The spiraling costs of drug discovery research and finding a cure for the disease are the key challenges for many clinical researchers. The lengthy process of drug discovery also complicates clinical trial research. Randomized In Vivo Clinical Trials (RVCTs) are commonly used to assess the safety and efficacy of pharmaceutical medications and therapies.

RVCTs test the candidate drug/treatment on a select group of patients. Unfortunately, this strategy has a number of challenges. To begin with, it is extremely time-consuming: in many situations, it takes more than a decade for a new drug to be approved by regulatory organizations. Second, it is extremely expensive: an RVCT typically costs hundreds of millions of dollars to complete. Third, due to expenses and a shortage of participant volunteers, it is only marginally useful for rare disorders¹.

It costs an average of $1.3 billion to the median cost of $985 million, to get a new drug to market in 2020². Some of the research work can take decades and the cost of failure of this research can be expensive for patients. 80% of clinical trials in the US fail to meet their patient recruitment timelines, according to the National Institutes of Health (NIH) report.

In Silico Clinical Trials (ISCTs) is one of the promising approaches to mitigate the challenges around the clinical trials. The term In-silico, which in Latin means 'in silicon,' was coined in 1987 as a term denoting biological experiments conducted on a computer or through computer simulation. This methodology can be used for modeling and simulation in both the pre-clinical trials and clinical evaluation of medical devices.

ISCTs replace the patient with a computer model, a Virtual Patient (VP). A virtual patient's pathophysiology is combined with the Pharmaco-Kinetics/Dynamics (PKPD) of relevant medications in a computational model. In this situation, safety and efficacy can be assessed by simulating the drug's effect on, preferably, all VPs, just as computerized verification should try to evaluate requirements under all potential operating circumstances.

To begin, we create a list of VPs that are compatible with physiology, Pharmacodynamics and kinetics, and in vivo clinical data. This action of model validation is completed once and for all. Second, simulate the safety and efficacy of a potential medicine using the in vivo validated Virtual Patients.