Digitalization of pharma labs will help in better decision making: Experts

Digitalization of pharma labs will help in better decision making: Experts

Besides enhancing workflow operations by connecting scientists with their lab instruments and data, digitalization holds the potential to acceleratee long-term business growth

  • By | June 02, 2022

Conventionally, chemicals and pharmaceutical laboratories have been using paper notebooks and control sheets to keep track of experimental data, analysis and findings. One of the biggest challenges of this system has been to maintain experiment integrity and data value. Bringing a paradigm shift, digital technologies are now enabling real-time control of lab procedures, speeding up quality control and making labs far more efficient.

“The next generation of scientists might not like to spend time going through the journal articles but rather rely on digital platforms. That’s where I think digitalization would be the key. Earlier when a resource would leave, we used to lose both information and expertise but with the digitalization in picture, it is probably very easy to start a project even with newcomers,” says Dr. Jayant D. Umarye, AVP - R&D, Godrej Agrovet.

Umarye spoke at an E-conference titled “Lab Digitalization – Delivering Efficiency & Sustainability” organized by the Indian Chemical News (ICN) on May 24, 2022. The virtual event supported by Dassault Systemes was moderated by Pravin Prashant, Editor, ICN.

Umarye elaborates further: “We look for data generation very early in the process so that we have less failure at scale up lab and pilot trials. By integrating instruments with the digital technologies, we generate data that has more value, so probably what we put in data will come out during our evaluation. So capturing data is the key for us. Technologies like flow chemistry and evaluation of biologicals through a sort of digitalization platform would play a major role going forward. While we generate a lot of data in our respective organizations, we need to protect this data and make it available to the scientists who are working on similar projects."

"With digitalization, we are currently in the transition from paper to electronic lab notebooks with integration also to the instruments and so on. So going forward, rather than searching outside databases like scifinder, we all would probably search inside our in-house databases. We are seeing that it's happening slowly for the last five years. Once implemented, I am sure we will be able to increase the efficiency of the R&D sector," Umarye adds.

“Apart from making money, the challenge before any company is to make its business sustainable and profitable. Therefore, I see digitization as a very important and integrated part of achieving this goal. If we are talking about 40 years down the line, we are talking about sustainability. We are talking about industry 4.0 and circularity and this is where I want digitization to play a part, whether it is with respect to efficiency or problem solving. Taken at a faster, much faster pace or whether it is related to walking hand in hand with industry 4.0 so that industry is able to achieve its goal with the help of digitization. So, digitization for us is not just a cool tool but something which is kind of walking hand in hand for a better future for all of us and the generations to come,” says Bijal Mathkar, R&I Director, Solvay Research and Innovation Center India.

“The digitization now allows us to check the plant environment remotely. Sitting at our home, I was able to see what's going on in the plant for whichever the unit for which I was responsible for. We had an extremely important project going on at that time and the project was delivered on time and in fact we exceeded the expectations. Digitalization plays a vital role in terms of business continuity and is important in today's collaborative system globally. So I'm sitting in India, but day in, day out and working with my colleagues and Europe in USA and Brazil and having the similar structure having the same thought. Also having the same platform to work upon is something which is very important and digitization can play,” Mathkar adds.

“If artificial intelligence and machine learning can be applied to come up with newer molecules and routes of synthesis, it will actually benefit mother nature. Better solutions not just for dyes and pigments but also the whole chemical industry which is looked upon as a highly polluting kind of industry,” says Ganesan Balakrishnan, GM – R&D and Special Projects, Sudarshan Chemical Industries.

"We had the privilege of working on both ends, digitization as well as non digitized. Thinking of total digitalization, I think some of us will be still coping to come to terms with that. But I think faster we know that would be the way we should be looking at it is better. Integrating all the data that is possibly generated by the various sections of a laboratory like analytical and R&D would be the way forward. I think the latest thing that is coming up is artificial intelligence and machine learning where you design the experiment before you get onto the table and start doing this stuff.  I think that would be the future. But having said that, you cannot remove that human factor from experiments because all these things can't work only on one factor. There not only has to be a wonderful merger of both digital technologies and human interventions but also a wonderful balance," opines Balakrishnan.

Talking about the challenges in a conventional paper-based laboratory, Dr. Frank Schaffer, Industry Process Consultant, Dassault Systemes says, “Our customers often talk about complexities associated with finding right data and inability to redo experiments due to time-consuming and unsustainable processes. What they're actually asking for is support for the innovation process on a scientific basis. It's really important to start with structured data collection which is the basis for whole data continuity. The scientific data needs to be collected and stored, and then on the structured data we can start easily to do data analytics which today take a bit of time. And this is what companies are doing already now on a regular basis. Now we want to take the data forward and build predictive models on materials formulations and predict their performance. Only then can we really empower the decision making and guide the scientists to the right direction. What kind of experiments to do and with that, accelerate the product research and decrease the time to market. This is the kind of data modeling engine that we are trying to power.”

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