Q9.7 Format
Lyric Maro
The Q notation is a way to specify the parameters of a binary fixed point number format. For example, in Q notation, the number format denoted by Q8.8 means that the fixed point numbers in this format have 8 bits for the integer part and 8 bits for the fraction part.
A variant of the Q notation has been in use by ARM. In this variant, the m number includes the sign bit. For example, a 16-bit signed integer would be denoted Q15.0 in the TI variant, but Q16.0 in the ARM variant.[2][3]
Because the denominator is a power of two, the multiplication can be implemented as an arithmetic shift to the left and the division as an arithmetic shift to the right; on many processors shifts are faster than multiplication and division.
To maintain accuracy, the intermediate multiplication and division results must be double precision and care must be taken in rounding the intermediate result before converting back to the desired Q number.
Unlike floating point Inf, saturated results are not sticky and will unsaturate on adding a negative value to a positive saturated value (0x7FFF) and vice versa in that implementation shown. In assembly language, the Signed Overflow flag can be used to avoid the typecasts needed for that C implementation.
The site is secure.
The ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.
The ICH Quality Implementation Working Group (Q-IWG) has prepared this Points to Consider document covering topics relevant to the implementation of ICH Q8(R2), Q9, and Q10, which supplement the existing guidance Q8, Q9, and Q10 Questions & Answers [3] and workshop training materials [4] already produced by this group. They should be considered all together.
The points to consider are based on questions raised during the ICH Q-IWG training workshop sessions in the three regions. The points to consider are not intended to be new guidance. They are intended to provide clarity to both industry and regulators and to facilitate the preparation, assessment, and inspection related to applications filed for marketing authorizations.
The development approach should be adapted based on the complexity and specificity of product and process; therefore, applicants are encouraged to contact regulatory authorities regarding questions related to specific information to be included in their application.
Using the Quality by Design (QbD) approach does not change regional regulatory requirements but can provide opportunities for more flexible approaches to meet them. In all cases, good manufacturing practice (GMP) compliance is expected.
Scientific rationale and quality risk management (QRM) processes are used to reach a conclusion on what are critical quality attributes (CQAs) and critical process parameters (CPPs) for a given product and process
The purpose of specifications and CoAs remains the same in the case of RTRT, but the way to develop them is different. Real-time release tests(RTRT) are considered to be specification testing methods and follow the established regional regulatory requirements for release specifications (as interpreted in e.g., ICH Q6A and ICH Q6B) together with other regional regulatory requirements (e.g., formats, GMP, batch acceptance decisions).
Different development approaches lead to different control strategies. Regardless of the control strategy, the batch release process should be followed. For a batch release decision, several elements should be considered. See in the figure below an illustration of the elements of the batch release process leading to the batch release decision.
The batch release process leading to the batch release decision can be performed by more than one quality individual depending on the regional regulatory requirements and company policy:
Following determination of the quality target product profile (QTPP) of the product under development, the applicant can use quality risk management (QRM, ICH Q9) tools to rank and select quality attributes (including material attributes) and/or process parameters that should be further evaluated and/or controlled within appropriate ranges to ensure the desired product quality. The applicant should consider providing information of sufficient detail to demonstrate how the conclusions were reached, which can include:
The factors to be studied in a DoE could come from the risk assessment exercise or prior knowledge. Inclusion of a full statistical evaluation of the DoEs performed at early development stages (e.g., screening) is not expected. A summary table of the factors and ranges studied and the conclusions reached will be helpful. For DoEs involving single- or multiple-unit operations that are used to establish CPPs and/or to define a Design Space (DS), the inclusion of the following information in the submission will greatly facilitate assessment by the regulators:
A model is a simplified representation of a system using mathematical terms. Models can enhance scientific understanding and possibly predict the behavior of a system under a set of conditions. Mathematical models can be used at every stage of development and manufacturing. They can be derived from first principles reflecting physical laws (such as mass balance, energy balance, and heat transfer relations), or from data, or from a combination of the two. There are many types of models and the selected one will depend on the existing knowledge about the system, the data available, and the objective of the study. This document is intended to highlight some points to consider when developing and implementing mathematical models during pharmaceutical product development, manufacturing and throughout the product lifecycle. Other approaches not described in this document can also be used.
For the purpose of implementation, models can also be categorized on the basis of the intended outcome of the model. Within each of these categories, models can be further classified as low, medium or high, on the basis of their impact in assuring product quality.
The following steps, if applicable, can be followed in a sequential manner, but occasionally, it might be appropriate to repeat an earlier step, thus imparting an iterative nature to this process. The overall steps are:
Model validation is an essential part of model development and implementation. Once a model is developed and implemented, verification continues throughout the lifecycle of the product.
The following elements can be considered for model validation and verification and generally are appropriate for high-impact models. In the case of well-established first principles-driven models, prior knowledge can be leveraged to support model validation and verification, if applicable. The applicability of the elements listed below for medium-impact or low-impact models can be considered on a case-by-case basis.
The level of detail for describing a model in a regulatory submission is dependent on the impact of its implementation in assuring the quality of the product. For the various types of models, the applicant can consider including:
A design space can be updated over the lifecycle as additional knowledge is gained. Risk assessments, as part of the risk management process, help steer the focus of development studies and define the design space. Operating within the design space is part of the control strategy. The design space associated with the control strategy ensures that the manufacturing process produces a product that meets the Quality Target Product Profile (QTPP) and Critical Quality Attributes (CQAs).
Since design spaces are typically developed at small scale, an effective control strategy helps manage potential residual risk after development and implementation. When developing a design space for a single-unit operation, the context of the overall manufacturing process can be considered, particularly immediate upstream and downstream steps that could interact with that unit operation. Potential linkages to CQAs should be evaluated in design space development.
In developing design spaces for existing products, multivariate models can be used for retrospective evaluation of historical production data. The level of variability present in the historical data will influence the ability to develop a design space, and additional studies might be appropriate.
Design spaces can be based on scientific first principles and/or empirical models. An appropriate statistical design of experiments incorporates a level of confidence that applies to the entire design space, including the edges of an approved design space. However, when operating the process near the edges of the design space, the risk of excursions from the design space could be higher because of normal process variation (common cause variation). The control strategy helps manage residual risk associated with the chosen point of operation within the design space. When changes are made (e.g., process, equipment, raw material suppliers), results of risk review can provide information regarding additional studies and/or testing that might verify the continued applicability of the design space and associated manufacturing steps after the change.
Capturing development knowledge and understanding contributes to design space implementation and continual improvement. Different approaches can be considered when implementing a design space (e.g., process ranges, mathematical expressions, or feedback controls to adjust parameters during processing (see also Figure 1d in ICH Q8(R2)). The chosen approach would be reflected in the control strategy to assure the inputs and process stay within the design space.
Although the entire design space does not have to be reestablished (e.g., DoE) at commercial scale, design spaces should be initially verified as suitable prior to commercial manufacturing. Design space verification should not be confused with process validation. However, it might be possible to conduct verification studies of the performance of the design space scale-dependent parameters as part of process validation. Design space verification includes monitoring or testing of CQAs that are influenced by scale-dependent parameters. Additional verification of a design space might be triggered by changes (e.g., site, scale, or equipment). Additional verification is typically guided by the results of risk assessments of the potential impacts of the change(s) on design space.
A risk-based approach can be applied to determine the design of any appropriate studies for assessment of the suitability of a design space across different scales. Prior knowledge and first principles, including simulation models and equipment scale-up factors, can be used to predict scale-independent parameters. Experimental studies could help verify these predictions.