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article imageOp-Ed: Personalized medicines and big data

By Tim Sandle     Sep 20, 2020 in Health
Personalized medicine proposes the customization of healthcare. This means medical decisions, practices, and drug products being tailored to the individual patient. How can big data assist with meeting these objectives?
In positioning medicine and health practices to meet the needs of the individual patient, this means rather than condition x needing drug y, diagnostic testing would be used to select the appropriate therapy for an individual person based on that person’s unique genetic makeup and physical characteristics. This aim brings with it the need to collect and process patient data.
Advances in genomics have helped make personalized medicine a clinical reality, and several patients have benefitted from the tailor-made approach.
There is another reason why personalized medicines could be important. Not only could treatments be more effective, focused treatments could avoid the misuse of medicines or the risks associated with patients being given general treatments not necessarily intended for their particular condition. Prescription drugs are the third most common cause of death after heart disease and cancer, for which psychiatric drugs (triggering falls) and non-steroidal, anti-inflammatory drugs (primarily by causing bleeding stomach ulcers and myocardial infarction) are most closely associated with fatalities.
Aside from direct risks, genetic medicines will not work with everyone. Examples include: attention deficit hyperactivity disorder medicine only works for one of 10 pre-schoolers; cancer drugs are effective for around 25 percent of patients; and anti-depression drugs work with just six of 10 patients.
READ MORE: Breakthrough in personalized breast cancer medicine
Big data assessments can aid with the development of personalized medicines. Typical health screens tend to miss the types of biomarkers that are necessary to match patients with specific medicines. Biomarkers can be anything from blood pressure to increasingly complex networks of individual traits. By using big data approaches, medical researchers can locate biomarkers for certain diseases and seek new molecular markers for specific disease risks.
This means that biomarkers can help in the assessment of disease targets and identification of suitable patient populations for the development program, as well as providing early signs of safety issues and efficacy in order to facilitate “go/no-go” decisions. The use of biomarkers in the development stage can also provide early indications of real-world effectiveness, which will be helpful for evaluation of the commercial viability of a drug early on.
To make personalized medicines a reality, further advances need to take place with genomics and with understanding the influence of the microorganisms that reside within the human body (the microbiome) have on the way medicines are processed.
Biological research runs the risk of being undermined by the poor design of the digital identifiers that tag data. To improve analysis of genetic material, for example, requires pragmatic guidelines to create, reference and maintain web-based identifiers to improve reproducibility, attribution, and scientific discovery.
Need for patient data
Gathering key patient and drug information requires big data analytics via computer analysis in order to make more accurate treatment decisions and to develop appropriate medicines. As an example, pharmaceutical scientists can a conduct a genome-wide association study to examine one disease, and then sequence the genome of many patients with that particular disease. [url=http://t deep-dive-into-big-pharma-ai-productivity-one-study-shaking-thepharmaceutical-industry/#5ac8ab82567d t=_blank]Automation and artificial intelligence can assist with this.
This allows the scientist to scan for shared mutations in the genome and then to select an appropriate treatment. Furthermore, with some types of cancer, computer modelling allows for the screening of combination treatments, which could involve more than one immunotherapeutic agent or a combination of immunotherapy and chemotherapy. This is far faster and probably more effective than is possible using traditional methods.
However, to safeguard the patient it is important that data is rendered anonymous and the privacy of the patient is assessed at all times.
This opinion article was written by an independent writer. The opinions and views expressed herein are those of the author and are not necessarily intended to reflect those of
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