The global life sciences and healthcare infrastructure is faced with an immense challenge, given the phenomenal rise in existing and new diseases, and the swelling population across the world. Although researchers and innovators across life sciences, biotechnology, and healthcare industries continue to pursue better and effective cures for prevailing and new conditions, the industries must also discover better ways to optimize costs and boost R&D efforts without compromising the quality of products and services intended to improve human lives. The cost of developing a new drug ranges between USD 1.5 and USD 2 billion, and the time taken to bring it to consumers can extend up to 10 to 12 years.
Technological advancements in Artificial Intelligence (AI) and Robotics are set to play a critical role in reducing cycle times, streamlining operations, cutting costs, and boosting productivity across the pharma value chain—drug discovery, clinical research, manufacturing, pharmacovigilance, regulatory compliance, and supply chain. For instance, collection and analysis of voluminous clinical as well as non-clinical data can now be done with utmost accuracy by tools that are powered by AI and Analytics and Insights. Therefore, efficiency in drug discovery, enhancement in trial productivity, and delivery of personalized treatments can be made possible with minimal errors, with less effort and at less cost.
What’s slowing down efficiency and innovation?
To ensure patient safety, regulators have stepped up the demand for systematic and precise reporting of trial activities. Policymakers are specifically demanding post-market evidence of pharmaceutical products’ efficacy and effectiveness before approving their reimbursement status. With steep increase in adverse event cases globally, and the evolving regulatory scenario across the world, an over-dependence on single point solutions and legacy IT infrastructure can impede discovery, development, and compliance management efforts.
Clinical Research Scientists working in and out of labs mostly lack access to critical reports and historical trial outcome records, especially during the early stages of drug development. Relevant data is largely stored in silos across trial sites and on systems maintained separately by healthcare providers, thereby giving rise to a fragmented, often incomplete picture of disease progression and drug efficacy.
Pharmaceutical IT ecosystem must therefore undergo rapid transformation that involves the creation and deployment of smart IT platforms, which possess the capability to support informed clinical decisions yet comply with cyber security and data privacy laws. By integrating unified systems that can capture data from a wide range of sources–consumer wearables and healthcare systems—pharma companies can provide regulators with greater, better visibility into adverse events, patient safety, and efficacy.
Reinvigorating R&D by adopting Business 4.0
There is an opportunity for pharma companies to wholly embrace and adopt emerging digital technologies that allow for convergence. They must embrace a Business 4.0 approach involving an agile, data-driven model that allows them to leverage AI, automation, and the cloud technologies.
This transformation can be led by a purpose-built, scalable platform which, in a structured manner, provides personalized clinical data to stakeholders across a product lifecycle – through intelligent search and analytics-led insights. An AI-enabled engine on such a platform can ingest and aggregate clinical data, identify disease trends, predict the efficacy of a drug, and provide pharmacovigilance assessments. This will help identify and prioritize clinical evidence for accelerating drug discovery and improving patient engagement. On a cloud-based platform, it can even screen and on-board study participants and reduce projected enrollment time by nearly half, thereby improving staff productivity and lowering dropout rates significantly.
Such a next generation solution will also help patients monitor their progress and play an active role in shaping their health outcomes. In turn, pharma companies will be able to:
- Lower drug development costs – by facilitating open, secure patient and clinical data exchange, and establishing a single source of truth. This system will integrate patient data, electronic medical records (ERMs), and real-time feeds from monitoring devices to analyze and suggest optimal treatments at every stage of a disease.
- Enable agile drug development—using analytics and machine learning to identify new drug targets, support systems biology programs, and simulate interactions. Insights gleaned from patient and trial outcome data can be used to establish program-specific key risk indicators (KRIs) which can be tracked through a risk-based monitoring (RBM) platform. Researchers can thereby improve quality oversight by discovering and predicting adverse events, such as protocol deviations. Artificial Intelligence-based pharmacovigilance is bringing in advancements in medical assessment and literature search and complaint handling in medical devices, among others.
- Ensure compliant, site-less trials and improve productivity– by leveraging data management and process automation capabilities to help study sponsors complete reporting and pre-trial paperwork processes.
The future of the pharma industry rests squarely on how quickly it is able to adopt unified platforms, which integrate clinical development operations, analytics, and information in a single collaborative environment. Ultimately, this will allow the industry to set up and complete trials effectively and quickly, become completely patient centric, and more importantly, work towards the betterment of human lives.