When diverse treatments are offered to different individuals or groups of patients based on their characteristics; it’s termed as personalised medicine which is usually interchangeable with precision medicine. However, according to the National Research Council, Precision medicine is a specific medical model based on genetic makeup, lifestyles, and environments.
The precise origin of personalised medicine is not clear however, it dates back to the thousand’s years ago, when Hippocrates used a person’s physique, age and the seasons to personalize treatments for his patients. Moreover, the discovery of ABO blood group system by Karl Landsteiner in 1901 that made people understand why blood transfusion was successful in some people whereas to others it wasn’t effective. One of the modern examples of personalized medicine was Herceptin drug was approved in 1998 by FDA for treating breast cancer patients who tested positive for HER2 protein and do not respond to standard therapy.
Rationale of Precision Medicine:
In the current treatment system every individual receives similar medication irrespective of our age, gender, lifestyle, genetic makeup and environments. Because of this, some individual reacts better to a specific drug and in others it causes side effects. The idea behind personalized medicine is to prescribe drugs to individuals based on their genetic makeup so that the drug would be safer and more effective.
Challenges of PPM:
The importance of PPM is not yet fully recognised by the current healthcare system. Many of the technologies that will be needed for the successful completion of PPM initiative are in the early stages of development. In addition, there are several potential challenges in future progress of PPM:
1) If the PPM approach is practiced regularly in the healthcare system then doctors and other healthcare providers need detailed knowledge on molecular genetics and biochemistry.
2) Drugs developed with PPM initiative will likely be expensive leading to problems with insurance companies to reimburse. It is critical to find the technique to reduce the cost in production.
3) Collection and storage of clinical data would be a challenging task and It will be imperative to protect participant’s privacy and the confidentiality of their health information
Benefits of Personalised Medicine:
PPM has potential to improve several aspects of health and healthcare treatments such as reduce adverse effects, increase patient compliance, shift the emphasis of medicine from reaction to prevention, improve disease detection and predict its susceptibility. It can also improve cost effectiveness and increase patient confidence post-marketing by approving novel therapeutic strategies and altering the perception of medicine in the healthcare system.
Personalised and precision medicine future perspective:
Recent studies have suggested along with genomic/DNA variability, transcriptomics profile, epigenomic, metabolites and microbiota also contribute significantly to different treatment outcomes for different individuals. In the last couple decades sequencing technology has made significant progress which led to reduction in time and cost of sequencing and brought off complete human genome sequence, sequencing the whole genome of several individual genomes and identification of disease-causing genes using whole genome sequencing. Next generation sequencing technologies led to an increase in production of massive amounts of data from tissue, sample, patients and single cells. The data obtained from these sequencing projects has immense application in personalized medical approaches for diagnosis, understanding and discovery of pathological mechanisms. With increasing amounts of data in biomedical sciences, the challenges to find connections and insights considering the complexity of biological systems. Big data analytics specially machine learning algorithms has potential to integrate multi-scale, multimodal and longitudinal patient’s data obtained from various techniques (e.g., genomic, transcriptomic, proteomic, metabolomic, etc.) together with clinical data to make accurate predictions to solve precision medical problems e.g. accurate disease diagnosis, disease detection and prediction, treatment optimization.