Rapid adoption of disruptive technologies are bringing in new approaches and outcomes in the pharma sector. An analysis By Lakshmipriya Nair
Wide-ranging factors like rising R&D costs to pricing pressures, and increasing regulatory burden to tax reforms in different countries have created an irrefutably perplexing innovation and commercial environment for pharma companies. Hence, they are increasingly looking at automation and digital technologies to reimagine their current strategies across the value chain.
As Amitabh Mishra, Chief Information Officer and Chief Digital Officer, Emcure Pharmaceuticals informs, “Group business leaders in pharma are realising the following: first, digital is a crucial business need in order to obtain a competitive advantage. Second, digital challenges status quo and enables a paradigm shift. The result of all this is that digital (and, by extension, automation, which is a subset of digital) asks the tough questions on major business processes, and in some cases, even redefines the business model itself. Pharma leaders are waking up to these truths, and committing large budgets to automation and digital. And they are open to accommodating changes to major processes as a part of the technology push.”
Ram Yeleswarapu, President and CEO, TAKE Solutions also points out, “Companies that have a deep domain understanding, a holistic top down process understanding and a bottom up operational understanding are ready to evolve and meet the changing needs of the industry which will define the next generation of blockbuster solutions for these sectors. While late to get off the blocks, the pharma industry is rapidly advancing automation and digitalisation across the board.”
So, let’s take a look at a few key aspects of the pharma industry wherein rapid adoption of these disruptive technologies are bringing in new approaches and outcomes.
Enhancing value creation
A report, ‘2018 Pharmaceutical and Life Sciences Trends’, from Clarkston Consulting points out, “As stakeholders in the healthcare value chain consolidate, align, and integrate for increased leverage and purchasing power, the life sciences industry will be further pressured to demonstrate the differentiated value they offer to patients, caregivers, and other partners in the healthcare landscape.”
To cite an interesting example, in 2017, Amazon applied for and received pharmacy-wholesaler licenses in several states of the US. This, in turn, led to a swift drop in the stock value of some of the largest wholesalers in the pharma industry. The foray of other tech giants/game changers like Apple, Microsoft, Google, and Facebook into the life sciences sector are also ushering newer business models and interesting collaborations to augment both, tech and pharma capabilities, thereby bringing about a paradigm shift in the way the sector operates.
Hence, the life sciences sector’s machines, methodologies, processes, and most importantly, its workforce are undergoing a transformation. Mishra explains it very well and says, “Machines cannot operate anymore on a standalone mode. They must be connected to a system that can track their operational parameters and record the numbers so that they can be retrieved if needed, for example, in times of an audit. Which brings us to an important point. Pharma companies’ profitability (and indeed their viability) depends on approvals from agencies such as the FDA, which depends on the results of audits. In the absence of digitalisation and automation, if a process document cannot be retrieved or a change in parameter value is found to be not documented, auditors could ban the company or lines of products, and cause billions of dollars in lost revenue. In order to support business in its push to clear regulatory audits, machines have to change (in order to be connected, monitored and optimised), as do methodologies (e.g. replacing manual techniques by automation), processes (unnecessary steps must be removed; processes must be simplified and automated 99 per cent or above) and workforce (workers need to be trained; professionals must be asked to think ‘digital first’ and innovation and ideation must
be built into the culture of the organisation).”
Giving an instance of how Emcure Pharma, is planning to utilise these technologies to derive more sales, he expounds, “The use of big data and analytics is going to enable us to increase sales by 5-10 per cent. We plan to collect, process and visualise sales data to the most granular level, and provide detailed dashboards that enable senior management to drill down the sales data to the product (SKU) level. Consequently, sales challenges will be handled in real-time and not retroactively after the month’s sales are over. All the data from our ERPs and other in-house software systems is being routed to a central analytics system that is capable of slicing and dicing the data in numerous ways. This consolidation and analysis of sales data is a brand-new capability for us, and is going to be a game-changer.”
And, thus, technology has emerged as a key enabler for pharma companies to derive enhanced value in their businesses.
Renewed approaches to problem solving
The pharma industry is also turning to next-gen technologies in its search for a panacea to deal with its bottlenecks and challenges. An Infosys research report titled, Digital Outlook for the Life Sciences Industry, clearly highlights, “Life sciences organisations are using a range of technologies, from analytics to AI, to discover new drugs and treatments to spur growth, cut operational costs, and engage better with patients. Digital technologies deployed at organisations are improving existing operations, solving new problems, and creating new opportunities.”
For instance, a Global Data study reveals, “Disruptive technologies such as AI, big data and block chain will impact the pharma industry, not only by helping to add value in terms of personalised treatment for patients, but also to counteract the unsustainability of skyrocketing drug prices.”
Yeleswarapu points out how these technologies are enabling compliance with regulations as well. He states, “The need by the regulators for ensuring clinical trial participation is extended to a globally stratified patient pool and the desire by biopharma companies to get their drugs approved and commercialised globally is yet another reason for adoption of digital technologies.”
He explains, “Regulations, quality and compliance requirements and above all the concern for patient safety have historically introduced redundancies and manual checks and balances and these are some of the reasons for a slower adoption rate within these sectors. With the advancements in technology, AI, machine learning (ML), and natural language processing (NLP) and the support extended by regulators to induce efficiencies while ensuring compliance with regulations are slowly settling in and this has warranted a new outlook towards methods and processes.”
Bettering R&D and manufacturing outcomes
Pharma companies are looking at disruptive technologies to deliver quicker, and more responsive approaches to discover, develop and manufacture new drugs.
As Yeleswarapu highlights, “The healthcare sector has been under duress to make therapies affordable while ensuring they are safe and effective, and this has led to the adoption of technology solutions that are seamlessly allowing for data and information flow for decision making, rather than the more complex stove pipe systems of yesteryears that were lending to costly custom development and maintenance.”
Gaurav Tripathi, CTO, Innoplexus, explains, “AI adoption will increase across all stages especially in drug discovery, with companies relying more on data driven drug discovery instead of conventional methods.”
Ascertaining this, the Global Data study elaborates, “One of the most time-consuming phases in the process of drug development is the discovery phase, which takes four to five years on average. Identifying promising drug targets is one of the biggest challenges and this is the most crucial phase, where the right decisions have to be made about whether to proceed through to clinical trials. Luckily, the highly developed AI programs now have the increasing capacity to delve into big data, identify patterns, and generate algorithms to explain them.”
Tripathi cites the example of Innoplexus’ offerings to explain this further, “Scientists working at pharma companies in drug discovery spend a lot of time in manually going through publications, scientific and clinical trials data online which is spread across few thousands of sources. They have to manually search for relevant documents while trying to remember all possible keywords and their synonyms to locate the right set of documents. Then they have to manually extract specific data points from those documents and collate them in a document for further analysis. A few of the world’s largest pharma companies using our products are able to save 50 per cent of that time by using our products which uses AI based ontologies, information discovery, search and computer vision based information extraction to automate 70 per cent of the process already. It is helping their scientists to free up their time performing manual redundant tasks around collecting and analysing information thereby helping them put that time in actual productive tasks.”
Thus, technology holds the potential to speed up the initial stages of R&D considerably and deliver outcomes faster.
Same is the case with pharma manufacturing as well. As Dipankar Kaul, Global Head, Compliance & Auditing, Asia/Pacific, Novartis Technical Operations, Sandoz illuminates, “The increase in automation and emergence of new technologies in 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.”
He further explains, “As 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.”
Recognising this fact, pharma manufacturing companies are also using cost efficient new technologies to augment operational performance and ensure agility and product safety.
Ways to optimise potential of disruptive technologies
Recognising this, the players of this industry are investing and strategising to create the underlying infrastructure in place. We are witnessing rapid adoption and installation of smart technologies to ensure a proven return on investment across the entire product and process chain in the pharma industry.
But, even as the industry ramps up its utilisation of technologies, the industry’s digital maturity lags in comparison to other industries. As Ankit Solanki, Co-Founder & CEO, Instinct Innovations points out, “Pharma is one of those few industries that needed a major digital revamp, given that different nodes in the ecosystem are still not digitised, so is the communication between these different nodes.”
So, how can life sciences companies work towards optimisation of their newly gained tech capabilities to further their progress?
Mishra points out that there are certain characteristics which organisations that have performed successful have and says, “First, the executive leadership team have a shared, clear vision that every one of the leaders has bought into. Second, they have a robust prioritisation framework that takes the hundreds of ideas around digital innovation, and reduces them to a list of about 10 or so priorities that can add the most value in the short run. Third, they invest time and energy into developing an organisation with the right kind of talent that sustains the momentum in the long run.
Finally, they adopt the best technology practices such as lean, agile, DevOps execution models.” He further recommends, “What is needed is a digital strategy. The first piece is the vision, which is a statement of where the company wants to be in three to five years. Second is leadership: we need to align digital to business goals through leadership alignment and stakeholder buy-in. The Chief Digital Officer needs to be excellent, but the most influential leaders of the organisation, that is the members of the executive council, need to rally behind the push for digital. The last piece consists of three components which are equally important. First is people – we need to enhance the skills of our employees, and put in place the correct digital organisational structure. Second is process – how to work analytics, paperless and other digital technologies into our processes. Finally comes the technology piece itself – how to bring in software and hardware systems based on IoT, analytics, AI, ML, and other staple digital technologies.”
Tripathi also recommends that the way forward is through integration and interconnection of all aspects of the pharma industry by mapping all the processes and finding out the ‘data handshake points’ between them. And, this is where he believes that partnerships with technology companies will be of help. He feels that these partnerships will let a technology partner focus on data while the pharma companies can focus on the science part.
Probably, the industry and the policymakers believe the same. As a result, we have seen several partnerships and strategic alignments between the life sciences and tech industries lately. To cite a couple of examples:
Example 1: Recently, NITI Aayog and Microsoft India entered into an agreement to leverage the benefits of AI for the growth of the country. As part of the agreement, Microsoft India will support NITI Aayog by combining the cloud, AI, research and its vertical expertise for new initiatives and solutions across several core areas including agriculture and healthcare and the environment. Microsoft will also accelerate the use of AI for the development and adoption of local language computing, in addition to building capacity for AI among the workforce through education.
Example 2: Indian Institute of Technology Mandi has tied up with RxDataScience Inc., a leading healthcare manufacturer in the United States, to create a portal documentation Artificial Intelligence and Machine Learning Research in the Pharmaceutical Sector. Reportedly, IIT Mandi team is also planning to work closely with RxDataScience Inc, to apply deep-learning methods and cognitive algorithms for discovering patterns among patient journeys and social ties among physicians. This is part of a long-term collaboration focused on performing machine-learning on healthcare datasets concerning patients and physicians and developing novel web-based visualisations.
Example 3: A lot of start-ups are attempting to iron out the various chinks in the pharma lifecycle, especially in distribution. For instance; Pharmeasy, NetMeds, Myra, 1mg, are apps which are enabling end consumers to order medicines online while the likes of PharmaRack and WantedNote enable SME retailers to order their inventory digitally from distributors, wholesalers and CNFs. Parallel to this are solutions like RedBook which enable each of these nodes to digitise themselves and hence lead to easier integration for aggregator or facilitators.
Multi-faceted role of next-gen technologies
Well, whatever, be the strategy, it is clear that the entire pharma ecosystem is undergoing a metamorphosis catalysed by technology.
A tech-driven in future in pharma, amongst other things, will include:
It is evident and inevitable that these new-age technologies like AI, big data, blockchain and other will continue to play a pivotal role in the pharma industry in times to come.
Role of disruptive technologies
Power of artificial intelligence
AI will drive more combination products, higher quality, and more personalisation across the industry. At its core, the power of AI for pharma lies in its ability to mine and analyse enormous sets of raw data, such as those generated through R&D – an area in life sciences with the most to gain in these nascent stages of AI adoption. AI stands to bring a stronger degree of certainty in the clinical stage by enabling a more thorough understanding of biological and disease complexities that would in turn enable a more targeted approach at the onset, thus increasing the likelihood of clinical success and decreasing the associated risks. As the volume of data collected increases, so too does the potential of AI to have a transformative impact on the industry. Like many technological innovations, AI success will depend on strong data management principles and the organisational ability to work cross-functionally as the tech will inevitably impact every side of the business.
Blockchain’s huge potential
Strategy and planning around the blockchain technology underpinning cryptocurrency will continue to evolve in life sciences, particularly in the clinical and supply chain aspects of the business. As a decentralised ledger of information, the blockchain could allow for:
A world of Internet of Things
With increasing frequency, the internet of things (IoT) has been touted as a cure-all through the use of live data for real-time decision-making. More life sciences companies will start to explore these capabilities in earnest. First and foremost, the benefits to the supply chain and manufacturing side of the business will drive IoT investments to maintain product quality via live temperature and stability monitoring, increase manage supply and demand through real-time tracking and oversight, and improve traceability of the supply chain to meet regulatory requirements around serialisation. But these are just a few of the dozens, if not hundreds, of applications of IoT to drive true business value. As the success of IoT largely depends on a business’s ability to manage and act on their data, life sciences companies should invest in strong data governance and data management initiatives to ultimately realise the vast returns that IoT can offer.
Source: Clarkston Consulting’s report on 2018 Pharmaceutical and Life Sciences Trends