Webinar on Personalized Medicine and Health Care will be hosted on July 26, 2021 at 9:30 AM (GM+4). Panel of speakers will be delivering their presentations on their recent research related with different specialties such as Endocrinologist, Cardiologist, Nephrologist, Orthopedic, Hematologists, Immunologists, Oncologists, Rheumatologist, Research scholars, Industrial professionals and Student delegates from Biomedical, Pharmaceuticals and Telemedicine and Healthcare Sectors. The current state of knowledge, its impact on the future will be discussed in detailed. Longdom invites all experts to be part of this webinar series and make it a perfect platform for knowledge sharing and networking.
Personalized medicine, precision medicine, or theranostics is a medical model that separates people into different groups with medical decisions, practices, interventions and/or products being tailored to the individual patient based on their predicted response or risk of disease. The terms personalized medicine, precision medicine, stratified medicine and P4 medicine are used interchangeably to describe this concept though some authors and organisations use these expressions separately to indicate particular nuances.
While the tailoring of treatment to patients dates back at least to the time of Hippocrates, the term has risen in usage in recent years given the growth of new diagnostic and informatics approaches that provide understanding of the molecular basis of disease, particularly genomics. This provides a clear evidence base on which to stratify (group) related patients
Research on cardiovascular genetics has had some spectacular successes in uncovering new therapeutic targets—for example, the finding that people with inactivating mutations in the gene encoding the trafficking protein PCSK9 are at a much lower risk for heart attacks led to the development of antibody therapy targeting this protein. However, when it comes to personalizing treatment for cardiovascular disease on the basis of an individual patient's genetic makeup or biomarker data, there are currently only a handful of options where such an approach has proven to be clinically useful.
There are currently three major approaches to T cell-based cancer immunotherapy, namely, active vaccination, adoptive cell transfer therapy and immune checkpoint blockade. Recently, this latter approach has demonstrated remarkable clinical benefits, putting cancer immunotherapy under the spotlight. Better understanding of the dynamics of anti-tumour immune responses (the "Cancer-Immunity Cycle") is crucial for the further development of this form of treatment. Tumours employ multiple strategies to escape from anti-tumour immunity, some of which result from the selection of cancer cells with immunosuppressive activity by the process of cancer immunoediting.
Apart from this selective process, anti-tumour immune responses can also be inhibited in multiple different ways which vary from patient to patient. This implies that cancer immunotherapy must be personalized to :
(1) identify the rate-limiting steps in any given patient,
(2) identify and combine strategies to overcome these hurdles.
(3) Proceed with the next round of the "Cancer-Immunity Cycle".
Advances in human genome research are opening the door to a new paradigm for practising medicine that promises to transform healthcare. Personalized medicine, the use of marker-assisted diagnosis and targeted therapies derived from an individual's molecular profile, will impact the way drugs are developed and medicine is practiced. Knowledge of the molecular basis of disease will lead to novel target identification, toxic genomic markers to screen compounds and improved selection of clinical trial patients, which will fundamentally change the pharmaceutical industry. The traditional linear process of drug discovery and development will be replaced by an integrated and heuristic approach. In addition, patient care will be revolutionized through the use of novel molecular predisposition, screening, diagnostic, prognostic, pharmacogenomics and monitoring markers. Although numerous challenges will need to be met to make personalized medicine a reality, with time, this approach will replace the traditional trial-and-error practice of medicine.
This perspective addresses conceptual approaches that can be undertaken to develop and apply clinical decision support in electronic health record systems to achieve personalized medical care. In addition, to represent meaningful benefits to personalized decision-making, a comparison of current and future applications of clinical decision support to enable individualized medical treatment plans is presented. If clinical decision support tools are to impact outcomes in a clear and positive manner, their development and deployment must therefore consider the needs of the providers, including specific practice needs, information workflow, and practice environment.
The development of cost-effective technologies able to comprehensively assess DNA, RNA, protein, and metabolites in patient tumours has fuelled efforts to tailor medical care. Indeed validated molecular tests assessing tumour tissue or patient germ line DNA already drive therapeutic decision making. However, many theoretical and regulatory challenges must still be overcome before fully realizing the promise of personalized molecular medicine. The masses of data generated by high-throughput technologies are challenging to manage, visualize, and convert to the knowledge required to improve patient outcomes. Systems biology integrates engineering, physics, and mathematical approaches with biologic and medical insights in an iterative process to visualize the interconnected events within a cell that determine how inputs from the environment and the network rewiring that occurs due to the genomic aberrations acquired by patient tumours determines cellular behaviour and patient outcomes.
The vision of personalized medicine, the practice of medicine where each patient receives the most appropriate medical treatments and the most fitting dosage and combination of drugs based on his or her genetic make-up, seems to become more realistic as our knowledge about the human genome rapidly expands. We already know the reason for many types of adverse drug reactions, which are often related to polymorphic gene alleles of drug metabolizing enzymes. Moreover, insight into reasons for poor drug efficacy, often related to single nucleotide polymorphisms or larger polymorphisms in genes encoding drug target proteins, has been gained. There is a growing need to incorporate this increasingly complex body of knowledge to the standard curriculum of medical schools, so that the forthcoming generation of clinicians and researchers will be familiar with the latest developments in pharmacogenomics and medical bioinformatics, and will be capable of providing patients with the expected benefits of personalized medicine.
The ability of biomarkers to improve treatment and reduce healthcare costs is potentially greater than in any other area of current medical research. For example, the American Society of Clinical Oncology estimates that routinely testing people with colon cancer for mutations in the K-RAS oncogene would save at least US $600 million a years. On the other side, thousands of papers in the course of biomarker discovery projects have been written, but only few clinically useful biomarkers have been successful validated for routine clinical practice The following are the major pitfalls in the translation from biomarker discovery to clinical utility:
1. Lack of making different selections before initiating the discovery phase.
2. Lack in biomarker characterisation/validation strategies.
3. Robustness of analysis techniques used in clinical trials.
At the current time, the field of vaccinology remains empirical in many respects. Vaccine development, vaccine immunogenicity, and vaccine efficacy have, for the most part, historically been driven by an empiric "isolate-inactivate-inject" paradigm. In turn, a population-level public health paradigm of "the same dose for everyone for every disease" model has been the normative thinking in regard to prevention of vaccine-preventable infectious diseases. In addition, up until recently, no vaccines had been designed specifically to overcome the immunosenescence of aging, consistent with a post-WWII mentality of developing vaccines and vaccine programs for children.
Precision medicine aims to provide the right treatment for the right patient at the right time with treatment directed on the basis of the targetable tumoral aberrations rather than just a traditional histologic subtype. However to facilitate this approach, clinicians require patient derived samples. Prostate cancer is challenging to culture in vitro. Recent development of novel organoid in vitro culture technology has led to the development of multiple new in vitro prostate cancer cell line models. We aim to apply organoid culture technology to develop novel in vitro prostate cancer cell line models and propagate patient derived samples to allow drug testing and next generation sequencing as part of a precision medicine approach to early recurrent prostate cancer.
Personalized medicine turns this approach on its head. It recognizes that complex diseases should no longer be considered as a single entity. One disease may have many different forms, or ‘subtypes’, resulting from the complex interaction of our biological make-up and the diverse pathological and physiological processes in our bodies. These will not only vary between patients who have the same disease but also within an individual patient as they get older and their body changes. As we integrate and analyses genomic and other data, we can find common factors and causes of variation, resulting in the discovery of new pathways of disease, changing how diseases are thought of and treated. It enables us to recognize that the same underlying change in our DNA or genome can lead to problems in very different parts of the body, which would not have been previously identified with a more traditional care approach.
Since the completion of the Human Genome project at the turn of the Century, there has been an unprecedented proliferation of genomic sequence data. A consequence of this is that the medical discoveries of the future will largely depend on our ability to process and analyse large genomic data sets, which continue to expand as the cost of sequencing decreases. Herein, we provide an overview of cloud computing and big data technologies, and discuss how such expertise can be used to deal with biology’s big data sets. In particular, big data technologies such as the Apache Hadoop project, which provides distributed and parallelised data processing and analysis of petabyte (PB) scale data sets, will be discussed, together with an overview of the current usage of Hadoop within the bioinformatics community.
Biospecimens contain molecules that can be analysed for indications of diseases. Biospecimens may confirm whether a disease is present or absent in a particular person, and research on biospecimens is especially helpful for understanding more about how disease processes may start and progress. This understanding may lead to better detection of diseases at the earliest stages and may permit design of more effective treatments.
1. Human Biospecimens
2. High Quality Bio-banking
3. BioSpecimen Sciences Program
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