Technology is transforming Pharma

Target identification and validation: DI uses advanced data analytics, machine learning, and AI to identify and validate potential drug targets, increasing the chances of success in the early stages of drug discovery.
Predictive modelling: It helps in predicting the outcomes of clinical trials by analysing historical data, which can lead to faster and more cost-effective development processes.
Compound screening: It assists in screening large libraries of compounds to identify those most likely to succeed, reducing time and costs.

Regulatory submission optimization: DI aids in structuring and optimizing data submissions to regulatory agencies, improving the chances of approval and reducing the time to deliver pharma dossiers by up to 90%.
Read more about how it works here
Post-market surveillance: It helps in monitoring drug safety and effectiveness aftermarket launch by analysing real-world data, ensuring ongoing compliance with regulatory requirements.
Life cycle management: Learn from our partners Global Value Web about how we bridge the gap between operational challenges and regulatory expectations by handling changes throughout the drug product life cycle here

Overall Equipment Efficiency: Combining the data from machine and environment in XpertFactory allows more insight and understanding of key control variables, which improves the quality of each component. Engineers can be alerted in real-time to problems before they occur, which results in consistent near zero error processes, an increase in uptime, and improved Overall Equipment Efficiency (OEE).
Digital twin: to optimise OEE, users can create a digital twin of processing to identify the service and maintenance requirements of equipment in all locations.
Real-Time Release Testing (RTRT): XpertFactory can evaluate product quality based on real-time data capture and advanced analytics  during the manufacturing process. 
Find out more about XpertFactory here

In a world-first innovation, ARGUS will monitor microbial growth within HPC chemical fluids and on surfaces, in real time and in-line, using innovative photonic sensors and manufacturing processes placed at multiple locations across the supply chain. Data from these microbial sensors will be shared and combined with other relevant data from supply chain partners in a new digital platform, allowing microbial insight to be shared. This novel in-line approach is a disruptive advance on present practice.

Personalized medicine: DI supports the development and implementation of personalized treatment plans by analysing patient data to predict individual responses to therapies.
Patient adherence and engagement: Analyse patterns in patient behaviour to develop strategies that improve medication adherence and patient engagement, leading to better health outcomes.
Telemedicine and remote monitoring: DI facilitates decision-making in telemedicine by integrating and analysing data from remote monitoring devices to provide real-time insights for patient care.

Factory capacity & inventory: There is always a balance between availability of manufacturing facilities and having the right materials in place to produce the finished products. This becomes even more complex with multiple products. Using DI and real-time data from operations just in time deliveries can be guaranteed.
Factory scheduling: With variable product demands, complex manufacturing processes and supply chain logistics, scheduling becomes a multifaceted problem. Utilising DI, heuristic knowledge and optimisation models of processes, the scheduling and availability of laboratory, QA and manufacturing facilities can be optimised to give optimal factory schedules.

Integrated order processing and process monitoring system that links together historic stock management and ERP systems, “Golden Batch” monitoring and advisory system, cycle time analysis and dashboarding to purchasing, manufacturing and sales operations to advise real time status and availability of products. Gives accurate lead times that mean JIT deliveries to customers is a reality.

AI and data governance: DI ensures that AI and machine learning models are used ethically, with a focus on transparency, fairness, and privacy, which is crucial when handling sensitive patient data.