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Archive for the ‘Population Health Management’ Category


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

 

When a baby is born through its mother’s birth canal, it is bathed in a soup of microbes. Those born by caesarean section (C-section) miss out on this bacterial baptism. The differences in microbe exposure at birth and later health could be caused by other factors, such as whether a mother takes antibiotics during her surgery, and whether a baby is breastfed or has a genetic predisposition to obesity. So, the researchers are sharply split on whether or not this missing of bacterial exposure increases the risk of chronic health problems such as obesity and asthma.

 

Researchers found that babies delivered surgically harboured different collections of bacteria than did those born vaginally. C-section babies, which comprise more than 30% of births in the United States, are also more prone to obesity and immune diseases such as diabetes. Experiments show that mice born by C-section are more prone to obesity and have impaired immune systems. There are fewer factors that could account for these differences in the rodents, which can be studied in controlled conditions, than in people.

 

A wave of clinical trials now under way could help to settle the question — and feed into the debate over whether seeding babies born by C-section with their mother’s vaginal bacteria is beneficial or potentially harmful. Several groups of researchers will be swabbing hundreds of C-section babies with their mother’s microbes, while comparing them to a control group. Each team plans to monitor its study participants over several years in the hope of learning more about how the collection of microbes in their bodies might influence weight, allergy risk and other factors.

 

But some scientists say that the trials could expose C-section babies to infection, or encourage mothers to try do-it-yourself swabbing, without much evidence that there is a benefit or risk. Moreover, there is no evidence that differing exposure to vaginal microbes at birth can help explain variation in people’s health over time. Presently the whole concept is in very much a state of uncertainty.

 

Researchers in near future will compare swabbed C-section babies with a placebo group and with infants delivered vaginally. They confirmed that their protocols will not increase the risk of infection for C-section babies. Scientists will also rigorously screen mothers participating in these trials for microbes such as HIV and group B streptococcus — a common vaginal bacterium that causes respiratory problems in newborns.

 

References:

 

https://www.nature.com/articles/d41586-019-02348-3?utm_source=Nature+Briefing

 

https://www.ncbi.nlm.nih.gov/pubmed/31431742

 

https://www.ncbi.nlm.nih.gov/pubmed/20566857

 

https://www.ncbi.nlm.nih.gov/pubmed/25452656

 

https://www.ncbi.nlm.nih.gov/pubmed/22939691

 

https://www.ncbi.nlm.nih.gov/pubmed/24030708

 

https://pharmaceuticalintelligence.com/2017/02/22/babys-microbiome-changing-due-to-caesarean-birth-and-formula-feeding/

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Artificial Intelligence and Cardiovascular Disease

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

 

Cardiology is a vast field that focuses on a large number of diseases specifically dealing with the heart, the circulatory system, and its functions. As such, similar symptomatologies and diagnostic features may be present in an individual, making it difficult for a doctor to easily isolate the actual heart-related problem. Consequently, the use of artificial intelligence aims to relieve doctors from this hurdle and extend better quality to patients. Results of screening tests such as echocardiograms, MRIs, or CT scans have long been proposed to be analyzed using more advanced techniques in the field of technology. As such, while artificial intelligence is not yet widely-used in clinical practice, it is seen as the future of healthcare.

 

The continuous development of the technological sector has enabled the industry to merge with medicine in order to create new integrated, reliable, and efficient methods of providing quality health care. One of the ongoing trends in cardiology at present is the proposed utilization of artificial intelligence (AI) in augmenting and extending the effectiveness of the cardiologist. This is because AI or machine-learning would allow for an accurate measure of patient functioning and diagnosis from the beginning up to the end of the therapeutic process. In particular, the use of artificial intelligence in cardiology aims to focus on research and development, clinical practice, and population health. Created to be an all-in-one mechanism in cardiac healthcare, AI technologies incorporate complex algorithms in determining relevant steps needed for a successful diagnosis and treatment. The role of artificial intelligence specifically extends to the identification of novel drug therapies, disease stratification or statistics, continuous remote monitoring and diagnostics, integration of multi-omic data, and extension of physician effectivity and efficiency.

 

Artificial intelligence – specifically a branch of it called machine learning – is being used in medicine to help with diagnosis. Computers might, for example, be better at interpreting heart scans. Computers can be ‘trained’ to make these predictions. This is done by feeding the computer information from hundreds or thousands of patients, plus instructions (an algorithm) on how to use that information. This information is heart scans, genetic and other test results, and how long each patient survived. These scans are in exquisite detail and the computer may be able to spot differences that are beyond human perception. It can also combine information from many different tests to give as accurate a picture as possible. The computer starts to work out which factors affected the patients’ outlook, so it can make predictions about other patients.

 

In current medical practice, doctors will use risk scores to make treatment decisions for their cardiac patients. These are based on a series of variables like weight, age and lifestyle. However, they do not always have the desired levels of accuracy. A particular example of the use of artificial examination in cardiology is the experimental study on heart disease patients, published in 2017. The researchers utilized cardiac MRI-based algorithms coupled with a 3D systolic cardiac motion pattern to accurately predict the health outcomes of patients with pulmonary hypertension. The experiment proved to be successful, with the technology being able to pick-up 30,000 points within the heart activity of 250 patients. With the success of the aforementioned study, as well as the promise of other researches on artificial intelligence, cardiology is seemingly moving towards a more technological practice.

 

One study was conducted in Finland where researchers enrolled 950 patients complaining of chest pain, who underwent the centre’s usual scanning protocol to check for coronary artery disease. Their outcomes were tracked for six years following their initial scans, over the course of which 24 of the patients had heart attacks and 49 died from all causes. The patients first underwent a coronary computed tomography angiography (CCTA) scan, which yielded 58 pieces of data on the presence of coronary plaque, vessel narrowing and calcification. Patients whose scans were suggestive of disease underwent a positron emission tomography (PET) scan which produced 17 variables on blood flow. Ten clinical variables were also obtained from medical records including sex, age, smoking status and diabetes. These 85 variables were then entered into an artificial intelligence (AI) programme called LogitBoost. The AI repeatedly analysed the imaging variables, and was able to learn how the imaging data interacted and identify the patterns which preceded death and heart attack with over 90% accuracy. The predictive performance using the ten clinical variables alone was modest, with an accuracy of 90%. When PET scan data was added, accuracy increased to 92.5%. The predictive performance increased significantly when CCTA scan data was added to clinical and PET data, with accuracy of 95.4%.

 

Another study findings showed that applying artificial intelligence (AI) to the electrocardiogram (ECG) enables early detection of left ventricular dysfunction and can identify individuals at increased risk for its development in the future. Asymptomatic left ventricular dysfunction (ALVD) is characterised by the presence of a weak heart pump with a risk of overt heart failure. It is present in three to six percent of the general population and is associated with reduced quality of life and longevity. However, it is treatable when found. Currently, there is no inexpensive, noninvasive, painless screening tool for ALVD available for diagnostic use. When tested on an independent set of 52,870 patients, the network model yielded values for the area under the curve, sensitivity, specificity, and accuracy of 0.93, 86.3 percent, 85.7 percent, and 85.7 percent, respectively. Furthermore, in patients without ventricular dysfunction, those with a positive AI screen were at four times the risk of developing future ventricular dysfunction compared with those with a negative screen.

 

In recent years, the analysis of big data database combined with computer deep learning has gradually played an important role in biomedical technology. For a large number of medical record data analysis, image analysis, single nucleotide polymorphism difference analysis, etc., all relevant research on the development and application of artificial intelligence can be observed extensively. For clinical indication, patients may receive a variety of cardiovascular routine examination and treatments, such as: cardiac ultrasound, multi-path ECG, cardiovascular and peripheral angiography, intravascular ultrasound and optical coherence tomography, electrical physiology, etc. By using artificial intelligence deep learning system, the investigators hope to not only improve the diagnostic rate and also gain more accurately predict the patient’s recovery, improve medical quality in the near future.

 

The primary issue about using artificial intelligence in cardiology, or in any field of medicine for that matter, is the ethical issues that it brings about. Physicians and healthcare professionals prior to their practice swear to the Hippocratic Oath—a promise to do their best for the welfare and betterment of their patients. Many physicians have argued that the use of artificial intelligence in medicine breaks the Hippocratic Oath since patients are technically left under the care of machines than of doctors. Furthermore, as machines may also malfunction, the safety of patients is also on the line at all times. As such, while medical practitioners see the promise of artificial technology, they are also heavily constricted about its use, safety, and appropriateness in medical practice.

 

Issues and challenges faced by technological innovations in cardiology are overpowered by current researches aiming to make artificial intelligence easily accessible and available for all. With that in mind, various projects are currently under study. For example, the use of wearable AI technology aims to develop a mechanism by which patients and doctors could easily access and monitor cardiac activity remotely. An ideal instrument for monitoring, wearable AI technology ensures real-time updates, monitoring, and evaluation. Another direction of cardiology in AI technology is the use of technology to record and validate empirical data to further analyze symptomatology, biomarkers, and treatment effectiveness. With AI technology, researchers in cardiology are aiming to simplify and expand the scope of knowledge on the field for better patient care and treatment outcomes.

 

References:

 

https://www.news-medical.net/health/Artificial-Intelligence-in-Cardiology.aspx

 

https://www.bhf.org.uk/informationsupport/heart-matters-magazine/research/artificial-intelligence

 

https://www.medicaldevice-network.com/news/heart-attack-artificial-intelligence/

 

https://www.nature.com/articles/s41569-019-0158-5

 

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5711980/

 

www.j-pcs.org/article.asp

http://www.onlinejacc.org/content/71/23/2668

http://www.scielo.br/pdf/ijcs/v30n3/2359-4802-ijcs-30-03-0187.pdf

 

https://www.escardio.org/The-ESC/Press-Office/Press-releases/How-artificial-intelligence-is-tackling-heart-disease-Find-out-at-ICNC-2019

 

https://clinicaltrials.gov/ct2/show/NCT03877614

 

https://www.europeanpharmaceuticalreview.com/news/82870/artificial-intelligence-ai-heart-disease/

 

https://www.frontiersin.org/research-topics/10067/current-and-future-role-of-artificial-intelligence-in-cardiac-imaging

 

https://www.news-medical.net/health/Artificial-Intelligence-in-Cardiology.aspx

 

https://www.sciencedaily.com/releases/2019/05/190513104505.htm

 

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The Journey of Antibiotic Discovery

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

 

The term ‘antibiotic’ was introduced by Selman Waksman as any small molecule, produced by a microbe, with antagonistic properties on the growth of other microbes. An antibiotic interferes with bacterial survival via a specific mode of action but more importantly, at therapeutic concentrations, it is sufficiently potent to be effective against infection and simultaneously presents minimal toxicity. Infectious diseases have been a challenge throughout the ages. From 1347 to 1350, approximately one-third of Europe’s population perished to Bubonic plague. Advances in sanitary and hygienic conditions sufficed to control further plague outbreaks. However, these persisted as a recurrent public health issue. Likewise, infectious diseases in general remained the leading cause of death up to the early 1900s. The mortality rate shrunk after the commercialization of antibiotics, which given their impact on the fate of mankind, were regarded as a ‘medical miracle’. Moreover, the non-therapeutic application of antibiotics has also greatly affected humanity, for instance those used as livestock growth promoters to increase food production after World War II.

 

Currently, more than 2 million North Americans acquire infections associated with antibiotic resistance every year, resulting in 23,000 deaths. In Europe, nearly 700 thousand cases of antibiotic-resistant infections directly develop into over 33,000 deaths yearly, with an estimated cost over €1.5 billion. Despite a 36% increase in human use of antibiotics from 2000 to 2010, approximately 20% of deaths worldwide are related to infectious diseases today. Future perspectives are no brighter, for instance, a government commissioned study in the United Kingdom estimated 10 million deaths per year from antibiotic resistant infections by 2050.

 

The increase in antibiotic-resistant bacteria, alongside the alarmingly low rate of newly approved antibiotics for clinical usage, we are on the verge of not having effective treatments for many common infectious diseases. Historically, antibiotic discovery has been crucial in outpacing resistance and success is closely related to systematic procedures – platforms – that have catalyzed the antibiotic golden age, namely the Waksman platform, followed by the platforms of semi-synthesis and fully synthetic antibiotics. Said platforms resulted in the major antibiotic classes: aminoglycosides, amphenicols, ansamycins, beta-lactams, lipopeptides, diaminopyrimidines, fosfomycins, imidazoles, macrolides, oxazolidinones, streptogramins, polymyxins, sulphonamides, glycopeptides, quinolones and tetracyclines.

 

The increase in drug-resistant pathogens is a consequence of multiple factors, including but not limited to high rates of antimicrobial prescriptions, antibiotic mismanagement in the form of self-medication or interruption of therapy, and large-scale antibiotic use as growth promotors in livestock farming. For example, 60% of the antibiotics sold to the USA food industry are also used as therapeutics in humans. To further complicate matters, it is estimated that $200 million is required for a molecule to reach commercialization, with the risk of antimicrobial resistance rapidly developing, crippling its clinical application, or on the opposing end, a new antibiotic might be so effective it is only used as a last resort therapeutic, thus not widely commercialized.

 

Besides a more efficient management of antibiotic use, there is a pressing need for new platforms capable of consistently and efficiently delivering new lead substances, which should attend their precursors impressively low rates of success, in today’s increasing drug resistance scenario. Antibiotic Discovery Platforms are aiming to screen large libraries, for instance the reservoir of untapped natural products, which is likely the next antibiotic ‘gold mine’. There is a void between phenotanypic screening (high-throughput) and omics-centered assays (high-information), where some mechanistic and molecular information complements antimicrobial activity, without the laborious and extensive application of various omics assays. The increasing need for antibiotics drives the relentless and continuous research on the foreground of antibiotic discovery. This is likely to expand our knowledge on the biological events underlying infectious diseases and, hopefully, result in better therapeutics that can swing the war on infectious diseases back in our favor.

 

During the genomics era came the target-based platform, mostly considered a failure due to limitations in translating drugs to the clinic. Therefore, cell-based platforms were re-instituted, and are still of the utmost importance in the fight against infectious diseases. Although the antibiotic pipeline is still lackluster, especially of new classes and novel mechanisms of action, in the post-genomic era, there is an increasingly large set of information available on microbial metabolism. The translation of such knowledge into novel platforms will hopefully result in the discovery of new and better therapeutics, which can sway the war on infectious diseases back in our favor.

 

References:

 

https://www.mdpi.com/2079-6382/8/2/45/htm

 

https://www.ncbi.nlm.nih.gov/pubmed/19515346

 

https://www.ajicjournal.org/article/S0196-6553(11)00184-2/fulltext

 

https://www.ncbi.nlm.nih.gov/pubmed/21700626

 

http://www.med.or.jp/english/journal/pdf/2009_02/103_108.pdf

 

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

 

RNA plays various roles in determining how the information in our genes drives cell behavior. One of its roles is to carry information encoded by our genes from the cell nucleus to the rest of the cell where it can be acted on by other cell components. Rresearchers have now defined how RNA also participates in transmitting information outside cells, known as extracellular RNA or exRNA. This new role of RNA in cell-to-cell communication has led to new discoveries of potential disease biomarkers and therapeutic targets. Cells using RNA to talk to each other is a significant shift in the general thought process about RNA biology.

 

Researchers explored basic exRNA biology, including how exRNA molecules and their transport packages (or carriers) were made, how they were expelled by producer cells and taken up by target cells, and what the exRNA molecules did when they got to their destination. They encountered surprising complexity both in the types of carriers that transport exRNA molecules between cells and in the different types of exRNA molecules associated with the carriers. The researchers had to be exceptionally creative in developing molecular and data-centric tools to begin making sense of the complexity, and found that the type of carrier affected how exRNA messages were sent and received.

 

As couriers of information between cells, exRNA molecules and their carriers give researchers an opportunity to intercept exRNA messages to see if they are associated with disease. If scientists could change or engineer designer exRNA messages, it may be a new way to treat disease. The researchers identified potential exRNA biomarkers for nearly 30 diseases including cardiovascular disease, diseases of the brain and central nervous system, pregnancy complications, glaucoma, diabetes, autoimmune diseases and multiple types of cancer.

 

As for example some researchers found that exRNA in urine showed promise as a biomarker of muscular dystrophy where current studies rely on markers obtained through painful muscle biopsies. Some other researchers laid the groundwork for exRNA as therapeutics with preliminary studies demonstrating how researchers might load exRNA molecules into suitable carriers and target carriers to intended recipient cells, and determining whether engineered carriers could have adverse side effects. Scientists engineered carriers with designer RNA messages to target lab-grown breast cancer cells displaying a certain protein on their surface. In an animal model of breast cancer with the cell surface protein, the researchers showed a reduction in tumor growth after engineered carriers deposited their RNA cargo.

 

Other than the above research work the scientists also created a catalog of exRNA molecules found in human biofluids like plasma, saliva and urine. They analyzed over 50,000 samples from over 2000 donors, generating exRNA profiles for 13 biofluids. This included over 1000 exRNA profiles from healthy volunteers. The researchers found that exRNA profiles varied greatly among healthy individuals depending on characteristics like age and environmental factors like exercise. This means that exRNA profiles can give important and detailed information about health and disease, but careful comparisons need to be made with exRNA data generated from people with similar characteristics.

 

Next the researchers will develop tools to efficiently and reproducibly isolate, identify and analyze different carrier types and their exRNA cargos and allow analysis of one carrier and its cargo at a time. These tools will be shared with the research community to fill gaps in knowledge generated till now and to continue to move this field forward.

 

References:

 

https://www.nih.gov/news-events/news-releases/scientists-explore-new-roles-rna

 

https://www.cell.com/consortium/exRNA

 

https://www.sciencedaily.com/releases/2016/06/160606120230.htm

 

https://www.pasteur.fr/en/multiple-roles-rnas

 

https://www.nature.com/scitable/topicpage/rna-functions-352

 

https://www.umassmed.edu/rti/biology/role-of-rna-in-biology/

 

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

 

Gender of a person can affect the kinds of cancer-causing mutations they develop, according to a genomic analysis spanning nearly 2,000 tumours and 28 types of cancer. The results show striking differences in the cancer-causing mutations found in people who are biologically male versus those who are biologically female — not only in the number of mutations lurking in their tumours, but also in the kinds of mutations found there.

 

Liver tumours from women were more likely to carry mutations caused by a faulty system of DNA mending called mismatch repair, for instance. And men with any type of cancer were more likely to exhibit DNA changes thought to be linked to a process that the body uses to repair DNA with two broken strands. These biases could point researchers to key biological differences in how tumours develop and evolve across sexes.

 

The data add to a growing realization that sex is important in cancer, and not only because of lifestyle differences. Lung and liver cancer, for example, are more common in men than in women — even after researchers control for disparities in smoking or alcohol consumption. The source of that bias, however, has remained unclear.

In 2014, the US National Institutes of Health began encouraging researchers to consider sex differences in preclinical research by, for example, including female animals and cell lines from women in their studies. And some studies have since found sex-linked biases in the frequency of mutations in protein-coding genes in certain cancer types, including some brain cancers and advanced melanoma.

 

But the present study is the most comprehensive study of sex differences in tumour genomes so far. It looks at mutations not only in genes that code for proteins, but also in the vast expanses of DNA that have other functions, such as controlling when genes are turned on or off. The study also compares male and female genomes across many different cancers, which can allow researchers to pick up on additional patterns of DNA mutations, in part by increasing the sample sizes.

 

Researchers analysed full genome sequences gathered by the International Cancer Genome Consortium. They looked at differences in the frequency of 174 mutations known to drive cancer, and found that some of these mutations occurred more frequently in men than in women, and vice versa. When they looked more broadly at the loss or duplication of DNA segments in the genome, they found 4,285 sex-biased genes spread across 15 chromosomes.

 

There were also differences found when some mutations seemed to arise during tumour development, suggesting that some cancers follow different evolutionary paths in men and women. Researchers also looked at particular patterns of DNA changes. Such patterns can, in some cases, reflect the source of the mutation. Tobacco smoke, for example, leaves behind a particular signature in the DNA.

 

Taken together, the results highlight the importance of accounting for sex, not only in clinical trials but also in preclinical studies. This could eventually allow researchers to pin down the sources of many of the differences found in this study. Liver cancer is roughly three times as common in men as in women in some populations, and its incidence is increasing in some countries. A better understanding of its aetiology may turn out to be really important for prevention strategies and treatments.

 

References:

 

https://www.nature.com/articles/d41586-019-00562-7?utm_source=Nature+Briefing

 

https://www.nature.com/news/policy-nih-to-balance-sex-in-cell-and-animal-studies-1.15195

 

https://www.ncbi.nlm.nih.gov/pubmed/26296643

 

https://www.biorxiv.org/content/10.1101/507939v1

 

https://www.ncbi.nlm.nih.gov/pubmed/25985759

 

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Digital Therapeutics: A Threat or Opportunity to Pharmaceuticals


Digital Therapeutics: A Threat or Opportunity to Pharmaceuticals

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

 

Digital Therapeutics (DTx) have been defined by the Digital Therapeutics Alliance (DTA) as “delivering evidence based therapeutic interventions to patients, that are driven by software to prevent, manage or treat a medical disorder or disease”. They might come in the form of a smart phone or computer tablet app, or some form of a cloud-based service connected to a wearable device. DTx tend to fall into three groups. Firstly, developers and mental health researchers have built digital solutions which typically provide a form of software delivered Cognitive-Behaviour Therapies (CBT) that help patients change behaviours and develop coping strategies around their condition. Secondly there are the group of Digital Therapeutics which target lifestyle issues, such as diet, exercise and stress, that are associated with chronic conditions, and work by offering personalized support for goal setting and target achievement. Lastly, DTx can be designed to work in combination with existing medication or treatments, helping patients manage their therapies and focus on ensuring the therapy delivers the best outcomes possible.

 

Pharmaceutical companies are clearly trying to understand what DTx will mean for them. They want to analyze whether it will be a threat or opportunity to their business. For a long time, they have been providing additional support services to patients who take relatively expensive drugs for chronic conditions. A nurse-led service might provide visits and telephone support to diabetics for example who self-inject insulin therapies. But DTx will help broaden the scope of support services because they can be delivered cost-effectively, and importantly have the ability to capture real-world evidence on patient outcomes. They will no-longer be reserved for the most expensive drugs or therapies but could apply to a whole range of common treatments to boost their efficacy. Faced with the arrival of Digital Therapeutics either replacing drugs, or playing an important role alongside therapies, pharmaceutical firms have three options. They can either ignore DTx and focus on developing drug therapies as they have done; they can partner with a growing number of DTx companies to develop software and services complimenting their drugs; or they can start to build their own Digital Therapeutics to work with their products.

 

Digital Therapeutics will have knock-on effects in health industries, which may be as great as the introduction of therapeutic apps and services themselves. Together with connected health monitoring devices, DTx will offer a near constant stream of data about an individuals’ behavior, real world context around factors affecting their treatment in their everyday lives and emotional and physiological data such as blood pressure and blood sugar levels. Analysis of the resulting data will help create support services tailored to each patient. But who stores and analyses this data is an important question. Strong data governance will be paramount to maintaining trust, and the highly regulated pharmaceutical industry may not be best-placed to handle individual patient data. Meanwhile, the health sector (payers and healthcare providers) is becoming more focused on patient outcomes, and payment for value not volume. The future will say whether pharmaceutical firms enhance the effectiveness of drugs with DTx, or in some cases replace drugs with DTx.

 

Digital Therapeutics have the potential to change what the pharmaceutical industry sells: rather than a drug it will sell a package of drugs and digital services. But they will also alter who the industry sells to. Pharmaceutical firms have traditionally marketed drugs to doctors, pharmacists and other health professionals, based on the efficacy of a specific product. Soon it could be paid on the outcome of a bundle of digital therapies, medicines and services with a closer connection to both providers and patients. Apart from a notable few, most pharmaceutical firms have taken a cautious approach towards Digital Therapeutics. Now, it is to be observed that how the pharmaceutical companies use DTx to their benefit as well as for the benefit of the general population.

 

References:

 

https://eloqua.eyeforpharma.com/LP=23674?utm_campaign=EFP%2007MAR19%20EFP%20Database&utm_medium=email&utm_source=Eloqua&elqTrackId=73e21ae550de49ccabbf65fce72faea0&elq=818d76a54d894491b031fa8d1cc8d05c&elqaid=43259&elqat=1&elqCampaignId=24564

 

https://www.s3connectedhealth.com/resources/white-papers/digital-therapeutics-pharmas-threat-or-opportunity/

 

http://www.pharmatimes.com/web_exclusives/digital_therapeutics_will_transform_pharma_and_healthcare_industries_in_2019._heres_how._1273671

 

https://www.mckinsey.com/industries/pharmaceuticals-and-medical-products/our-insights/exploring-the-potential-of-digital-therapeutics

 

https://player.fm/series/digital-health-today-2404448/s9-081-scaling-digital-therapeutics-the-opportunities-and-challenges

 

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Immunoediting can be a constant defense in the cancer landscape


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

 

There are many considerations in the cancer immunoediting landscape of defense and regulation in the cancer hallmark biology. The cancer hallmark biology in concert with key controls of the HLA compatibility affinity mechanisms are pivotal in architecting a unique patient-centric therapeutic application. Selection of random immune products including neoantigens, antigens, antibodies and other vital immune elements creates a high level of uncertainty and risk of undesirable immune reactions. Immunoediting is a constant process. The human innate and adaptive forces can either trigger favorable or unfavorable immunoediting features. Cancer is a multi-disease entity. There are multi-factorial initiators in a certain disease process. Namely, environmental exposures, viral and / or microbiome exposure disequilibrium, direct harm to DNA, poor immune adaptability, inherent risk and an individual’s own vibration rhythm in life.

 

When a human single cell is crippled (Deranged DNA) with mixed up molecular behavior that is the initiator of the problem. A once normal cell now transitioned into full threatening molecular time bomb. In the modeling and creation of a tumor it all begins with the singular molecular crisis and crippling of a normal human cell. At this point it is either chop suey (mixed bit responses) or a productive defensive and regulation response and posture of the immune system. Mixed bits of normal DNA, cancer-laden DNA, circulating tumor DNA, circulating normal cells, circulating tumor cells, circulating immune defense cells, circulating immune inflammatory cells forming a moiety of normal and a moiety of mess. The challenge is to scavenge the mess and amplify the normal.

 

Immunoediting is a primary push-button feature that is definitely required to be hit when it comes to initiating immune defenses against cancer and an adaptation in favor of regression. As mentioned before that the tumor microenvironment is a “mixed bit” moiety, which includes elements of the immune system that can defend against circulating cancer cells and tumor growth. Personalized (Precision-Based) cancer vaccines must become the primary form of treatment in this case. Current treatment regimens in conventional therapy destroy immune defenses and regulation and create more serious complications observed in tumor progression, metastasis and survival. Commonly resistance to chemotherapeutic agents is observed. These personalized treatments will be developed in concert with cancer hallmark analytics and immunocentrics affinity and selection mapping. This mapping will demonstrate molecular pathway interface and HLA compatibility and adaptation with patientcentricity.

References:

 

https://www.linkedin.com/pulse/immunoediting-cancer-landscape-john-catanzaro/

 

https://www.cell.com/cell/fulltext/S0092-8674(16)31609-9

 

https://www.researchgate.net/publication/309432057_Circulating_tumor_cell_clusters_What_we_know_and_what_we_expect_Review

 

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4190561/

 

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5840207/

 

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5593672/

 

https://www.frontiersin.org/articles/10.3389/fimmu.2018.00414/full

 

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5593672/

 

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4190561/

 

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4388310/

 

https://www.linkedin.com/pulse/cancer-hallmark-analytics-omics-data-pathway-studio-review-catanzaro/

 

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