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Strategic funding priorities in the pharmaceutical sciences allied to Quality by Design (QbD) and Process Analytical Technology (PAT)

Substantial changes in Pharmaceutical R&D strategy are required to address existing issues of low productivity, imminent patent expirations and pressures on pricing. Moves towards personalized healthcare and increasing diversity in the nature of portfolios including the rise of biopharmaceuticals however have the potential to provide considerable challenges to the establishment of cost effective and robust supply chains. To guarantee product quality and surety of supply for essential medicines it is necessary that manufacturing science keeps pace with advances in pharmaceutical R&D. In this position paper, the EUFEPS QbD and PAT Sciences network make recommendations that European industry, academia and health agencies focus attention on delivering step changes in science and technology in a number of key themes. These subject areas, all underpinned by the sciences allied to QbD and PAT, include product design and development for personalized healthcare, continuous-processing in pharmaceutical product manufacture, quantitative quality risk assessment for pharmaceutical development including life cycle management and the downstream processing of biopharmaceutical products. Plans are being established to gain commitment for inclusion of these themes into future funding priorities for the Innovative Medicines Initiative (IMI).

Quality by design case study 1: Design of 5-fluorouracil loaded lipid nanoparticles by the W/O/W double emulsion - Solvent evaporation method.

With Quality by Design (QbD), a systematic approach involving design and development of all production processes to achieve the final product with a predetermined quality, you work within a design space that determines the critical formulation and process parameters. Verification of the quality of the final product is no longer necessary. In the current study, the QbD approach was used in the preparation of lipid nanoparticle formulations to improve skin penetration of 5-Fluorouracil, a widely-used compound for treating non-melanoma skin cancer. 5-Fluorouracil-loaded lipid nanoparticles were prepared by the W/O/W double emulsion - solvent evaporation method. Artificial neural network software was used to evaluate the data obtained from the lipid nanoparticle formulations, to establish the design space, and to optimize the formulations. Two different artificial neural network models were developed. The limit values of the design space of the inputs and outputs obtained by both models were found to be within the knowledge space. The optimal formulations recommended by the models were prepared and the critical quality attributes belonging to those formulations were assigned. The experimental results remained within the design space limit values. Consequently, optimal formulations with the critical quality attributes determined to achieve the Quality Target Product Profile were successfully obtained within the design space by following the QbD steps.

A quality by design approach using artificial intelligence techniques to control the critical quality attributes of ramipril tablets manufactured by wet granulation.

Quality by design (QbD) is an essential part of the modern approach to pharmaceutical quality. This study was conducted in the framework of a QbD project involving ramipril tablets. Preliminary work included identification of the critical quality attributes (CQAs) and critical process parameters (CPPs) based on the quality target product profiles (QTPPs) using the historical data and risk assessment method failure mode and effect analysis (FMEA). Compendial and in-house specifications were selected as QTPPs for ramipril tablets. CPPs that affected the product and process were used to establish an experimental design. The results thus obtained can be used to facilitate definition of the design space using tools such as design of experiments (DoE), the response surface method (RSM) and artificial neural networks (ANNs). The project was aimed at discovering hidden knowledge associated with the manufacture of ramipril tablets using a range of artificial intelligence-based software, with the intention of establishing a multi-dimensional design space that ensures consistent product quality. At the end of the study, a design space was developed based on the study data and specifications, and a new formulation was optimized. On the basis of this formulation, a new laboratory batch formulation was prepared and tested. It was confirmed that the explored formulation was within the design space.

Quality by design approach: application of artificial intelligence techniques of tablets manufactured by direct compression.

The publication of the International Conference of Harmonization (ICH) Q8, Q9, and Q10 guidelines paved the way for the standardization of quality after the Food and Drug Administration issued current Good Manufacturing Practices guidelines in 2003. “Quality by Design”, mentioned in the ICH Q8 guideline, offers a better scientific understanding of critical process and product qualities using knowledge obtained during the life cycle of a product. In this scope, the “knowledge space” is a summary of all process knowledge obtained during product development, and the “design space” is the area in which a product can be manufactured within acceptable limits. To create the spaces, artificial neural networks (ANNs) can be used to emphasize the multidimensional interactions of input variables and to closely bind these variables to a design space. This helps guide the experimental design process to include interactions among the input variables, along with modeling and optimization of pharmaceutical formulations. The objective of this study was to develop an integrated multivariate approach to obtain a quality product based on an understanding of the cause–effect relationships between formulation ingredients and product properties with ANNs and genetic programming on the ramipril tablets prepared by the direct compression method. In this study, the data are generated through the systematic application of the design of experiments (DoE) principles and optimization studies using artificial neural networks and neurofuzzy logic programs.

Impacts of chemical enhancers on skin permeation and deposition of terbinafine

Abstract Context/Objective: The addition of chemical enhancers into formulations is the most commonly employed approach to overcome the skin barrier. The objective of this work was to evaluate the effect of vehicle and chemical enhancers on the skin permeation and accumulation of terbinafine, an allylamine antifungal drug. Methods: Terbinafine (1% w/w) was formulated as a Carbopol 934 P gel formulation in presence and absence of three chemical enhancers, nerolidol, dl-limonene and urea. Terbinafine distribution and deposition in stratum corneum (SC) and skin following 8-h ex vivo permeation study was determined using a sequential tape stripping procedure. The conformational order of SC lipids was investigated by ATR-FTIR spectroscopy. Results and discussion: Nerolidol containing gel formulation produced significantly higher enhancement in terbinafine permeation through skin and its skin accumulation was increased. ATR-FTIR results showed enhancer induced lipid bilayer disruption in SC. Urea resulted in enhanced permeation of terbinafine across the skin and a balanced distribution to the SC was achieved. But, dl-limonene could not minimize the accumulation of terbinafine in the upper SC. Conclusion: Nerolidol dramatically improved the skin permeation and deposition of terbinafine in the skin that might help to optimize targeting of the drug to the epidermal sites as required for both of superficial and deep cutaneous fungal infections.

Quetiapine Fumarate Extended-release Tablet Formulation Design Using Artificial Neural Networks

Objectives: This design study was implemented within the scope of the quality by design approach, which included the “International Conference on Harmonization” guidelines. We evaluated the quality of a modified-release tablet formulation of quetiapine fumarate, which was designed using artificial neural networks (ANN), and determined a new formulation that was similar to the reference product. Materials and Methods: Twelve different formulations were produced and tested. The reference product’s results and our experimental results were used as outputs for the training of the ANN programs of Intelligensys Ltd. Results: Dissolution tests were performed with the new formulation (F13) suggested by the INForm V.4 ANN program in three different pHs of the gastrointestinal system. The compliance of this formulation was confirmed by comparing the results with an f2 similarity test. Conclusion: Use of these programs supports research and development processes with multiple evaluation methods and alternative formulations may be determined faster and at lower cost.

Formulation design of the oral disintegrating tablets including alfuzosin hydrochloride with risk evaluation via quality by design

*Corresponding author: Yıldız Özsoy E-mail: yozsoy@istanbul.edu.tr INTRODUCTION Today, it is generally accepted that quality cannot be tested or inspected into a finished product, but rather that quality, safety and effectiveness must be “designed” and built into a product and its manufacturing process. In the traditional approach, the production processes and process parameters are determined to be unchanged in order to avoid any variety in the quality of the product. Thus, required specifications are met and the product quality is measured by finished product tests1. In order to design quality into a product, the requirements for the product design and performance must be well understood in the early design phase. In pharmaceuticals, these product requirements can be justified as Quality Target Product Profile (QTPP) which is also defined as “a prospective summary of ABSTRACT

QbD Implementation in Biotechnological Product Development Studies

Biotechnological drug development is an extensive area still growing and coming into prominence day by day. Since biotechnological product manufacturing is irreversible, highly expensive, and contains so many critical parameters throughout the process, quality control tests applied to the finished product become inefficacious; therefore, maintaining predefined quality is crucial. Quality by Design (QbD), a systematic approach, is designing and optimizing of formulation and production processes in order to provide a predefined product quality by following a risk and scientific‐based path. Determining the critical variables for biotechnological products and their manufactur‐ ing via risk assessment is the first and most vital stage of QbD approach, before exploring the multivariate relations among the independent and dependent critical variables by mathematical modeling with the assistive technologies. Response Surface Method (RSM), Artificial Neural Network (ANN), and Genetic Algorithm (GA) are some of the assistive technologies used to perform mathematical modeling. After modeling, additional knowledge is vested and this provides the chance to find a range in which the product quality is always ensured, called as “Design space”. So, product quality is procured all along the process by keeping the critical variables under control with less effort, money, and mistakes.

New quality concepts in pharmaceuticals

Büyük hızla yeniliklere adapte olmaya çalışan ilaç sanayii, son yıllarda, üretim bilgilerinde, kalite yönetim sistemlerinde ve risk yönetiminde önemli ilerlemeler yaşamış ve üretim kalitesinin sağlanmasına yardımcı olmak için kullanılabilecek modern üretim araçları geliştirmiştir. Bu yeni araçlar üreticilerin problemleri saptamasına, analiz etmesine, düzeltmesine, önlemesine ve üretim süreçlerini sürekli olarak iyileştirmesine yardım etmektedir. Bu gelişmeler üzerine, 2002 yılında Amerikan Gıda ve İlaç Dairesi (FDA), ilaç üretimini düzenleyen kuralları ve ürün kalitesini daha da geliştirmek ve modernize etmek amacı ile Güncel İyi Üretim Uygulamalarındaki (cGMP) yenilikleri ilaç endüstrisine duyurmuştur. ABD, Japonya ve Avrupa’da ilaç endüstrisinin yetkilileri ve uzmanlardan oluşan, ilaç ürünleri için teknik gereksinimlerin bu üç bölgede uyumlaştırılması amacıyla kurulmuş bir forum olan Uluslararası Harmonizasyon Kurumu (ICH), endüstriye Tasarımla Kalite (QbD) olarak adlandırılan yeni bir yaklaşım getirmek için güncel kılavuzları (ICH Q8, Q9, Q10 ve Q11) yayınlamıştır. Kaliteyi test etmek yerine tasarımla sağlayan bu yeni yaklaşımın bir sonucu olarak, üründe kalite artışı, üretim maliyetinde azalma ve ürünün piyasaya daha kısa sürede sunulması sağlanmaktadır. Ayrıca, hastanın güvenliği ve daha kaliteli ilaca daha kısa sürede ulaşması üzerinde durulmaktadır. Anahtar sözcükler: Üretim Kalitesi, ICH, Tasarımla kalite ABS TRACT New quality concepts in pharmaceuticals