Question

Please assist with this. Please elaborate at least half of a page or one page. Thanks!

What are the ethical, legal, political, and moral implications of technologies such as CRISPR-cas9, and those associated with it – e.g., “Big Data Analytics”; cybersecurity; etc.?

CRISPR-cas9 technology relies very heavily on so-called “Big Data Analytics”. Why might that be the case?

Answer 1

Ethical reasons and Moral Implications: Safety (a possibility remains in terms of edits in wrong places, mosaicism i.e. some cells carry edits, while others do not etc.) and experts believe that the risks do not justify the potential benefits and it should not be used for clinical reproductive purposes. Bioethicists also believe that allowing the practice to take roots, will open doors to problematic prospects including,  non-therapeutic and enhancement purposes. Concerns regarding informed consent remain as it is impossible to obtain informed consent for therapy because the patients affected by the edits are the embryo and future generations². Concern that process will be available only to wealthy and will increase existing disparities in access to health care and other interventions and can create individuals defined by the quality of engineered genome

Legal and Political implications: genome editing "for reproductive purposes will be regulated differently inside and outside of the U.S., leading to uses considered objectionable to the American public."¹ In the US, Federal funds cannot be used for any research that creates or destroys embryos. Also, NIH does not fund any use of gene editing in human embryo³ political economy of drug development, an issue that even scientists with substantial commercial interests typically regard as lying outside their remit, remains highly controversial. Across the world, there are varied and contingent regulatory landscape surrounds gene editing and to date to internationally agreed-upon regulatory framework for gene editing exists, so each country is in the process of evaluating whether, and to what extent, current regulations are adequate for research conducted with, and applications and products related to, gene editing. The most contentious areas of debate seem to revolve around gene-edited agriculture and food products and whether they should be regulated differently from genetically modified organism. For instance, in Canada, human germline editing is banned and in Germany experiments on human germline are currently limited due to the Embryo Protection Act of 1990 and South Korea’s Bioethics and Biosafety Act prohibits genetic experimentation with and modification of human embryos and more

CRISPR-cas9 technology relies very heavily on so-called “Big Data Analytics”. Why might that be the case?

New biomedical techniques, such as next-generation genome sequencing, are generating large volumes of data and scientists are struggling to analyze it for insight generation, efficiently. Being a genetic researcher means working with algorithms that process big data in genetics, and data processing software. Machine Learning (ML) is where the data-analytical technique is applied to multi-dimensional dataset so that predictive models can be built and insights gained from the data. ML helps scientists study and understand complex cellular systems, such as genome or gene editing, and allows them to create models that learn from big data sets and generate predictable outcomes. Genetic research and data genetics are working together to create advances in science’s understanding of diseases. The large volumes of data that are now available to scientists, and that ML and data technology are able to process, is accelerating the development of new drugs and personalised therapies. Big data also provides healthcare professionals with access to the information needed to prescribe doses that are tailored to each patient, reducing the risk of side effects and drug resistance. The prohibitive cost of this personalised-medicine approach has resulted in widespread resistance to its uptake