Unexpected events — whether catastrophic like the oil leak in the Gulf of Mexico or a disruptive supply chain shortage — can change the future of a company. To prevent such difficulties or at least minimize their impact, life sciences companies spend millions of dollars on daily analysis of enterprise-wide risks.
Whereas supply chain and logistics are traditionally a focus of risk analysis teams, manufacturing and quality teams are now charged with improving process predictability. Through process understanding, that not only helps minimize risk in the event of a disaster, but it also provides immediate benefits such as predictable product supply, lower cost of goods sold (COGS) and inventory levels, and enhanced product quality, technology transfer, and regulatory compliance. A predictable process helps a company realize those benefits even when using a contract manufacturing organization (CMO).
Predictable Product Supply
Although supply chain logistics are basic to a predictable product supply, process predictability can be a challenge for some companies. Many drugs are made by complex processes involving multiple steps, splits, and recombinations that may take place at different facilities. Pressure to be first to market makes accelerated scale-up through process development a priority. Supply chain disruptions increase the pressure and the importance to produce additional supplies quickly and reliably. Predictable product supply is also critical to successful clinical trials, which could be interrupted or cancelled for lack of product.
Cost of Goods: A predictable process leads to fewer failed batches, better consistency, and higher product quality. It also can be more easily improved to enhance yields, which requires fewer batches to meet demand and may lower capital expenditures for plant or line expansions.
Inventory and Product Quality: Predictable processes enable rapid replenishment, which can improve a company’s balance sheet by reducing the need for stockpiling. A well-defined process provides for consistent product release and improved product quality.
Technology Transfer: A process that has been well-defined in development is more easily transferred to captive (or contract) manufacturing operations and directly affects COGs, time to market, compliance, and so on. Technology transfer risk can be reduced by better process design early in a product life cycle — one of the central tenets of quality by design (QbD).
Regulatory Compliance: To improve product quality, the US Food and Drug Administration (FDA) has enhanced its focus on process quality. Inherent in that effort is a requirement for manufacturers to understand the cause of their process failures. Quality and manufacturing teams can reduce the duration of site inspections and audits when they have well-defined processes. A critical factor for reducing technology transfer risk is to provide on-demand data access and aggregation capabilities directly to end-users so each multidisciplinary tech transfer team can collaborate productively.
Achieving Predictability and Understanding
Manufacturing intelligence solutions can help make processes more predictable while helping companies identify manufacturing process characteristics and trends that pose threats. Here are some critical requirements for a software solution to be used as a collaboration platform for process development and manufacturing teams with their CMO partners:
- User-centric interface to provide direct, on-demand access to all data from disparate sources
- Data capture from paper records to make information easily available in electronic form
- Analysis of continuous (online) and discrete data
- Sharing data, analysis results, and reports across functional disciplines and locations
- Rapid compilation and distribution of both descriptive (dashboard) and investigational (cause-and-effect) analytical results
- Automated generation of periodic reviews and reports for batches and campaigns
- Identifying critical process parameters (CPPs) and understanding their relationship to critical quality attributes (CQAs).
That last point may be most important to reducing the risk of manufacturing defects and increasing process understanding in pharmaceutical and biotech operations. CPPs are in-process parameters most responsible for driving variability in CQAs. Multivariate analysis techniques help identify likely process outcomes and are critical to best practices in risk mitigation. Leading companies are institutionalizing relevant knowledge so their staffs can learn from past experience and focus attention on higher-risk products and processes.
Effective solutions must be collaborative, maintain process context and lot genealogy, and enable ad hoc investigational analytics, with trending and reporting capabilities. All these features will allow manufacturers to reduce operational risk, ensure product quality, and support operational excellence initiatives.
Robert DiScipio is president and CEO at Aegis Analytical Corporation, 1380 Forest Park Circle, Suite 200, Lafayette, CO, 80026; 1-303-625-2101; firstname.lastname@example.org; www.aegiscorp.com.