AI-first Research & Development

Data driven & intelligent

In the R&D lab of the future, an interconnected ecosystem of data, platforms, instruments, and advanced analytical tools supports scientists across teams and geographies to rapidly discover breakthrough therapies. They identify disease-specific signatures and biomarkers through real-world evidence (RWE) data including -omics, outcomes and histology data to improve disease understanding and identify, validate and optimize targets that have a lower risk of failing during development. They speed up lead identification, selection and optimization through predictive modelling and AI.

Lifesciences companies have historically focused on generating and guarding their own data, but external data will be increasingly important to R&D success. Insights from data and advanced analytics enable high performers to gain advantage in how they identify targets, how quickly they develop and market them, how they adjust developmental plans to reduce failure, and doing all of these not just faster, but cheaper.

Various types of patient data, such as genomic data enable better target selection, and RWE for in-silico modelling during discovery facilitates accelerated timelines and better drug candidate selection. Patient data from clinical trials or electronic medical records is used to construct synthetic control arms. Decentralisation of clinical trials  creates a continuous streams of data collected passively and actively between clinic visits.

AI capabilities are increasingly used as the only feasible technology to extract signals from these expanded data sources, generating novel insights from unstructured data such as notes in patient electronic medical records or from disparate fields of scientific study, and in feature detection in continuous patient monitoring data.

AI-native biotech companies offer a glimpse of how R&D will move to an AI-first model. Many have stacked capabilities end to end, reshaping the drug discovery and development process and harnessing the operational benefits of a redefined value chain. To accelerate discovery and ensure that they focus on competitively differentiating capabilities, these AI biotechs often use an ecosystem of partners, including academic researchers to provide target expertise, contract research organizations (CROs) to do wet-lab experimentation, and other industry partners to codevelop and commercialize assets.

Drives software development, cloud, networking, artificial intelligence, analytics, security and workplace.

Smart Manufacturing

Continuous & intelligent

Life science organisations are scaling smart factory capabilities and continuous manufacturing to boost agility.

Companies are investing in fully digitising and integrating information technology (IT) and operational technology (OT) capabilities in manufacturing. By seamlessly connecting and integrating disparate manufacturing systems and processes, companies increase visibility and performance capabilities. Smart factories employ Industry 4.0 data-driven technologies like artificial intelligence (AI), machine learning (ML), and the Internet of Things (IOT).

Continuous manufacturing is a way for drug manufacturers to more easily adapt supply to demand. Fully end-to-end Continuous Manufacturing systems, seek to encompass both API (active pharmaceutical ingredient) and final dosage form manufacturing in one integrated system.

 Utilises business applications, data analytics, artificial intelligence, automation, networking, cloud, Industry 4.0& IoT, digital workplace and security.

Digital Patient & Practitioner Engagement

Multichannel & personalised

The pandemic helped to create more patient-centric channels. Digital technologies, in particular, were globally adopted, and telemedicine became broadly available. Digital health, digital medicine, and digital therapeutics offer life sciences and stakeholders an opportunity to create more personalized experiences and new ways to become patient-centric. Patient engagement has become an essential part of biopharma research and development and disease management. AI-driven engagement, connected patient, and health care provider (HCP) platforms may provide patients and partners timely access to content and treatments that are relevant and personalized. Going forward,  weaving digital tools and analytics into every aspect of a product launch will be a prerequisite to engaging with patients.

During the COVID-19 pandemic, physicians have come to appreciate how efficient and effective virtual engagement with pharma companies can be. According to a recent BCG survey, three-quarters of them prefer to maintain or increase the number of virtual (as opposed to face-to-face) engagements with reps. Pharma companies are rethinking the role of sales representatives. Face-to-face visits and telephone interactions by sales reps are still essential, but virtual channels give physicians access to the information they need in an easy and convenient manner. The same holds true for information on new products: physicians highly value webinars, virtual training, and speaker programs. Artificial intelligence–integrated customer relationship management, patient and physician portals, digital companion apps, and predictable reimbursement are rapidly becoming minimum requirements for competition. Winning pharma companies will use data- and analytics-based platforms to convey the right messages to the right customers at the right times. Ultimately, this can create a stronger, more seamless omnichannel experience.

Requires the integration of networking, digital workplace, cloud, AI and security.