Biopharmaceutical manufacturers are plagued by high costs and lengthy timelines to bring new therapies to market. These factors can contribute to global drug shortages, causing delays in patients accessing necessary treatments.
The drug development process is intricate and can take decades, with a lengthy road from R&D to clinical trials to commercial manufacturing. Throughout this journey, organizations face numerous challenges in integrating and analyzing data, and a lack of real-time insights can slow critical decision-making as therapies progress.
In addition to market-specific pressures and regulatory obstacles, the pharmaceutical industry faces general manufacturing issues — such as equipment downtime and inefficient maintenance scheduling —that affect all process industries. To tackle these challenges, the sector must adapt and modernize their facility strategies by embracing emerging technologies like artificial intelligence (AI).
By adopting AI-inclusive advanced analytics platforms into daily workflows, organizations can implement predictive maintenance, enhance product development operations and scheduling, and accelerate delivery timelines. These proactive approaches not only improve operational efficiency, but also reduce maintenance and operational costs throughout facilities.
Maintenance shortcomings
In pharma, as with any process industry, equipment failures are all too common, and unexpected asset failures can significantly delay production. For contract manufacturing organizations (CMOs) and large manufacturing facilities, where multiple production schedules are tightly balanced, the effects of these delays can ripple throughout the entire plant downstream to the supply chain. These delays prevent efficient delivery of therapies to patients who rely on them, often for life-critical needs.
These issues are often the result of insufficient equipment health visibility, which is required to obtain predictive insights. Additionally, comparing production behavior against historical patterns can help users understand long-term trends, but the process of aggregating the data necessary to do so can be time consuming and tedious.
As a result, manufacturing teams often schedule routine interval maintenance instead of condition-based maintenance. This prescheduled maintenance can be costly and excessive for some equipment, while equipment requiring more frequent maintenance may not receive adequate attention. Furthermore, interval-based maintenance ties up personnel and equipment resources that could be better used driving life-saving therapies to market.
AI-equipped advanced analytics
Addressing these and other challenges, advanced analytics platforms empower pharma companies to effectively consolidate data throughout the entire drug product lifecycle. By centralizing data, these platforms facilitate the comparison of drug processing across critical development stages. This integration is crucial for maintaining key process parameters when transitioning operations from a development facility to a CMO for production.
Once data is in a centralized location for comparison, it can be challenging to isolate process inefficiencies or optimize process parameters. Advanced analytics platforms do not require users — consisting of process engineers, managers, operators and more — to possess analytics expertise to garner key insights, enabling personnel of any discipline to leverage the software as efficiently as data scientists. Using tools, such as built-in AI assistants, users need only to key in or verbalize questions to gain insights about their processes. Similarly, no-code machine learning algorithms simplify analyzing historical data against real-time process parameters to predict and optimize operational outcomes.
In drug development, laboratory testing is critical for assessing drug efficacy and determining necessary process adjustments. However, lab equipment delays can hinder the timely availability of this information. Advanced analytics platforms help address this issue as well by integrating historical lab data with real-time process data to create predictive analytics and forecast development outcomes. This empowers SMEs to make process adjustments in real-time without needing to wait for lab results, enabling early identification of potential operational issues to mitigate risks and avoid costly delays, such as a lost batch.
Furthermore, AI-enhanced platforms leverage continuous data monitoring in real-time to accelerate insights into key process metrics, providing an immediate and informed feedback loop to ensure consistent product quality. This guidance helps inform automated batch review reporting build-out (Exhibit 1).
Efficient process management with proactive adjustments help reduce R&D cycle times, while real-time monitoring and data-informed process improvements enhance product quality. Collectively, these factors reduce the risk of batch rejections and compliance issues.
Streamlining data analysis
By minimizing the need for manual intervention, these platforms streamline batch review and help organizations maintain regulatory compliance.
Additionally, data centralization through all stages of product development enhances collaboration among departments and companies, enhancing decision-making.
As process efficiency improves and re-manufacturing of failed batches becomes less of an issue, organizations can reduce operational costs. Savings previously spent on unnecessary trials and manufacturing adjustments can instead be redirected toward capital improvements and optimization efforts. This shift, driven by AI predictions, enhances output potential and accelerates the introduction of new drugs to the market.
By utilizing integrated, automated reporting and AI-enhanced documentation, organizations ensure regulatory compliance throughout the entire development process, from R&D to large-scale commercialization. Improved documentation and validation procedures also streamline regulatory submissions in late-stage drug candidates.
These benefits empower technical teams with actionable insights about their processes, enabling informed decision-making at every step of development. This helps optimize processes and quickly address deviations, all while consolidating information into a single, accessible location.
Integration, dashboarding, and automated alerting
With data spread across numerous equipment lines, or even among multiple facilities, maintaining real-time insights into production processes is critical. Advanced analytics platforms enable the required data integration across various sensors, control systems and plants to provide these insights, generating excursion notifications before faults occur.
Equipment failures are often multivariate in nature, and therefore difficult to detect with standard tag-by-tag monitoring. The machine learning algorithms incorporated into routine advanced analytics workflows make equipment failures more predictable by leveraging pattern analysis and comparisons with historical equipment performance.
After combining trends across sensors and sites into a single platform feeding predictive algorithms, the next step is communicating key findings to plant personnel. This can be done by building out comprehensive analytics dashboards for display in centralized plant areas for operations and management staff alike. These dashboards can be customized to team- or site-specific needs, and they enable easy visualization of equipment health metrics and maintenance status.
When building dashboards in advanced analytics platforms, modern AI features can reduce the workload burden even further. For example, users can simply ask the AI performance engine which key performance indicators (KPIs) are deviating, or have the AI populate trend and dashboard views with specific tables and visualizations (Exhibit 2).
While dashboards are useful for engineering and operations teams working in a facility, key stakeholders off the plant floor often cannot consistently monitor dashboards to stay informed of plant processes. Automated alerts and notifications fill this void, providing communication when a process trends out of specification, prompting action prior to impending failures.
With a shift towards proactive as opposed to reactive downtime and maintenance, scheduling teams can optimize production for ideal balance between producing therapies and ensuring reliable equipment operation. This facilitates equipment maintenance scheduling during low-impact times, with minimal disruptions to production.
Enabling predictive maintenance
AI can help engineers quickly and effectively implement predictive maintenance calculations and dashboards in production facilities. For example, one pharmaceutical manufacturer used an AI-inclusive advanced analytics platform to develop a pump health indicator for its CIP pumps. This prompted a complete shift to condition-based monitoring and maintenance, saving the company $200k per year.
Another manufacturer used the same platform to develop a similar health indication algorithm for critical heat exchangers throughout its facility. The team also configured alerts to notify plant personnel in the event that deviations from baseline production data occur. This saves the company an estimated $10k per year for each heat exchanger by eliminating unnecessary maintenance and significantly reducing downtime and associated production outages (Exhibit 3).
Deploying and streamlining predictive maintenance programs can quickly traverse facility fence lines, permeating entire organizations. Higher efficiency and optimized resource allocation help boost both productivity and overall operational efficiency, and data-based as opposed to time-based maintenance strategies enable critical maintenance prioritization. This enables scheduling around production needs to maximize uptime and production.
Additionally, performing equipment service based on actual needs can increase asset lifespans, reducing required expenditures on equipment replacement. Instead, these resources can be allocated toward process optimization, new therapy development, and facility expansion projects.
Harnessing advanced analytics
Pharmaceutical processors must adapt to market fluctuations and increasing demand for therapies by modernizing facilities with advanced analytics tools to accelerate and streamline outcomes. Integrating advanced analytics and AI platforms enables faster drug development, mitigates equipment failures, addresses inefficiencies, and shortens time-to-market. Furthermore, AI-driven insights and real-time monitoring ensure high production quality, reducing batch rejections and compliance issues.
By managing data across R&D, clinical trials and manufacturing phases within a unified system, organizations facilitate smooth transitions and break down communication barriers, enhancing decision-making and fostering innovation.
To stay competitive and deliver therapies quickly, pharma companies must keep their facilities up to date, leveraging insightful automation software tools to help maintain timely and high-quality patient care.
