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EMA Guideline on Setting Health-Based Exposure LimitsThe results of an industry workgroup’s examination of EMA’s guide on shared facilities are presented.
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EMA Guideline on Setting Health-Based Exposure LimitsThe results of an industry workgroup’s examination of EMA’s guide on shared facilities are presented.
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Cleaning Limits—Why the 10-ppm Criterion should be AbandonedThe 10-ppm criterion for the acceptable concentration of potential API in cleaning validation to minimize cross contamination into next product has been employed for many years. This article describes why the 10-ppm criterion, which was established based on analytical limitations and estimates of acceptability, is no longer necessary and why a risk-based approach should be universally adopted.
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Cleaning Limits—Why the 10-ppm and 0.001-Dose Criteria Should be Abandoned, Part IIConventional limit-setting techniques are not health-based and can make risk assessment more difficult.
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Process Validation: Do We Need Brainwashing?Six years after the guidance, it’s time to change our quality assurance vocabulary.
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Comparison of Permitted Daily Exposure with 0.001 Minimal Daily Dose for Cleaning ValidationIn this study, the authors investigated the relationship between the 0.001 MinDD and the PDE values for 140 drug substances as an attempt to identify high-risk groups of products for patient safety. This comparison can serve as a method for prioritization of APIs for development of PDEs.
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Comparison of Permitted Daily Exposure with 0.001 Minimal Daily Dose for Cleaning ValidationIn this study, the authors investigated the relationship between the 0.001 MinDD and the PDE values for 140 drug substances as an attempt to identify high-risk groups of products for patient safety. This comparison can serve as a method for prioritization of APIs for development of PDEs.
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EMA Guideline on Setting Health-Based Exposure LimitsThe results of an industry workgroup’s examination of EMA’s guide on shared facilities are presented.
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EMA Guideline on Setting Health-Based Exposure LimitsThe results of an industry workgroup’s examination of EMA’s guide on shared facilities are presented.
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What Does the Future Hold for the Animal Rule? Examining FDAs Final GuidanceThe author reviews FDA's final Animal Rule guidance.
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Calculating And Understanding Particulate Contamination RiskThe author presents a method to calculate the relationship between supply air volume flow and airborne particle concentrations.
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Error Assessment of Drug Concentration in Multicomponent Pharmaceutical MixturesA novel method, based on differential calculus, was used to calculate the maximum potential error associated with the drug concentration in pharmaceutical mixtures composed of an infinite number of ingredients measured on an infinite number of balances with different sensitivities. The method was further applied to calculate the ingredients’ least allowable quantities. This approach ensures that the pharmaceutical formulation is prepared within a given maximum permissible error in drug dose.
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Evolving your Micro QC Lab with LIMS integration for Colony CountingWednesday, February 16, 2022 at 9 AM EST | 6 AM PST | 2PM GMT | 3 PM CET & 11AM EST | 8AM PST | 4PM GMT | 5PM CET How are you approaching Pharma 4.0 and the QC Lab of the future with automation? Join us for an informative Webinar on the benefits of end-to-end integration with LIMS and QC Micro automation.
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UV Spectroscopy Gains Use in Dissolution TestingKeith Moore, vice-president of analytical services, Metrics Contract Services discusses gains use in dissolution testing.
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FDA Perspectives: Understanding Challenges to Quality by DesignDirectors from FDA's Center for Drug Evaluation and Research summarize findings in an FDA-commissioned report on QbD and propose actions the agency can take to encourage full-scale QbD implementation.
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Scientific Approach Ensures Accurate Weighing ResultsUnderstanding of the basic principles of balance and scale enables a user to achieve a qualified weighing process.
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Statistical Tools to Aid in the Assessment of Critical Process ParametersThere are many different approaches for assessing process parameter criticality, and assessing which process parameters have a significant impact on critical quality attributes (CQAs) is a particular challenge. Including an unimportant process parameter as a critical process parameter (CPP) in a control strategy can be detrimental. The authors present a statistical approach to determine when a statistically significant relationship between a process parameter and a CQA is large enough to make a practically meaningful impact (i.e., practical significance).
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Statistical Tools to Aid in the Assessment of Critical Process ParametersThere are many different approaches for assessing process parameter criticality, and assessing which process parameters have a significant impact on critical quality attributes (CQAs) is a particular challenge. Including an unimportant process parameter as a critical process parameter (CPP) in a control strategy can be detrimental. The authors present a statistical approach to determine when a statistically significant relationship between a process parameter and a CQA is large enough to make a practically meaningful impact (i.e., practical significance).
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Statistical Tools to Aid in the Assessment of Critical Process ParametersThere are many different approaches for assessing process parameter criticality, and assessing which process parameters have a significant impact on critical quality attributes (CQAs) is a particular challenge. Including an unimportant process parameter as a critical process parameter (CPP) in a control strategy can be detrimental. The authors present a statistical approach to determine when a statistically significant relationship between a process parameter and a CQA is large enough to make a practically meaningful impact (i.e., practical significance).
Latest:
Statistical Tools to Aid in the Assessment of Critical Process ParametersThere are many different approaches for assessing process parameter criticality, and assessing which process parameters have a significant impact on critical quality attributes (CQAs) is a particular challenge. Including an unimportant process parameter as a critical process parameter (CPP) in a control strategy can be detrimental. The authors present a statistical approach to determine when a statistically significant relationship between a process parameter and a CQA is large enough to make a practically meaningful impact (i.e., practical significance).
