In his carrier spanning over three decades, Ravi has played instrumental roles in several pharmaceutical companies and is currently responsible for running Lupin Bioresearch Center to conduct BA/BE studies for several regulated markets in support of generic product development.

Artificial Intelligence(AI) is the ability of a computer or machine to simulate human intelligence and is dependent on three elements massive amounts of data, sophisticated algorithms, and high performance parallel processors. It is a broad term encompassing various ways in which computers process and find patterns within complex information and generate inferences or predictions from that data.

The idea of using artificial intelligence(AI) to accelerate drug discovery process and boost a success rate of pharmaceutical research programs has inspired a surge of activity in this area over the last several years.It can be applied to various types of healthcare data (structured and unstructured). The increasing availability of healthcare data and rapid development of big data analytic methods has made possible the recent successful applications of AI in healthcare.

With the ability to find hidden and unintuitive patterns in vast amounts of data in ways that no human can do AI represents a considerable promise to transform many industries including pharma and biotech.

AI in Various Phases of R&D
With more comprehensive understanding of the biological basis of disease through AI the R&D process will be more streamlined to develop therapies from the offset, with optimal trial and errors. Reducing the failure rate will in turn save the industry billions of dollars.

•AI for analyzing research literature, publications and patents
AI-driven supercomputer for accelerating analysis and tests of hypotheses by researchers using “massive volumes of disparate data sources” that include million sources of laboratory data reports as well as medical literature. AI is helpful for scientific data mining, data contextualization and deriving hypotheses. Navigating through all this information to draw meaningful insights about drug candidates is where AI-based algorithms become indispensable.

Deep learning screening of biomarkers includes genome, proteome, metabolome, lipidome data of the biological samples to unravel the complex biological networks playing roles in diseases and help identify medications for specific patient populations. AI tool is used to identify molecular signatures and potential biomarkers for assessing the vaccine immunological response.

•AI in Drug Manufacturing
Through integration with AI self learning machines, the

complex operations in pharma manufacturing plants can be simplified to a greater degree. While this will certainly reduce the time and increase the efficiency, it will also enhance the reporting quality.

These developing technologies will enable operations to become intelligent and efficient although they pose a challenge for the policy makers and regulators to redefine the way we understand the current good manufacturing practices(cGMP).

AI-enabled machines will emphasize a means of rejecting the root cause(s) for the output going ‘Out-of-Specification’. Understanding the logics and patterns predicting the variations and adjusting the process beforehand will preempt unnecessary product failures. This will also minimize any redundancy in the process, improve the yield ensure consistency and stabilize quality and ultimately exhibit complete regulatory compliance resulting in a product fulfilling its critical quality attributes. Thus, the expectations of the regulatory agencies in regards continuous quality verification(CQV) will evidently be satisfied by AI and its applications through machine learning tools.

AI-enabled machines will emphasize a means of rejecting the rootcause(s) for the output going ‘Out-of-Specification’. Understanding the logics and patterns predicting the variations and adjusting the process before hand will preempt unnecessary product failures. This will also minimize any redundancy in the process improve the yield, ensure consistency and stabilize quality and ultimately exhibit complete regulatory compliance resulting in a product fulfilling its critical quality attributes. Thus, the expectations of the regulatory agencies in regards continuous quality verification(CQV) will evidently be satisfied by AI and its applications through machine learning tools.

•AI in Clinical Trial Management
Artificial intelligence(AI)technology, combined with big data, hold the potential to solve many key clinical trial challenges with a better protocol design, patient enrolment and retention and study startup as prime areas for improvement.

Technologies such as digital reporting apps, as well as wearables, allow for realtime engagement and communication, and support patient centric trials. Patients can send feed back on treatment symptoms and manage medication intake and can share information with researchers with more meaningful clinically relevant insights and be used to assess and develop trial objectives, endpoints and procedures, reducing or eliminating the need for patients to travel to sites, which increases patient adherence and compliance. AI analysis of live remote data also can detect when patients may not be compliant, allowing clinical personnel to intervene before a patient’s data must be excluded.

Data driven strategies powered by advanced AI algorithms processing data collected from mobile sensors and apps electronic medical and administrative records and other sources have the potential to reduce trial costs.

Ai in Healthcare
AI in healthcare is currently geared towards improving patient outcomes aligning the interests of various stakeholders and reducing healthcare costs. It is beginning to play a role in quantifying the quality of service patients receive at hospitals.