Methods development troubleshooting

Contracting out of activities previously only conducted in-house is already becoming quite common and will probably continue to develop. In the past a so-called full-service pharmaceutical company took direct responsibility for all the activities required for the formulation, manufacture, quality control, and regulatory approval of its drug products. Nowadays the use of specialist contract houses to perform activities such as formulation, analytical methods development, manufacture of clinical trials supplies, supervision of the assembly of an NDA, postmarketing surveillance, and even troubleshooting may be contracted for even by some of the largest companies. 

An understanding of the mechanisms of solid-phase extraction (SPE) are crucial for effective methods development. The four mechanisms outlined in Chapter 2 are sufficient for the majority of SPE and are an effective set of tools for methods development. The molecule s structure and the sample matrix are the main features used to choose a mechanism of isolation and separation. This chapter will discuss a six-step approach to methods development, how to execute the SPE recovery experiment, troubleshooting and optimizing conditions for the SPE recovery experiment, and how to critically evaluate previously published methods. 

With nonaqueous samples (part B of Fig. 3.8), the decisions for sorbents are somewhat reversed. For example, an analyte that is polar and ionic is best recovered with ion exchange. This example is similar to that of an aqueous sample. If the analyte is polar but nonionic, then the sorbent choice could be reversed phase or normal phase. The choice depends on the organic solvent, either polar or nonpolar, respectively. Finally, if the analyte is nonpolar, the sorbent choice is reversed phase. The second step in methods development is to execute the SPE experiment. Lastly, one has to optimize and troubleshoot the SPE method. 

Most importantly, this book was written as an updated reference guide for busy laboratory analysts and researchers. Topics covered include HPLC operation, method development, maintenance/troubleshooting, and regulatory aspects. This book can serve as a supplementary text for students pursuing a career in analytical chemistry. A reader with a science degree and a basic understanding of chemistry is assumed. 

A summary update of best practices in HPLC operation, method development, maintenance, troubleshooting, and regulatory compliance. 

Practical HPLC Methodology and Applications Practical HPLC Method development (Both the last ones are suitable for careful study of a method development and optimization.) Troubleshooting HPLC Systems, a Bench Manual (general hints, maintenance and troubleshooting of the HPLC equipment). 

This equates to approximately 50% reduction in the flow rate (from 1.0 to 0.5 mL min 0- Refer to Chapters 6 and 10 on method development and troubleshooting, respectively. 

Weinberger, R. Lombardi, R. Method Development, Optimization and Troubleshooting for High Performance Capillary Electrophoresis Simon and Schuster Custom Publishing Needham Heights, MA, 1997. 

Many of the techniques described above, excipient compatibility, blend uniformity by HPLC dissolution, and content uniformity/assay by HPLC can be effectively automated by robotic sample preparation. Each of these techniques requires that the sample under study be dissolved in an appropriate solvent and fully extracted from any excipients. There are a number of commercially available products that have proven to be effective and robust in this sample preparation role. This robotic process can reduce both the analyst hours required to prepare a number of samples, and turnaround time on the sample analysis, since the robotic systems will operate unattended over night and on weekends. There is of course a cost to pay for laboratory automation. There is a significant capital cost, and then an ongoing maintenance cost for the continued operation of the system. Also it is critical that a specialist be available in-house to care for the system, develop the methods, and troubleshoot any issues with the system. The cost of the system and specialist must be weighed against the advantages of speed and lab capacity enhancement realized with a successful automation implementation. 

The need for weU-trained technical service professionals is expected to continue as an essential aspect of the chemical industry, despite the phenomenal growth ia electronic methods of information storage, retrieval, and transmission. Advanced troubleshooting of complex customer processes and accelerated accurate product development and market introductions should continue to be principal elements of technical service personnel duties. Increased levels of integration, perhaps blurring the lines between suppHer and customer, may come to pass. There are already instances of personnel swapping between customers and suppHers for extended periods to allow cross-fertilization of ideas and provide more accurate perspectives for the companies involved in these efforts. Technical service and research personnel have been those persons most directly involved in such efforts. 

Overview Reconciliation adjusts the measurements to close constraints subject to their uncertainty. The numerical methods for reconciliation are based on the restriction that the measurements are only subject to random errors. Since all measurements have some unknown bias, this restriction is violated. The resultant adjusted measurements propagate these biases. Since troubleshooting, model development, ana parameter estimation will ultimately be based on these adjusted measurements, the biases will be incorporated into the conclusions, models, and parameter estimates. This potentially leads to errors in operation, control, and design. 

Bioinformatics requires people. It always has, and probably always will. To expect informatics to behave differently from experimental science is, at best, hopeful and overly optimistic and, at worse, naive or disingenuous. Experimental science is becoming ever more reliant on instrumental analysis and robotics, yet people are still required to troubleshoot and to make sense of the results. Much the same holds for bioinformatics We can devolve work that is routine to automation—scanning genomes, etc.— but people are still needed to ensure such automation works and to assess the results. New methods need to be developed and their results used and applied. There is... 

The level 3 method is used until synthetic routes and formulations have been finalized and forced degradation and preliminary stability studies have been conducted i.e., until the components that need to be separated in the final DS and in the final DP have been clearly determined. At this juncture, the focus shifts to the development of fast, robust and transferable final methods to be used for primary stability studies and post-approval analyses. Freqnently, separate methods are developed for DS and DP since the goals of each method are different (see Section I). Orthogonal methods continue to be of importance to troubleshoot any questions that may arise during the subsequent life cycle of the drug. 

Finally, process analytics methods can be used in commercial manufacturing, either as temporary methods for gaining process information or troubleshooting, or as permanent installations for process monitoring and control. The scope of these applications is often more narrowly defined than those in development scenarios. It will be most relevant for manufacturing operations to maintain process robustness and/or reduce variability. Whereas the scientific scope is typically much more limited in permanent installations in production, the practical implementation aspects are typically much more complex than in an R D environment. The elements of safety, convenience, reliability, validation and maintenance are of equal importance for the success of the application in a permanent installation. Some typical attributes of process analytics applications and how they are applied differently in R D and manufacturing are listed in Table 2.1. 

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