December 3, 2020

Super generics have been the obvious choice in the pharma industry for those who want to improve their ROI with products that have a good pharmacokinetic profile and a quick and efficient application process. The only challenges lay in finding new delivery systems and studies to support alternate applications for the therapeutic entities. However, with the advent of AI-enabled apps, these challenges can be overcome to create smoother processes that can help find treatments for new maladies.

Need for super generics

Drug makers today are burdened by the huge costs incurred in research and development. The average R&D-to-marketplace cost for a new medicine is nearly USD 4 billion, sometimes exceeding USD 10 billion. Moreover, out of all NCEs developed worldwide, only 12% make it to the clinical trials. This is one of the major reasons for declining ROI in the research and development of therapeutics - new chemical entities and new biologic entities.

The industry is exploring strategies to keep profits and margins on the higher end. And this has led to a renaissance of super generics, also known as value-added generics or new therapeutic entities (NTE). These re-innovated products offer advantages in terms of patient convenience, new dosage form, route of delivery, pharmacokinetic profile, safety, efficacy, stability, manufacturing process, etc.

For the US, the situation can be summarized as follows:


New Molecular Entity


Super generics

Regulatory Procedure




Time for market entry

10-15 Years

2 to 3 years

3 to 5 years





Rejections by HA








A major advantage of these products is the low chance of rejection by statutory bodies, owing to their prior approval in different forms/applications. Moreover, only minimal amount of data and studies need to be generated, as a lot of the data is already available from the original submissions.

Reports state that the application of these drugs in diverse therapeutic areas and the consideration of novel and unconventional drug delivery systems will enable the field to evolve at the CAGR (compound annual growth rate) of ~17%.

Overcoming challenges with Industry 4.0

Super generics can work wonders for pharma companies given the high ROI and low possibility of rejection. However, they also pose two major challenges:

1. Identifying alternate application and/or delivery system for an existing application

2. Identifying and optimizing the studies/data requirement for submission of the alternate application

Both these challenges can be addressed using technologies in Industry 4.0 to obtain an unequivocal solution.

The first challenge can be addressed by the use of artificial intelligence (AI) to explore different applications of a molecule. An example of this application is the robot Eve that helped discover that Triclosan, a common ingredient in toothpaste, could potentially treat drug-resistant malaria parasites. AI can also be utilized to assist the exploration of drug delivery systems. In recent times, AI is also being used to screen known moieties to evaluate their potential in treatment and/or prophylaxis of COVID-19 caused by SARS-COV-2. With the introduction of the Coronavirus Treatment Acceleration Program (CTAP) by the US FDA to expedite the development of safe and effective life-saving treatments, the potential of super generics is well worth exploring.

AI can also be utilized to quickly find existing studies and evaluate future studies required to be presented in a marketing application for the use of existing anti-viral drugs as therapeutics in pandemic-like situations. This idea is supported by the fact that today, AI-enabled applications are being used to optimize clinical trial studies.

The ability of AI-enabled applications to analyze the available data and accurately predict further data requirements for a NTE application will have a huge impact on the regulatory affairs department.

Advantages of leveraging AI-enabled apps for regulatory estimation of applications and data include:

1. Bringing parity and consistency in review, as approaches and knowledge vary between humans

2. Reducing the time required to evaluate molecules and applications

Leveraging AI in regulatory evaluation of the application and development of super generic drugs can have significant business benefits. It ensures better quality and helps reduce cost and time by lowering the overall efforts of the regulatory specialist by at least 10% to 15%. Additionally, it will enable establishments to look for quick and robust support for the scientific community to deliver solutions in challenging situations.

Sudip has over 14 years of experience in the pharmaceutical and life sciences domain. He holds a Post Graduate degree in Pharmacy and has worked with parent as well as consulting organizations over the years. He also has a rich experience in regulation and quality domains. Sudip has participated in various regulatory submissions as well as regulatory compliance activities across the globe.