Delivering on the promise of science in life sciences

Kavita Rekhraj

CHANGE will persist throughout 2022 and beyond for the life sciences sector in Southeast Asia. Armed with new sources of insights and real-world evidence, companies will be solving problems for diseases that were once thought intractable. New processes adopted during the pandemic to expedite Covid-19 vaccines and therapeutic products will also be applied to accelerate the development of other drugs and treatments.

In the year ahead, the greatest challenge confronting life sciences leaders will be to ensure that they accelerate the valuable progress made – and not revert to old ways. To address this, they will need to focus on three strategic imperatives to capitalise on their digital progress, and fundamentally change the drug development paradigm:

Delivering a patient-centric experience 

From concept to launch, a more patient-centric model could change many of the micro and macro decisions that life sciences companies make – including what to research, how to develop, how to distribute, and ultimately, how to measure patient outcomes. Achieving this will require companies to fundamentally re-examine how they operationalise patient services programmes (PSPs) through which they gather data on patient interactions, and perform service interventions to improve the patient experience.

While there isn’t a single best answer, the argument for moving PSPs in-house has been gaining momentum, as companies recognise both the growing complexity and greater patient lifetime value that such programmes bring. In Southeast Asia, where chronic disease incidence rates are rising and demand for more personalised therapies are growing, a number of market opportunities may also exist to launch more comprehensive PSPs. 

As they look to scale their PSPs, life sciences companies will need to consider how they can best obtain the specialised solutions that they need to deliver these programmes – including but not limited to scheduling services, pharmacy e-commerce, distribution networks, and other digital applications – and weigh the trade-offs between buying and building such capabilities. 

Furthermore, as PSPs evolve from point workflows focused on onboarding and access, to more holistic patient engagement models, companies may also see the need to own their digital patient platforms – and consequently, patient data. Given that delivering a unified patient experience would require the collaboration of multiple ecosystem partners, such a platform should also be one that is capable of driving the standardisation and automation of workflows – such as enrolment, integration with insurance systems, and authorisations – through the use of application programming interfaces.

Accelerating digital with AI

On the back of the pandemic, more holistic and enterprise digital transformation is no longer a question of if or when, but how. As life sciences companies push digital at scale across the value chain, we are seeing digital transformation being tackled head-on by executive leadership – not just by chief information officers or chief digital officers, but by entire management teams. The digital imperative is being embedded in every business function – R&D, manufacturing, supply chain, and commercial—as well as core functions such as HR, with the expectation that companies must now evolve from just ‘doing digital’ to truly ‘being digital’.

While digital transformation entails many aspects, one particular area of concern that is actively being discussed at the board and C-suite level is the use of artificial intelligence (AI) applications. This is not a surprise: AI has potentially limitless use cases for the sector, including expediting drug development, providing better decision-making for diagnosis and surgeries, and making supply chains smarter and more responsive. 

The crux of the matter, however, is that while AI is becoming mainstream, enterprise AI at scale remains elusive for many companies in Southeast Asia, not least because of the difficulties associated with identifying business cases with the highest value, and challenges with managing data across the organisation from preliminary research and clinical trials, to manufacturing, supply chain, and commercialisation.

Looking ahead, life sciences companies will need to double down on efforts to integrate AI more holistically across their processes, starting by gaining access to the rich data that AI systems require in order to function. Often, this means having to overcome historically separate and siloed organisational structures that impede the accessibility of quality data, as well as cleaning, curating, and managing that data in a coordinated way across the enterprise.

Future-proofing supply chains

Covid-19 has created a renewed urgency for life sciences companies to put in place agile manufacturing processes, and more resilient supply chains. As disruptions to logistics and transportation impacted the timely delivery of products, companies rapidly digitalised their supply chain operations, leveraging Internet of Things (IoT) solutions to track and track product shipments in real-time, and plug gaps in supply chain visibility. 

Looking ahead, we can also expect to see a growing urgency to reduce dependence on bulk active pharmaceutical ingredients (API) and generic drugs sourced from offshore markets, and an increase in the reshoring and regionalisation of critical materials supply. As companies move towards greater in-country API development and manufacturing, many are already deploying the use of innovative, streamlined, and automated manufacturing techniques to more quickly adapt supply to demand. 

These include, for instance, the implementation of end-to-end continuous manufacturing platforms that encompass both API and final dosage form manufacturing in a single integrated system. Through the use of continuous flow chemistry manufacturing techniques, these facilities also conduct small-scale operations – only a fraction of the size of a conventional batch process – that are more cost-efficient and environmentally-friendly.

As life sciences companies seek out deeper insights across their supply chain, they should also consider how they can leverage a variety of different risk assessments and data analytics tools to improve demand prediction and support data-sharing with customers and partners. Some examples are control towers or data hubs that merge internal data, such as production and inventory data, with data from intermediaries and partners to provide real-time longitudinal visibility into material and product flow; as well as self-healing AI solutions that analyse supply chain, manufacturing, and market data to highlight potential issues, analyse their root causes, and suggest next steps for supply chain operators.

Delivering on the promise of science

At this juncture, it is worthwhile to note that the Covid-19 pandemic has brought life sciences companies closer to their purpose. In the last two years, we have witnessed companies gamely taking on the challenge of accelerating drug development and increasing access to vaccines – all while expanding their environmental, social, and governance (ESG) goals, including those relating to sustainability, as well as diversity, equity and inclusion.

This too, is hard-won progress that must be protected and accelerated. As life sciences companies look to advance some of their most immediate business priorities, they should also consider how they can better integrate these priorities with longer-term ESG goals aligned to their organisational purpose. This is, of course, no mean feat – but one that they must tackle to truly deliver on the promise of science.

The writer is life sciences & health care industry leader at Deloitte Southeast Asia