO LEAD TO INCREDIBLE CURES AND BREAKTHROUGHS IN THE YEARS TO COME. DR. LANDER NOW SERVES AS THE PRESIDENT AND FOUNDING DIRECTOR OF THE BRODE INSTITUTE AT M.I.T. AND HARVARD, THE WORLD’S FOREMOST NONPROFIT GENETIC RESEARCH ORGANIZATION. AND I CAME TO APPRECIATE DR. LANDER’S EXTRAORDINARY MIND WHEN HE SERVED AS THE CO-CHAIR OF THE PRESIDENT’S COUNCIL ON ADVISORS AND SCIENCE AND TECHNOLOGY DURING THE OBAMA-BIDEN ADMINISTRATION. AND I’M GRATEFUL, I’M GRATEFUL THAT WE CAN WORK TOGETHER AGAIN. I’VE ALWAYS SAID THAT BIDEN-HARRIS ADMINISTRATION WILL ALSO LEAD AND WE’RE GOING TO LEAD WITH SCIENCE AND TRUTH. WE BELIEVE IN BOTH. [LAUGHTER] GOD WILLING OVERCOME THE PANDEMIC AND BUILD OUR COUNTRY BETTER THAN IT WAS BEFORE. AND THAT’S WHY FOR THE FIRST TIME IN HISTORY, I’M GOING TO BE ELEVATING THE PRESIDENTIAL SCIENCE ADVISOR TO A CABINET RANK BECAUSE WE THINK IT’S THAT IMPORTANT. AS DEPUTY DIRECTOR OF THE OFFICE OF SCIENCE AND TECHNOLOGY POLICY AND SCIENCE AND — SCIENCE AND SOCIETY, I APPOINT DR. NELSON. SHE’S A PROFESSOR AT THE INSTITUTE OF ADVANCED STUDIES AT PRINCETON UNIVERSITY. THE PRESIDENT OF THE SOCIAL SCIENCE RESEARCH COUNCIL. AND ONE OF AMERICA’S LEADING SCHOLARS IN THE — AN AWARD-WINNING AUTHOR AND RESEARCHER AND EXPLORING THE CONNECTIONS BETWEEN SCIENCE AND OUR SOCIETY. THE DAUGHTER OF A MILITARY FAMILY, HER DAD SERVED IN THE UNITED STATES NAVY AND HER MOM WAS AN ARMY CRIPPING TO RAFFER. DR. NELSON DEVELOPED A LOVE OF TECHNOLOGY AT A VERY YOUNG AGE PARTICULARLY WITH THE EARLY COMPUTER PRODUCTS. COMPUTING PRODUCTS AND CODE-BREAKING EQUIPMENT THAT EVERY KID HAS AROUND THEIR HOUSE. AND SHE GREW UP WITHIN HER HOME. WHEN I WROTE THAT DOWN, I THOUGHT TO MYSELF, I MEAN, HOW MANY KIDS — ANY WAY, THAT PASSION WAS A PASSION FORGED A LIFELONG CURIOSITY ABOUT THE INEQUITIES AND THE POWER DIAMONDICS THAT SIT BENEATH THE SURFACE OF SCIENTIFIC RESEARCH AND THE TECHNOLOGY WE BUILD. DR. NELSON IS FOCUSED ON THOSE INSIGHTS. AND THE SCIENCE, TECHNOLOGY AND SOCIETY, LIKE FEW BEFORE HER EVER HAVE IN AMERICAN HISTORY. BREAKING NEW GROUND ON OUR UNDERSTANDING OF THE ROLE SCIENCE PLAYS IN AMERICAN LIFE AND OPENING THE DOOR TO — TO A FUTURE WHICH SCIENCE BETTER SERVES ALL PEOPLE. AS CO-CHAIR OF THE PRESIDENT’S COUNCIL ON ADVISORS OF SCIENCE AND TECHNOLOGY,APPOINT DR. FRANCIS ARNOLD, DIRECTOR OF THE ROSE BIOENGINEERING CENTER AT CALTECH AND ONE OF THE WORLD’S LEADING EXPERTS IN PROTEIN ENGINEERING, A LIFE-LONG CHAMPION OF RENEWABLE ENERGY SOLUTIONS WHO HAS BEEN INDUCTED INTO THE NATIONAL INVENTORS’ HALL OF FAME. THAT AIN’T A BAD PLACE TO BE. NOT ONLY IS SHE THE FIRST WOMAN TO BE ELECTED TO ALL THREE NATIONAL ACADEMIES OF SCIENCE, MEDICINE AND ENGINEERING AND ALSO THE FIRST WOMAN, AMERICAN WOMAN, TO WIN A NOBEL PRIZE IN CHEMISTRY. A VERY SLOW LEARNER, SLOW STARTER, THE DAUGHTER OF PITTSBURGH, SHE WORKED AS A CAB DRIVER, A JAZZ CLUB SERVER, BEFORE MAKING HER WAY TO BERKELEY AND A CAREER ON THE LEADING EDGE OF HUMAN DISCOVERY. AND I WANT TO MAKE THAT POINT AGAIN. I WANT — IF ANY OF YOUR CHILDREN ARE WATCHING, LET THEM KNOW YOU CAN DO ANYTHING. THIS COUNTRY CAN DO ANYTHING. ANYTHING AT ALL. AND SO SHE SURVIVED BREAST CANCER, OVERCAME A TRAGIC LOSS IN HER FAMILY WHILE RISING TO THE TOP OF HER FIELD, STILL OVERWHELMINGLY DOMINATED BY MEN. HER PASSION HAS BEEN A STEADFAST COMMITMENT TO RENEWABLE ENERGY FOR THE BETTERMENT OF OUR PLANET AND HUMANKIND. SHE IS AN INSPIRING FIGURE TO SCIENTISTS ACROSS THE FIELD AND ACROSS NATIONS. AND I WANT TO THANK DR. ARNOLD FOR AGREEING TO CO-CHAIR A FIRST ALL WOMAN TEAM TO LEAD THE PRESIDENT’S COUNCIL OF ADVISORS ON SCIENCE AND TECHNOLOGY WHICH LEADS ME TO THE NEXT MEMBER OF THE TEAM. AS CO-CHAIR, THE PRESIDENT’S COUNCIL OF ADVISORS ON SCIENCE AND TECHNOLOGY, I APPOINT DR. MARIE ZUBER. A TRAIL BLAZER BRAISING GEO PHYSICIST AND PLANETARY SCIENTIST A. FORMER CHAIR OF THE NATIONAL SCIENCE BOARD. FIRST WOMAN TO LEAD THE SCIENCE DEPARTMENT AT M.I.T. AND THE FIRST WOMAN TO LEAD NASA’S ROBOTIC PLANETARY MISSION. GROWING UP IN COLE COUNTRY NOT FAR FROM HEAVEN, SCRANTON, PENNSYLVANIA, IN CARBON COUNTY, PENNSYLVANIA, ABOUT 50 MILES SOUTH OF WHERE I WAS A KID, SHE DREAMED OF EXPLORING OUTER SPACE. COULD HAVE TOLD HER SHE WOULD JUST GO TO GREEN REACH IN SCRANTON AND FIND WHERE IT WAS. AND I SHOULDN’T BE SO FLIPPANT. BUT I’M SO EXCITED ABOUT THESE FOLKS. YOU KNOW, READING EVERY BOOK SHE COULD FIND AND LISTENING TO HER MOM’S STORIES ABOUT WATCHING THE EARLIEST ROCKET LAUNCH ON TELEVISION, MARIE BECAME THE FIRST PERSON IN HER FAMILY TO GO TO COLLEGE AND NEVER LET GO OF HER DREAM. TODAY SHE OVERSEES THE LINCOLN LABORATORY AT M.I.T. AND LEADS THE INSTITUTION’S CLIMATE ACTION PLAN. GROWING UP IN COLD COUNTRY, NOT AND FINALLY, COULD NOT BE HERE TODAY, BUT I’M PLEASED TO ANNOUNCE THAT I’VE HAD A LONG CONVERSATION WITH DR. FRANCIS COLLINS AND COULD NOT BE HERE TODAY. AND I’VE ASKED THEM TO STAY ON AS DIRECTOR OF THE INSTITUTE OF HEALTH AND — AT THIS CRITICAL MOMENT. I’VE KNOWN DR. COLLINS FOR MANY YEARS. I WORKED WITH HIM CLOSELY. HE’S BRILLIANT. A PIONEER. A TRUE LEADER. AND ABOVE ALL, HE’S A MODEL OF PUBLIC SERVICE AND I’M HONORED TO BE WORKING WITH HIM AGAIN. AND IT IS — IN HIS ABSENCE I WANT TO THANK HIM AGAIN FOR BEING WILLING TO STAY ON. I KNOW THAT WASN’T HIS ORIGINAL PLAN. BUT WE WORKED AN AWFUL LOT ON THE MOON SHOT AND DEALING WITH CANCER AND I JUST WANT TO THANK HIM AGAIN. AND TO EACH OF YOU AND YOUR FAMILIES, AND I SAY YOUR FAMILIES, THANK YOU FOR THE WILLINGNESS TO SERVE. AND NOT THAT YOU HAVEN’T BEEN SERVING ALREADY BUT TO SERVE IN THE ADMINISTRATION. AND THE AMERICAN PEOPLE, TO ALL THE AMERICAN PEOPLE, THIS IS A TEAM THAT’S GOING TO HELP RESTORE YOUR FAITH IN AMERICA’S PLACE IN THE FRONTIER OF SCIENCE AND DISCOVER AND HOPE. I’M NOW GOING TO TURN THIS OVER STARTING WITH DR. LANDER, TO EACH OF OUR NOMINEES AND THEN WITH — HEAR FROM THE VICE PRESIDENT. BUT AGAIN, JUST CAN’T THANK YOU ENOUGH AND I REALLY MEAN IT. THANK YOU, THANK YOU, THANK YOU FOR WILLING TO DO THIS. DOCTOR, IT’S ALL YOURS. I BETTER PUT MY MASK ON OR I’M GOING TO GET IN TROUBLE.

 

Director’s Page

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Crowdsourcing Difficult-to-Collect Epidemiological Data in Pandemics: Lessons from Ebola to the current COVID-19 Pandemic

 

Curator: Stephen J. Williams, Ph.D.

 

At the onset of the COVID-19 pandemic, epidemiological data from the origin of the Sars-Cov2 outbreak, notably from the Wuhan region in China, was sparse.  In fact, official individual patient data rarely become available early on in an outbreak, when that data is needed most. Epidemiological data was just emerging from China as countries like Italy, Spain, and the United States started to experience a rapid emergence of the outbreak in their respective countries.  China, made of 31 geographical provinces, is a vast and complex country, with both large urban and rural areas.

 

 

 

As a result of this geographical diversity and differences in healthcare coverage across the country, epidemiological data can be challenging.  For instance, cancer incidence data for regions and whole country is difficult to calculate as there are not many regional cancer data collection efforts, contrasted with the cancer statistics collected in the United States, which is meticulously collected by cancer registries in each region, state and municipality.  Therefore, countries like China must depend on hospital record data and autopsy reports in order to back-extrapolate cancer incidence data.  This is the case in some developed countries like Italy where cancer registry is administered by a local government and may not be as extensive (for example in the Napoli region of Italy).

 

 

 

 

 

 

Population density China by province. Source https://www.unicef.cn/en/figure-13-population-density-province-2017

 

 

 

Epidemiologists, in areas in which data collection may be challenging, are relying on alternate means of data collection such as using devices connected to the internet-of-things such as mobile devices, or in some cases, social media is becoming useful to obtain health related data.  Such as effort to acquire pharmacovigilance data, patient engagement, and oral chemotherapeutic adherence using the social media site Twitter has been discussed in earlier posts: (see below)

Twitter is Becoming a Powerful Tool in Science and Medicine at https://pharmaceuticalintelligence.com/2014/11/06/twitter-is-becoming-a-powerful-tool-in-science-and-medicine/

 

 

 

 

 

Now epidemiologists are finding crowd-sourced data from social media and social networks becoming useful in collecting COVID-19 related data in those countries where health data collection efforts may be sub-optimal.  In a recent paper in The Lancet Digital Health [1], authors Kaiyuan Sun, Jenny Chen, and Cecile Viboud present data from the COVID-19 outbreak in China using information collected over social network sites as well as public news outlets and find strong correlations with later-released government statistics, showing the usefulness in such social and crowd-sourcing strategies to collect pertinent time-sensitive data.  In particular, the authors aim was to investigate this strategy of data collection to reduce the time delays between infection and detection, isolation and reporting of cases.

The paper is summarized below:

Kaiyuan Sun, PhD Jenny Chen, BScn Cécile Viboud, PhD . (2020).  Early epidemiological analysis of the coronavirus disease 2019 outbreak based on crowdsourced data: a population-level observational study.  The Lancet: Digital Health; Volume 2, Issue 4, E201-E208.

Summary

Background

As the outbreak of coronavirus disease 2019 (COVID-19) progresses, epidemiological data are needed to guide situational awareness and intervention strategies. Here we describe efforts to compile and disseminate epidemiological information on COVID-19 from news media and social networks.

Methods

In this population-level observational study, we searched DXY.cn, a health-care-oriented social network that is currently streaming news reports on COVID-19 from local and national Chinese health agencies. We compiled a list of individual patients with COVID-19 and daily province-level case counts between Jan 13 and Jan 31, 2020, in China. We also compiled a list of internationally exported cases of COVID-19 from global news media sources (Kyodo News, The Straits Times, and CNN), national governments, and health authorities. We assessed trends in the epidemiology of COVID-19 and studied the outbreak progression across China, assessing delays between symptom onset, seeking care at a hospital or clinic, and reporting, before and after Jan 18, 2020, as awareness of the outbreak increased. All data were made publicly available in real time.

Findings

We collected data for 507 patients with COVID-19 reported between Jan 13 and Jan 31, 2020, including 364 from mainland China and 143 from outside of China. 281 (55%) patients were male and the median age was 46 years (IQR 35–60). Few patients (13 [3%]) were younger than 15 years and the age profile of Chinese patients adjusted for baseline demographics confirmed a deficit of infections among children. Across the analysed period, delays between symptom onset and seeking care at a hospital or clinic were longer in Hubei province than in other provinces in mainland China and internationally. In mainland China, these delays decreased from 5 days before Jan 18, 2020, to 2 days thereafter until Jan 31, 2020 (p=0·0009). Although our sample captures only 507 (5·2%) of 9826 patients with COVID-19 reported by official sources during the analysed period, our data align with an official report published by Chinese authorities on Jan 28, 2020.

Interpretation

News reports and social media can help reconstruct the progression of an outbreak and provide detailed patient-level data in the context of a health emergency. The availability of a central physician-oriented social network facilitated the compilation of publicly available COVID-19 data in China. As the outbreak progresses, social media and news reports will probably capture a diminishing fraction of COVID-19 cases globally due to reporting fatigue and overwhelmed health-care systems. In the early stages of an outbreak, availability of public datasets is important to encourage analytical efforts by independent teams and provide robust evidence to guide interventions.

A Few notes on Methodology:

  • The authors used crowd-sourced reports from DXY.cn, a social network for Chinese physicians, health-care professionals, pharmacies and health-care facilities. This online platform provides real time coverage of the COVID-19 outbreak in China
  • More data was curated from news media, television and includes time-stamped information on COVID-19 cases
  • These reports are publicly available, de-identified patient data
  • No patient consent was needed and no ethics approval was required
  • Data was collected between January 20, 2020 and January 31,2020
  • Sex, age, province of identification, travel history, dates of symptom development was collected
  • Additional data was collected for other international sites of the pandemic including Cambodia, Canada, France, Germany, Hong Kong, India, Italy, Japan, Malaysia, Nepal, Russia, Singapore, UK, and USA
  • All patients in database had laboratory confirmation of infection

 

Results

  • 507 patient data was collected with 153 visited and 152 resident of Wuhan
  • Reported cases were skewed toward males however the overall population curve is skewed toward males in China
  • Most cases (26%) were from Beijing (urban area) while an equal amount were from rural areas combined (Shaanzi and Yunnan)
  • Age distribution of COVID cases were skewed toward older age groups with median age of 45 HOWEVER there were surprisingly a statistically high amount of cases less than 5 years of age
  • Outbreak progression based on the crowd-sourced patient line was consistent with the data published by the China Center for Disease Control
  • Median reporting delay in the authors crowd-sourcing data was 5 days
  • Crowd-sourced data was able to detect apparent rapid growth of newly reported cases during the collection period in several provinces outside of Hubei province, which is consistent with local government data

The following graphs show age distribution for China in 2017 and predicted for 2050.

projected age distribution China 2050. Source https://chinapower.csis.org/aging-problem/

 

 

 

 

 

 

 

 

 

 

 

 

The authors have previously used this curation of news methodology to analyze the Ebola outbreak[2].

A further use of the crowd-sourced database was availability of travel histories for patients returning from Wuhan and onset of symptoms, allowing for estimation of incubation periods.

The following published literature has also used these datasets:

Backer JA, Klinkenberg D, Wallinga J: Incubation period of 2019 novel coronavirus (2019-nCoV) infections among travellers from Wuhan, China, 20-28 January 2020. Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin 2020, 25(5).

Lauer SA, Grantz KH, Bi Q, Jones FK, Zheng Q, Meredith HR, Azman AS, Reich NG, Lessler J: The Incubation Period of Coronavirus Disease 2019 (COVID-19) From Publicly Reported Confirmed Cases: Estimation and Application. Annals of internal medicine 2020, 172(9):577-582.

Li Q, Guan X, Wu P, Wang X, Zhou L, Tong Y, Ren R, Leung KSM, Lau EHY, Wong JY et al: Early Transmission Dynamics in Wuhan, China, of Novel Coronavirus-Infected Pneumonia. The New England journal of medicine 2020, 382(13):1199-1207.

Dataset is available on the Laboratory for the Modeling of Biological and Socio-technical systems website of Northeastern University at https://www.mobs-lab.org/.

References

  1. Sun K, Chen J, Viboud C: Early epidemiological analysis of the coronavirus disease 2019 outbreak based on crowdsourced data: a population-level observational study. The Lancet Digital health 2020, 2(4):e201-e208.
  2. Cleaton JM, Viboud C, Simonsen L, Hurtado AM, Chowell G: Characterizing Ebola Transmission Patterns Based on Internet News Reports. Clinical infectious diseases : an official publication of the Infectious Diseases Society of America 2016, 62(1):24-31.

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Reporter: Stephen J. Williams, PhD

10:00-10:45 AM The Davids vs. the Cancer Goliath Part 1

Startups from diagnostics, biopharma, medtech, digital health and emerging tech will have 8 minutes to articulate their visions on how they aim to tame the beast.

Start Time End Time Company
10:00 10:08 Belong.Life
10:09 10:17 Care+Wear
10:18 10:26 OncoPower
10:27 10:35 PolyAurum LLC
10:36 10:44 Seeker Health

Speakers:
Karthik Koduru, MD, Co-Founder and Chief Oncologist, OncoPower
Eliran Malki, Co-Founder and CEO, Belong.Life
Chaitenya Razdan, Co-founder and CEO, Care+Wear @_crazdan
Debra Shipley Travers, President & CEO, PolyAurum LLC @polyaurum
Sandra Shpilberg, Founder and CEO, Seeker Health @sandrashpilberg

Belong Life

  • 10,000 cancer patients a month helping patients navigate cancer care with Belong App
  • Belong Eco system includes all their practitioners and using a trigger based content delivery (posts, articles etc)
  • most important taking unstructured health data (images, social activity, patient compilance) and converting to structured data

Care+Wear

personally design picc line cover for oncology patients

partners include NBA Major league baseball, Oscar de la Renta,

designs easy access pic line gowns and shirts

OncoPower :Digital Health in a Blockchain Ecosystem

problems associated with patient adherence and developed a product to address this

  1. OncoPower Blockchain: HIPAA compliant using the coin Oncopower security token to incentiavize patients and oncologists to consult with each other or oncologists with tumor boards; this is not an initial coin offering

PolyArum

  • spinout from UPENN; developing a nanoparticle based radiation therapy; glioblastoma muse model showed great response with gold based nanoparticle and radiation
  • they see enhanced tumor penetration, and retention of the gold nanoparticles
  • however most nanoparticles need to be a large size greater than 5 nm to see effect so they used a polymer based particle; see good uptake but excretion past a week so need to re-dose with Au nanoparticles
  • they are looking for capital and expect to start trials in 2020

Seeker Health

  • tying to improve the efficiency of clinical trial enrollment
  • using social networks to find the patients to enroll in clinical trials
  • steps they use 1) find patients on Facebook, Google, Twitter 2) engage patient screen 3) screening at clinical sites
  • Seeker Portal is a patient management system: patients referred to a clinical site now can be tracked

11:00- 11:45 AM Breakout: How to Scale Precision Medicine

The potential for precision medicine is real, but is limited by access to patient datasets. How are government entities, hospitals and startups bringing the promise of precision medicine to the masses of oncology patients

Moderator: Sandeep Burugupalli, Senior Manager, Real World Data Innovation, Pfizer @sandeepburug
Speakers:
Ingo ​Chakravarty, President and CEO, Navican @IngoChakravarty
Eugean Jiwanmall, Senior Research Analyst for Medical Policy & Technology Evaluation , Independence Blue Cross @IBX
Andrew Norden, M.D., Chief Medical Officer, Cota @ANordenMD
Ankur Parikh M.D, Medical Director of Precision Medicine, Cancer Treatment Centers of America @CancerCenter

Ingo: data is not ordered, only half of patients are tracked in some database, reimbursement a challenge

Eugean: identifying mutations as patients getting more comprehensive genomic coverage, clinical trials are expanding more rapidly as seen in 2018 ASCO

Ingo: general principals related to health outcomes or policy or reimbursement.. human studies are paramount but payers may not allowing for general principals (i.e. an Alk mutation in lung cancer and crizotanib treatment may be covered but maybe not for glioblastoma or another cancer containing similar ALK mutation; payers still depend on clinical trial results)

Andrew: using gene panels and NGS but only want to look for actionable targets; they establish an expert panel which reviews these NGS sequence results to determine actionable mutations

Ankur:  they have molecular tumor boards but still if want to prescribe off label and can’t find a clinical trial there is no reimbursement

Andrew: going beyond actionable mutations, although many are doing WES (whole exome sequencing) can we use machine learning to see if there are actionable data from a WES

Ingo: we forget in datasets is that patients have needs today and we need those payment systems and structures today

Eugean: problem is the start from cost (where the cost starts at and was it truly medically necessary)

Norden: there are not enough data sharing to make a decision; an enormous amount of effort to get businesses and technical limitations in data sharing; possibly there are policies needed to be put in place to assimilate datasets and promote collaborations

Ingo: need to take out the middle men between sequencing of patient tumor and treatment decision; middle men are taking out value out of the ‘supply chain’;

Andrew: PATIENTS DON’T OWN their DATA but MOST clinicians agree THEY SHOULD

Ankur: patients are willing to share data but the HIPAA compliance is a barrier

 

11:50- 12:30 AM Fireside Chat with Michael Pellini, M.D.

Building a Precision Medicine Business from the Ground Up: An Operating and Venture Perspective

Dr. Pellini has spent more than 20 years working on the operating side of four companies, each of which has pushed the boundaries of the standard of care. He will describe his most recent experience at Foundation Medicine, at the forefront of precision medicine, and how that experience can be leveraged on the venture side, where he now evaluates new healthcare technologies.

Speaker:
Michael Pellini, M.D., Managing Partner, Section 32 and Chairman, Foundation Medicine @MichaelPellini

Roche just bought Foundation Medicine for $2.5 billion.  They negotiated over 7 months but aside from critics they felt it was a great deal because it gives them, as a diagnostic venture, the international reach and biotech expertise.  Foundation Medicine offered Roche expertise on the diagnostic space including ability to navigate payers and regulatory aspects of the diagnostic business.  He feels it benefits all aspects of patient care and the work they do with other companies.

Moderatore: Roche is doing multiple deals to ‘own’ a disease state.

Dr. Pellini:  Roche is closing a deal with Flatiron just like how Merck closed deals with genomics companies.  He feels best to build the best company on a stand alone basis and provide for patients, then good things will happen.  However the problem of achieving scale for Precision Medicine is reimbursement by payers.  They still have to keep collecting data and evolving services to suit pharma.  They didn’t know if there model would work but when he met with FDA in 2011 they worked with Precision Medicine, said collect the data and we will keep working with you,

However the payers aren’t contributing to the effort.  They need to assist some of the young companies that can’t raise the billion dollars needed for all the evidence that payers require.  Precision Medicine still have problems, even though they have collected tremendous amounts of data and raised significant money.  From the private payer perspective there is no clear roadmap for success.

They recognized that the payers would be difficult but they had a plan but won’t invest in companies that don’t have a plan for getting reimbursement from payers.

Moderator: What is section 32?

Pellini:  Their investment arm invests in the spectrum of precision healtcare companies including tech companies.  They started with a digital path imaging system that went from looking through a scope and now looking at a monitor with software integrated with medical records. Section 32 has $130 million under management and may go to $400 Million but they want to stay small.

Pellini: we get 4-5 AI pitches a week.

Moderator: Are you interested in companion diagnostics?

Pellini:  There may be 24 expected 2018 drug approvals and 35% of them have a companion diagnostic (CDX) with them.  however going out ten years 70% may have a CDX associated with them.  Payers need to work with companies to figure out how to pay with these CDXs.

 

 

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Medcity Converge 2018 Philadelphia: Live Coverage @pharma_BI

Stephen J. Williams: Reporter

3.3.3

3.3.3   Medcity Converge 2018 Philadelphia: Live Coverage @pharma_BI, Volume 2 (Volume Two: Latest in Genomics Methodologies for Therapeutics: Gene Editing, NGS and BioInformatics, Simulations and the Genome Ontology), Part 2: CRISPR for Gene Editing and DNA Repair

MedCity CONVERGE is a two-day executive summit that gathers innovative thought leaders from across all healthcare sectors to provide actionable insight on where oncology innovation is heading.

On July 11-12, 2018 in Philadelphia, MedCity CONVERGE will gather technology disruptors, payers, providers, life science companies, venture capitalists and more to discuss how AI, Big Data and Precision Medicine are changing the game in cancer. See agenda.

The conference highlights innovation and best practices across the continuum—from research to technological innovation to transformations of treatment and care delivery, and most importantly, patient empowerment—from some of the country’s most innovative healthcare organizations managing the disease.

Meaningful networking opportunities abound, with executives driving the innovation from diverse entities: leading hospital systems, medical device firms, biotech, pharma, emerging technology startups and health IT, as well as the investment community.

Day 1: Wednesday, July 11, 2018

7:30 AM

2nd Floor – Paris Foyer

Registration + Breakfast

8:15 AM–8:30 AM

Paris Ballroom

Welcome Remarks: Arundhati Parmar, VP and Editor-in-Chief, MedCity News

8:30 AM–9:15 AM

Paris Ballroom

Practical Applications of AI in Cancer

We are far from machine learning dictating clinical decision making, but AI has important niche applications in oncology. Hear from a panel of innovative startups and established life science players about how machine learning and AI can transform different aspects in healthcare, be it in patient recruitment, data analysis, drug discovery or care delivery.

Moderator: Ayan Bhattacharya, Advanced Analytics Specialist Leader, Deloitte Consulting LLP
Speakers:
Wout Brusselaers, CEO and Co-Founder, Deep 6 AI @woutbrusselaers ‏
Tufia Haddad, M.D., Chair of Breast Medical Oncology and Department of Oncology Chair of IT, Mayo Clinic
Carla Leibowitz, Head of Corporate Development, Arterys @carlaleibowitz
John Quackenbush, Ph.D., Professor and Director of the Center for Cancer Computational Biology, Dana-Farber Cancer Institute

9:15 AM–9:45 AM

Paris Ballroom

Opening Keynote: Dr. Joshua Brody, Medical Oncologist, Mount Sinai Health System

The Promise and Hype of Immunotherapy

Immunotherapy is revolutionizing oncology care across various types of cancers, but it is also necessary to sort the hype from the reality. In his keynote, Dr. Brody will delve into the history of this new therapy mode and how it has transformed the treatment of lymphoma and other diseases. He will address the hype surrounding it, why so many still don’t respond to the treatment regimen and chart the way forward—one that can lead to more elegant immunotherapy combination paths and better outcomes for patients.

Speaker:
Joshua Brody, M.D., Assistant Professor, Mount Sinai School of Medicine @joshuabrodyMD

9:45 AM–10:00 AM

Paris Foyer

Networking Break + Showcase

10:00 AM–10:45 AM

Paris Ballroom

The Davids vs. the Cancer Goliath Part 1

Startups from diagnostics, biopharma, medtech, digital health and emerging tech will have 8 minutes to articulate their visions on how they aim to tame the beast.

Start Time End Time Company
10:00 10:08 Belong.Life
10:09 10:17 Care+Wear
10:18 10:26 OncoPower
10:27 10:35 PolyAurum LLC
10:36 10:44 Seeker Health

Speakers:
Karthik Koduru, MD, Co-Founder and Chief Oncologist, OncoPower
Eliran Malki, Co-Founder and CEO, Belong.Life
Chaitenya Razdan, Co-founder and CEO, Care+Wear @_crazdan
Debra Shipley Travers, President & CEO, PolyAurum LLC @polyaurum
Sandra Shpilberg, Founder and CEO, Seeker Health @sandrashpilberg

10:45 AM–11:00 AM

Paris Foyer

Networking Break + Showcase

11:00 AM–11:45 AM

Montpellier – 3rd Floor

Breakout: Biopharma Gets Its Feet Wet in Digital Health

In the last few years, biotech and pharma companies have been leveraging digital health tools in everything from oncology trials, medication adherence to patient engagement. What are the lessons learned?

Moderator: Anthony Green, Ph.D., Vice President, Technology Commercialization Group, Ben Franklin Technology Partners
Speakers:
Derek Bowen, VP of Business Development & Strategy, Blackfynn, Inc.
Gyan Kapur, Vice President, Activate Venture Partners
Tom Kottler, Co-Founder & CEO, HealthPrize Technologies @HealthPrize

11:00 AM–11:45 AM

Paris Ballroom

Breakout: How to Scale Precision Medicine

The potential for precision medicine is real, but is limited by access to patient datasets. How are government entities, hospitals and startups bringing the promise of precision medicine to the masses of oncology patients

Moderator: Sandeep Burugupalli, Senior Manager, Real World Data Innovation, Pfizer @sandeepburug
Speakers:
Ingo ​Chakravarty, President and CEO, Navican @IngoChakravarty
Eugean Jiwanmall, Senior Research Analyst for Medical Policy & Technology Evaluation , Independence Blue Cross @IBX
Andrew Norden, M.D., Chief Medical Officer, Cota @ANordenMD
Ankur Parikh M.D, Medical Director of Precision Medicine, Cancer Treatment Centers of America @CancerCenter

11:50 AM–12:30 PM

Paris Ballroom

Fireside Chat with Michael Pellini, M.D.

Building a Precision Medicine Business from the Ground Up: An Operating and Venture Perspective

Dr. Pellini has spent more than 20 years working on the operating side of four companies, each of which has pushed the boundaries of the standard of care. He will describe his most recent experience at Foundation Medicine, at the forefront of precision medicine, and how that experience can be leveraged on the venture side, where he now evaluates new healthcare technologies.

Speaker:
Michael Pellini, M.D., Managing Partner, Section 32 and Chairman, Foundation Medicine @MichaelPellini

12:30 PM–1:30 PM

Chez Colette Restaurant – Lobby

Lunch Reception

1:30 PM–2:15 PM

Paris Ballroom

Clinical Trials 2.0

The randomized, controlled clinical trial is the gold standard, but it may be time for a new model. How can patient networks and new technology be leveraged to boost clinical trial recruitment and manage clinical trials more efficiently?

Moderator: John Reites, Chief Product Officer, Thread @johnreites
Speakers:
Andrew Chapman M.D., Chief of Cancer Services , Sidney Kimmel Cancer Center, Thomas Jefferson University Hospital
Michelle Longmire, M.D., Founder, Medable @LongmireMD
Sameek Roychowdhury MD, PhD, Medical Oncologist and Researcher, Ohio State University Comprehensive Cancer Center @OSUCCC_James

2:20 PM–3:00 PM

Paris Ballroom

CONVERGEnce on Steroids: Why Comcast and Independence Blue Cross?

This year has seen a great deal of convergence in health care.  One of the most innovative collaborations announced was that of Cable and Media giant Comcast Corporation and health plan Independence Blue Cross.  This fireside chat will explore what the joint venture is all about, the backstory of how this unlikely partnership came to be, and what it might mean for our industry.

sponsored by Independence Blue Cross

Moderator: Tom Olenzak, Managing Director Strategic Innovation Portfolio, Independence Blue Cross @IBX
Speakers:
Marc Siry, VP, Strategic Development, Comcast
Michael Vennera, SVP, Chief Information Officer, Independence Blue Cross

3:00 PM–3:15 PM

Paris Foyer

Networking Break + Showcase

3:15 PM–4:00 PM

Montpellier – 3rd Floor

Breakout: Charting the Way Forward in Gene and Cell Therapy

There is a boom underway in cell and gene therapies that are being wielded to tackle cancer and other diseases at the cellular level. FDA has approved a few drugs in the space. These innovations raise important questions about patient access, patient safety, and personalized medicine. Hear from interesting startups and experts about the future of gene therapy.

Moderator: Alaric DeArment, Senior Reporter, MedCity News
Speakers:
Amy DuRoss, CEO, Vineti
Andre Goy, M.D., Chairman and Director of John Theurer Cancer Center , Hackensack University Medical Center

3:15 PM–4:00 PM

Paris Ballroom

Breakout: What’s A Good Model for Value-Based Care in Oncology?

How do you implement a value-based care model in oncology? Medicare has created a bundled payment model in oncology and there are lessons to be learned from that and other programs. Listen to two presentations from experts in the field.

Moderator: Mahek Shah, M.D., Senior Researcher, Harvard Business School @Mahek_MD
Speakers:
Charles Saunders M.D., CEO, Integra Connect
Mari Vandenburgh, Director of Value-Based Reimbursement Operations, Highmark @Highmark

4:00 PM–4:10 PM

Paris Foyer

Networking Break + Showcase

4:10 PM–4:55 PM

Montpellier – 3rd Floor

Breakout: Trends in Oncology Investing

A panel of investors interested in therapeutics, diagnostics, digital health and emerging technology will discuss what is hot in cancer investing.

Moderator: Stephanie Baum, Director of Special Projects, MedCity News @StephLBaum
Speakers:
Karen Griffith Gryga, Chief Investment Officer, Dreamit Ventures @karengg 
Stacey Seltzer, Partner, Aisling Capital
David Shaywitz, M.D., Ph.D., Senior Partner, Takeda Ventures

4:10 PM–4:55 PM

Paris Ballroom

Breakout: What Patients Want and Need On Their Journey

Cancer patients are living with an existential threat every day. A panel of patients and experts in oncology care management will discuss what’s needed to make the journey for oncology patients a bit more bearable.

sponsored by CEO Council for Growth

Moderator: Amanda Woodworth, M.D., Director of Breast Health, Drexel University College of Medicine
Speakers:
Kezia Fitzgerald, Chief Innovation Officer & Co-Founder, CareAline® Products, LLC
Sara Hayes, Senior Director of Community Development, Health Union @SaraHayes_HU
Katrece Nolen, Cancer Survivor and Founder, Find Cancer Help @KatreceNolen
John Simpkins, Administrative DirectorService Line Director of the Cancer Center, Children’s Hospital of Philadelphia

5:00 PM–5:45 PM

Paris Ballroom

Early Diagnosis Through Predictive Biomarkers, NonInvasive Testing

Diagnosing cancer early is often the difference between survival and death. Hear from experts regarding the new and emerging technologies that form the next generation of cancer diagnostics.

Moderator: Heather Rose, Director of Licensing, Thomas Jefferson University
Speakers:
Bonnie Anderson, Chairman and CEO, Veracyte @BonnieAndDx
Kevin Hrusovsky, Founder and Chairman, Powering Precision Health @KevinHrusovsky

5:45 PM–7:00 PM

Paris Foyer

Networking Reception

Day 2: Thursday, July 12, 2018

7:30 AM

Paris Foyer

Breakfast + Registration

8:30 AM–8:40 AM

Paris Ballroom

Opening Remarks: Arundhati Parmar, VP and Editor-in-Chief, MedCity News

8:40 AM–9:25 AM

Paris Ballroom

The Davids vs. the Cancer Goliath Part 2

Startups from diagnostics, biopharma, medtech, digital health and emerging tech will have 8 minutes to articulate their visions on how they aim to tame the beast.

Start Time End Time Company
8:40 8:48 3Derm
8:49 8:57 CNS Pharmaceuticals
8:58 9:06 Cubismi
9:07 9:15 CytoSavvy
9:16 9:24 PotentiaMetrics

Speakers:
Liz Asai, CEO & Co-Founder, 3Derm Systems, Inc. @liz_asai
John M. Climaco, CEO, CNS Pharmaceuticals @cns_pharma 
John Freyhof, CEO, CytoSavvy
Robert Palmer, President & CEO, PotentiaMetrics @robertdpalmer 
Moira Schieke M.D., Founder, Cubismi, Adjunct Assistant Prof UW Madison @cubismi_inc

9:30 AM–10:15 AM

Paris Ballroom

Liquid Biopsy and Gene Testing vs. Reimbursement Hurdles

Genetic testing, whether broad-scale or single gene-testing, is being ordered by an increasing number of oncologists, but in many cases, patients are left to pay for these expensive tests themselves. How can this dynamic be shifted? What can be learned from the success stories?

Moderator: Shoshannah Roth, Assistant Director of Health Technology Assessment and Information Services , ECRI Institute @Ecri_Institute
Speakers:
Rob Dumanois, Manager – reimbursement strategy, Thermo Fisher Scientific
Eugean Jiwanmall, Senior Research Analyst for Medical Policy & Technology Evaluation , Independence Blue Cross @IBX
Michael Nall, President and Chief Executive Officer, Biocept

10:15 AM–10:25 AM

Paris Foyer

Networking Break + Showcase

10:25 AM–11:10 AM

Paris Ballroom

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

11:10 AM–11:20 AM

Networking Break + Showcase

11:20 AM–12:05 PM

Paris Ballroom

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

12:10 PM–12:40 PM

Paris Ballroom

Closing Keynote: Anne Stockwell, Cancer Survivor, Founder, Well Again

Finding Your Well Again
Anne Stockwell discusses her mission to help cancer survivors heal their emotional trauma and regain their balance after treatment. A multi-skilled artist as well as a three-time cancer survivor, Anne learned through experience that the emotional impact of cancer often strikes after treatment, isolating a survivor rather than lighting the way forward. Anne realized that her well-trained imagination as an artist was key to her successful reentry after cancer. Now she helps other survivors develop their own creative tools to help them find their way forward with joy.

Speaker:
Anne Stockwell, Founder and President, Well Again @annewellagain

12:40 PM–12:45 PM

Closing Remarks

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Please see related articles on Live Coverage of Previous Meetings on this Open Access Journal

LIVE – Real Time – 16th Annual Cancer Research Symposium, Koch Institute, Friday, June 16, 9AM – 5PM, Kresge Auditorium, MIT

Real Time Coverage and eProceedings of Presentations on 11/16 – 11/17, 2016, The 12th Annual Personalized Medicine Conference, HARVARD MEDICAL SCHOOL, Joseph B. Martin Conference Center, 77 Avenue Louis Pasteur, Boston

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BIO 2018! June 4-7, 2018 at Boston Convention & Exhibition Center

https://pharmaceuticalintelligence.com/press-coverage/

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Reporter and Curator: Dr. Sudipta Saha, Ph.D.

 

Anti-Müllerian Hormone (AMH), is secreted by growing follicles that contains the egg or ovum. According to regular practice low AMH and high Follicle Stimulating Hormone (FSH) are generally considered as indicators of diminished egg quantity in a female. But, there are several cases the female conceived absolutely normally without any support even after low AMH was reported.

 

Therefore, a new research published in the Journal of the American Medical Association declares that AMH doesn’t dictate a woman’s reproductive potential. Although AMH testing is one of the most common ways that doctors assess a woman’s fertility. Present research says that all it takes is one egg each cycle and AMH is not a marker of whether a female can or cannot become pregnant. So, for women who haven’t yet tried to get pregnant and who are wondering whether they are fertile, an AMH value isn’t going to be helpful in that context. In addition, AMH is not necessarily a good marker to predict that whether one has to cryopreserve her eggs. So, practically doctors don’t yet have a way to definitively predict egg quality or a woman’s long-term ability to conceive, but age is obviously one of the most important factors.

 

The above mentioned study followed 750 women between the ages of 30 and 44 who had been trying to conceive for three months or less. During the 12-month observation period, those with low AMH values of less than 0.7 were not less likely to conceive than those who had normal AMH values. The study had various limitations, however, that are worth noting. The researchers only included women who did not have a history of infertility. Women who sought fertility treatments (about 6 percent) were withdrawn. And only 12 percent of the women were in the 38-to-44 age range. In addition, the number of live births was unavailable.

 

Among women aged 30 to 44 years without a history of infertility who had been trying to conceive for 3 months or less, biomarkers indicating diminished ovarian reserve compared with normal ovarian reserve were not associated with reduced fertility. These findings do not support the use of urinary or blood FSH tests or AMH levels to assess natural fertility for women with these characteristics. The researchers’ next want to see whether low AMH is associated with a higher risk of miscarriage among the women who conceived.

 

Although AMH testing isn’t designed to be an overall gauge of a woman’s fertility, it can still provide valuable information, especially for women who are infertile and seeking treatment. It can assist in diagnosing polycystic ovarian syndrome, and identify when a woman is getting closer to menopause. Previous research also showed that AMH is good predictor of a woman’s response to ovarian stimulation for in vitro fertilization and therefore it can predict the probability of conceiving via in vitro fertilization (I.V.F.).

 

References:

 

https://jamanetwork.com/journals/jama/article-abstract/2656811?JamaNetworkReader=True

 

https://www.nytimes.com/2017/10/16/health/fertility-test-ovarian-reserve.html

 

https://academic.oup.com/humrep/article/26/11/2925/656065

 

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3339896/

 

https://www.ncbi.nlm.nih.gov/pubmed/27179263

 

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Reporter and Curator: Dr. Sudipta Saha, Ph.D.

Researchers have classified a brand-new organ inside human body. Known as the mesentery, the new organ is found in our digestive systems, and was long thought to be made up of fragmented, separate structures. But recent research has shown that it’s actually one, continuous organ. The evidence for the organ’s reclassification is now published in The Lancet Gastroenterology & Hepatology. Although we now know about the structure of this new organ, its function is still poorly understood, and studying it could be the key to better understanding and treatment of abdominal and digestive disease.

mesentery

J Calvin Coffey, a researcher from the University Hospital Limerick in Ireland, who first discovered that the mesentery was an organ. In 2012, Coffey and his colleagues showed through detailed microscopic examinations that the mesentery is actually a continuous structure. Over the past four years, they’ve gathered further evidence that the mesentery should actually be classified as its own distinct organ, and the latest paper makes it official. Mesentery is a double fold of peritoneum – the lining of the abdominal cavity – that holds our intestine to the wall of our abdomen. It was described by the Italian polymath Leanardo da Vinci in 1508, but it has been ignored throughout the centuries, until now. Although there are generally considered to be five organs in the human body, there are in fact now 79, including the mesentery. The heart, brain, liver, lungs and kidneys are the vital organs, but there are another 74 that play a role in keeping us healthy. The distinctive anatomical and functional features of mesentery have been revealed that justify designation of the mesentery as an organ. Accordingly, the mesentery should be subjected to the same investigatory focus that is applied to other organs and systems. This provides a platform from which to direct future scientific investigation of the human mesentery in health and disease.

References:

http://www.thelancet.com/journals/langas/article/PIIS2468-1253(16)30026-7/abstract

http://www.sciencealert.com/it-s-official-a-brand-new-human-organ-has-been-classified

http://www.bbc.com/news/health-38506708

http://www.independent.co.uk/news/science/new-organ-mesentery-found-human-body-digestive-system-classified-abdominal-grays-anatomy-a7507396.html

https://in.news.yahoo.com/scientists-discover-human-organ-064207997.html

https://en.wikipedia.org/wiki/Mesentery

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New NIH breast cancer research to focus on prevention

Reporter: Stephen J. Williams, PhD

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A new phase of the Breast Cancer and the Environment Research Program (BCERP), focused on prevention, is being launched at the National Institutes of Health. Grant-funded researchers will now work across scientific disciplines, involve new racially and ethnically diverse communities, and expand the study of risk factors that precede breast cancer, such as breast density.

These new directions reflect recommendations made by the Interagency Breast Cancer and Environmental Research Coordinating Committee (IBCERCC) in 2013. IBCERCC was congressionally mandated to review the state of the science around breast cancer and environmental influences by the Breast Cancer and Environmental Research Act. Recommendations included prioritizing prevention, involving transdisciplinary research teams, engaging public stakeholders, collaborating across federal agencies, and communicating the science to the public.

This broadened research focus will add to the growing knowledge of environmental and genetic factors that may influence breast cancer risk across the lifespan. The six new BCERP projects, plus a new coordinating center promoting cross-project collaboration, are jointly funded by the National Institute of Environmental Health Sciences (NIEHS) and the National Cancer Institute. All projects involve strong partnerships between researchers and organizations focused on breast cancer prevention or environmental health.

The new research will be conducted at the following institutions

  • Brigham and Women’s Hospital, Boston
  • City of Hope/Beckman Research Institute, Duarte, California
  • Columbia University, New York City
  • Georgetown Lombardi Comprehensive Cancer Center, Washington, D.C.
  • Michigan State University, Lansing
  • University of Massachusetts, Amherst
  • University of Wisconsin – Madison (Coordinating Center)

“The beauty of this research is that scientific discoveries and community observations inform each other, in order to dive deeper into the complex causes of breast cancer,” said Gwen Collman, Ph.D., director of NIEHS Division of Extramural Research and Training.

The focus on minority and socio-economically disadvantaged women is an important step in addressing disparities in breast cancer outcomes. Although African-American women are diagnosed with breast cancer less often than white women, more aggressive cancers and breast cancer deaths are more common among African-American women.

Another new direction for BCERP is research on the role of breast density as a possible intermediate risk factor for breast cancer. Dense breast tissue is one of the most common risk factors for breast cancer. Identifying links between environmental exposures and high breast density may provide new insights into prevention.

“These priorities reflect our continued commitment to breast cancer prevention,” noted Caroline Dilworth, Ph.D., BCERP program lead at NIEHS. “Our goal is to build on the high quality science we’ve been funding for more than a decade, while also being responsive to the expert recommendations of the IBCERCC report.”

Grant Numbers: U01ES026130, U01ES026137, U01ES026122, U01ES026132, U01ES026119, U01ES026140, U01ES026127

NIEHS supports research to understand the effects of the environment on human health and is part of NIH. For more information on environmental health topics, visit www.niehs.nih.gov. Subscribe to one or more of the NIEHS news lists to stay current on NIEHS news, press releases, grant opportunities, training, events, and publications.

The National Cancer Institute leads the National Cancer Program and the NIH’s efforts to dramatically reduce the prevalence of cancer and improve the lives of cancer patients and their families, through research into prevention and cancer biology, the development of new interventions, and the training and mentoring of new researchers. For more information about cancer, please visit the NCI website at http://www.cancer.gov or call NCI’s Cancer Information Service at 1-800-4-CANCER.

About the National Institutes of Health (NIH): NIH, the nation’s medical research agency, includes 27 Institutes and Centers and is a component of the U.S. Department of Health and Human Services. NIH is the primary federal agency conducting and supporting basic, clinical, and translational medical research, and is investigating the causes, treatments, and cures for both common and rare diseases. For more information about NIH and its programs, visit www.nih.gov.

Other posts on this site on  Cancer and Early Detection  include

Early Detection of Prostate Cancer: American Urological Association (AUA) Guideline

Mechanism involved in Breast Cancer Cell Growth: Function in Early Detection & Treatment

Warning signs may lead to better early detection of ovarian cancer

‘Mosaicism’ is Associated with Aging and Chronic Diseases like Cancer: detection of genetic mosaicism could be an early marker for detecting cancer.

CDC Findings: Due to Aging Population, Actual Number of Cancer Deaths is Rising while Risk of Dying From Cancer is Falling in the US

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Treatments other than Chemotherapy for Leukemias and Lymphomas

Author, Curator, Editor: Larry H. Bernstein, MD, FCAP

2.5.1 Radiation Therapy 

http://www.lls.org/treatment/types-of-treatment/radiation-therapy

Radiation therapy, also called radiotherapy or irradiation, can be used to treat leukemia, lymphoma, myeloma and myelodysplastic syndromes. The type of radiation used for radiotherapy (ionizing radiation) is the same that’s used for diagnostic x-rays. Radiotherapy, however, is given in higher doses.

Radiotherapy works by damaging the genetic material (DNA) within cells, which prevents them from growing and reproducing. Although the radiotherapy is directed at cancer cells, it can also damage nearby healthy cells. However, current methods of radiotherapy have been improved upon, minimizing “scatter” to nearby tissues. Therefore its benefit (destroying the cancer cells) outweighs its risk (harming healthy cells).

When radiotherapy is used for blood cancer treatment, it’s usually part of a treatment plan that includes drug therapy. Radiotherapy can also be used to relieve pain or discomfort caused by an enlarged liver, lymph node(s) or spleen.

Radiotherapy, either alone or with chemotherapy, is sometimes given as conditioning treatment to prepare a patient for a blood or marrow stem cell transplant. The most common types used to treat blood cancer are external beam radiation (see below) and radioimmunotherapy.
External Beam Radiation

External beam radiation is the type of radiotherapy used most often for people with blood cancers. A focused radiation beam is delivered outside the body by a machine called a linear accelerator, or linac for short. The linear accelerator moves around the body to deliver radiation from various angles. Linear accelerators make it possible to decrease or avoid skin reactions and deliver targeted radiation to lessen “scatter” of radiation to nearby tissues.

The dose (total amount) of radiation used during treatment depends on various factors regarding the patient, disease and reason for treatment, and is established by a radiation oncologist. You may receive radiotherapy during a series of visits, spread over several weeks (from two to 10 weeks, on average). This approach, called dose fractionation, lessens side effects. External beam radiation does not make you radioactive.

2.5.2  Bone marrow (BM) transplantation

http://www.nlm.nih.gov/medlineplus/ency/article/003009.htm

There are three kinds of bone marrow transplants:

Autologous bone marrow transplant: The term auto means self. Stem cells are removed from you before you receive high-dose chemotherapy or radiation treatment. The stem cells are stored in a freezer (cryopreservation). After high-dose chemotherapy or radiation treatments, your stems cells are put back in your body to make (regenerate) normal blood cells. This is called a rescue transplant.

Allogeneic bone marrow transplant: The term allo means other. Stem cells are removed from another person, called a donor. Most times, the donor’s genes must at least partly match your genes. Special blood tests are done to see if a donor is a good match for you. A brother or sister is most likely to be a good match. Sometimes parents, children, and other relatives are good matches. Donors who are not related to you may be found through national bone marrow registries.

Umbilical cord blood transplant: This is a type of allogeneic transplant. Stem cells are removed from a newborn baby’s umbilical cord right after birth. The stem cells are frozen and stored until they are needed for a transplant. Umbilical cord blood cells are very immature so there is less of a need for matching. But blood counts take much longer to recover.

Before the transplant, chemotherapy, radiation, or both may be given. This may be done in two ways:

Ablative (myeloablative) treatment: High-dose chemotherapy, radiation, or both are given to kill any cancer cells. This also kills all healthy bone marrow that remains, and allows new stem cells to grow in the bone marrow.

Reduced intensity treatment, also called a mini transplant: Patients receive lower doses of chemotherapy and radiation before a transplant. This allows older patients, and those with other health problems to have a transplant.

A stem cell transplant is usually done after chemotherapy and radiation is complete. The stem cells are delivered into your bloodstream usually through a tube called a central venous catheter. The process is similar to getting a blood transfusion. The stem cells travel through the blood into the bone marrow. Most times, no surgery is needed.

Donor stem cells can be collected in two ways:

  • Bone marrow harvest. This minor surgery is done under general anesthesia. This means the donor will be asleep and pain-free during the procedure. The bone marrow is removed from the back of both hip bones. The amount of marrow removed depends on the weight of the person who is receiving it.
  • Leukapheresis. First, the donor is given 5 days of shots to help stem cells move from the bone marrow into the blood. During leukapheresis, blood is removed from the donor through an IV line in a vein. The part of white blood cells that contains stem cells is then separated in a machine and removed to be later given to the recipient. The red blood cells are returned to the donor.

Why the Procedure is Performed

A bone marrow transplant replaces bone marrow that either is not working properly or has been destroyed (ablated) by chemotherapy or radiation. Doctors believe that for many cancers, the donor’s white blood cells can attach to any remaining cancer cells, similar to when white cells attach to bacteria or viruses when fighting an infection.

Your doctor may recommend a bone marrow transplant if you have:

Certain cancers, such as leukemia, lymphoma, and multiple myeloma

A disease that affects the production of bone marrow cells, such as aplastic anemia, congenital neutropenia, severe immunodeficiency syndromes, sickle cell anemia, thalassemia

Had chemotherapy that destroyed your bone

2.5.3 Autologous stem cell transplantation

Phase II trial of 131I-B1 (anti-CD20) antibody therapy with autologous stem cell transplantation for relapsed B cell lymphomas

O.W Press,  F Appelbaum,  P.J Martin, et al.
http://www.thelancet.com/journals/lancet/article/PIIS0140-6736(95)92225-3/abstract

25 patients with relapsed B-cell lymphomas were evaluated with trace-labelled doses (2·5 mg/kg, 185-370 MBq [5-10 mCi]) of 131I-labelled anti-CD20 (B1) antibody in a phase II trial. 22 patients achieved 131I-B1 biodistributions delivering higher doses of radiation to tumor sites than to normal organs and 21 of these were treated with therapeutic infusions of 131I-B1 (12·765-29·045 GBq) followed by autologous hemopoietic stem cell reinfusion. 18 of the 21 treated patients had objective responses, including 16 complete remissions. One patient died of progressive lymphoma and one died of sepsis. Analysis of our phase I and II trials with 131I-labelled B1 reveal a progression-free survival of 62% and an overall survival of 93% with a median follow-up of 2 years. 131I-anti-CD20 (B1) antibody therapy produces complete responses of long duration in most patients with relapsed B-cell lymphomas when given at maximally tolerated doses with autologous stem cell rescue.

Autologous (Self) Transplants

http://www.leukaemia.org.au/treatments/stem-cell-transplants/autologous-self-transplants

An autologous transplant (or rescue) is a type of transplant that uses the person’s own stem cells. These cells are collected in advance and returned at a later stage. They are used to replace stem cells that have been damaged by high doses of chemotherapy, used to treat the person’s underlying disease.

In most cases, stem cells are collected directly from the bloodstream. While stem cells normally live in your marrow, a combination of chemotherapy and a growth factor (a drug that stimulates stem cells) called Granulocyte Colony Stimulating Factor (G-CSF) is used to expand the number of stem cells in the marrow and cause them to spill out into the circulating blood. From here they can be collected from a vein by passing the blood through a special machine called a cell separator, in a process similar to dialysis.

Most of the side effects of an autologous transplant are caused by the conditioning therapy used. Although they can be very unpleasant at times it is important to remember that most of them are temporary and reversible.

Procedure of Hematopoietic Stem Cell Transplantation

Hematopoietic stem cell transplantation (HSCT) is the transplantation of multipotent hematopoietic stem cells, usually derived from bone marrow, peripheral blood, or umbilical cord blood. It may be autologous (the patient’s own stem cells are used) or allogeneic (the stem cells come from a donor).

Hematopoietic Stem Cell Transplantation

Author: Ajay Perumbeti, MD, FAAP; Chief Editor: Emmanuel C Besa, MD
http://emedicine.medscape.com/article/208954-overview

Hematopoietic stem cell transplantation (HSCT) involves the intravenous (IV) infusion of autologous or allogeneic stem cells to reestablish hematopoietic function in patients whose bone marrow or immune system is damaged or defective.

The image below illustrates an algorithm for typically preferred hematopoietic stem cell transplantation cell source for treatment of malignancy.

An algorithm for typically preferred hematopoietic stem cell transplantation cell source for treatment of malignancy: If a matched sibling donor is not available, then a MUD is selected; if a MUD is not available, then choices include a mismatched unrelated donor, umbilical cord donor(s), and a haploidentical donor.

Supportive Therapies

2.5.4  Blood transfusions – risks and complications of a blood transfusion

  • Allogeneic transfusion reaction (acute or delayed hemolytic reaction)
  • Allergic reaction
  • Viruses Infectious Diseases

The risk of catching a virus from a blood transfusion is very low.

HIV. Your risk of getting HIV from a blood transfusion is lower than your risk of getting killed by lightning. Only about 1 in 2 million donations might carry HIV and transmit HIV if given to a patient.

Hepatitis B and C. The risk of having a donation that carries hepatitis B is about 1 in 205,000. The risk for hepatitis C is 1 in 2 million. If you receive blood during a transfusion that contains hepatitis, you’ll likely develop the virus.

Variant Creutzfeldt-Jakob disease (vCJD). This disease is the human version of Mad Cow Disease. It’s a very rare, yet fatal brain disorder. There is a possible risk of getting vCJD from a blood transfusion, although the risk is very low. Because of this, people who may have been exposed to vCJD aren’t eligible blood donors.

  • Fever
  • Iron Overload
  • Lung Injury
  • Graft-Versus-Host Disease

Graft-versus-host disease (GVHD) is a condition in which white blood cells in the new blood attack your tissues.

2.5.5 Erythropoietin

Erythropoietin, (/ɨˌrɪθrɵˈpɔɪ.ɨtɨn/UK /ɛˌrɪθr.pˈtɪn/) also known as EPO, is a glycoprotein hormone that controls erythropoiesis, or red blood cell production. It is a cytokine (protein signaling molecule) for erythrocyte (red blood cell) precursors in the bone marrow. Human EPO has a molecular weight of 34 kDa.

Also called hematopoietin or hemopoietin, it is produced by interstitial fibroblasts in the kidney in close association with peritubular capillary and proximal convoluted tubule. It is also produced in perisinusoidal cells in the liver. While liver production predominates in the fetal and perinatal period, renal production is predominant during adulthood. In addition to erythropoiesis, erythropoietin also has other known biological functions. For example, it plays an important role in the brain’s response to neuronal injury.[1] EPO is also involved in the wound healing process.[2]

Exogenous erythropoietin is produced by recombinant DNA technology in cell culture. Several different pharmaceutical agents are available with a variety ofglycosylation patterns, and are collectively called erythropoiesis-stimulating agents (ESA). The specific details for labelled use vary between the package inserts, but ESAs have been used in the treatment of anemia in chronic kidney disease, anemia in myelodysplasia, and in anemia from cancer chemotherapy. Boxed warnings include a risk of death, myocardial infarction, stroke, venous thromboembolism, and tumor recurrence.[3]

2.5.6  G-CSF (granulocyte-colony stimulating factor)

Granulocyte-colony stimulating factor (G-CSF or GCSF), also known as colony-stimulating factor 3 (CSF 3), is a glycoprotein that stimulates the bone marrow to produce granulocytes and stem cells and release them into the bloodstream.

There are different types, including

  • Lenograstim (Granocyte)
  • Filgrastim (Neupogen, Zarzio, Nivestim, Ratiograstim)
  • Long acting (pegylated) filgrastim (pegfilgrastim, Neulasta) and lipegfilgrastim (Longquex)

Pegylated G-CSF stays in the body for longer so you have treatment less often than with the other types of G-CSF.

2.5.7  Plasma Exchange (plasmapheresis)

http://emedicine.medscape.com/article/1895577-overview

Plasmapheresis is a term used to refer to a broad range of procedures in which extracorporeal separation of blood components results in a filtered plasma product.[1, 2] The filtering of plasma from whole blood can be accomplished via centrifugation or semipermeable membranes.[3] Centrifugation takes advantage of the different specific gravities inherent to various blood products such as red cells, white cells, platelets, and plasma.[4] Membrane plasma separation uses differences in particle size to filter plasma from the cellular components of blood.[3]

Traditionally, in the United States, most plasmapheresis takes place using automated centrifuge-based technology.[5] In certain instances, in particular in patients already undergoing hemodialysis, plasmapheresis can be carried out using semipermeable membranes to filter plasma.[4]

In therapeutic plasma exchange, using an automated centrifuge, filtered plasma is discarded and red blood cells along with replacement colloid such as donor plasma or albumin is returned to the patient. In membrane plasma filtration, secondary membrane plasma fractionation can selectively remove undesired macromolecules, which then allows for return of the processed plasma to the patient instead of donor plasma or albumin. Examples of secondary membrane plasma fractionation include cascade filtration,[6] thermofiltration, cryofiltration,[7] and low-density lipoprotein pheresis.

The Apheresis Applications Committee of the American Society for Apheresis periodically evaluates potential indications for apheresis and categorizes them from I to IV based on the available medical literature. The following are some of the indications, and their categorization, from the society’s 2010 guidelines.[2]

  • The only Category I indication for hemopoietic malignancy is Hyperviscosity in monoclonal gammopathies

2.5.8  Platelet Transfusions

Indications for platelet transfusion in children with acute leukemia

Scott Murphy, Samuel Litwin, Leonard M. Herring, Penelope Koch, et al.
Am J Hematol Jun 1982; 12(4): 347–356
http://onlinelibrary.wiley.com/doi/10.1002/ajh.2830120406/abstract;jsessionid=A6001D9D865EA1EBC667EF98382EF20C.f03t01
http://dx.doi.org:/10.1002/ajh.2830120406

In an attempt to determine the indications for platelet transfusion in thrombocytopenic patients, we randomized 56 children with acute leukemia to one of two regimens of platelet transfusion. The prophylactic group received platelets when the platelet count fell below 20,000 per mm3 irrespective of clinical events. The therapeutic group was transfused only when significant bleeding occurred and not for thrombocytopenia alone. The time to first bleeding episode was significantly longer and the number of bleeding episodes were significantly reduced in the prophylactic group. The survival curves of the two groups could not be distinguished from each other. Prior to the last month of life, the total number of days on which bleeding was present was significantly reduced by prophylactic therapy. However, in the terminal phase (last month of life), the duration of bleeding episodes was significantly longer in the prophylactic group. This may have been due to a higher incidence of immunologic refractoriness to platelet transfusion. Because of this terminal bleeding, comparison of the two groups for total number of days on which bleeding was present did not show a significant difference over the entire study period.

Clinical and Laboratory Aspects of Platelet Transfusion Therapy
Yuan S, Goldfinger D
http://www.uptodate.com/contents/clinical-and-laboratory-aspects-of-platelet-transfusion-therapy

INTRODUCTION — Hemostasis depends on an adequate number of functional platelets, together with an intact coagulation (clotting factor) system. This topic covers the logistics of platelet use and the indications for platelet transfusion in adults. The approach to the bleeding patient, refractoriness to platelet transfusion, and platelet transfusion in neonates are discussed elsewhere.

Pooled Platelets – A single unit of platelets can be isolated from every unit of donated blood, by centrifuging the blood within the closed collection system to separate the platelets from the red blood cells (RBC). The number of platelets per unit varies according to the platelet count of the donor; a yield of 7 x 1010 platelets is typical [1]. Since this number is inadequate to raise the platelet count in an adult recipient, four to six units are pooled to allow transfusion of 3 to 4 x 1011 platelets per transfusion [2]. These are called whole blood-derived or random donor pooled platelets.

Advantages of pooled platelets include lower cost and ease of collection and processing (a separate donation procedure and pheresis equipment are not required). The major disadvantage is recipient exposure to multiple donors in a single transfusion and logistic issues related to bacterial testing.

Apheresis (single donor) Platelets – Platelets can also be collected from volunteer donors in the blood bank, in a one- to two-hour pheresis procedure. Platelets and some white blood cells are removed, and red blood cells and plasma are returned to the donor. A typical apheresis platelet unit provides the equivalent of six or more units of platelets from whole blood (ie, 3 to 6 x 1011 platelets) [2]. In larger donors with high platelet counts, up to three units can be collected in one session. These are called apheresis or single donor platelets.

Advantages of single donor platelets are exposure of the recipient to a single donor rather than multiple donors, and the ability to match donor and recipient characteristics such as HLA type, cytomegalovirus (CMV) status, and blood type for certain recipients.

Both pooled and apheresis platelets contain some white blood cells (WBC) that were collected along with the platelets. These WBC can cause febrile non-hemolytic transfusion reactions (FNHTR), alloimmunization, and transfusion-associated graft-versus-host disease (ta-GVHD) in some patients.

Platelet products also contain plasma, which can be implicated in adverse reactions including transfusion-related acute lung injury (TRALI) and anaphylaxis. (See ‘Complications of platelet transfusion’ .)

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Classification of Microbiota –

An Overview of Clinical Microbiology, Classification, and Antimicrobial Resistance

Author and Curator: Larry H. Bernstein, MD, FCAP

Classification of Microbiota

Introduction to Overview of Microbiology

This is a contribution to a series of pieces on the history of biochemistry, molecular biology, physiology and medicine in the 20th century.  Here I describe the common microbial organisms encountered in the clinical laboratory, the method of their collection, plating, culture and identification, and antibiotic sensitivity testing and resistant strains.

I may begin with the recognition that there are common strains in the environment that are not pathogenic, and there are pathogenic bacteria.
In addition, there are bacteria that coexist in the body habitat under specific conditions so that we are able to map the types expected to location, such as, skin, mouth and nasal cavities, the colon, the vagina and urinary system.  Meningitides occur as a result of extension from the nasal cavity to the brain.  When bacteria invade the circulation, it is referred to as septicemia, and the bacteria can cause valvular heart damage.

Bacteriology can be traced to origins in the 19th century.  The clinical features of localized infection are classically referred to as redness, heat, a raised lesion (pustule), and exudate (serous or purulent – watery or cellular).  This not only holds for a focal lesion (as skin), but also for pneumonia, urinary infection, and genital. It may be accompanied by cough, or bloody cough and wheezing, or by an unclear urine. In the case of septicemia, there is fever, and there may be seizures or delirium.

Collection and handling of specimens

Specimens are collected by sterile technique by a nurse or physician and sent to a lab as a swab, or as a blood specimen.  In the case of a febrile illness, blood cultures may be obtained from opposite arms, and another an hour later.  This is related to the possible cyclical seeding of bacteria into the circulation.  If the specimen is collected from a site of infection, a swab may be put onto a glass slide for gram staining.  The specimen collected is sent to the laboratory.

We may consider syphilis and tuberculosis special cases that I’ll set aside.  I shall not go into virology either, although I may referred to smallpox, influenza, polio, HIV under epidemic.  The first step in identification is the Gram stain, developed in the 19th century.  Organisms of the skin are Gram positive and appear blue on staining.  They are cocci, or circular, organized in characteristic clusters (staphylococcus, streptococcus) or in pairs (diplococci, eg. Pneumococcus), and if from the intestine (enterococcus).  If they are elongated rods, they might be coliform.  If they stain red, they are Gram negative.  Gram negative rods are coliform, and are enterobacteriaceae. Meningococci are Gram negative cocci.  So we have certain information about these organisms before we plate them for growth.

Laboratory growth characteristics

The specimen is applied to an agar plate with a metal rod applicator, or perhaps onto more than one agar plate.  The agar plate contains a growth media or a growth inhibitor that is more favorable to certain species than to others.  The bacteria are grown at 37o C in an incubator and colonies develop that are white or nonwhite, and they are smooth or wrinkled.  The appearance of the colonies is characteristic for certain strains.  If there is no contamination, all of the colonies look the same.  The next step is to:

  • Gram stain from a colony
  • Transfer samples from the colony to a series of growth media that identify presence or absence of specific nutrient requirements for growth (which is presumed from the prior findings).

In addition, the colony samples are grown on an agar to which is applied antibiotic tabs.  The tabs either allow or repress growth.  It wa some 50 years ago that the infectious disease physician and microbiologist Abraham Braude would culture the bacteria on agar plates that had a gradient of antibiotic to check for concentration that would inhibit growth.

Principles of Diagnosis (Extracts)

By John A. Washington

The clinical presentation of an infectious disease reflects the interaction between the host and the microorganism. This interaction is affected by the host immune status and microbial virulence factors. Signs and symptoms vary according to the site and severity of infection. Diagnosis requires a composite of information, including history, physical examination, radiographic findings, and laboratory data.

Microbiologic Examination

Direct Examination and Techniques: Direct examination of specimens reveals gross pathology. Microscopy may identify microorganisms. Immunofluorescence, immuno-peroxidase staining, and other immunoassays may detect specific microbial antigens. Genetic probes identify genus- or species-specific DNA or RNA sequences.

Culture: Isolation of infectious agents frequently requires specialized media. Nonselective (noninhibitory) media permit the growth of many microorganisms. Selective media contain inhibitory substances that permit the isolation of specific types of microorganisms.

Microbial Identification: Colony and cellular morphology may permit preliminary identification. Growth characteristics under various conditions, utilization of carbohydrates and other substrates, enzymatic activity, immunoassays, and genetic probes are also used.

Serodiagnosis: A high or rising titer of specific IgG antibodies or the presence of specific IgM antibodies may suggest or confirm a diagnosis.

Antimicrobial Susceptibility: Microorganisms, particularly bacteria, are tested in vitro to determine whether they are susceptible to antimicrobial agents.

Diagnostic medical microbiology is the discipline that identifies etiologic agents of disease. The job of the clinical microbiology laboratory is to test specimens from patients for microorganisms that are, or may be, a cause of the illness and to provide information (when appropriate) about the in vitro activity of antimicrobial drugs against the microorganisms identified (Fig. 1).

Laboratory procedures used in confirming a clinical diagnosis of infectious disease with a bacterial etiology

http://www.ncbi.nlm.nih.gov/books/NBK8014/bin/ch10f1.jpg

A variety of microscopic, immunologic, and hybridization techniques have been developed for rapid diagnosis

techniques have been developed for rapid diagnosis

techniques have been developed for rapid diagnosis

From: Chapter 10, Principles of Diagnosis
Medical Microbiology. 4th edition.
Baron S, editor.
Galveston (TX): University of Texas Medical Branch at Galveston; 1996.

For immunologic detection of microbial antigens, latex particle agglutination, coagglutination, and enzyme-linked immunosorbent assay (ELISA) are the most frequently used techniques in the clinical laboratory. Antibody to a specific antigen is bound to latex particles or to a heat-killed and treated protein A-rich strain of Staphylococcus aureus to produce agglutination (Fig. 10-2). There are several approaches to ELISA; the one most frequently used for the detection of microbial antigens uses an antigen-specific antibody that is fixed to a solid phase, which may be a latex or metal bead or the inside surface of a well in a plastic tray. Antigen present in the specimen binds to the antibody as inFig. 10-2. The test is then completed by adding a second antigen-specific antibody bound to an enzyme that can react with a substrate to produce a colored product. The initial antigen antibody complex forms in a manner similar to that shown inFigure 10-2. When the enzyme-conjugated antibody is added, it binds to previously unbound antigenic sites, and the antigen is, in effect, sandwiched between the solid phase and the enzyme-conjugated antibody. The reaction is completed by adding the enzyme substrate.

agglutination test ch10f2

agglutination test ch10f2

Figure 2 Agglutination test in which inert particles (latex beads or heat-killed S aureus Cowan 1 strain with protein A) are coated with antibody to any of a variety of antigens and then used to detect the antigen in specimens or in isolated bacteria

http://www.ncbi.nlm.nih.gov/books/NBK8014/bin/ch10f2.jpg

Genetic probes are based on the detection of unique nucleotide sequences with the DNA or RNA of a microorganism. Once such a unique nucleotide sequence, which may represent a portion of a virulence gene or of chromosomal DNA, is found, it is isolated and inserted into a cloning vector (plasmid), which is then transformed into Escherichia coli to produce multiple copies of the probe. The sequence is then reisolated from plasmids and labeled with an isotope or substrate for diagnostic use. Hybridization of the sequence with a complementary sequence of DNA or RNA follows cleavage of the double-stranded DNA of the microorganism in the specimen.

The use of molecular technology in the diagnoses of infectious diseases has been further enhanced by the introduction of gene amplication techniques, such as the polymerase chain reaction (PCR) in which DNA polymerase is able to copy a strand of DNA by elongating complementary strands of DNA that have been initiated from a pair of closely spaced oligonucleotide primers. This approach has had major applications in the detection of infections due to microorganisms that are difficult to culture (e.g. the human immunodeficiency virus) or that have not as yet been successfully cultured (e.g. the Whipple’s disease bacillus).

Solid media, although somewhat less sensitive than liquid media, provide isolated colonies that can be quantified if necessary and identified. Some genera and species can be recognized on the basis of their colony morphologies.

In some instances one can take advantage of differential carbohydrate fermentation capabilities of microorganisms by incorporating one or more carbohydrates in the medium along with a suitable pH indicator. Such media are called differential media (e.g., eosin methylene blue or MacConkey agar) and are commonly used to isolate enteric bacilli. Different genera of the Enterobacteriaceae can then be presumptively identified by the color as well as the morphology of colonies.

Culture media can also be made selective by incorporating compounds such as antimicrobial agents that inhibit the indigenous flora while permitting growth of specific microorganisms resistant to these inhibitors. One such example is Thayer-Martin medium, which is used to isolate Neisseria gonorrhoeae. This medium contains vancomycin to inhibit Gram-positive bacteria, colistin to inhibit most Gram-negative bacilli, trimethoprim-sulfamethoxazole to inhibit Proteus species and other species that are not inhibited by colistin and anisomycin to inhibit fungi. The pathogenic Neisseria species, N gonorrhoeae and N meningitidis, are ordinarily resistant to the concentrations of these antimicrobial agents in the medium.

Infection of the bladder (cystitis) or kidney (pyelone-phritis) is usually accompanied by bacteriuria of about ≥ 104 CFU/ml. For this reason, quantitative cultures (Fig. 10-3) of urine must always be performed. For most other specimens a semiquantitative streak method (Fig. 10-3) over the agar surface is sufficient. For quantitative cultures, a specific volume of specimen is spread over the agar surface and the number of colonies per milliliter is estimated.

Identification of bacteria (including mycobacteria) is based on growth characteristics (such as the time required for growth to appear or the atmosphere in which growth occurs), colony and microscopic morphology, and biochemical, physiologic, and, in some instances, antigenic or nucleotide sequence characteristics. The selection and number of tests for bacterial identification depend upon the category of bacteria present (aerobic versus anaerobic, Gram-positive versus Gram-negative, cocci versus bacilli) and the expertise of the microbiologist examining the culture. Gram-positive cocci that grow in air with or without added CO2 may be identified by a relatively small number of tests. The identification of most Gram-negative bacilli is far more complex and often requires panels of 20 tests for determining biochemical and physiologic characteristics.

Antimicrobial susceptibility tests are performed by either disk diffusion or a dilution method. In the former, a standardized suspension of a particular microorganism is inoculated onto an agar surface to which paper disks containing various antimicrobial agents are applied. Following overnight incubation, any zone diameters of inhibition about the disks are measured. An alternative method is to dilute on a log2 scale each antimicrobial agent in broth to provide a range of concentrations and to inoculate each tube or, if a microplate is used, each well containing the antimicrobial agent in broth with a standardized suspension of the microorganism to be tested. The lowest concentration of antimicrobial agent that inhibits the growth of the microorganism is the minimal inhibitory concentration.

Classification Principles

This Week’s Citation Classic®_______ Sneath P H A & Sokal R R.
Numerical taxonomy: the principles and practice of
numerical classification. San Francisco: Freeman, 1973. 573 p.
[Medical Research Council Microbial Systematics Unit, Univ. Leicester, England
and Dept. Ecology and Evolution, State Univ. New York, Stony Brook, NY]
Numerical taxonomy establishes classification
of organisms based on their similarities. It utilizes
many equally weighted characters and employs
clustering and similar algorithms to yield
objective groupings. It can beextended to give
phylogenetic or diagnostic systems and can be
applied to many other fields of endeavour.

Mathematical Foundations of Computer Science 1998
Lecture Notes in Computer Science Volume 1450, 1998, pp 474-482
Date: 28 May 2006
Positive Turing and truth-table completeness for NEXP are incomparable 1998
Levke Bentzien

The truth-table method [matrix method] is one of the decision procedures for sentence logic (q.v., §3.2). The method is based on the fact that the truth value of a compound formula of sentence logic, construed as a truth-function, is determined by the truth values of its arguments (cf. “Sentence logic” §2.2). To decide whether a formula A is a tautology or not, we list all possible combinations of truth values to the variables in A: A is a tautology if it takes the value truth under each assignment.

Using ideas introduced by Buhrman et al. ([2], [3]) to separate various completeness notions for NEXP = NTIME (2poly), positive Turing complete sets for NEXP are studied. In contrast to many-one completeness and bounded truth-table completeness with norm 1 which are known to coincide on NEXP ([3]), whence any such set for NEXP is positive Turing complete, we give sets A and B such that

A is ≤ bT(2) P -complete but not ≤ posT P -complete for NEXP

B is ≤ posT P -complete but not ≤ tt P -complete for NEXP. These results come close to optimality since a further strengthening of (1), as was done by Buhrman in [1] for EXP = DTIME(2poly), seems to require the assumption NEXP = co-NEXP.

Computability and Models
The University Series in Mathematics 2003, pp 1-10
Truth-Table Complete Computably Enumerable Sets
Marat M. Arslanov

We prove a truth-table completeness criterion for computably enumerable sets.
The authors research was partially supported by Russian Foundation of Basic Research, Project 99-01-00830, and RFBR-INTAS, Project 97-91-71991.

TRUTH TABLE CLASSIFICATION AND IDENTIFICATION*
EUGENE W. RYPKA
Department of Microbiology, Lovelace Foundation for Medical Education and Research,
Albuquerque, N.M. 87108, U.S.A.
Space life sciences 1971-12-1; 3(2): pp 135-156
http://dx.doi.org:/10.1007/BF00927988
(Received 15 July, 1971)
Abstract. A logical basis for classification is that elements grouped together and higher categories of elements should have a high degree of similarity with the provision that all groups and categories be disjoint to some degree. A methodology has been developed for constructing classifications automatically that gives
nearly instantaneous correlations of character patterns of organisms with time and clusters with apparent similarity. This means that automatic numerical identification will always construct schemes from which disjoint answers can be obtained if test sensitivities for characters are correct. Unidentified organisms are recycled through continuous classification with reconstruction of identification schemes. This process is
cyclic and self-correcting. The method also accumulates and analyzes data which updates and presents a more accurate biological picture.

Syndromic classification: A process for amplifying information using S-clustering

Eugene W. Rypka, PHD

http://dx.doi.org:/10.1016/S0899-9007(96)00315-2

Optimal classification/Rypka < Optimal classification>

Contents

1 Rypka’s Method

1.1 Equations

1.2 Examples

2 Notes and References

Rypka’s Method

Rypka’s[1] method[2] utilizes the theoretical and empirical separatory equations shown below to perform the task of optimal classification. The method finds the optimal order of the fewest attributes, which in combination define a bounded class of elements.

Application of the method begins with construction of an attribute-valued system in truth table[3] or spreadsheet form with elements listed in the left most column beginning in the second row. Characteristics[4] are listed in the first row beginning in the second column with the code name of the data in the upper left most cell. The values which connect each characteristic with each element are placed in the intersecting cells. Selecting appropriate characteristics to universally define the class of elements may be the most difficult part for the classifier of utilizing this method.

The elements are first sorted in descending order according to their truth table value, which is calculated from the existing sequence and value of characteristics for each element. Duplicate truth table values or multisets for the entire bounded class reveal either the need to eliminate duplicate elements or the need to include additional characteristics.

An empirical separatory value is calculated for each characteristic in the set and the characteristic with the greatest empirical separatory value is exchanged with the characteristic which occupies the most significant attribute position.

Next the second most significant characteristic is found by calculating an empirical separatory value for each remaining characteristic in combination with the first characteristic. The characteristic which produces the greatest separatory value is then exchanged with the characteristic which occupies the second most significant attribute position.

Next the third most significant characteristic is found by calculating an empirical separatory value for each remaining characteristic in combination with the first and second characteristics. The characteristic which produces the greatest empirical separatory value is then exchanged with the characteristic which occupies the third most significant attribute position. This procedure may continue until all characteristics have been processed or until one hundred percent separation of the elements has been achieved.

A larger radix will allow faster identification by excluding a greater percentage of elements per characteristic. A binary radix for instance excludes only fifty percent of the elements per characteristic whereas a five-valued radix excludes eighty percent of the elements per characteristic.[5] What follows is an elucidation of the matrix and separatory equations.[6]

Computational Example
Bounded Class Data

bounded class data

Bounded Class Dimensions

G = 28 – 28 elements – i = 0…G-1[1]

C = 10 – 10 characteristics or attributes – j = 0…C-1

V = 5 – 5 valued logic – l = 0…V-1

Order of Elements

order of elements

Count multisets

count multisets

Squared multiset Counts

squared multiset counts

Separatory Values

separatory values

T=

max(T) = 309 = S8 = highest initial separatory value

Notes

Mathcad’s ORIGIN function applies to all arrays such that if more than one array is being used and one array requires a zero origin then the other arrays must use a zero origin with all variables being adapted as well.

Rypka’s Method Edit

Rypka’s[1] method[2] utilizes the theoretical and empirical separatory equations shown below to perform the task of optimal classification. The method finds the optimal order of the fewest attributes, which in combination define a bounded class of elements.

Application of the method begins with construction of an attribute-valued system in truth table[3] or spreadsheet form with elements listed in the left most column beginning in the second row. Characteristics[4] are listed in the first row beginning in the second column with the title of the attributes in the upper left most cell. Normally the file name of the data is given the title of the element class. The values which connect each characteristic with each element are placed in the intersecting cells. Selecting characteristics which all elements share may be the most difficult part of creating a database which can utilizing this method.

The elements are first sorted in descending order according to their truth table value, which is calculated from the existing sequence and value of characteristics for each element. Duplicate truth table values or multisets for the entire bounded class reveal either the need to eliminate duplicate elements or the need to include additional characteristics.

An empirical separatory value is calculated for each characteristic in the set and the characteristic with the greatest empirical separatory value is exchanged with the characteristic which occupies the most significant attribute position.

Next the second most significant characteristic is found by calculating an empirical separatory value for each remaining characteristic in combination with the first characteristic. The characteristic which produces the greatest separatory value is then exchanged with the characteristic which occupies the second most significant attribute position.

Next the third most significant characteristic is found by calculating an empirical separatory value for each remaining characteristic in combination with the first and second characteristics. The characteristic which produces the greatest empirical separatory value is then exchanged with the characteristic which occupies the third most significant attribute position. This procedure may continue until all characteristics have been processed or until one hundred percent separation of the elements has been achieved.

A larger radix will allow faster identification by excluding a greater percentage of elements per characteristic. A binary radix for instance excludes only fifty percent of the elements per characteristic whereas a five-valued radix excludes eighty percent of the elements per characteristic.[5] What follows is an elucidation of the matrix and separatory equations.[6]

Syndromic Classification: A Process for Amplifying Information Using S-Clustering

Eugene W. Rypka, PhD
University of New Mexico, Albuquerque, New Mexico, USA
Statistics Editor: Marcello Pagano, PhD
Harvard School of Public Health, Boston, Massachusetts, USA
Nutrition 1996; 12(11/12): 827-829

In a previous issue of Nutrition, Drs. Bernstein and Pleban’ use the method of S-clustering to aid in nutritional classification of patients directly on-line. Classification of this type is called primary or syndromic classification.* It is created by a process called separatory (S-) clustering (E. Rypka, unpublished observations). The authors use S-clustering in Table I.  S-clustering extracts features (analytes, variables) from endogenous data that amplify or maximize structural information to create classes of patients (pathophysiologic events) which are the most disjointed or separable. S-clustering differs from other classificatory methods because it finds in a database a theoretic- or more- number of variables with the required variety that map closest to an ideal, theoretic, or structural information standard. In Table I of their article, Bernstein and Pleban’ indicate there would have to be 3 ’ = 243 rows to show all possible patterns. In Table II of this article, I have used a 33 = 27 row truth table to convey the notion of mapping amplified information to an ideal, theoretic standard using just the first three columns. Variables are scaled for use in S-clustering.

A Survey of Binary Similarity and Distance Measures
Seung-Seok Choi, Sung-Hyuk Cha, Charles C. Tappert
SYSTEMICS, CYBERNETICS AND INFORMATICS 2010; 8(1): 43-48
The binary feature vector is one of the most common
representations of patterns and measuring similarity and
distance measures play a critical role in many problems
such as clustering, classification, etc. Ever since Jaccard
proposed a similarity measure to classify ecological
species in 1901, numerous binary similarity and distance
measures have been proposed in various fields. Applying
appropriate measures results in more accurate data
analysis. Notwithstanding, few comprehensive surveys
on binary measures have been conducted. Hence we
collected 76 binary similarity and distance measures used
over the last century and reveal their correlations through
the hierarchical clustering technique.

This paper is organized as follows. Section 2 describes
the definitions of 76 binary similarity and dissimilarity
measures. Section 3 discusses the grouping of those
measures using hierarchical clustering. Section 4
concludes this work.

Historically, all the binary measures observed above have
had a meaningful performance in their respective fields.
The binary similarity coefficients proposed by Peirce,
Yule, and Pearson in 1900s contributes to the evolution
of the various correlation based binary similarity
measures. The Jaccard coefficient proposed at 1901 is
still widely used in the various fields such as ecology and
biology. The discussion of inclusion or exclusion of
negative matches was actively arisen by Sokal & Sneath
in during 1960s and by Goodman & Kruskal in 1970s.

Polyphasic Taxonomy of the Genus Vibrio: Numerical Taxonomy of Vibrio cholerae, Vibrio
parahaemolyticus, and Related Vibrio Species
R. R. COLWELL
JOURNAL OF BACTERIOLOGY, Oct. 1970;  104(1): 410-433
A set of 86 bacterial cultures, including 30 strains of Vibrio cholerae, 35 strains of
V. parahaemolyticus, and 21 representative strains of Pseudomonas, Spirillum,
Achromobacter, Arthrobacter, and marine Vibrio species were tested for a total of 200
characteristics. Morphological, physiological, and biochemical characteristics were
included in the analysis. Overall deoxyribonucleic acid (DNA) base compositions
and ultrastructure, under the electron microscope, were also examined. The taxonomic
data were analyzed by computer by using numerical taxonomy programs
designed to sort and cluster strains related phenetically. The V. cholerae strains
formed an homogeneous cluster, sharing overall S values of >75%. Two strains,
V. cholerae NCTC 30 and NCTC 8042, did not fall into the V. cholerae species
group when tested by the hypothetical median organism calculation. No separation
of “classic” V. cholerae, El Tor vibrios, and nonagglutinable vibrios was observed.
These all fell into a single, relatively homogeneous, V. cholerae species cluster.
PJ. parahaemolyticus strains, excepting 5144, 5146, and 5162, designated members
of the species V. alginolyticus, clustered at S >80%. Characteristics uniformly
present in all the Vibrio species examined are given, as are also characteristics and
frequency of occurrence for V. cholerae and V. parahaemolyticus. The clusters formed
in the numerical taxonomy analyses revealed similar overall DNA base compositions,
with the range for the Vibrio species of 40 to 48% guanine plus cytosine. Generic
level of relationship of V. cholerae and V. parahaemolyticus is considered
dubious. Intra- and intergroup relationships obtained from the numerical taxonomy
studies showed highly significant correlation with DNA/DNA reassociation data.

A Numerical Classification of the Genus Bacillus
By FERGUS G . PRIEST, MICHAEL GOODFELLOW AND CAROLE TODD
Journal of General Microbiology (1988), 134, 1847-1882.

Three hundred and sixty-eight strains of aerobic, endospore-forming bacteria which included type and reference cultures of Bacillus and environmental isolates were studied. Overall similarities of these strains for 118 unit characters were determined by the SSMS,, and Dp coefficients and clustering achieved using the UPGMA algorithm. Test error was within acceptable limits. Six cluster-groups were defined at 70% SSM which corresponded to 69% Sp and 48-57% SJ.G roupings obtained with the three coefficients were generally similar but there were some changes in the definition and membership of cluster-groups and clusters, particularly with the SJ coefficient. The Bacillus strains were distributed among 31 major (4 or more strains), 18 minor (2 or 3 strains) and 30 single-member clusters at the 83% SsMle vel. Most of these clusters can be regarded as taxospecies. The heterogeneity of several species, including Bacillus breuis, B. circulans, B. coagulans, B. megateriun, B . sphaericus and B . stearothermophilus, has been indicated  and the species status of several taxa of hitherto uncertain validity confirmed. Thus on the basis of the numerical phenetic and appropriate (published) molecular genetic data, it is proposed
that the following names be recognized; BacillusJlexus (Batchelor) nom. rev., Bacillus fusiformis (Smith et al.) comb. nov., Bacillus kaustophilus (Prickett) nom. rev., Bacilluspsychrosaccharolyticus (Larkin & Stokes) nom. rev. and Bacillus simplex (Gottheil) nom. rev. Other phenetically well-defined taxospecies included ‘ B. aneurinolyticus’, ‘B. apiarius’, ‘B. cascainensis’, ‘B. thiaminolyticus’ and three clusters of environmental isolates related to B . firmus and previously described as ‘B. firmus-B. lentus intermediates’. Future developments in the light of the numerical phenetic data are discussed.

Numerical Classification of Bacteria
Part II. * Computer Analysis of Coryneform Bacteria (2)
Comparison of Group-Formations Obtained on Two
Different Methods of Scoring Data
By Eitaro MASUOan d Toshio NAKAGAWA
[Agr. Biol. Chem., 1969; 33(8): 1124-1133.
Sixty three organisms selected from 12 genera of bacteria were subjected to numerical analysis. The purpose of this work is to examine the relationships among 38 coryneform bacteria included in the test organisms by two coding methods-Sneath’s and Lockhart’s systems-, and to compare the results with conventional classification. In both cases of codification, five groups and one or two single item(s) were found in the resultant classifications. Different codings brought, however, a few distinct differences in some groups , especially in a group of sporogenic bacilli or lactic-acid bacteria. So far as the present work concerns, the result obtained on Lockhart’s coding rather than that obtained on Sneath’s coding resembled the conventional classification. The taxonomic positions of corynebacteria were quite different from those of the conventional classification, regardless
of which coding method was applied.
Though animal corynebacteria have conventionally been considered to occupy the
taxonomic position neighboring to genera Arthrobacter and Cellulornonas and regarded to be the nucleus of so-called “coryneform bacteria,’ the present work showed that many of the corynebacteria are akin to certain mycobacteria rather than to the organisms belonging to the above two genera.

Numerical Classification of Bacteria
Part III. Computer Analysis of “Coryneform Bacteria” (3)
Classification Based on DNA Base Compositions
By EitaroM ASUaOnd ToshioN AKAGAWA
Agr. Biol. Chem., 1969; 33(11): 1570-1576
It has been known that the base compositions of deoxyribonucleic acids (DNA) are
quite different from organism to organism. A pertinent example of this diversity is
found in bacterial species. The base compositions of DNA isolated from a wide variety
of bacteria are distributed in a range from 25 to 75 GC mole-percent (100x(G+C)/
(A+T+G+C)).1) The usefulness of the information of DNA base composition for
the taxonomy of bacteria has been emphasized by several authors. Lee et al.,” Sueoka,” and Freese) have speculated on the evolutionary significance of microbial DNA base composition. They pointed out that closely related microorganisms generally showed similar base compositions of DNA, and suggested that phylogenetic relationship should be reflected in the GC content.
In the present paper are compared the results of numerical classifications of 45
bacteria based on the two different similarity matrices: One representing the overall
similarities of phenotypic properties, the other representing the similarities of GC contents.

Advanced computational algorithms for microbial community analysis using massive 16S rRNA
sequence data
Y Sun, Y Cai, V Mai, W Farmerie, F Yu, J Li and S Goodison
Nucleic Acids Research, 2010; 38(22): e205
http://dx.doi.org:/10.1093/nar/gkq872

With the aid of next-generation sequencing technology, researchers can now obtain millions of microbial signature sequences for diverse applications ranging from human epidemiological studies to global ocean surveys. The development of advanced computational strategies to maximally extract pertinent information from massive nucleotide data has become a major focus of the bioinformatics community. Here, we describe a novel analytical strategy including discriminant and topology analyses that enables researchers to deeply investigate the hidden world of microbial communities, far beyond basic microbial diversity estimation. We demonstrate the utility of our
approach through a computational study performed on a previously published massive human gut 16S rRNA data set. The application of discriminant and
topology analyses enabled us to derive quantitative disease-associated microbial signatures and describe microbial community structure in far more detail than previously achievable. Our approach provides rigorous statistical tools for sequence based studies aimed at elucidating associations between known or unknown organisms and a variety of physiological or environmental conditions.

What is Drug Resistance?

Antimicrobial resistance is the ability of microbes, such as bacteria, viruses, parasites, or fungi, to grow in the presence of a chemical (drug) that would normally kill it or limit its growth.

Diagram showing the difference between non-resistant bacteria and drug resistant bacteria.

Credit: NIAID

DrugResistance difference between non-resistant bacteria and drug resistant bacteria

DrugResistance difference between non-resistant bacteria and drug resistant bacteria

http://www.niaid.nih.gov/SiteCollectionImages/topics/antimicrobialresistance/1whatIsDrugResistance.gif

Diagram showing the difference between non-resistant bacteria and drug resistant bacteria. Non-resistant bacteria multiply, and upon drug treatment, the bacteria die. Drug resistant bacteria multiply as well, but upon drug treatment, the bacteria continue to spread.

Between 5 and 10 percent of all hospital patients develop an infection. About 90,000 of these patients die each year as a result of their infection, up from 13,300 patient deaths in 1992.

According to the Centers for Disease Control and Prevention (April 2011), antibiotic resistance in the United States costs an estimated $20 billion a year in excess health care costs, $35 million in other societal costs and more than 8 million additional days that people spend in the hospital.

Resistance to Antibiotics: Are We in the Post-Antibiotic Era?

Alfonso J. Alanis
Archives of Medical Research 36 (2005) 697–705
http://dx.doi.org:/10.1016/j.arcmed.2005.06.009

Serious infections caused by bacteria that have become resistant to commonly used antibiotics have become a major global healthcare problem in the 21st century. They not only are more severe and require longer and more complex treatments, but they are also significantly more expensive to diagnose and to treat. Antibiotic resistance, initially a problem of the hospital setting associated with an increased number of hospital acquired infections usually in critically ill and immunosuppressed patients, has now extended into the community causing severe infections difficult to diagnose and treat. The molecular mechanisms by which bacteria have become resistant to antibiotics are diverse and complex. Bacteria have developed resistance to all different classes of antibiotics discovered to date. The most frequent type of resistance is acquired and transmitted horizontally via the conjugation of a plasmid. In recent times new mechanisms of resistance have resulted in the simultaneous development of resistance to several antibiotic classes creating very dangerous multidrug-resistant (MDR) bacterial strains, some also known as ‘‘superbugs’’. The indiscriminate and inappropriate use of antibiotics in outpatient clinics, hospitalized patients and in the food industry is the single largest factor leading to antibiotic resistance. The pharmaceutical industry, large academic institutions or the government are not investing the necessary resources to produce the next generation of newer safe and effective antimicrobial drugs. In many cases, large pharmaceutical companies have terminated their anti-infective research programs altogether due to economic reasons. The potential negative consequences of all these events are relevant because they put society at risk for the spread of potentially serious MDR bacterial infections.

Targeting the Human Macrophage with Combinations of Drugs and Inhibitors of Ca2+ and K+ Transport to Enhance the Killing of Intracellular Multi-Drug Resistant M. tuberculosis (MDR-TB) – a Novel, Patentable Approach to Limit the Emergence of XDR-TB

Marta Martins
Recent Patents on Anti-Infective Drug Discovery, 2011, 6, 000-000

The emergence of resistance in Tuberculosis has become a serious problem for the control of this disease. For that reason, new therapeutic strategies that can be implemented in the clinical setting are urgently needed. The design of new compounds active against mycobacteria must take into account that Tuberculosis is mainly an intracellular infection of the alveolar macrophage and therefore must maintain activity within the host cells. An alternative therapeutic approach will be described in this review, focusing on the activation of the phagocytic cell and the subsequent killing of the internalized bacteria. This approach explores the combined use of antibiotics and phenothiazines, or Ca2+ and K+ flux inhibitors, in the infected macrophage. Targeting the infected macrophage and not the internalized bacteria could overcome the problem of bacterial multi-drug resistance. This will potentially eliminate the appearance of new multi-drug resistant tuberculosis (MDR-TB) cases and subsequently prevent the emergence of extensively-drug resistant tuberculosis (XDR-TB). Patents resulting from this novel and innovative approach could be extremely valuable if they can be implemented in the clinical setting. Other patents will also be discussed such as the treatment of TB using immunomodulator compounds (for example: betaglycans).

Six Epigenetic Faces of Streptococcus

Kevin Mayer
http://www.genengnews.com/gen-news-highlights/six-epigenetic-faces-of-streptococcus/81250430/

Medical illustration of Streptococcus pneumonia. [CDC]

Streptococcus pneumonia

Streptococcus pneumonia

It appears that S. pneumoniae has even more personalities, each associated with a different proclivity toward invasive, life-threatening disease. In fact, any of six personalities may emerge depending on the action of a single genetic switch.

To uncover the switch, an international team of scientists conducted a study in genomics, but they looked beyond nucleotide polymorphisms or accessory regions as possible phenotype-shifting mechanisms. Instead, they focused on the potential of restriction-modification (RM) systems to mediate gene regulation via epigenetic changes.

Scientists representing the University of Leicester, Griffith University’s Institute for Glycomics, theUniversity of Adelaide, and Pacific Biosciences realized that the S. pneumoniae genome contains two Type I, three Type II, and one Type IV RM systems. Of these, only the DpnI Type II RM system had been described in detail. Switchable Type I systems had been described previously, but these reports did not provide evidence for differential methylation or for phenotypic impact.

As it turned out, the Type I system embodied a mechanism capable of randomly changing the bacterium’s characteristics into six alternative states. The mechanism’s details were presented September 30 in Nature Communications, in an article entitled, “A random six-phase switch regulates pneumococcal virulence via global epigenetic changes.”

“The underlying mechanism for such phase variation consists of genetic rearrangements in a Type I restriction-modification system (SpnD39III),” wrote the authors. “The rearrangements generate six alternative specificities with distinct methylation patterns, as defined by single-molecule, real-time (SMRT) methylomics.”

Eradication of multidrug-resistant A. baumanniii in burn wounds by antiseptic pulsed electric field.

A Golberg, GF Broelsch, D Vecchio,S Khan, MR Hamblin, WG Austen, Jr, RL Sheridan,  ML Yarmush.

Emerging bacterial resistance to multiple drugs is an increasing problem in burn wound management. New non-pharmacologic interventions are needed for wound disinfection. Here we report on a novel physical method for disinfection: antiseptic pulsed electric field (PEF) applied externally to the infected wounds.  In an animal model, we show that PEF can reduce the load of multidrug resistant Acinetobacter baumannii present in a full thickness burn wound by more than four orders of magnitude, as detected by bioluminescence imaging. Furthermore, using a finite element numerical model, we demonstrate that PEF provides non-thermal, homogeneous, full thickness treatment for the burn wound, thus, overcoming the limitation of treatment depth for many topical antimicrobials. These modeling tools and our in vivo results will be extremely useful for further translation of the PEF technology to the clinical setting. We believe that PEF, in combination with systemic antibiotics, will synergistically eradicate multidrug-resistant burn wound infections, prevent biofilm formation and restore natural skin microbiome. PEF provides a new platform for infection combat in patients, therefore it has a potential to significantly decreasing morbidity and mortality.

Golberg, A. & Yarmush, M. L. Nonthermal irreversible electroporation: fundamentals, applications, and challenges. IEEE Trans Biomed Eng 60, 707-14 (2013).

Mechanisms Of Antibiotic Resistance In Salmonella: Efflux Pumps, Genetics, Quorum Sensing And Biofilm Formation.

Martins M, McCusker M, Amaral L, Fanning S
Perspectives in Drug Discovery and Design 02/2011; 8:114-123.

In Salmonella the main mechanisms of antibiotic resistance are mutations in target genes (such as DNA gyrase and topoisomerase IV) and the over-expression of efflux pumps. However, other mechanisms such as changes in the cell envelope; down regulation of membrane porins; increased lipopolysaccharide (LPS) component of the outer cell membrane; quorum sensing and biofilm formation can also contribute to the resistance seen in this microorganism. To overcome this problem new therapeutic approaches are urgently needed. In the case of efflux-mediated multidrug resistant isolates, one of the treatment options could be the use of efflux pump inhibitors (EPIs) in combination with the antibiotics to which the bacteria is resistant. By blocking the efflux pumps resistance is partly or wholly reversed, allowing antibiotics showing no activity against the MDR strains to be used to treat these infections. Compounds that show potential as an EPI are therefore of interest, as well as new strategies to target the efflux systems. Quorum sensing (QS) and biofilm formation are systems also known to be involved in antibiotic resistance. Consequently, compounds that can disrupt or inhibit these bacterial “communication systems” will be of use in the treatment of these infections.

Role of Phenothiazines and Structurally Similar Compounds of Plant Origin in the Fight against Infections by Drug Resistant Bacteria

SG Dastidar, JE Kristiansen, J Molnar and L Amaral
Antibiotics 2013, 2, 58-71;
http://dx.doi.org:/10.3390/antibiotics2010058

Phenothiazines have their primary effects on the plasma membranes of prokaryotes and eukaryotes. Among the components of the prokaryotic plasma membrane affected are efflux pumps, their energy sources and energy providing enzymes, such as ATPase, and genes that regulate and code for the permeability aspect of a bacterium. The response of multidrug and extensively drug resistant tuberculosis to phenothiazines shows an alternative therapy for its treatment. Many phenothiazines have shown synergistic activity with several antibiotics thereby lowering the doses of antibiotics administered for specific bacterial infections. Trimeprazine is synergistic with trimethoprim. Flupenthixol (Fp) has been found to be synergistic with penicillin and chlorpromazine (CPZ); in addition, some antibiotics are also synergistic. Along with the antibacterial action described in this review, many phenothiazines possess plasmid curing activities, which render the bacterial carrier of the plasmid sensitive to antibiotics. Thus, simultaneous applications of a phenothiazine like TZ would not only act as an additional antibacterial agent but also would help to eliminate drug resistant plasmid from the infectious bacterial cells.

Multidrug Efflux Pumps Described for Staphylococcus aureus

Efflux Pump  Family Regulator(s) Substrate Specificity  References 
Chromosomally-encoded Efflux Systems 
NorA MFS MgrA, NorG(?) Hydrophilic fluoroquinolones (ciprofloxacin, norfloxacin)QACs (tetraphenylphosphonium, benzalkonium chloride)

Dyes (e.g. ethidium bromide, rhodamine)

[16,18,19]
NorB MFS MgrA, NorG Fluoroquinolones (e.g. hydrophilic: ciprofloxacin, norfloxacin and hydrophobic: moxifloxacin,
sparfloxacin)TetracyclineQACs (e.g. tetraphenylphosphonium, cetrimide)Dyes (e.g. ethidium bromide)
[31]
NorC MFS MgrA(?), NorG Fluoroquinolones (e.g. hydrophilic: ciprofloxacin and hydrophobic: moxifloxacin)Dyes (e.g. rhodamine) [35,36]
MepA MATE MepR Fluoroquinolones (e.g. hydrophilic: ciprofloxacin, norfloxacin and hydrophobic: moxifloxacin,
sparfloxacin)Glycylcyclines (e.g. tigecycline)QACs (e.g. tetraphenylphosphonium, cetrimide, benzalkonium chloride)Dyes (e.g. ethidium bromide)
[37,38]
MdeA MFS n.i. Hydrophilic fluoroquinolones (e.g. ciprofloxacin, norfloxacin)Virginiamycin, novobiocin, mupirocin, fusidic acid

QACs (e.g. tetraphenylphosphonium, benzalkonium chloride, dequalinium)

Dyes (e.g. ethidium bromide)

[39,40]
SepA n.d. n.i. QACs (e.g. benzalkonium chloride)Biguanidines (e.g. chlorhexidine)

Dyes (e.g. acriflavine)

[41]
SdrM MFS n.i. Hydrophilic fluoroquinolones (e.g. norfloxacin)Dyes (e.g. ethidium bromide, acriflavine) [42]
LmrS MFS n.i. Oxazolidinone (linezolid)Phenicols (e.g. choramphenicol, florfenicol)

Trimethoprim, erythromycin, kanamycin, fusidic acid

QACs (e.g. tetraphenylphosphonium)

Detergents (e.g. sodium docecyl sulphate)

Dyes (e.g. ethidium bromide)

[43]

Plasmid-encoded Efflux Systems

QacA MFS QacR QACs (e.g. tetraphenylphosphonium, benzalkonium chloride, dequalinium)Biguanidines (e.g. chlorhexidine)

Diamidines (e.g. pentamidine)

Dyes (e.g. ethidium bromide, rhodamine, acriflavine)

[45,49]
QacB MFS QacR QACs (e.g. tetraphenylphosphonium, benzalkonium chloride)Dyes (e.g. ethidium bromide, rhodamine, acriflavine) [53]
Smr SMR n.i. QACs (e.g. benzalkonium chloride, cetrimide)Dyes (e.g. ethidium bromide) [58,61]
QacG SMR n.i. QACs (e.g. benzalkonium chloride, cetyltrymethylammonium)Dyes (e.g. ethidium bromide) [67]
QacH SMR n.i. QACs (e.g. benzalkonium chloride, cetyltrymethylammonium)Dyes (e.g. ethidium bromide) [68]
QacJ SMR n.i. QACs (e.g. benzalkonium chloride, cetyltrymethylammonium)Dyes (e.g. ethidium bromide) [69]

a n.d.: The family of transporters to which SepA belongs is not elucidated to date.
b n.i.: The transporter has no regulator identified to date.
QACs: quaternary ammonium compounds
The importance of efflux pumps in bacterial antibiotic resistance

  1. A. Webber and L. J. V. Piddock
    Journal of Antimicrobial Chemotherapy (2003) 51, 9–11
    http://dx.doi.org:/10.1093/jac/dkg050Efflux pumps are transport proteins involved in the extrusion of toxic substrates (including virtually all classes of clinically relevant antibiotics) from within cells into the external environment. These proteins are found in both Gram-positive and -negative bacteria as well as in eukaryotic organisms. Pumps may be specific for one substrate or may transport a range of structurally dissimilar compounds (including antibiotics of multiple classes); such pumps can be associated with multiple drug resistance (MDR). In the prokaryotic kingdom there are five major families of efflux transporter: MF (major facilitator), MATE (multidrug and toxic efflux), RND (resistance-nodulation-division), SMR (small multidrug resistance) and ABC (ATP binding cassette). All these systems utilize the proton motive force as an energy source. Advances in DNA technology have led to the identification of members of the above families. Transporters that efflux multiple substrates, including antibiotics, have not evolved in response to the stresses of the antibiotic era. All bacterial genomes studied contain efflux pumps that indicate their ancestral origins. It has been estimated that ∼5–10% of all bacterial genes are involved in transport and a large proportion of these encode efflux pumps.

The efflux pump

The efflux pump

Multidrug-resistance efflux pumps — not just for resistance

Laura J. V. Piddock
Nature Reviews | Microbiology | Aug 2006; 4: 629

It is well established that multidrug-resistance efflux pumps encoded by bacteria can confer clinically relevant resistance to antibiotics. It is now understood that these efflux pumps also have a physiological role(s). They can confer resistance to natural substances produced by the host, including bile, hormones and host defense molecules. In addition, some efflux pumps of the resistance nodulation division (RND) family have been shown to have a role in the colonization and the persistence of bacteria in the host. Here, I present the accumulating evidence that multidrug-resistance efflux pumps have roles in bacterial pathogenicity and propose that these pumps therefore have greater clinical relevance than is usually attributed to them.

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