Saransh Chaudhary, President, Global Critical Care Division, Venus Remedies, and CEO, Venus Medicine Research Centre details how data analytics and data science can potentially revolutionise the entire pharma value chain by improving efficiencies and outcomes
The Indian pharma industry is one of the largest and most dynamic in the world, supplying affordable and quality medicines to millions of people globally. However, the industry also faces several challenges like increasing competition, regulatory hurdles, pricing pressures and rising R&D costs. To overcome these challenges and achieve sustainable growth, the pharma industry needs to leverage the power of data science and data analytics, which can provide insights, solutions and innovations across the entire value chain.
The disciplines of data science and data analytics use scientific methods, algorithms and systems to extract knowledge and insights from structured and unstructured data. They can help the pharma industry in various ways to improve efficiencies and outcomes.
An analysis by McKinsey has projected that on account of broader adoption of these technologies, the EBIDTA earnings of the Indian pharma industry will go up by 45-75 per cent in the next 10 years. Furthermore, viewing it from a global perspective, artificial intelligence (AI)-enabled automation and advanced data analytics will result in a 60-70 per cent reduction in process timelines and a 30 per cent reduction in operational costs, according to another study conducted by PwC.
The big pharma in India has already taken a lead by adopting a range of technologies like artificial intelligence, machine learning and cloud computing to advance drug discovery, optimise clinical trials, improve supply chain management and accelerate product development.
Boost for research
Data science and data analytics can accelerate and enhance the research process by enabling faster and more accurate identification of drug targets, biomarkers and pathways, as well as better understanding of disease mechanisms and patient subgroups. For example, AI-assisted clinical management can help researchers design and execute more efficient, faster and effective clinical trials through predictive modelling, reducing the time and cost of drug development. Similarly, molecular design can rely on machine learning algorithms to generate novel and optimised drug candidates and, in turn, reduce the need for extensive screening and testing.
Drug discovery
Data-driven technologies can revolutionise the drug discovery process by enabling the discovery of new drugs, repurposing of existing drugs and optimisation of drug properties. For example, drug repurposing can use data mining and network analysis to identify new indications for approved or failed drugs, thus saving time and resources. Toxicity prediction can employ computational models to predict the potential toxicity of drug candidates and hence reduce the risk of adverse effects and attrition.
Drug development
These new-age technologies can improve the drug development process by enabling faster and more reliable validation, optimisation and scale-up of drug candidates. For example, material waste reduction can use data-driven methods to optimise the process parameters and reduce the material consumption and environmental impact of drug production. Process intelligence can utilise data visualisation and analytics to monitor and control the quality and performance of drug manufacturing processes so as to ensure compliance and consistency. A leading multinational is aptly using AI systems to build clinical decision-making tools to develop personalised medicine for cancer patients.
Supply chain management
Data technologies can optimise the supply chain management of the pharma industry by enabling better forecasting, planning and coordination of drug demand-supply dynamics. For example, forecasting of patient flow and demand can use data modelling and simulation to predict the future demand for a particular drug or medicine, helping pharma companies adjust their production and inventory accordingly. Supply chain logistics can use data optimisation and automation to improve the transportation and distribution of drugs, reducing costs and delays. These advancements are also enhancing traceability and end-to-end visibility in the supply chain across manufacturing, warehouse and distribution centres, besides combating counterfeit drugs.
Impetus to marketing
These advanced technological interventions can give an edge to the marketing efforts of the pharma industry by enabling more effective and personalised communication, promotion and engagement of customers and stakeholders. For instance, segmentation analysis can use data clustering and classification to identify and target different customer segments based on their needs, preferences and behaviours. Social media analytics can use data mining and sentiment analysis to monitor and analyse the online reputation and feedback of pharma brands, products and services to achieve the end objective of improving customer satisfaction and loyalty.
Cost management
Data-enabled technologies can reduce the costs of the pharma industry by enabling more efficient and economical use of resources, assets and processes. For example, data prediction and prevention can be used in preventive maintenance to anticipate and prevent the breakdown of equipment and machinery, thus reducing downtime and repair costs. The pharma industry can also use data analytics as a tool to assist sales representatives in taking optimal decisions regarding pricing, discounts and incentives in order to maximise revenue and profits.
Make the most of it
The Indian pharma industry has a huge potential to leverage data science and data analytics for improved business outcomes as it has access to a large and diverse pool of data, talent and technology. However, in order to realise this potential, the industry also needs to overcome barriers like data quality, security and privacy, data integration and interoperability, data literacy and culture, and data governance and regulation. The industry also needs to collaborate with other stakeholders like academia, government and healthcare providers to create a data-driven ecosystem that fosters innovation and value creation.
Data analytics and data science are the key enablers of Pharma 4.0, which signifies the next wave of digital transformation in the pharma industry. By harnessing the power of data, the pharma sector can become more efficient, competitive and quality-oriented, which will in turn improve health outcomes.
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