Issuu on Google+

active pharmaceutical ingredients

Do You Have a Pharmaceutical Process? The process was going crazy around the world. A lot would be beautiful - High performance excellent purity , no problems . Then the next batch would be terrible - half the production of the lot before him and of lower quality. Operators swore that each batch prepared exactly how records of batches required. And since each lot was worth about $ 300,000, process failures were costing a lot of money and keep the plant manager and supervisors at night. Each time a batch no, they would review the records of manufacture made to try to understand what was " different." They knew that something must have changed , but could not find . Sometimes they thought they had discovered and strengthen process control and recycling operators , but still maddening variability return. So they learned to live with it. And spend the money. And planning for batch failures . But they do not have to. With process variability unexplained - or "out of control" processes - which may be impossible to discover the underlying causes by matching batch records together or even looking at trending data graphs and process control . Local Anesthetics Why? Due to the variability of the process may be caused by complex interactions among multiple process parameters that do not affect the process individually . For example , I found that the variability of the process in an active pharmaceutical ingredient (API ) manufacturing process was caused by a combination of temperature, residual water, and the concentration for a given process step. The effect was not observed if one or two of the parameters were in the ranges that caused variability - only if all three were in these ranges together! So how do you go about finding these relationships? You can maximize your chances of discovering the causes of the variability of unwanted processes and find out how to get it under control by following these six steps . These steps quantitative statistical

analysis and data mining processes with experience in qualitative and systematic evaluation are integrated to identify non- obvious and subtle cause-effect relationships that lead to variability. Identify the product or process variability to be addressed . Create a database of historical process parameters from a variety of sources, such as batch records , test results of raw materials, and incident reports . Create a " Robustness Assessment Report " integral containing a detailed analysis of each step of the process and the critical parameters of the process using the documentation and discussions with personnel with the necessary expertise. lidocaine hydrochloride Perform a statistical analysis of the database using data mining software , such as JMP to find drivers of change and interactions on process variability . Generate hypotheses about process experts identified drivers of change to determine if they are truly causal or merely correlated. Development of a predictive model and identify the optimum or near optimum operating parameters to reduce process variability . At the end of this type of robustness study of the process , usually three main categories of findings discovered : 1. Actions that can be taken now : These are process changes that can be made within the current regulatory document to improve the process now . Often these changes can be made within the current parameters of batch record or may require a revalidation process . 2. Parameters that require further study : These are usually gaps in the batch record and analytical data that could be useful in both process control and provide additional information on the variability of the process. 3. Future action : these process changes that may be outside the regulatory filings or advice on items such as new equipment or procedures , laboratory and Process Analytical Technology (PAT ) opportunities.

This systematic statistical mixture of quantitative and qualitative analysis empirical scientific analysis is a powerful approach for identifying drivers of change and improve or optimize process performance , so it is vital to have both quantitative statistical and qualitative process knowledge in their teams process robustness . Statisticians can both find and identify sources of process variation and process experts can help guide the search and help determine whether a statistical correlation is truly causal .

The application program of API process robustness problems or drug production processes is one of the most profitable and fastest ways to dramatically improve performance pharmaceutical process , performance and quality forms. Ideally create a robust process that uses team managing portfolios of projects to prioritize each process on the basis of value to your company and then systematically evaluate each product process using the steps described above. If you are interested in learning more about making pharmaceutical processes perform better or how a management application portfolio can maximize the value of his portfolio, be sure to visit Optsee 速 to learn about a management tool integrated portfolio of projects to prioritize and optimize the portfolios of corporate projects. By automatically analyzing your project portfolio in thousands of scenarios and then optimizing against multiple constraints such as limited resources and funding , Optsee 速 quickly shows your chances of an optimal portfolio return. Mahendra Chemicals is an US FDA approved ISO 9001:2008 certified Lidocaine Drug and pharmaceutical manufacturer.

Do You Have a Pharmaceutical Process?