Comparing a Statistical Model and Bayesian Approach to Establish the Design Space for the Coating of Ciprofloxacin HCl Beads at Different Scales of Production.

The primary objective of this study was to compare two methods for establishing a design space for critical process parameters that affect ethylcellulose film coating of multiparticulate beads and assess this design space validity across manufacturing scales. While there are many factors that can affect film coating, this study will focus on the effects processing conditions have on the quality and extent of film formation, as evaluated by their impact coating yield and drug release. Ciprofloxacin HCl layered beads were utilized as an active substrate core, ethylcellulose aqueous dispersion as a controlled release polymer, and triethyl citrate as a plasticizer. Thirty experiments were conducted using a central composite design to optimize the coating process and map the response surface to build a design space using either statistical least squares or a Bayesian approach. The response surface was fitted using a linear two-factor interaction model with spraying temperature, curing temperature, and curing time as significant model terms. The design spaces established by the two approaches were in close agreement with the statistical least squares approach being more conservative than the Bayesian approach. The design space established for the critical process parameters using small-scale batches was tested using scale-up batches and found to be scale-independent. The robustness of the design space was confirmed across scales and was successfully utilized to establish process signature for the coating process.

A Systematic Approach of Employing Quality by Design Principles: Risk Assessment and Design of Experiments to Demonstrate Process Understanding and Identify the Critical Process Parameters for Coating of the Ethylcellulose Pseudolatex Dispersion Using Non-Conventional Fluid Bed Process.

The goal of this study was to utilize risk assessment techniques and statistical design of experiments (DoE) to gain process understanding and to identify critical process parameters for the manufacture of controlled release multiparticulate beads using a novel disk-jet fluid bed technology. The material attributes and process parameters were systematically assessed using the Ishikawa fish bone diagram and failure mode and effect analysis (FMEA) risk assessment methods. The high risk attributes identified by the FMEA analysis were further explored using resolution V fractional factorial design. To gain an understanding of the processing parameters, a resolution V fractional factorial study was conducted. Using knowledge gained from the resolution V study, a resolution IV fractional factorial study was conducted; the purpose of this IV study was to identify the critical process parameters (CPP) that impact the critical quality attributes and understand the influence of these parameters on film formation. For both studies, the microclimate, atomization pressure, inlet air volume, product temperature (during spraying and curing), curing time, and percent solids in the coating solutions were studied. The responses evaluated were percent agglomeration, percent fines, percent yield, bead aspect ratio, median particle size diameter (d50), assay, and drug release rate. Pyrobuttons® were used to record real-time temperature and humidity changes in the fluid bed. The risk assessment methods and process analytical tools helped to understand the novel disk-jet technology and to systematically develop models of the coating process parameters like process efficiency and the extent of curing during the coating process.

Quality-by-Design II: Application of Quantitative Risk Analysis to the Formulation of Ciprofloxacin Tablets.

Qualitative risk assessment methods are often used as the first step to determining design space boundaries; however, quantitative assessments of risk with respect to the design space, i.e., calculating the probability of failure for a given severity, are needed to fully characterize design space boundaries. Quantitative risk assessment methods in design and operational spaces are a significant aid to evaluating proposed design space boundaries. The goal of this paper is to demonstrate a relatively simple strategy for design space definition using a simplified Bayesian Monte Carlo simulation. This paper builds on a previous paper that used failure mode and effects analysis (FMEA) qualitative risk assessment and Plackett-Burman design of experiments to identity the critical quality attributes. The results show that the sequential use of qualitative and quantitative risk assessments can focus the design of experiments on a reduced set of critical material and process parameters that determine a robust design space under conditions of limited laboratory experimentation. This approach provides a strategy by which the degree of risk associated with each known parameter can be calculated and allocates resources in a manner that manages risk to an acceptable level.

Clinical pharmacology of topiramate versus lamotrigine versus phenobarbital: comparison of efficacy and side effects using odds ratios.

Clinical pharmacologists, neurologists, internists, and all health care givers must consider the efficacy, safety, and side effect profile of a given antiepileptic drug (AED) when determining which drug is best for a given patient. The first purpose of this paper is to address whether the "new" AEDs have advantages over the "old" drugs. The second purpose is to teach those interested in clinical pharmacology about the use of Web-based information access to answer a neurology/clinical pharmacology problem: to compare the efficacy and side effects of topiramate versus lamotrigine versus phenobarbital using odds ratios. Cost of all three AEDs was also compared. A number of new AEDs, including topiramate and lamotrigine, have been developed for chronic focal and secondarily generalized epileptic seizures. Efficacy of these drugs as anticonvulsants does not seem to be superior to that of traditional anticonvulsants such as phenobarbital. However, the advantage of the new drugs is a different spectrum of possible adverse events. Newer AEDs may or may not induce sedation and may minimize noncompliance by reducing side effects of lethargy and cognitive impairment. The difficulty in achieving therapeutic dosage because of side effects makes one consider whether these agents are "better" than the oldest and most side effect-prone AED, phenobarbital. The new AEDs have less frequent interactions, leading to improved tolerability with comedication. This exercise compares two "new" AEDs, topiramate and lamotrigine, with phenobarbital by evaluating efficacies and side effects using relative odds ratios, a method commonly used in drug development research. Development of new algorithms and/or new knowledge will bring beneficial tools to all clinical pharmacologists.

Antimicrobial resistance risk assessment in food safety.

Microbiological risk assessments generally focus on estimating adverse human health risks from exposures to human pathogenic microbes. The assessment of potential human health risks posed by pathogens that have acquired resistance to antimicrobial drugs is a new application of risk assessment that is closely related to microbiological risk assessment. Antimicrobial resistance risk assessment is a risk analyticalprocess that focuses on resistance determinants as hazardous agents that might lead to drug-resistant microbial infections in humans exposed to bacteria carrying the determinants. Antimicrobial-resistant infections could occur directly from actively inavading or opportunistic pathogens or indirectly from the transfer of resistance genes to other bacteria. Here, we discuss risk assessment models that might be employed to estimate risks from drug-resistant bacteria in the animal food pathway and the types of models and data that may be used for microbiological risk assessments or antimicrobial resistance risk assessments.

Probability concepts in quality risk management.

Essentially any concept of risk is built on fundamental concepts of chance, likelihood, or probability. Although risk is generally a probability of loss of something of value, given that a risk-generating event will occur or has occurred, it is ironic that the quality risk management literature and guidelines on quality risk management tools are relatively silent on the meaning and uses of "probability." The probability concept is typically applied by risk managers as a combination of frequency-based calculation and a "degree of belief" meaning of probability. Probability as a concept that is crucial for understanding and managing risk is discussed through examples from the most general, scenario-defining and ranking tools that use probability implicitly to more specific probabilistic tools in risk management. A rich history of probability in risk management applied to other fields suggests that high-quality risk management decisions benefit from the implementation of more thoughtful probability concepts in both risk modeling and risk management. LAY ABSTRACT: Essentially any concept of risk is built on fundamental concepts of chance, likelihood, or probability. Although "risk" generally describes a probability of loss of something of value, given that a risk-generating event will occur or has occurred, it is ironic that the quality risk management literature and guidelines on quality risk management methodologies and respective tools focus on managing severity but are relatively silent on the in-depth meaning and uses of "probability." Pharmaceutical manufacturers are expanding their use of quality risk management to identify and manage risks to the patient that might occur in phases of the pharmaceutical life cycle from drug development to manufacture, marketing to product discontinuation. A probability concept is typically applied by risk managers as a combination of data-based measures of probability and a subjective "degree of belief" meaning of probability. Probability as a concept that is crucial for understanding and managing risk is discussed through examples from the most general, scenario-defining and ranking tools that use probability implicitly to more specific probabilistic tools in risk management.

The reliability-quality relationship for quality systems and quality risk management.

Engineering reliability typically refers to the probability that a system, or any of its components, will perform a required function for a stated period of time and under specified operating conditions. As such, reliability is inextricably linked with time-dependent quality concepts, such as maintaining a state of control and predicting the chances of losses from failures for quality risk management. Two popular current good manufacturing practice (cGMP) and quality risk management tools, failure mode and effects analysis (FMEA) and root cause analysis (RCA) are examples of engineering reliability evaluations that link reliability with quality and risk. Current concepts in pharmaceutical quality and quality management systems call for more predictive systems for maintaining quality; yet, the current pharmaceutical manufacturing literature and guidelines are curiously silent on engineering quality. This commentary discusses the meaning of engineering reliability while linking the concept to quality systems and quality risk management. The essay also discusses the difference between engineering reliability and statistical (assay) reliability. LAY ABSTRACT: The assurance of quality in a pharmaceutical product is no longer measured only "after the fact" of manufacturing. Rather, concepts of quality systems and quality risk management call for designing quality assurance into all stages of the pharmaceutical product life cycle. Interestingly, most assays for quality are essentially static and inform product quality over the life cycle only by being repeated over time. Engineering process reliability is the fundamental concept that is meant to anticipate quality failures over the life cycle of the product. Reliability is a well-developed theory and practice for other types of manufactured products and manufacturing processes. Thus, it is well known to be an appropriate index of manufactured product quality. This essay discusses the meaning of reliability and its linkages with quality systems and quality risk management.

Rapid benefit-risk assessments: no escape from expert judgments in risk management.

The "human health impacts assessment" described by Cox and Popken (this issue) is intended to be a benefit-risk tool that avoids pitfalls of using expert judgments for policy analysis or during strict application of the precautionary principle in risk management. The proposed benefit-risk calculation uses numerous assumptions and suppositions to calculate a ratio of quality-adjusted life years (QALYs) lost for the number of human illness days prevented by the use of a food-animal antimicrobial drug, to the number of human illness days caused by the use of the antimicrobial drug. Assumptions about data--e.g., expert judgments on the representativeness of parameter estimates--are commonly used in risk assessment and risk management, including Cox and Popken's method. Cox and Popken apply the technique to specific examples of enrofloxacin and macrolides antimicrobial drugs, sometimes used in broiler chickens for human food. Although enthusiastically portrayed as a straightforward calculation of QALYs lost for two decision alternatives, Cox and Popken were silent on the pivotal expert judgment subsumed in their method: quality weights for illnesses caused by antimicrobial-resistant and antimicrobial-sensitive microbes are tacitly assumed to be equal. Yet, the costs in terms of prolonged illness, additional medications, repeat medical visits, and dread of more serious sequelae are expected to differ substantially for antimicrobial-resistant versus antimicrobial-sensitive illnesses. Despite their enthusiasm to the contrary, the "human health impacts assessment" by Cox and Popken is not immune from expert judgments in risk management.