Dipankar Kaul -Head GMP Audits, Asia-Pacific- Novartis Technical Operations, envisages the many ways in which AI can transform pharma manufacturing operations and the present perspective of the science of pharma development, manufacturing and quality assurance
As the pharma manufacturing plants operate on computerised/programmable logic-controlled machines, instruments or technologies within fixed operating parameters to produce products of standard quality and specifications, compatibility towards highly automated and robotic machineries is evident. Through integration with AI self-learning machines, these complex operations can be simplified to a greater degree. The further development of these technologies will facilitate ensuring that these operations become more intelligent and efficient; however, they pose a challenge for the policy makers and regulators to redefine our knowledge of the current good manufacturing practices.
Some of the AI applications useful in text, speech, and video recognition can be utilised further to interpret the pattern and convert it into a meaningful and chronological script of events. While this will certainly reduce the time and increase the efficiency, it will also enhance the reporting quality. Machine learning thus employs statistical applications to identify patterns in the data then make predictions using those patterns. The AI lead machine-learning processes are capable of reading and recognising the key process parameters responsible for raising product quality.
Academics, researchers and a few industry players have been working on AI for several decades now, from as far back as 1955 when John McCarthy coined its definition.  Substantial material has already been published on AI and its prospects; however, its usability in various industrial and commercial applications is yet to be fully applied and benefitted. As stated, not all technological revolutions are straightforward and thus, in like manner concerns continue to arise on the ways that AI will integrate within the domain of pharma engineering and regulatory compliance. At least, this is the dilemma confronting the current pharma scientists, engineers and manufacturers. There is an interesting cultural challenge to overcome, which is technological innovation that supports AI, while the pharma industry has been built on
a strong backbone of ‘traditional’ science that the regulators expect.
A cautious approach has been made by data scientists from various tech giants, who are continuously engaged in constructing some of the AI systems for the pharma industry which has a necessary fence of regulations and is controlled by the health authorities. The present need is to determine that AI solutions are both feasible for use, and safe for adoption.
Perhaps many such technologies had existed earlier, but today they are poised to become mainstream systems with advancements in technologies brought in by IT giants. At the same time, the increase in automation and emergence of new technologies within the pharma industry over the last decades have altered our perspective of the science of pharma development and manufacturing operations. Also, the global expectations of the health authorities, to rationalise and validate pharma processes right from the moment of their development and establish scientific rationales undergirding the principal quality attributes and controlling measures are gaining greater acceptance. Consequently, the systems used to automate the process steps during manufacture, which are uninterruptedly evolving with the implementation of new
instrumentation, are well within the gamut of regulatory compliance and this further diversifies the need for various autonomous technologies.
Integrating machine learning with pharma operations
Plant efficiency and reliability are often cited as the potential reasons for developing and applying AI techniques, developing a variety of algorithms and expert systems to the control and operations.  AI is otherwise referred to as a process of evolving machines or a device to communicate, learn, plan and solve problems in a manner similar to the ways humans do.
Machine learning lies at the core of AI. Learning in the absence of any kind of supervision requires an ability to identify patterns in the streams of inputs, whereas learning with adequate supervision involves classification and numerical regressions.  Although, over time, machines become increasingly capable, and some of the tasks are removed from the list of ‘intelligence’, for e.g. playing chess is no more considered by some as AI which continues to be debatable as AI continuously evolves. However, with various technology giants, robotics companies and IT start-ups expressing greater interest and venturing into AI, the boundary between automation and AI appears to be more distinct. Therefore, innovative ideas continue to appear almost every year, and inventing autonomous machines is signaling the beginning of the AI revolution.
AI and pharma product development
AI applications have already been established and proven for pharmaceutical development. There are various publications demonstrating successful AI interfaces to develop pharmaceutical formulations or predict high yielding combination of chemical reactions. Utilising AI in technology can save time, money and resources, while providing a better understanding of the relationships between the different chemical reactions, physical processes and other related process parameters.
The right algorithms, which are a set of rules to be followed in order to calculate or perform problem-solving operations using computing devices, are fundamental to the designing of the AI architecture for a process industry. Neural networks are such rapidly growing technologies that can be applied to the development and processing of pharma substances and
products. Neural networks are the learning algorithms used within machine learning. In the recent years, neural networks have been demonstrated to be able to offer an alternative approach. Neural networks are mathematical constructs with their capacity to “learn” relationships within data, with no prior knowledge required from the user.
The current trends in pharma sciences portend a good outcome with the developments of information technology and AI. The “Quality by Design” cited in the ICH Q8 Guideline, provides enhanced scientific understanding of critical process and product qualities using the knowledge obtained during the life cycle of a product. In light of this development, “design space” is the area in which a product can be manufactured within acceptable limits.
The Neural Network generates and assesses a range of models to identify the one that will best fit the experimental data provided to it. As such, increasingly, artificial neural networks (often termed ANNs) are used to model complex behaviors in issues like formulation and processing of pharmaceuticals.  To create these spaces, artificial neural networks (ANNs) can be trained to emphasise the multidimensional interactions of the input variables and closely bind these variables to a design space. This assists in guiding the experimental design process to include interactions among the input variables, together with the modeling and optimisation of the pharma formulations. 
There are various machine learning algorithms viz. genetic algorithms or fuzzy logic which have proven to be effective and useful tools in predicting the results that arise from alterations in the input para