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The new era of analytics has revolutionized human life by improving varied areas including healthcare. The conventional drug discovery process is complicated, time-consuming and costly, and some of the main factors that make it amenable to failure include lack of effectiveness, adverse reactions, poor pharmacokinetics and marketable reasons. Traditional medicine (TM), used conventionally, is subjected to rigorous research for its efficacy and safety, which increases both the time and cost of the process. With rapidly changing economic scenario, the need for utilizing novel approaches for evidence-based clinical drug development that overcomes these facets of TM has increased leading to the increased usage of predictive modelling. The whole process of predictive analytics finds its roots with drug design and development. Some latest approaches in predictive healthcare for predicting clinical outcomes are done by modelling to optimize the dosage of drugs and also to evaluate potential adverse mechanism. Big data analytics, data mining and text mining are the latest technologies being used in predictive analytics. These technologies have helped in the prediction, treatment and effective diagnosis of diseases which has brought a lot of significant changes such as in the service quality which is being provided and most importantly cost reduction. This chapter involves various steps being followed for the development of a drug in the USA. Development of drug from identifying the protein to its clinical testing and post-market trials has many phases and involves approval of certain applications. |
