Precision medicine: Concept and tools

Abstract

Precision medicine is the new age medicine and refers to tailoring treatments to a subpopulation who have a common susceptibility to a particular disease or similar response to a particular drug. Although the concept existed even during the times of Sir William Osler, it was given a shot in the arm with the Precision Medicine Initiative launched by Barack Obama in 2015. The main tools of precision medicine are Big data, artificial intelligence, the various omics, pharmaco-omics, environmental and social factors and the integration of these with preventive and population medicine. Big data can be acquired from electronic health records of patients and includes various biomarkers (clinical and omics based), laboratory and radiological investigations and these can be analysed through machine learning by various complex flowcharts setting up an algorithm for the management of specific subpopulations. So, there is a move away from the traditional "one size fits all" treatment to precision-based medicine. Research in "omics" has increased in leaps and bounds and advancements have included the fields of genomics, epigenomics, proteomics, transcriptomics, metabolomics and microbiomics. Pharmaco-omics has also come to the forefront with development of new drugs and suiting a particular drug to a particular subpopulation, thus avoiding their prescription to non-responders, preventing unwanted adverse effects and proving economical in the long run. Environmental, social and behavioural factors are as important or in fact more important than genetic factors in most complex diseases and managing these factors form an important part of precision medicine. Finally integrating precision with preventive and public health makes "precision medicine" a complete final product which will change the way medicine will be practised in future.

Keywords: Big data; Epigenetics; Omics; Precision medicine; Preventive medicine.

Fig. 1

Tools of precision medicine.

Fig. 2

Multi-omics approaches.

Fig. 3

Factors affecting omics signature.

Fig. 5

Effect of pharmacokinetic and pharmacodynamics on drug effects and toxicity.

Fig. 6

Genetic polymorphisms in drug metabolizing enzymes. (A) CYP2D6 polymorphism affecting disposition of codeine. (B) CYP2C19 polymorphism affecting disposition of clopidogrel.

Fig. 7

Genetic polymorphism in drug transporters. Organic anion transporter P1B1 (OAT P1B1) polymorphism reduces uptake of clopidogrel into the liver thus causing increased plasma and increases the plasma concentrations (and adverse effects).

Fig. 8

Genetic polymorphism in CYP2C9 inhibits metabolism of warfarin and increases inhibition of VKOR (vitamin K epoxide reductase) which reduces vitamin K which is required for the carboxylation (activation) of coagulation factors VII, IX, and X – bleeding. Genetic polymorphism in VKOR (target for warfarin) prevents reduction of vitamin K – Bleeding. Polymorphisms of both, much higher incidence of bleeding.

Fig. 4

Translation of genetic polymorphisms to observable phenotypes.